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Mathematics : Publications since January 2023

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@article{fds376060,
   Author = {Chew, J and Hirn, M and Krishnaswamy, S and Needell, D and Perlmutter,
             M and Steach, H and Viswanath, S and Wu, HT},
   Title = {Geometric scattering on measure spaces},
   Journal = {Applied and Computational Harmonic Analysis},
   Volume = {70},
   Year = {2024},
   Month = {May},
   url = {http://dx.doi.org/10.1016/j.acha.2024.101635},
   Abstract = {The scattering transform is a multilayered, wavelet-based
             transform initially introduced as a mathematical model of
             convolutional neural networks (CNNs) that has played a
             foundational role in our understanding of these networks'
             stability and invariance properties. In subsequent years,
             there has been widespread interest in extending the success
             of CNNs to data sets with non-Euclidean structure, such as
             graphs and manifolds, leading to the emerging field of
             geometric deep learning. In order to improve our
             understanding of the architectures used in this new field,
             several papers have proposed generalizations of the
             scattering transform for non-Euclidean data structures such
             as undirected graphs and compact Riemannian manifolds
             without boundary. Analogous to the original scattering
             transform, these works prove that these variants of the
             scattering transform have desirable stability and invariance
             properties and aim to improve our understanding of the
             neural networks used in geometric deep learning. In this
             paper, we introduce a general, unified model for geometric
             scattering on measure spaces. Our proposed framework
             includes previous work on compact Riemannian manifolds
             without boundary and undirected graphs as special cases but
             also applies to more general settings such as directed
             graphs, signed graphs, and manifolds with boundary. We
             propose a new criterion that identifies to which groups a
             useful representation should be invariant and show that this
             criterion is sufficient to guarantee that the scattering
             transform has desirable stability and invariance properties.
             Additionally, we consider finite measure spaces that are
             obtained from randomly sampling an unknown manifold. We
             propose two methods for constructing a data-driven graph on
             which the associated graph scattering transform approximates
             the scattering transform on the underlying manifold.
             Moreover, we use a diffusion-maps based approach to prove
             quantitative estimates on the rate of convergence of one of
             these approximations as the number of sample points tends to
             infinity. Lastly, we showcase the utility of our method on
             spherical images, a directed graph stochastic block model,
             and on high-dimensional single-cell data.},
   Doi = {10.1016/j.acha.2024.101635},
   Key = {fds376060}
}

@article{fds376241,
   Author = {Ji, H and Witelski, TP},
   Title = {Coarsening of Thin Films with Weak Condensation},
   Journal = {SIAM Journal on Applied Mathematics},
   Volume = {84},
   Number = {2},
   Pages = {362-386},
   Publisher = {Society for Industrial & Applied Mathematics
             (SIAM)},
   Year = {2024},
   Month = {April},
   url = {http://dx.doi.org/10.1137/23m1559336},
   Doi = {10.1137/23m1559336},
   Key = {fds376241}
}

@article{fds374862,
   Author = {Feng, Y and Li, L and Liu, JG and Xu, X},
   Title = {EXISTENCE OF WEAK SOLUTIONS TO p-NAVIER-STOKES
             EQUATIONS},
   Journal = {Discrete and Continuous Dynamical Systems - Series
             B},
   Volume = {29},
   Number = {4},
   Pages = {1868-1890},
   Publisher = {American Institute of Mathematical Sciences
             (AIMS)},
   Year = {2024},
   Month = {April},
   url = {http://dx.doi.org/10.3934/dcdsb.2023159},
   Abstract = {We study the existence of weak solutions to the
             p-Navier-Stokes equations with a symmetric p-Laplacian on
             bounded domains. We construct a particular Schauder basis in
             W01, p(Ω) with divergence free constraint and prove
             existence of weak solutions using the Galerkin approximation
             via this basis. Meanwhile, in the proof, we establish a
             chain rule for the Lp integral of the weak solutions, which
             fixes a gap in our previous work. The equality of energy
             dissipation is also established for the weak solutions
             considered.},
   Doi = {10.3934/dcdsb.2023159},
   Key = {fds374862}
}

@article{fds376686,
   Author = {Blekherman, G and Raymond, A and Wei, F},
   Title = {Undecidability of polynomial inequalities in weighted graph
             homomorphism densities},
   Journal = {Forum of Mathematics, Sigma},
   Volume = {12},
   Year = {2024},
   Month = {March},
   url = {http://dx.doi.org/10.1017/fms.2024.19},
   Abstract = {Many problems and conjectures in extremal combinatorics
             concern polynomial inequalities between homomorphism
             densities of graphs where we allow edges to have real
             weights. Using the theory of graph limits, we can
             equivalently evaluate polynomial expressions in homomorphism
             densities on kernels W, that is, symmetric, bounded and
             measurable functions W from. In 2011, Hatami and Norin
             proved a fundamental result that it is undecidable to
             determine the validity of polynomial inequalities in
             homomorphism densities for graphons (i.e., the case where
             the range of W is, which corresponds to unweighted graphs
             or, equivalently, to graphs with edge weights between and).
             The corresponding problem for more general sets of kernels,
             for example, for all kernels or for kernels with range,
             remains open. For any 0$ ]]>, we show undecidability of
             polynomial inequalities for any set of kernels which
             contains all kernels with range. This result also answers a
             question raised by Lovász about finding computationally
             effective certificates for the validity of homomorphism
             density inequalities in kernels.},
   Doi = {10.1017/fms.2024.19},
   Key = {fds376686}
}

@article{fds376265,
   Author = {Chidyagwai, SG and Kaplan, MS and Jensen, CW and Chen, JS and Chamberlain, RC and Hill, KD and Barker, PCA and Slesnick, TC and Randles, A},
   Title = {Surgical Modulation of Pulmonary Artery Shear Stress: A
             Patient-Specific CFD Analysis of the Norwood
             Procedure.},
   Journal = {Cardiovasc Eng Technol},
   Year = {2024},
   Month = {March},
   url = {http://dx.doi.org/10.1007/s13239-024-00724-3},
   Abstract = {PURPOSR: This study created 3D CFD models of the Norwood
             procedure for hypoplastic left heart syndrome (HLHS) using
             standard angiography and echocardiogram data to investigate
             the impact of shunt characteristics on pulmonary artery (PA)
             hemodynamics. Leveraging routine clinical data offers
             advantages such as availability and cost-effectiveness
             without subjecting patients to additional invasive
             procedures. METHODS: Patient-specific geometries of the
             intrathoracic arteries of two Norwood patients were
             generated from biplane cineangiograms. "Virtual surgery" was
             then performed to simulate the hemodynamics of alternative
             PA shunt configurations, including shunt type (modified
             Blalock-Thomas-Taussig shunt (mBTTS) vs. right
             ventricle-to-pulmonary artery shunt (RVPAS)), shunt
             diameter, and pulmonary artery anastomosis angle. Left-right
             pulmonary flow differential, Qp/Qs, time-averaged wall shear
             stress (TAWSS), and oscillatory shear index (OSI) were
             evaluated. RESULTS: There was strong agreement between
             clinically measured data and CFD model output throughout the
             patient-specific models. Geometries with a RVPAS tended
             toward more balanced left-right pulmonary flow, lower Qp/Qs,
             and greater TAWSS and OSI than models with a mBTTS. For both
             shunt types, larger shunts resulted in a higher Qp/Qs and
             higher TAWSS, with minimal effect on OSI. Low TAWSS areas
             correlated with regions of low flow and changing the
             PA-shunt anastomosis angle to face toward low TAWSS regions
             increased TAWSS. CONCLUSION: Excellent correlation between
             clinically measured and CFD model data shows that 3D CFD
             models of HLHS Norwood can be developed using standard
             angiography and echocardiographic data. The CFD analysis
             also revealed consistent changes in PA TAWSS, flow
             differential, and OSI as a function of shunt
             characteristics.},
   Doi = {10.1007/s13239-024-00724-3},
   Key = {fds376265}
}

@article{fds372258,
   Author = {Kirveslahti, H and Mukherjee, S},
   Title = {Representing fields without correspondences: the lifted
             Euler characteristic transform},
   Journal = {Journal of Applied and Computational Topology},
   Volume = {8},
   Number = {1},
   Pages = {1-34},
   Year = {2024},
   Month = {March},
   url = {http://dx.doi.org/10.1007/s41468-023-00133-w},
   Abstract = {Topological transforms have been very useful in statistical
             analysis of shapes or surfaces without restrictions that the
             shapes are diffeomorphic and requiring the estimation of
             correspondence maps. In this paper we introduce two
             topological transforms that generalize from shapes to
             fields, f:R3→R. Both transforms take a field and associate
             to each direction v∈Sd-1 a summary obtained by scanning
             the field in the direction v. The transforms we introduce
             are of interest for both applications as well as their
             theoretical properties. The topological transforms for
             shapes are based on an Euler calculus on sets. A key insight
             in this paper is that via a lifting argument one can develop
             an Euler calculus on real valued functions from the standard
             Euler calculus on sets, this idea is at the heart of the two
             transforms we introduce. We prove the transforms are
             injective maps. We show for particular moduli spaces of
             functions we can upper bound the number of directions needed
             determine any particular function.},
   Doi = {10.1007/s41468-023-00133-w},
   Key = {fds372258}
}

@article{fds374249,
   Author = {Chung, YM and Huang, WK and Wu, HT},
   Title = {Topological data analysis assisted automated sleep stage
             scoring using airflow signals},
   Journal = {Biomedical Signal Processing and Control},
   Volume = {89},
   Pages = {105760-105760},
   Publisher = {Elsevier BV},
   Year = {2024},
   Month = {March},
   url = {http://dx.doi.org/10.1016/j.bspc.2023.105760},
   Abstract = {Objective: Breathing pattern variability (BPV), as a
             universal physiological feature, encodes rich health
             information. We aim to show that, a high-quality automatic
             sleep stage scoring based on a proper quantification of BPV
             extracting from the single airflow signal can be achieved.
             Methods: Topological data analysis (TDA) is applied to
             characterize BPV from the intrinsically nonstationary
             airflow signal. The extracted features from TDA are utilized
             to train an automatic sleep stage scoring model using the
             XGBoost learner. Additionally, the noise and artifacts that
             are typically present in the air flow signal are leveraged
             to improve the performance of the trained system. To
             evaluate the effectiveness of the proposed approach, a
             state-of-the-art method is implemented for comparison
             purposes. Results: A leave-one-subject-out cross-validation
             was conducted on a dataset comprising 30 whole-night
             polysomnogram signals with standard annotations. The results
             show that the proposed features outperform those considered
             in the state-of-the-art work in terms of overall accuracy
             (78.8% ± 8.7% vs. 75.0% ± 9.6%) and Cohen's kappa (0.56 ±
             0.15 vs. 0.50 ± 0.15) for automatically scoring wake, rapid
             eye movement (REM), and non-REM (NREM) stages. An external
             validation conducted on a dataset comprising 80 whole-night
             polysomnogram signals with standard annotations shows a
             result of overall accuracy 74.1%±11.6% and Cohen's kappa
             0.42±0.15, which again outperforms the state-of-the-art
             work. Furthermore, the analysis of feature importance
             reveals that the TDA features provide complementary
             information to the traditional features commonly used in the
             literature, and the respiratory quality index is identified
             as an essential component. Conclusion: The proposed
             TDA-assisted automatic annotation system can accurately
             distinguish wake, REM and NREM from the airflow signal.
             Significance: The utilization of a single air flow channel
             and the universality of BPV suggest the potential of
             TDA-assisted signal processing in addressing various
             biomedical signals and homecare issues beyond sleep stage
             annotation.},
   Doi = {10.1016/j.bspc.2023.105760},
   Key = {fds374249}
}

@article{fds375271,
   Author = {Liu, T-C and Chen, Y-C and Chen, P-L and Tu, P-H and Yeh, C-H and Yeap,
             M-C and Wu, Y-H and Chen, C-C and Wu, H-T},
   Title = {Removal of electrical stimulus artifact in local field
             potential recorded from subthalamic nucleus by using
             manifold denoising.},
   Journal = {Journal of neuroscience methods},
   Volume = {403},
   Pages = {110038},
   Year = {2024},
   Month = {March},
   url = {http://dx.doi.org/10.1016/j.jneumeth.2023.110038},
   Abstract = {<h4>Background</h4>Deep brain stimulation (DBS) is an
             effective treatment for movement disorders such as
             Parkinson's disease (PD). However, local field potentials
             (LFPs) recorded through lead externalization during
             high-frequency stimulation (HFS) are contaminated by
             stimulus artifacts, which require to be removed before
             further analysis.<h4>New method</h4>In this study, a novel
             stimulus artifact removal algorithm based on manifold
             denoising, termed Shrinkage and Manifold-based Artifact
             Removal using Template Adaptation (SMARTA), was proposed to
             remove artifacts by deriving a template for each stimulus
             artifact and subtracting it from the signal. Under a
             low-dimensional manifold assumption, a matrix denoising
             technique called optimal shrinkage was applied to design a
             similarity metric such that the template for stimulus
             artifacts could be accurately recovered.<h4>Result</h4>SMARTA
             was evaluated using semirealistic signals, which were the
             combination of semirealistic stimulus artifacts recorded in
             an agar brain model and LFPs of PD patients with no
             stimulation, and realistic LFP signals recorded in patients
             with PD during HFS. The results indicated that SMARTA
             removes stimulus artifacts with a modest distortion in LFP
             estimates.<h4>Comparison with existing methods</h4>SMARTA
             was compared with moving-average subtraction,
             sample-and-interpolate technique, and Hampel
             filtering.<h4>Conclusion</h4>The proposed SMARTA algorithm
             helps the exploration of the neurophysiological mechanisms
             of DBS effects.},
   Doi = {10.1016/j.jneumeth.2023.110038},
   Key = {fds375271}
}

@article{fds375222,
   Author = {Ding, X and Wu, HT},
   Title = {How do kernel-based sensor fusion algorithms behave under
             high-dimensional noise?},
   Journal = {Information and Inference},
   Volume = {13},
   Number = {1},
   Publisher = {Oxford University Press (OUP)},
   Year = {2024},
   Month = {March},
   url = {http://dx.doi.org/10.1093/imaiai/iaad051},
   Abstract = {We study the behavior of two kernel based sensor fusion
             algorithms, nonparametric canonical correlation analysis
             (NCCA) and alternating diffusion (AD), under the nonnull
             setting that the clean datasets collected from two sensors
             are modeled by a common low-dimensional manifold embedded in
             a high-dimensional Euclidean space and the datasets are
             corrupted by high-dimensional noise. We establish the
             asymptotic limits and convergence rates for the eigenvalues
             of the associated kernel matrices assuming that the sample
             dimension and sample size are comparably large, where NCCA
             and AD are conducted using the Gaussian kernel. It turns out
             that both the asymptotic limits and convergence rates depend
             on the signal-to-noise ratio (SNR) of each sensor and
             selected bandwidths. On one hand, we show that if NCCA and
             AD are directly applied to the noisy point clouds without
             any sanity check, it may generate artificial information
             that misleads scientists' interpretation. On the other hand,
             we prove that if the bandwidths are selected adequately,
             both NCCA and AD can be made robust to high-dimensional
             noise when the SNRs are relatively large.},
   Doi = {10.1093/imaiai/iaad051},
   Key = {fds375222}
}

@article{fds376235,
   Author = {Ciocanel, M-V and Ding, L and Mastromatteo, L and Reichheld, S and Cabral, S and Mowry, K and Sandstede, B},
   Title = {Parameter Identifiability in PDE Models of Fluorescence
             Recovery After Photobleaching.},
   Journal = {Bulletin of mathematical biology},
   Volume = {86},
   Number = {4},
   Pages = {36},
   Year = {2024},
   Month = {March},
   url = {http://dx.doi.org/10.1007/s11538-024-01266-4},
   Abstract = {Identifying unique parameters for mathematical models
             describing biological data can be challenging and often
             impossible. Parameter identifiability for partial
             differential equations models in cell biology is especially
             difficult given that many established in vivo measurements
             of protein dynamics average out the spatial dimensions.
             Here, we are motivated by recent experiments on the binding
             dynamics of the RNA-binding protein PTBP3 in RNP granules of
             frog oocytes based on fluorescence recovery after
             photobleaching (FRAP) measurements. FRAP is a widely-used
             experimental technique for probing protein dynamics in
             living cells, and is often modeled using simple
             reaction-diffusion models of the protein dynamics. We show
             that current methods of structural and practical parameter
             identifiability provide limited insights into
             identifiability of kinetic parameters for these PDE models
             and spatially-averaged FRAP data. We thus propose a pipeline
             for assessing parameter identifiability and for learning
             parameter combinations based on re-parametrization and
             profile likelihoods analysis. We show that this method is
             able to recover parameter combinations for synthetic FRAP
             datasets and investigate its application to real
             experimental data.},
   Doi = {10.1007/s11538-024-01266-4},
   Key = {fds376235}
}

@article{fds372447,
   Author = {YU, J and LAI, R and LI, W and OSHER, S},
   Title = {A FAST PROXIMAL GRADIENT METHOD AND CONVERGENCE ANALYSIS FOR
             DYNAMIC MEAN FIELD PLANNING},
   Journal = {Mathematics of Computation},
   Volume = {93},
   Number = {346},
   Pages = {603-642},
   Publisher = {American Mathematical Society (AMS)},
   Year = {2024},
   Month = {March},
   url = {http://dx.doi.org/10.1090/mcom/3879},
   Abstract = {In this paper, we propose an efficient and flexible
             algorithm to solve dynamic mean-field planning problems
             based on an accelerated proximal gradient method. Besides an
             easy-to-implement gradient descent step in this algorithm, a
             crucial projection step becomes solving an elliptic equation
             whose solution can be obtained by conventional methods
             efficiently. By induction on iterations used in the
             algorithm, we theoretically show that the proposed discrete
             solution converges to the underlying continuous solution as
             the grid becomes finer. Furthermore, we generalize our
             algorithm to meanfield game problems and accelerate it using
             multilevel and multigrid strategies. We conduct
             comprehensive numerical experiments to confirm the
             convergence analysis of the proposed algorithm, to show its
             efficiency and mass preservation property by comparing it
             with state-of-the-art methods, and to illustrate its
             flexibility for handling various mean-field variational
             problems.},
   Doi = {10.1090/mcom/3879},
   Key = {fds372447}
}

@article{fds375300,
   Author = {Mémoli, F and Stefanou, A and Zhou, L},
   Title = {Persistent cup product structures and related
             invariants},
   Journal = {Journal of Applied and Computational Topology},
   Volume = {8},
   Number = {1},
   Pages = {93-148},
   Publisher = {Springer Science and Business Media LLC},
   Year = {2024},
   Month = {March},
   url = {http://dx.doi.org/10.1007/s41468-023-00138-5},
   Abstract = {<jats:title>Abstract</jats:title><jats:p>One-dimensional
             persistent homology is arguably the most important and
             heavily used computational tool in topological data
             analysis. Additional information can be extracted from
             datasets by studying multi-dimensional persistence modules
             and by utilizing cohomological ideas, e.g. the
             cohomological cup product. In this work, given a single
             parameter filtration, we investigate a certain 2-dimensional
             persistence module structure associated with persistent
             cohomology, where one parameter is the cup-length
             <jats:inline-formula><jats:alternatives><jats:tex-math>$$\ell
             \ge 0$$</jats:tex-math>?? <mml:mrow> <mml:mi>ℓ</mml:mi>
             <mml:mo>≥</mml:mo> <mml:mn>0</mml:mn> </mml:mrow>
             </mml:math></jats:alternatives></jats:inline-formula> and
             the other is the filtration parameter. This new persistence
             structure, called the <jats:italic>persistent cup
             module</jats:italic>, is induced by the cohomological cup
             product and adapted to the persistence setting. Furthermore,
             we show that this persistence structure is stable. By fixing
             the cup-length parameter <jats:inline-formula><jats:alternatives><jats:tex-math>$$\ell
             $$</jats:tex-math>?? <mml:mi>ℓ</mml:mi>
             </mml:math></jats:alternatives></jats:inline-formula>, we
             obtain a 1-dimensional persistence module, called the
             persistent <jats:inline-formula><jats:alternatives><jats:tex-math>$$\ell
             $$</jats:tex-math>?? <mml:mi>ℓ</mml:mi>
             </mml:math></jats:alternatives></jats:inline-formula>-cup
             module, and again show it is stable in the interleaving
             distance sense, and study their associated generalized
             persistence diagrams. In addition, we consider a generalized
             notion of a <jats:italic>persistent invariant</jats:italic>,
             which extends both the <jats:italic>rank
             invariant</jats:italic> (also referred to as
             <jats:italic>persistent Betti number</jats:italic>),
             Puuska’s rank invariant induced by epi-mono-preserving
             invariants of abelian categories, and the recently-defined
             <jats:italic>persistent cup-length invariant</jats:italic>,
             and we establish their stability. This generalized notion of
             persistent invariant also enables us to lift the
             Lyusternik-Schnirelmann (LS) category of topological spaces
             to a novel stable persistent invariant of filtrations,
             called the <jats:italic>persistent LS-category
             invariant</jats:italic>.</jats:p>},
   Doi = {10.1007/s41468-023-00138-5},
   Key = {fds375300}
}

@article{fds372954,
   Author = {Agarwal, PK and Katz, MJ and Sharir, M},
   Title = {On reverse shortest paths in geometric proximity
             graphs},
   Journal = {Computational Geometry: Theory and Applications},
   Volume = {117},
   Year = {2024},
   Month = {February},
   url = {http://dx.doi.org/10.1016/j.comgeo.2023.102053},
   Abstract = {Let S be a set of n geometric objects of constant complexity
             (e.g., points, line segments, disks, ellipses) in R2, and
             let ϱ:S×S→R≥0 be a distance function on S. For a
             parameter r≥0, we define the proximity graph G(r)=(S,E)
             where E={(e1,e2)∈S×S|e1≠e2,ϱ(e1,e2)≤r}. Given S,
             s,t∈S, and an integer k≥1, the reverse-shortest-path
             (RSP) problem asks for computing the smallest value r⁎≥0
             such that G(r⁎) contains a path from s to t of length at
             most k. In this paper we present a general randomized
             technique that solves the RSP problem efficiently for a
             large family of geometric objects and distance functions.
             Using standard, and sometimes more involved, semi-algebraic
             range-searching techniques, we first give an efficient
             algorithm for the decision problem, namely, given a value
             r≥0, determine whether G(r) contains a path from s to t of
             length at most k. Next, we adapt our decision algorithm and
             combine it with a random-sampling method to compute r⁎, by
             efficiently performing a binary search over an implicit set
             of O(n2) candidate ‘critical’ values that contains r⁎.
             We illustrate the versatility of our general technique by
             applying it to a variety of geometric proximity graphs. For
             example, we obtain (i) an O⁎(n4/3) expected-time
             randomized algorithm (where O⁎(⋅) hides polylog(n)
             factors) for the case where S is a set of (possibly
             intersecting) line segments in R2 and ϱ(e1,e2)=minx∈e1,y∈e2⁡‖x−y‖
             (where ‖⋅‖ is the Euclidean distance), and (ii) an
             O⁎(n+m4/3) expected-time randomized algorithm for the case
             where S is a set of m points lying on an x-monotone
             polygonal chain T with n vertices, and ϱ(p,q), for p,q∈S,
             is the smallest value h such that the points p′:=p+(0,h)
             and q′:=q+(0,h) are visible to each other, i.e., all
             points on the segment p′q′ lie above or on the polygonal
             chain T.},
   Doi = {10.1016/j.comgeo.2023.102053},
   Key = {fds372954}
}

@article{fds376258,
   Author = {Huang, J and Morsomme, R and Dunson, D and Xu, J},
   Title = {Detecting changes in the transmission rate of a stochastic
             epidemic model.},
   Journal = {Statistics in medicine},
   Year = {2024},
   Month = {February},
   url = {http://dx.doi.org/10.1002/sim.10050},
   Abstract = {Throughout the course of an epidemic, the rate at which
             disease spreads varies with behavioral changes, the
             emergence of new disease variants, and the introduction of
             mitigation policies. Estimating such changes in transmission
             rates can help us better model and predict the dynamics of
             an epidemic, and provide insight into the efficacy of
             control and intervention strategies. We present a method for
             likelihood-based estimation of parameters in the stochastic
             susceptible-infected-removed model under a
             time-inhomogeneous transmission rate comprised of piecewise
             constant components. In doing so, our method simultaneously
             learns change points in the transmission rate via a Markov
             chain Monte Carlo algorithm. The method targets the exact
             model posterior in a difficult missing data setting given
             only partially observed case counts over time. We validate
             performance on simulated data before applying our approach
             to data from an Ebola outbreak in Western Africa and
             COVID-19 outbreak on a university campus.},
   Doi = {10.1002/sim.10050},
   Key = {fds376258}
}

@article{fds376051,
   Author = {Aquino, W and Rouse, J and Bonnet, M},
   Title = {Active design of diffuse acoustic fields in
             enclosures.},
   Journal = {The Journal of the Acoustical Society of
             America},
   Volume = {155},
   Number = {2},
   Pages = {1297-1307},
   Year = {2024},
   Month = {February},
   url = {http://dx.doi.org/10.1121/10.0024770},
   Abstract = {This paper presents a numerical framework for designing
             diffuse fields in rooms of any shape and size, driven at
             arbitrary frequencies. That is, we aim at overcoming the
             Schroeder frequency limit for generating diffuse fields in
             an enclosed space. We formulate the problem as a Tikhonov
             regularized inverse problem and propose a low-rank
             approximation of the spatial correlation that results in
             significant computational gains. Our approximation is
             applicable to arbitrary sets of target points and allows us
             to produce an optimal design at a computational cost that
             grows only linearly with the (potentially large) number of
             target points. We demonstrate the feasibility of our
             approach through numerical examples where we approximate
             diffuse fields at frequencies well below the Schroeder
             limit.},
   Doi = {10.1121/10.0024770},
   Key = {fds376051}
}

@article{fds374395,
   Author = {Li, X and Pura, J and Allen, A and Owzar, K and Lu, J and Harms, M and Xie,
             J},
   Title = {DYNATE: Localizing rare-variant association regions via
             multiple testing embedded in an aggregation
             tree.},
   Journal = {Genet Epidemiol},
   Volume = {48},
   Number = {1},
   Pages = {42-55},
   Year = {2024},
   Month = {February},
   url = {http://dx.doi.org/10.1002/gepi.22542},
   Abstract = {Rare-variants (RVs) genetic association studies enable
             researchers to uncover the variation in phenotypic traits
             left unexplained by common variation. Traditional
             single-variant analysis lacks power; thus, researchers have
             developed various methods to aggregate the effects of RVs
             across genomic regions to study their collective impact.
             Some existing methods utilize a static delineation of
             genomic regions, often resulting in suboptimal effect
             aggregation, as neutral subregions within the test region
             will result in an attenuation of signal. Other methods use
             varying windows to search for signals but often result in
             long regions containing many neutral RVs. To pinpoint short
             genomic regions enriched for disease-associated RVs, we
             developed a novel method, DYNamic Aggregation TEsting
             (DYNATE). DYNATE dynamically and hierarchically aggregates
             smaller genomic regions into larger ones and performs
             multiple testing for disease associations with a controlled
             weighted false discovery rate. DYNATE's main advantage lies
             in its strong ability to identify short genomic regions
             highly enriched for disease-associated RVs. Extensive
             numerical simulations demonstrate the superior performance
             of DYNATE under various scenarios compared with existing
             methods. We applied DYNATE to an amyotrophic lateral
             sclerosis study and identified a new gene, EPG5, harboring
             possibly pathogenic mutations.},
   Doi = {10.1002/gepi.22542},
   Key = {fds374395}
}

@article{fds375267,
   Author = {Gressman, PT and Pierce, LB and Roos, J and Yung,
             PL},
   Title = {A NEW TYPE OF SUPERORTHOGONALITY},
   Journal = {Proceedings of the American Mathematical
             Society},
   Volume = {152},
   Number = {2},
   Pages = {665-675},
   Year = {2024},
   Month = {February},
   url = {http://dx.doi.org/10.1090/proc/16631},
   Abstract = {We provide a simple criterion on a family of functions that
             implies a square function estimate on Lp for every even
             integer p ≥ 2. This defines a new type of
             superorthogonality that is verified by checking a less
             restrictive criterion than any other type of
             superorthogonality that is currently known.},
   Doi = {10.1090/proc/16631},
   Key = {fds375267}
}

@article{fds373972,
   Author = {Chiu, N-T and Chuang, B and Anakmeteeprugsa, S and Shelley, KH and Alian, AA and Wu, H-T},
   Title = {Signal quality assessment of peripheral venous
             pressure.},
   Journal = {Journal of clinical monitoring and computing},
   Volume = {38},
   Number = {1},
   Pages = {101-112},
   Year = {2024},
   Month = {February},
   url = {http://dx.doi.org/10.1007/s10877-023-01071-9},
   Abstract = {Develop a signal quality index (SQI) for the widely
             available peripheral venous pressure waveform (PVP). We
             focus on the quality of the cardiac component in PVP. We
             model PVP by the adaptive non-harmonic model. When the
             cardiac component in PVP is stronger, the PVP is defined to
             have a higher quality. This signal quality is quantified by
             applying the synchrosqueezing transform to decompose the
             cardiac component out of PVP, and the SQI is defined as a
             value between 0 and 1. A database collected during the lower
             body negative pressure experiment is utilized to validate
             the developed SQI. All signals are labeled into categories
             of low and high qualities by experts. A support vector
             machine (SVM) learning model is trained for practical
             purpose. The developed signal quality index coincide with
             human experts' labels with the area under the curve 0.95. In
             a leave-one-subject-out cross validation (LOSOCV), the SQI
             achieves accuracy 0.89 and F1 0.88, which is consistently
             higher than other commonly used signal qualities, including
             entropy, power and mean venous pressure. The trained SVM
             model trained with SQI, entropy, power and mean venous
             pressure could achieve an accuracy 0.92 and F1 0.91 under
             LOSOCV. An exterior validation of SQI achieves accuracy 0.87
             and F1 0.92; an exterior validation of the SVM model
             achieves accuracy 0.95 and F1 0.96. The developed SQI has a
             convincing potential to help identify high quality PVP
             segments for further hemodynamic study. This is the first
             work aiming to quantify the signal quality of the widely
             applied PVP waveform.},
   Doi = {10.1007/s10877-023-01071-9},
   Key = {fds373972}
}

@article{fds374250,
   Author = {Li, X and Pura, J and Allen, A and Owzar, K and Lu, J and Harms, M and Xie,
             J},
   Title = {DYNATE: Localizing rare-variant association regions via
             multiple testing embedded in an aggregation
             tree.},
   Journal = {Genet Epidemiol},
   Volume = {48},
   Number = {1},
   Pages = {42-55},
   Year = {2024},
   Month = {February},
   url = {http://dx.doi.org/10.1002/gepi.22542},
   Abstract = {Rare-variants (RVs) genetic association studies enable
             researchers to uncover the variation in phenotypic traits
             left unexplained by common variation. Traditional
             single-variant analysis lacks power; thus, researchers have
             developed various methods to aggregate the effects of RVs
             across genomic regions to study their collective impact.
             Some existing methods utilize a static delineation of
             genomic regions, often resulting in suboptimal effect
             aggregation, as neutral subregions within the test region
             will result in an attenuation of signal. Other methods use
             varying windows to search for signals but often result in
             long regions containing many neutral RVs. To pinpoint short
             genomic regions enriched for disease-associated RVs, we
             developed a novel method, DYNamic Aggregation TEsting
             (DYNATE). DYNATE dynamically and hierarchically aggregates
             smaller genomic regions into larger ones and performs
             multiple testing for disease associations with a controlled
             weighted false discovery rate. DYNATE's main advantage lies
             in its strong ability to identify short genomic regions
             highly enriched for disease-associated RVs. Extensive
             numerical simulations demonstrate the superior performance
             of DYNATE under various scenarios compared with existing
             methods. We applied DYNATE to an amyotrophic lateral
             sclerosis study and identified a new gene, EPG5, harboring
             possibly pathogenic mutations.},
   Doi = {10.1002/gepi.22542},
   Key = {fds374250}
}

@article{fds375372,
   Author = {Bryant, R},
   Title = {The generality of closed G_2 solitons},
   Journal = {Pure and Applied Mathematics Quarterly},
   Volume = {19},
   Number = {6},
   Pages = {2827-2840},
   Publisher = {International Press},
   Editor = {Cheng, S-Y and Lima-Filho, P and Yau, SS-T and Yau,
             S-T},
   Year = {2024},
   Month = {January},
   Abstract = {The local generality of the space of solitons for the
             Laplacian flow of closed G2-structures is analyzed, and it
             is shown that the germs of such structures depend, up to
             diffeomorphism, on 16 functions of 6 variables (in the sense
             of É. Cartan). The method is to construct a natural
             exterior differential system whose integral manifolds
             describe such solitons and to show that it is involutive in
             Cartan’s sense, so that Cartan-Kähler theory can be
             applied. Meanwhile, it turns out that, for the more special
             case of gradient solitons, the natural exterior differential
             system is not involutive, and the generality of these
             structures remains a mystery.},
   Key = {fds375372}
}

@article{fds375373,
   Author = {Bryant, R and Cheeger, J and Lima-Filho, P and Rosenberg, J and White,
             B},
   Title = {The mathematical work of H. Blaine Lawson,
             Jr.},
   Journal = {Pure and Applied Mathematics Quarterly},
   Volume = {19},
   Number = {6},
   Pages = {2627-2662},
   Publisher = {International Press},
   Year = {2024},
   Month = {January},
   Key = {fds375373}
}

@article{fds375395,
   Author = {Stevens, JB and Riley, BA and Je, J and Gao, Y and Wang, C and Mowery, YM and Brizel, DM and Yin, F-F and Liu, J-G and Lafata, KJ},
   Title = {Radiomics on spatial-temporal manifolds via Fokker-Planck
             dynamics.},
   Journal = {Med Phys},
   Year = {2024},
   Month = {January},
   url = {http://dx.doi.org/10.1002/mp.16905},
   Abstract = {BACKGROUND: Delta radiomics is a high-throughput
             computational technique used to describe quantitative
             changes in serial, time-series imaging by considering the
             relative change in radiomic features of images extracted at
             two distinct time points. Recent work has demonstrated a
             lack of prognostic signal of radiomic features extracted
             using this technique. We hypothesize that this lack of
             signal is due to the fundamental assumptions made when
             extracting features via delta radiomics, and that other
             methods should be investigated. PURPOSE: The purpose of this
             work was to show a proof-of-concept of a new radiomics
             paradigm for sparse, time-series imaging data, where
             features are extracted from a spatial-temporal manifold
             modeling the time evolution between images, and to assess
             the prognostic value on patients with oropharyngeal cancer
             (OPC). METHODS: To accomplish this, we developed an
             algorithm to mathematically describe the relationship
             between two images acquired at time t = 0 $t = 0$ and t > 0
             $t &gt; 0$ . These images serve as boundary conditions of a
             partial differential equation describing the transition from
             one image to the other. To solve this equation, we propagate
             the position and momentum of each voxel according to
             Fokker-Planck dynamics (i.e., a technique common in
             statistical mechanics). This transformation is driven by an
             underlying potential force uniquely determined by the
             equilibrium image. The solution generates a spatial-temporal
             manifold (3 spatial dimensions + time) from which we define
             dynamic radiomic features. First, our approach was
             numerically verified by stochastically sampling dynamic
             Gaussian processes of monotonically decreasing noise. The
             transformation from high to low noise was compared between
             our Fokker-Planck estimation and simulated ground-truth. To
             demonstrate feasibility and clinical impact, we applied our
             approach to 18 F-FDG-PET images to estimate early metabolic
             response of patients (n = 57) undergoing definitive
             (chemo)radiation for OPC. Images were acquired pre-treatment
             and 2-weeks intra-treatment (after 20 Gy). Dynamic radiomic
             features capturing changes in texture and morphology were
             then extracted. Patients were partitioned into two groups
             based on similar dynamic radiomic feature expression via
             k-means clustering and compared by Kaplan-Meier analyses
             with log-rank tests (p < 0.05). These results were
             compared to conventional delta radiomics to test the added
             value of our approach. RESULTS: Numerical results confirmed
             our technique can recover image noise characteristics given
             sparse input data as boundary conditions. Our technique was
             able to model tumor shrinkage and metabolic response. While
             no delta radiomics features proved prognostic, Kaplan-Meier
             analyses identified nine significant dynamic radiomic
             features. The most significant feature was
             Gray-Level-Size-Zone-Matrix gray-level variance
             (p = 0.011), which demonstrated prognostic improvement
             over its corresponding delta radiomic feature (p = 0.722).
             CONCLUSIONS: We developed, verified, and demonstrated the
             prognostic value of a novel, physics-based radiomics
             approach over conventional delta radiomics via data
             assimilation of quantitative imaging and differential
             equations.},
   Doi = {10.1002/mp.16905},
   Key = {fds375395}
}

@article{fds375396,
   Author = {Nguyen, DL and Shelley Hwang and E and Ryser, MD and Grimm,
             LJ},
   Title = {Imaging Changes and Outcomes of Patients Undergoing Active
             Monitoring for Ductal Carcinoma In Situ: Seven-Year
             Follow-up Study.},
   Journal = {Acad Radiol},
   Year = {2024},
   Month = {January},
   url = {http://dx.doi.org/10.1016/j.acra.2023.12.021},
   Abstract = {RATIONALE AND OBJECTIVES: To determine the imaging changes
             and their associated positive predictive value (PPV) for
             invasive breast cancer in women undergoing active monitoring
             for ductal carcinoma in situ (DCIS). MATERIALS AND METHODS:
             In this seven-year follow-up retrospective IRB-exempted
             cohort study, we reviewed patients diagnosed with DCIS who
             elected active monitoring between 2003 and 2022 at a single
             academic institution. Imaging characteristics,
             histopathology at initial diagnosis, and subsequent
             follow-up were recorded. Low-risk DCIS was defined as low or
             intermediate grade and hormone receptor (HR) positive
             (estrogen and/or progesterone receptor positive) disease
             diagnosed in women at least 40 years of age. Progression was
             defined as subsequent ipsilateral invasive breast cancer
             diagnosis. RESULTS: There were 39 patients with a median age
             of 58.4 years (IQR: 51.1-69.6 years) and a median follow-up
             of 4.3 years (range: 0.6-16.4 years). Nearly two thirds of
             patients (64%, 25/39) had stable imaging (range: 0.6-16.4
             years) and remained progression-free during active
             monitoring. Among the remaining 14 patients (36%), there
             were 24 imaging findings which prompted 22 subsequent core
             needle biopsies (range: 1-3 biopsies per patient) and two
             surgical biopsies. The PPV of invasive cancer was 29%
             (7/24) overall and 38% (3/8) for masses, 33% (3/9) for
             calcifications, 17% (1/6) for non-mass enhancement, and 0%
             (0/1) for architectural distortion. CONCLUSION: Of the
             radiographic changes prompting an additional biopsy,
             development of a new mass (38%) and new calcifications (33%)
             had the highest PPV for invasive progression. Close imaging
             follow-up should be a critical component for patients
             undergoing monitoring for DCIS.},
   Doi = {10.1016/j.acra.2023.12.021},
   Key = {fds375396}
}

@article{fds361709,
   Author = {Earle, G and Mattingly, JC},
   Title = {Convergence of stratified MCMC sampling of non-reversible
             dynamics},
   Journal = {Stochastics and Partial Differential Equations: Analysis and
             Computations},
   Year = {2024},
   Month = {January},
   url = {http://dx.doi.org/10.1007/s40072-024-00325-0},
   Abstract = {We present a form of stratified MCMC algorithm built with
             non-reversible stochastic dynamics in mind. It can also be
             viewed as a generalization of the exact milestoning method
             or form of NEUS. We prove the convergence of the method
             under certain assumptions, with expressions for the
             convergence rate in terms of the process’s behavior within
             each stratum and large-scale behavior between strata. We
             show that the algorithm has a unique fixed point which
             corresponds to the invariant measure of the process without
             stratification. We will show how the convergence speeds of
             two versions of the algorithm, one with an extra eigenvalue
             problem step and one without, related to the mixing rate of
             a discrete process on the strata, and the mixing probability
             of the process being sampled within each stratum. The
             eigenvalue problem version also relates to local and global
             perturbation results of discrete Markov chains, such as
             those given by Van Koten, Weare et. al.},
   Doi = {10.1007/s40072-024-00325-0},
   Key = {fds361709}
}

@article{fds374408,
   Author = {Melikechi, O and Dunson, DB},
   Title = {Ellipsoid fitting with the Cayley transform.},
   Journal = {IEEE transactions on signal processing : a publication of
             the IEEE Signal Processing Society},
   Volume = {72},
   Pages = {70-83},
   Year = {2024},
   Month = {January},
   url = {http://dx.doi.org/10.1109/tsp.2023.3332560},
   Abstract = {We introduce Cayley transform ellipsoid fitting (CTEF), an
             algorithm that uses the Cayley transform to fit ellipsoids
             to noisy data in any dimension. Unlike many ellipsoid
             fitting methods, CTEF is ellipsoid specific, meaning it
             always returns elliptic solutions, and can fit arbitrary
             ellipsoids. It also significantly outperforms other fitting
             methods when data are not uniformly distributed over the
             surface of an ellipsoid. Inspired by growing calls for
             interpretable and reproducible methods in machine learning,
             we apply CTEF to dimension reduction, data visualization,
             and clustering in the context of cell cycle and circadian
             rhythm data and several classical toy examples. Since CTEF
             captures global curvature, it extracts nonlinear features in
             data that other machine learning methods fail to identify.
             For example, on the clustering examples CTEF outperforms 10
             popular algorithms.},
   Doi = {10.1109/tsp.2023.3332560},
   Key = {fds374408}
}

@article{fds376095,
   Author = {Datta, J and Banerjee, S and Dunson, DB},
   Title = {Nonparametric Bayes multiresolution testing for
             high-dimensional rare events},
   Journal = {Journal of Nonparametric Statistics},
   Year = {2024},
   Month = {January},
   url = {http://dx.doi.org/10.1080/10485252.2024.2309978},
   Abstract = {In a variety of application areas, there is interest in
             assessing evidence of differences in the intensity of event
             realizations between groups. For example, in cancer genomic
             studies collecting data on rare variants, the focus is on
             assessing whether and how the variant profile changes with
             the disease subtype. Motivated by this application, we
             develop multiresolution nonparametric Bayes tests for
             differential mutation rates across groups. The
             multiresolution approach yields fast and accurate detection
             of spatial clusters of rare variants, and our nonparametric
             Bayes framework provides great flexibility for modelling the
             intensities of rare variants. Some theoretical properties
             are also assessed, including weak consistency of our
             Dirichlet Process-Poisson-Gamma mixture over multiple
             resolutions. Simulation studies illustrate excellent small
             sample properties relative to competitors, and we apply the
             method to detect rare variants related to common variable
             immunodeficiency from whole exome sequencing data on 215
             patients and over 60,027 control subjects.},
   Doi = {10.1080/10485252.2024.2309978},
   Key = {fds376095}
}

@article{fds376122,
   Author = {Solomon, YE and Bendich, P},
   Title = {Convolutional persistence transforms},
   Journal = {Journal of Applied and Computational Topology},
   Year = {2024},
   Month = {January},
   url = {http://dx.doi.org/10.1007/s41468-024-00164-x},
   Abstract = {In this paper, we consider topological featurizations of
             data defined over simplicial complexes, like images and
             labeled graphs, obtained by convolving this data with
             various filters before computing persistence. Viewing a
             convolution filter as a local motif, the persistence diagram
             of the resulting convolution describes the way the motif is
             distributed across the simplicial complex. This pipeline,
             which we call convolutional persistence, extends the
             capacity of topology to observe patterns in such data.
             Moreover, we prove that (generically speaking) for any two
             labeled complexes one can find some filter for which they
             produce different persistence diagrams, so that the
             collection of all possible convolutional persistence
             diagrams is an injective invariant. This is proven by
             showing convolutional persistence to be a special case of
             another topological invariant, the Persistent Homology
             Transform. Other advantages of convolutional persistence are
             improved stability, greater flexibility for data-dependent
             vectorizations, and reduced computational complexity for
             certain data types. Additionally, we have a suite of
             experiments showing that convolutions greatly improve the
             predictive power of persistence on a host of classification
             tasks, even if one uses random filters and vectorizes the
             resulting diagrams by recording only their total
             persistences.},
   Doi = {10.1007/s41468-024-00164-x},
   Key = {fds376122}
}

@article{fds374555,
   Author = {Duprez, F and Crombin, M and Daubechies, I and Devries, N and Durant, V and El Khalil and M and Audag, N},
   Title = {[Update on manual bronchial clearance techniques (adults and
             adolescents)].},
   Journal = {Revue des maladies respiratoires},
   Volume = {41},
   Number = {1},
   Pages = {43-50},
   Year = {2024},
   Month = {January},
   url = {http://dx.doi.org/10.1016/j.rmr.2023.10.006},
   Abstract = {In adults and teenagers, airway clearance physiotherapy
             techniques (ACPT) are various and numerous. However, they
             for still awaiting scientific validation. Among ACPTs, Slow
             Expiration with the Glottis Opened in the Lateral Posture
             (ELTGOL), Autogenic Drainage (DA), and Active Cycling
             Breathing Technique (ACBT) present a Grade B level of
             evidence with weak recommendations. Even though these
             maneuvers are widely applied, precise description of chest
             physiotherapy (CP) is largely absent from the scientific
             literature; it is difficult to standardize its
             implementation and reproduce the results; scientific
             validation and faithful execution of the techniques are
             consequently problematic. In this paper, the authors aim to
             depict each of the three CP techniques as precisely as
             possible; with this in mind, graphic modeling of the
             different respiratory exercises is presented in such a way
             that they can be easily learned, applied and reproduced by
             physiotherapists.},
   Doi = {10.1016/j.rmr.2023.10.006},
   Key = {fds374555}
}

@article{fds374248,
   Author = {Jing, Y and Chen, J and Li, L and Lu, J},
   Title = {A Machine Learning Framework for Geodesics Under Spherical
             Wasserstein–Fisher–Rao Metric and Its Application for
             Weighted Sample Generation},
   Journal = {Journal of Scientific Computing},
   Volume = {98},
   Number = {1},
   Year = {2024},
   Month = {January},
   url = {http://dx.doi.org/10.1007/s10915-023-02396-y},
   Abstract = {Wasserstein–Fisher–Rao (WFR) distance is a family of
             metrics to gauge the discrepancy of two Radon measures,
             which takes into account both transportation and weight
             change. Spherical WFR distance is a projected version of WFR
             distance for probability measures so that the space of Radon
             measures equipped with WFR can be viewed as metric cone over
             the space of probability measures with spherical WFR.
             Compared to the case for Wasserstein distance, the
             understanding of geodesics under the spherical WFR is less
             clear and still an ongoing research focus. In this paper, we
             develop a deep learning framework to compute the geodesics
             under the spherical WFR metric, and the learned geodesics
             can be adopted to generate weighted samples. Our approach is
             based on a Benamou–Brenier type dynamic formulation for
             spherical WFR. To overcome the difficulty in enforcing the
             boundary constraint brought by the weight change, a
             Kullback–Leibler divergence term based on the inverse map
             is introduced into the cost function. Moreover, a new
             regularization term using the particle velocity is
             introduced as a substitute for the Hamilton–Jacobi
             equation for the potential in dynamic formula. When used for
             sample generation, our framework can be beneficial for
             applications with given weighted samples, especially in the
             Bayesian inference, compared to sample generation with
             previous flow models.},
   Doi = {10.1007/s10915-023-02396-y},
   Key = {fds374248}
}

@article{fds373608,
   Author = {Shnitzer, T and Wu, HT and Talmon, R},
   Title = {Spatiotemporal analysis using Riemannian composition of
             diffusion operators},
   Journal = {Applied and Computational Harmonic Analysis},
   Volume = {68},
   Year = {2024},
   Month = {January},
   url = {http://dx.doi.org/10.1016/j.acha.2023.101583},
   Abstract = {Multivariate time-series have become abundant in recent
             years, as many data-acquisition systems record information
             through multiple sensors simultaneously. In this paper, we
             assume the variables pertain to some geometry and present an
             operator-based approach for spatiotemporal analysis. Our
             approach combines three components that are often considered
             separately: (i) manifold learning for building operators
             representing the geometry of the variables, (ii) Riemannian
             geometry of symmetric positive-definite matrices for
             multiscale composition of operators corresponding to
             different time samples, and (iii) spectral analysis of the
             composite operators for extracting different dynamic modes.
             We propose a method that is analogous to the classical
             wavelet analysis, which we term Riemannian multi-resolution
             analysis (RMRA). We provide some theoretical results on the
             spectral analysis of the composite operators, and we
             demonstrate the proposed method on simulations and on real
             data.},
   Doi = {10.1016/j.acha.2023.101583},
   Key = {fds373608}
}

