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Mathematics : Publications since January 2019

List all publications in the database.    :chronological  alphabetical  combined  bibtex listing:

Agarwal, Pankaj K.

  1. Lowe, A; Agarwal, PK, Flood-risk analysis on terrains under the multiflow-direction model, Acm Transactions on Spatial Algorithms and Systems, vol. 5 no. 4 (September, 2019) [doi]  [abs]
  2. Agarwal, PK; Chang, HC; Xiao, A, Efficient algorithms for geometric partial matching, Leibniz International Proceedings in Informatics, Lipics, vol. 129 (June, 2019), ISBN 9783959771047 [doi]  [abs]
  3. Agarwal, PK; Aronov, B; Ezra, E; Zahl, J, An efficient algorithm for generalized polynomial partitioning and its applications, Leibniz International Proceedings in Informatics, Lipics, vol. 129 (June, 2019), ISBN 9783959771047 [doi]  [abs]
  4. Agarwal, PK; Cohen, R; Halperin, D; Mulzer, W, Maintaining the union of unit discs under insertions with near-optimal overhead, Leibniz International Proceedings in Informatics, Lipics, vol. 129 (June, 2019), ISBN 9783959771047 [doi]  [abs]
  5. Rav, M; Lowe, A; Agarwal, PK, Flood risk analysis on terrains, Acm Transactions on Spatial Algorithms and Systems, vol. 5 no. 1 (May, 2019) [doi]  [abs]
  6. Agarwal, PK; Pan, J, Near-Linear Algorithms for Geometric Hitting Sets and Set Covers, Discrete & Computational Geometry (January, 2019) [doi]  [abs]

Agazzi, Andrea

  1. Li, L; Krznar, P; Erban, A; Agazzi, A; Martin-Levilain, J; Supale, S; Kopka, J; Zamboni, N; Maechler, P, Metabolomics Identifies a Biomarker Revealing In Vivo Loss of Functional β-Cell Mass Before Diabetes Onset., Diabetes, vol. 68 no. 12 (December, 2019), pp. 2272-2286 [doi]  [abs]

Akin, Victoria S

  1. Akin, V, An algebraic characterization of the point-pushing subgroup, Journal of Algebra, vol. 541 (January, 2020), pp. 98-125 [doi]  [abs]

Autry, Eric A.

  1. Clifton, SM; Hill, K; Karamchandani, AJ; Autry, EA; McMahon, P; Sun, G, Mathematical model of gender bias and homophily in professional hierarchies., Chaos (Woodbury, N.Y.), vol. 29 no. 2 (February, 2019), pp. 023135 [doi]  [abs]

Beale, J. Thomas

  1. Tlupova, S; Beale, JT, Regularized single and double layer integrals in 3D Stokes flow, Journal of Computational Physics, vol. 386 (June, 2019), pp. 568-584 [doi]  [abs]
  2. Beale, JT; Ying, W, Solution of the Dirichlet problem by a finite difference analog of the boundary integral equation, Numerische Mathematik, vol. 141 no. 3 (March, 2019), pp. 605-626 [doi]  [abs]

Beckman, Erin

  1. Beckman, E; Frank, N; Jiang, Y; Junge, M; Tang, S, The frog model on trees with drift, Electronic Communications in Probability, vol. 24 (January, 2019), Institute of Mathematical Statistics [doi]  [abs]

Bendich, Paul L

  1. Bendich, P; Bubenik, P; Wagner, A, Stabilizing the unstable output of persistent homology computations, Journal of Applied and Computational Topology (November, 2019), pp. 1-30, SPRINGER  [abs]
  2. Tralie, CJ; Bendich, P; Harer, J, Multi-Scale Geometric Summaries for Similarity-Based Sensor Fusion, Ieee Aerospace Conference Proceedings, vol. 2019-March (March, 2019), ISBN 9781538668542 [doi]  [abs]
  3. Bendich, P, Topology, geometry, and machine-learning for tracking and sensor fusion, Smart Structures and Materials 2005: Active Materials: Behavior and Mechanics, vol. 11017 (January, 2019), pp. lxxxiii-cii, ISBN 9781510627017