@article{fds376687,
   Author = {Ruotolo, J and Wang, K and Wei, F},
   Title = {An Asymptotically Sharp Bound on the Maximum Number of
             Independent Transversals},
   Journal = {Electronic Journal of Combinatorics},
   Volume = {31},
   Number = {1},
   Year = {2024},
   Month = {January},
   url = {http://dx.doi.org/10.37236/11670},
   Abstract = {Let G be a multipartite graph with partition V1, V2, …, Vk
             of V (G). Let di,j denote the edge density of the pair (Vi,
             Vj). An independent transversal is an independent set of G
             with exactly one vertex in each Vi . In this paper, we prove
             an asymptotically sharp upper bound on the maximum number of
             independent transversals given the di,j ’s.},
   Doi = {10.37236/11670},
   Key = {fds376687}
}

@article{fds375344,
   Author = {Franz, L and Viljoen, M and Askew, S and Brown, M and Dawson, G and Di
             Martino, JM and Sapiro, G and Sebolai, K and Seris, N and Shabalala, N and Stahmer, A and Turner, EL and de Vries, PJ},
   Title = {Autism Caregiver Coaching in Africa (ACACIA): Protocol for a
             type 1-hybrid effectiveness-implementation
             trial.},
   Journal = {PLoS One},
   Volume = {19},
   Number = {1},
   Pages = {e0291883},
   Year = {2024},
   url = {http://dx.doi.org/10.1371/journal.pone.0291883},
   Abstract = {BACKGROUND: While early autism intervention can
             significantly improve outcomes, gaps in implementation exist
             globally. These gaps are clearest in Africa, where forty
             percent of the world's children will live by 2050.
             Task-sharing early intervention to non-specialists is a key
             implementation strategy, given the lack of specialists in
             Africa. Naturalistic Developmental Behavioral Interventions
             (NDBI) are a class of early autism intervention that can be
             delivered by caregivers. As a foundational step to address
             the early autism intervention gap, we adapted a
             non-specialist delivered caregiver coaching NDBI for the
             South African context, and pre-piloted this cascaded
             task-sharing approach in an existing system of care.
             OBJECTIVES: First, we will test the effectiveness of the
             caregiver coaching NDBI compared to usual care. Second, we
             will describe coaching implementation factors within the
             Western Cape Department of Education in South Africa.
             METHODS: This is a type 1 effectiveness-implementation
             hybrid design; assessor-blinded, group randomized controlled
             trial. Participants include 150 autistic children (18-72
             months) and their caregivers who live in Cape Town, South
             Africa, and those involved in intervention implementation.
             Early Childhood Development practitioners, employed by the
             Department of Education, will deliver 12, one hour, coaching
             sessions to the intervention group. The control group will
             receive usual care. Distal co-primary outcomes include the
             Communication Domain Standard Score (Vineland Adaptive
             Behavior Scales, Third Edition) and the Language and
             Communication Developmental Quotient (Griffiths Scales of
             Child Development, Third Edition). Proximal secondary
             outcome include caregiver strategies measured by the sum of
             five items from the Joint Engagement Rating Inventory. We
             will describe key implementation determinants. RESULTS:
             Participant enrolment started in April 2023. Estimated
             primary completion date is March 2027. CONCLUSION: The
             ACACIA trial will determine whether a cascaded task-sharing
             intervention delivered in an educational setting leads to
             meaningful improvements in communication abilities of
             autistic children, and identify implementation barriers and
             facilitators. TRIAL REGISTRATION: NCT05551728 in Clinical
             Trial Registry (https://clinicaltrials.gov).},
   Doi = {10.1371/journal.pone.0291883},
   Key = {fds375344}
}

@article{fds374859,
   Author = {Gao, Y and Liu, J-G},
   Title = {A Selection Principle for Weak KAM Solutions via
             Freidlin–Wentzell Large Deviation Principle of Invariant
             Measures},
   Journal = {SIAM Journal on Mathematical Analysis},
   Volume = {55},
   Number = {6},
   Pages = {6457-6495},
   Publisher = {Society for Industrial & Applied Mathematics
             (SIAM)},
   Year = {2023},
   Month = {December},
   url = {http://dx.doi.org/10.1137/22m1519717},
   Doi = {10.1137/22m1519717},
   Key = {fds374859}
}

@article{fds374860,
   Author = {Gao, Y and Liu, J-G},
   Title = {Large Deviation Principle and Thermodynamic Limit of
             Chemical Master Equation via Nonlinear Semigroup},
   Journal = {Multiscale Modeling & Simulation},
   Volume = {21},
   Number = {4},
   Pages = {1534-1569},
   Publisher = {Society for Industrial & Applied Mathematics
             (SIAM)},
   Year = {2023},
   Month = {December},
   url = {http://dx.doi.org/10.1137/22m1505633},
   Doi = {10.1137/22m1505633},
   Key = {fds374860}
}

@article{fds373953,
   Author = {Cruikshank, A and Nijhout, HF and Best, J and Reed,
             M},
   Title = {Dynamical questions in volume transmission.},
   Journal = {Journal of biological dynamics},
   Volume = {17},
   Number = {1},
   Pages = {2269986},
   Year = {2023},
   Month = {December},
   url = {http://dx.doi.org/10.1080/17513758.2023.2269986},
   Abstract = {In volume transmission (or neuromodulation) neurons do not
             make one-to-one connections to other neurons, but instead
             simply release neurotransmitter into the extracellular space
             from numerous varicosities. Many well-known
             neurotransmitters including serotonin (5HT), dopamine (DA),
             histamine (HA), Gamma-Aminobutyric Acid (GABA) and
             acetylcholine (ACh) participate in volume transmission.
             Typically, the cell bodies are in one volume and the axons
             project to a distant volume in the brain releasing the
             neurotransmitter there. We introduce volume transmission and
             describe mathematically two natural homeostatic mechanisms.
             In some brain regions several neurotransmitters in the
             extracellular space affect each other's release. We
             investigate the dynamics created by this comodulation in two
             different cases: serotonin and histamine; and the
             comodulation of 4 neurotransmitters in the striatum and we
             compare to experimental data. This kind of comodulation
             poses new dynamical questions as well as the question of how
             these biochemical networks influence the
             electrophysiological networks in the brain.},
   Doi = {10.1080/17513758.2023.2269986},
   Key = {fds373953}
}

@article{fds374394,
   Author = {Witt, CE and Mena, S and Holmes, J and Hersey, M and Buchanan, AM and Parke, B and Saylor, R and Honan, LE and Berger, SN and Lumbreras, S and Nijhout, FH and Reed, MC and Best, J and Fadel, J and Schloss, P and Lau,
             T and Hashemi, P},
   Title = {Serotonin is a common thread linking different classes of
             antidepressants.},
   Journal = {Cell chemical biology},
   Volume = {30},
   Number = {12},
   Pages = {1557-1570.e6},
   Year = {2023},
   Month = {December},
   url = {http://dx.doi.org/10.1016/j.chembiol.2023.10.009},
   Abstract = {Depression pathology remains elusive. The monoamine
             hypothesis has placed much focus on serotonin, but due to
             the variable clinical efficacy of monoamine reuptake
             inhibitors, the community is looking for alternative
             therapies such as ketamine (neurogenesis theory of
             antidepressant action). There is evidence that different
             classes of antidepressants may affect serotonin levels; a
             notion we test here. We measure hippocampal serotonin in
             mice with voltammetry and study the effects of acute
             challenges of escitalopram, fluoxetine, reboxetine, and
             ketamine. We find that pseudo-equivalent doses of these
             drugs similarly raise ambient serotonin levels, despite
             their differing pharmacodynamics because of differences in
             Uptake 1 and 2, rapid SERT trafficking, and modulation of
             serotonin by histamine. These antidepressants have different
             pharmacodynamics but have strikingly similar effects on
             extracellular serotonin. Our findings suggest that serotonin
             is a common thread that links clinically effective
             antidepressants, synergizing different theories of
             depression (synaptic plasticity, neurogenesis, and the
             monoamine hypothesis).},
   Doi = {10.1016/j.chembiol.2023.10.009},
   Key = {fds374394}
}

@article{fds371873,
   Author = {Buch, DA and Johndrow, JE and Dunson, DB},
   Title = {Explaining transmission rate variations and forecasting
             epidemic spread in multiple regions with a semiparametric
             mixed effects SIR model.},
   Journal = {Biometrics},
   Volume = {79},
   Number = {4},
   Pages = {2987-2997},
   Year = {2023},
   Month = {December},
   url = {http://dx.doi.org/10.1111/biom.13901},
   Abstract = {The transmission rate is a central parameter in mathematical
             models of infectious disease. Its pivotal role in outbreak
             dynamics makes estimating the current transmission rate and
             uncovering its dependence on relevant covariates a core
             challenge in epidemiological research as well as public
             health policy evaluation. Here, we develop a method for
             flexibly inferring a time-varying transmission rate
             parameter, modeled as a function of covariates and a smooth
             Gaussian process (GP). The transmission rate model is
             further embedded in a hierarchy to allow information
             borrowing across parallel streams of regional incidence
             data. Crucially, the method makes use of optional
             vaccination data as a first step toward modeling of endemic
             infectious diseases. Computational techniques borrowed from
             the Bayesian spatial analysis literature enable fast and
             reliable posterior computation. Simulation studies reveal
             that the method recovers true covariate effects at nominal
             coverage levels. We analyze data from the COVID-19 pandemic
             and validate forecast intervals on held-out data.
             User-friendly software is provided to enable practitioners
             to easily deploy the method in public health research.},
   Doi = {10.1111/biom.13901},
   Key = {fds371873}
}

@article{fds374279,
   Author = {Nazaret, A and Tonekaboni, S and Darnell, G and Ren, SY and Sapiro, G and Miller, AC},
   Title = {Modeling personalized heart rate response to exercise and
             environmental factors with wearables data},
   Journal = {npj Digital Medicine},
   Volume = {6},
   Number = {1},
   Year = {2023},
   Month = {December},
   url = {http://dx.doi.org/10.1038/s41746-023-00926-4},
   Abstract = {Heart rate (HR) response to workout intensity reflects
             fitness and cardiorespiratory health. Physiological models
             have been developed to describe such heart rate dynamics and
             characterize cardiorespiratory fitness. However, these
             models have been limited to small studies in controlled lab
             environments and are challenging to apply to noisy—but
             ubiquitous—data from wearables. We propose a hybrid
             approach that combines a physiological model with flexible
             neural network components to learn a personalized,
             multidimensional representation of fitness. The
             physiological model describes the evolution of heart rate
             during exercise using ordinary differential equations
             (ODEs). ODE parameters are dynamically derived via a neural
             network connecting personalized representations to external
             environmental factors, from area topography to weather and
             instantaneous workout intensity. Our approach efficiently
             fits the hybrid model to a large set of 270,707 workouts
             collected from wearables of 7465 users from the Apple Heart
             and Movement Study. The resulting model produces fitness
             representations that accurately predict full HR response to
             exercise intensity in future workouts, with a per-workout
             median error of 6.1 BPM [4.4–8.8 IQR]. We further
             demonstrate that the learned representations correlate with
             traditional metrics of cardiorespiratory fitness, such as
             VO2 max (explained variance 0.81 ± 0.003). Lastly, we
             illustrate how our model is naturally interpretable and
             explicitly describes the effects of environmental factors
             such as temperature and humidity on heart rate, e.g., high
             temperatures can increase heart rate by 10%. Combining
             physiological ODEs with flexible neural networks can yield
             interpretable, robust, and expressive models for health
             applications.},
   Doi = {10.1038/s41746-023-00926-4},
   Key = {fds374279}
}

@article{fds375268,
   Author = {Chu, R and Pierce, LB},
   Title = {Generalizations of the Schrödinger maximal operator:
             building arithmetic counterexamples},
   Journal = {Journal d'Analyse Mathematique},
   Volume = {151},
   Number = {1},
   Pages = {59-114},
   Year = {2023},
   Month = {December},
   url = {http://dx.doi.org/10.1007/s11854-023-0335-7},
   Abstract = {Let TtP2f(x) denote the solution to the linear Schrödinger
             equation at time t, with initial value function f, where P
             2(ξ) = ∣ξ∣2. In 1980, Carleson asked for the minimal
             regularity of f that is required for the pointwise a.e.
             convergence of TtP2f(x) to f(x) as t → 0. This was
             recently resolved by work of Bourgain, and Du and Zhang.
             This paper considers more general dispersive equations, and
             constructs counterexamples to pointwise a.e. convergence for
             a new class of real polynomial symbols P of arbitrary
             degree, motivated by a broad question: what occurs for
             symbols lying in a generic class? We construct the
             counterexamples using number-theoretic methods, in
             particular the Weil bound for exponential sums, and the
             theory of Dwork-regular forms. This is the first case in
             which counterexamples are constructed for indecomposable
             forms, moving beyond special regimes where P has some
             diagonal structure.},
   Doi = {10.1007/s11854-023-0335-7},
   Key = {fds375268}
}

@article{fds374353,
   Author = {Deng, H and Robles, C},
   Title = {Completion of two-parameter period maps by nilpotent
             orbits},
   Year = {2023},
   Month = {December},
   Abstract = {We show that every two-parameter period map admits a
             Kato--Nakayama--Usui completion to a morphism of log
             manifolds.},
   Key = {fds374353}
}

@article{fds374414,
   Author = {Nan, J and Roychowdhury, S and Randles, A},
   Title = {Investigating the Influence of Heterogeneity Within Cell
             Types on Microvessel Network Transport.},
   Journal = {Cellular and molecular bioengineering},
   Volume = {16},
   Number = {5-6},
   Pages = {497-507},
   Year = {2023},
   Month = {December},
   url = {http://dx.doi.org/10.1007/s12195-023-00790-y},
   Abstract = {<h4>Background</h4>Current research on the biophysics of
             circulating tumor cells often overlooks the heterogeneity of
             cell populations, focusing instead on average cellular
             properties. This study aims to address the gap by
             considering the diversity of cell biophysical
             characteristics and their implications on cancer
             spread.<h4>Methods</h4>We utilized computer simulations to
             assess the influence of variations in cell size and membrane
             elasticity on the behavior of cells within fluid
             environments. The study controlled cell and fluid properties
             to systematically investigate the transport of tumor cells
             through a simulated network of branching
             channels.<h4>Results</h4>The simulations revealed that even
             minor differences in cellular properties, such as slight
             changes in cell radius or shear elastic modulus, lead to
             significant changes in the fluid conditions that cells
             experience, including velocity and wall shear stress
             (p < 0.001).<h4>Conclusion</h4>The findings underscore
             the importance of considering cell heterogeneity in
             biophysical studies and suggest that small variations in
             cellular characteristics can profoundly impact the dynamics
             of tumor cell circulation. This has potential implications
             for understanding the mechanisms of cancer metastasis and
             the development of therapeutic strategies.},
   Doi = {10.1007/s12195-023-00790-y},
   Key = {fds374414}
}

@article{fds374493,
   Author = {Gong, Y and Kiselev, A},
   Title = {A simple reaction-diffusion system as a possible model for
             the origin of chemotaxis.},
   Journal = {Journal of biological dynamics},
   Volume = {17},
   Number = {1},
   Pages = {2260833},
   Year = {2023},
   Month = {December},
   url = {http://dx.doi.org/10.1080/17513758.2023.2260833},
   Abstract = {Chemotaxis is a directed cell movement in response to
             external chemical stimuli. In this paper, we propose a
             simple model for the origin of chemotaxis - namely how a
             directed movement in response to an external chemical signal
             may occur based on purely reaction-diffusion equations
             reflecting inner working of the cells. The model is inspired
             by the well-studied role of the rho-GTPase Cdc42 regulator
             of cell polarity, in particular in yeast cells. We analyse
             several versions of the model to better understand its
             analytic properties and prove global regularity in one and
             two dimensions. Using computer simulations, we demonstrate
             that in the framework of this model, at least in certain
             parameter regimes, the speed of the directed movement
             appears to be proportional to the size of the gradient of
             signalling chemical. This coincides with the form of the
             chemical drift in the most studied mean field model of
             chemotaxis, the Keller-Segel equation.},
   Doi = {10.1080/17513758.2023.2260833},
   Key = {fds374493}
}

@article{fds371626,
   Author = {Wang, S-C and Ting, C-K and Chen, C-Y and Liu, C and Lin, N-C and Loong,
             C-C and Wu, H-T and Lin, Y-T},
   Title = {Arterial blood pressure waveform in liver transplant surgery
             possesses variability of morphology reflecting recipients'
             acuity and predicting short term outcomes.},
   Journal = {Journal of clinical monitoring and computing},
   Volume = {37},
   Number = {6},
   Pages = {1521-1531},
   Year = {2023},
   Month = {December},
   url = {http://dx.doi.org/10.1007/s10877-023-01047-9},
   Abstract = {We investigated clinical information underneath the
             beat-to-beat fluctuation of the arterial blood pressure
             (ABP) waveform morphology. We proposed the Dynamical
             Diffusion Map algorithm (DDMap) to quantify the variability
             of morphology.  The underlying physiology could be the
             compensatory mechanisms involving complex interactions
             between various physiological mechanisms to regulate the
             cardiovascular system. As a liver transplant surgery
             contains distinct periods, we investigated its clinical
             behavior in different surgical steps. Our study used DDmap
             algorithm, based on unsupervised manifold learning, to
             obtain a quantitative index for the beat-to-beat variability
             of morphology. We examined the correlation between the
             variability of ABP morphology and disease acuity as
             indicated by Model for End-Stage Liver Disease (MELD)
             scores, the postoperative laboratory data, and 4 early
             allograft failure (EAF) scores. Among the 85 enrolled
             patients, the variability of morphology obtained during the
             presurgical phase was best correlated with MELD-Na scores.
             The neohepatic phase variability of morphology was
             associated with EAF scores as well as postoperative
             bilirubin levels, international normalized ratio, aspartate
             aminotransferase levels, and platelet count. Furthermore,
             variability of morphology presents more associations with
             the above clinical conditions than the common BP measures
             and their BP variability indices. The variability of
             morphology obtained during the presurgical phase is
             indicative of patient acuity, whereas those during the
             neohepatic phase are indicative of short-term surgical
             outcomes.},
   Doi = {10.1007/s10877-023-01047-9},
   Key = {fds371626}
}

@article{fds374285,
   Author = {Topaz, CM and Ning, S and Ciocanel, MV and Bushway,
             S},
   Title = {Federal criminal sentencing: race-based disparate impact and
             differential treatment in judicial districts},
   Journal = {Humanities and Social Sciences Communications},
   Volume = {10},
   Number = {1},
   Year = {2023},
   Month = {December},
   url = {http://dx.doi.org/10.1057/s41599-023-01879-5},
   Abstract = {Race-based inequity in federal criminal sentencing is widely
             acknowledged, and yet our understanding of it is far from
             complete. Inequity may arise from several sources, including
             direct bias of courtroom actors and structural bias that
             produces racially disparate impacts. Irrespective of these
             sources, inequity may also originate from different loci
             within the federal system. We bring together the questions
             of the sources and loci of inequity. The purpose of our
             study is to quantify race-based disparate impact and
             differential treatment at the national level and at the
             level of individual federal judicial districts. We analyze
             over one-half million sentencing records publicly available
             from the United States Sentencing Commission database,
             spanning the years 2006 to 2020. At the system-wide level,
             Black and Hispanic defendants receive average sentences that
             are approximately 19 months longer and 5 months longer,
             respectively. Demographic factors and sentencing guideline
             elements account for nearly 17 of the 19 months for Black
             defendants and all five of the months for Hispanic
             defendants, demonstrating the disparate impact of the system
             at the national level. At the individual district level,
             even after controlling for each district’s unique
             demographics and implementation of sentencing factors, 14
             districts show significant differences for minoritized
             defendants as compared to white ones. These unexplained
             differences are evidence of possible differential treatment
             by judges, prosecutors, and defense attorneys.},
   Doi = {10.1057/s41599-023-01879-5},
   Key = {fds374285}
}

@article{fds368358,
   Author = {Drivas, TD and Elgindi, TM and La, J},
   Title = {Propagation of singularities by Osgood vector fields and for
             2D inviscid incompressible fluids},
   Journal = {Mathematische Annalen},
   Volume = {387},
   Number = {3-4},
   Pages = {1691-1718},
   Year = {2023},
   Month = {December},
   url = {http://dx.doi.org/10.1007/s00208-022-02498-2},
   Abstract = {We show that certain singular structures (Hölderian cusps
             and mild divergences) are transported by the flow of
             homeomorphisms generated by an Osgood velocity field. The
             structure of these singularities is related to the modulus
             of continuity of the velocity and the results are shown to
             be sharp in the sense that slightly more singular structures
             cannot generally be propagated. For the 2D Euler equation,
             we prove that certain singular structures are preserved by
             the motion, e.g. a system of log log +(1 / | x|) vortices
             (and those that are slightly less singular) travel with the
             fluid in a nonlinear fashion, up to bounded perturbations.
             We also give stability results for weak Euler solutions away
             from their singular set.},
   Doi = {10.1007/s00208-022-02498-2},
   Key = {fds368358}
}

@article{fds370528,
   Author = {Moris, D and Barfield, R and Chan, C and Chasse, S and Stempora, L and Xie,
             J and Plichta, JK and Thacker, J and Harpole, DH and Purves, T and Lagoo-Deenadayalan, S and Hwang, E-SS and Kirk,
             AD},
   Title = {Immune Phenotype and Postoperative Complications After
             Elective Surgery.},
   Journal = {Ann Surg},
   Volume = {278},
   Number = {6},
   Pages = {873-882},
   Year = {2023},
   Month = {December},
   url = {http://dx.doi.org/10.1097/SLA.0000000000005864},
   Abstract = {OBJECTIVES: To characterize and quantify accumulating
             immunologic alterations, pre and postoperatively in patients
             undergoing elective surgical procedures. BACKGROUND:
             Elective surgery is an anticipatable, controlled human
             injury. Although the human response to injury is generally
             stereotyped, individual variability exists. This makes
             surgical outcomes less predictable, even after standardized
             procedures, and may provoke complications in patients unable
             to compensate for their injury. One potential source of
             variation is found in immune cell maturation, with
             phenotypic changes dependent on an individual's unique,
             lifelong response to environmental antigens. METHODS: We
             enrolled 248 patients in a prospective trial facilitating
             comprehensive biospecimen and clinical data collection in
             patients scheduled to undergo elective surgery. Peripheral
             blood was collected preoperatively, and immediately on
             return to the postanesthesia care unit. Postoperative
             complications that occurred within 30 days after surgery
             were captured. RESULTS: As this was an elective surgical
             cohort, outcomes were generally favorable. With a median
             follow-up of 6 months, the overall survival at 30 days was
             100%. However, 20.5% of the cohort experienced a
             postoperative complication (infection, readmission, or
             system dysfunction). We identified substantial heterogeneity
             of immune senescence and terminal differentiation phenotypes
             in surgical patients. More importantly, phenotypes
             indicating increased T-cell maturation and senescence were
             associated with postoperative complications and were evident
             preoperatively. CONCLUSIONS: The baseline immune repertoire
             may define an immune signature of resilience to surgical
             injury and help predict risk for surgical
             complications.},
   Doi = {10.1097/SLA.0000000000005864},
   Key = {fds370528}
}

@article{fds374556,
   Author = {Deng, H and Robles, C},
   Title = {Completion of two-parameter period maps by nilpotent
             orbits},
   Year = {2023},
   Month = {December},
   Abstract = {We show that every two-parameter period map admits a
             Kato--Nakayama--Usui completion to a morphism of log
             manifolds.},
   Key = {fds374556}
}

@article{fds374391,
   Author = {Falcinelli, SD and Cooper-Volkheimer, AD and Semenova, L and Wu, E and Richardson, A and Ashokkumar, M and Margolis, DM and Archin, NM and Rudin, CD and Murdoch, D and Browne, EP},
   Title = {Impact of Cannabis Use on Immune Cell Populations and the
             Viral Reservoir in People With HIV on Suppressive
             Antiretroviral Therapy.},
   Journal = {J Infect Dis},
   Volume = {228},
   Number = {11},
   Pages = {1600-1609},
   Year = {2023},
   Month = {November},
   url = {http://dx.doi.org/10.1093/infdis/jiad364},
   Abstract = {BACKGROUND: Human immunodeficiency virus (HIV) infection
             remains incurable due to the persistence of a viral
             reservoir despite antiretroviral therapy (ART). Cannabis
             (CB) use is prevalent amongst people with HIV (PWH), but the
             impact of CB on the latent HIV reservoir has not been
             investigated. METHODS: Peripheral blood cells from a cohort
             of PWH who use CB and a matched cohort of PWH who do not use
             CB on ART were evaluated for expression of
             maturation/activation markers, HIV-specific T-cell
             responses, and intact proviral DNA. RESULTS: CB use was
             associated with increased abundance of naive T cells,
             reduced effector T cells, and reduced expression of
             activation markers. CB use was also associated with reduced
             levels of exhausted and senescent T cells compared to
             nonusing controls. HIV-specific T-cell responses were
             unaffected by CB use. CB use was not associated with intact
             or total HIV DNA frequency in CD4 T cells. CONCLUSIONS: This
             analysis is consistent with the hypothesis that CB use
             reduces activation, exhaustion, and senescence in the T
             cells of PWH, and does not impair HIV-specific CD8 T-cell
             responses. Longitudinal and interventional studies with
             evaluation of CB exposure are needed to fully evaluate the
             impact of CB use on the HIV reservoir.},
   Doi = {10.1093/infdis/jiad364},
   Key = {fds374391}
}

@article{fds372445,
   Author = {Bezemek, Z and Heldman, M},
   Title = {Importance Sampling for the Empirical Measure of Weakly
             Interacting Diffusions},
   Journal = {Applied Mathematics and Optimization},
   Volume = {89},
   Publisher = {Springer},
   Year = {2023},
   Month = {November},
   Key = {fds372445}
}

@article{fds374019,
   Author = {Martin, A and Liu, G and Ladd, W and Lee, S and Gounley, J and Vetter, J and Patel, S and Rizzi, S and Mateevitsi, V and Insley, J and Randles,
             A},
   Title = {Performance Evaluation of Heterogeneous GPU Programming
             Frameworks for Hemodynamic Simulations},
   Journal = {ACM International Conference Proceeding Series},
   Pages = {1126-1137},
   Publisher = {ACM},
   Year = {2023},
   Month = {November},
   ISBN = {9798400707858},
   url = {http://dx.doi.org/10.1145/3624062.3624188},
   Abstract = {Preparing for the deployment of large scientific and
             engineering codes on upcoming exascale systems with
             GPU-dense nodes is made challenging by the unprecedented
             diversity of device architectures and heterogeneous
             programming models. In this work, we evaluate the process of
             porting a massively parallel, fluid dynamics code written in
             CUDA to SYCL, HIP, and Kokkos with a range of backends,
             using a combination of automated tools and manual tuning. We
             use a proxy application along with a custom performance
             model to inform the results and identify additional
             optimization strategies. At scale performance of the
             programming model implementations are evaluated on
             pre-production GPU node architectures for Frontier and
             Aurora, as well as on current NVIDIA device-based systems
             Summit and Polaris. Real-world workloads representing 3D
             blood flow calculations in complex vasculature are assessed.
             Our analysis highlights critical trade-offs between code
             performance, portability, and development
             time.},
   Doi = {10.1145/3624062.3624188},
   Key = {fds374019}
}

@article{fds374020,
   Author = {Valero-Lara, P and Vetter, J and Gounley, J and Randles,
             A},
   Title = {Moment Representation of Regularized Lattice Boltzmann
             Methods on NVIDIA and AMD GPUs},
   Journal = {ACM International Conference Proceeding Series},
   Pages = {1697-1704},
   Publisher = {ACM},
   Year = {2023},
   Month = {November},
   ISBN = {9798400707858},
   url = {http://dx.doi.org/10.1145/3624062.3624250},
   Abstract = {The lattice Boltzmann method is a highly scalable
             Navier-Stokes solver that has been applied to flow problems
             in a wide array of domains. However, the method is
             bandwidth-bound on modern GPU accelerators and has a large
             memory footprint. In this paper, we present new 2D and 3D
             GPU implementations of two different regularized lattice
             Boltzmann methods, which are not only able to achieve an
             acceleration of ~1.4 × w.r.t. reference lattice Boltzmann
             implementations but also reduce the memory requirements by
             up to 35% and 47% in 2D and 3D simulations respectively.
             These new approaches are evaluated on NVIDIA and AMD GPU
             architectures.},
   Doi = {10.1145/3624062.3624250},
   Key = {fds374020}
}

@article{fds374021,
   Author = {Tanade, C and Rakestraw, E and Ladd, W and Draeger, E and Randles,
             A},
   Title = {Cloud Computing to Enable Wearable-Driven Longitudinal
             Hemodynamic Maps},
   Journal = {Proceedings of the International Conference for High
             Performance Computing, Networking, Storage and Analysis, SC
             2023},
   Publisher = {ACM},
   Year = {2023},
   Month = {November},
   ISBN = {9798400701092},
   url = {http://dx.doi.org/10.1145/3581784.3607101},
   Abstract = {Tracking hemodynamic responses to treatment and stimuli over
             long periods remains a grand challenge. Moving from
             established single-heartbeat technology to longitudinal
             profiles would require continuous data describing how the
             patient's state evolves, new methods to extend the temporal
             domain over which flow is sampled, and high-throughput
             computing resources. While personalized digital twins can
             accurately measure 3D hemodynamics over several heartbeats,
             state-of-the-art methods would require hundreds of years of
             wallclock time on leadership scale systems to simulate one
             day of activity. To address these challenges, we propose a
             cloud-based, parallel-in-time framework leveraging
             continuous data from wearable devices to capture the first
             3D patient-specific, longitudinal hemodynamic maps. We
             demonstrate the validity of our method by establishing
             ground truth data for 750 beats and comparing the results.
             Our cloud-based framework is based on an initial fixed set
             of simulations to enable the wearable-informed creation of
             personalized longitudinal hemodynamic maps.},
   Doi = {10.1145/3581784.3607101},
   Key = {fds374021}
}

@article{fds374482,
   Author = {Pierce, L and Chu, R},
   Title = {Generalizations of the Schrödinger maximal operator:
             building arithmetic counterexamples},
   Booktitle = {https://arxiv.org/abs/2309.05872},
   Year = {2023},
   Month = {November},
   Key = {fds374482}
}

@article{fds371622,
   Author = {Chapman, SJ and Dallaston, MC and Kalliadasis, S and Trinh, PH and Witelski, TP},
   Title = {The role of exponential asymptotics and complex
             singularities in self-similarity, transitions, and branch
             merging of nonlinear dynamics},
   Journal = {Physica D: Nonlinear Phenomena},
   Volume = {453},
   Year = {2023},
   Month = {November},
   url = {http://dx.doi.org/10.1016/j.physd.2023.133802},
   Abstract = {We study a prototypical example in nonlinear dynamics where
             transition to self-similarity in a singular limit is
             fundamentally changed as a parameter is varied. Here, we
             focus on the complicated dynamics that occur in a
             generalised unstable thin-film equation that yields
             finite-time rupture. A parameter, n, is introduced to model
             more general disjoining pressures. For the standard case of
             van der Waals intermolecular forces, n=3, it was previously
             established that a countably infinite number of self-similar
             solutions exist leading to rupture. Each solution can be
             indexed by a parameter, ϵ=ϵ1>ϵ2>⋯>0, and the prediction
             of the discrete set of solutions requires examination of
             terms beyond-all-orders in ϵ. However, recent numerical
             results have demonstrated the surprising complexity that
             exists for general values of n. In particular, the
             bifurcation structure of self-similar solutions now exhibits
             branch merging as n is varied. In this work, we shall
             present key ideas of how branch merging can be interpreted
             via exponential asymptotics.},
   Doi = {10.1016/j.physd.2023.133802},
   Key = {fds371622}
}

@article{fds374205,
   Author = {Wang, Z and Zhang, Z and Lu, J and Li, Y},
   Title = {Coordinate Descent Full Configuration Interaction for
             Excited States.},
   Journal = {Journal of chemical theory and computation},
   Volume = {19},
   Number = {21},
   Pages = {7731-7739},
   Year = {2023},
   Month = {November},
   url = {http://dx.doi.org/10.1021/acs.jctc.3c00452},
   Abstract = {An efficient excited state method, named xCDFCI, in the
             configuration interaction framework is proposed. xCDFCI
             extends the unconstrained nonconvex optimization problem in
             coordinate descent full configuration interaction (CDFCI) to
             a multicolumn version for low-lying excited states
             computation. The optimization problem is addressed via a
             tailored coordinate descent method. In each iteration, a
             determinant is selected based on an approximated gradient,
             and coefficients of all states associated with the selected
             determinant are updated. A deterministic compression is
             applied to limit memory usage. We test xCDFCI applied to
             H<sub>2</sub>O and N<sub>2</sub> molecules under the cc-pVDZ
             basis set. For both systems, five low-lying excited states
             in the same symmetry sector are calculated, together with
             the ground state. xCDFCI also produces accurate binding
             curves of the carbon dimer in the cc-pVDZ basis with
             chemical accuracy, where the ground state and four excited
             states in the same symmetry sector are benchmarked.},
   Doi = {10.1021/acs.jctc.3c00452},
   Key = {fds374205}
}

@article{fds374022,
   Author = {Roychowdhury, S and Balogh, P and Mahmud, ST and Puleri, DF and Martin,
             A and Gounley, J and Draeger, EW and Randles, A},
   Title = {Enhancing Adaptive Physics Refinement Simulations Through
             the Addition of Realistic Red Blood Cell
             Counts.},
   Journal = {International Conference for High Performance Computing,
             Networking, Storage and Analysis : [proceedings]. SC
             (Conference : Supercomputing)},
   Volume = {2023},
   Pages = {41},
   Publisher = {ACM},
   Year = {2023},
   Month = {November},
   ISBN = {9798400701092},
   url = {http://dx.doi.org/10.1145/3581784.3607105},
   Abstract = {Simulations of cancer cell transport require accurately
             modeling mm-scale and longer trajectories through a
             circulatory system containing trillions of deformable red
             blood cells, whose intercellular interactions require
             submicron fidelity. Using a hybrid CPU-GPU approach, we
             extend the advanced physics refinement (APR) method to
             couple a finely-resolved region of explicitly-modeled red
             blood cells to a coarsely-resolved bulk fluid domain. We
             further develop algorithms that: capture the dynamics at the
             interface of differing viscosities, maintain hematocrit
             within the cell-filled volume, and move the finely-resolved
             region and encapsulated cells while tracking an individual
             cancer cell. Comparison to a fully-resolved fluid-structure
             interaction model is presented for verification. Finally, we
             use the advanced APR method to simulate cancer cell
             transport over a mm-scale distance while maintaining a local
             region of RBCs, using a fraction of the computational power
             required to run a fully-resolved model.},
   Doi = {10.1145/3581784.3607105},
   Key = {fds374022}
}

@article{fds374290,
   Author = {Repasky, M and Cheng, X and Xie, Y},
   Title = {Neural Stein Critics with Staged L2-Regularization},
   Journal = {IEEE Transactions on Information Theory},
   Volume = {69},
   Number = {11},
   Pages = {7246-7275},
   Year = {2023},
   Month = {November},
   url = {http://dx.doi.org/10.1109/TIT.2023.3299258},
   Abstract = {Learning to differentiate model distributions from observed
             data is a fundamental problem in statistics and machine
             learning, and high-dimensional data remains a challenging
             setting for such problems. Metrics that quantify the
             disparity in probability distributions, such as the Stein
             discrepancy, play an important role in high-dimensional
             statistical testing. In this paper, we investigate the role
             of L2 regularization in training a neural network Stein
             critic so as to distinguish between data sampled from an
             unknown probability distribution and a nominal model
             distribution. Making a connection to the Neural Tangent
             Kernel (NTK) theory, we develop a novel staging procedure
             for the weight of regularization over training time, which
             leverages the advantages of highly-regularized training at
             early times. Theoretically, we prove the approximation of
             the training dynamic by the kernel optimization, namely the
             'lazy training', when the L2 regularization weight is large,
             and training on n samples converge at a rate of O(n-1/2) up
             to a log factor. The result guarantees learning the optimal
             critic assuming sufficient alignment with the leading
             eigen-modes of the zero-time NTK. The benefit of the staged
             L2 regularization is demonstrated on simulated high
             dimensional data and an application to evaluating generative
             models of image data.},
   Doi = {10.1109/TIT.2023.3299258},
   Key = {fds374290}
}

@article{fds374488,
   Author = {An, J and Henderson, C and Ryzhik, L},
   Title = {Voting models and semilinear parabolic equations},
   Journal = {Nonlinearity},
   Volume = {36},
   Number = {11},
   Year = {2023},
   Month = {November},
   url = {http://dx.doi.org/10.1088/1361-6544/ad001c},
   Abstract = {We present probabilistic interpretations of solutions to
             semi-linear parabolic equations with polynomial
             nonlinearities in terms of the voting models on the
             genealogical trees of branching Brownian motion (BBM). These
             extend McKean’s connection between the Fisher-KPP equation
             and BBM (McKean 1975 Commun. Pure Appl. Math. 28 323-31). In
             particular, we present ‘random outcome’ and ‘random
             threshold’ voting models that yield any polynomial
             nonlinearity f satisfying f ( 0 ) = f ( 1 ) = 0 and a
             ‘recursive up the tree’ model that allows to go beyond
             this restriction on f. We compute several examples of
             particular interest; for example, we obtain a curious
             interpretation of the heat equation in terms of a nontrivial
             voting model and a ‘group-based’ voting rule that leads
             to a probabilistic view of the pushed-pulled transition for
             a class of nonlinearities introduced by Ebert and van
             Saarloos.},
   Doi = {10.1088/1361-6544/ad001c},
   Key = {fds374488}
}

@article{fds371968,
   Author = {Dunlap, A and Gu, Y and Komorowski, T},
   Title = {Fluctuation exponents of the KPZ equation on a large
             torus},
   Journal = {Communications on Pure and Applied Mathematics},
   Volume = {76},
   Number = {11},
   Pages = {3104-3149},
   Publisher = {Wiley},
   Year = {2023},
   Month = {November},
   url = {http://dx.doi.org/10.1002/cpa.22110},
   Abstract = {We study the one‐dimensional KPZ equation on a large
             torus, started at equilibrium. The main results are optimal
             variance bounds in the super‐relaxation regime and part of
             the relaxation regime.},
   Doi = {10.1002/cpa.22110},
   Key = {fds371968}
}

@article{fds372375,
   Author = {An, D and Fang, D and Jordan, S and Liu, J-P and Low, GH and Wang,
             J},
   Title = {Efficient quantum algorithm for nonlinear reaction-diffusion
             equations and energy estimation},
   Journal = {Commun. Math. Phys.},
   Volume = {404},
   Pages = {963-1020},
   Booktitle = {arXiv 2205.01141},
   Year = {2023},
   Month = {October},
   url = {http://dx.doi.org/10.48550/arXiv.2205.01141},
   Doi = {10.48550/arXiv.2205.01141},
   Key = {fds372375}
}

@article{fds373607,
   Author = {Schmitz, RSJM and van den Belt-Dusebout, AW and Clements, K and Ren,
             Y and Cresta, C and Timbres, J and Liu, Y-H and Byng, D and Lynch, T and Menegaz, BA and Collyar, D and Hyslop, T and Thomas, S and Love, JK and Schaapveld, M and Bhattacharjee, P and Ryser, MD and Sawyer, E and Hwang, ES and Thompson, A and Wesseling, J and Lips, EH and Schmidt, MK and Grand Challenge PRECISION consortium},
   Title = {Association of DCIS size and margin status with risk of
             developing breast cancer post-treatment: multinational,
             pooled cohort study.},
   Journal = {BMJ},
   Volume = {383},
   Pages = {e076022},
   Year = {2023},
   Month = {October},
   url = {http://dx.doi.org/10.1136/bmj-2023-076022},
   Abstract = {OBJECTIVE: To examine the association between size and
             margin status of ductal carcinoma in situ (DCIS) and risk of
             developing ipsilateral invasive breast cancer and
             ipsilateral DCIS after treatment, and stage and subtype of
             ipsilateral invasive breast cancer. DESIGN: Multinational,
             pooled cohort study. SETTING: Four large international
             cohorts. PARTICIPANTS: Patient level data on 47 695 women
             with a diagnosis of pure, primary DCIS between 1999 and 2017
             in the Netherlands, UK, and US who underwent surgery, either
             breast conserving or mastectomy, often followed by
             radiotherapy or endocrine treatment, or both. MAIN OUTCOME
             MEASURES: The main outcomes were 10 year cumulative
             incidence of ipsilateral invasive breast cancer and
             ipsilateral DCIS estimated in relation to DCIS size and
             margin status, and adjusted hazard ratios and 95% confidence
             intervals, estimated using multivariable Cox proportional
             hazards analyses with multiple imputed data RESULTS: The 10
             year cumulative incidence of ipsilateral invasive breast
             cancer was 3.2%. In women who underwent breast conserving
             surgery with or without radiotherapy, only adjusted risks
             for ipsilateral DCIS were significantly increased for larger
             DCIS (20-49 mm) compared with DCIS <20 mm (hazard ratio
             1.38, 95% confidence interval 1.11 to 1.72). Risks for both
             ipsilateral invasive breast cancer and ipsilateral DCIS were
             significantly higher with involved compared with clear
             margins (invasive breast cancer 1.40, 1.07 to 1.83; DCIS
             1.39, 1.04 to 1.87). Use of adjuvant endocrine treatment was
             not significantly associated with a lower risk of
             ipsilateral invasive breast cancer compared to treatment
             with breast conserving surgery only (0.86, 0.62 to 1.21). In
             women who received breast conserving treatment with or
             without radiotherapy, higher DCIS grade was not
             significantly associated with ipsilateral invasive breast
             cancer, only with a higher risk of ipsilateral DCIS (grade
             1: 1.42, 1.08 to 1.87; grade 3: 2.17, 1.66 to 2.83). Higher
             age at diagnosis was associated with lower risk (per year)
             of ipsilateral DCIS (0.98, 0.97 to 0.99) but not ipsilateral
             invasive breast cancer (1.00, 0.99 to 1.00). Women with
             large DCIS (≥50 mm) more often developed stage III and IV
             ipsilateral invasive breast cancer compared to women with
             DCIS <20 mm. No such association was found between involved
             margins and higher stage of ipsilateral invasive breast
             cancer. Associations between larger DCIS and hormone
             receptor negative and human epidermal growth factor receptor
             2 positive ipsilateral invasive breast cancer and involved
             margins and hormone receptor negative ipsilateral invasive
             breast cancer were found. CONCLUSIONS: The association of
             DCIS size and margin status with ipsilateral invasive breast
             cancer and ipsilateral DCIS was small. When these two
             factors were added to other known risk factors in
             multivariable models, clinicopathological risk factors alone
             were found to be limited in discriminating between low and
             high risk DCIS.},
   Doi = {10.1136/bmj-2023-076022},
   Key = {fds373607}
}

@article{fds373482,
   Author = {Getz, J and Hsu, C-H and Leslie, S},
   Title = {Harmonic analysis on certain spherical varieties},
   Journal = {Journal of the European Mathematical Society},
   Publisher = {EMS Press},
   Year = {2023},
   Month = {October},
   url = {http://dx.doi.org/10.4171/JEMS/1381},
   Doi = {10.4171/JEMS/1381},
   Key = {fds373482}
}

@article{fds373473,
   Author = {Getz, J and Hsu, C-H and Leslie, S},
   Title = {Harmonic analysis on certain spherical varieties},
   Journal = {Journal of the European Mathematical Society},
   Publisher = {EMS Press},
   Year = {2023},
   Month = {October},
   url = {http://dx.doi.org/10.4171/JEMS/1381},
   Doi = {10.4171/JEMS/1381},
   Key = {fds373473}
}

@article{fds374286,
   Author = {Nelson, AC and Rolls, MM and Ciocanel, M-V and McKinley,
             SA},
   Title = {Minimal Mechanisms of Microtubule Length Regulation in
             Living Cells},
   Year = {2023},
   Month = {October},
   Key = {fds374286}
}

@article{fds373668,
   Author = {Ryser, MD and Greenwald, MA and Sorribes, IC and King, LM and Hall, A and Geradts, J and Weaver, DL and Mallo, D and Holloway, S and Monyak, D and Gumbert, G and Vaez-Ghaemi, S and Wu, E and Murgas, K and Grimm, LJ and Maley, CC and Marks, JR and Shibata, D and Hwang,
             ES},
   Title = {Growth Dynamics of Ductal Carcinoma in Situ Recapitulate
             Normal Breast Development.},
   Journal = {bioRxiv},
   Year = {2023},
   Month = {October},
   url = {http://dx.doi.org/10.1101/2023.10.01.560370},
   Abstract = {Ductal carcinoma in situ (DCIS) and invasive breast cancer
             share many morphologic, proteomic, and genomic alterations.
             Yet in contrast to invasive cancer, many DCIS tumors do not
             progress and may remain indolent over decades. To better
             understand the heterogenous nature of this disease, we
             reconstructed the growth dynamics of 18 DCIS tumors based on
             the geo-spatial distribution of their somatic mutations. The
             somatic mutation topographies revealed that DCIS is
             multiclonal and consists of spatially discontinuous
             subclonal lesions. Here we show that this pattern of spread
             is consistent with a new 'Comet' model of DCIS
             tumorigenesis, whereby multiple subclones arise early and
             nucleate the buds of the growing tumor. The discontinuous,
             multiclonal growth of the Comet model is analogous to the
             branching morphogenesis of normal breast development that
             governs the rapid expansion of the mammary epithelium during
             puberty. The branching morphogenesis-like dynamics of the
             proposed Comet model diverges from the canonical model of
             clonal evolution, and better explains observed genomic
             spatial data. Importantly, the Comet model allows for the
             clinically relevant scenario of extensive DCIS spread,
             without being subjected to the selective pressures of
             subclone competition that promote the emergence of
             increasingly invasive phenotypes. As such, the normal cell
             movement inferred during DCIS growth provides a new
             explanation for the limited risk of progression in DCIS and
             adds biologic rationale for ongoing clinical efforts to
             reduce DCIS overtreatment.},
   Doi = {10.1101/2023.10.01.560370},
   Key = {fds373668}
}

@article{fds371236,
   Author = {Agarwal, PK and Geft, T and Halperin, D and Taylor,
             E},
   Title = {Multi-robot motion planning for unit discs with revolving
             areas},
   Journal = {Computational Geometry: Theory and Applications},
   Volume = {114},
   Year = {2023},
   Month = {October},
   url = {http://dx.doi.org/10.1016/j.comgeo.2023.102019},
   Abstract = {We study the problem of motion planning for a collection of
             n labeled unit disc robots in a polygonal environment. We
             assume that the robots have revolving areas around their
             start and final positions: that each start and each final is
             contained in a radius 2 disc lying in the free space, not
             necessarily concentric with the start or final position,
             which is free from other start or final positions. This
             assumption allows a weakly-monotone motion plan, in which
             robots move according to an ordering as follows: during the
             turn of a robot R in the ordering, it moves fully from its
             start to final position, while other robots do not leave
             their revolving areas. As R passes through a revolving area,
             a robot R′ that is inside this area may move within the
             revolving area to avoid a collision. Notwithstanding the
             existence of a motion plan, we show that minimizing the
             total traveled distance in this setting, specifically even
             when the motion plan is restricted to be weakly-monotone, is
             APX-hard, ruling out any polynomial-time
             (1+ε)-approximation algorithm. On the positive side, we
             present the first constant-factor approximation algorithm
             for computing a feasible weakly-monotone motion plan. The
             total distance traveled by the robots is within an O(1)
             factor of that of the optimal motion plan, which need not be
             weakly monotone. Our algorithm extends to an online setting
             in which the polygonal environment is fixed but the initial
             and final positions of robots are specified in an online
             manner. Finally, we observe that the overhead in the overall
             cost that we add while editing the paths to avoid
             robot-robot collision can vary significantly depending on
             the ordering we chose. Finding the best ordering in this
             respect is known to be NP-hard, and we provide a polynomial
             time O(log⁡nlog⁡log⁡n)-approximation algorithm for
             this problem.},
   Doi = {10.1016/j.comgeo.2023.102019},
   Key = {fds371236}
}

@article{fds373536,
   Author = {Qi, D and Liu, J-G},
   Title = {High-order moment closure models with random batch method
             for efficient computation of multiscale turbulent
             systems.},
   Journal = {Chaos (Woodbury, N.Y.)},
   Volume = {33},
   Number = {10},
   Pages = {103133},
   Year = {2023},
   Month = {October},
   url = {http://dx.doi.org/10.1063/5.0160057},
   Abstract = {We propose a high-order stochastic-statistical moment
             closure model for efficient ensemble prediction of
             leading-order statistical moments and probability density
             functions in multiscale complex turbulent systems. The
             statistical moment equations are closed by a precise
             calibration of the high-order feedbacks using ensemble
             solutions of the consistent stochastic equations, suitable
             for modeling complex phenomena including non-Gaussian
             statistics and extreme events. To address challenges
             associated with closely coupled spatiotemporal scales in
             turbulent states and expensive large ensemble simulation for
             high-dimensional systems, we introduce efficient
             computational strategies using the random batch method
             (RBM). This approach significantly reduces the required
             ensemble size while accurately capturing essential
             high-order structures. Only a small batch of small-scale
             fluctuation modes is used for each time update of the
             samples, and exact convergence to the full model statistics
             is ensured through frequent resampling of the batches during
             time evolution. Furthermore, we develop a reduced-order
             model to handle systems with really high dimensions by
             linking the large number of small-scale fluctuation modes to
             ensemble samples of dominant leading modes. The
             effectiveness of the proposed models is validated by
             numerical experiments on the one-layer and two-layer Lorenz
             '96 systems, which exhibit representative chaotic features
             and various statistical regimes. The full and reduced-order
             RBM models demonstrate uniformly high skill in capturing the
             time evolution of crucial leading-order statistics,
             non-Gaussian probability distributions, while achieving
             significantly lower computational cost compared to direct
             Monte-Carlo approaches. The models provide effective tools
             for a wide range of real-world applications in prediction,
             uncertainty quantification, and data assimilation.},
   Doi = {10.1063/5.0160057},
   Key = {fds373536}
}

@article{fds373425,
   Author = {Baek, Y and Aquino, W and Mukherjee, S},
   Title = {Generalized Bayes approach to inverse problems with model
             misspecification.},
   Journal = {Inverse problems},
   Volume = {39},
   Number = {10},
   Pages = {105011},
   Year = {2023},
   Month = {October},
   url = {http://dx.doi.org/10.1088/1361-6420/acf51c},
   Abstract = {We propose a general framework for obtaining probabilistic
             solutions to PDE-based inverse problems. Bayesian methods
             are attractive for uncertainty quantification but assume
             knowledge of the likelihood model or data generation
             process. This assumption is difficult to justify in many
             inverse problems, where the specification of the data
             generation process is not obvious. We adopt a Gibbs
             posterior framework that directly posits a regularized
             variational problem on the space of probability
             distributions of the parameter. We propose a novel model
             comparison framework that evaluates the optimality of a
             given loss based on its "predictive performance". We provide
             cross-validation procedures to calibrate the regularization
             parameter of the variational objective and compare multiple
             loss functions. Some novel theoretical properties of Gibbs
             posteriors are also presented. We illustrate the utility of
             our framework via a simulated example, motivated by
             dispersion-based wave models used to characterize arterial
             vessels in ultrasound vibrometry.},
   Doi = {10.1088/1361-6420/acf51c},
   Key = {fds373425}
}

@article{fds373966,
   Author = {Schlesinger, O and Kundu, R and Goetz, S and Sapiro, G and Peterchev,
             AV and Di Martino and JM},
   Title = {Automatic Neurocranial Landmarks Detection from Visible
             Facial Landmarks Leveraging 3D Head Priors.},
   Journal = {Clin Image Based Proced Fairness AI Med Imaging Ethical
             Philos Issues Med Imaging (2023)},
   Volume = {14242},
   Pages = {12-20},
   Year = {2023},
   Month = {October},
   ISBN = {9783031452482},
   url = {http://dx.doi.org/10.1007/978-3-031-45249-9_2},
   Abstract = {The localization and tracking of neurocranial landmarks is
             essential in modern medical procedures, e.g., transcranial
             magnetic stimulation (TMS). However, state-of-the-art
             treatments still rely on the manual identification of head
             targets and require setting retroreflective markers for
             tracking. This limits the applicability and scalability of
             TMS approaches, making them time-consuming, dependent on
             expensive hardware, and prone to errors when retroreflective
             markers drift from their initial position. To overcome these
             limitations, we propose a scalable method capable of
             inferring the position of points of interest on the scalp,
             e.g., the International 10-20 System's neurocranial
             landmarks. In contrast with existing approaches, our method
             does not require human intervention or markers; head
             landmarks are estimated leveraging visible facial landmarks,
             optional head size measurements, and statistical head model
             priors. We validate the proposed approach on ground truth
             data from 1,150 subjects, for which facial 3D and head
             information is available; our technique achieves a
             localization RMSE of 2.56 mm on average, which is of the
             same order as reported by high-end techniques in TMS. Our
             implementation is available at https://github.com/odedsc/ANLD.},
   Doi = {10.1007/978-3-031-45249-9_2},
   Key = {fds373966}
}

@article{fds373329,
   Author = {Perochon, S and Di Martino and JM and Carpenter, KLH and Compton, S and Davis, N and Eichner, B and Espinosa, S and Franz, L and Krishnappa
             Babu, PR and Sapiro, G and Dawson, G},
   Title = {Early detection of autism using digital behavioral
             phenotyping.},
   Journal = {Nat Med},
   Volume = {29},
   Number = {10},
   Pages = {2489-2497},
   Year = {2023},
   Month = {October},
   url = {http://dx.doi.org/10.1038/s41591-023-02574-3},
   Abstract = {Early detection of autism, a neurodevelopmental condition
             associated with challenges in social communication, ensures
             timely access to intervention. Autism screening
             questionnaires have been shown to have lower accuracy when
             used in real-world settings, such as primary care, as
             compared to research studies, particularly for children of
             color and girls. Here we report findings from a multiclinic,
             prospective study assessing the accuracy of an autism
             screening digital application (app) administered during a
             pediatric well-child visit to 475 (17-36 months old)
             children (269 boys and 206 girls), of which 49 were
             diagnosed with autism and 98 were diagnosed with
             developmental delay without autism. The app displayed
             stimuli that elicited behavioral signs of autism, quantified
             using computer vision and machine learning. An algorithm
             combining multiple digital phenotypes showed high diagnostic
             accuracy with the area under the receiver operating
             characteristic curve = 0.90, sensitivity = 87.8%,
             specificity = 80.8%, negative predictive
             value = 97.8% and positive predictive value = 40.6%.
             The algorithm had similar sensitivity performance across
             subgroups as defined by sex, race and ethnicity. These
             results demonstrate the potential for digital phenotyping to
             provide an objective, scalable approach to autism screening
             in real-world settings. Moreover, combining results from
             digital phenotyping and caregiver questionnaires may
             increase autism screening accuracy and help reduce
             disparities in access to diagnosis and intervention.},
   Doi = {10.1038/s41591-023-02574-3},
   Key = {fds373329}
}

@article{fds372698,
   Author = {Cao, Y and Lu, J and Wang, L},
   Title = {On Explicit L2 -Convergence Rate Estimate for
             Underdamped Langevin Dynamics},
   Journal = {Archive for Rational Mechanics and Analysis},
   Volume = {247},
   Number = {5},
   Year = {2023},
   Month = {October},
   url = {http://dx.doi.org/10.1007/s00205-023-01922-4},
   Abstract = {We provide a refined explicit estimate of the exponential
             decay rate of underdamped Langevin dynamics in the L2
             distance, based on a framework developed in Albritton et al.
             (Variational methods for the kinetic Fokker–Planck
             equation, arXiv arXiv:1902.04037 , 2019). To achieve this,
             we first prove a Poincaré-type inequality with a Gibbs
             measure in space and a Gaussian measure in momentum. Our
             estimate provides a more explicit and simpler expression of
             the decay rate; moreover, when the potential is convex with
             a Poincaré constant m≪ 1 , our estimate shows the decay
             rate of O(m) after optimizing the choice of the friction
             coefficient, which is much faster than m for the overdamped
             Langevin dynamics.},
   Doi = {10.1007/s00205-023-01922-4},
   Key = {fds372698}
}

@article{fds373363,
   Author = {Garrett, BL and Rudin, C},
   Title = {Interpretable algorithmic forensics.},
   Journal = {Proceedings of the National Academy of Sciences of the
             United States of America},
   Volume = {120},
   Number = {41},
   Pages = {e2301842120},
   Year = {2023},
   Month = {October},
   url = {http://dx.doi.org/10.1073/pnas.2301842120},
   Abstract = {One of the most troubling trends in criminal investigations
             is the growing use of "black box" technology, in which law
             enforcement rely on artificial intelligence (AI) models or
             algorithms that are either too complex for people to
             understand or they simply conceal how it functions. In
             criminal cases, black box systems have proliferated in
             forensic areas such as DNA mixture interpretation, facial
             recognition, and recidivism risk assessments. The champions
             and critics of AI argue, mistakenly, that we face a catch
             22: While black box AI is not understandable by people, they
             assume that it produces more accurate forensic evidence. In
             this Article, we question this assertion, which has so
             powerfully affected judges, policymakers, and academics. We
             describe a mature body of computer science research showing
             how "glass box" AI-designed to be interpretable-can be more
             accurate than black box alternatives. Indeed, black box AI
             performs predictably <i>worse</i> in settings like the
             criminal system. Debunking the black box performance myth
             has implications for forensic evidence, constitutional
             criminal procedure rights, and legislative policy. Absent
             some compelling-or even credible-government interest in
             keeping AI as a black box, and given the constitutional
             rights and public safety interests at stake, we argue that a
             substantial burden rests on the government to justify black
             box AI in criminal cases. We conclude by calling for
             judicial rulings and legislation to safeguard a right to
             interpretable forensic AI.},
   Doi = {10.1073/pnas.2301842120},
   Key = {fds373363}
}

@article{fds374489,
   Author = {An, J and Henderson, C and Ryzhik, L},
   Title = {Quantitative Steepness, Semi-FKPP Reactions, and
             Pushmi-Pullyu Fronts},
   Journal = {Archive for Rational Mechanics and Analysis},
   Volume = {247},
   Number = {5},
   Year = {2023},
   Month = {October},
   url = {http://dx.doi.org/10.1007/s00205-023-01924-2},
   Abstract = {We uncover a seemingly previously unnoticed algebraic
             structure of a large class of reaction–diffusion equations
             and use it to study the long time behavior of the solutions
             and their convergence to traveling waves in the pulled and
             pushed regimes, as well as at the pushmi-pullyu boundary.
             One such new object introduced in this paper is the shape
             defect function, which, indirectly, measures the difference
             between the profiles of the solution and the traveling wave.
             While one can recast the classical notion of ‘steepness’
             in terms of the positivity of the shape defect function, its
             positivity can, surprisingly, be used in numerous
             quantitative ways. In particular, the positivity is used in
             a new weighted Hopf-Cole transform and in a relative entropy
             approach that play a key role in the stability arguments.
             The shape defect function also gives a new connection
             between reaction–diffusion equations and reaction
             conservation laws at the pulled-pushed transition. Other
             simple but seemingly new algebraic constructions in the
             present paper supply various unexpected inequalities
             sprinkled throughout the paper. Of note is a new variational
             formulation that applies equally to pulled and pushed
             fronts, opening the door to an as-yet-elusive variational
             analysis in the pulled case.},
   Doi = {10.1007/s00205-023-01924-2},
   Key = {fds374489}
}

@article{fds374292,
   Author = {Landa, B and Cheng, X},
   Title = {Robust Inference of Manifold Density and Geometry by Doubly
             Stochastic Scaling},
   Journal = {SIAM Journal on Mathematics of Data Science},
   Volume = {5},
   Number = {3},
   Pages = {589-614},
   Publisher = {Society for Industrial & Applied Mathematics
             (SIAM)},
   Year = {2023},
   Month = {September},
   url = {http://dx.doi.org/10.1137/22m1516968},
   Doi = {10.1137/22m1516968},
   Key = {fds374292}
}

@article{fds373376,
   Author = {Hughes, J},
   Title = {Weave-realizability for D–type},
   Journal = {Algebraic & Geometric Topology},
   Volume = {23},
   Number = {6},
   Pages = {2735-2776},
   Publisher = {Mathematical Sciences Publishers},
   Year = {2023},
   Month = {September},
   url = {http://dx.doi.org/10.2140/agt.2023.23.2735},
   Doi = {10.2140/agt.2023.23.2735},
   Key = {fds373376}
}

@article{fds374287,
   Author = {Ciocanel, M-V and Goldrosen, N and Topaz, C},
   Title = {Quantifying Federal Sentence Disparities with Inferred
             Sentencing Records},
   Journal = {SIAM News Blogs},
   Year = {2023},
   Month = {September},
   Key = {fds374287}
}

@article{fds374498,
   Author = {Akin, V and Bookman, J and Braley, E},
   Title = {Modeling Active Learning in Professional Development for
             Teaching},
   Journal = {The journal of faculty development},
   Volume = {37},
   Number = {3},
   Pages = {28-39},
   Publisher = {Magna Publications},
   Year = {2023},
   Month = {September},
   Key = {fds374498}
}

@article{fds370378,
   Author = {Xu, J and Li, Y and Yang, H and Dunson, D and Daubechies,
             I},
   Title = {PiPs: A kernel-based optimization scheme for analyzing
             non-stationary 1D signals},
   Journal = {Applied and Computational Harmonic Analysis},
   Volume = {66},
   Pages = {1-17},
   Year = {2023},
   Month = {September},
   url = {http://dx.doi.org/10.1016/j.acha.2023.04.002},
   Abstract = {This paper proposes a novel kernel-based optimization scheme
             to handle tasks in the analysis, e.g., signal spectral
             estimation and single-channel source separation of 1D
             non-stationary oscillatory data. The key insight of our
             optimization scheme for reconstructing the time-frequency
             information is that when a nonparametric regression is
             applied on some input values, the output regressed points
             would lie near the oscillatory pattern of the oscillatory 1D
             signal only if these input values are a good approximation
             of the ground-truth phase function. In this work, Gaussian
             Process (GP) is chosen to conduct this nonparametric
             regression: the oscillatory pattern is encoded as the
             Pattern-inducing Points (PiPs) which act as the training
             data points in the GP regression; while the targeted phase
             function is fed in to compute the correlation kernels,
             acting as the testing input. Better approximated phase
             function generates more precise kernels, thus resulting in
             smaller optimization loss error when comparing the
             kernel-based regression output with the original signals. To
             the best of our knowledge, this is the first algorithm that
             can satisfactorily handle fully non-stationary oscillatory
             data, close and crossover frequencies, and general
             oscillatory patterns. Even in the example of a signal
             produced by slow variation in the parameters of a
             trigonometric expansion, we show that PiPs admits
             competitive or better performance in terms of accuracy and
             robustness than existing state-of-the-art
             algorithms.},
   Doi = {10.1016/j.acha.2023.04.002},
   Key = {fds370378}
}

@article{fds372678,
   Author = {Rigon, T and Herring, AH and Dunson, DB},
   Title = {A generalized Bayes framework for probabilistic
             clustering},
   Journal = {Biometrika},
   Volume = {110},
   Number = {3},
   Pages = {559-578},
   Year = {2023},
   Month = {September},
   url = {http://dx.doi.org/10.1093/biomet/asad004},
   Abstract = {Loss-based clustering methods, such as k-means clustering
             and its variants, are standard tools for finding groups in
             data. However, the lack of quantification of uncertainty in
             the estimated clusters is a disadvantage. Model-based
             clustering based on mixture models provides an alternative
             approach, but such methods face computational problems and
             are highly sensitive to the choice of kernel. In this
             article we propose a generalized Bayes framework that
             bridges between these paradigms through the use of Gibbs
             posteriors. In conducting Bayesian updating, the
             loglikelihood is replaced by a loss function for clustering,
             leading to a rich family of clustering methods. The Gibbs
             posterior represents a coherent updating of Bayesian beliefs
             without needing to specify a likelihood for the data, and
             can be used for characterizing uncertainty in clustering. We
             consider losses based on Bregman divergence and pairwise
             similarities, and develop efficient deterministic algorithms
             for point estimation along with sampling algorithms for
             uncertainty quantification. Several existing clustering
             algorithms, including k-means, can be interpreted as
             generalized Bayes estimators in our framework, and thus we
             provide a method of uncertainty quantification for these
             approaches, allowing, for example, calculation of the
             probability that a data point is well clustered.},
   Doi = {10.1093/biomet/asad004},
   Key = {fds372678}
}

@article{fds370363,
   Author = {Xu, J and Li, Y and Yang, H and Dunson, D and Daubechies,
             I},
   Title = {PiPs: A kernel-based optimization scheme for analyzing
             non-stationary 1D signals},
   Journal = {Applied and Computational Harmonic Analysis},
   Volume = {66},
   Pages = {1-17},
   Year = {2023},
   Month = {September},
   url = {http://dx.doi.org/10.1016/j.acha.2023.04.002},
   Abstract = {This paper proposes a novel kernel-based optimization scheme
             to handle tasks in the analysis, e.g., signal spectral
             estimation and single-channel source separation of 1D
             non-stationary oscillatory data. The key insight of our
             optimization scheme for reconstructing the time-frequency
             information is that when a nonparametric regression is
             applied on some input values, the output regressed points
             would lie near the oscillatory pattern of the oscillatory 1D
             signal only if these input values are a good approximation
             of the ground-truth phase function. In this work, Gaussian
             Process (GP) is chosen to conduct this nonparametric
             regression: the oscillatory pattern is encoded as the
             Pattern-inducing Points (PiPs) which act as the training
             data points in the GP regression; while the targeted phase
             function is fed in to compute the correlation kernels,
             acting as the testing input. Better approximated phase
             function generates more precise kernels, thus resulting in
             smaller optimization loss error when comparing the
             kernel-based regression output with the original signals. To
             the best of our knowledge, this is the first algorithm that
             can satisfactorily handle fully non-stationary oscillatory
             data, close and crossover frequencies, and general
             oscillatory patterns. Even in the example of a signal
             produced by slow variation in the parameters of a
             trigonometric expansion, we show that PiPs admits
             competitive or better performance in terms of accuracy and
             robustness than existing state-of-the-art
             algorithms.},
   Doi = {10.1016/j.acha.2023.04.002},
   Key = {fds370363}
}

@article{fds372370,
   Author = {Balmaceda, JM and Clemens, CH and Daubechies, I and Pineda, AR and Rusu,
             G and Waldschmidt, M},
   Title = {Graduate Assistantships in Developing Countries (GRAID)
             Supporting Mathematics Graduate Students in the Countries
             that Need it Most},
   Journal = {Notices of the American Mathematical Society},
   Volume = {70},
   Number = {8},
   Pages = {1281-1284},
   Year = {2023},
   Month = {September},
   url = {http://dx.doi.org/10.1090/noti2749},
   Doi = {10.1090/noti2749},
   Key = {fds372370}
}

@article{fds373965,
   Author = {Nazaret, A and Sapiro, G},
   Title = {A large-scale observational study of the causal effects of a
             behavioral health nudge.},
   Journal = {Science advances},
   Volume = {9},
   Number = {38},
   Pages = {eadi1752},
   Year = {2023},
   Month = {September},
   url = {http://dx.doi.org/10.1126/sciadv.adi1752},
   Abstract = {Nudges are interventions promoting healthy behavior without
             forbidding options or substantial incentives; the Apple
             Watch, for example, encourages users to stand by delivering
             a notification if they have been sitting for the first 50
             minutes of an hour. On the basis of 76 billion minutes of
             observational standing data from 160,000 subjects in the
             public Apple Heart and Movement Study, we estimate the
             causal effect of this notification using a regression
             discontinuity design for time series data with time-varying
             treatment. We show that the nudge increases the probability
             of standing by up to 43.9% and remains effective with time.
             The nudge's effectiveness increases with age and is
             independent of gender. Closing Apple Watch Activity Rings, a
             visualization of participants' daily progress in Move,
             Exercise, and Stand, further increases the nudge's impact.
             This work demonstrates the effectiveness of behavioral
             health interventions and introduces tools for investigating
             their causal effect from large-scale observations.},
   Doi = {10.1126/sciadv.adi1752},
   Key = {fds373965}
}

@article{fds374499,
   Author = {Akin, V and Viel, S},
   Title = {Equity in Grading Systems: Moving Away From “Fair”
             Towards Transparency and Inclusion in Coordinated Calculus
             Courses},
   Volume = {96},
   Booktitle = {Justice through the lens of calculus: Framing new
             possibilities for diversity, equity, and
             inclusion.},
   Publisher = {MAA Press},
   Editor = {Voigt, M and Hagaman, J and Gehrtz, J and Ratliff, B and Alexander, N and Levy, R},
   Year = {2023},
   Month = {September},
   Key = {fds374499}
}

@article{fds374500,
   Author = {Akin, V and Bookman, J and Braley, E},
   Title = {Modeling Active Learning in Professional Development for
             Teaching},
   Journal = {The journal of faculty development},
   Volume = {37},
   Number = {3},
   Pages = {28-39},
   Publisher = {Magna Publications},
   Year = {2023},
   Month = {September},
   Key = {fds374500}
}

@article{fds374501,
   Author = {Akin, V and Viel, S},
   Title = {Equity in Grading Systems: Moving Away From “Fair”
             Towards Transparency and Inclusion in Coordinated Calculus
             Courses},
   Volume = {96},
   Booktitle = {Justice through the lens of calculus: Framing new
             possibilities for diversity, equity, and
             inclusion.},
   Publisher = {MAA Press},
   Editor = {Voigt, M and Hagaman, J and Gehrtz, J and Ratliff, B and Alexander, N and Levy, R},
   Year = {2023},
   Month = {September},
   Key = {fds374501}
}

@article{fds372441,
   Author = {Bachmann, T and Wickelgren, K},
   Title = {On quadratically enriched excess and residual
             intersections},
   Journal = {Journal fur die Reine und Angewandte Mathematik},
   Volume = {2023},
   Number = {802},
   Pages = {77-123},
   Year = {2023},
   Month = {September},
   url = {http://dx.doi.org/10.1515/crelle-2023-0041},
   Abstract = {We use recent duality results of Eisenbud and Ulrich to give
             tools to study quadratically enriched residual intersections
             when there is no excess bundle. We use this to prove a
             formula for the Witt-valued Euler number of an almost
             complete intersection. We give example computations of
             quadratically enriched excess and residual
             intersections.},
   Doi = {10.1515/crelle-2023-0041},
   Key = {fds372441}
}

@article{fds372277,
   Author = {Drivas, TD and Dunlap, A and Graham, C and La, J and Ryzhik,
             L},
   Title = {Invariant measures for stochastic conservation laws on the
             line},
   Journal = {Nonlinearity},
   Volume = {36},
   Number = {9},
   Pages = {4553-4594},
   Year = {2023},
   Month = {September},
   url = {http://dx.doi.org/10.1088/1361-6544/acdb3a},
   Abstract = {We consider a stochastic conservation law on the line with
             solution-dependent diffusivity, a super-linear,
             sub-quadratic Hamiltonian, and smooth, spatially-homogeneous
             kick-type random forcing. We show that this Markov process
             admits a unique ergodic spatially-homogeneous invariant
             measure for each mean in a non-explicit unbounded set. This
             generalises previous work on the stochastic Burgers
             equation.},
   Doi = {10.1088/1361-6544/acdb3a},
   Key = {fds372277}
}

@article{fds372446,
   Author = {Huang, H and Yu, J and Chen, J and Lai, R},
   Title = {Bridging mean-field games and normalizing flows with
             trajectory regularization},
   Journal = {Journal of Computational Physics},
   Volume = {487},
   Pages = {112155-112155},
   Publisher = {Elsevier BV},
   Year = {2023},
   Month = {August},
   url = {http://dx.doi.org/10.1016/j.jcp.2023.112155},
   Abstract = {Mean-field games (MFGs) are a modeling framework for systems
             with a large number of interacting agents. They have
             applications in economics, finance, and game theory.
             Normalizing flows (NFs) are a family of deep generative
             models that compute data likelihoods by using an invertible
             mapping typically parameterized by neural networks. They are
             useful for density modeling and data generation. While
             active research has been conducted on both models, few noted
             the relationship between the two. In this work, we unravel
             the connections between MFGs and NFs by contextualizing the
             training of an NF as solving the MFG. This is achieved by
             reformulating the MFG problem in terms of agent trajectories
             and parameterizing a discretization of the resulting MFG
             with flow architectures. With this connection, we explore
             two research directions. First, we employ expressive NF
             architectures to accurately solve high-dimensional MFGs,
             sidestepping the curse of dimensionality in traditional
             numerical methods. Compared with other deep learning
             approaches, our trajectory-based formulation encodes the
             continuity equation in the network architecture to better
             approximate population dynamics. Second, we regularize the
             training of NFs with transport costs and show the
             effectiveness on controlling the model's Lipschitz bound,
             resulting in better generalization performance. We
             demonstrate numerical results through comprehensive
             experiments on a variety of synthetic and real-life
             datasets.},
   Doi = {10.1016/j.jcp.2023.112155},
   Key = {fds372446}
}

@article{fds372809,
   Author = {Hahn, S and Zhu, R and Mak, S and Rudin, C and Jiang,
             Y},
   Title = {An Interpretable, Flexible, and Interactive Probabilistic
             Framework for Melody Generation},
   Journal = {Proceedings of the ACM SIGKDD International Conference on
             Knowledge Discovery and Data Mining},
   Pages = {4089-4099},
   Year = {2023},
   Month = {August},
   ISBN = {9798400701030},
   url = {http://dx.doi.org/10.1145/3580305.3599772},
   Abstract = {The fast-growing demand for algorithmic music generation is
             found throughout entertainment, art, education, etc.
             Unfortunately, most recent models are practically impossible
             to interpret or musically fine-tune, as they use deep neural
             networks with thousands of parameters. We introduce an
             interpretable, flexible, and interactive model,
             SchenkComposer, for melody generation that empowers users to
             be creative in all aspects of the music generation pipeline
             and allows them to learn from the process. We divide the
             task of melody generation into steps based on the process
             that a human composer using music-theoretical domain
             knowledge might use. First, the model determines phrase
             structure based on form analysis and identifies an
             appropriate number of measures. Using concepts from
             Schenkerian analysis, the model then finds a fitting
             harmonic rhythm, middleground harmonic progression,
             foreground rhythm, and melody in a hierarchical, scaffolded
             approach using a probabilistic context-free grammar based on
             musical contours. By incorporating theories of musical form
             and harmonic structure, our model produces music with
             long-term structural coherence. In extensive human
             experiments, we find that music generated with our approach
             successfully passes a Turing test in human experiments while
             current state-of-the-art approaches fail, and we further
             demonstrate superior performance and preference for our
             melodies compared to existing melody generation methods.
             Additionally, we developed and deployed a public website for
             SchenkComposer, and conducted preliminary user surveys.
             Through analysis, we show the strong viability and
             enjoyability of SchenkComposer.},
   Doi = {10.1145/3580305.3599772},
   Key = {fds372809}
}

@article{fds372520,
   Author = {Gao, Q and Ji, Z and Wang, L and Owzar, K and Li, Q-J and Chan, C and Xie,
             J},
   Title = {SifiNet: A robust and accurate method to identify feature
             gene sets and annotate cells.},
   Journal = {bioRxiv},
   Year = {2023},
   Month = {August},
   url = {http://dx.doi.org/10.1101/2023.05.24.541352},
   Abstract = {SifiNet is a robust and accurate computational pipeline for
             identifying distinct gene sets, extracting and annotating
             cellular subpopulations, and elucidating intrinsic
             relationships among these subpopulations. Uniquely, SifiNet
             bypasses the cell clustering stage, commonly integrated into
             other cellular annotation pipelines, thereby circumventing
             potential inaccuracies in clustering that may compromise
             subsequent analyses. Consequently, SifiNet has demonstrated
             superior performance in multiple experimental datasets
             compared with other state-of-the-art methods. SifiNet can
             analyze both single-cell RNA and ATAC sequencing data,
             thereby rendering comprehensive multiomic cellular profiles.
             It is conveniently available as an open-source R
             package.},
   Doi = {10.1101/2023.05.24.541352},
   Key = {fds372520}
}

@article{fds371623,
   Author = {Autry, E and Carter, D and Herschlag, GJ and Hunter, Z and Mattingly,
             JC},
   Title = {METROPOLIZED FOREST RECOMBINATION FOR MONTE CARLO SAMPLING
             OF GRAPH PARTITIONS},
   Journal = {SIAM Journal on Applied Mathematics},
   Volume = {83},
   Number = {4},
   Pages = {1366-1391},
   Publisher = {Society for Industrial & Applied Mathematics
             (SIAM)},
   Year = {2023},
   Month = {August},
   url = {http://dx.doi.org/10.1137/21M1418010},
   Abstract = {We develop a new Markov chain on graph partitions that makes
             relatively global moves yet is computationally feasible to
             be used as the proposal in the Metropolis-Hastings method.
             Our resulting algorithm is able to sample from a specified
             measure on partitions or spanning forests. Being able to
             sample from a specified measure is a requirement of what we
             consider as the gold standard in quantifying the extent to
             which a particular map is a gerrymander. Our proposal chain
             modifies the recently developed method called recombination
             (ReCom), which draws spanning trees on joined partitions and
             then randomly cuts them to repartition. We improve the
             computational efficiency by augmenting the statespace from
             partitions to spanning forests. The extra information
             accelerates the computation of the forward and backward
             proposal probabilities which are required for the
             Metropolis-Hastings algorithm. We demonstrate this method by
             sampling redistricting plans on several measures of interest
             and find promising convergence results on several key
             observables of interest. We also explore some limitations in
             the measures that are efficient to sample from and
             investigate the feasibility of using parallel tempering to
             extend this space of measures.},
   Doi = {10.1137/21M1418010},
   Key = {fds371623}
}

@article{fds371472,
   Author = {Liu, R and Li, M and Dunson, DB},
   Title = {PPA: Principal parcellation analysis for brain connectomes
             and multiple traits.},
   Journal = {NeuroImage},
   Volume = {276},
   Pages = {120214},
   Year = {2023},
   Month = {August},
   url = {http://dx.doi.org/10.1016/j.neuroimage.2023.120214},
   Abstract = {Our understanding of the structure of the brain and its
             relationships with human traits is largely determined by how
             we represent the structural connectome. Standard practice
             divides the brain into regions of interest (ROIs) and
             represents the connectome as an adjacency matrix having
             cells measuring connectivity between pairs of ROIs.
             Statistical analyses are then heavily driven by the (largely
             arbitrary) choice of ROIs. In this article, we propose a
             human trait prediction framework utilizing a
             tractography-based representation of the brain connectome,
             which clusters fiber endpoints to define a data-driven white
             matter parcellation targeted to explain variation among
             individuals and predict human traits. This leads to
             Principal Parcellation Analysis (PPA), representing
             individual brain connectomes by compositional vectors
             building on a basis system of fiber bundles that captures
             the connectivity at the population level. PPA eliminates the
             need to choose atlases and ROIs a priori, and provides a
             simpler, vector-valued representation that facilitates
             easier statistical analysis compared to the complex graph
             structures encountered in classical connectome analyses. We
             illustrate the proposed approach through applications to
             data from the Human Connectome Project (HCP) and show that
             PPA connectomes improve power in predicting human traits
             over state-of-the-art methods based on classical
             connectomes, while dramatically improving parsimony and
             maintaining interpretability. Our PPA package is publicly
             available on GitHub, and can be implemented routinely for
             diffusion image data.},
   Doi = {10.1016/j.neuroimage.2023.120214},
   Key = {fds371472}
}

@article{fds376096,
   Author = {Talbot, A and Dunson, D and Dzirasa, K and Carlson,
             D},
   Title = {Estimating a brain network predictive of stress and genotype
             with supervised autoencoders.},
   Journal = {J R Stat Soc Ser C Appl Stat},
   Volume = {72},
   Number = {4},
   Pages = {912-936},
   Year = {2023},
   Month = {August},
   url = {http://dx.doi.org/10.1093/jrsssc/qlad035},
   Abstract = {Targeted brain stimulation has the potential to treat mental
             illnesses. We develop an approach to help design protocols
             by identifying relevant multi-region electrical dynamics.
             Our approach models these dynamics as a superposition of
             latent networks, where the latent variables predict a
             relevant outcome. We use supervised autoencoders (SAEs) to
             improve predictive performance in this context, describe the
             conditions where SAEs improve predictions, and provide
             modelling constraints to ensure biological relevance. We
             experimentally validate our approach by finding a network
             associated with stress that aligns with a previous
             stimulation protocol and characterizing a genotype
             associated with bipolar disorder.},
   Doi = {10.1093/jrsssc/qlad035},
   Key = {fds376096}
}

@article{fds371669,
   Author = {Chen, J and Engelhard, M and Henao, R and Berchuck, S and Eichner, B and Perrin, EM and Sapiro, G and Dawson, G},
   Title = {Enhancing early autism prediction based on electronic
             records using clinical narratives.},
   Journal = {J Biomed Inform},
   Volume = {144},
   Pages = {104390},
   Year = {2023},
   Month = {August},
   url = {http://dx.doi.org/10.1016/j.jbi.2023.104390},
   Abstract = {Recent work has shown that predictive models can be applied
             to structured electronic health record (EHR) data to
             stratify autism likelihood from an early age (<1 year).
             Integrating clinical narratives (or notes) with structured
             data has been shown to improve prediction performance in
             other clinical applications, but the added predictive value
             of this information in early autism prediction has not yet
             been explored. In this study, we aimed to enhance the
             performance of early autism prediction by using both
             structured EHR data and clinical narratives. We built models
             based on structured data and clinical narratives separately,
             and then an ensemble model that integrated both sources of
             data. We assessed the predictive value of these models from
             Duke University Health System over a 14-year span to
             evaluate ensemble models predicting later autism diagnosis
             (by age 4 years) from data collected from ages 30 to
             360 days. Our sample included 11,750 children above by age
             3 years (385 meeting autism diagnostic criteria). The
             ensemble model for autism prediction showed superior
             performance and at age 30 days achieved 46.8% sensitivity
             (95% confidence interval, CI: 22.0%, 52.9%), 28.0% positive
             predictive value (PPV) at high (90%) specificity (CI: 2.0%,
             33.1%), and AUC4 (with at least 4-year follow-up for
             controls) reaching 0.769 (CI: 0.715, 0.811). Prediction by
             360 days achieved 44.5% sensitivity (CI: 23.6%, 62.9%),
             and 13.7% PPV at high (90%) specificity (CI: 9.6%, 18.9%),
             and AUC4 reaching 0.797 (CI: 0.746, 0.840). Results show
             that incorporating clinical narratives in early autism
             prediction achieved promising accuracy by age 30 days,
             outperforming models based on structured data only.
             Furthermore, findings suggest that additional features
             learned from clinician narratives might be hypothesis
             generating for understanding early development in
             autism.},
   Doi = {10.1016/j.jbi.2023.104390},
   Key = {fds371669}
}

@article{fds374487,
   Author = {Autry, E and Carter, D and Herschlag, GJ and Hunter, Z and Mattingly,
             JC},
   Title = {METROPOLIZED FOREST RECOMBINATION FOR MONTE CARLO SAMPLING
             OF GRAPH PARTITIONS},
   Journal = {SIAM Journal on Applied Mathematics},
   Volume = {83},
   Number = {4},
   Pages = {1366-1391},
   Year = {2023},
   Month = {August},
   url = {http://dx.doi.org/10.1137/21M1418010},
   Abstract = {We develop a new Markov chain on graph partitions that makes
             relatively global moves yet is computationally feasible to
             be used as the proposal in the Metropolis-Hastings method.
             Our resulting algorithm is able to sample from a specified
             measure on partitions or spanning forests. Being able to
             sample from a specified measure is a requirement of what we
             consider as the gold standard in quantifying the extent to
             which a particular map is a gerrymander. Our proposal chain
             modifies the recently developed method called recombination
             (ReCom), which draws spanning trees on joined partitions and
             then randomly cuts them to repartition. We improve the
             computational efficiency by augmenting the statespace from
             partitions to spanning forests. The extra information
             accelerates the computation of the forward and backward
             proposal probabilities which are required for the
             Metropolis-Hastings algorithm. We demonstrate this method by
             sampling redistricting plans on several measures of interest
             and find promising convergence results on several key
             observables of interest. We also explore some limitations in
             the measures that are efficient to sample from and
             investigate the feasibility of using parallel tempering to
             extend this space of measures.},
   Doi = {10.1137/21M1418010},
   Key = {fds374487}
}

@article{fds371244,
   Author = {Parikh, H and Hoffman, K and Sun, H and Zafar, SF and Ge, W and Jing, J and Liu, L and Sun, J and Struck, A and Volfovsky, A and Rudin, C and Westover,
             MB},
   Title = {Effects of epileptiform activity on discharge outcome in
             critically ill patients in the USA: a retrospective
             cross-sectional study.},
   Journal = {The Lancet. Digital health},
   Volume = {5},
   Number = {8},
   Pages = {e495-e502},
   Year = {2023},
   Month = {August},
   url = {http://dx.doi.org/10.1016/s2589-7500(23)00088-2},
   Abstract = {<h4>Background</h4>Epileptiform activity is associated with
             worse patient outcomes, including increased risk of
             disability and death. However, the effect of epileptiform
             activity on neurological outcome is confounded by the
             feedback between treatment with antiseizure medications and
             epileptiform activity burden. We aimed to quantify the
             heterogeneous effects of epileptiform activity with an
             interpretability-centred approach.<h4>Methods</h4>We did a
             retrospective, cross-sectional study of patients in the
             intensive care unit who were admitted to Massachusetts
             General Hospital (Boston, MA, USA). Participants were aged
             18 years or older and had electrographic epileptiform
             activity identified by a clinical neurophysiologist or
             epileptologist. The outcome was the dichotomised modified
             Rankin Scale (mRS) at discharge and the exposure was
             epileptiform activity burden defined as mean or maximum
             proportion of time spent with epileptiform activity in 6 h
             windows in the first 24 h of electroencephalography. We
             estimated the change in discharge mRS if everyone in the
             dataset had experienced a specific epileptiform activity
             burden and were untreated. We combined pharmacological
             modelling with an interpretable matching method to account
             for confounding and epileptiform activity-antiseizure
             medication feedback. The quality of the matched groups was
             validated by the neurologists.<h4>Findings</h4>Between Dec
             1, 2011, and Oct 14, 2017, 1514 patients were admitted to
             Massachusetts General Hospital intensive care unit, 995
             (66%) of whom were included in the analysis. Compared with
             patients with a maximum epileptiform activity of 0 to less
             than 25%, patients with a maximum epileptiform activity
             burden of 75% or more when untreated had a mean 22·27% (SD
             0·92) increased chance of a poor outcome (severe disability
             or death). Moderate but long-lasting epileptiform activity
             (mean epileptiform activity burden 2% to <10%) increased the
             risk of a poor outcome by mean 13·52% (SD 1·93). The
             effect sizes were heterogeneous depending on preadmission
             profile-eg, patients with hypoxic-ischaemic encephalopathy
             or acquired brain injury were more adversely affected
             compared with patients without these conditions.<h4>Interpretation</h4>Our
             results suggest that interventions should put a higher
             priority on patients with an average epileptiform activity
             burden 10% or greater, and treatment should be more
             conservative when maximum epileptiform activity burden is
             low. Treatment should also be tailored to individual
             preadmission profiles because the potential for epileptiform
             activity to cause harm depends on age, medical history, and
             reason for admission.<h4>Funding</h4>National Institutes of
             Health and National Science Foundation.},
   Doi = {10.1016/s2589-7500(23)00088-2},
   Key = {fds371244}
}

@article{fds371608,
   Author = {Peloquin, J and Kirillova, A and Rudin, C and Brinson, LC and Gall,
             K},
   Title = {Prediction of tensile performance for 3D printed
             photopolymer gyroid lattices using structural porosity, base
             material properties, and machine learning},
   Journal = {Materials and Design},
   Volume = {232},
   Year = {2023},
   Month = {August},
   url = {http://dx.doi.org/10.1016/j.matdes.2023.112126},
   Abstract = {Advancements in additive manufacturing (AM) technology and
             three-dimensional (3D) modeling software have enabled the
             fabrication of parts with combinations of properties that
             were impossible to achieve with traditional manufacturing
             techniques. Porous designs such as truss-based and
             sheet-based lattices have gained much attention in recent
             years due to their versatility. The multitude of lattice
             design possibilities, coupled with a growing list of
             available 3D printing materials, has provided a vast range
             of 3D printable structures that can be used to achieve
             desired performance. However, the process of computationally
             or experimentally evaluating many combinations of base
             material and lattice design for a given application is
             impractical. This research proposes a framework for quickly
             predicting key mechanical properties of 3D printed gyroid
             lattices using information about the base material and
             porosity of the structure. Experimental data was gathered to
             train a simple, interpretable, and accurate kernel ridge
             regression machine learning model. The performance of the
             model was then compared to numerical simulation data and
             demonstrated similar accuracy at a fraction of the
             computation time. Ultimately, the model development serves
             as an advancement in ML-driven mechanical property
             prediction that can be used to guide extension of current
             and future models.},
   Doi = {10.1016/j.matdes.2023.112126},
   Key = {fds371608}
}

@article{fds371888,
   Author = {Peloquin, J and Kirillova, A and Mathey, E and Rudin, C and Brinson, LC and Gall, K},
   Title = {Tensile performance data of 3D printed photopolymer gyroid
             lattices.},
   Journal = {Data in brief},
   Volume = {49},
   Pages = {109396},
   Year = {2023},
   Month = {August},
   url = {http://dx.doi.org/10.1016/j.dib.2023.109396},
   Abstract = {Additive manufacturing has provided the ability to
             manufacture complex structures using a wide variety of
             materials and geometries. Structures such as triply periodic
             minimal surface (TPMS) lattices have been incorporated into
             products across many fields due to their unique combinations
             of mechanical, geometric, and physical properties. Yet, the
             near limitless possibility of combining geometry and
             material into these lattices leaves much to be discovered.
             This article provides a dataset of experimentally gathered
             tensile stress-strain curves and measured porosity values
             for 389 unique gyroid lattice structures manufactured using
             vat photopolymerization 3D printing. The lattice samples
             were printed from one of twenty different photopolymer
             materials available from either Formlabs, LOCTITE AM, or
             ETEC that range from strong and brittle to elastic and
             ductile and were printed on commercially available 3D
             printers, specifically the Formlabs Form2, Prusa SL1, and
             ETEC Envision One cDLM Mechanical. The stress-strain curves
             were recorded with an MTS Criterion C43.504 mechanical
             testing apparatus and following ASTM standards, and the void
             fraction or "porosity" of each lattice was measured using a
             calibrated scale. This data serves as a valuable resource
             for use in the development of novel printing materials and
             lattice geometries and provides insight into the influence
             of photopolymer material properties on the printability,
             geometric accuracy, and mechanical performance of 3D printed
             lattice structures. The data described in this article was
             used to train a machine learning model capable of predicting
             mechanical properties of 3D printed gyroid lattices based on
             the base mechanical properties of the printing material and
             porosity of the lattice in the research article
             [1].},
   Doi = {10.1016/j.dib.2023.109396},
   Key = {fds371888}
}

@article{fds373056,
   Author = {McDonald, SM and Augustine, EK and Lanners, Q and Rudin, C and Catherine
             Brinson, L and Becker, ML},
   Title = {Applied machine learning as a driver for polymeric
             biomaterials design.},
   Journal = {Nature communications},
   Volume = {14},
   Number = {1},
   Pages = {4838},
   Year = {2023},
   Month = {August},
   url = {http://dx.doi.org/10.1038/s41467-023-40459-8},
   Abstract = {Polymers are ubiquitous to almost every aspect of modern
             society and their use in medical products is similarly
             pervasive. Despite this, the diversity in commercial
             polymers used in medicine is stunningly low. Considerable
             time and resources have been extended over the years towards
             the development of new polymeric biomaterials which address
             unmet needs left by the current generation of medical-grade
             polymers. Machine learning (ML) presents an unprecedented
             opportunity in this field to bypass the need for
             trial-and-error synthesis, thus reducing the time and
             resources invested into new discoveries critical for
             advancing medical treatments. Current efforts pioneering
             applied ML in polymer design have employed combinatorial and
             high throughput experimental design to address data
             availability concerns. However, the lack of available and
             standardized characterization of parameters relevant to
             medicine, including degradation time and biocompatibility,
             represents a nearly insurmountable obstacle to ML-aided
             design of biomaterials. Herein, we identify a gap at the
             intersection of applied ML and biomedical polymer design,
             highlight current works at this junction more broadly and
             provide an outlook on challenges and future
             directions.},
   Doi = {10.1038/s41467-023-40459-8},
   Key = {fds373056}
}

@article{fds374518,
   Author = {Li, S and Zhang, C and Zhang, Z and Zhao, H},
   Title = {A data-driven and model-based accelerated Hamiltonian Monte
             Carlo method for Bayesian elliptic inverse
             problems},
   Journal = {Statistics and Computing},
   Volume = {33},
   Number = {4},
   Year = {2023},
   Month = {August},
   url = {http://dx.doi.org/10.1007/s11222-023-10262-y},
   Abstract = {In this paper, we consider a Bayesian inverse problem
             modeled by elliptic partial differential equations (PDEs).
             Specifically, we propose a data-driven and model-based
             approach to accelerate the Hamiltonian Monte Carlo (HMC)
             method in solving large-scale Bayesian inverse problems. The
             key idea is to exploit (model-based) and construct
             (data-based) intrinsic approximate low-dimensional structure
             of the underlying problem which consists of two
             components—a training component that computes a set of
             data-driven basis to achieve significant dimension reduction
             in the solution space, and a fast solving component that
             computes the solution and its derivatives for a newly
             sampled elliptic PDE with the constructed data-driven basis.
             Hence we develop an effective data and model-based approach
             for the Bayesian inverse problem and overcome the typical
             computational bottleneck of HMC—repeated evaluation of the
             Hamiltonian involving the solution (and its derivatives)
             modeled by a complex system, a multiscale elliptic PDE in
             our case. Finally, we present numerical examples to
             demonstrate the accuracy and efficiency of the proposed
             method.},
   Doi = {10.1007/s11222-023-10262-y},
   Key = {fds374518}
}

@article{fds372525,
   Author = {Przybyło, J and Wei, F},
   Title = {On the Asymptotic Confirmation of the Faudree–Lehel
             Conjecture for General Graphs},
   Journal = {Combinatorica},
   Volume = {43},
   Number = {4},
   Pages = {791-826},
   Year = {2023},
   Month = {August},
   url = {http://dx.doi.org/10.1007/s00493-023-00036-5},
   Abstract = {Given a simple graph G, the irregularity strength of G,
             denoted by s(G), is the least positive integer k such that
             there is a weight assignment on edges f: E(G) → { 1 , 2 ,
             ⋯ , k} attributing distinct weighted degrees: f~ (v) : =
             ∑ u:{u,v}∈E(G)f({ u, v}) to all vertices v∈ V(G) . It
             is straightforward that s(G) ≥ n/ d for every d-regular
             graph G on n vertices with d> 1 . In 1987, Faudree and Lehel
             conjectured in turn that there is an absolute constant c
             such that s(G) ≤ n/ d+ c for all such graphs. Even though
             the conjecture has remained open in almost all relevant
             cases, it is more generally believed that there exists a
             universal constant c such that s(G) ≤ n/ δ+ c for every
             graph G on n vertices with minimum degree δ≥ 1 which does
             not contain an isolated edge; In this paper we confirm that
             the generalized Faudree–Lehel Conjecture holds for graphs
             with δ≥ nβ where β is any fixed constant larger than
             0.8; Furthermore, we confirm that the conjecture holds in
             general asymptotically. That is, we prove that for any ε∈
             (0 , 0.25) there exist absolute constants c1, c2 such that
             for all graphs G on n vertices with minimum degree δ≥ 1
             and without isolated edges, s(G)≤nδ(1+c1δε)+c2 ; We
             thereby extend in various aspects and strengthen a recent
             result of Przybyło, who showed that s(G)≤nd(1+1lnε/19n)=nd(1+o(1))
             for d-regular graphs with d∈ [ln 1+εn, n/ ln εn] . We
             also improve the earlier general upper bound: s(G)<6nδ+6 of
             Kalkowski, Karoński and Pfender.},
   Doi = {10.1007/s00493-023-00036-5},
   Key = {fds372525}
}

@article{fds372207,
   Author = {Ciocanel, M-V and Ding, L and Mastromatteo, L and Reichheld, S and Cabral, S and Mowry, K and Sandstede, B},
   Title = {Parameter identifiability in PDE models of fluorescence
             recovery after photobleaching},
   Year = {2023},
   Month = {July},
   Key = {fds372207}
}

@article{fds373378,
   Author = {Hughes, J},
   Title = {Lagrangian fillings in $A$-type and their Kálmán loop
             orbits},
   Journal = {Revista Matemática Iberoamericana},
   Volume = {39},
   Number = {5},
   Pages = {1681-1723},
   Publisher = {European Mathematical Society - EMS - Publishing House
             GmbH},
   Year = {2023},
   Month = {July},
   url = {http://dx.doi.org/10.4171/rmi/1436},
   Doi = {10.4171/rmi/1436},
   Key = {fds373378}
}