Bertozzi, Andrea L

  1. J. B. Greer and A. L. Bertozzi, H-1 solutions of a class of fourth order nonlinear equations for image processing, Discrete And Continuous Dynamical Systems, vol. 10 no. 1-2 (2004), pp. 349 -- 366

Bray, Hubert

  1. Bray, H; Hamm, B; Hirsch, S; Wheeler, J; Zhang, Y, Flatly foliated relativity, Pure and Applied Mathematics Quarterly, vol. 15 no. 2 (January, 2019), pp. 707-747, International Press of Boston [doi]  [abs]

Bryant, Robert   (search)

  1. Bryant, RL; Eastwood, MG; Gover, AR; Neusser, K, Some differential complexes within and beyond parabolic geometry, Advanced Studies in Pure Mathematics, vol. 82 no. Differential Geometry and Tanaka Theory (November, 2019), pp. 13-40, Mathematical Society of Japan  [abs]
  2. Bryant, R; Buckmire, R; Khadjavi, L; Lind, D, The origins of spectra, an organization for LGBT mathematicians, Notices of the American Mathematical Society, vol. 66 no. 6 (June, 2019), pp. 875-882 [doi]

Calderbank, Robert

  1. Beirami, A; Calderbank, R; Christiansen, MM; Duffy, KR; Medard, M, A Characterization of Guesswork on Swiftly Tilting Curves, Ieee Transactions on Information Theory, vol. 65 no. 5 (May, 2019), pp. 2850-2871 [doi]  [abs]
  2. Michelusi, N; Nokleby, M; Mitra, U; Calderbank, R, Multi-Scale Spectrum Sensing in Dense Multi-Cell Cognitive Networks, Ieee Transactions on Communications, vol. 67 no. 4 (April, 2019), pp. 2673-2688 [doi]  [abs]
  3. Vahid, A; Calderbank, R, Throughput region of spatially correlated interference packet networks, Ieee Transactions on Information Theory, vol. 65 no. 2 (February, 2019), pp. 1220-1235 [doi]  [abs]

Cao, Yu

  1. Cao, Y; Lu, J; Lu, Y, Exponential Decay of Rényi Divergence Under Fokker–Planck Equations, Journal of Statistical Physics, vol. 176 no. 5 (September, 2019), pp. 1172-1184 [doi]  [abs]
  2. Cao, Y; Lu, J; Lu, Y, Gradient flow structure and exponential decay of the sandwiched Rényi divergence for primitive Lindblad equations with GNS-detailed balance, Journal of Mathematical Physics, vol. 60 no. 5 (May, 2019), pp. 052202-052202, AIP Publishing [doi]  [abs]

Cheng, Cheng

  1. Cheng, C; Jiang, Y; Sun, Q, Spatially distributed sampling and reconstruction, Applied and Computational Harmonic Analysis, vol. 47 no. 1 (July, 2019), pp. 109-148, Elsevier BV [doi]

Cheng, Xiuyuan

  1. Cheng, X; Cloninger, A; Coifman, RR, Two-sample statistics based on anisotropic kernels, Information and Inference (December, 2019), Oxford University Press (OUP) [doi]  [abs]
  2. Cheng, X; Qiu, Q; Calderbank, R; Sapiro, G, RotDCF: Decomposition of convolutional filters for rotation-equivariant deep networks (May, 2019)
  3. Cheng, X; Rachh, M; Steinerberger, S, On the diffusion geometry of graph Laplacians and applications, Applied and Computational Harmonic Analysis, vol. 46 no. 3 (May, 2019), pp. 674-688, Elsevier BV [doi]

Cruz, Joshua

  1. Cruz, J; Giusti, C; Itskov, V; Kronholm, B, On Open and Closed Convex Codes, Discrete & Computational Geometry, vol. 61 no. 2 (March, 2019), pp. 247-270, Springer Science and Business Media LLC [doi]

Dasgupta, Samit

  1. Dasgupta, S; Spiess, M, On the characteristic polynomial of the gross regulator matrix, Transactions of the American Mathematical Society, vol. 372 no. 2 (January, 2019), pp. 803-827 [doi]  [abs]