@article{fds371671,
   Author = {Feiger, B and Jensen, CW and Bryner, BS and Segars, WP and Randles,
             A},
   Title = {Modeling the effect of patient size on cerebral perfusion
             during veno-arterial extracorporeal membrane
             oxygenation.},
   Journal = {Perfusion},
   Pages = {2676591231187962},
   Year = {2023},
   Month = {July},
   url = {http://dx.doi.org/10.1177/02676591231187962},
   Abstract = {INTRODUCTION: A well-known complication of veno-arterial
             extracorporeal membrane oxygenation (VA ECMO) is
             differential hypoxia, in which poorly-oxygenated blood
             ejected from the left ventricle mixes with and displaces
             well-oxygenated blood from the circuit, thereby causing
             cerebral hypoxia and ischemia. We sought to characterize the
             impact of patient size and anatomy on cerebral perfusion
             under a range of different VA ECMO flow conditions. METHODS:
             We use one-dimensional (1D) flow simulations to investigate
             mixing zone location and cerebral perfusion across 10
             different levels of VA ECMO support in eight semi-idealized
             patient geometries, for a total of 80 scenarios. Measured
             outcomes included mixing zone location and cerebral blood
             flow (CBF). RESULTS: Depending on patient anatomy, we found
             that a VA ECMO support ranging between 67-97% of a patient's
             ideal cardiac output was needed to perfuse the brain. In
             some cases, VA ECMO flows exceeding 90% of the patient's
             ideal cardiac output are needed for adequate cerebral
             perfusion. CONCLUSIONS: Individual patient anatomy markedly
             affects mixing zone location and cerebral perfusion in VA
             ECMO. Future fluid simulations of VA ECMO physiology should
             incorporate varied patient sizes and geometries in order to
             best provide insights toward reducing neurologic injury and
             improved outcomes in this patient population.},
   Doi = {10.1177/02676591231187962},
   Key = {fds371671}
}

@article{fds371278,
   Author = {Coffman, M and Di Martino and JM and Aiello, R and Carpenter, KLH and Chang, Z and Compton, S and Eichner, B and Espinosa, S and Flowers, J and Franz, L and Perochon, S and Krishnappa Babu and PR and Sapiro, G and Dawson, G},
   Title = {Relationship between quantitative digital behavioral
             features and clinical profiles in young autistic
             children.},
   Journal = {Autism Res},
   Volume = {16},
   Number = {7},
   Pages = {1360-1374},
   Year = {2023},
   Month = {July},
   url = {http://dx.doi.org/10.1002/aur.2955},
   Abstract = {Early behavioral markers for autism include differences in
             social attention and orienting in response to one's name
             when called, and differences in body movements and motor
             abilities. More efficient, scalable, objective, and reliable
             measures of these behaviors could improve early screening
             for autism. This study evaluated whether objective and
             quantitative measures of autism-related behaviors elicited
             from an app (SenseToKnow) administered on a smartphone or
             tablet and measured via computer vision analysis (CVA) are
             correlated with standardized caregiver-report and clinician
             administered measures of autism-related behaviors and
             cognitive, language, and motor abilities. This is an
             essential step in establishing the concurrent validity of a
             digital phenotyping approach. In a sample of 485 toddlers,
             43 of whom were diagnosed with autism, we found that
             CVA-based gaze variables related to social attention were
             associated with the level of autism-related behaviors. Two
             language-related behaviors measured via the app, attention
             to people during a conversation and responding to one's name
             being called, were associated with children's language
             skills. Finally, performance during a bubble popping game
             was associated with fine motor skills. These findings
             provide initial support for the concurrent validity of the
             SenseToKnow app and its potential utility in identifying
             clinical profiles associated with autism. Future research is
             needed to determine whether the app can be used as an autism
             screening tool, can reliably stratify autism-related
             behaviors, and measure changes in autism-related behaviors
             over time.},
   Doi = {10.1002/aur.2955},
   Key = {fds371278}
}

@article{fds371136,
   Author = {Roychowdhury, S and Draeger, EW and Randles, A},
   Title = {Establishing metrics to quantify spatial similarity in
             spherical and red blood cell distributions},
   Journal = {Journal of Computational Science},
   Volume = {71},
   Year = {2023},
   Month = {July},
   url = {http://dx.doi.org/10.1016/j.jocs.2023.102060},
   Abstract = {As computational power increases and systems with millions
             of red blood cells can be simulated, it is important to note
             that varying spatial distributions of cells may affect
             simulation outcomes. Since a single simulation may not
             represent the ensemble behavior, many different
             configurations may need to be sampled to adequately assess
             the entire collection of potential cell arrangements. In
             order to determine both the number of distributions needed
             and which ones to run, we must first establish methods to
             identify well-generated, randomly placed cell distributions
             and to quantify distinct cell configurations. We utilize
             metrics to assess (1) the presence of any underlying
             structure to the initial cell distribution and (2)
             similarity between cell configurations. We propose the use
             of the radial distribution function to identify long-range
             structure in a cell configuration and apply it to a randomly
             distributed and structured set of red blood cells. To
             quantify spatial similarity between two configurations, we
             make use of the Jaccard index, and characterize sets of red
             blood cell and sphere initializations. As an extension to
             our work submitted to the International Conference on
             Computational Science (Roychowdhury et al., 2022), we
             significantly increase our data set size from 72 to 1048
             cells, include a similar set of studies using spheres,
             compare the effects of varying sphere size, and utilize the
             Jaccard index distribution to probe sets of extremely
             similar configurations. Our results show that the radial
             distribution function can be used as a metric to determine
             long-range structure in both distributions of spheres and
             RBCs. We determine that the ideal case of spheres within a
             cube versus bi-concave shaped cells within a cylinder
             affects the shape of the Jaccard index distributions, as
             well as the range of Jaccard values, showing that both the
             shape of particle and the domain may play a role. We also
             find that the distribution is able to capture very similar
             configurations through Jaccard index values greater than 95%
             when appending several nearly identical configurations into
             the data set.},
   Doi = {10.1016/j.jocs.2023.102060},
   Key = {fds371136}
}

@article{fds371517,
   Author = {Tanade, C and Putney, S and Randles, A},
   Title = {Establishing massively parallel models to examine the
             influence of cell heterogeneity on tumor
             growth},
   Journal = {Journal of Computational Science},
   Volume = {71},
   Year = {2023},
   Month = {July},
   url = {http://dx.doi.org/10.1016/j.jocs.2023.102059},
   Abstract = {Parallel 3D cellular automaton models of tumor growth can
             efficiently capture emergent morphology. We extended a 2D
             growth model to 3D to examine the influence of symmetric
             division in heterogeneous tumors on growth dynamics. As
             extending to 3D severely increased time-to-solution, we
             parallelized the model using N-body, lattice halo exchange,
             and adaptive communication schemes. Supplementing prior work
             from Tanade et al. (2022), we demonstrated over 55x speedup
             and evaluated performance on ≤30 nodes of Stampede2. This
             work established a framework to parametrically study 3D
             growth dynamics, and of the cancer phenotypes we studied,
             the parallel model better scaled when tumor boundaries were
             radially symmetric.},
   Doi = {10.1016/j.jocs.2023.102059},
   Key = {fds371517}
}

@article{fds371627,
   Author = {Eid, A-M and Elgamal, M and Gonzalez-Fiol, A and Shelley, KH and Wu,
             H-T and Alian, AA},
   Title = {Using the ear photoplethysmographic waveform as an early
             indicator of central hypovolemia in healthy volunteers
             utilizing LBNP induced hypovolemia model.},
   Journal = {Physiological measurement},
   Volume = {44},
   Number = {5},
   Year = {2023},
   Month = {July},
   url = {http://dx.doi.org/10.1088/1361-6579/acd165},
   Abstract = {<i>Objective</i>. To study the photoplethysmographic (PPG)
             waveforms of different locations (ear and finger) during
             lower body negative pressure (LBNP) induced hypovolemia.
             Then, to determine whether the PPG waveform can be used to
             detect hypovolemia during the early stage of
             LBNP.<i>Approach</i>. 36 healthy volunteers were recruited
             for progressive LBNP induced hypovolemia, with an endpoint
             of -60 mmHg or development of hypoperfusion symptoms,
             whichever comes first. Subjects tolerating the entire
             protocol without symptoms were designated as high tolerance
             (HT), while symptomatic subjects were designated as low
             tolerance (LT). Subjects were monitored with an
             electrocardiogram, continuous noninvasive blood pressure
             monitor, and two pulse oximetry probes, one on the ear
             (Xhale) and one the finger (Nellcor). Stroke volume was
             measured non-invasively utilizing Non-Invasive Cardiac
             Output Monitor (NICOM, Cheetah Medical). The waveform
             morphology was analyzed using novel PPG waveforms indices,
             including phase hemodynamic index (PHI) and amplitude
             hemodyamaic index and were evaluated from the ear PPG and
             finger PPG at different LBNP stages.<i>Main results</i>. The
             PHI, particularly the phase relationship between the second
             harmonic and the fundamental component of the ear PPG
             denoted as∇φ2,during the early stage of LBNP (-15 mmHg)
             in the HT and LT groups is statistically significantly
             different (<i>p</i>value = 0.0033) with the area under curve
             0.81 (CI: 0.616-0.926). The other indices are not
             significantly different. The 5 fold cross validation shows
             that∇φ2during the early stage of LBNP (-15 mmHg) as the
             single index could predict the tolerance of the subject with
             the sensitivity, specificity, accuracy and<i>F</i>1 as 0.771
             ± 0.192, 0.71 ± 0.107, 0.7 ± 0.1 and 0.771 ± 0.192
             respectively.<i>Significance</i>. The ear's PPG PHI which
             compares the phases of the fundamental and second harmonic
             has the potential to be used as an early predictor of
             central hypovolemia.},
   Doi = {10.1088/1361-6579/acd165},
   Key = {fds371627}
}

@article{fds370318,
   Author = {Soloveychik, I and Tarokh, V},
   Title = {Region selection in Markov random fields: Gaussian
             case},
   Journal = {Journal of Multivariate Analysis},
   Volume = {196},
   Year = {2023},
   Month = {July},
   url = {http://dx.doi.org/10.1016/j.jmva.2023.105178},
   Abstract = {We consider the problem of model selection in Gaussian
             Markov fields in the sample deficient scenario. The
             benchmark information-theoretic results in the case of
             d-regular graphs require the number of samples to be at
             least proportional to the logarithm of the number of
             vertices to allow consistent graph recovery. When the number
             of samples is less than this amount, reliable recovery of
             all edges is impossible. In many applications, it is more
             important to learn the distribution of the edge (coupling)
             parameters over the network than the specific locations of
             the edges. Assuming that the entire graph can be partitioned
             into a number of spatial regions with similar edge
             parameters and reasonably regular boundaries, we develop new
             information-theoretic sample complexity bounds and show that
             a bounded number of samples can be sufficient to
             consistently recover these regions. Finally, we introduce
             and analyze an efficient region growing algorithm capable of
             recovering the regions with high accuracy. We show that it
             is consistent and demonstrate its performance benefits in
             synthetic simulations.},
   Doi = {10.1016/j.jmva.2023.105178},
   Key = {fds370318}
}

@article{fds370311,
   Author = {Rotzoll, M and Regan, MH and Husty, ML and Hayes,
             MJD},
   Title = {Kinematic geometry of spatial RSSR mechanisms},
   Journal = {Mechanism and Machine Theory},
   Volume = {185},
   Year = {2023},
   Month = {July},
   url = {http://dx.doi.org/10.1016/j.mechmachtheory.2023.105335},
   Abstract = {Two different novel methods to derive the input–output
             (IO) equation of arbitrary RSSR linkages are described. Both
             methods share some common elements, i.e., they use the
             standard Denavit–Hartenberg notation to first describe the
             linkage as an open kinematic chain, and Study's kinematic
             mapping to describe the displacement of the coordinate frame
             attached to the end-effector of the chain with respect to
             the relatively non-moving base frame. The kinematic closure
             equation is obtained in the seven-dimensional projective
             kinematic mapping image space by equating the eight Study
             soma coordinates to the identity array. Then two methods are
             successfully applied to eliminate the intermediate joint
             angle parameters leading to the degree four biquadratic
             implicit algebraic IO equation: (a) the linear
             implicitisation algorithm, which can be applied after
             rearranging the closure equation such that the linkage can
             be viewed as two serial RS chains, and (b) numerical
             elimination theory using pseudowitness sets. Both approaches
             lead to the same IO equation. The utility of this algebraic
             form of the IO equation is illustrated with three detailed
             application examples.},
   Doi = {10.1016/j.mechmachtheory.2023.105335},
   Key = {fds370311}
}

@article{fds371967,
   Author = {Dunlap, A and Gu, Y and Li, L},
   Title = {Localization length of the $1+1$ continuum directed random
             polymer},
   Journal = {Annales Henri Poincaré},
   Volume = {24},
   Number = {7},
   Pages = {2537-2555},
   Publisher = {Springer Science and Business Media LLC},
   Year = {2023},
   Month = {July},
   url = {http://dx.doi.org/10.1007/s00023-023-01288-z},
   Abstract = {In this paper, we study the localization length of the
             continuum directed polymer, defined as the distance between
             the endpoints of two paths sampled independently from the
             quenched polymer measure. We show that the localization
             length converges in distribution in the thermodynamic limit,
             and derive an explicit density formula of the limiting
             distribution. As a consequence, we prove the $\frac32$-power
             law decay of the density, confirming the physics prediction
             of Hwa and Fisher (Phys Rev B 49(5):3136, 1994). Our proof
             uses the recent result of Das and Zhu (Localization of the
             continuum directed random polymer, 2022).},
   Doi = {10.1007/s00023-023-01288-z},
   Key = {fds371967}
}

@article{fds372448,
   Author = {Yu, J and Lai, R and Li, W and Osher, S},
   Title = {Computational mean-field games on manifolds},
   Journal = {Journal of Computational Physics},
   Volume = {484},
   Pages = {112070-112070},
   Publisher = {Elsevier BV},
   Year = {2023},
   Month = {July},
   url = {http://dx.doi.org/10.1016/j.jcp.2023.112070},
   Abstract = {Conventional Mean-field games/control study the behavior of
             a large number of rational agents moving in Euclidean
             spaces. In this work, we explore the mean-field games on
             Riemannian manifolds. We formulate the mean-field game Nash
             Equilibrium on manifolds. We also establish the equivalence
             between the PDE system and the optimality conditions of the
             associated variational form on manifolds. Based on the
             triangular mesh representation of two-dimensional manifolds,
             we design a proximal gradient method for variational
             mean-field games. Our comprehensive numerical experiments on
             various manifolds illustrate the effectiveness and
             flexibility of the proposed model and numerical
             methods.},
   Doi = {10.1016/j.jcp.2023.112070},
   Key = {fds372448}
}

@article{fds372436,
   Author = {Zhang, R and Xin, R and Seltzer, M and Rudin, C},
   Title = {Optimal Sparse Regression Trees},
   Journal = {Proceedings of the 37th AAAI Conference on Artificial
             Intelligence, AAAI 2023},
   Volume = {37},
   Pages = {11270-11279},
   Year = {2023},
   Month = {June},
   ISBN = {9781577358800},
   Abstract = {Regression trees are one of the oldest forms of AI models,
             and their predictions can be made without a calculator,
             which makes them broadly useful, particularly for
             high-stakes applications. Within the large literature on
             regression trees, there has been little effort towards full
             provable optimization, mainly due to the computational
             hardness of the problem. This work proposes a
             dynamic-programming-with-bounds approach to the construction
             of provably-optimal sparse regression trees. We leverage a
             novel lower bound based on an optimal solution to the
             k-Means clustering algorithm on one dimensional data. We are
             often able to find optimal sparse trees in seconds, even for
             challenging datasets that involve large numbers of samples
             and highly-correlated features.},
   Key = {fds372436}
}

@article{fds374307,
   Author = {Hunt, S and Daily, SB and Viel, S and Boyd-Sinkler,
             K},
   Title = {Examining the Impact of Introductory Mathematics Courses on
             Undergraduate Students' Desire to Pursue a STEM
             Major},
   Journal = {ASEE Annual Conference and Exposition, Conference
             Proceedings},
   Year = {2023},
   Month = {June},
   Key = {fds374307}
}

@article{fds374631,
   Author = {Iyer, G and Lu, E and Nolen, J},
   Title = {Using Bernoulli maps to accelerate mixing of a random walk
             on the torus},
   Journal = {Quarterly of Applied Mathematics},
   Volume = {82},
   Number = {2},
   Pages = {359-390},
   Publisher = {American Mathematical Society (AMS)},
   Year = {2023},
   Month = {June},
   url = {http://dx.doi.org/10.1090/qam/1668},
   Abstract = {<p>We study the mixing time of a random walk on the torus,
             alternated with a Lebesgue measure preserving Bernoulli map.
             Without the Bernoulli map, the mixing time of the random
             walk alone is <inline-formula content-type="math/mathml"> ??
             <mml:semantics> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo
             stretchy="false">(</mml:mo> <mml:mn>1</mml:mn> <mml:mrow
             class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow>
             <mml:msup> <mml:mi>ε<!-- ε --></mml:mi> <mml:mn>2</mml:mn>
             </mml:msup> <mml:mo stretchy="false">)</mml:mo> </mml:mrow>
             <mml:annotation encoding="application/x-tex">O(1/\varepsilon
             ^2)</mml:annotation> </mml:semantics> </mml:math>
             </inline-formula>, where <inline-formula
             content-type="math/mathml"> ?? <mml:semantics>
             <mml:mi>ε<!-- ε --></mml:mi> <mml:annotation
             encoding="application/x-tex">\varepsilon</mml:annotation>
             </mml:semantics> </mml:math> </inline-formula> is the step
             size. Our main results show that for a class of Bernoulli
             maps, when the random walk is alternated with the Bernoulli
             map <inline-formula content-type="math/mathml"> ??
             <mml:semantics> <mml:mi>φ<!-- φ --></mml:mi>
             <mml:annotation encoding="application/x-tex">\varphi</mml:annotation>
             </mml:semantics> </mml:math> </inline-formula> the mixing
             time becomes <inline-formula content-type="math/mathml"> ??
             <mml:semantics> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo
             stretchy="false">(</mml:mo> <mml:mo fence="false"
             stretchy="false">|<!-- | --></mml:mo> <mml:mi>ln</mml:mi>
             <mml:mo>⁡<!-- ⁡ --></mml:mo> <mml:mi>ε<!-- ε
             --></mml:mi> <mml:mo fence="false" stretchy="false">|<!-- |
             --></mml:mo> <mml:mo stretchy="false">)</mml:mo> </mml:mrow>
             <mml:annotation encoding="application/x-tex">O(\lvert \ln
             \varepsilon \rvert )</mml:annotation> </mml:semantics>
             </mml:math> </inline-formula>. We also study the
             <italic>dissipation time</italic> of this process, and
             obtain <inline-formula content-type="math/mathml"> ??
             <mml:semantics> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo
             stretchy="false">(</mml:mo> <mml:mo fence="false"
             stretchy="false">|<!-- | --></mml:mo> <mml:mi>ln</mml:mi>
             <mml:mo>⁡<!-- ⁡ --></mml:mo> <mml:mi>ε<!-- ε
             --></mml:mi> <mml:mo fence="false" stretchy="false">|<!-- |
             --></mml:mo> <mml:mo stretchy="false">)</mml:mo> </mml:mrow>
             <mml:annotation encoding="application/x-tex">O(\lvert \ln
             \varepsilon \rvert )</mml:annotation> </mml:semantics>
             </mml:math> </inline-formula> upper and lower bounds with
             explicit constants.</p>},
   Doi = {10.1090/qam/1668},
   Key = {fds374631}
}

@article{fds371293,
   Author = {Motta, FC and McGoff, K and Moseley, RC and Cho, C-Y and Kelliher, CM and Smith, LM and Ortiz, MS and Leman, AR and Campione, SA and Devos, N and Chaorattanakawee, S and Uthaimongkol, N and Kuntawunginn, W and Thongpiam, C and Thamnurak, C and Arsanok, M and Wojnarski, M and Vanchayangkul, P and Boonyalai, N and Smith, PL and Spring, MD and Jongsakul, K and Chuang, I and Harer, J and Haase,
             SB},
   Title = {The parasite intraerythrocytic cycle and human circadian
             cycle are coupled during malaria infection.},
   Journal = {Proceedings of the National Academy of Sciences of the
             United States of America},
   Volume = {120},
   Number = {24},
   Pages = {e2216522120},
   Year = {2023},
   Month = {June},
   url = {http://dx.doi.org/10.1073/pnas.2216522120},
   Abstract = {During infections with the malaria parasites <i>Plasmodium
             vivax</i>, patients exhibit rhythmic fevers every 48 h.
             These fever cycles correspond with the time the parasites
             take to traverse the intraerythrocytic cycle (IEC). In other
             <i>Plasmodium</i> species that infect either humans or mice,
             the IEC is likely guided by a parasite-intrinsic clock
             [Rijo-Ferreira<i>et al.</i>, <i>Science</i> <b>368</b>,
             746-753 (2020); Smith <i>et al</i>., <i>Science</i>
             <b>368</b>, 754-759 (2020)], suggesting that intrinsic clock
             mechanisms may be a fundamental feature of malaria
             parasites. Moreover, because <i>Plasmodium</i> cycle times
             are multiples of 24 h, the IECs may be coordinated with the
             host circadian clock(s). Such coordination could explain the
             synchronization of the parasite population in the host and
             enable alignment of IEC and circadian cycle phases. We
             utilized an ex vivo culture of whole blood from patients
             infected with <i>P. vivax</i> to examine the dynamics of the
             host circadian transcriptome and the parasite IEC
             transcriptome. Transcriptome dynamics revealed that the
             phases of the host circadian cycle and the parasite IEC are
             correlated across multiple patients, showing that the cycles
             are phase coupled. In mouse model systems, host-parasite
             cycle coupling appears to provide a selective advantage for
             the parasite. Thus, understanding how host and parasite
             cycles are coupled in humans could enable antimalarial
             therapies that disrupt this coupling.},
   Doi = {10.1073/pnas.2216522120},
   Key = {fds371293}
}

@article{fds369395,
   Author = {Mubayi, D and Mukherjee, S},
   Title = {Triangles in graphs without bipartite suspensions},
   Journal = {Discrete Mathematics},
   Volume = {346},
   Number = {6},
   Year = {2023},
   Month = {June},
   url = {http://dx.doi.org/10.1016/j.disc.2023.113355},
   Abstract = {Given graphs T and H, the generalized Turán number
             ex(n,T,H) is the maximum number of copies of T in an
             n-vertex graph with no copies of H. Alon and Shikhelman,
             using a result of Erdős, determined the asymptotics of
             ex(n,K3,H) when the chromatic number of H is greater than
             three and proved several results when H is bipartite. We
             consider this problem when H has chromatic number three.
             Even this special case for the following relatively simple
             three chromatic graphs appears to be challenging. The
             suspension Hˆ of a graph H is the graph obtained from H by
             adding a new vertex adjacent to all vertices of H. We give
             new upper and lower bounds on ex(n,K3,Hˆ) when H is a path,
             even cycle, or complete bipartite graph. One of the main
             tools we use is the triangle removal lemma, but it is
             unclear if much stronger statements can be proved without
             using the removal lemma.},
   Doi = {10.1016/j.disc.2023.113355},
   Key = {fds369395}
}

@article{fds369944,
   Author = {Shi, A and Berchuck, SI and Jammal, AA and Singh, G and Hunt, S and Roche,
             K and Mukherjee, S and Medeiros, FA},
   Title = {Identifying Risk Factors for Blindness From Glaucoma at
             First Presentation to a Tertiary Clinic.},
   Journal = {Am J Ophthalmol},
   Volume = {250},
   Pages = {130-137},
   Year = {2023},
   Month = {June},
   url = {http://dx.doi.org/10.1016/j.ajo.2023.02.006},
   Abstract = {PURPOSE: Glaucoma is the leading cause of irreversible
             blindness, a crippling disability resulting in higher risks
             of chronic health conditions. To better understand
             disparities in blindness risk, we identified risk factors of
             blindness on first presentation to a glaucoma clinic using a
             large clinical database. DESIGN: Retrospective
             cross-sectional study. METHODS: We used electronic health
             records of glaucoma patients from the Duke Ophthalmic
             Registry. International Classification of Diseases codes
             were used to identify glaucoma and exclude concurrent
             diseases. Blindness classification was based on the
             definition of legal blindness. Risk factors included gender,
             race, marital status, age, intraocular pressure, diabetes
             history, income level, and education. Odds ratios (ORs) and
             95% CIs were calculated for risk factors using univariable
             and multivariable logistic regression. RESULTS: Our cohort
             consisted of 3753 patients, with 192 (5%) blind on first
             presentation. In univariable models, African American /
             Black race (OR 2.48, 95% CI 1.83-3.36), single marital
             status (1.74, 95% CI 1.25-2.44), prior diabetes diagnosis
             (2.23, 95% CI 1.52-3.27), and higher intraocular pressure
             (1.29 per 1 SD higher, 95% CI 1.13-1.46) were associated
             with increased risk of presenting blind, whereas higher
             annual income (0.75, 95% CI 0.65-0.86) and education (0.77,
             95% CI 0.69-0.85) were associated with lower risk. These
             associations remained significant and in the same direction
             in a multivariable model apart from income, which became
             insignificant. CONCLUSIONS: Using a large real-world
             clinical database, we identified risk factors associated
             with presentation with blindness among glaucoma patients.
             Our results highlight disparities in health care outcomes
             and indicate the importance of targeted education to reduce
             disparities in blindness.},
   Doi = {10.1016/j.ajo.2023.02.006},
   Key = {fds369944}
}

@article{fds368760,
   Author = {Wang, Y and Li, X and Konanur, M and Konkel, B and Seyferth, E and Brajer,
             N and Liu, J-G and Bashir, MR and Lafata, KJ},
   Title = {Towards optimal deep fusion of imaging and clinical data via
             a model-based description of fusion quality.},
   Journal = {Med Phys},
   Volume = {50},
   Number = {6},
   Pages = {3526-3537},
   Year = {2023},
   Month = {June},
   url = {http://dx.doi.org/10.1002/mp.16181},
   Abstract = {BACKGROUND: Due to intrinsic differences in data formatting,
             data structure, and underlying semantic information, the
             integration of imaging data with clinical data can be
             non-trivial. Optimal integration requires robust data
             fusion, that is, the process of integrating multiple data
             sources to produce more useful information than captured by
             individual data sources. Here, we introduce the concept of
             fusion quality for deep learning problems involving imaging
             and clinical data. We first provide a general theoretical
             framework and numerical validation of our technique. To
             demonstrate real-world applicability, we then apply our
             technique to optimize the fusion of CT imaging and hepatic
             blood markers to estimate portal venous hypertension, which
             is linked to prognosis in patients with cirrhosis of the
             liver. PURPOSE: To develop a measurement method of optimal
             data fusion quality deep learning problems utilizing both
             imaging data and clinical data. METHODS: Our approach is
             based on modeling the fully connected layer (FCL) of a
             convolutional neural network (CNN) as a potential function,
             whose distribution takes the form of the classical Gibbs
             measure. The features of the FCL are then modeled as random
             variables governed by state functions, which are interpreted
             as the different data sources to be fused. The probability
             density of each source, relative to the probability density
             of the FCL, represents a quantitative measure of
             source-bias. To minimize this source-bias and optimize CNN
             performance, we implement a vector-growing encoding scheme
             called positional encoding, where low-dimensional clinical
             data are transcribed into a rich feature space that
             complements high-dimensional imaging features. We first
             provide a numerical validation of our approach based on
             simulated Gaussian processes. We then applied our approach
             to patient data, where we optimized the fusion of CT images
             with blood markers to predict portal venous hypertension in
             patients with cirrhosis of the liver. This patient study was
             based on a modified ResNet-152 model that incorporates both
             images and blood markers as input. These two data sources
             were processed in parallel, fused into a single FCL, and
             optimized based on our fusion quality framework. RESULTS:
             Numerical validation of our approach confirmed that the
             probability density function of a fused feature space
             converges to a source-specific probability density function
             when source data are improperly fused. Our numerical results
             demonstrate that this phenomenon can be quantified as a
             measure of fusion quality. On patient data, the fused model
             consisting of both imaging data and positionally encoded
             blood markers at the theoretically optimal fusion quality
             metric achieved an AUC of 0.74 and an accuracy of 0.71. This
             model was statistically better than the imaging-only model
             (AUC = 0.60; accuracy = 0.62), the blood marker-only model
             (AUC = 0.58; accuracy = 0.60), and a variety of purposely
             sub-optimized fusion models (AUC = 0.61-0.70; accuracy =
             0.58-0.69). CONCLUSIONS: We introduced the concept of data
             fusion quality for multi-source deep learning problems
             involving both imaging and clinical data. We provided a
             theoretical framework, numerical validation, and real-world
             application in abdominal radiology. Our data suggests that
             CT imaging and hepatic blood markers provide complementary
             diagnostic information when appropriately
             fused.},
   Doi = {10.1002/mp.16181},
   Key = {fds368760}
}

@article{fds372739,
   Author = {Lange, J and Zhao, Y and Gogebakan, KC and Olivas-Martinez, A and Ryser,
             MD and Gard, CC and Etzioni, R},
   Title = {Test sensitivity in a prospective cancer screening program:
             A critique of a common proxy measure.},
   Journal = {Stat Methods Med Res},
   Volume = {32},
   Number = {6},
   Pages = {1053-1063},
   Year = {2023},
   Month = {June},
   url = {http://dx.doi.org/10.1177/09622802221142529},
   Abstract = {The true sensitivity of a cancer screening test, defined as
             the frequency with which the test returns a positive result
             if the cancer is present, is a key indicator of diagnostic
             performance. Given the challenges of directly assessing test
             sensitivity in a prospective screening program, proxy
             measures for true sensitivity are frequently reported. We
             call one such proxy empirical sensitivity, as it is given by
             the observed ratio of screen-detected cancers to the sum of
             screen-detected and interval cancers. In the setting of the
             canonical three-state Markov model for progression from
             preclinical onset to clinical diagnosis, we formulate a
             mathematical relationship for how empirical sensitivity
             varies with the screening interval and the mean preclinical
             sojourn time and identify conditions under which empirical
             sensitivity exceeds or falls short of true sensitivity. In
             particular, when the inter-screening interval is short
             relative to the mean sojourn time, empirical sensitivity
             tends to exceed true sensitivity, unless true sensitivity is
             high. The Breast Cancer Surveillance Consortium (BCSC) has
             reported an estimate of 0.87 for the empirical sensitivity
             of digital mammography. We show that this corresponds to a
             true sensitivity of 0.82 under a mean sojourn time of 3.6
             years estimated based on breast cancer screening trials.
             However, the BCSC estimate of empirical sensitivity
             corresponds to even lower true sensitivity under more
             contemporary, longer estimates of mean sojourn time.
             Consistently applied nomenclature that distinguishes
             empirical sensitivity from true sensitivity is needed to
             ensure that published estimates of sensitivity from
             prospective screening studies are properly
             interpreted.},
   Doi = {10.1177/09622802221142529},
   Key = {fds372739}
}

@article{fds362970,
   Author = {Wang, C and Han, B and Patel, B and Rudin, C},
   Title = {In Pursuit of Interpretable, Fair and Accurate Machine
             Learning for Criminal Recidivism Prediction},
   Journal = {Journal of Quantitative Criminology},
   Volume = {39},
   Number = {2},
   Pages = {519-581},
   Year = {2023},
   Month = {June},
   url = {http://dx.doi.org/10.1007/s10940-022-09545-w},
   Abstract = {Objectives: We study interpretable recidivism prediction
             using machine learning (ML) models and analyze performance
             in terms of prediction ability, sparsity, and fairness.
             Unlike previous works, this study trains interpretable
             models that output probabilities rather than binary
             predictions, and uses quantitative fairness definitions to
             assess the models. This study also examines whether models
             can generalize across geographic locations. Methods: We
             generated black-box and interpretable ML models on two
             different criminal recidivism datasets from Florida and
             Kentucky. We compared predictive performance and fairness of
             these models against two methods that are currently used in
             the justice system to predict pretrial recidivism: the
             Arnold PSA and COMPAS. We evaluated predictive performance
             of all models on predicting six different types of crime
             over two time spans. Results: Several interpretable ML
             models can predict recidivism as well as black-box ML models
             and are more accurate than COMPAS or the Arnold PSA. These
             models are potentially useful in practice. Similar to the
             Arnold PSA, some of these interpretable models can be
             written down as a simple table. Others can be displayed
             using a set of visualizations. Our geographic analysis
             indicates that ML models should be trained separately for
             separate locations and updated over time. We also present a
             fairness analysis for the interpretable models. Conclusions:
             Interpretable ML models can perform just as well as
             non-interpretable methods and currently-used risk assessment
             scales, in terms of both prediction accuracy and fairness.
             ML models might be more accurate when trained separately for
             distinct locations and kept up-to-date.},
   Doi = {10.1007/s10940-022-09545-w},
   Key = {fds362970}
}

@article{fds374494,
   Author = {Kiselev, A and Luo, X},
   Title = {Illposedness of C2 Vortex Patches},
   Journal = {Archive for Rational Mechanics and Analysis},
   Volume = {247},
   Number = {3},
   Year = {2023},
   Month = {June},
   url = {http://dx.doi.org/10.1007/s00205-023-01892-7},
   Abstract = {It is well known that vortex patches are wellposed in C1,α
             if 0 < α< 1 . In this paper, we prove the illposedness of
             C2 vortex patches. The setup is to consider the vortex
             patches in Sobolev spaces W2,p where the curvature of the
             boundary is Lp integrable. In this setting, we show the
             persistence of W2,p regularity when 1 < p< ∞ and construct
             C2 initial patch data for which the curvature of the patch
             boundary becomes unbounded immediately for t> 0 , though it
             regains C2 regularity precisely at all integer times without
             being time periodic. The key ingredient is the evolution
             equation for the curvature, the dominant term in which turns
             out to be linear and dispersive.},
   Doi = {10.1007/s00205-023-01892-7},
   Key = {fds374494}
}

@article{fds370952,
   Author = {Kiselev, A and Luo, X},
   Title = {Illposedness of C2 Vortex Patches},
   Journal = {Archive for Rational Mechanics and Analysis},
   Volume = {247},
   Number = {3},
   Publisher = {Springer Science and Business Media LLC},
   Year = {2023},
   Month = {June},
   url = {http://dx.doi.org/10.1007/s00205-023-01892-7},
   Abstract = {It is well known that vortex patches are wellposed in C1,α
             if 0 < α< 1 . In this paper, we prove the illposedness of
             C2 vortex patches. The setup is to consider the vortex
             patches in Sobolev spaces W2,p where the curvature of the
             boundary is Lp integrable. In this setting, we show the
             persistence of W2,p regularity when 1 < p< ∞ and construct
             C2 initial patch data for which the curvature of the patch
             boundary becomes unbounded immediately for t> 0 , though it
             regains C2 regularity precisely at all integer times without
             being time periodic. The key ingredient is the evolution
             equation for the curvature, the dominant term in which turns
             out to be linear and dispersive.},
   Doi = {10.1007/s00205-023-01892-7},
   Key = {fds370952}
}

@article{fds370612,
   Author = {Poe, JC and Fang, J and Zhang, D and Lee, MR and DiCioccio, RA and Su, H and Qin, X and Zhang, JY and Visentin, J and Bracken, SJ and Ho, VT and Wang,
             KS and Rose, JJ and Pavletic, SZ and Hakim, FT and Jia, W and Suthers, AN and Curry-Chisolm, IM and Horwitz, ME and Rizzieri, DA and McManigle, WC and Chao, NJ and Cardones, AR and Xie, J and Owzar, K and Sarantopoulos,
             S},
   Title = {Single-cell landscape analysis unravels molecular
             programming of the human B cell compartment in chronic
             GVHD.},
   Journal = {JCI insight},
   Volume = {8},
   Number = {11},
   Pages = {e169732},
   Year = {2023},
   Month = {June},
   url = {http://dx.doi.org/10.1172/jci.insight.169732},
   Abstract = {Alloreactivity can drive autoimmune syndromes. After
             allogeneic hematopoietic stem cell transplantation
             (allo-HCT), chronic graft-versus-host disease (cGVHD), a B
             cell-associated autoimmune-like syndrome, commonly occurs.
             Because donor-derived B cells continually develop under
             selective pressure from host alloantigens, aberrant B cell
             receptor (BCR) activation and IgG production can emerge and
             contribute to cGVHD pathobiology. To better understand
             molecular programing of B cells in allo-HCT, we performed
             scRNA-Seq analysis on high numbers of purified B cells from
             patients. An unsupervised analysis revealed 10 clusters,
             distinguishable by signature genes for maturation,
             activation, and memory. Within the memory B cell
             compartment, we found striking transcriptional differences
             in allo-HCT patients compared with healthy or infected
             individuals, including potentially pathogenic atypical B
             cells (ABCs) that were expanded in active cGVHD. To identify
             intrinsic alterations in potentially pathological B cells,
             we interrogated all clusters for differentially expressed
             genes (DEGs) in active cGVHD versus patients who never had
             signs of immune tolerance loss (no cGVHD). Active cGVHD DEGs
             occurred in both naive and BCR-activated B cell clusters.
             Remarkably, some DEGs occurred across most clusters,
             suggesting common molecular programs that may promote B cell
             plasticity. Our study of human allo-HCT and cGVHD provides
             understanding of altered B cell memory during chronic
             alloantigen stimulation.},
   Doi = {10.1172/jci.insight.169732},
   Key = {fds370612}
}

@article{fds370641,
   Author = {Krishnappa Babu and PR and Aikat, V and Di Martino and JM and Chang, Z and Perochon, S and Espinosa, S and Aiello, R and L H Carpenter and K and Compton, S and Davis, N and Eichner, B and Flowers, J and Franz, L and Dawson, G and Sapiro, G},
   Title = {Blink rate and facial orientation reveal distinctive
             patterns of attentional engagement in autistic toddlers: a
             digital phenotyping approach.},
   Journal = {Sci Rep},
   Volume = {13},
   Number = {1},
   Pages = {7158},
   Year = {2023},
   Month = {May},
   url = {http://dx.doi.org/10.1038/s41598-023-34293-7},
   Abstract = {Differences in social attention are well-documented in
             autistic individuals, representing one of the earliest signs
             of autism. Spontaneous blink rate has been used to index
             attentional engagement, with lower blink rates reflecting
             increased engagement. We evaluated novel methods using
             computer vision analysis (CVA) for automatically quantifying
             patterns of attentional engagement in young autistic
             children, based on facial orientation and blink rate, which
             were captured via mobile devices. Participants were 474
             children (17-36 months old), 43 of whom were diagnosed with
             autism. Movies containing social or nonsocial content were
             presented via an iPad app, and simultaneously, the device's
             camera recorded the children's behavior while they watched
             the movies. CVA was used to extract the duration of time the
             child oriented towards the screen and their blink rate as
             indices of attentional engagement. Overall, autistic
             children spent less time facing the screen and had a higher
             mean blink rate compared to neurotypical children.
             Neurotypical children faced the screen more often and
             blinked at a lower rate during the social movies compared to
             the nonsocial movies. In contrast, autistic children faced
             the screen less often during social movies than during
             nonsocial movies and showed no differential blink rate to
             social versus nonsocial movies.},
   Doi = {10.1038/s41598-023-34293-7},
   Key = {fds370641}
}

@article{fds375282,
   Author = {Jin, B and Dunson, DB and Rager, JE and Reif, DM and Engel, SM and Herring,
             AH},
   Title = {Bayesian matrix completion for hypothesis
             testing.},
   Journal = {Journal of the Royal Statistical Society. Series C, Applied
             statistics},
   Volume = {72},
   Number = {2},
   Pages = {254-270},
   Year = {2023},
   Month = {May},
   url = {http://dx.doi.org/10.1093/jrsssc/qlac005},
   Abstract = {We aim to infer bioactivity of each chemical by assay
             endpoint combination, addressing sparsity of toxicology
             data. We propose a Bayesian hierarchical framework which
             borrows information across different chemicals and assay
             endpoints, facilitates out-of-sample prediction of activity
             for chemicals not yet assayed, quantifies uncertainty of
             predicted activity, and adjusts for multiplicity in
             hypothesis testing. Furthermore, this paper makes a novel
             attempt in toxicology to simultaneously model
             heteroscedastic errors and a nonparametric mean function,
             leading to a broader definition of activity whose need has
             been suggested by toxicologists. Real application identifies
             chemicals most likely active for neurodevelopmental
             disorders and obesity.},
   Doi = {10.1093/jrsssc/qlac005},
   Key = {fds375282}
}

@article{fds368952,
   Author = {Liu, X and Mukherjee, S},
   Title = {Stability theorems for some Kruskal–Katona type
             results},
   Journal = {European Journal of Combinatorics},
   Volume = {110},
   Year = {2023},
   Month = {May},
   url = {http://dx.doi.org/10.1016/j.ejc.2022.103666},
   Abstract = {The classical Kruskal–Katona theorem gives a tight upper
             bound for the size of an r-uniform hypergraph H as a
             function of the size of its shadow. Its stability version
             was obtained by Keevash who proved that if the size of H is
             close to the maximum with respect to the size of its shadow,
             then H is structurally close to a complete r-uniform
             hypergraph. We prove similar stability results for two
             classes of hypergraphs whose extremal properties have been
             investigated by many researchers: the cancellative
             hypergraphs and hypergraphs without expansion of
             cliques.},
   Doi = {10.1016/j.ejc.2022.103666},
   Key = {fds368952}
}

@article{fds369943,
   Author = {Lahkar, R and Mukherjee, S and Roy, S},
   Title = {The logit dynamic in supermodular games with a continuum of
             strategies: A deterministic approximation
             approach},
   Journal = {Games and Economic Behavior},
   Volume = {139},
   Pages = {133-160},
   Year = {2023},
   Month = {May},
   url = {http://dx.doi.org/10.1016/j.geb.2023.02.003},
   Abstract = {We consider large population supermodular games with
             pairwise interaction and a continuous strategy set. Our
             objective is to establish convergence of the logit dynamic
             in such games to logit equilibria. For this purpose, we
             apply the deterministic approximation approach, which
             interprets a deterministic dynamic as an approximation of a
             stochastic process. We first establish the closeness of this
             dynamic with a step–wise approximation. We then show that
             the logit stochastic process is close to the step–wise
             logit dynamic in a discrete approximation of the original
             game. Combining the two results, we obtain our deterministic
             approximation result. We then apply this result to
             supermodular games. Over finite but sufficiently long time
             horizons, the logit stochastic process converges to logit
             equilibria in a discrete approximation of the supermodular
             game. By the deterministic approximation approach, so does
             the logit dynamic in the continuum supermodular
             game.},
   Doi = {10.1016/j.geb.2023.02.003},
   Key = {fds369943}
}

@article{fds369091,
   Author = {Liu, Y and Claus, S and Kerfriden, P and Chen, J and Zhong, P and Dolbow,
             JE},
   Title = {Model-based simulations of pulsed laser ablation using an
             embedded finite element method.},
   Journal = {International Journal of Heat and Mass Transfer},
   Volume = {204},
   Pages = {123843},
   Year = {2023},
   Month = {May},
   url = {http://dx.doi.org/10.1016/j.ijheatmasstransfer.2022.123843},
   Abstract = {A model of thermal ablation with application to multi-pulsed
             laser lithotripsy is presented. The approach is based on a
             one-sided Stefan-Signorini model for thermal ablation, and
             relies on a level-set function to represent the moving
             interface between the solid phase and a fictitious gas phase
             (representing the ablated material). The model is
             discretized with an embedded finite element method, wherein
             the interface geometry can be arbitrarily located relative
             to the background mesh. Nitsche's method is adopted to
             impose the Signorini condition on the moving interface. A
             bound constraint is also imposed to deal with thermal shocks
             that can arise during representative simulations of pulsed
             ablation with high-power lasers. We report simulation
             results based on experiments for pulsed laser ablation of
             wet BegoStone samples treated in air, where Begostone has
             been used as a phantom material for kidney stone. The model
             is calibrated against experimental measurements by adjusting
             the percentage of incoming laser energy absorbed at the
             surface of the stone sample. Simulation results are then
             validated against experimental observations for the crater
             area, volume, and geometry as a function of laser pulse
             energy and duration. Our studies illustrate how the
             spreading of the laser beam from the laser fiber tip with
             concomitantly reduced incident laser irradiance on the
             damaged crater surface explains trends in both the
             experimental observations and the model-based simulation
             results.},
   Doi = {10.1016/j.ijheatmasstransfer.2022.123843},
   Key = {fds369091}
}

@article{fds370839,
   Author = {An, C and Chu, R and Pierce, LB},
   Title = {Counterexamples for High-Degree Generalizations of the
             Schrödinger Maximal Operator},
   Journal = {International Mathematics Research Notices},
   Volume = {2023},
   Number = {10},
   Pages = {8371-8418},
   Publisher = {Oxford University Press (OUP)},
   Year = {2023},
   Month = {May},
   url = {http://dx.doi.org/10.1093/imrn/rnac088},
   Abstract = {In 1980 Carleson posed a question on the minimal regularity
             of an initial data function in a Sobolev space that implies
             pointwise convergence for the solution of the linear
             Schrödinger equation. After progress by many authors, this
             was recently resolved (up to the endpoint) by Bourgain,
             whose counterexample construction for the Schrödinger
             maximal operator proved a necessary condition on the
             regularity, and Du and Zhang, who proved a sufficient
             condition. Analogues of Carleson's question remain open for
             many other dispersive partial differential equations. We
             develop a flexible new method to approach such problems and
             prove that for any integer, if a degree generalization of
             the Schrödinger maximal operator is bounded from to, then
             In dimensions, for every degree, this is the first result
             that exceeds a long-standing barrier at. Our methods are
             number-theoretic, and in particular apply the Weil bound, a
             consequence of the truth of the Riemann Hypothesis over
             finite fields.},
   Doi = {10.1093/imrn/rnac088},
   Key = {fds370839}
}

@article{fds371252,
   Author = {Wu, H-T and Harezlak, J},
   Title = {Application of de-shape synchrosqueezing to estimate gait
             cadence from a single-sensor accelerometer placed in
             different body locations.},
   Journal = {Physiological measurement},
   Volume = {44},
   Number = {5},
   Year = {2023},
   Month = {May},
   url = {http://dx.doi.org/10.1088/1361-6579/accefe},
   Abstract = {<i>Objective.</i>Commercial and research-grade wearable
             devices have become increasingly popular over the past
             decade. Information extracted from devices using
             accelerometers is frequently summarized as 'number of steps'
             (commercial devices) or 'activity counts' (research-grade
             devices). Raw accelerometry data that can be easily
             extracted from accelerometers used in research, for instance
             ActiGraph GT3X+, are frequently discarded.<i>Approach.</i>Our
             primary goal is proposing an innovative use of
             the<i>de-shape synchrosqueezing transform</i>to analyze the
             raw accelerometry data recorded from a single sensor
             installed in different body locations, particularly the
             wrist, to extract<i>gait cadence</i>when a subject is
             walking. The proposed methodology is tested on data
             collected in a semi-controlled experiment with 32
             participants walking on a one-kilometer predefined course.
             Walking was executed on a flat surface as well as on the
             stairs (up and down).<i>Main results.</i>The cadences of
             walking on a flat surface, ascending stairs, and descending
             stairs, determined from the wrist sensor, are 1.98 ± 0.15
             Hz, 1.99 ± 0.26 Hz, and 2.03 ± 0.26 Hz respectively. The
             cadences are 1.98 ± 0.14 Hz, 1.97 ± 0.25 Hz, and 2.02 ±
             0.23 Hz, respectively if determined from the hip sensor,
             1.98 ± 0.14 Hz, 1.93 ± 0.22 Hz and 2.06 ± 0.24 Hz,
             respectively if determined from the left ankle sensor, and
             1.98 ± 0.14 Hz, 1.97 ± 0.22 Hz, and 2.04 ± 0.24 Hz,
             respectively if determined from the right ankle sensor. The
             difference is statistically significant indicating that the
             cadence is fastest while descending stairs and slowest when
             ascending stairs. Also, the standard deviation when the
             sensor is on the wrist is larger. These findings are in line
             with our expectations.<i>Conclusion.</i>We show that our
             proposed algorithm can extract the cadence with high
             accuracy, even when the sensor is placed on the
             wrist.},
   Doi = {10.1088/1361-6579/accefe},
   Key = {fds371252}
}

@article{fds367062,
   Author = {Petkova, I and Wong, B},
   Title = {Twisted Mazur Pattern Satellite Knots & Bordered Floer
             Theory},
   Journal = {Michigan Mathematical Journal},
   Volume = {73},
   Number = {2},
   Pages = {255-304},
   Publisher = {Michigan Mathematical Journal},
   Year = {2023},
   Month = {May},
   url = {http://dx.doi.org/10.1307/mmj/20205927},
   Abstract = {We use bordered Floer theory to study properties of twisted
             Mazur pattern satellite knots Qn(K). We prove that Qn(K) is
             not Floer homologically thin, with two exceptions. We
             calculate the 3-genus of Qn(K) in terms of the twisting
             parameter n and the 3-genus of the companion K, and we
             determine when Qn(K) is fibered. As an application to our
             results on Floer thickness and 3-genus, we verify the
             Cosmetic Surgery Conjecture for many of these satellite
             knots.},
   Doi = {10.1307/mmj/20205927},
   Key = {fds367062}
}

@article{fds372372,
   Author = {Huang, H-Y and Tong, Y and Fang, D and Su, Y},
   Title = {Learning Many-Body Hamiltonians with Heisenberg-Limited
             Scaling.},
   Journal = {Physical review letters},
   Volume = {130},
   Number = {20},
   Pages = {200403},
   Year = {2023},
   Month = {May},
   url = {http://dx.doi.org/10.1103/physrevlett.130.200403},
   Abstract = {Learning a many-body Hamiltonian from its dynamics is a
             fundamental problem in physics. In this Letter, we propose
             the first algorithm to achieve the Heisenberg limit for
             learning an interacting N-qubit local Hamiltonian. After a
             total evolution time of O(ε^{-1}), the proposed algorithm
             can efficiently estimate any parameter in the N-qubit
             Hamiltonian to ε error with high probability. Our algorithm
             uses ideas from quantum simulation to decouple the unknown
             N-qubit Hamiltonian H into noninteracting patches and learns
             H using a quantum-enhanced divide-and-conquer approach. The
             proposed algorithm is robust against state preparation and
             measurement error, does not require eigenstates or thermal
             states, and only uses polylog(ε^{-1}) experiments. In
             contrast, the best existing algorithms require O(ε^{-2})
             experiments and total evolution time. We prove a matching
             lower bound to establish the asymptotic optimality of our
             algorithm.},
   Doi = {10.1103/physrevlett.130.200403},
   Key = {fds372372}
}

@article{fds372524,
   Author = {Liu, CH and Wei, F},
   Title = {Phase transition of degeneracy in minor-closed
             families},
   Journal = {Advances in Applied Mathematics},
   Volume = {146},
   Year = {2023},
   Month = {May},
   url = {http://dx.doi.org/10.1016/j.aam.2023.102489},
   Abstract = {Given an infinite family G of graphs and a monotone property
             P, an (upper) threshold for G and P is a “fastest
             growing” function p:N→[0,1] such that
             limn→∞⁡Pr⁡(Gn(p(n))∈P)=1 for any sequence
             (Gn)n∈N over G with limn→∞⁡|V(Gn)|=∞, where
             Gn(p(n)) is the random subgraph of Gn such that each edge
             remains independently with probability p(n). In this paper
             we study the upper threshold for the family of H-minor free
             graphs and the property of being (r−1)-degenerate and
             apply it to study the thresholds for general minor-closed
             families and the properties for being r-choosable and
             r-colorable. Even a constant factor approximation for the
             upper threshold for all pairs (r,H) is expected to be
             challenging by its close connection to a major open question
             in extremal graph theory. We determine asymptotically the
             thresholds (up to a constant factor) for being
             (r−1)-degenerate (and r-choosable, respectively) for a
             large class of pairs (r,H), including all graphs H of
             minimum degree at least r and all graphs H with no
             vertex-cover of size at most r, and provide lower bounds for
             the rest of the pairs of (r,H).},
   Doi = {10.1016/j.aam.2023.102489},
   Key = {fds372524}
}

@article{fds370385,
   Author = {Isaev, DY and Sabatos-DeVito, M and Di Martino and JM and Carpenter, K and Aiello, R and Compton, S and Davis, N and Franz, L and Sullivan, C and Dawson, G and Sapiro, G},
   Title = {Computer Vision Analysis of Caregiver-Child Interactions in
             Children with Neurodevelopmental Disorders: A Preliminary
             Report.},
   Journal = {J Autism Dev Disord},
   Year = {2023},
   Month = {April},
   url = {http://dx.doi.org/10.1007/s10803-023-05973-0},
   Abstract = {We report preliminary results of computer vision analysis of
             caregiver-child interactions during free play with children
             diagnosed with autism (N = 29, 41-91 months),
             attention-deficit/hyperactivity disorder (ADHD, N = 22,
             48-100 months), or combined autism + ADHD (N = 20,
             56-98 months), and neurotypical children (NT, N = 7,
             55-95 months). We conducted micro-analytic analysis of
             'reaching to a toy,' as a proxy for initiating or responding
             to a toy play bout. Dyadic analysis revealed two clusters of
             interaction patterns, which differed in frequency of
             'reaching to a toy' and caregivers' contingent responding to
             the child's reach for a toy by also reaching for a toy.
             Children in dyads with higher caregiver responsiveness had
             less developed language, communication, and socialization
             skills. Clusters were not associated with diagnostic groups.
             These results hold promise for automated methods of
             characterizing caregiver responsiveness in dyadic
             interactions for assessment and outcome monitoring in
             clinical trials.},
   Doi = {10.1007/s10803-023-05973-0},
   Key = {fds370385}
}

@article{fds370898,
   Author = {Mahzarnia, A and Stout, JA and Anderson, RJ and Moon, HS and Yar Han and Z and Beck, K and Browndyke, JN and Dunson, DB and Johnson, KG and O'Brien,
             RJ and Badea, A},
   Title = {Identifying vulnerable brain networks associated with
             Alzheimer's disease risk.},
   Journal = {Cereb Cortex},
   Volume = {33},
   Number = {9},
   Pages = {5307-5322},
   Year = {2023},
   Month = {April},
   url = {http://dx.doi.org/10.1093/cercor/bhac419},
   Abstract = {The selective vulnerability of brain networks in individuals
             at risk for Alzheimer's disease (AD) may help differentiate
             pathological from normal aging at asymptomatic stages,
             allowing the implementation of more effective interventions.
             We used a sample of 72 people across the age span, enriched
             for the APOE4 genotype to reveal vulnerable networks
             associated with a composite AD risk factor including age,
             genotype, and sex. Sparse canonical correlation analysis
             (CCA) revealed a high weight associated with genotype, and
             subgraphs involving the cuneus, temporal, cingulate
             cortices, and cerebellum. Adding cognitive metrics to the
             risk factor revealed the highest cumulative degree of
             connectivity for the pericalcarine cortex, insula, banks of
             the superior sulcus, and the cerebellum. To enable scaling
             up our approach, we extended tensor network principal
             component analysis, introducing CCA components. We developed
             sparse regression predictive models with errors of 17% for
             genotype, 24% for family risk factor for AD, and 5 years for
             age. Age prediction in groups including cognitively impaired
             subjects revealed regions not found using only normal
             subjects, i.e. middle and transverse temporal, paracentral
             and superior banks of temporal sulcus, as well as the
             amygdala and parahippocampal gyrus. These modeling
             approaches represent stepping stones towards single subject
             prediction.},
   Doi = {10.1093/cercor/bhac419},
   Key = {fds370898}
}

@article{fds370609,
   Author = {Lu, J and Wu, Y and Xiang, Y},
   Title = {Score-based Transport Modeling for Mean-Field Fokker-Planck
             Equations},
   Volume = {503},
   Year = {2023},
   Month = {April},
   url = {http://dx.doi.org/10.1016/j.jcp.2024.112859},
   Abstract = {We use the score-based transport modeling method to solve
             the mean-field Fokker-Planck equations, which we call MSBTM.
             We establish an upper bound on the time derivative of the
             Kullback-Leibler (KL) divergence to MSBTM numerical
             estimation from the exact solution, thus validates the MSBTM
             approach. Besides, we provide an error analysis for the
             algorithm. In numerical experiments, we study three types of
             mean-field Fokker-Planck equation and their corresponding
             dynamics of particles in interacting systems. The MSBTM
             algorithm is numerically validated through qualitative and
             quantitative comparison between the MSBTM solutions, the
             results of integrating the associated stochastic
             differential equation and the analytical solutions if
             available.},
   Doi = {10.1016/j.jcp.2024.112859},
   Key = {fds370609}
}

@article{fds371510,
   Author = {Gu, Y and Dunson, DB},
   Title = {Bayesian Pyramids: identifiable multilayer discrete latent
             structure models for discrete data},
   Journal = {Journal of the Royal Statistical Society. Series B:
             Statistical Methodology},
   Volume = {85},
   Number = {2},
   Pages = {399-426},
   Year = {2023},
   Month = {April},
   url = {http://dx.doi.org/10.1093/jrsssb/qkad010},
   Abstract = {High-dimensional categorical data are routinely collected in
             biomedical and social sciences. It is of great importance to
             build interpretable parsimonious models that perform
             dimension reduction and uncover meaningful latent structures
             from such discrete data. Identifiability is a fundamental
             requirement for valid modeling and inference in such
             scenarios, yet is challenging to address when there are
             complex latent structures. In this article, we propose a
             class of identifiable multilayer (potentially deep) discrete
             latent structure models for discrete data, termed Bayesian
             Pyramids. We establish the identifiability of Bayesian
             Pyramids by developing novel transparent conditions on the
             pyramid-shaped deep latent directed graph. The proposed
             identifiability conditions can ensure Bayesian posterior
             consistency under suitable priors. As an illustration, we
             consider the two-latent-layer model and propose a Bayesian
             shrinkage estimation approach. Simulation results for this
             model corroborate the identifiability and estimatability of
             model parameters. Applications of the methodology to DNA
             nucleotide sequence data uncover useful discrete latent
             features that are highly predictive of sequence types. The
             proposed framework provides a recipe for interpretable
             unsupervised learning of discrete data and can be a useful
             alternative to popular machine learning methods.},
   Doi = {10.1093/jrsssb/qkad010},
   Key = {fds371510}
}

@article{fds369224,
   Author = {Byng, D and Thomas, SM and Rushing, CN and Lynch, T and McCarthy, A and Francescatti, AB and Frank, ES and Partridge, AH and Thompson, AM and Retèl, VP and van Harten, WH and Grimm, LJ and Hyslop, T and Hwang, ES and Ryser, MD},
   Title = {Surveillance Imaging after Primary Diagnosis of Ductal
             Carcinoma in Situ.},
   Journal = {Radiology},
   Volume = {307},
   Number = {1},
   Pages = {e221210},
   Year = {2023},
   Month = {April},
   url = {http://dx.doi.org/10.1148/radiol.221210},
   Abstract = {Background Guidelines recommend annual surveillance imaging
             after diagnosis of ductal carcinoma in situ (DCIS).
             Guideline adherence has not been characterized in a
             contemporary cohort. Purpose To identify uptake and
             determinants of surveillance imaging in women who underwent
             treatment for DCIS. Materials and Methods A stratified
             random sample of women who underwent breast-conserving
             surgery for primary DCIS between 2008 and 2014 was
             retrospectively selected from 1330 facilities in the United
             States. Imaging examinations were recorded from date of
             diagnosis until first distant recurrence, death, loss to
             follow-up, or end of study (November 2018). Imaging after
             treatment was categorized into 10 12-month periods
             starting 6 months after diagnosis. Primary outcome was
             per-period receipt of asymptomatic surveillance imaging
             (mammography, MRI, or US). Secondary outcome was diagnosis
             of ipsilateral invasive breast cancer. Multivariable
             logistic regression with repeated measures and generalized
             estimating equations was used to model receipt of imaging.
             Rates of diagnosis with ipsilateral invasive breast cancer
             were compared between women who did and those who did not
             undergo imaging in the 6-18-month period after diagnosis
             using inverse probability-weighted Kaplan-Meier estimators.
             Results A total of 12 559 women (median age, 60 years;
             IQR, 52-69 years) were evaluated. Uptake of surveillance
             imaging was 75% in the first period and decreased over time
             (P < .001). Across the first 5 years after treatment, 52% of
             women participated in consistent annual surveillance.
             Surveillance was lower in Black (adjusted odds ratio [OR],
             0.80; 95% CI: 0.74, 0.88; P < .001) and Hispanic (OR, 0.82;
             95% CI: 0.72, 0.94; P = .004) women than in White women.
             Women who underwent surveillance in the first period had a
             higher 6-year rate of diagnosis of invasive cancer (1.6%;
             95% CI: 1.3, 1.9) than those who did not (1.1%; 95% CI: 0.7,
             1.4; difference: 0.5%; 95% CI: 0.1, 1.0; P = .03).
             Conclusion Half of women did not consistently adhere to
             imaging surveillance guidelines across the first 5 years
             after treatment, with racial disparities in adherence rates.
             © RSNA, 2023 Supplemental material is available for this
             article. See also the editorial by Rahbar and Dontchos in
             this issue.},
   Doi = {10.1148/radiol.221210},
   Key = {fds369224}
}

@article{fds370417,
   Author = {Isaev, DY and Vlasova, RM and Di Martino and JM and Stephen, CD and Schmahmann, JD and Sapiro, G and Gupta, AS},
   Title = {Uncertainty of Vowel Predictions as a Digital Biomarker for
             Ataxic Dysarthria.},
   Journal = {Cerebellum (London, England)},
   Year = {2023},
   Month = {April},
   url = {http://dx.doi.org/10.1007/s12311-023-01539-z},
   Abstract = {Dysarthria is a common manifestation across cerebellar
             ataxias leading to impairments in communication, reduced
             social connections, and decreased quality of life. While
             dysarthria symptoms may be present in other neurological
             conditions, ataxic dysarthria is a perceptually distinct
             motor speech disorder, with the most prominent
             characteristics being articulation and prosody abnormalities
             along with distorted vowels. We hypothesized that
             uncertainty of vowel predictions by an automatic speech
             recognition system can capture speech changes present in
             cerebellar ataxia. Speech of participants with ataxia (N=61)
             and healthy controls (N=25) was recorded during the "picture
             description" task. Additionally, participants' dysarthric
             speech and ataxia severity were assessed on a Brief Ataxia
             Rating Scale (BARS). Eight participants with ataxia had
             speech and BARS data at two timepoints. A neural network
             trained for phoneme prediction was applied to speech
             recordings. Average entropy of vowel tokens predictions
             (AVE) was computed for each participant's recording,
             together with mean pitch and intensity standard deviations
             (MPSD and MISD) in the vowel segments. AVE and MISD
             demonstrated associations with BARS speech score (Spearman's
             rho=0.45 and 0.51), and AVE demonstrated associations with
             BARS total (rho=0.39). In the longitudinal cohort, Wilcoxon
             pairwise signed rank test demonstrated an increase in BARS
             total and AVE, while BARS speech and acoustic measures did
             not significantly increase. Relationship of AVE to both BARS
             speech and BARS total, as well as the ability to capture
             disease progression even in absence of measured speech
             decline, indicates the potential of AVE as a digital
             biomarker for cerebellar ataxia.},
   Doi = {10.1007/s12311-023-01539-z},
   Key = {fds370417}
}

@article{fds367820,
   Author = {Shi, H and Vardhan, M and Randles, A},
   Title = {The Role of Immersion for Improving Extended Reality
             Analysis of Personalized Flow Simulations.},
   Journal = {Cardiovascular engineering and technology},
   Volume = {14},
   Number = {2},
   Pages = {194-203},
   Year = {2023},
   Month = {April},
   url = {http://dx.doi.org/10.1007/s13239-022-00646-y},
   Abstract = {<h4>Purpose</h4>Computational models of flow in
             patient-derived arterial geometries have become a key
             paradigm of biomedical research. These fluid models are
             often challenging to visualize due to high spatial
             heterogeneity and visual complexity. Virtual immersive
             environments can offer advantageous visualization of
             spatially heterogeneous and complex systems. However, as
             different VR devices offer varying levels of immersion,
             there remains a crucial lack of understanding regarding what
             level of immersion is best suited for interactions with
             patient-specific flow models.<h4>Methods</h4>We conducted a
             quantitative user evaluation with multiple VR devices
             testing an important use of hemodynamic simulations-analysis
             of surface parameters within complex patient-specific
             geometries. This task was compared for the semi-immersive
             zSpace 3D monitor and the fully immersive HTC Vive
             system.<h4>Results</h4>The semi-immersive device was more
             accurate than the fully immersive device. The two devices
             showed similar results for task duration and performance
             (accuracy/duration). The accuracy of the semi-immersive
             device was also higher for arterial geometries of greater
             complexity and branching.<h4>Conclusion</h4>This assessment
             demonstrates that the level of immersion plays a significant
             role in the accuracy of assessing arterial flow models. We
             found that the semi-immersive VR device was a generally
             optimal choice for arterial visualization.},
   Doi = {10.1007/s13239-022-00646-y},
   Key = {fds367820}
}

@article{fds374495,
   Author = {Kiselev, A and Luo, X},
   Title = {On Nonexistence of Splash Singularities for the α -SQG
             Patches},
   Journal = {Journal of Nonlinear Science},
   Volume = {33},
   Number = {2},
   Year = {2023},
   Month = {April},
   url = {http://dx.doi.org/10.1007/s00332-023-09893-2},
   Abstract = {In this paper, we consider patch solutions to the α-SQG
             equation and derive new criteria for the absence of splash
             singularity where different patches or parts of the same
             patch collide in finite time. Our criterion refines a result
             due to Gancedo and Strain Gancedo and Strain (2014),
             providing a condition on the growth of curvature of the
             patch necessary for the splash and an exponential in time
             lower bound on the distance between patches with bounded
             curvature.},
   Doi = {10.1007/s00332-023-09893-2},
   Key = {fds374495}
}

@article{fds370969,
   Author = {Liu, GR and Sheu, YC and Wu, HT},
   Title = {CENTRAL AND NONCENTRAL LIMIT THEOREMS ARISING FROM THE
             SCATTERING TRANSFORM AND ITS NEURAL ACTIVATION
             GENERALIZATION},
   Journal = {SIAM Journal on Mathematical Analysis},
   Volume = {55},
   Number = {2},
   Pages = {1170-1213},
   Year = {2023},
   Month = {April},
   url = {http://dx.doi.org/10.1137/21M1454511},
   Abstract = {Motivated by the analysis of complicated time series, we
             examine a generalization of the scattering transform that
             includes broad neural activation functions. This
             generalization is the neural activation scattering transform
             (NAST). NAST comprises a sequence of "neural processing
             units," each of which applies a high pass filter to the
             input from the previous layer followed by a composition with
             a nonlinear function as the output to the next neuron. Here,
             the nonlinear function models how a neuron gets excited by
             the input signal. In addition to showing properties like
             nonexpansion, horizontal translational invariability, and
             insensitivity to local deformation, we explore the
             statistical properties of the second-order NAST of a
             Gaussian process with various dependence structures and its
             interaction with the chosen wavelets and activation
             functions. We also provide central limit theorem (CLT) and
             non-CLT results. Numerical simulations demonstrate the
             developed theorems. Our results explain how NAST processes
             complicated time series, paving a way toward statistical
             inference based on NAST for real-world applications.},
   Doi = {10.1137/21M1454511},
   Key = {fds370969}
}

@article{fds369942,
   Author = {Hamid, I and Korunes, KL and Schrider, DR and Goldberg,
             A},
   Title = {Localizing Post-Admixture Adaptive Variants with Object
             Detection on Ancestry-Painted Chromosomes.},
   Journal = {Molecular biology and evolution},
   Volume = {40},
   Number = {4},
   Pages = {msad074},
   Publisher = {Oxford University Press (OUP)},
   Editor = {Rogers, R},
   Year = {2023},
   Month = {April},
   url = {http://dx.doi.org/10.1093/molbev/msad074},
   Abstract = {Gene flow between previously differentiated populations
             during the founding of an admixed or hybrid population has
             the potential to introduce adaptive alleles into the new
             population. If the adaptive allele is common in one source
             population, but not the other, then as the adaptive allele
             rises in frequency in the admixed population, genetic
             ancestry from the source containing the adaptive allele will
             increase nearby as well. Patterns of genetic ancestry have
             therefore been used to identify post-admixture positive
             selection in humans and other animals, including examples in
             immunity, metabolism, and animal coloration. A common method
             identifies regions of the genome that have local ancestry
             "outliers" compared with the distribution across the rest of
             the genome, considering each locus independently. However,
             we lack theoretical models for expected distributions of
             ancestry under various demographic scenarios, resulting in
             potential false positives and false negatives. Further,
             ancestry patterns between distant sites are often not
             independent. As a result, current methods tend to infer wide
             genomic regions containing many genes as under selection,
             limiting biological interpretation. Instead, we develop a
             deep learning object detection method applied to images
             generated from local ancestry-painted genomes. This approach
             preserves information from the surrounding genomic context
             and avoids potential pitfalls of user-defined summary
             statistics. We find the method is robust to a variety of
             demographic misspecifications using simulated data. Applied
             to human genotype data from Cabo Verde, we localize a known
             adaptive locus to a single narrow region compared with
             multiple or long windows obtained using two other
             ancestry-based methods.},
   Doi = {10.1093/molbev/msad074},
   Key = {fds369942}
}

@article{fds365445,
   Author = {Cook, NA and Nguyen, HH and Yakir, O and Zeitouni,
             O},
   Title = {Universality of Poisson Limits for Moduli of Roots of Kac
             Polynomials},
   Journal = {International Mathematics Research Notices},
   Volume = {2023},
   Number = {8},
   Pages = {6648-6690},
   Publisher = {Oxford University Press (OUP)},
   Year = {2023},
   Month = {April},
   url = {http://dx.doi.org/10.1093/imrn/rnac021},
   Abstract = {We give a new proof of a recent resolution [18] by Michelen
             and Sahasrabudhe of a conjecture of Shepp and Vanderbei [19]
             that the moduli of roots of Gaussian Kac polynomials of
             degree $n$, centered at $1$ and rescaled by $n^2$, should
             form a Poisson point process. We use this new approach to
             verify a conjecture from [18] that the Poisson statistics
             are in fact universal.},
   Doi = {10.1093/imrn/rnac021},
   Key = {fds365445}
}

@article{fds369692,
   Author = {Kiselev, A and Luo, X},
   Title = {On Nonexistence of Splash Singularities for the α -SQG
             Patches},
   Journal = {Journal of Nonlinear Science},
   Volume = {33},
   Number = {2},
   Publisher = {Springer Science and Business Media LLC},
   Year = {2023},
   Month = {April},
   url = {http://dx.doi.org/10.1007/s00332-023-09893-2},
   Abstract = {In this paper, we consider patch solutions to the α-SQG
             equation and derive new criteria for the absence of splash
             singularity where different patches or parts of the same
             patch collide in finite time. Our criterion refines a result
             due to Gancedo and Strain Gancedo and Strain (2014),
             providing a condition on the growth of curvature of the
             patch necessary for the splash and an exponential in time
             lower bound on the distance between patches with bounded
             curvature.},
   Doi = {10.1007/s00332-023-09893-2},
   Key = {fds369692}
}

@article{fds361408,
   Author = {Li, X and Sung, A and Xie, J},
   Title = {DART: Distance Assisted Recursive Testing},
   Journal = {Journal of Machine Learning Research},
   Volume = {24},
   Number = {169},
   Year = {2023},
   Month = {April},
   Abstract = {In many applications, a large number of features are
             collected with the goal to identify a few important ones.
             Sometimes, these features lie in a metric space with a known
             distance matrix, which partially reflects their
             co-importance pattern. Proper use of the distance matrix
             will boost the power of identifying important features.
             Hence, we develop a new multiple testing framework named the
             Distance Assisted Recursive Testing (DART). DART has two
             stages. In stage 1, we transform the distance matrix into an
             aggregation tree, where each node represents a set of
             features. In stage 2, based on the aggregation tree, we set
             up dynamic node hypotheses and perform multiple testing on
             the tree. All rejections are mapped back to the features.
             Under mild assumptions, the false discovery proportion of
             DART converges to the desired level in high probability
             converging to one. We illustrate by theory and simulations
             that DART has superior performance under various models
             compared to the existing methods. We applied DART to a
             clinical trial in the allogeneic stem cell transplantation
             study to identify the gut microbiota whose abundance will be
             impacted by the after-transplant care.},
   Key = {fds361408}
}

@article{fds369852,
   Author = {Arapura, D and Matsuki, K and Patel, D and Włodarczyk,
             J},
   Title = {A Kawamata–Viehweg type formulation of the logarithmic
             Akizuki–Nakano vanishing theorem},
   Journal = {Mathematische Zeitschrift},
   Volume = {303},
   Number = {4},
   Year = {2023},
   Month = {April},
   url = {http://dx.doi.org/10.1007/s00209-023-03225-6},
   Abstract = {In this article, we present a Kawamata–Viehweg type
             formulation of the (logarithmic) Akizuki–Nakano Vanishing
             Theorem. We give two proofs: one by reduction to an older
             theorem of Steenbrink via Kawamata’s covering lemma, and
             another by mod p reduction using results of
             Deligne–Illusie and Hara. We also include two
             applications.},
   Doi = {10.1007/s00209-023-03225-6},
   Key = {fds369852}
}

@article{fds373605,
   Author = {Witt, CE and Mena, S and Holmes, J and Hersey, M and Buchanan, AM and Parke, B and Saylor, R and Honan, LE and Berger, SN and Lumbreras, S and Nijhout, FH and Reed, MC and Best, J and Fadel, J and Schloss, P and Lau,
             T and Hashemi, P},
   Title = {Serotonin is a Common Thread Linking Different Classes of
             Antidepressants.},
   Journal = {Res Sq},
   Year = {2023},
   Month = {March},
   url = {http://dx.doi.org/10.21203/rs.3.rs-2741902/v1},
   Abstract = {Depression pathology remains elusive. The monoamine
             hypothesis has placed much focus on serotonin, but due to
             the variable clinical efficacy of monoamine reuptake
             inhibitors, the community is looking for alternative
             therapies such as ketamine (synaptic plasticity and
             neurogenesis theory of antidepressant action). There is
             evidence that different classes of antidepressants may
             affect serotonin levels; a notion we test here. We measure
             hippocampal serotonin in mice with voltammetry and study the
             effects of acute challenges of antidepressants. We find that
             pseudo-equivalent doses of these drugs similarly raise
             ambient serotonin levels, despite their differing
             pharmacodynamics because of differences in Uptake 1 and 2,
             rapid SERT trafficking and modulation of serotonin by
             histamine. These antidepressants have different
             pharmacodynamics but have strikingly similar effects on
             extracellular serotonin. Our findings suggest that serotonin
             is a common thread that links clinically effective
             antidepressants, synergizing different theories of
             depression (synaptic plasticity, neurogenesis and the
             monoamine hypothesis).},
   Doi = {10.21203/rs.3.rs-2741902/v1},
   Key = {fds373605}
}

@article{fds374584,
   Author = {L. Chariker and A. De Masi and J. Lebowitz and E.
             Presutti},
   Title = {Scaling limit of a generalized contact process},
   Journal = {J. Stat. Phys},
   Volume = {190},
   Number = {3},
   Year = {2023},
   Month = {March},
   ISSN = {1572-9613},
   url = {https://www.researchwithrutgers.com/en/publications/scaling-limit-of-a-generalized-contact-process},
   Keywords = {Generalized contact process Hydrodynamic limit Integrate and
             fire Mean field Neurons with discrete voltage Spatial
             dependence},
   Abstract = {https://www.researchwithrutgers.com/en/publications/scaling-limit-of-a-generalized-contact-process},
   Doi = {10.1007/s10955-022-03050-x},
   Key = {fds374584}
}

@article{fds358017,
   Author = {Bachmann, T and Wickelgren, K},
   Title = {EULER CLASSES: SIX-FUNCTORS FORMALISM, DUALITIES,
             INTEGRALITY AND LINEAR SUBSPACES OF COMPLETE
             INTERSECTIONS},
   Journal = {Journal of the Institute of Mathematics of
             Jussieu},
   Volume = {22},
   Number = {2},
   Pages = {681-746},
   Year = {2023},
   Month = {March},
   url = {http://dx.doi.org/10.1017/S147474802100027X},
   Abstract = {We equate various Euler classes of algebraic vector bundles,
             including those of [12] and one suggested by M. J. Hopkins,
             A. Raksit, and J.-P. Serre. We establish integrality results
             for this Euler class and give formulas for local indices at
             isolated zeros, both in terms of the six-functors formalism
             of coherent sheaves and as an explicit recipe in the
             commutative algebra of Scheja and Storch. As an application,
             we compute the Euler classes enriched in bilinear forms
             associated to arithmetic counts of d-planes on complete
             intersections in in terms of topological Euler numbers over
             and.},
   Doi = {10.1017/S147474802100027X},
   Key = {fds358017}
}

@article{fds367648,
   Author = {Wang, YG and Womersley, RS and Wu, HT and Yu, WH},
   Title = {Numerical computation of triangular complex spherical
             designs with small mesh ratio},
   Journal = {Journal of Computational and Applied Mathematics},
   Volume = {421},
   Year = {2023},
   Month = {March},
   url = {http://dx.doi.org/10.1016/j.cam.2022.114796},
   Abstract = {This paper provides triangular spherical designs for the
             complex unit sphere Ωd⊂ℂd by exploiting the natural
             correspondence with the real unit sphere S2d−1⊂R2d. A
             variational characterization of triangular complex designs
             is provided, with particular emphasis on numerical
             computation of efficient triangular complex designs with
             good geometric properties as measured by their mesh ratio.
             We give numerical examples of triangular spherical t-designs
             on complex unit spheres of dimension d=2 to
             6.},
   Doi = {10.1016/j.cam.2022.114796},
   Key = {fds367648}
}

@article{fds368028,
   Author = {Beauville, A and Schoen, C},
   Title = {A Non-Hyperelliptic Curve with Torsion Ceresa Cycle Modulo
             Algebraic Equivalence},
   Journal = {International Mathematics Research Notices},
   Volume = {2023},
   Number = {5},
   Pages = {3671-3675},
   Publisher = {Oxford University Press (OUP)},
   Year = {2023},
   Month = {March},
   url = {http://dx.doi.org/10.1093/imrn/rnab344},
   Abstract = {We exhibit a non-hyperelliptic curve C of genus 3 such that
             the class of the Ceresa cycle [C]-[C-] in JC modulo
             algebraic equivalence is torsion.},
   Doi = {10.1093/imrn/rnab344},
   Key = {fds368028}
}

@article{fds370567,
   Author = {Bowen, M and King, JR and Witelski, TP},
   Title = {CAUCHY-DIRICHLET PROBLEMS FOR THE POROUS MEDIUM
             EQUATION},
   Journal = {Discrete and Continuous Dynamical Systems- Series
             A},
   Volume = {43},
   Number = {3-4},
   Pages = {1143-1174},
   Publisher = {American Institute of Mathematical Sciences
             (AIMS)},
   Year = {2023},
   Month = {March},
   url = {http://dx.doi.org/10.3934/dcds.2022182},
   Abstract = {We consider the porous medium equation subject to
             zero-Dirichlet conditions on a variety of two-dimensional
             domains, namely strips, slender domains and sectors,
             allowing us to capture a number of different classes of
             behaviours. Our focus is on intermediate-asymptotic
             descriptions, derived by formal arguments and validated
             against numerical computations. While our emphasis is on
             non-negative solutions to the slow-diffusion case, we also
             derive a number of results for sign-change solutions and for
             fast diffusion. Self-similar solutions of various kinds play
             a central role, alongside the identification of suitable
             conserved quantities. The characterisation of domains
             exhibiting infinite-time hole closure is a particular upshot
             and we highlight a number of open problems.},
   Doi = {10.3934/dcds.2022182},
   Key = {fds370567}
}

@article{fds366912,
   Author = {Dou, X and Liu, JG and Zhou, Z},
   Title = {A TUMOR GROWTH MODEL WITH AUTOPHAGY: THE
             REACTION-(CROSS-)DIFFUSION SYSTEM AND ITS FREE BOUNDARY
             LIMIT},
   Journal = {Discrete and Continuous Dynamical Systems - Series
             B},
   Volume = {28},
   Number = {3},
   Pages = {1964-1992},
   Publisher = {American Institute of Mathematical Sciences
             (AIMS)},
   Year = {2023},
   Month = {March},
   url = {http://dx.doi.org/10.3934/dcdsb.2022154},
   Abstract = {In this paper, we propose a tumor growth model to
             incorporate and investigate the spatial effects of
             autophagy. The cells are classified into two phases: normal
             cells and autophagic cells, whose dynamics are also coupled
             with the nutrients. First, we construct a
             reaction-(cross-)diffusion system describing the evolution
             of cell densities, where the drift is determined by the
             negative gradient of the joint pressure, and the reaction
             terms manifest the unique mechanism of autophagy. Next, in
             the incompressible limit, such a cell density model
             naturally connects to a free boundary system, describing the
             geometric motion of the tumor region. Analyzing the free
             boundary model in a special case, we show that the ratio of
             the two phases of cells exponentially converges to a
             “well-mixed” limit. Within this “well-mixed” limit,
             we obtain an analytical solution of the free boundary system
             which indicates the exponential growth of the tumor size in
             the presence of autophagy in contrast to the linear growth
             without it. Numerical simulations are also provided to
             illustrate the analytical properties and to explore more
             scenarios.},
   Doi = {10.3934/dcdsb.2022154},
   Key = {fds366912}
}

@article{fds369041,
   Author = {Gao, Y and Li, T and Li, X and Liu, JG},
   Title = {TRANSITION PATH THEORY FOR LANGEVIN DYNAMICS ON MANIFOLDS:
             OPTIMAL CONTROL AND DATA-DRIVEN SOLVER},
   Journal = {Multiscale Modeling and Simulation},
   Volume = {21},
   Number = {1},
   Pages = {1-33},
   Publisher = {Society for Industrial & Applied Mathematics
             (SIAM)},
   Year = {2023},
   Month = {March},
   url = {http://dx.doi.org/10.1137/21M1437883},
   Abstract = {We present a data-driven point of view for rare events,
             which represent conformational transitions in biochemical
             reactions modeled by overdamped Langevin dynamics on
             manifolds in high dimensions. We first reinterpret the
             transition state theory and the transition path theory from
             the optimal control viewpoint. Given a point cloud probing
             the manifold, we construct a discrete Markov chain with a
             Q-matrix computed from an approximated Voronoi tesselation
             via the point cloud. We use this Q-matrix to compute a
             discrete committor function whose level set automatically
             orders the point cloud. Then based on the committor
             function, an optimally controlled random walk on point
             clouds is constructed and utilized to efficiently sample
             transition paths, which become an almost sure event in O(1)
             time instead of a rare event in the original reaction
             dynamics. To compute the mean transition path efficiently, a
             local averaging algorithm based on the optimally controlled
             random walk is developed, which adapts the finite
             temperature string method to the controlled Monte Carlo
             samples. Numerical examples on sphere/torus including a
             conformational transition for the alanine dipeptide in
             vacuum are conducted to illustrate the data-driven solver
             for the transition path theory on point clouds. The mean
             transition path obtained via the controlled Monte Carlo
             simulations highly coincides with the computed dominant
             transition path in the transition path theory.},
   Doi = {10.1137/21M1437883},
   Key = {fds369041}
}

@article{fds374549,
   Author = {Miller, E and Zhang, J},
   Title = {Geodesic complexity of convex polyhedra},
   Year = {2023},
   Month = {March},
   Key = {fds374549}
}

@article{fds369850,
   Author = {Wang, M and Lu, J},
   Title = {Neural Network-Based Variational Methods for Solving
             Quadratic Porous Medium Equations in High
             Dimensions},
   Journal = {Communications in Mathematics and Statistics},
   Volume = {11},
   Number = {1},
   Pages = {21-57},
   Year = {2023},
   Month = {March},
   url = {http://dx.doi.org/10.1007/s40304-023-00339-5},
   Abstract = {In this paper, we propose and study neural network-based
             methods for solutions of high-dimensional quadratic porous
             medium equation (QPME). Three variational formulations of
             this nonlinear PDE are presented: a strong formulation and
             two weak formulations. For the strong formulation, the
             solution is directly parameterized with a neural network and
             optimized by minimizing the PDE residual. It can be proved
             that the convergence of the optimization problem guarantees
             the convergence of the approximate solution in the L1 sense.
             The weak formulations are derived following (Brenier in
             Examples of hidden convexity in nonlinear PDEs, 2020) which
             characterizes the very weak solutions of QPME. Specifically
             speaking, the solutions are represented with intermediate
             functions who are parameterized with neural networks and are
             trained to optimize the weak formulations. Extensive
             numerical tests are further carried out to investigate the
             pros and cons of each formulation in low and high
             dimensions. This is an initial exploration made along the
             line of solving high-dimensional nonlinear PDEs with neural
             network-based methods, which we hope can provide some useful
             experience for future investigations.},
   Doi = {10.1007/s40304-023-00339-5},
   Key = {fds369850}
}

@article{fds367604,
   Author = {Ding, X and Wu, HT},
   Title = {Impact of Signal-to-Noise Ratio and Bandwidth on Graph
             Laplacian Spectrum From High-Dimensional Noisy Point
             Cloud},
   Journal = {IEEE Transactions on Information Theory},
   Volume = {69},
   Number = {3},
   Pages = {1899-1931},
   Publisher = {Institute of Electrical and Electronics Engineers
             (IEEE)},
   Year = {2023},
   Month = {March},
   url = {http://dx.doi.org/10.1109/TIT.2022.3216561},
   Abstract = {We systematically study the spectrum of kernel-based graph
             Laplacian (GL) constructed from high-dimensional and noisy
             random point cloud in the nonnull setup. The problem is
             motived by studying the model when the clean signal is
             sampled from a manifold that is embedded in a
             low-dimensional Euclidean subspace, and corrupted by
             high-dimensional noise. We quantify how the signal and noise
             interact in different regions of signal-to-noise ratio
             (SNR), and report the resulting peculiar spectral behavior
             of GL. In addition, we explore the impact of chosen kernel
             bandwidth on the spectrum of GL over different regions of
             SNR, which lead to an adaptive choice of kernel bandwidth
             that coincides with the common practice in real data. This
             result paves the way to a theoretical understanding of how
             practitioners apply GL when the dataset is
             noisy.},
   Doi = {10.1109/TIT.2022.3216561},
   Key = {fds367604}
}

@article{fds368886,
   Author = {Steinerberger, S and Wu, HT},
   Title = {Fundamental component enhancement via adaptive nonlinear
             activation functions},
   Journal = {Applied and Computational Harmonic Analysis},
   Volume = {63},
   Pages = {135-143},
   Year = {2023},
   Month = {March},
   url = {http://dx.doi.org/10.1016/j.acha.2022.11.007},
   Abstract = {In many real world oscillatory signals, the fundamental
             component of a signal f(t) might be weak or does not exist.
             This makes it difficult to estimate the instantaneous
             frequency of the signal. A traditional approach is to apply
             the rectification trick, working with |f(t)| or ReLu(f(t))
             instead, to enhance the fundamental component. This raises
             an interesting question: what type of nonlinear function
             g:R→R has the property that g(f(t)) has a more pronounced
             fundamental frequency? g(t)=|t| and g(t)=ReLu(t) seem to
             work well in practice; we propose a variant of
             g(t)=1/(1−|t|) and provide a theoretical guarantee.
             Several simulated signals and real signals are analyzed to
             demonstrate the performance of the proposed
             solution.},
   Doi = {10.1016/j.acha.2022.11.007},
   Key = {fds368886}
}

@article{fds374288,
   Author = {Benson, J and Bessonov, M and Burke, K and Cassani, S and Ciocanel, M-V and Cooney, DB and Volkening, A},
   Title = {How do classroom-turnover times depend on lecture-hall
             size?},
   Journal = {Mathematical biosciences and engineering :
             MBE},
   Volume = {20},
   Number = {5},
   Pages = {9179-9207},
   Year = {2023},
   Month = {March},
   url = {http://dx.doi.org/10.3934/mbe.2023403},
   Abstract = {Academic spaces in colleges and universities span classrooms
             for 10 students to lecture halls that hold over 600 people.
             During the break between consecutive classes, students from
             the first class must leave and the new class must find their
             desks, regardless of whether the room holds 10 or 600
             people. Here we address the question of how the size of
             large lecture halls affects classroom-turnover times,
             focusing on non-emergency settings. By adapting the
             established social-force model, we treat students as
             individuals who interact and move through classrooms to
             reach their destinations. We find that social interactions
             and the separation time between consecutive classes strongly
             influence how long it takes entering students to reach their
             desks, and that these effects are more pronounced in larger
             lecture halls. While the median time that individual
             students must travel increases with decreased separation
             time, we find that shorter separation times lead to shorter
             classroom-turnover times overall. This suggests that the
             effects of scheduling gaps and lecture-hall size on
             classroom dynamics depends on the perspective-individual
             student or whole class-that one chooses to
             take.},
   Doi = {10.3934/mbe.2023403},
   Key = {fds374288}
}

@article{fds367059,
   Author = {Bernal, EA and Hauenstein, JD and Mehta, D and Regan, MH and Tang,
             T},
   Title = {Machine learning the real discriminant locus},
   Journal = {Journal of Symbolic Computation},
   Volume = {115},
   Pages = {409-426},
   Year = {2023},
   Month = {March},
   url = {http://dx.doi.org/10.1016/j.jsc.2022.08.001},
   Abstract = {Parameterized systems of polynomial equations arise in many
             applications in science and engineering with the real
             solutions describing, for example, equilibria of a dynamical
             system, linkages satisfying design constraints, and scene
             reconstruction in computer vision. Since different parameter
             values can have a different number of real solutions, the
             parameter space is decomposed into regions whose boundary
             forms the real discriminant locus. This article views
             locating the real discriminant locus as a supervised
             classification problem in machine learning where the goal is
             to determine classification boundaries over the parameter
             space, with the classes being the number of real solutions.
             This article presents a novel sampling method which
             carefully samples a multidimensional parameter space. At
             each sample point, homotopy continuation is used to obtain
             the number of real solutions to the corresponding polynomial
             system. Machine learning techniques including nearest
             neighbors, support vector classifiers, and neural networks
             are used to efficiently approximate the real discriminant
             locus. One application of having learned the real
             discriminant locus is to develop a real homotopy method that
             only tracks real solution paths unlike traditional methods
             which track all complex solution paths. Examples show that
             the proposed approach can efficiently approximate
             complicated solution boundaries such as those arising from
             the equilibria of the N=4 Kuramoto model which was
             previously intractable using traditional
             methods.},
   Doi = {10.1016/j.jsc.2022.08.001},
   Key = {fds367059}
}

@article{fds369807,
   Author = {Pura, JA and Li, X and Chan, C and Xie, J},
   Title = {TEAM: A MULTIPLE TESTING ALGORITHM ON THE AGGREGATION TREE
             FOR FLOW CYTOMETRY ANALYSIS},
   Journal = {Annals of Applied Statistics},
   Volume = {17},
   Number = {1},
   Pages = {621-640},
   Year = {2023},
   Month = {March},
   url = {http://dx.doi.org/10.1214/22-AOAS1645},
   Abstract = {In immunology studies, flow cytometry is a commonly used
             multivariate single-cell assay. One key goal in flow
             cytometry analysis is to detect the immune cells responsive
             to certain stimuli. Statistically, this problem can be
             translated into comparing two protein expression probability
             density functions (PDFs) before and after the stimulus; the
             goal is to pinpoint the regions where these two PDFs differ.
             Further screening of these differential regions can be
             performed to identify enriched sets of responsive cells. In
             this paper we model identifying differential density regions
             as a multiple testing problem. First, we partition the
             sample space into small bins. In each bin we form a
             hypothesis to test the existence of differential PDFs.
             Second, we develop a novel multiple testing method, called
             TEAM (testing on the aggregation tree method), to identify
             those bins that harbor differential PDFs while controlling
             the false discovery rate (FDR) under the desired level. TEAM
             embeds the testing procedure into an aggregation tree to
             test from fine-to coarse-resolution. The procedure achieves
             the statistical goal of pinpointing density differences to
             the smallest possible regions. TEAM is computationally
             efficient, capable of analyzing large flow cytometry data
             sets in much shorter time compared with competing methods.
             We applied TEAM and competing methods on a flow cytometry
             data set to identify T cells responsive to the
             cytomegalovirus (CMV)-pp65 antigen stimulation. With
             additional downstream screening, TEAM successfully
             identified enriched sets containing monofunctional,
             bifunctional, and polyfunctional T cells. Competing methods
             either did not finish in a reasonable time frame or provided
             less interpretable results. Numerical simulations and
             theoretical justifications demonstrate that TEAM has
             asymptotically valid, powerful, and robust performance.
             Overall, TEAM is a computationally efficient and
             statistically powerful algorithm that can yield meaningful
             biological insights in flow cytometry studies.},
   Doi = {10.1214/22-AOAS1645},
   Key = {fds369807}
}

@article{fds367922,
   Author = {Faigenbaum-Golovin, S and Levin, D},
   Title = {Manifold reconstruction and denoising from scattered data in
             high dimension},
   Journal = {Journal of Computational and Applied Mathematics},
   Volume = {421},
   Pages = {114818-114818},
   Publisher = {Elsevier BV},
   Year = {2023},
   Month = {March},
   url = {http://dx.doi.org/10.1016/j.cam.2022.114818},
   Doi = {10.1016/j.cam.2022.114818},
   Key = {fds367922}
}

@article{fds372443,
   Author = {Bezemek, ZW and Spiliopoulos, K},
   Title = {Rate of homogenization for fully-coupled McKean-Vlasov
             SDEs},
   Journal = {Stochastics and Dynamics},
   Volume = {23},
   Number = {2},
   Publisher = {World Scientific Pub Co Pte Ltd},
   Year = {2023},
   Month = {March},
   url = {http://dx.doi.org/10.1142/S0219493723500132},
   Abstract = {In this paper, we consider a fully-coupled slow-fast system
             of McKean-Vlasov stochastic differential equations with full
             dependence on the slow and fast component and on the law of
             the slow component and derive convergence rates to its
             homogenized limit. We do not make periodicity assumptions,
             but we impose conditions on the fast motion to guarantee
             ergodicity. In the course of the proof we obtain related
             ergodic theorems and we gain results on the regularity of
             Poisson type of equations and of the associated Cauchy
             problem on the Wasserstein space that are of independent
             interest.},
   Doi = {10.1142/S0219493723500132},
   Key = {fds372443}
}

@article{fds372623,
   Author = {Alon, N and Wei, F},
   Title = {Irregular subgraphs},
   Journal = {Combinatorics, Probability and Computing},
   Volume = {32},
   Number = {2},
   Pages = {269-283},
   Publisher = {Cambridge University Press (CUP)},
   Year = {2023},
   Month = {March},
   url = {http://dx.doi.org/10.1017/s0963548322000220},
   Abstract = {<jats:title>Abstract</jats:title><jats:p>We suggest two
             related conjectures dealing with the existence of spanning
             irregular subgraphs of graphs. The first asserts that any
             <jats:inline-formula><jats:alternatives>??<jats:tex-math>
             $d$ </jats:tex-math></jats:alternatives></jats:inline-formula>-regular
             graph on <jats:inline-formula><jats:alternatives>??<jats:tex-math>
             $n$ </jats:tex-math></jats:alternatives></jats:inline-formula>
             vertices contains a spanning subgraph in which the number of
             vertices of each degree between <jats:inline-formula><jats:alternatives>??<jats:tex-math>
             $0$ </jats:tex-math></jats:alternatives></jats:inline-formula>
             and <jats:inline-formula><jats:alternatives>??<jats:tex-math>
             $d$ </jats:tex-math></jats:alternatives></jats:inline-formula>
             deviates from <jats:inline-formula><jats:alternatives>??<jats:tex-math>
             $\frac{n}{d+1}$ </jats:tex-math></jats:alternatives></jats:inline-formula>
             by at most <jats:inline-formula><jats:alternatives>??<jats:tex-math>
             $2$ </jats:tex-math></jats:alternatives></jats:inline-formula>.
             The second is that every graph on <jats:inline-formula><jats:alternatives>??<jats:tex-math>
             $n$ </jats:tex-math></jats:alternatives></jats:inline-formula>
             vertices with minimum degree <jats:inline-formula><jats:alternatives>??<jats:tex-math>
             $\delta$ </jats:tex-math></jats:alternatives></jats:inline-formula>
             contains a spanning subgraph in which the number of vertices
             of each degree does not exceed <jats:inline-formula><jats:alternatives>??<jats:tex-math>
             $\frac{n}{\delta +1}+2$ </jats:tex-math></jats:alternatives></jats:inline-formula>.
             Both conjectures remain open, but we prove several
             asymptotic relaxations for graphs with a large number of
             vertices <jats:inline-formula><jats:alternatives>??<jats:tex-math>
             $n$ </jats:tex-math></jats:alternatives></jats:inline-formula>.
             In particular we show that if <jats:inline-formula><jats:alternatives>??<jats:tex-math>
             $d^3 \log n \leq o(n)$ </jats:tex-math></jats:alternatives></jats:inline-formula>
             then every <jats:inline-formula><jats:alternatives>??<jats:tex-math>
             $d$ </jats:tex-math></jats:alternatives></jats:inline-formula>-regular
             graph with <jats:inline-formula><jats:alternatives>??<jats:tex-math>
             $n$ </jats:tex-math></jats:alternatives></jats:inline-formula>
             vertices contains a spanning subgraph in which the number of
             vertices of each degree between <jats:inline-formula><jats:alternatives>??<jats:tex-math>
             $0$ </jats:tex-math></jats:alternatives></jats:inline-formula>
             and <jats:inline-formula><jats:alternatives>??<jats:tex-math>
             $d$ </jats:tex-math></jats:alternatives></jats:inline-formula>
             is <jats:inline-formula><jats:alternatives>??<jats:tex-math>
             $(1+o(1))\frac{n}{d+1}$ </jats:tex-math></jats:alternatives></jats:inline-formula>.
             We also prove that any graph with <jats:inline-formula><jats:alternatives>??<jats:tex-math>
             $n$ </jats:tex-math></jats:alternatives></jats:inline-formula>
             vertices and minimum degree <jats:inline-formula><jats:alternatives>??<jats:tex-math>
             $\delta$ </jats:tex-math></jats:alternatives></jats:inline-formula>
             contains a spanning subgraph in which no degree is repeated
             more than <jats:inline-formula><jats:alternatives>??<jats:tex-math>
             $(1+o(1))\frac{n}{\delta +1}+2$ </jats:tex-math></jats:alternatives></jats:inline-formula>
             times.</jats:p>},
   Doi = {10.1017/s0963548322000220},
   Key = {fds372623}
}

@article{fds372373,
   Author = {Fang, D and Vilanova, AT},
   Title = {Observable Error Bounds of the Time-Splitting Scheme for
             Quantum-Classical Molecular Dynamics},
   Journal = {SIAM Journal on Numerical Analysis},
   Volume = {61},
   Number = {1},
   Pages = {26-44},
   Publisher = {Society for Industrial & Applied Mathematics
             (SIAM)},
   Year = {2023},
   Month = {February},
   url = {http://dx.doi.org/10.1137/21m1462349},
   Doi = {10.1137/21m1462349},
   Key = {fds372373}
}

@article{fds369338,
   Author = {Robles, C},
   Title = {Pseudoconvexity at infinity in Hodge theory: a codimension
             one example},
   Year = {2023},
   Month = {February},
   Abstract = {The generalization of the Satake--Baily--Borel
             compactification to arbitrary period maps has been reduced
             to a certain extension problem on certain "neighborhoods at
             infinity". Extension problems of this type require that the
             neighborhood be pseudoconvex. The purpose of this note is to
             establish the desired pseudoconvexity in one relatively
             simple, but non-trivial, example: codimension one
             degenerations of a period map of weight two Hodge structures
             with first Hodge number $h^{2,0}$ equal to
             2.},
   Key = {fds369338}
}

@article{fds369339,
   Author = {Robles, C},
   Title = {Extension of Hodge norms at infinity},
   Year = {2023},
   Month = {February},
   Abstract = {It is a long-standing problem in Hodge theory to generalize
             the Satake--Baily--Borel (SBB) compactification of a locally
             Hermitian symmetric space to arbitrary period maps. A proper
             topological SBB-type completion has been constructed, and
             the problem of showing that the construction is algebraic
             has been reduced to showing that the compact fibres A of the
             completion admit neighborhoods X satisfying certain
             properties. All but one of those properties has been
             established; the outstanding problem is to show that
             holomorphic functions on certain divisors "at infinity"
             extend to $X$. Extension theorems of this type require that
             the complex manifold X be pseudoconvex; that is, admit a
             plurisubharmonic exhaustion function. The neighborhood X is
             stratified, and the strata admit Hodge norms which are may
             be used to produce plurisubharmonic functions on the strata.
             One would like to extend these norms to X so that they may
             be used to construct the desired plurisubharmonic exhaustion
             of X. The purpose of this paper is show that there exists a
             function that simultaneously extends all the Hodge norms
             along the strata that intersect the fibre A
             nontrivially.},
   Key = {fds369339}
}

@article{fds369364,
   Author = {Perochon, S and Matias Di Martino and J and Carpenter, KLH and Compton,
             S and Davis, N and Espinosa, S and Franz, L and Rieder, AD and Sullivan, C and Sapiro, G and Dawson, G},
   Title = {A tablet-based game for the assessment of visual motor
             skills in autistic children.},
   Journal = {NPJ Digit Med},
   Volume = {6},
   Number = {1},
   Pages = {17},
   Year = {2023},
   Month = {February},
   url = {http://dx.doi.org/10.1038/s41746-023-00762-6},
   Abstract = {Increasing evidence suggests that early motor impairments
             are a common feature of autism. Thus, scalable, quantitative
             methods for measuring motor behavior in young autistic
             children are needed. This work presents an engaging and
             scalable assessment of visual-motor abilities based on a
             bubble-popping game administered on a tablet. Participants
             are 233 children ranging from 1.5 to 10 years of age (147
             neurotypical children and 86 children diagnosed with autism
             spectrum disorder [autistic], of which 32 are also diagnosed
             with co-occurring attention-deficit/hyperactivity disorder
             [autistic+ADHD]). Computer vision analyses are used to
             extract several game-based touch features, which are
             compared across autistic, autistic+ADHD, and neurotypical
             participants. Results show that younger (1.5-3 years)
             autistic children pop the bubbles at a lower rate, and their
             ability to touch the bubble's center is less accurate
             compared to neurotypical children. When they pop a bubble,
             their finger lingers for a longer period, and they show more
             variability in their performance. In older children
             (3-10-years), consistent with previous research, the
             presence of co-occurring ADHD is associated with greater
             motor impairment, reflected in lower accuracy and more
             variable performance. Several motor features are correlated
             with standardized assessments of fine motor and cognitive
             abilities, as evaluated by an independent clinical
             assessment. These results highlight the potential of
             touch-based games as an efficient and scalable approach for
             assessing children's visual-motor skills, which can be part
             of a broader screening tool for identifying early signs
             associated with autism.},
   Doi = {10.1038/s41746-023-00762-6},
   Key = {fds369364}
}

@article{fds371406,
   Author = {Kim, R and Nijhout, HF and Reed, MC},
   Title = {Mathematical insights into the role of dopamine signaling in
             circadian entrainment.},
   Journal = {Mathematical biosciences},
   Volume = {356},
   Pages = {108956},
   Year = {2023},
   Month = {February},
   url = {http://dx.doi.org/10.1016/j.mbs.2022.108956},
   Abstract = {The circadian clock in the mammalian brain comprises
             interlocked molecular feedback loops that have downstream
             effects on important physiological functions such as the
             sleep-wake cycle and hormone regulation. Experiments have
             shown that the circadian clock also modulates the synthesis
             and breakdown of the neurotransmitter dopamine. Imbalances
             in dopamine are linked to a host of neurological conditions
             including Parkinson's disease, attention-deficit/hyperactivity
             disorder, and mood disorders, and these conditions are often
             accompanied by circadian disruptions. We have previously
             created a mathematical model using nonlinear ordinary
             differential equations to describe the influences of the
             circadian clock on dopamine at the molecular level. Recent
             experiments suggest that dopamine reciprocally influences
             the circadian clock. Dopamine receptor D1 (DRD1) signaling
             has been shown to aid in the entrainment of the clock to the
             24-hour light-dark cycle, but the underlying mechanisms are
             not well understood. In this paper, we use our mathematical
             model to support the experimental hypothesis that DRD1
             signaling promotes circadian entrainment by modulating the
             clock's response to light. We model the effects of a phase
             advance or delay, as well as the therapeutic potential of a
             REV-ERB agonist. In addition to phase shifts, we study the
             influences of photoperiod, or day length, in the
             mathematical model, connect our findings with the
             experimental and clinical literature, and determine the
             parameter that affects the critical photoperiod that signals
             seasonal changes to physiology.},
   Doi = {10.1016/j.mbs.2022.108956},
   Key = {fds371406}
}

@article{fds368122,
   Author = {Zito, A and Rigon, T and Dunson, DB},
   Title = {Inferring taxonomic placement from DNA barcoding aiding in
             discovery of new taxa},
   Journal = {Methods in Ecology and Evolution},
   Volume = {14},
   Number = {2},
   Pages = {529-542},
   Year = {2023},
   Month = {February},
   url = {http://dx.doi.org/10.1111/2041-210X.14009},
   Abstract = {Predicting the taxonomic affiliation of DNA sequences
             collected from biological samples is a fundamental step in
             biodiversity assessment. This task is performed by
             leveraging existing databases containing reference DNA
             sequences endowed with a taxonomic identification. However,
             environmental sequences can be from organisms that are
             either unknown to science or for which there are no
             reference sequences available. Thus, taxonomic novelty of a
             sequence needs to be accounted for when doing
             classification. We propose Bayesian nonparametric taxonomic
             classifiers, BayesANT, which use species sampling model
             priors to allow unobserved taxa to be discovered at each
             taxonomic rank. Using a simple product multinomial
             likelihood with conjugate Dirichlet priors at the lowest
             rank, a highly flexible supervised algorithm is developed to
             provide a probabilistic prediction of the taxa placement of
             each sequence at each rank. As an illustration, we run our
             algorithm on a carefully annotated library of Finnish
             arthropods (FinBOL). To assess the ability of BayesANT to
             recognize novelty and to predict known taxonomic
             affiliations correctly, we test it on two training-test
             splitting scenarios, each with a different proportion of
             taxa unobserved in training. We show how our algorithm
             attains accurate predictions and reliably quantifies
             classification uncertainty, especially when many sequences
             in the test set are affiliated to taxa unknown in training.
             By enabling taxonomic predictions for DNA barcodes to
             identify unseen branches, we believe BayesANT will be of
             broad utility as a tool for DNA metabarcoding within
             bioinformatics pipelines.},
   Doi = {10.1111/2041-210X.14009},
   Key = {fds368122}
}

@article{fds368056,
   Author = {Bray, H and Hirsch, S and Kazaras, D and Khuri, M and Zhang,
             Y},
   Title = {Spacetime Harmonic Functions and Applications to
             Mass},
   Journal = {Perspectives in Scalar Curvature},
   Publisher = {World Scientific},
   Editor = {Gromov, ML and Lawson, HB},
   Year = {2023},
   Month = {February},
   Abstract = {In the pioneering work of Stern, level sets of harmonic
             functions have been shown to be an effective tool in the
             study of scalar curvature in dimension 3. Generalizations of
             this idea, utilizing level sets of so called spacetime
             harmonic functions as well as other elliptic equations, are
             similarly effective in treating geometric inequalities
             involving the ADM mass. In this paper, we survey recent
             results in this context, focusing on applications of
             spacetime harmonic functions to the asymptotically flat and
             asymptotically hyperbolic versions of the spacetime positive
             mass theorem, and additionally introduce a new concept of
             total mass valid in both settings which is encoded in
             interpolation regions between generic initial data and model
             geometries. Furthermore, a novel and elementary proof of the
             positive mass theorem with charge is presented, and the
             level set approach to the Penrose inequality given by
             Huisken and Ilmanen is related to the current developments.
             Lastly, we discuss several open problems.},
   Key = {fds368056}
}

@article{fds369849,
   Author = {Qi, D and Liu, J-G},
   Title = {A random batch method for efficient ensemble forecasts of
             multiscale turbulent systems.},
   Journal = {Chaos (Woodbury, N.Y.)},
   Volume = {33},
   Number = {2},
   Pages = {023113},
   Year = {2023},
   Month = {February},
   url = {http://dx.doi.org/10.1063/5.0129127},
   Abstract = {A new efficient ensemble prediction strategy is developed
             for a multiscale turbulent model framework with emphasis on
             the nonlinear interactions between large and small-scale
             variables. The high computational cost in running large
             ensemble simulations of high-dimensional equations is
             effectively avoided by adopting a random batch decomposition
             of the wide spectrum of the fluctuation states, which is a
             characteristic feature of the multiscale turbulent systems.
             The time update of each ensemble sample is then only subject
             to a small portion of the small-scale fluctuation modes in
             one batch, while the true model dynamics with multiscale
             coupling is respected by frequent random resampling of the
             batches at each time updating step. We investigate both
             theoretical and numerical properties of the proposed method.
             First, the convergence of statistical errors in the random
             batch model approximation is shown rigorously independent of
             the sample size and full dimension of the system. Next, the
             forecast skill of the computational algorithm is tested on
             two representative models of turbulent flows exhibiting many
             key statistical phenomena with a direct link to realistic
             turbulent systems. The random batch method displays robust
             performance in capturing a series of crucial statistical
             features with general interests, including highly
             non-Gaussian fat-tailed probability distributions and
             intermittent bursts of instability, while requires a much
             lower computational cost than the direct ensemble approach.
             The efficient random batch method also facilitates the
             development of new strategies in uncertainty quantification
             and data assimilation for a wide variety of general complex
             turbulent systems in science and engineering.},
   Doi = {10.1063/5.0129127},
   Key = {fds369849}
}

@article{fds369365,
   Author = {Engelhard, MM and Henao, R and Berchuck, SI and Chen, J and Eichner, B and Herkert, D and Kollins, SH and Olson, A and Perrin, EM and Rogers, U and Sullivan, C and Zhu, Y and Sapiro, G and Dawson, G},
   Title = {Predictive Value of Early Autism Detection Models Based on
             Electronic Health Record Data Collected Before Age 1
             Year.},
   Journal = {JAMA Netw Open},
   Volume = {6},
   Number = {2},
   Pages = {e2254303},
   Year = {2023},
   Month = {February},
   url = {http://dx.doi.org/10.1001/jamanetworkopen.2022.54303},
   Abstract = {IMPORTANCE: Autism detection early in childhood is critical
             to ensure that autistic children and their families have
             access to early behavioral support. Early correlates of
             autism documented in electronic health records (EHRs) during
             routine care could allow passive, predictive model-based
             monitoring to improve the accuracy of early detection.
             OBJECTIVE: To quantify the predictive value of early autism
             detection models based on EHR data collected before age 1
             year. DESIGN, SETTING, AND PARTICIPANTS: This retrospective
             diagnostic study used EHR data from children seen within the
             Duke University Health System before age 30 days between
             January 2006 and December 2020. These data were used to
             train and evaluate L2-regularized Cox proportional hazards
             models predicting later autism diagnosis based on data
             collected from birth up to the time of prediction (ages
             30-360 days). Statistical analyses were performed between
             August 1, 2020, and April 1, 2022. MAIN OUTCOMES AND
             MEASURES: Prediction performance was quantified in terms of
             sensitivity, specificity, and positive predictive value
             (PPV) at clinically relevant model operating thresholds.
             RESULTS: Data from 45 080 children, including 924 (1.5%)
             meeting autism criteria, were included in this study.
             Model-based autism detection at age 30 days achieved 45.5%
             sensitivity and 23.0% PPV at 90.0% specificity. Detection by
             age 360 days achieved 59.8% sensitivity and 17.6% PPV at
             81.5% specificity and 38.8% sensitivity and 31.0% PPV at
             94.3% specificity. CONCLUSIONS AND RELEVANCE: In this
             diagnostic study of an autism screening test, EHR-based
             autism detection achieved clinically meaningful accuracy by
             age 30 days, improving by age 1 year. This automated
             approach could be integrated with caregiver surveys to
             improve the accuracy of early autism screening.},
   Doi = {10.1001/jamanetworkopen.2022.54303},
   Key = {fds369365}
}

@article{fds369337,
   Author = {Bierman, J and Li, Y and Lu, J},
   Title = {Improving the Accuracy of Variational Quantum Eigensolvers
             with Fewer Qubits Using Orbital Optimization.},
   Journal = {Journal of chemical theory and computation},
   Volume = {19},
   Number = {3},
   Pages = {790-798},
   Year = {2023},
   Month = {February},
   url = {http://dx.doi.org/10.1021/acs.jctc.2c00895},
   Abstract = {Near-term quantum computers will be limited in the number of
             qubits on which they can process information as well as the
             depth of the circuits that they can coherently carry out. To
             date, experimental demonstrations of algorithms such as the
             Variational Quantum Eigensolver (VQE) have been limited to
             small molecules using minimal basis sets for this reason. In
             this work we propose incorporating an orbital optimization
             scheme into quantum eigensolvers wherein a parametrized
             partial unitary transformation is applied to the basis
             functions set in order to reduce the number of qubits
             required for a given problem. The optimal transformation is
             found by minimizing the ground state energy with respect to
             this partial unitary matrix. Through numerical simulations
             of small molecules up to 16 spin orbitals, we demonstrate
             that this method has the ability to greatly extend the
             capabilities of near-term quantum computers with regard to
             the electronic structure problem. We find that VQE paired
             with orbital optimization consistently achieves lower ground
             state energies than traditional VQE when using the same
             number of qubits and even frequently achieves lower ground
             state energies than VQE methods using more
             qubits.},
   Doi = {10.1021/acs.jctc.2c00895},
   Key = {fds369337}
}

@article{fds366158,
   Author = {Coskun, MC and Liva, G and Graell I Amat and A and Lentmaier, M and Pfister, HD},
   Title = {Successive Cancellation Decoding of Single Parity-Check
             Product Codes: Analysis and Improved Decoding},
   Journal = {Ieee Transactions on Information Theory},
   Volume = {69},
   Number = {2},
   Pages = {823-841},
   Publisher = {Institute of Electrical and Electronics Engineers
             (IEEE)},
   Year = {2023},
   Month = {February},
   url = {http://dx.doi.org/10.1109/TIT.2022.3207802},
   Abstract = {A product code with single parity-check component codes can
             be described via the tools of a multi-kernel polar code,
             where the rows of the generator matrix are chosen according
             to the constraints imposed by the product code construction.
             Following this observation, successive cancellation decoding
             of such codes is introduced. In particular, the error
             probability of single parity-check product codes over binary
             memoryless symmetric channels under successive cancellation
             decoding is characterized. A bridge with the analysis of
             product codes introduced by Elias is also established for
             the binary erasure channel. Successive cancellation list
             decoding of single parity-check product codes is then
             described. For the provided example, simulations over the
             binary input additive white Gaussian channel show that
             successive cancellation list decoding outperforms belief
             propagation decoding applied to the code graph. Finally, the
             performance of the concatenation of a product code with a
             high-rate outer code is investigated via distance spectrum
             analysis. Examples of concatenations performing within 0.7
             dB from the random coding union bound are
             provided.},
   Doi = {10.1109/TIT.2022.3207802},
   Key = {fds366158}
}

@article{fds368929,
   Author = {Pepona, M and Gounley, J and Randles, A},
   Title = {Effect of constitutive law on the erythrocyte membrane
             response to large strains.},
   Journal = {Computers & mathematics with applications (Oxford, England :
             1987)},
   Volume = {132},
   Pages = {145-160},
   Year = {2023},
   Month = {February},
   url = {http://dx.doi.org/10.1016/j.camwa.2022.12.009},
   Abstract = {Three constitutive laws, that is the Skalak, neo-Hookean and
             Yeoh laws, commonly employed for describing the erythrocyte
             membrane mechanics are theoretically analyzed and
             numerically investigated to assess their accuracy for
             capturing erythrocyte deformation characteristics and
             morphology. Particular emphasis is given to the nonlinear
             deformation regime, where it is known that the discrepancies
             between constitutive laws are most prominent. Hence, the
             experiments of optical tweezers and micropipette aspiration
             are considered here, for which relationships between the
             individual shear elastic moduli of the constitutive laws can
             also be established through analysis of the
             tension-deformation relationship. All constitutive laws were
             found to adequately predict the axial and transverse
             deformations of a red blood cell subjected to stretching
             with optical tweezers for a constant shear elastic modulus
             value. As opposed to Skalak law, the neo-Hookean and Yeoh
             laws replicated the erythrocyte membrane folding, that has
             been experimentally observed, with the trade-off of
             sustaining significant area variations. For the micropipette
             aspiration, the suction pressure-aspiration length
             relationship could be excellently predicted for a fixed
             shear elastic modulus value only when Yeoh law was
             considered. Importantly, the neo-Hookean and Yeoh laws
             reproduced the membrane wrinkling at suction pressures close
             to those experimentally measured. None of the constitutive
             laws suffered from membrane area compressibility in the
             micropipette aspiration case.},
   Doi = {10.1016/j.camwa.2022.12.009},
   Key = {fds368929}
}

@article{fds368885,
   Author = {Kiselev, A and Yao, Y},
   Title = {Small Scale Formations in the Incompressible Porous Media
             Equation},
   Journal = {Archive for Rational Mechanics and Analysis},
   Volume = {247},
   Number = {1},
   Year = {2023},
   Month = {February},
   url = {http://dx.doi.org/10.1007/s00205-022-01830-z},
   Abstract = {We construct examples of solutions to the incompressible
             porous media (IPM) equation that must exhibit infinite in
             time growth of derivatives provided they remain smooth. As
             an application, this allows us to obtain nonlinear
             instability for a class of stratified steady states of
             IPM.},
   Doi = {10.1007/s00205-022-01830-z},
   Key = {fds368885}
}

@article{fds364956,
   Author = {Alian, A and Shelley, K and Wu, H-T},
   Title = {Amplitude and phase measurements from harmonic analysis may
             lead to new physiologic insights: lower body negative
             pressure photoplethysmographic waveforms as an
             example.},
   Journal = {Journal of clinical monitoring and computing},
   Volume = {37},
   Number = {1},
   Pages = {127-137},
   Year = {2023},
   Month = {February},
   url = {http://dx.doi.org/10.1007/s10877-022-00866-6},
   Abstract = {The photoplethysmographic (PPG) waveform contains
             hemodynamic information in its oscillations. We provide a
             new method for quantitative study of the waveform morphology
             and its relationship to the hemodynamics. A data adaptive
             modeling of the waveform shape is used to describe the PPG
             waveforms recorded from ear and finger. Several indices,
             based on the phase and amplitude information of different
             harmonics, are proposed to describe the PPG morphology. The
             proposed approach is illustrated by analyzing PPG waveforms
             recorded during a lower body negative pressure (LBNP)
             experiment. Different phase and amplitude dynamics are
             observed during the LBNP experiment. Specifically, we
             observe that the phase difference between the high order
             harmonics and fundamental components change more
             significantly when the PPG signal is recorded from the ear
             than the finger at the beginning of the study. In contrast,
             the finger PPG amplitude changes more when compared to the
             ear PPG during the recovery period. A more complete harmonic
             analysis of the PPG appears to provide new hemodynamic
             information when used during a LBNP experiment. We encourage
             other investigators who possess modulated clinical waveform
             data (e.g. PPG, arterial pressure, respiratory, and
             autonomic) to re-examine their data, using phase information
             and higher harmonics as a potential source of new insights
             into underlying physiologic mechanisms.},
   Doi = {10.1007/s10877-022-00866-6},
   Key = {fds364956}
}

@article{fds369139,
   Author = {Murdoch, DM and Barfield, R and Chan, C and Towe, SL and Bell, RP and Volkheimer, A and Choe, J and Hall, SA and Berger, M and Xie, J and Meade,
             CS},
   Title = {Neuroimaging and immunological features of neurocognitive
             function related to substance use in people with
             HIV.},
   Journal = {J Neurovirol},
   Volume = {29},
   Number = {1},
   Pages = {78-93},
   Year = {2023},
   Month = {February},
   url = {http://dx.doi.org/10.1007/s13365-022-01102-2},
   Abstract = {This study sought to identify neuroimaging and immunological
             factors associated with substance use and that contribute to
             neurocognitive impairment (NCI) in people with HIV (PWH). We
             performed cross-sectional immunological phenotyping,
             neuroimaging, and neurocognitive testing on virally
             suppressed PWH in four substance groups: cocaine only users
             (COC), marijuana only users (MJ), dual users (Dual), and
             Non-users. Participants completed substance use assessments,
             multimodal MRI brain scan, neuropsychological testing, and
             blood and CSF sampling. We employed a two-stage analysis of
             305 possible biomarkers of cognitive function associated
             with substance use. Feature reduction (Kruskal Wallis
             p-value < 0.05) identified 53 biomarkers associated with
             substance use (22 MRI and 31 immunological) for model
             inclusion along with clinical and demographic variables. We
             employed eXtreme Gradient Boosting (XGBoost) with these
             markers to predict cognitive function (global T-score).
             SHapley Additive exPlanations (SHAP) values were calculated
             to rank features for impact on model output and NCI.
             Participants were 110 PWH with sustained HIV viral
             suppression (33 MJ, 12 COC, 22 Dual, and 43 Non-users). The
             ten highest ranking biomarkers for predicting global T-score
             were 4 neuroimaging biomarkers including functional
             connectivity, gray matter volume, and white matter
             integrity; 5 soluble biomarkers (plasma glycine, alanine,
             lyso-phosphatidylcholine (lysoPC) aC17.0,
             hydroxy-sphingomyelin (SM.OH) C14.1, and
             phosphatidylcholinediacyl (PC aa) C28.1); and 1 clinical
             variable (nadir CD4 count). The results of our machine
             learning model suggest that substance use may indirectly
             contribute to NCI in PWH through both metabolomic and
             neuropathological mechanisms.},
   Doi = {10.1007/s13365-022-01102-2},
   Key = {fds369139}
}

@article{fds369693,
   Author = {Nelson, AC and Fogelson, AL},
   Title = {Towards understanding the effect of fibrinogen interactions
             on fibrin gel structure.},
   Journal = {Physical review. E},
   Volume = {107},
   Number = {2-1},
   Pages = {024413},
   Publisher = {American Physical Society (APS)},
   Year = {2023},
   Month = {February},
   url = {http://dx.doi.org/10.1103/physreve.107.024413},
   Abstract = {Fibrin gelation involves the enzymatic conversion of the
             plasma protein fibrinogen to fibrin monomers which then
             polymerize to form the gel that is a major structural
             component of a blood clot. Because fibrinogen provides the
             material from which fibrin is made, it is generally regarded
             as promoting the gelation process. However, fibrinogen can
             bind to a site on a fibrin oligomer, preventing another
             fibrin oligomer from binding there, thus slowing the
             polymerization process. "Soluble fibrin oligomers," which
             are mixtures of fibrin and fibrinogen, are found in the
             blood plasma and serve as biomarkers for various clotting
             disorders, so understanding the interplay between fibrin and
             fibrinogen during fibrin polymerization may have medical
             importance. We present a kinetic gelation model of fibrin
             polymerization which accounts for the dual and antagonistic
             roles of fibrinogen. It builds on our earlier model of
             fibrin polymerization that proposed a novel mechanism for
             branch formation, which is a necessary component of
             gelation. This previous model captured salient experimental
             observations regarding the determinants of the structure of
             the gel, but did not include fibrinogen binding. Here, we
             add to that model reactions between fibrinogen and fibrin,
             so oligomers are now mixtures of fibrin and fibrinogen, and
             characterizing their dynamics leads to equations of
             substantially greater complexity than previously. Using a
             moment generating function approach, we derive a closed
             system of moment equations and we track their dynamics
             until the finite time blow-up of specific second moments
             indicates that a gel has formed. In simulations begun with
             an initial mixture of fibrin and fibrinogen monomers, a
             sufficiently high relative concentration of fibrinogen
             prevents gelation; the critical concentration increases with
             the branch formation rate. In simulations begun with only
             fibrinogen monomers that are converted to fibrin at a
             specified rate, the rates of conversion, fibrinogen binding
             to oligomers, and branch formation together determine
             whether a gel forms, how long it takes to form, and the
             structural properties of the gel that results.},
   Doi = {10.1103/physreve.107.024413},
   Key = {fds369693}
}

@article{fds360122,
   Author = {Smith, CM and Goldrosen, N and Ciocanel, M-V and Santorella, R and Topaz, CM and Sen, S},
   Title = {Racial Disparities in Criminal Sentencing Vary Considerably
             across Federal Judges},
   Journal = {Journal of Institutional and Theoretical
             Economics},
   Volume = {179},
   Publisher = {Mohr Siebeck},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1628/jite-2023-0005},
   Doi = {10.1628/jite-2023-0005},
   Key = {fds360122}
}

@article{fds361536,
   Author = {Herzog, DP and Mattingly, JC and Nguyen, HD},
   Title = {Gibbsian dynamics and the generalized Langevin
             equation},
   Journal = {Electronic Journal of Probability},
   Volume = {28},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1214/23-EJP904},
   Abstract = {We study the statistically invariant structures of the
             nonlinear generalized Langevin equation (GLE) with a
             power-law memory kernel. For a broad class of memory
             kernels, including those in the subdiffusive regime, we
             construct solutions of the GLE using a Gibbsian framework,
             which does not rely on existing Markovian approximations.
             Moreover, we provide conditions on the decay of the memory
             to ensure uniqueness of statistically steady states,
             generalizing previous known results for the GLE under
             particular kernels as a sum of exponentials.},
   Doi = {10.1214/23-EJP904},
   Key = {fds361536}
}

@article{fds365095,
   Author = {Zito, A and Rigon, T and Ovaskainen, O and Dunson,
             DB},
   Title = {Bayesian Modeling of Sequential Discoveries.},
   Journal = {Journal of the American Statistical Association},
   Volume = {118},
   Number = {544},
   Pages = {2521-2532},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1080/01621459.2022.2060835},
   Abstract = {We aim at modeling the appearance of distinct tags in a
             sequence of labeled objects. Common examples of this type of
             data include words in a corpus or distinct species in a
             sample. These sequential discoveries are often summarized
             via accumulation curves, which count the number of distinct
             entities observed in an increasingly large set of objects.
             We propose a novel Bayesian method for species sampling
             modeling by directly specifying the probability of a new
             discovery, therefore, allowing for flexible specifications.
             The asymptotic behavior and finite sample properties of such
             an approach are extensively studied. Interestingly, our
             enlarged class of sequential processes includes highly
             tractable special cases. We present a subclass of models
             characterized by appealing theoretical and computational
             properties, including one that shares the same discovery
             probability with the Dirichlet process. Moreover, due to
             strong connections with logistic regression models, the
             latter subclass can naturally account for covariates. We
             finally test our proposal on both synthetic and real data,
             with special emphasis on a large fungal biodiversity study
             in Finland. Supplementary materials for this article are
             available online.},
   Doi = {10.1080/01621459.2022.2060835},
   Key = {fds365095}
}

@article{fds371511,
   Author = {Papadogeorgou, G and Bello, C and Ovaskainen, O and Dunson,
             DB},
   Title = {Covariate-Informed Latent Interaction Models: Addressing
             Geographic & Taxonomic Bias in Predicting Bird–Plant
             Interactions},
   Journal = {Journal of the American Statistical Association},
   Volume = {118},
   Number = {544},
   Pages = {2250-2261},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1080/01621459.2023.2208390},
   Abstract = {Reductions in natural habitats urge that we better
             understand species’ interconnection and how biological
             communities respond to environmental changes. However,
             ecological studies of species’ interactions are limited by
             their geographic and taxonomic focus which can distort our
             understanding of interaction dynamics. We focus on
             bird–plant interactions that refer to situations of
             potential fruit consumption and seed dispersal. We develop
             an approach for predicting species’ interactions that
             accounts for errors in the recorded interaction networks,
             addresses the geographic and taxonomic biases of existing
             studies, is based on latent factors to increase flexibility
             and borrow information across species, incorporates
             covariates in a flexible manner to inform the latent
             factors, and uses a meta-analysis dataset from 85 individual
             studies. We focus on interactions among 232 birds and 511
             plants in the Atlantic Forest, and identify 5% of pairs of
             species with an unrecorded interaction, but posterior
             probability that the interaction is possible over 80%.
             Finally, we develop a permutation-based variable importance
             procedure for latent factor network models and identify that
             a bird’s body mass and a plant’s fruit diameter are
             important in driving the presence of species interactions,
             with a multiplicative relationship that exhibits both a
             thresholding and a matching behavior. Supplementary
             materials for this article are available
             online.},
   Doi = {10.1080/01621459.2023.2208390},
   Key = {fds371511}
}

@article{fds372788,
   Author = {Barrientos, AF and Sen, D and Page, GL and Dunson,
             DB},
   Title = {Bayesian Inferences on Uncertain Ranks and Orderings:
             Application to Ranking Players and Lineups},
   Journal = {Bayesian Analysis},
   Volume = {18},
   Number = {3},
   Pages = {777-806},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1214/22-BA1324},
   Abstract = {It is common to be interested in rankings or order
             relationships among entities. In complex settings where one
             does not directly measure a univariate statistic upon which
             to base ranks, such inferences typically rely on statistical
             models having entity-specific parameters. These can be
             treated as random effects in hierarchical models
             characterizing variation among the entities. In this paper,
             we are particularly interested in the problem of ranking
             basketball players in terms of their contribution to team
             performance. Using data from the National Basketball
             Association (NBA) in the United States, we find that many
             players have similar latent ability levels, making any
             single estimated ranking highly misleading. The current
             literature fails to provide summaries of order relationships
             that adequately account for uncertainty. Motivated by this,
             we propose a Bayesian strategy for characterizing
             uncertainty in inferences on order relationships among
             players and lineups. Our approach adapts to scenarios in
             which uncertainty in ordering is high by producing more
             conservative results that improve interpretability. This is
             achieved through a reward function within a decision
             theoretic framework. We apply our approach to data from the
             2009–2010 NBA season.},
   Doi = {10.1214/22-BA1324},
   Key = {fds372788}
}

@article{fds372789,
   Author = {Sachs, M and Sen, D and Lu, J and Dunson, D},
   Title = {Posterior Computation with the Gibbs Zig-Zag
             Sampler},
   Journal = {Bayesian Analysis},
   Volume = {18},
   Number = {3},
   Pages = {909-927},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1214/22-BA1319},
   Abstract = {An intriguing new class of piecewise deterministic Markov
             processes (PDMPs) has recently been proposed as an
             alternative to Markov chain Monte Carlo (MCMC). We propose a
             new class of PDMPs termed Gibbs zig-zag samplers, which
             allow parameters to be updated in blocks with a zig-zag
             sampler applied to certain parameters and traditional
             MCMC-style updates to others. We demonstrate the flexibility
             of this framework on posterior sampling for logistic models
             with shrinkage priors for high-dimensional regression and
             random effects, and provide conditions for geometric
             ergodicity and the validity of a central limit
             theorem.},
   Doi = {10.1214/22-BA1319},
   Key = {fds372789}
}

@article{fds373933,
   Author = {Li, D and Nguyen, P and Zhang, Z and Dunson, D},
   Title = {Tree representations of brain structural connectivity via
             persistent homology.},
   Journal = {Frontiers in neuroscience},
   Volume = {17},
   Pages = {1200373},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.3389/fnins.2023.1200373},
   Abstract = {The brain structural connectome is generated by a collection
             of white matter fiber bundles constructed from diffusion
             weighted MRI (dMRI), acting as highways for neural activity.
             There has been abundant interest in studying how the
             structural connectome varies across individuals in relation
             to their traits, ranging from age and gender to
             neuropsychiatric outcomes. After applying tractography to
             dMRI to get white matter fiber bundles, a key question is
             how to represent the brain connectome to facilitate
             statistical analyses relating connectomes to traits. The
             current standard divides the brain into regions of interest
             (ROIs), and then relies on an <i>adjacency matrix</i> (AM)
             representation. Each cell in the AM is a measure of
             connectivity, e.g., number of fiber curves, between a pair
             of ROIs. Although the AM representation is intuitive, a
             disadvantage is the high-dimensionality due to the large
             number of cells in the matrix. This article proposes a
             simpler tree representation of the brain connectome, which
             is motivated by ideas in computational topology and takes
             topological and biological information on the cortical
             surface into consideration. We demonstrate that our tree
             representation preserves useful information and
             interpretability, while reducing dimensionality to improve
             statistical and computational efficiency. Applications to
             data from the Human Connectome Project (HCP) are considered
             and code is provided for reproducing our
             analyses.},
   Doi = {10.3389/fnins.2023.1200373},
   Key = {fds373933}
}

@article{fds374277,
   Author = {Chakraborty, A and Ou, R and Dunson, DB},
   Title = {Bayesian Inference on High-Dimensional Multivariate Binary
             Responses},
   Journal = {Journal of the American Statistical Association},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1080/01621459.2023.2260053},
   Abstract = {It has become increasingly common to collect
             high-dimensional binary response data; for example, with the
             emergence of new sampling techniques in ecology. In smaller
             dimensions, multivariate probit (MVP) models are routinely
             used for inferences. However, algorithms for fitting such
             models face issues in scaling up to high dimensions due to
             the intractability of the likelihood, involving an integral
             over a multivariate normal distribution having no analytic
             form. Although a variety of algorithms have been proposed to
             approximate this intractable integral, these approaches are
             difficult to implement and/or inaccurate in high dimensions.
             Our main focus is in accommodating high-dimensional binary
             response data with a small-to-moderate number of covariates.
             We propose a two-stage approach for inference on model
             parameters while taking care of uncertainty propagation
             between the stages. We use the special structure of latent
             Gaussian models to reduce the highly expensive computation
             involved in joint parameter estimation to focus inference on
             marginal distributions of model parameters. This essentially
             makes the method embarrassingly parallel for both stages. We
             illustrate performance in simulations and applications to
             joint species distribution modeling in ecology.
             Supplementary materials for this article are available
             online.},
   Doi = {10.1080/01621459.2023.2260053},
   Key = {fds374277}
}

@article{fds371114,
   Author = {Koplik, G and Borggren, N and Voisin, S and Angeloro, G and Hineman, J and Johnson, T and Bendich, P},
   Title = {Topological Simplification of Signals for Inference and
             Approximate Reconstruction},
   Journal = {IEEE Aerospace Conference Proceedings},
   Volume = {2023-March},
   Year = {2023},
   Month = {January},
   ISBN = {9781665490320},
   url = {http://dx.doi.org/10.1109/AERO55745.2023.10115654},
   Abstract = {As Internet of Things (loT) devices become both cheaper and
             more powerful, researchers are increasingly finding
             solutions to their scientific curiosities both financially
             and com-putationally feasible. When operating with
             restricted power or communications budgets, however, devices
             can only send highly-compressed data. Such circumstances are
             common for devices placed away from electric grids that can
             only communicate via satellite, a situation particularly
             plausible for environmental sensor networks. These
             restrictions can be further complicated by potential
             variability in the communications budget, for ex-ample a
             solar-powered device needing to expend less energy when
             transmitting data on a cloudy day. We propose a novel,
             topology-based, lossy compression method well-equipped for
             these restrictive yet variable circumstances. This
             technique, Topological Signal Compression, allows sending
             compressed sig-nals that utilize the entirety of a variable
             communications budget. To demonstrate our algorithm's
             capabilities, we per-form entropy calculations as well as a
             classification exercise on increasingly topologically
             simplified signals from the Free-Spoken Digit Dataset and
             explore the stability of the resulting performance against
             common baselines.},
   Doi = {10.1109/AERO55745.2023.10115654},
   Key = {fds371114}
}

@article{fds376284,
   Author = {Solomon, E and Wagner, A and Bendich, P},
   Title = {FROM GEOMETRY TO TOPOLOGY: INVERSE THEOREMS FOR DISTRIBUTED
             PERSISTENCE},
   Journal = {Journal of Computational Geometry},
   Volume = {14},
   Number = {2 Special Issue},
   Pages = {172-196},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.20382/jocg.v14i2a8},
   Abstract = {What is the “right” topological invariant of a large
             point cloud X? Prior research has focused on estimating the
             full persistence diagram of X, a quantity that is very
             expensive to compute, unstable to outliers, and far from
             injective. We therefore propose that, in many cases, the
             collection of persistence diagrams of many small subsets of
             X is a better invariant. This invariant, which we call
             “distributed persistence,” is perfectly parallelizable,
             more stable to outliers, and has a rich inverse theory. The
             map from the space of metric spaces (with the quasi-isometry
             distance) to the space of distributed persistence invariants
             (with the Hausdorff-Bottleneck distance) is globally
             bi-Lipschitz. This is a much stronger property than simply
             being injective, as it implies that the inverse image of a
             small neighborhood is a small neighborhood, and is to our
             knowledge the only result of its kind in the TDA literature.
             Moreover, the inverse Lipschitz constant depends on the size
             of the subsets taken, so that as the size of these subsets
             goes from small to large, the invariant interpolates between
             a purely geometric one and a topological one. Lastly, we
             note that our inverse results do not actually require
             considering all subsets of a fixed size (an enormous
             collection), but a relatively small collection satisfying
             simple covering properties. These theoretical results are
             complemented by synthetic experiments demonstrating the use
             of distributed persistence in practice.},
   Doi = {10.20382/jocg.v14i2a8},
   Key = {fds376284}
}

@article{fds371501,
   Author = {Fraiman, N and Mukherjee, S and Thoppe, G},
   Title = {The Shadow Knows: Empirical Distributions of Minimum
             Spanning Acycles and Persistence Diagrams of Random
             Complexes},
   Journal = {Discrete Analysis},
   Volume = {2023},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.19086/da.73323},
   Abstract = {In 1985, Frieze showed that the expected sum of the edge
             weights of the minimum spanning tree (MST) in the uniformly
             weighted graph converges to z (3). Recently, Hino and
             Kanazawa extended this result to a uniformly weighted
             simplicial complex, where the role of the MST is played by
             its higher-dimensional analog—the Minimum Spanning Acycle
             (MSA). Our work goes beyond and describes the histogram of
             all the weights in this random MST and random MSA.
             Specifically, we show that their empirical distributions
             converge to a measure based on a concept called the shadow.
             The shadow of a graph is the set of all the missing
             transitive edges and, for a simplicial complex, it is a
             related topological generalization. As a corollary, we
             obtain a similar claim for the death times in the
             persistence diagram corresponding to the above weighted
             complex, a result of interest in applied
             topology.},
   Doi = {10.19086/da.73323},
   Key = {fds371501}
}

@article{fds367868,
   Author = {Caprio, M and Mukherjee, S},
   Title = {Ergodic theorems for dynamic imprecise probability
             kinematics},
   Journal = {International Journal of Approximate Reasoning},
   Volume = {152},
   Pages = {325-343},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1016/j.ijar.2022.10.016},
   Abstract = {We formulate an ergodic theory for the (almost sure) limit
             PE˜co of a sequence (PEnco) of successive dynamic imprecise
             probability kinematics (DIPK, introduced in [10]) updates of
             a set PE0co representing the initial beliefs of an agent. As
             a consequence, we formulate a strong law of large
             numbers.},
   Doi = {10.1016/j.ijar.2022.10.016},
   Key = {fds367868}
}

@article{fds370291,
   Author = {Fei, F and Costa, A and Dolbow, JE and Settgast, RR and Cusini,
             M},
   Title = {Phase-Field Simulation of Near-Wellbore Nucleation and
             Propagation of Hydraulic Fractures in Enhanced Geothermal
             Systems (EGS)},
   Journal = {Society of Petroleum Engineers Spe Reservoir Simulation
             Conference, Rsc 2023},
   Year = {2023},
   Month = {January},
   ISBN = {9781613998717},
   url = {http://dx.doi.org/10.2118/212251-MS},
   Abstract = {Enhanced geothermal systems (EGS) rely on the artificial
             creation of fractures (i.e., hydraulic fractures) to enhance
             the permeability of the formation which would, otherwise, be
             too low to allow for fluid circulation. Hydraulic fracturing
             involves complex nucleation and propagation processes, which
             are key to the analysis and prediction of well productivity.
             Numerical simulations are commonly employed to understand
             the specific mechanisms behind nucleation and propagation of
             hydraulic fractures. However, most numerical approaches face
             tremendous challenges in tracking and accommodating the
             evolving fracture geometry, especially when curved and
             branched fractures occur. The phase-field method can
             overcome this obstacle, as it can model fracture propagation
             without the need for tracking the fracture tip nor for
             remeshing. However, the most common phase-field formulation
             is unable to accurately capture fracture nucleation. In this
             work, we develop a new phase-field approach for hydraulic
             fracturing that accounts for fracture nucleation due to the
             strengths of geologic material and the existence of small
             defects. Verification examples show that the proposed
             formulation can accurately predict near-wellbore nucleation
             and propagation of hydraulic fractures and the wellbore
             breakdown pressure. Simulation of a three-dimensional
             wellbore problem further demonstrates the efficiency of the
             proposed phase-field method in handling fracture nucleation
             and propagation.},
   Doi = {10.2118/212251-MS},
   Key = {fds370291}
}

@article{fds367493,
   Author = {Gao, Y and Liu, JG and Wu, N},
   Title = {Data-driven efficient solvers for Langevin dynamics on
             manifold in high dimensions},
   Journal = {Applied and Computational Harmonic Analysis},
   Volume = {62},
   Pages = {261-309},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1016/j.acha.2022.09.003},
   Abstract = {We study the Langevin dynamics of a physical system with
             manifold structure M⊂Rp based on collected sample points
             {xi}i=1n⊂M that probe the unknown manifold M. Through the
             diffusion map, we first learn the reaction coordinates
             {yi}i=1n⊂N corresponding to {xi}i=1n, where N is a
             manifold diffeomorphic to M and isometrically embedded in
             Rℓ with ℓ≪p. The induced Langevin dynamics on N in
             terms of the reaction coordinates captures the slow time
             scale dynamics such as conformational changes in biochemical
             reactions. To construct an efficient and stable
             approximation for the Langevin dynamics on N, we leverage
             the corresponding Fokker-Planck equation on the manifold N
             in terms of the reaction coordinates y. We propose an
             implementable, unconditionally stable, data-driven finite
             volume scheme for this Fokker-Planck equation, which
             automatically incorporates the manifold structure of N.
             Furthermore, we provide a weighted L2 convergence analysis
             of the finite volume scheme to the Fokker-Planck equation on
             N. The proposed finite volume scheme leads to a Markov chain
             on {yi}i=1n with an approximated transition probability and
             jump rate between the nearest neighbor points. After an
             unconditionally stable explicit time discretization, the
             data-driven finite volume scheme gives an approximated
             Markov process for the Langevin dynamics on N and the
             approximated Markov process enjoys detailed balance,
             ergodicity, and other good properties.},
   Doi = {10.1016/j.acha.2022.09.003},
   Key = {fds367493}
}

@article{fds370086,
   Author = {Liu, JG and Tang, Y and Zhao, Y},
   Title = {ON THE EQUILIBRIUM OF THE POISSON-NERNST-PLANCK-BIKERMANN
             MODEL EQUIPPING WITH THE STERIC AND CORRELATION
             EFFECTS},
   Journal = {Communications in Mathematical Sciences},
   Volume = {21},
   Number = {2},
   Pages = {485-515},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.4310/CMS.2023.v21.n2.a8},
   Abstract = {The Poisson-Nernst-Planck-Bikermann (PNPB) model, in which
             the ions and water molecules are treated as different
             species with non-uniform sizes and valences with
             interstitial voids, can describe the steric and correlation
             effects in ionic solution neglected by the
             Poisson-Nernst-Planck and Poisson-Boltzmann theories with
             point charge assumption. In the PNPB model, the electric
             potential is governed by the fourth-order Poisson-Bikermann
             (4PBik) equation instead of the Poisson equation so that it
             can describe the correlation effect. Moreover, the steric
             potential is included in the ionic and water fluxes as well
             as the equilibrium Fermi-like distributions which
             characterizes the steric effect quantitatively. In this
             work, we analyze the self-adjointness and the kernel of the
             fourth-order operator of the 4PBik equation. Also, we show
             the positivity of the void volume function and the convexity
             of the free energy. Following these properties, the
             well-posedness of the PNPB model in equilibrium is given.
             Furthermore, because the PNPB model has an energy dissipated
             structure, we adopt a finite volume scheme which preserves
             the energy dissipated property at the semi-discrete level.
             Various numerical investigations are given to show the
             parameter dependence of the steric effect to the steady
             state},
   Doi = {10.4310/CMS.2023.v21.n2.a8},
   Key = {fds370086}
}

@article{fds372916,
   Author = {Gao, Y and Liu, JG},
   Title = {Random Walk Approximation for Irreversible Drift-Diffusion
             Process on Manifold: Ergodicity, Unconditional Stability and
             Convergence},
   Journal = {Communications in Computational Physics},
   Volume = {34},
   Number = {1},
   Pages = {132-172},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.4208/cicp.OA-2023-0021},
   Abstract = {Irreversible drift-diffusion processes are very common in
             biochemical reactions. They have a non-equilibrium
             stationary state (invariant measure) which does not satisfy
             detailed balance. For the corresponding Fokker-Planck
             equation on a closed manifold, using Voronoi tessellation,
             we propose two upwind finite volume schemes with or without
             the information of the invariant measure. Both schemes
             possess stochastic Q-matrix structures and can be decomposed
             as a gradient flow part and a Hamiltonian flow part,
             enabling us to prove unconditional stability, ergodicity and
             error estimates. Based on the two upwind schemes, several
             numerical examples – including sampling accelerated by a
             mixture flow, image transformations and simulations for
             stochastic model of chaotic system – are conducted. These
             two structure-preserving schemes also give a natural random
             walk approximation for a generic irreversible
             drift-diffusion process on a manifold. This makes them
             suitable for adapting to manifold-related computations that
             arise from high-dimensional molecular dynamics
             simulations.},
   Doi = {10.4208/cicp.OA-2023-0021},
   Key = {fds372916}
}

@article{fds373900,
   Author = {Huang, X and Durrett, R},
   Title = {Corrigendum to: The contact process on periodic trees
             (Electronic Communications in Probability)},
   Journal = {Electronic Communications in Probability},
   Volume = {28},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1214/23-ECP518},
   Abstract = {In [1] we considered periodic trees in which the number of
             children in successive generations is (n, a1, …, ak ) with
             maxi ai ≤ Cn1−δ and (log ai)/ log n → bi as n →
             ∞. Our proof contained an error. In this note we correct
             the proof. The theorem has changed: the critical value for
             local survival is asymptotically√¯ck (log n)/n where lk =
             max{i: 0 ≤ i ≤ k, ai ≠ 1} and ¯ck = min{k + 1 − lk
             − blk, (k − b)/2}, where b = limn→∞ log(a1a2 · ·
             · ak )/ log n.},
   Doi = {10.1214/23-ECP518},
   Key = {fds373900}
}

@article{fds365842,
   Author = {Daubechies, I and Devore, R and Dym, N and Faigenbaum-Golovin, S and Kovalsky, SZ and Lin, KC and Park, J and Petrova, G and Sober,
             B},
   Title = {Neural Network Approximation of Refinable
             Functions},
   Journal = {IEEE Transactions on Information Theory},
   Volume = {69},
   Number = {1},
   Pages = {482-495},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1109/TIT.2022.3199601},
   Abstract = {In the desire to quantify the success of neural networks in
             deep learning and other applications, there is a great
             interest in understanding which functions are efficiently
             approximated by the outputs of neural networks. By now,
             there exists a variety of results which show that a wide
             range of functions can be approximated with sometimes
             surprising accuracy by these outputs. For example, it is
             known that the set of functions that can be approximated
             with exponential accuracy (in terms of the number of
             parameters used) includes, on one hand, very smooth
             functions such as polynomials and analytic functions and, on
             the other hand, very rough functions such as the Weierstrass
             function, which is nowhere differentiable. In this paper, we
             add to the latter class of rough functions by showing that
             it also includes refinable functions. Namely, we show that
             refinable functions are approximated by the outputs of deep
             ReLU neural networks with a fixed width and increasing depth
             with accuracy exponential in terms of their number of
             parameters. Our results apply to functions used in the
             standard construction of wavelets as well as to functions
             constructed via subdivision algorithms in Computer Aided
             Geometric Design.},
   Doi = {10.1109/TIT.2022.3199601},
   Key = {fds365842}
}

@article{fds371115,
   Author = {Pu, W and Sober, B and Daly, N and Zhou, C and Sabetsarvestani, Z and Higgitt, C and Daubechies, I and Rodrigues, MRD},
   Title = {Image Separation With Side Information: A Connected
             Auto-Encoders Based Approach.},
   Journal = {IEEE transactions on image processing : a publication of the
             IEEE Signal Processing Society},
   Volume = {32},
   Pages = {2931-2946},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1109/tip.2023.3275872},
   Abstract = {X-radiography (X-ray imaging) is a widely used imaging
             technique in art investigation. It can provide information
             about the condition of a painting as well as insights into
             an artist's techniques and working methods, often revealing
             hidden information invisible to the naked eye. X-radiograpy
             of double-sided paintings results in a mixed X-ray image and
             this paper deals with the problem of separating this mixed
             image. Using the visible color images (RGB images) from each
             side of the painting, we propose a new Neural Network
             architecture, based upon 'connected' auto-encoders, designed
             to separate the mixed X-ray image into two simulated X-ray
             images corresponding to each side. This connected
             auto-encoders architecture is such that the encoders are
             based on convolutional learned iterative shrinkage
             thresholding algorithms (CLISTA) designed using algorithm
             unrolling techniques, whereas the decoders consist of simple
             linear convolutional layers; the encoders extract sparse
             codes from the visible image of the front and rear paintings
             and mixed X-ray image, whereas the decoders reproduce both
             the original RGB images and the mixed X-ray image. The
             learning algorithm operates in a totally self-supervised
             fashion without requiring a sample set that contains both
             the mixed X-ray images and the separated ones. The
             methodology was tested on images from the double-sided wing
             panels of the Ghent Altarpiece, painted in 1432 by the
             brothers Hubert and Jan van Eyck. These tests show that the
             proposed approach outperforms other state-of-the-art X-ray
             image separation methods for art investigation
             applications.},
   Doi = {10.1109/tip.2023.3275872},
   Key = {fds371115}
}

@article{fds371294,
   Author = {Shan, S and Daubechies, I},
   Title = {Diffusion Maps: Using the Semigroup Property for Parameter
             Tuning},
   Volume = {Part F6},
   Pages = {409-424},
   Booktitle = {Applied and Numerical Harmonic Analysis},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1007/978-3-030-45847-8_18},
   Abstract = {Diffusion maps (DM) constitute a classic dimension reduction
             technique, for data lying on or close to a (relatively)
             low-dimensional manifold embedded in a much larger
             dimensional space. It consists in constructing a spectral
             parametrization for the manifold from simulated random walks
             or diffusion paths on the dataset. However, DM is hard to
             tune in practice. In particular, the task to set a diffusion
             time t when constructing the diffusion kernel matrix is
             critical. We address this problem by using the semigroup
             property of the diffusion operator. We propose a semigroup
             criterion for picking the “right” value for t.
             Experiments show that this principled approach is effective
             and robust.},
   Doi = {10.1007/978-3-030-45847-8_18},
   Key = {fds371294}
}

@article{fds374632,
   Author = {Etzioni, R and Gulati, R and Owens, L and Lange, J and Ryser,
             MD},
   Title = {Abstract IA018: Opportunity for interception as a driver of
             benefit in cancer early detection: implications for
             multi-cancer early detection testing},
   Journal = {Cancer Prevention Research},
   Volume = {16},
   Number = {1_Supplement},
   Pages = {IA018-IA018},
   Publisher = {American Association for Cancer Research
             (AACR)},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1158/1940-6215.precprev22-ia018},
   Abstract = {<jats:title>Abstract</jats:title> <jats:p>Early detection
             cannot succeed unless there is adequate opportunity for
             cancer to be diagnosed and intercepted within its early
             preclinical phase. An understanding of opportunity for early
             detection and interception is therefore critical in
             predicting potential mortality reduction due to screening.
             Opportunity is not directly observable but may be learned
             using data from prospectively screened cohorts and
             populations. In this presentation I will share a history of
             methods for learning about early detection opportunity and
             will present examples of how we have built on this work to
             study opportunity for early detection in prostate and breast
             cancer. I will describe a generic model of how opportunity
             and sensitivity combine to produce stage shift and mortality
             reduction and will briefly explore whether a lack of
             opportunity may have been behind the results of the UCKCTOCS
             trial. I will use this learning to motivate why I believe a
             prospective study to investigate opportunity for
             multi-cancer detection and interception is warranted before
             or alongside ongoing and planned screening trials. This work
             in in collaboration with Roman Gulat i and Lukas Owens (Fred
             Hutch), Jane Lange (OHSU) and Marc Ryser (Duke University).
             We acknowledge funding from the National Cancer Institute
             and collaboration with and data from the Breast Cancer
             Surveillance Consortium</jats:p> <jats:p>Citation Format:
             Ruth Etzioni, Roman Gulati, Lukas Owens, Jane Lange, Marc D.
             Ryser. Opportunity for interception as a driver of benefit
             in cancer early detection: implications for multi-cancer
             early detection testing. [abstract]. In: Proceedings of the
             AACR Special Conference: Precision Prevention, Early
             Detection, and Interception of Cancer; 2022 Nov 17-19;
             Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1
             Suppl): Abstract nr IA018.</jats:p>},
   Doi = {10.1158/1940-6215.precprev22-ia018},
   Key = {fds374632}
}

@article{fds365564,
   Author = {Krishnappa Babu and PR and Di Martino and JM and Chang, Z and Perochon, S and Aiello, R and Carpenter, KLH and Compton, S and Davis, N and Franz, L and Espinosa, S and Flowers, J and Dawson, G and Sapiro,
             G},
   Title = {Complexity analysis of head movements in autistic
             toddlers.},
   Journal = {J Child Psychol Psychiatry},
   Volume = {64},
   Number = {1},
   Pages = {156-166},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1111/jcpp.13681},
   Abstract = {BACKGROUND: Early differences in sensorimotor functioning
             have been documented in young autistic children and infants
             who are later diagnosed with autism. Previous research has
             demonstrated that autistic toddlers exhibit more frequent
             head movement when viewing dynamic audiovisual stimuli,
             compared to neurotypical toddlers. To further explore this
             behavioral characteristic, in this study, computer vision
             (CV) analysis was used to measure several aspects of head
             movement dynamics of autistic and neurotypical toddlers
             while they watched a set of brief movies with social and
             nonsocial content presented on a tablet. METHODS: Data were
             collected from 457 toddlers, 17-36 months old, during
             their well-child visit to four pediatric primary care
             clinics. Forty-one toddlers were subsequently diagnosed with
             autism. An application (app) displayed several brief movies
             on a tablet, and the toddlers watched these movies while
             sitting on their caregiver's lap. The front-facing camera in
             the tablet recorded the toddlers' behavioral responses. CV
             was used to measure the participants' head movement rate,
             movement acceleration, and complexity using multiscale
             entropy. RESULTS: Autistic toddlers exhibited significantly
             higher rate, acceleration, and complexity in their head
             movements while watching the movies compared to neurotypical
             toddlers, regardless of the type of movie content (social
             vs. nonsocial). The combined features of head movement
             acceleration and complexity reliably distinguished the
             autistic and neurotypical toddlers. CONCLUSIONS: Autistic
             toddlers exhibit differences in their head movement dynamics
             when viewing audiovisual stimuli. Higher complexity of their
             head movements suggests that their movements were less
             predictable and less stable compared to neurotypical
             toddlers. CV offers a scalable means of detecting subtle
             differences in head movement dynamics, which may be helpful
             in identifying early behaviors associated with autism and
             providing insight into the nature of sensorimotor
             differences associated with autism.},
   Doi = {10.1111/jcpp.13681},
   Key = {fds365564}
}

@article{fds371718,
   Author = {Solomon, O and Patriat, R and Braun, H and Palnitkar, TE and Moeller, S and Auerbach, EJ and Ugurbil, K and Sapiro, G and Harel,
             N},
   Title = {Motion robust magnetic resonance imaging via efficient
             Fourier aggregation.},
   Journal = {Medical image analysis},
   Volume = {83},
   Pages = {102638},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1016/j.media.2022.102638},
   Abstract = {We present a method for suppressing motion artifacts in
             anatomical magnetic resonance acquisitions. Our proposed
             technique, termed MOTOR-MRI, can recover and salvage images
             which are otherwise heavily corrupted by motion induced
             artifacts and blur which renders them unusable. Contrary to
             other techniques, MOTOR-MRI operates on the reconstructed
             images and not on k-space data. It relies on breaking the
             standard acquisition protocol into several shorter ones
             (while maintaining the same total acquisition time) and
             subsequent efficient aggregation in Fourier space of locally
             sharp and consistent information among them, producing a
             sharp and motion mitigated image. We demonstrate the
             efficacy of the technique on T<sub>2</sub>-weighted turbo
             spin echo magnetic resonance brain scans with severe motion
             corruption from both 3 T and 7 T scanners and show
             significant qualitative and quantitative improvement in
             image quality. MOTOR-MRI can operate independently, or in
             conjunction with additional motion correction
             methods.},
   Doi = {10.1016/j.media.2022.102638},
   Key = {fds371718}
}

@article{fds368436,
   Author = {Cai, Z and Lu, J and Yang, S},
   Title = {NUMERICAL ANALYSIS FOR INCHWORM MONTE CARLO METHOD: SIGN
             PROBLEM AND ERROR GROWTH},
   Journal = {Mathematics of Computation},
   Volume = {92},
   Number = {341},
   Pages = {1141-1209},
   Publisher = {American Mathematical Society (AMS)},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1090/MCOM/3785},
   Abstract = {We consider the numerical analysis of the inchworm Monte
             Carlo method, which is proposed recently to tackle the
             numerical sign problem for open quantum systems. We focus on
             the growth of the numerical error with respect to the
             simulation time, for which the inchworm Monte Carlo method
             shows a flatter curve than the direct application of Monte
             Carlo method to the classical Dyson series. To better
             understand the underlying mechanism of the inchworm Monte
             Carlo method, we distinguish two types of exponential error
             growth, which are known as the numerical sign problem and
             the error amplification. The former is due to the fast
             growth of variance in the stochastic method, which can be
             observed from the Dyson series, and the latter comes from
             the evolution of the numerical solution. Our analysis
             demonstrates that the technique of partial resummation can
             be considered as a tool to balance these two types of error,
             and the inchworm Monte Carlo method is a successful case
             where the numerical sign problem is effectively suppressed
             by such means. We first demonstrate our idea in the context
             of ordinary differential equations, and then provide
             complete analysis for the inchworm Monte Carlo method.
             Several numerical experiments are carried out to verify our
             theoretical results.},
   Doi = {10.1090/MCOM/3785},
   Key = {fds368436}
}

@article{fds370310,
   Author = {Chen, Z and Lu, J and Lu, Y and Zhou, S},
   Title = {A REGULARITY THEORY FOR STATIC SCHRÖDINGER EQUATIONS ON R
             d IN SPECTRAL BARRON SPACES},
   Journal = {SIAM Journal on Mathematical Analysis},
   Volume = {55},
   Number = {1},
   Pages = {557-570},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1137/22M1478719},
   Abstract = {Spectral Barron spaces have received considerable interest
             recently, as it is the natural function space for
             approximation theory of two-layer neural networks with a
             dimension-free convergence rate. In this paper, we study the
             regularity of solutions to the whole-space static
             Schrödinger equation in spectral Barron spaces. We prove
             that if the source of the equation lies in the spectral
             Barron space B s(R d) and the potential function admitting a
             nonnegative lower bound decomposes as a positive constant
             plus a function in B s(R d), then the solution lies in the
             spectral Barron space B s+2(R d).},
   Doi = {10.1137/22M1478719},
   Key = {fds370310}
}

@article{fds371889,
   Author = {Chen, Z and Lu, J and Qian, H and Wang, X and Yin, W},
   Title = {HeteRSGD: Tackling Heterogeneous Sampling Costs via Optimal
             Reweighted Stochastic Gradient Descent},
   Journal = {Proceedings of Machine Learning Research},
   Volume = {206},
   Pages = {10732-10781},
   Year = {2023},
   Month = {January},
   Abstract = {One implicit assumption in current stochastic gradient
             descent (SGD) algorithms is the identical cost for sampling
             each component function of the finite-sum objective.
             However, there are applications where the costs differ
             substantially, for which SGD schemes with uniform sampling
             invoke a high sampling load. We investigate the use of
             importance sampling (IS) as a cost saver in this setting, in
             contrast to its traditional use for variance reduction. The
             key ingredient is a novel efficiency metric for IS that
             advocates low sampling costs while penalizing high gradient
             variances. We then propose HeteRSGD, an SGD scheme that
             performs gradient sampling according to optimal probability
             weights stipulated by the metric, and establish theories on
             its optimal asymptotic and finite-time convergence rates
             among all possible IS-based SGD schemes. We show that the
             relative efficiency gain of HeteRSGD can be arbitrarily
             large regardless of the problem dimension and number of
             components. Our theoretical results are validated
             numerically for both convex and nonconvex
             problems.},
   Key = {fds371889}
}

@article{fds371890,
   Author = {Lee, H and Lu, J and Tan, Y},
   Title = {Convergence of score-based generative modeling for general
             data distributions},
   Journal = {Proceedings of Machine Learning Research},
   Volume = {201},
   Pages = {946-985},
   Year = {2023},
   Month = {January},
   Abstract = {Score-based generative modeling (SGM) has grown to be a
             hugely successful method for learning to generate samples
             from complex data distributions such as that of images and
             audio. It is based on evolving an SDE that transforms white
             noise into a sample from the learned distribution, using
             estimates of the score function, or gradient log-pdf.
             Previous convergence analyses for these methods have
             suffered either from strong assumptions on the data
             distribution or exponential dependencies, and hence fail to
             give efficient guarantees for the multimodal and non-smooth
             distributions that arise in practice and for which good
             empirical performance is observed. We consider a popular
             kind of SGM—denoising diffusion models—and give
             polynomial convergence guarantees for general data
             distributions, with no assumptions related to functional
             inequalities or smoothness. Assuming L2-accurate score
             estimates, we obtain Wasserstein distance guarantees for any
             distribution of bounded support or sufficiently decaying
             tails, as well as TV guarantees for distributions with
             further smoothness assumptions.},
   Key = {fds371890}
}

@article{fds372260,
   Author = {Chen, Z and Li, Y and Lu, J},
   Title = {ON THE GLOBAL CONVERGENCE OF RANDOMIZED COORDINATE GRADIENT
             DESCENT FOR NONCONVEX OPTIMIZATION*},
   Journal = {SIAM Journal on Optimization},
   Volume = {33},
   Number = {2},
   Pages = {713-738},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1137/21M1460375},
   Abstract = {In this work, we analyze the global convergence property of
             a coordinate gradient descent with random choice of
             coordinates and stepsizes for nonconvex optimization
             problems. Under generic assumptions, we prove that the
             algorithm iterate will almost surely escape strict saddle
             points of the objective function. As a result, the algorithm
             is guaranteed to converge to local minima if all saddle
             points are strict. Our proof is based on viewing the
             coordinate descent algorithm as a nonlinear random dynamical
             system and a quantitative finite block analysis of its
             linearization around saddle points.},
   Doi = {10.1137/21M1460375},
   Key = {fds372260}
}

@article{fds372774,
   Author = {Sachs, M and Sen, D and Lu, J and Dunson, D},
   Title = {Posterior Computation with the Gibbs Zig-Zag
             Sampler},
   Journal = {Bayesian Analysis},
   Volume = {18},
   Number = {3},
   Pages = {909-927},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1214/22-BA1319},
   Abstract = {An intriguing new class of piecewise deterministic Markov
             processes (PDMPs) has recently been proposed as an
             alternative to Markov chain Monte Carlo (MCMC). We propose a
             new class of PDMPs termed Gibbs zig-zag samplers, which
             allow parameters to be updated in blocks with a zig-zag
             sampler applied to certain parameters and traditional
             MCMC-style updates to others. We demonstrate the flexibility
             of this framework on posterior sampling for logistic models
             with shrinkage priors for high-dimensional regression and
             random effects, and provide conditions for geometric
             ergodicity and the validity of a central limit
             theorem.},
   Doi = {10.1214/22-BA1319},
   Key = {fds372774}
}

@article{fds372815,
   Author = {Huang, H and Landsberg, JM and Lu, J},
   Title = {GEOMETRY OF BACKFLOW TRANSFORMATION ANSATZE FOR QUANTUM
             MANY-BODY FERMIONIC WAVEFUNCTIONS},
   Journal = {Communications in Mathematical Sciences},
   Volume = {21},
   Number = {5},
   Pages = {1447-1453},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.4310/CMS.2023.v21.n5.a12},
   Abstract = {Wave function ansatze based on the backflow transformation
             are widely used to parametrize anti-symmetric multivariable
             functions for many-body quantum problems. We study the
             geometric aspects of such ansatze, in particular we show
             that in general totally antisymmetric polynomials cannot be
             efficiently represented by backflow transformation ansatze
             at least in the category of polynomials. In fact, if there
             are N particles in the system, one needs a linear
             combination of at least O(N3N−3) determinants to represent
             a generic totally antisymmetric polynomial. Our proof is
             based on bounding the dimension of the source of the ansatze
             from above and bounding the dimension of the target from
             below.},
   Doi = {10.4310/CMS.2023.v21.n5.a12},
   Key = {fds372815}
}

@article{fds373339,
   Author = {Bal, G and Becker, S and Drouot, A and Kammerer, CF and Lu, J and Watson,
             AB},
   Title = {EDGE STATE DYNAMICS ALONG CURVED INTERFACES},
   Journal = {SIAM Journal on Mathematical Analysis},
   Volume = {55},
   Number = {5},
   Pages = {4219-4254},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1137/22M1489708},
   Abstract = {We study the propagation of wavepackets along weakly curved
             interfaces between topologically distinct media. Our
             Hamiltonian is an adiabatic modulation of Dirac operators
             omnipresent in the topological insulators literature. Using
             explicit formulas for straight edges, we construct a family
             of solutions that propagates, for long times,
             unidirectionally and dispersion-free along the curved edge.
             We illustrate our results through various numerical
             simulations.},
   Doi = {10.1137/22M1489708},
   Key = {fds373339}
}

@article{fds373537,
   Author = {Zhang, S and Lu, J and Zhao, H},
   Title = {On Enhancing Expressive Power via Compositions of Single
             Fixed-Size ReLU Network},
   Journal = {Proceedings of Machine Learning Research},
   Volume = {202},
   Pages = {41452-41487},
   Year = {2023},
   Month = {January},
   Abstract = {This paper explores the expressive power of deep neural
             networks through the framework of function compositions. We
             demonstrate that the repeated compositions of a single
             fixed-size ReLU network exhibit surprising expressive power,
             despite the limited expressive capabilities of the
             individual network itself. Specifically, we prove by
             construction that L2◦g◦r◦L1 can approximate
             1-Lipschitz continuous functions on [0, 1]d with an error
             O(r−1/d), where g is realized by a fixed-size ReLU
             network, L1 and L2 are two affine linear maps matching the
             dimensions, and g◦r denotes the r-times composition of g.
             Furthermore, we extend such a result to generic continuous
             functions on [0, 1]d with the approximation error
             characterized by the modulus of continuity. Our results
             reveal that a continuous-depth network generated via a
             dynamical system has immense approximation power even if its
             dynamics function is time-independent and realized by a
             fixed-size ReLU network.},
   Key = {fds373537}
}

@article{fds373538,
   Author = {Chen, H and Lee, H and Lu, J},
   Title = {Improved Analysis of Score-based Generative Modeling:
             User-Friendly Bounds under Minimal Smoothness
             Assumptions},
   Journal = {Proceedings of Machine Learning Research},
   Volume = {202},
   Pages = {5367-5382},
   Year = {2023},
   Month = {January},
   Abstract = {We give an improved theoretical analysis of score-based
             generative modeling. Under a score estimate with small L2
             error (averaged across timesteps), we provide efficient
             convergence guarantees for any data distribution with
             second-order moment, by either employing early stopping or
             assuming a smoothness condition on the score function of the
             data distribution. Our result does not rely on any
             log-concavity or functional inequality assumption and has a
             logarithmic dependence on the smoothness. In particular, we
             show that under only a finite second moment condition,
             approximating the following in reverse KL divergence in
             ϵ-accuracy can be done in (equation presented)Õ (
             dlog(1ϵ/δ) ) steps: 1) the variance-δ Gaussian
             perturbation of any data distribution; 2) data distributions
             with 1/δ-smooth score functions. Our analysis also provides
             a quantitative comparison between different discrete
             approximations and may guide the choice of discretization
             points in practice.},
   Key = {fds373538}
}

@article{fds373539,
   Author = {Agazzi, A and Lu, J and Mukherjee, S},
   Title = {Global optimality of Elman-type RNNs in the mean-field
             regime},
   Journal = {Proceedings of Machine Learning Research},
   Volume = {202},
   Pages = {196-227},
   Year = {2023},
   Month = {January},
   Abstract = {We analyze Elman-type Recurrent Reural Networks (RNNs) and
             their training in the mean-field regime. Specifically, we
             show convergence of gradient descent training dynamics of
             the RNN to the corresponding mean-field formulation in the
             large width limit. We also show that the fixed points of the
             limiting infinite-width dynamics are globally optimal, under
             some assumptions on the initialization of the weights. Our
             results establish optimality for feature-learning with wide
             RNNs in the mean-field regime.},
   Key = {fds373539}
}

@article{fds373540,
   Author = {Marwah, T and Lipton, ZC and Lu, J and Risteski, A},
   Title = {Neural Network Approximations of PDEs Beyond Linearity: A
             Representational Perspective},
   Journal = {Proceedings of Machine Learning Research},
   Volume = {202},
   Pages = {24139-24172},
   Year = {2023},
   Month = {January},
   Abstract = {A burgeoning line of research leverages deep neural networks
             to approximate the solutions to high dimensional PDEs,
             opening lines of theoretical inquiry focused on explaining
             how it is that these models appear to evade the curse of
             dimensionality. However, most prior theoretical analyses
             have been limited to linear PDEs. In this work, we take a
             step towards studying the representational power of neural
             networks for approximating solutions to nonlinear PDEs. We
             focus on a class of PDEs known as nonlinear elliptic
             variational PDEs, whose solutions minimize an Euler-Lagrange
             energy functional E(u) = RΩ L(x, u(x), ∇u(x)) −
             f(x)u(x)dx. We show that if composing a function with Barron
             norm b with partial derivatives of L produces a function of
             Barron norm at most BLbp, the solution to the PDE can be
             ϵ-approximated in the L2 sense by a function with Barron
             norm O ( (dBL)max{p log(1/ϵ),plog(1/ϵ) }). By a classical
             result due to (Barron, 1993), this correspondingly bounds
             the size of a 2-layer neural network needed to approximate
             the solution. Treating p, ϵ, BL as constants, this quantity
             is polynomial in dimension, thus showing neural networks can
             evade the curse of dimensionality. Our proof technique
             involves neurally simulating (preconditioned) gradient in an
             appropriate Hilbert space, which converges exponentially
             fast to the solution of the PDE, and such that we can bound
             the increase of the Barron norm at each iterate. Our results
             subsume and substantially generalize analogous prior results
             for linear elliptic PDEs over a unit hypercube.},
   Key = {fds373540}
}

@article{fds372232,
   Author = {Ladd, W and Jensen, C and Vardhan, M and Ames, J and Hammond, JR and Draeger, EW and Randles, A},
   Title = {Optimizing Cloud Computing Resource Usage for Hemodynamic
             Simulation},
   Journal = {Proceedings - 2023 IEEE International Parallel and
             Distributed Processing Symposium, IPDPS 2023},
   Pages = {568-578},
   Year = {2023},
   Month = {January},
   ISBN = {9798350337662},
   url = {http://dx.doi.org/10.1109/IPDPS54959.2023.00063},
   Abstract = {Cloud computing resources are becoming an increasingly
             attractive option for simulation workflows but require users
             to assess a wider variety of hardware options and associated
             costs than required by traditional in-house hardware or
             fixed allocations at leadership computing facilities. The
             pay-as-you-go model used by cloud providers gives users the
             opportunity to make more nuanced cost-benefit decisions at
             runtime by choosing hardware that best matches a given
             workload, but creates the risk of suboptimal allocation
             strategies or inadvertent cost overruns. In this work, we
             propose the use of an iteratively-refined performance model
             to optimize cloud simulation campaigns against overall cost,
             throughput, or maximum time to solution. Hemodynamic
             simulations represent an excellent use case for these
             assessments, as the relative costs and dominant terms in the
             performance model can vary widely with hardware, numerical
             parameters and physics models. Performance and scaling
             behavior of hemodynamic simulations on multiple cloud
             services as well as a traditional compute cluster are
             collected and evaluated, and an initial performance model is
             proposed along with a strategy for dynamically refining it
             with additional experimental data.},
   Doi = {10.1109/IPDPS54959.2023.00063},
   Key = {fds372232}
}

@article{fds373528,
   Author = {Chidambaram, M and Wu, C and Cheng, Y and Ge, R},
   Title = {Hiding Data Helps: On the Benefits of Masking for Sparse
             Coding},
   Journal = {Proceedings of Machine Learning Research},
   Volume = {202},
   Pages = {5600-5615},
   Year = {2023},
   Month = {January},
   Abstract = {Sparse coding, which refers to modeling a signal as sparse
             linear combinations of the elements of a learned dictionary,
             has proven to be a successful (and interpretable) approach
             in applications such as signal processing, computer vision,
             and medical imaging. While this success has spurred much
             work on provable guarantees for dictionary recovery when the
             learned dictionary is the same size as the ground-truth
             dictionary, work on the setting where the learned dictionary
             is larger (or over-realized) with respect to the ground
             truth is comparatively nascent. Existing theoretical results
             in this setting have been constrained to the case of
             noise-less data. We show in this work that, in the presence
             of noise, minimizing the standard dictionary learning
             objective can fail to recover the elements of the
             ground-truth dictionary in the over-realized regime,
             regardless of the magnitude of the signal in the
             data-generating process. Furthermore, drawing from the
             growing body of work on self-supervised learning, we propose
             a novel masking objective for which recovering the
             ground-truth dictionary is in fact optimal as the signal
             increases for a large class of data-generating processes. We
             corroborate our theoretical results with experiments across
             several parameter regimes showing that our proposed
             objective also enjoys better empirical performance than the
             standard reconstruction objective.},
   Key = {fds373528}
}

@article{fds373529,
   Author = {Zhou, M and Ge, R},
   Title = {Implicit Regularization Leads to Benign Overfitting for
             Sparse Linear Regression},
   Journal = {Proceedings of Machine Learning Research},
   Volume = {202},
   Pages = {42543-42573},
   Year = {2023},
   Month = {January},
   Abstract = {In deep learning, often the training process finds an
             interpolator (a solution with 0 training loss), but the test
             loss is still low. This phenomenon, known as benign
             overfitting, is a major mystery that received a lot of
             recent attention. One common mechanism for benign
             overfitting is implicit regularization, where the training
             process leads to additional properties for the interpolator,
             often characterized by minimizing certain norms. However,
             even for a simple sparse linear regression problem y =
             β∗Tx + ξ with sparse β∗, neither minimum ℓ1 or ℓ2
             norm interpolator gives the optimal test loss. In this work,
             we give a different parametrization of the model which leads
             to a new implicit regularization effect that combines the
             benefit of ℓ1 and ℓ2 interpolators. We show that
             training our new model via gradient descent leads to an
             interpolator with near-optimal test loss. Our result is
             based on careful analysis of the training dynamics and
             provides another example of implicit regularization effect
             that goes beyond norm minimization.},
   Key = {fds373529}
}

@article{fds374447,
   Author = {Chidambaram, M and Wang, X and Wu, C and Ge, R},
   Title = {Provably Learning Diverse Features in Multi-View Data with
             Midpoint Mixup},
   Journal = {Proceedings of Machine Learning Research},
   Volume = {202},
   Pages = {5563-5599},
   Year = {2023},
   Month = {January},
   Abstract = {Mixup is a data augmentation technique that relies on
             training using random convex combinations of data points and
             their labels. In recent years, Mixup has become a standard
             primitive used in the training of state-of-the-art image
             classification models due to its demonstrated benefits over
             empirical risk minimization with regards to generalization
             and robustness. In this work, we try to explain some of this
             success from a feature learning perspective. We focus our
             attention on classification problems in which each class may
             have multiple associated features (or views) that can be
             used to predict the class correctly. Our main theoretical
             results demonstrate that, for a non-trivial class of data
             distributions with two features per class, training a
             2-layer convolutional network using empirical risk
             minimization can lead to learning only one feature for
             almost all classes while training with a specific
             instantiation of Mixup succeeds in learning both features
             for every class. We also show empirically that these
             theoretical insights extend to the practical settings of
             image benchmarks modified to have multiple
             features.},
   Key = {fds374447}
}

@article{fds376017,
   Author = {Zhao, H and Panigrahi, A and Ge, R and Arora, S},
   Title = {Do Transformers Parse while Predicting the Masked
             Word?},
   Journal = {EMNLP 2023 - 2023 Conference on Empirical Methods in Natural
             Language Processing, Proceedings},
   Pages = {16513-16542},
   Year = {2023},
   Month = {January},
   ISBN = {9798891760608},
   Abstract = {Pre-trained language models have been shown to encode
             linguistic structures like parse trees in their embeddings
             while being trained unsupervised. Some doubts have been
             raised whether the models are doing parsing or only some
             computation weakly correlated with it. Concretely: (a) Is it
             possible to explicitly describe transformers with realistic
             embedding dimensions, number of heads, etc. that are capable
             of doing parsing -or even approximate parsing? (b) Why do
             pre-trained models capture parsing structure? This paper
             takes a step toward answering these questions in the context
             of generative modeling with PCFGs. We show that masked
             language models like BERT or RoBERTa of moderate sizes can
             approximately execute the Inside-Outside algorithm for the
             English PCFG (Marcus et al., 1993). We also show that the
             Inside-Outside algorithm is optimal for masked language
             modeling loss on the PCFG-generated data. We conduct probing
             experiments on models pre-trained on PCFG-generated data to
             show that this not only allows recovery of approximate parse
             tree, but also recovers marginal span probabilities computed
             by the Inside-Outside algorithm, which suggests an implicit
             bias of masked language modeling towards this
             algorithm.},
   Key = {fds376017}
}

@article{fds371245,
   Author = {Ou, YJ and Barnett, AJ and Mitra, A and Schwartz, FR and Chen, C and Grimm,
             L and Lo, JY and Rudin, C},
   Title = {A user interface to communicate interpretable AI decisions
             to radiologists},
   Journal = {Progress in Biomedical Optics and Imaging - Proceedings of
             SPIE},
   Volume = {12467},
   Year = {2023},
   Month = {January},
   ISBN = {9781510660397},
   url = {http://dx.doi.org/10.1117/12.2654068},
   Abstract = {Tools for computer-aided diagnosis based on deep learning
             have become increasingly important in the medical field.
             Such tools can be useful, but require effective
             communication of their decision-making process in order to
             safely and meaningfully guide clinical decisions. Inherently
             interpretable models provide an explanation for each
             decision that matches their internal decision-making
             process. We present a user interface that incorporates the
             Interpretable AI Algorithm for Breast Lesions (IAIA-BL)
             model, which interpretably predicts both mass margin and
             malignancy for breast lesions. The user interface displays
             the most relevant aspects of the model's explanation
             including the predicted margin value, the AI confidence in
             the prediction, and the two most highly activated prototypes
             for each case. In addition, this user interface includes
             full-field and cropped images of the region of interest, as
             well as a questionnaire suitable for a reader study. Our
             preliminary results indicate that the model increases the
             readers' confidence and accuracy in their decisions on
             margin and malignancy.},
   Doi = {10.1117/12.2654068},
   Key = {fds371245}
}

@article{fds372810,
   Author = {Lanners, Q and Parikh, H and Volfovsky, A and Rudin, C and Page,
             D},
   Title = {Variable Importance Matching for Causal Inference},
   Journal = {Proceedings of Machine Learning Research},
   Volume = {216},
   Pages = {1174-1184},
   Year = {2023},
   Month = {January},
   Abstract = {Our goal is to produce methods for observational causal
             inference that are auditable, easy to troubleshoot, accurate
             for treatment effect estimation, and scalable to
             high-dimensional data. We describe a general framework
             called Model-to-Match that achieves these goals by (i)
             learning a distance metric via outcome modeling, (ii)
             creating matched groups using the distance metric, and (iii)
             using the matched groups to estimate treatment effects.
             Model-to-Match uses variable importance measurements to
             construct a distance metric, making it a flexible framework
             that can be adapted to various applications. Concentrating
             on the scalability of the problem in the number of potential
             confounders, we operationalize the Model-to-Match framework
             with LASSO. We derive performance guarantees for settings
             where LASSO outcome modeling consistently identifies all
             confounders (importantly without requiring the linear model
             to be correctly specified). We also provide experimental
             results demonstrating the method's auditability, accuracy,
             and scalability as well as extensions to more general
             nonparametric outcome modeling.},
   Key = {fds372810}
}

@article{fds373003,
   Author = {Agnew, E and Qiu, M and Zhu, L and Wiseman, S and Rudin,
             C},
   Title = {The Mechanical Bard: An Interpretable Machine Learning
             Approach to Shakespearean Sonnet Generation},
   Journal = {Proceedings of the Annual Meeting of the Association for
             Computational Linguistics},
   Volume = {2},
   Pages = {1627-1638},
   Year = {2023},
   Month = {January},
   ISBN = {9781959429715},
   Abstract = {We consider the automated generation of sonnets, a poetic
             form constrained according to meter, rhyme scheme, and
             length. Sonnets generally also use rhetorical figures,
             expressive language, and a consistent theme or narrative.
             Our constrained decoding approach allows for the generation
             of sonnets within preset poetic constraints, while using a
             relatively modest neural backbone. Human evaluation confirms
             that our approach produces Shakespearean sonnets that
             resemble human-authored sonnets, and which adhere to the
             genre’s defined constraints and contain lyrical language
             and literary devices.},
   Key = {fds373003}
}

@article{fds373531,
   Author = {Chen, Z and Tan, S and Chajewska, U and Rudin, C and Caruana,
             R},
   Title = {Missing Values and Imputation in Healthcare Data: Can
             Interpretable Machine Learning Help?},
   Journal = {Proceedings of Machine Learning Research},
   Volume = {209},
   Pages = {86-99},
   Year = {2023},
   Month = {January},
   Abstract = {Missing values are a fundamental problem in data science.
             Many datasets have missing values that must be properly
             handled because the way missing values are treated can have
             large impact on the resulting machine learning model. In
             medical applications, the consequences may affect healthcare
             decisions. There are many methods in the literature for
             dealing with missing values, including state-of-the-art
             methods which often depend on black-box models for
             imputation. In this work, we show how recent advances in
             interpretable machine learning provide a new perspective for
             understanding and tackling the missing value problem. We
             propose methods based on high-accuracy glass-box Explainable
             Boosting Machines (EBMs) that can help users (1) gain new
             insights on missingness mechanisms and better understand the
             causes of missingness, and (2) detect – or even alleviate
             – potential risks introduced by imputation algorithms.
             Experiments on real-world medical datasets illustrate the
             effectiveness of the proposed methods.},
   Key = {fds373531}
}

@article{fds374496,
   Author = {Kiselev, A and Nazarov, F and Ryzhik, L and Yao, Y},
   Title = {Chemotaxis and reactions in biology},
   Journal = {Journal of the European Mathematical Society},
   Volume = {25},
   Number = {7},
   Pages = {2641-2696},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.4171/JEMS/1247},
   Abstract = {Chemotaxis plays a crucial role in a variety of processes in
             biology and ecology. Quite often it acts to improve
             efficiency of biological reactions. One example is the
             immune system signalling, where infected tissues release
             chemokines attracting monocytes to fight invading bacteria.
             Another example is reproduction, where eggs release
             pheromones that attract sperm. A macro scale example is
             flower scent appealing to pollinators. In this paper we
             consider a system of PDEs designed to model such processes.
             Our interest is to quantify the effect of chemotaxis on
             reaction rates compared to pure reaction-diffusion. We limit
             consideration to surface chemotaxis, which is well motivated
             from the point of view of many applications. Our results
             provide the first insight into situations where chemotaxis
             can be crucial for reaction success, and where its effect is
             likely to be limited. The proofs are based on new analytical
             tools; a significant part of the paper is dedicated to
             building up the linear machinery that can be useful in more
             general settings. In particular, we establish precise
             estimates on the rates of convergence to the ground state
             for a class of Fokker–Planck operators with potentials
             that grow at a logarithmic rate at infinity. These estimates
             are made possible by a new sharp weak weighted Poincaré
             inequality.},
   Doi = {10.4171/JEMS/1247},
   Key = {fds374496}
}

@article{fds375269,
   Author = {Levine, AS},
   Title = {A note on rationally slice knots},
   Journal = {New York Journal of Mathematics},
   Volume = {29},
   Pages = {1363-1372},
   Year = {2023},
   Month = {January},
   Abstract = {Kawauchi proved that every strongly negative amphichiral
             knot (Formula Presented) bounds a smoothly embedded disk in
             some rational homology ball VK, whose construction a priori
             depends on K. We show that VK is inde-pendent of K up to
             diffeomorphism. Thus, a single 4-manifold, along with
             connected sums thereof, accounts for all known examples of
             knots that are rationally slice but not slice.},
   Key = {fds375269}
}

@article{fds374293,
   Author = {Lee, J and Xie, Y and Cheng, X},
   Title = {Training Neural Networks for Sequential Change-Point
             Detection},
   Journal = {ICASSP, IEEE International Conference on Acoustics, Speech
             and Signal Processing - Proceedings},
   Volume = {2023-June},
   Year = {2023},
   Month = {January},
   ISBN = {9781728163277},
   url = {http://dx.doi.org/10.1109/ICASSP49357.2023.10095005},
   Abstract = {Detecting an abrupt distributional shift of a data stream,
             known as change-point detection, is a fundamental problem in
             statistics and machine learning. We introduce a novel
             approach for online change-point detection using neural
             net-works. To be specific, our approach is training neural
             net-works to compute the cumulative sum of a detection
             statistic sequentially, which exhibits a significant change
             when a change-point occurs. We demonstrated the superiority
             and potential of the proposed method in detecting
             change-point using both synthetic and real-world
             data.1},
   Doi = {10.1109/ICASSP49357.2023.10095005},
   Key = {fds374293}
}

@article{fds365844,
   Author = {Chen, Z and Wu, HT},
   Title = {Disentangling modes with crossover instantaneous frequencies
             by synchrosqueezed chirplet transforms, from theory to
             application},
   Journal = {Applied and Computational Harmonic Analysis},
   Volume = {62},
   Pages = {84-122},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1016/j.acha.2022.08.004},
   Abstract = {Analysis of signals with oscillatory modes with crossover
             instantaneous frequencies is a challenging problem in time
             series analysis. One way to handle this problem is lifting
             the 2-dimensional time-frequency representation to a
             3-dimensional representation, called time-frequency-chirp
             rate (TFC) representation, by adding one extra chirp rate
             parameter so that crossover frequencies are disentangled in
             higher dimension. The chirplet transform is an algorithm for
             this lifting idea, which leads to a TFC representation.
             However, in practice, we found that it has a strong
             “blurring” effect in the chirp rate axis, which limits
             its application in real-world data. Moreover, to our
             knowledge, we have limited mathematical understanding of the
             chirplet transform in the literature. Motivated by the need
             for the real-world data analysis, in this paper, we propose
             the synchrosqueezed chirplet transform (SCT) that enhances
             the TFC representation given by the chirplet transform. The
             resulting concentrated TFC representation has high contrast
             so that one can better distinguish different modes with
             crossover instantaneous frequencies. The basic idea is to
             use the phase information in the chirplet transform to
             determine a reassignment rule that sharpens the TFC
             representation determined by the chirplet transform. We also
             analyze the chirplet transform and provide theoretical
             guarantees of SCT.},
   Doi = {10.1016/j.acha.2022.08.004},
   Key = {fds365844}
}

@article{fds369945,
   Author = {Colominas, MA and Wu, HT},
   Title = {An Iterative Warping and Clustering Algorithm to Estimate
             Multiple Wave-Shape Functions From a Nonstationary
             Oscillatory Signal},
   Journal = {IEEE Transactions on Signal Processing},
   Volume = {71},
   Pages = {701-712},
   Publisher = {Institute of Electrical and Electronics Engineers
             (IEEE)},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1109/TSP.2023.3252883},
   Abstract = {Nonsinusoidal oscillatory signals are everywhere. In
             practice, the nonsinusoidal oscillatory pattern, modeled as
             a 1-periodic wave-shape function (WSF), might vary from
             cycle to cycle. When there are finite different WSFs, s1,sK,
             so that the WSF jumps from one to another suddenly, the
             different WSFs and jumps encode useful information. We
             present an iterative warping and clustering algorithm to
             estimate s1,sK from a nonstationary oscillatory signal with
             time-varying amplitude and frequency, and hence the change
             points of the WSFs. The algorithm is a novel combination of
             time-frequency analysis, singular value decomposition
             entropy and vector spectral clustering. We demonstrate the
             efficiency of the proposed algorithm with simulated and real
             signals, including the voice signal, arterial blood
             pressure, electrocardiogram and accelerometer signal.
             Moreover, we provide a mathematical justification of the
             algorithm under the assumption that the amplitude and
             frequency of the signal are slowly time-varying and there
             are finite change points that model sudden changes from one
             wave-shape function to another one.},
   Doi = {10.1109/TSP.2023.3252883},
   Key = {fds369945}
}

@article{fds375272,
   Author = {Chen, HY and Wu, HT and Chen, CY},
   Title = {Quality Aware Sleep Stage Classification over RIP Signals
             with Persistence Diagrams},
   Journal = {2023 IEEE 19th International Conference on Body Sensor
             Networks, BSN 2023 - Proceedings},
   Year = {2023},
   Month = {January},
   ISBN = {9798350338416},
   url = {http://dx.doi.org/10.1109/BSN58485.2023.10331130},
   Abstract = {Automated sleep stage classification is a valuable tool for
             analyzing sleep patterns and has numerous applications in
             wearable healthcare systems. However, the accuracy of sleep
             stage classification using signals from wearable devices can
             be affected by data quality issues such as signal
             interference or packet loss. In this study, we present an
             algorithm that addresses packet loss in respiratory
             inductive plethysmography (RIP) signals for sleep stage
             detection. RIP signals can be conveniently collected using
             abdominal and thoracic belts. By exploring the rich
             structural patterns in such signals, we utilize persistence
             diagrams to uncover macro-structures for sleep stage
             classification, which is particularly suitable for high data
             missing rates. Our model achieves a promising performance of
             76% accuracy and a 0.54 Cohen's kappa coefficient for
             three-stage classification. Additionally, we evaluate the
             model across different missing data rates and highlight the
             superior fault tolerance of persistence diagram features
             compared to other conventional temporal and spectral
             features.},
   Doi = {10.1109/BSN58485.2023.10331130},
   Key = {fds375272}
}

@article{fds368211,
   Author = {Dasgupta, S and Kakde, M},
   Title = {On the Brumer-Stark conjecture},
   Journal = {Annals of Mathematics},
   Volume = {197},
   Number = {1},
   Pages = {289-388},
   Publisher = {Annals of Mathematics},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.4007/annals.2023.197.1.5},
   Abstract = {Let H=F be a finite abelian extension of number fields with
             F totally real and H a CM field. Let S and T be disjoint
             finite sets of places of F satisfying the standard
             conditions. The Brumer-Stark conjecture states that the
             Stickelberger element ΦH/FS,T annihilates the T-smoothed
             class group ClT(H). We prove this conjecture away from p=2,
             that is, after tensoring with Z[1/2]. We prove a stronger
             version of this result conjectured by Kurihara that gives a
             formula for the 0th Fitting ideal of the minus part of the
             Pontryagin dual of [Formula Presented] in terms of
             Stickelberger elements. We also show that this stronger
             result implies Rubin's higher rank version of the
             Brumer-Stark conjecture, again away from 2. Our technique is
             a generalization of Ribet's method, building upon on our
             earlier work on the Gross-Stark conjecture. Here we work
             with group ring valued Hilbert modular forms as introduced
             by Wiles. A key aspect of our approach is the construction
             of congruences between cusp forms and Eisenstein series that
             are stronger than usually expected, arising as shadows of
             the trivial zeroes of p-adic L-functions. These stronger
             congruences are essential to proving that the cohomology
             classes we construct are unramified at p.},
   Doi = {10.4007/annals.2023.197.1.5},
   Key = {fds368211}
}

@article{fds369772,
   Author = {Tackett, M and Viel, S and Manturuk, K},
   Title = {A validation of the short-form classroom community scale for
             undergraduate mathematics and statistics
             students},
   Journal = {Journal of University Teaching and Learning
             Practice},
   Volume = {20},
   Number = {1},
   Pages = {1-17},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.53761/1.20.01.08},
   Abstract = {This study examines Cho & Demmans Epp’s short-form
             adaptation of Rovai’s well-known Classroom Community Scale
             (CCS-SF) as a measure of classroom community among
             introductory undergraduate math and statistics students. A
             series of statistical analyses were conducted to investigate
             the validity of the CCS-SF for this new population. Data
             were collected from 351 students enrolled in 21 online
             classes, offered for credit in Fall 2020 and Spring 2021 at
             a private university in the United States. Further
             confirmatory analysis was conducted with data from 128
             undergraduates enrolled in 13 in-person and hybrid classes,
             offered for credit in Fall 2021 at the same institution.
             Following Rovai’s original 20-item CCS, the 8-item CCS-SF
             yields two interpretable factors, connectedness and
             learning. This study confirms the two-factor structure of
             the CCS-SF, and concludes that it is a valid measure of
             classroom community among undergraduate students enrolled in
             remote, hybrid, and in-person introductory mathematics and
             statistics courses. Practitioner Notes 1. Cho & Demmans
             Epp's 2019 Classroom Community Scale Short Form (CCS-SF) is
             a promising but relatively new instrument for measuring
             students’ sense of community, previously validated only
             for graduate online courses. This research article validates
             the instrument's use for undergraduate students in online,
             hybrid, and in-person courses. 2. According to Rovai’s
             original Classroom Community Scale from which the CCS-SF is
             adapted, students’ sense of community can be understood by
             two subscales, connectedness and learning. These subscales
             measure how students relate to their peers and their
             perception of being in a supportive learning environment. 3.
             Through exploratory factor analysis, we have shown more
             nuanced views of the subscales demonstrating the multiple
             facets in which students evaluate belongingness among their
             peers and perception of having shared learning goals. 4.
             With this validation article, instructors can now administer
             the CCS-SF instrument in undergraduate courses to assess the
             classroom community, as well as using the instrument for
             research on undergraduate students. 5. With this validation
             article, educational researchers can use the CCS-SF to
             better understand situational factors and pedagogies
             associated with students’ sense of community and how these
             associations vary for students with different
             identities.},
   Doi = {10.53761/1.20.01.08},
   Key = {fds369772}
}

@article{fds374300,
   Author = {Davis, R and Pries, R and Wickelgren, K},
   Title = {The Galois action on the lower central series of the
             fundamental group of the Fermat curve},
   Journal = {Israel Journal of Mathematics},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1007/s11856-023-2571-z},
   Abstract = {Information about the absolute Galois group GK of a number
             field K is encoded in how it acts on the étale fundamental
             group π of a curve X defined over K. In the case that K =
             ℚ(ζn) is the cyclotomic field and X is the Fermat curve
             of degree n ≥ 3, Anderson determined the action of GK on
             the étale homology with coefficients in ℤ/nℤ. The
             étale homology is the first quotient in the lower central
             series of the étale fundamental group. In this paper, we
             determine the Galois module structure of the graded Lie
             algebra for π. As a consequence, this determines the action
             of GK on all degrees of the associated graded quotient of
             the lower central series of the étale fundamental group of
             the Fermat curve of degree n, with coefficients in
             ℤ/nℤ.},
   Doi = {10.1007/s11856-023-2571-z},
   Key = {fds374300}
}

@article{fds370199,
   Author = {Kush, D and Rossman, B},
   Title = {TREE-DEPTH AND THE FORMULA COMPLEXITY OF SUBGRAPH
             ISOMORPHISM},
   Journal = {SIAM Journal on Computing},
   Volume = {52},
   Number = {1},
   Pages = {273-325},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1137/20M1372925},
   Abstract = {For a fixed "pattern"" graph G, the colored G-subgraph
             isomorphism problem (denoted by SUB(G)) asks, given an
             n-vertex graph H and a coloring V (H) → V (G), whether H
             contains a properly colored copy of G. The complexity of
             this problem is tied to parameterized versions of P =? NP
             and L =? NL, among other questions. An overarching goal is
             to understand the complexity of SUB(G), under different
             computational models, in terms of natural invariants of the
             pattern graph G. In this paper, we establish a close
             relationship between the formula complexity of SUB(G) and an
             invariant known as tree-depth (denoted by (G)). SUB(G) is
             known to be solvable by monotone AC0 formulas of size O(n
             (G)). Our main result is an n Ω ((G)1/3) lower bound for
             formulas that are monotone or have sublogarithmic depth.
             This complements a lower bound of Li, Razborov, and Rossman
             [SIAM J. Comput., 46 (2017), pp. 936-971] relating
             tree-width and AC0 circuit size. As a corollary, it implies
             a stronger homomorphism preservation theorem for firstorder
             logic on finite structures [B. Rossman, An improved
             homomorphism preservation theorem from lower bounds in
             circuit complexity, in 8th Innovations in Theoretical
             Computer Science Conference, LIPIcs. Leibniz Int. Proc.
             Inform. 67, Schloss Dagstuhl. Leibniz-Zent. Inform., Wadern,
             Germany, 2017, 27]. The technical core of this result is an
             nΩ (k) lower bound in the special case where G is a
             complete binary tree of height k, which we establish using
             the pathset framework introduced in B. Rossman [SIAM J.
             Comput., 47 (2018), pp. 1986-2028]. (The lower bound for
             general patterns follows via a recent excluded-minor
             characterization of tree-depth [W. Czerwiński, W. Nadara,
             and M. Pilipczuk, SIAM J. Discrete Math., 35 (2021), pp.
             934-947; K. Kawarabayashi and B. Rossman, A polynomial
             excluded-minor approximation of treedepth, in Proceedings of
             the 2018 Annual ACMSIAM Symposium on Discrete Algorithms,
             2018, pp. 234-246]. Additional results of this paper extend
             the pathset framework and improve upon both the best known
             upper and lower bounds on the average-case formula size of
             SUB(G) when G is a path.},
   Doi = {10.1137/20M1372925},
   Key = {fds370199}
}

@article{fds369327,
   Author = {He, W and Rossman, B},
   Title = {Symmetric Formulas for Products of Permutations},
   Journal = {Leibniz International Proceedings in Informatics,
             LIPIcs},
   Volume = {251},
   Year = {2023},
   Month = {January},
   ISBN = {9783959772631},
   url = {http://dx.doi.org/10.4230/LIPIcs.ITCS.2023.68},
   Abstract = {We study the formula complexity of the word problem WordSn,k
             : (0, 1)kn2 → (0, 1): given n-by-n permutation matrices
             M1,..., Mk, compute the (1, 1)-entry of the matrix product
             M1 · · · Mk. An important feature of this function is
             that it is invariant under action of Snk−1 given by
             (π1,..., πk−1)(M1,..., Mk) = (M1π1−1,
             π1M2π2−1,..., πk−2Mk−1πk−−11, πk−1Mk). This
             symmetry is also exhibited in the smallest known unbounded
             fan-in (and, or, not)-formulas for WordSn,k, which have size
             nO(log k). In this paper we prove a matching nΩ(log k)
             lower bound for Snk−1-invariant formulas computing
             WordSn,k. This result is motivated by the fact that a
             similar lower bound for unrestricted (non-invariant)
             formulas would separate complexity classes NC1 and Logspace.
             Our more general main theorem gives a nearly tight
             nd(k1/d−1) lower bound on the Gk−1invariant depth-d
             (maj, and, or, not)-formula size of WordG,k for any finite
             simple group G whose minimum permutation representation has
             degree n. We also give nearly tight lower bounds on the
             Gk−1-invariant depth-d (and, or, not)-formula size in the
             case where G is an abelian group.},
   Doi = {10.4230/LIPIcs.ITCS.2023.68},
   Key = {fds369327}
}

@article{fds374519,
   Author = {He, Y and Zhao, H and Zhong, Y},
   Title = {How Much Can One Learn a Partial Differential Equation from
             Its Solution?},
   Journal = {Foundations of Computational Mathematics},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1007/s10208-023-09620-z},
   Abstract = {In this work, we study the problem of learning a partial
             differential equation (PDE) from its solution data. PDEs of
             various types are used to illustrate how much the solution
             data can reveal the PDE operator depending on the underlying
             operator and initial data. A data-driven and data-adaptive
             approach based on local regression and global consistency is
             proposed for stable PDE identification. Numerical
             experiments are provided to verify our analysis and
             demonstrate the performance of the proposed
             algorithms.},
   Doi = {10.1007/s10208-023-09620-z},
   Key = {fds374519}
}

@article{fds374520,
   Author = {Zhang, S and Lu, J and Zhao, H},
   Title = {On Enhancing Expressive Power via Compositions of Single
             Fixed-Size ReLU Network},
   Journal = {Proceedings of Machine Learning Research},
   Volume = {202},
   Pages = {41452-41487},
   Year = {2023},
   Month = {January},
   Abstract = {This paper explores the expressive power of deep neural
             networks through the framework of function compositions. We
             demonstrate that the repeated compositions of a single
             fixed-size ReLU network exhibit surprising expressive power,
             despite the limited expressive capabilities of the
             individual network itself. Specifically, we prove by
             construction that L2◦g◦r◦L1 can approximate
             1-Lipschitz continuous functions on [0, 1]d with an error
             O(r−1/d), where g is realized by a fixed-size ReLU
             network, L1 and L2 are two affine linear maps matching the
             dimensions, and g◦r denotes the r-times composition of g.
             Furthermore, we extend such a result to generic continuous
             functions on [0, 1]d with the approximation error
             characterized by the modulus of continuity. Our results
             reveal that a continuous-depth network generated via a
             dynamical system has immense approximation power even if its
             dynamics function is time-independent and realized by a
             fixed-size ReLU network.},
   Key = {fds374520}
}

@article{fds368023,
   Author = {Dawson, M and Dudley, C and Omoma, S and Tung, H-R and Ciocanel,
             M-V},
   Title = {Characterizing emerging features in cell dynamics using
             topological data analysis methods.},
   Journal = {Mathematical biosciences and engineering :
             MBE},
   Volume = {20},
   Number = {2},
   Pages = {3023-3046},
   Publisher = {American Institute of Mathematical Sciences
             (AIMS)},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.3934/mbe.2023143},
   Abstract = {Filament-motor interactions inside cells play essential
             roles in many developmental as well as other biological
             processes. For instance, actin-myosin interactions drive the
             emergence or closure of ring channel structures during wound
             healing or dorsal closure. These dynamic protein
             interactions and the resulting protein organization lead to
             rich time-series data generated by using fluorescence
             imaging experiments or by simulating realistic stochastic
             models. We propose methods based on topological data
             analysis to track topological features through time in cell
             biology data consisting of point clouds or binary images.
             The framework proposed here is based on computing the
             persistent homology of the data at each time point and on
             connecting topological features through time using
             established distance metrics between topological summaries.
             The methods retain aspects of monomer identity when
             analyzing significant features in filamentous structure
             data, and capture the overall closure dynamics when
             assessing the organization of multiple ring structures
             through time. Using applications of these techniques to
             experimental data, we show that the proposed methods can
             describe features of the emergent dynamics and
             quantitatively distinguish between control and perturbation
             experiments.},
   Doi = {10.3934/mbe.2023143},
   Key = {fds368023}
}

@article{fds374131,
   Author = {Fintzen, J and Kaletha, T and Spice, L},
   Title = {A TWISTED YU CONSTRUCTION, HARISH-CHANDRA CHARACTERS, AND
             ENDOSCOPY},
   Journal = {Duke Mathematical Journal},
   Volume = {172},
   Number = {12},
   Pages = {2241-2301},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1215/00127094-2022-0080},
   Abstract = {We give a modification of Yu’s construction of
             supercuspidal representations of a connected reductive group
             G over a non-Archimedean local field F . This modification
             restores the validity of certain key intertwining property
             claims made by Yu, which were recently proved to be false
             for the original construction. This modification is also an
             essential ingredient in the construction of supercuspidal
             L-packets in a preprint by the second author. As further
             applications, we prove the stability and many instances of
             endoscopic character identities of these supercuspidal
             L-packets, subject to some conditions on the base field F .
             In particular, for regular supercuspidal parameters, we
             prove all instances of standard endoscopy. In addition, we
             prove that these supercuspidal L-packets satisfy a recent
             conjecture by the second author, which, together with
             standard endoscopy, uniquely characterizes the local
             Langlands correspondence for supercuspidal L-packets (again
             subject to the conditions on F ). These results are based on
             a statement of the Harish-Chandra character formula for the
             supercuspidal representations arising from the twisted Yu
             construction.},
   Doi = {10.1215/00127094-2022-0080},
   Key = {fds374131}
}

@article{fds374132,
   Author = {Fintzen, J},
   Title = {Supercuspidal representations in non-defining
             characteristics},
   Journal = {Journal of Algebra},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1016/j.jalgebra.2023.05.019},
   Abstract = {We show that a mod-ℓ-representation of a p-adic group
             arising from the analogue of Yu's construction is
             supercuspidal if and only if it arises from a supercuspidal
             representation of a finite reductive group. This has been
             previously shown by Henniart and Vigneras under the
             assumption that the second adjointness holds.},
   Doi = {10.1016/j.jalgebra.2023.05.019},
   Key = {fds374132}
}

@article{fds373671,
   Author = {Zhang, S and Lu, J and Zhao, H},
   Title = {On Enhancing Expressive Power via Compositions of Single
             Fixed-Size ReLU Network},
   Journal = {Proceedings of Machine Learning Research},
   Volume = {202},
   Pages = {41452-41487},
   Year = {2023},
   Month = {January},
   Abstract = {This paper explores the expressive power of deep neural
             networks through the framework of function compositions. We
             demonstrate that the repeated compositions of a single
             fixed-size ReLU network exhibit surprising expressive power,
             despite the limited expressive capabilities of the
             individual network itself. Specifically, we prove by
             construction that L2◦g◦r◦L1 can approximate
             1-Lipschitz continuous functions on [0, 1]d with an error
             O(r−1/d), where g is realized by a fixed-size ReLU
             network, L1 and L2 are two affine linear maps matching the
             dimensions, and g◦r denotes the r-times composition of g.
             Furthermore, we extend such a result to generic continuous
             functions on [0, 1]d with the approximation error
             characterized by the modulus of continuity. Our results
             reveal that a continuous-depth network generated via a
             dynamical system has immense approximation power even if its
             dynamics function is time-independent and realized by a
             fixed-size ReLU network.},
   Key = {fds373671}
}

@article{fds374491,
   Author = {Bréchet, P and Papagiannouli, K and An, J and Montúfar,
             G},
   Title = {Critical Points and Convergence Analysis of Generative Deep
             Linear Networks Trained with Bures-Wasserstein
             Loss},
   Journal = {Proceedings of Machine Learning Research},
   Volume = {202},
   Pages = {3106-3147},
   Year = {2023},
   Month = {January},
   Abstract = {We consider a deep matrix factorization model of covariance
             matrices trained with the Bures-Wasserstein distance. While
             recent works have made advances in the study of the
             optimization problem for overparametrized low-rank matrix
             approximation, much emphasis has been placed on
             discriminative settings and the square loss. In contrast,
             our model considers another type of loss and connects with
             the generative setting. We characterize the critical points
             and minimizers of the Bures-Wasserstein distance over the
             space of rank-bounded matrices. The Hessian of this loss at
             low-rank matrices can theoretically blow up, which creates
             challenges to analyze convergence of gradient optimization
             methods. We establish convergence results for gradient flow
             using a smooth perturbative version of the loss as well as
             convergence results for finite step size gradient descent
             under certain assumptions on the initial
             weights.},
   Key = {fds374491}
}

@article{fds372374,
   Author = {Fang, D and Lin, L and Tong, Y},
   Title = {Time-marching based quantum solvers for time-dependent
             linear differential equations},
   Journal = {Quantum},
   Volume = {7},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.22331/Q-2023-03-20-955},
   Abstract = {The time-marching strategy, which propagates the solution
             from one time step to the next, is a natural strategy for
             solving time-dependent differential equations on classical
             computers, as well as for solving the Hamiltonian simulation
             problem on quantum computers. For more general homogeneous
             linear differential equations ddt|ψ(t)i = A(t)|ψ(t)i,
             |ψ(0)i = |ψ0i, a time-marching based quantum solver can
             suffer from exponentially vanishing success probability with
             respect to the number of time steps and is thus considered
             impractical. We solve this problem by repeatedly invoking a
             technique called the uniform singular value amplification,
             and the overall success probability can be lower bounded by
             a quantity that is independent of the number of time steps.
             The success probability can be further improved using a
             compression gadget lemma. This provides a path of designing
             quantum differential equation solvers that is alternative to
             those based on quantum linear systems algorithms (QLSA). We
             demonstrate the performance of the time-marching strategy
             with a high-order integrator based on the truncated Dyson
             series. The complexity of the algorithm depends linearly on
             the amplification ratio, which quantifies the deviation from
             a unitary dynamics. We prove that the linear dependence on
             the amplification ratio attains the query complexity lower
             bound and thus cannot be improved in the worst case. This
             algorithm also surpasses existing QLSA based solvers in
             three aspects: (1) A(t) does not need to be diagonalizable.
             (2) A(t) can be non-smooth, and is only of bounded
             variation. (3) It can use fewer queries to the initial state
             |ψ0i. Finally, we demonstrate the time-marching strategy
             with a first-order truncated Magnus series, which simplifies
             the implementation compared to high-order truncated Dyson
             series approach, while retaining the aforementioned
             benefits. Our analysis also raises some open questions
             concerning the differences between time-marching and QLSA
             based methods for solving differential equations.},
   Doi = {10.22331/Q-2023-03-20-955},
   Key = {fds372374}
}

@article{fds372442,
   Author = {Bezemek, ZW and Spiliopoulos, K},
   Title = {Moderate deviations for fully coupled multiscale weakly
             interacting particle systems},
   Journal = {Stochastics and Partial Differential Equations: Analysis and
             Computations},
   Publisher = {Springer Science and Business Media LLC},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1007/s40072-023-00301-0},
   Abstract = {We consider a collection of fully coupled weakly interacting
             diffusion processes moving in a two-scale environment. We
             study the moderate deviations principle of the empirical
             distribution of the particles’ positions in the combined
             limit as the number of particles grow to infinity and the
             time-scale separation parameter goes to zero simultaneously.
             We make use of weak convergence methods, which provide a
             convenient representation for the moderate deviations rate
             function in a variational form in terms of an effective mean
             field control problem. We rigorously obtain equivalent
             representation for the moderate deviations rate function in
             an appropriate “negative Sobolev” form, proving their
             equivalence, which is reminiscent of the large deviations
             rate function form for the empirical measure of weakly
             interacting diffusions obtained in the 1987 seminal paper by
             Dawson–Gärtner. In the course of the proof we obtain
             related ergodic theorems and we consider the regularity of
             Poisson type of equations associated to McKean–Vlasov
             problems, both of which are topics of independent interest.
             A novel “doubled corrector problem” is introduced in
             order to control derivatives in the measure arguments of the
             solutions to the related Poisson equations used to control
             behavior of fluctuation terms.},
   Doi = {10.1007/s40072-023-00301-0},
   Key = {fds372442}
}

@article{fds372444,
   Author = {Bezemek, ZW and Spiliopoulos, K},
   Title = {Large deviations for interacting multiscale particle
             systems},
   Journal = {Stochastic Processes and their Applications},
   Volume = {155},
   Pages = {27-108},
   Publisher = {Elsevier BV},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1016/j.spa.2022.09.010},
   Abstract = {We consider a collection of weakly interacting diffusion
             processes moving in a two-scale locally periodic
             environment. We study the large deviations principle of the
             empirical distribution of the particles’ positions in the
             combined limit as the number of particles grow to infinity
             and the time-scale separation parameter goes to zero. We
             make use of weak convergence methods providing a convenient
             representation for the large deviations rate function, which
             allow us to characterize the effective controlled mean field
             dynamics. In addition, we rigorously obtain equivalent
             non-variational representations for the large deviations
             rate function as introduced by Dawson–Gärtner.},
   Doi = {10.1016/j.spa.2022.09.010},
   Key = {fds372444}
}

@article{fds374606,
   Author = {Lang, Q and Lu, F},
   Title = {IDENTIFIABILITY OF INTERACTION KERNELS IN MEAN-FIELD
             EQUATIONS OF INTERACTING PARTICLES},
   Journal = {Foundations of Data Science},
   Volume = {5},
   Pages = {480-502},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.3934/fods.2023007},
   Abstract = {This study examines the identifiability of interaction
             kernels in mean-field equations of interacting particles or
             agents, an area of growing interest across various
             scientific and engineering fields. The main focus is
             identifying data-dependent function spaces where a quadratic
             loss functional possesses a unique minimizer. We consider
             two data-adaptive L2 spaces: one weighted by a data-adaptive
             measure and the other using the Lebesgue measure. In each L2
             space, we show that the function space of identifiability is
             the closure of the RKHS associated with the integral
             operator of inversion. Alongside prior research, our study
             completes a full characterization of identifiability in
             interacting particle systems with either finite or infinite
             particles, highlighting critical differences between these
             two settings. Moreover, the identifiability analysis has
             important implications for computational practice. It shows
             that the inverse problem is ill-posed, necessitating
             regularization. Our numerical demonstrations show that the
             weighted L2 space is preferable over the unweighted L2
             space, as it yields more accurate regularized
             estimators.},
   Doi = {10.3934/fods.2023007},
   Key = {fds374606}
}

@article{fds372526,
   Author = {Conlon, D and Fox, J and Sudakov, B and Wei, F},
   Title = {Threshold Ramsey multiplicity for paths and even
             cycles},
   Journal = {European Journal of Combinatorics},
   Volume = {107},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.1016/j.ejc.2022.103612},
   Abstract = {The Ramsey number r(H) of a graph H is the minimum integer n
             such that any two-coloring of the edges of the complete
             graph Kn contains a monochromatic copy of H. While this
             definition only asks for a single monochromatic copy of H,
             it is often the case that every two-edge-coloring of the
             complete graph on r(H) vertices contains many monochromatic
             copies of H. The minimum number of such copies over all
             two-colorings of Kr(H) will be referred to as the threshold
             Ramsey multiplicity of H. Addressing a problem of Harary and
             Prins, who were the first to systematically study this
             quantity, we show that there is a positive constant c such
             that the threshold Ramsey multiplicity of a path or an even
             cycle on k vertices is at least (ck)k. This bound is tight
             up to the constant c. We prove a similar result for odd
             cycles in a companion paper.},
   Doi = {10.1016/j.ejc.2022.103612},
   Key = {fds372526}
}

@article{fds374607,
   Author = {Przybyłlo, J and Wei, F},
   Title = {Short Proof of the Asymptotic Confirmation of the
             Faudree-Lehel Conjecture},
   Journal = {Electronic Journal of Combinatorics},
   Volume = {30},
   Number = {4},
   Year = {2023},
   Month = {January},
   url = {http://dx.doi.org/10.37236/11413},
   Abstract = {Given a simple graph G, the irregularity strength of G,
             denoted s(G), is the least positive integer k such that
             there is a weight assignment on edges (Formula present) for
             which each vertex weight (Formula present) is unique amongst
             all (Formula present). In 1987, Faudree and Lehel
             conjectured that there is a constant c such that (Formula
             present) for all d-regular graphs G on n vertices with d >
             1, whereas it is trivial that (Formula present) In this
             short note we prove that the Faudree-Lehel Conjecture holds
             (Formula present) for any fixed ɛ > 0, with a small
             additive constant c = 28 for n large enough. Furthermore, we
             confirm the conjecture asymptotically by proving that for
             any fixed (Formula present) there is a constant C such that
             for all d-regular graphs (Formula present) extending and
             improving a recent result of Przybyłlo that (Formula
             present) and n is large enough.},
   Doi = {10.37236/11413},
   Key = {fds374607}
}

@article{fds374583,
   Author = {B. Li and J. Lu},
   Title = {Quantum variational embedding for ground-state energy
             problems: sum of squares and cluster selection},
   Year = {2023},
   url = {http://arxiv.org/abs/2305.18571},
   Key = {fds374583}
}

@article{fds370635,
   Author = {Young, AL and van den Boom, W and Schroeder, RA and Krishnamoorthy,
             V and Raghunathan, K and Wu, H-T and Dunson, DB},
   Title = {Mutual information: Measuring nonlinear dependence in
             longitudinal epidemiological data.},
   Journal = {PLoS One},
   Volume = {18},
   Number = {4},
   Pages = {e0284904},
   Year = {2023},
   url = {http://dx.doi.org/10.1371/journal.pone.0284904},
   Abstract = {Given a large clinical database of longitudinal patient
             information including many covariates, it is computationally
             prohibitive to consider all types of interdependence between
             patient variables of interest. This challenge motivates the
             use of mutual information (MI), a statistical summary of
             data interdependence with appealing properties that make it
             a suitable alternative or addition to correlation for
             identifying relationships in data. MI: (i) captures all
             types of dependence, both linear and nonlinear, (ii) is zero
             only when random variables are independent, (iii) serves as
             a measure of relationship strength (similar to but more
             general than R2), and (iv) is interpreted the same way for
             numerical and categorical data. Unfortunately, MI typically
             receives little to no attention in introductory statistics
             courses and is more difficult than correlation to estimate
             from data. In this article, we motivate the use of MI in the
             analyses of epidemiologic data, while providing a general
             introduction to estimation and interpretation. We illustrate
             its utility through a retrospective study relating
             intraoperative heart rate (HR) and mean arterial pressure
             (MAP). We: (i) show postoperative mortality is associated
             with decreased MI between HR and MAP and (ii) improve
             existing postoperative mortality risk assessment by
             including MI and additional hemodynamic statistics.},
   Doi = {10.1371/journal.pone.0284904},
   Key = {fds370635}
}

@article{fds374861,
   Author = {Gao, Y and Liu, J-G and Li, W},
   Title = {Master equations for finite state mean field games with
             nonlinear activations},
   Journal = {Discrete and Continuous Dynamical Systems -
             B},
   Publisher = {American Institute of Mathematical Sciences
             (AIMS)},
   Year = {2023},
   url = {http://dx.doi.org/10.3934/dcdsb.2023204},
   Doi = {10.3934/dcdsb.2023204},
   Key = {fds374861}
}

@article{fds373606,
   Author = {Gao, Y and Liu, J-G},
   Title = {Stochastic Chemical Reaction Systems in Biology},
   Journal = {SIAM REVIEW},
   Volume = {65},
   Number = {2},
   Pages = {593-+},
   Year = {2023},
   Key = {fds373606}
}

@article{fds360099,
   Author = {Miller, E},
   Title = {Stratifications of real vector spaces from constructible
             sheaves with conical microsupport},
   Journal = {Journal of Applied and Computational Topology},
   Volume = {7},
   Number = {3},
   Pages = {473-489},
   Publisher = {Springer},
   Year = {2023},
   url = {http://dx.doi.org/10.1007/s41468-023-00112-1},
   Doi = {10.1007/s41468-023-00112-1},
   Key = {fds360099}
}

@article{fds374550,
   Author = {Miller, E and Geist, N},
   Title = {Global dimension of real-exponent polynomial
             rings},
   Journal = {Algebra and Number Theory},
   Volume = {17},
   Number = {10},
   Pages = {1779-1788},
   Publisher = {Mathematical Sciences Publishers (MSP)},
   Year = {2023},
   url = {http://dx.doi.org/10.2140/ant.2023.17.1779},
   Doi = {10.2140/ant.2023.17.1779},
   Key = {fds374550}
}

@article{fds370133,
   Author = {Etzioni, R and Gulati, R and Owens, L and Lange, J and Ryser,
             MD},
   Title = {Opportunity for interception as a driver of benefit in
             cancer early detection: implications for multi-cancer early
             detection testing.},
   Journal = {CANCER PREVENTION RESEARCH},
   Volume = {16},
   Number = {1},
   Pages = {6-6},
   Year = {2023},
   Key = {fds370133}
}

@article{fds373669,
   Author = {Lynch, T and Basila, D and Schnitt, SJ and Marks, JR and Strand, SH and Hyslop, T and Badve, SS and Watson, MA and Le-Petross, HT and Grimm, L and West, RB and Weiss, A and Rapperport, A and King, L and Factor, RE and Ryser, MD and Partridge, AH and Hwang, E-SS and Thompson, AM and Collyar, DE},
   Title = {From the lab to the clinic: Lessons learned from a
             translational working group.},
   Journal = {JOURNAL OF CLINICAL ONCOLOGY},
   Volume = {41},
   Number = {16},
   Year = {2023},
   Key = {fds373669}
}

@article{fds359483,
   Author = {Babu, PRK and Di Martino and JM and Chang, Z and Perochon, S and Carpenter,
             KLH and Compton, S and Espinosa, S and Dawson, G and Sapiro,
             G},
   Title = {Exploring Complexity of Facial Dynamics in Autism Spectrum
             Disorder.},
   Journal = {IEEE Trans Affect Comput},
   Volume = {14},
   Number = {2},
   Pages = {919-930},
   Year = {2023},
   url = {http://dx.doi.org/10.1109/taffc.2021.3113876},
   Abstract = {Atypical facial expression is one of the early symptoms of
             autism spectrum disorder (ASD) characterized by reduced
             regularity and lack of coordination of facial movements.
             Automatic quantification of these behaviors can offer novel
             biomarkers for screening, diagnosis, and treatment
             monitoring of ASD. In this work, 40 toddlers with ASD and
             396 typically developing toddlers were shown
             developmentally-appropriate and engaging movies presented on
             a smart tablet during a well-child pediatric visit. The
             movies consisted of social and non-social dynamic scenes
             designed to evoke certain behavioral and affective
             responses. The front-facing camera of the tablet was used to
             capture the toddlers' face. Facial landmarks' dynamics were
             then automatically computed using computer vision
             algorithms. Subsequently, the complexity of the landmarks'
             dynamics was estimated for the eyebrows and mouth regions
             using multiscale entropy. Compared to typically developing
             toddlers, toddlers with ASD showed higher complexity (i.e.,
             less predictability) in these landmarks' dynamics. This
             complexity in facial dynamics contained novel information
             not captured by traditional facial affect analyses. These
             results suggest that computer vision analysis of facial
             landmark movements is a promising approach for detecting and
             quantifying early behavioral symptoms associated with
             ASD.},
   Doi = {10.1109/taffc.2021.3113876},
   Key = {fds359483}
}

@article{fds368049,
   Author = {Garrett, BL and Rudin, C},
   Title = {The Right to a Glass Box: Rethinking the Use of Artificial
             Intelligence in Criminal Justice},
   Journal = {Cornell Law Review},
   Year = {2023},
   Key = {fds368049}
}

@article{fds370611,
   Author = {Young, AL and van den Boom, W and Schroeder, RA and Krishnamoorthy,
             V and Raghunathan, K and Wu, H-T and Dunson, DB},
   Title = {Mutual information: Measuring nonlinear dependence in
             longitudinal epidemiological data.},
   Journal = {PLoS One},
   Volume = {18},
   Number = {4},
   Pages = {e0284904},
   Year = {2023},
   url = {http://dx.doi.org/10.1371/journal.pone.0284904},
   Abstract = {Given a large clinical database of longitudinal patient
             information including many covariates, it is computationally
             prohibitive to consider all types of interdependence between
             patient variables of interest. This challenge motivates the
             use of mutual information (MI), a statistical summary of
             data interdependence with appealing properties that make it
             a suitable alternative or addition to correlation for
             identifying relationships in data. MI: (i) captures all
             types of dependence, both linear and nonlinear, (ii) is zero
             only when random variables are independent, (iii) serves as
             a measure of relationship strength (similar to but more
             general than R2), and (iv) is interpreted the same way for
             numerical and categorical data. Unfortunately, MI typically
             receives little to no attention in introductory statistics
             courses and is more difficult than correlation to estimate
             from data. In this article, we motivate the use of MI in the
             analyses of epidemiologic data, while providing a general
             introduction to estimation and interpretation. We illustrate
             its utility through a retrospective study relating
             intraoperative heart rate (HR) and mean arterial pressure
             (MAP). We: (i) show postoperative mortality is associated
             with decreased MI between HR and MAP and (ii) improve
             existing postoperative mortality risk assessment by
             including MI and additional hemodynamic statistics.},
   Doi = {10.1371/journal.pone.0284904},
   Key = {fds370611}
}

@article{fds374521,
   Author = {Zhao, H and Zhong, Y},
   Title = {How much can one learn from a single solution of a
             PDE?},
   Journal = {Pure and Applied Functional Analysis},
   Volume = {8},
   Number = {2},
   Pages = {751-773},
   Year = {2023},
   Key = {fds374521}
}

@article{fds372521,
   Author = {Hou, L and Ji, Z and Wang, J and Xie, J},
   Title = {Editorial: Statistical and computational methods for
             single-cell sequencing analysis.},
   Journal = {Front Genet},
   Volume = {14},
   Pages = {1235174},
   Year = {2023},
   url = {http://dx.doi.org/10.3389/fgene.2023.1235174},
   Doi = {10.3389/fgene.2023.1235174},
   Key = {fds372521}
}

@article{fds376402,
   Author = {Li, X and Sung, AD and Xie, J},
   Title = {DART: Distance Assisted Recursive Testing},
   Journal = {JOURNAL OF MACHINE LEARNING RESEARCH},
   Volume = {24},
   Year = {2023},
   Key = {fds376402}
}

@inproceedings{MM:EEG,
   Author = {E Causevic and R~R Coifman and R Isenhart and A Jacquin and E~R John and M Maggioni and L~S Prichep and F~J
             Warner},
   Title = {{QEEG}-based classification with wavelet packets and
             microstate features for triage applications in the
             {ER}},
   Year = {2005},
   Key = {MM:EEG}
}

@inproceedings{csb05-poly,
   Author = {L. Wang and R. Mettu and B. R. Donald},
   Title = {An Algebraic Geometry Approach to Protein Backbone Structure
             Determination from {NMR} Data},
   Pages = {235--246},
   Booktitle = {Proceedings of the {IEEE} Computational Systems
             Bioinformatics Conference ({CSB})},
   Address = {Stanford, CA},
   Year = {2005},
   Key = {csb05-poly}
}

@inproceedings{isrr-05,
   Author = {B. R. Donald and C. Levey and C. McGray and I. Paprotny and D. Rus},
   Title = {A Steerable, Untethered, 250 $\times$ 60 $\mu$m {MEMS}
             Mobile Micro-Robot},
   Booktitle = {Proceedings of the 12th {\it International Symposium of
             Robotics Research (ISRR)}},
   Address = {San Francisco, CA.},
   Year = {2005},
   Key = {isrr-05}
}

@booklet{Greer04a,
   Author = {J. B. Greer and A. L. Bertozzi},
   Title = {H-1 solutions of a class of fourth order nonlinear equations
             for image processing},
   Journal = {Discrete And Continuous Dynamical Systems},
   Volume = {10},
   Number = {1-2},
   Pages = {349 -- 366},
   Year = {2004},
   Key = {Greer04a}
}

@misc{PathNIH2004,
   Author = {GL Davis and Mauro Maggioni and FJ Warner and FB Geshwind and AC Coppi and RA DeVerse and RR Coifman},
   Title = {Hyper-spectral Analysis of normal and malignant colon tissue
             microarray sections using a novel DMD system},
   Year = {2004},
   Key = {PathNIH2004}
}

@techreport{CMTech,
   Author = {Ronald R Coifman and Mauro Maggioni},
   Title = {Multiresolution Analysis associated to diffusion semigroups:
             construction and fast algorithms},
   Number = {YALE/DCS/TR-1289},
   Organization = {Dept. Comp. Sci., Yale University},
   Institution = {Dept. Comp. Sci., Yale University},
   Year = {2004},
   Key = {CMTech}
}

@misc{gordon04-poster-langmead,
   Author = {C. Langmead and B. R. Donald},
   Title = {A Framework for Automated {NMR} Resonance Assignments and
             3{D} Structural Homology Detection},
   Address = {Ventura, CA},
   Year = {2004},
   Key = {gordon04-poster-langmead}
}

@misc{gordon04-poster-wang,
   Author = {L. Wang and R. Mettu and R. Lilien and A. Yan and B. R.
             Donald},
   Title = {Exact Solutions for Internuclear Vectors and Dihedral Angles
             from Two {RDC}s and Their Application in a Systematic Search
             Algorithm for Determining Protein Backbone
             Structure},
   Address = {Ventura, CA},
   Year = {2004},
   Key = {gordon04-poster-wang}
}

@techreport{Dartmouth:TR2004-492,
   Author = {Ryan H. Lilien and Mohini Sridharan and Bruce R.
             Donald},
   Title = {{Identification of Novel Small Molecule Inhibitors of
             Core-Binding Factor Dimerization by Computational Screening
             against NMR Molecular Ensembles}},
   Number = {TR2004-492},
   Organization = {Dartmouth College, Computer Science},
   Institution = {Dartmouth College, Computer Science},
   Address = {Hanover, NH},
   Year = {2004},
   url = {ftp://ftp.cs.dartmouth.edu/TR/TR2004-492.pdf},
   Key = {Dartmouth:TR2004-492}
}

@inproceedings{wafr04,
   Author = {B. R. Donald},
   Title = {Plenary lecture: {Algorithmic} Challenges in Structural
             Molecular Biology and Proteomics},
   Pages = {1--10},
   Booktitle = {Proceedings of the Sixth International Workshop on the
             Algorithmic Foundations of Robotics (WAFR)},
   Publisher = {University of Utrecht},
   Address = {Utrecht/Zeist, The Netherlands},
   Year = {2004},
   Key = {wafr04}
}

@inproceedings{recomb-04langmead-poster,
   Author = {C. Langmead and B. R. Donald},
   Title = {High-Throughput 3{D} homology Detection via {NMR} Resonance
             Assignment},
   Series = {Eighth Annual International Conference on Research in
             Computational Molecular Biology ({RECOMB})},
   Pages = {522},
   Booktitle = {Currents in Computational Molecular Biology,
             2004},
   Address = {San Diego},
   Editor = {A. Gramada and P. Bourne},
   Year = {2004},
   Key = {recomb-04langmead-poster}
}

@inproceedings{recomb-04a,
   Author = {A. Yan and C. Langmead and B. R. Donald},
   Title = {A Probability-Based Similarity Measure for Saupe Alignment
             Tensors with Applications to Residual Dipolar Couplings in
             {NMR} Structural Biology},
   Series = {Eighth Annual International Conference on Research in
             Computational Molecular Biology ({RECOMB})},
   Pages = {437--438},
   Booktitle = {Currents in Computational Molecular Biology,
             2004},
   Address = {San Diego},
   Editor = {A. Gramada and P. Bourne},
   Year = {2004},
   Key = {recomb-04a}
}

@inproceedings{WangDonald-csb04,
   Author = {L. Wang and B. R. Donald},
   Title = {Analysis of a Systematic Search-Based Algorithm for
             Determining Protein Backbone Structure from a Minimal Number
             of Residual Dipolar Couplings},
   Pages = {319--330},
   Booktitle = {Proceedings of the {IEEE} Computational Systems
             Bioinformatics Conference ({CSB})},
   Address = {Stanford, CA},
   Year = {2004},
   Key = {WangDonald-csb04}
}

@misc{acs03-poster-anderson,
   Author = {A. Anderson and R. Lilien and V. Popov and B. R.
             Donald},
   Title = {Ensembles of Active Site Conformations Allow Structure-Based
             Redesign and Drug Design},
   Address = {New Orleans},
   Year = {2003},
   Key = {acs03-poster-anderson}
}

@techreport{Dartmouth:TR2004-494,
   Author = {Christopher J. Langmead and Bruce R. Donald},
   Title = {{An Improved Nuclear Vector Replacement Algorithm for
             Nuclear Magnetic Resonance Assignment}},
   Number = {TR2004-494},
   Organization = {Dartmouth College, Computer Science},
   Institution = {Dartmouth College, Computer Science},
   Address = {Hanover, NH},
   Year = {2003},
   url = {ftp://ftp.cs.dartmouth.edu/TR/TR2004-494.pdf},
   Key = {Dartmouth:TR2004-494}
}

@inproceedings{isrr-03,
   Author = {B. R. Donald and C. Levey and C. McGray and D. Rus and M.
             Sinclair},
   Title = {Untethered Micro-Actuators for Autonomous Micro-robot
             Locomotion: Design, Fabrication, Control, and
             Performance},
   Booktitle = {Proceedings of the 11th {\it International Symposium of
             Robotics Research}},
   Address = {Siena, Italy},
   Year = {2003},
   Key = {isrr-03}
}

@inproceedings{recomb02-poster,
   Author = {R. Lilien and A. Anderson and B. Donald},
   Title = {Modeling Protein Flexibility for Structure-Based Active Site
             Redesign},
   Series = {The Sixth Annual International Conference on Research in
             Computational Molecular Biology (RECOMB)},
   Pages = {122-123},
   Booktitle = {Currents in Computational Molecular Biology},
   Address = {Washington DC},
   Editor = {L. Florea and others},
   Year = {2002},
   Key = {recomb02-poster}
}

@misc{ismb00-poster-langmead,
   Author = {C. J. Langmead and B. R. Donald},
   Title = {Time-frequency Analysis of Protein {NMR}
             Data},
   Year = {2000},
   Key = {ismb00-poster-langmead}
}

@misc{ismb00-poster-cbk,
   Author = {C. Bailey-Kellogg and A. Widge and J. J. {Kelley III} and M.
             J. Berardi and J. H. Bushweller and B. R.
             Donald},
   Title = {The {NOESY} {Jigsaw}: Automated Protein Secondary Structure
             and Main-Chain Assignment from Sparse, Unassigned {NMR}
             Data},
   Year = {2000},
   Key = {ismb00-poster-cbk}
}

@misc{ismb00-poster-lilien,
   Author = {R. Lilien and M. Sridharan and X. Huang and J. H. Bushweller and B. R. Donald},
   Title = {Computational Screening Studies for Core Binding Factor
             Beta: Use of Multiple Conformations to Model Receptor
             Flexibility},
   Year = {2000},
   Key = {ismb00-poster-lilien}
}

@inproceedings{recomb00,
   Author = {C. Bailey-Kellogg and A. Widge and J. J. {Kelley III} and M.
             J. Berardi and J. H. Bushweller and B. R.
             Donald},
   Title = {The {NOESY} {Jigsaw}: Automated Protein Secondary Structure
             and Main-Chain Assignment from Sparse, Unassigned {NMR}
             Data},
   Pages = {33--44},
   Booktitle = {The Fourth Annual International Conference on Research in
             Computational Molecular Biology ({RECOMB-2000})},
   Year = {2000},
   Key = {recomb00}
}

@inproceedings{BaileyKelloggZhaoDonald00,
   Author = {C. Bailey-Kellogg and F. Zhao and B. R. Donald},
   Title = {Spatial Aggregation in Scientific Data Mining},
   Booktitle = {Proceedings of the First {SIAM} Conference on Computational
             Science and Engineering},
   Address = {Washington, DC},
   Year = {2000},
   Key = {BaileyKelloggZhaoDonald00}
}

@article{BohringerDonaldMacDonald97A,
   Author = {K.-F.~B{\"o}hringer and B.~R.~Donald and N.~C.~MacDonald},
   Title = {{\em Programmable Vector Fields for Distributed
             Manipulation, with Applications to MEMS Actuator Arrays and
             Vibratory Parts Feeders}},
   Journal = {International Journal of Robotics Research},
   Volume = {18},
   Number = {2},
   Year = {1999},
   Key = {BohringerDonaldMacDonald97A}
}

@inproceedings{icra99b,
   Author = {K.-F.~B{\"o}hringer and B.~R.~Donald and F.~Lamiraux and L.~Kavraki},
   Title = {Part Orientation with One or Two Stable Equilibria Using
             Programmable Force Fields},
   Booktitle = {IEEE International Conference on Robotics and Automation,
             Workshop on Distributed Manipulation},
   Year = {1999},
   Key = {icra99b}
}

@inproceedings{BohringerLamirauxKavrakiDonald99,
   Author = {K.-F. B{\"o}hringer and B. R. Donald and F. Lamiraux and L.
             Kavraki},
   Title = {Part Orientation with One or Two Stable Equilibria Using
             Programmable Vector Fields},
   Booktitle = {{IEEE} International Conference on Robotics and Automation,
             Workshop on Distributed Manipulation},
   Address = {Detroit},
   Year = {1999},
   Key = {BohringerLamirauxKavrakiDonald99}
}

@inproceedings{isrr99,
   Author = {K.-F.~B{\"o}hringer and B.~R.~Donald and F.~Lamiraux and L.~Kavraki},
   Title = {A Single Universal Force Field Can Uniquely Pose Any Part Up
             To Symmetry},
   Booktitle = {9th International Symposium of Robotics Research
             (ISRR)},
   Year = {1999},
   Key = {isrr99}
}

@inproceedings{GariepyRusDonald99B,
   Author = {B. R. Donald and L. Gariepy and D. Rus},
   Title = {Experiments in Constrained Prehensile Manipulation:
             Distributed Manipulation with Ropes},
   Booktitle = {{IEEE} International Conference on Robotics and Automation,
             Workshop on Distributed Manipulation},
   Address = {Detroit},
   Year = {1999},
   Key = {GariepyRusDonald99B}
}

@inproceedings{icra99a,
   Author = {J.~Suh and B.~Darling and K.-F.~B{\"o}hringer and B.~R.~Donald and H.~Baltes and G.~Kovacs},
   Title = {{CMOS} Integrated Organic Ciliary Actuator Array as a
             General-Purpose Micromanipulation Tool},
   Booktitle = {IEEE International Conference on Robotics and Automation,
             Workshop on Distributed Manipulation},
   Year = {1999},
   Key = {icra99a}
}

@inproceedings{BohringerDonald98,
   Author = {K.-F. B{\"o}hringer and B. R. Donald},
   Title = {Algorithmic {MEMS}},
   Booktitle = {Proceedings of the 3rd International Workshop on the
             Algorithmic Foundations of Robotics {WAFR}},
   Address = {Houston, TX},
   Year = {1998},
   Key = {BohringerDonald98}
}

@inproceedings{BriggsDonald96,
   Author = {A. Briggs and B. R. Donald},
   Title = {Robust Geometric Algorithms for Sensor Planning},
   Booktitle = {Proceedings of the International Workshop on the Algorithmic
             Foundations of Robotics {WAFR}},
   Address = {Toulouse, France},
   Year = {1996},
   Key = {BriggsDonald96}
}

@misc{JenningsRusDonald-poster-94,
   Author = {B. R. Donald and J. Jennings and D. Rus},
   Title = {Cooperating Autonomous Mobile Robots: Theory and
             Experiments},
   Address = {{MIT}, Cambridge, MA},
   Year = {1994},
   Key = {JenningsRusDonald-poster-94}
}

@article{PaiDonald93,
   Author = {B. R. Donald and D. Pai},
   Title = {The Motion of Planar Compliantly-Connected Rigid Bodies in
             Contact, with Applications to Automatic Fastening},
   Journal = {International Journal of Robotics Research},
   Volume = {12},
   Number = {4},
   Pages = {307--338},
   Year = {1993},
   Key = {PaiDonald93}
}

@inproceedings{BrownChewDonald93,
   Author = {R. Brown and P. Chew and B. R. Donald},
   Title = {Mobile Robots, Map-making, Shape Metrics, and
             Localization},
   Booktitle = {Proceedings of the International Association of Science and
             Technology for Development ({IASTED}) International
             Conference on Robotics and Manufacturing},
   Address = {Oxford, England},
   Year = {1993},
   Key = {BrownChewDonald93}
}

@inproceedings{JenningsRusDonald93B,
   Author = {J. Jennings and D. Rus and B. R. Donald},
   Title = {Experimental Information Invariants for Cooperating
             Autonomous Mobile Robots},
   Booktitle = {Proceedings of the International Joint Conference on
             Artificial Intelligence ({IJCAI}) Workshop on Dynamically
             Interacting Robots},
   Address = {Chambery, France},
   Year = {1993},
   Key = {JenningsRusDonald93B}
}

@inproceedings{JenningsRusDonald93A,
   Author = {B. R. Donald and J. Jennings and D. Rus},
   Title = {Towards a Theory of Information Invariants for Cooperating
             Autonomous Mobile Robots},
   Booktitle = {Proceedings of the International Symposium of Robotics
             Research {ISRR}},
   Address = {Hidden Valley, PA},
   Year = {1993},
   Key = {JenningsRusDonald93A}
}

@article{ieee-92,
   Author = {B. R. Donald},
   Title = {Robot Motion Planning},
   Journal = {{IEEE} Trans. on Robotics and Automation},
   Volume = {8},
   Number = {2},
   Year = {1992},
   Key = {ieee-92}
}

@inproceedings{CannyResslerDonald92,
   Author = {J. Canny and B. R. Donald and G. Ressler},
   Title = {A Rational Rotation Method for Robust Geometric
             Algorithms},
   Pages = {251--260},
   Booktitle = {Proc. {ACM} Symposium on Computational Geometry},
   Address = {Berlin},
   Year = {1992},
   Key = {CannyResslerDonald92}
}

@inproceedings{JenningsDonald91A,
   Author = {J. Jennings and B. R. Donald},
   Title = {Programming Autonomous Agents: A theory of Perceptual
             Equivalence},
   Booktitle = {Proceedings of the 1st {AAAI} Fall Symposium on Sensory
             Aspects of Robotic Intelligence},
   Address = {Asilomar, CA},
   Year = {1991},
   Key = {JenningsDonald91A}
}

@inproceedings{XavierDonald89B,
   Author = {B. R. Donald and P. Xavier},
   Title = {A Provably Good Approximation Algorithm for Optimal-Time
             Trajectory Planning},
   Pages = {958--964},
   Booktitle = {Proc. {IEEE} International Conference on Robotics and
             Automation},
   Address = {Scottsdale, AZ},
   Year = {1989},
   Key = {XavierDonald89B}
}

@inproceedings{Donald88,
   Author = {B. R. Donald},
   Title = {The Complexity of Planar Compliant Motion Planning with
             Uncertainty},
   Pages = {309--318},
   Booktitle = {Proc. 4th {ACM} Symposium on Computational
             Geometry},
   Address = {Urbana. IL},
   Year = {1988},
   Key = {Donald88}
}

@inproceedings{Donald86A,
   Author = {B. R. Donald},
   Title = {A Theory of Error Detection and Recovery: Robot Motion
             Planning with Uncertainty in the Geometric Models of the
             Robot and Environment},
   Booktitle = {Proceedings of the International Workshop on Geometric
             Reasoning},
   Address = {Oxford University, England},
   Year = {1986},
   Key = {Donald86A}
}

 

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Mathematics Department
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