Daubechies, Ingrid

  1. Sabetsarvestani, Z; Sober, B; Higgitt, C; Daubechies, I; Rodrigues, MRD, Artificial intelligence for art investigation: Meeting the challenge of separating x-ray images of the Ghent Altarpiece., Science Advances, vol. 5 no. 8 (August, 2019), pp. eaaw7416 [doi]  [abs]
  2. Alaifari, R; Daubechies, I; Grohs, P; Yin, R, Stable Phase Retrieval in Infinite Dimensions, Foundations of Computational Mathematics, vol. 19 no. 4 (August, 2019), pp. 869-900, Springer Nature America, Inc [doi]  [abs]
  3. Shan, S; Kovalsky, SZ; Winchester, JM; Boyer, DM; Daubechies, I, ariaDNE: A robustly implemented algorithm for Dirichlet energy of the normal, Methods in Ecology and Evolution, vol. 10 no. 4 (April, 2019), pp. 541-552 [doi]  [abs]

Ding, Xiucai

  1. Ding, X, Singular vector distribution of sample covariance matrices, Advances in Applied Probability, vol. 51 no. 01 (March, 2019), pp. 236-267, Cambridge University Press (CUP) [doi]  [abs]

Dolbow, John E.

  1. Jiang, W; Spencer, BW; Dolbow, JE, Ceramic nuclear fuel fracture modeling with the extended finite element method, Engineering Fracture Mechanics, vol. 223 (January, 2020) [doi]  [abs]
  2. Guilleminot, J; Dolbow, JE, Data-driven enhancement of fracture paths in random composites, Mechanics Research Communications, vol. 103 (January, 2020) [doi]  [abs]
  3. Geelen, RJM; Liu, Y; Hu, T; Tupek, MR; Dolbow, JE, A phase-field formulation for dynamic cohesive fracture, Computer Methods in Applied Mechanics and Engineering, vol. 348 (May, 2019), pp. 680-711 [doi]  [abs]
  4. Asareh, I; Kim, TY; Song, JH; Dolbow, JE, Corrigendum to “A linear complete extended finite element method for dynamic fracture simulation with non-nodal enrichments” [Finite Elem. Anal. Des. 152, 2018](S0168874X18305080)(10.1016/j.finel.2018.09.002), Finite Elements in Analysis and Design, vol. 157 (May, 2019), pp. 50 [doi]  [abs]
  5. Liu, Y; Peco, C; Dolbow, J, A fully coupled mixed finite element method for surfactants spreading on thin liquid films, Computer Methods in Applied Mechanics and Engineering, vol. 345 (March, 2019), pp. 429-453, Elsevier BV [doi]  [abs]
  6. Peco, C; Liu, Y; Rhea, C; Dolbow, JE, Models and simulations of surfactant-driven fracture in particle rafts, International Journal of Solids and Structures, vol. 156-157 (January, 2019), pp. 194-209, Elsevier BV [doi]  [abs]

Dunson, David B.   (search)

  1. Tikhonov, G; Duan, L; Abrego, N; Newell, G; White, M; Dunson, D; Ovaskainen, O, Computationally efficient joint species distribution modeling of big spatial data., Ecology (November, 2019), pp. e02929 [doi]  [abs]
  2. Zhang, Z; Allen, GI; Zhu, H; Dunson, D, Tensor network factorizations: Relationships between brain structural connectomes and traits., Neuroimage, vol. 197 (August, 2019), pp. 330-343 [doi]  [abs]
  3. Li, C; Lin, L; Dunson, DB, On posterior consistency of tail index for Bayesian kernel mixture models, Bernoulli, vol. 25 no. 3 (August, 2019), pp. 1999-2028, Bernoulli Society for Mathematical Statistics and Probability [doi]
  4. Miller, JW; Dunson, DB, Robust Bayesian Inference via Coarsening, Journal of the American Statistical Association, vol. 114 no. 527 (July, 2019), pp. 1113-1125, Informa UK Limited [doi]  [abs]
  5. Johndrow, JE; Smith, A; Pillai, N; Dunson, DB, MCMC for Imbalanced Categorical Data, Journal of the American Statistical Association, vol. 114 no. 527 (July, 2019), pp. 1394-1403 [doi]  [abs]
  6. Niu, M; Cheung, P; Lin, L; Dai, Z; Lawrence, N; Dunson, D, Intrinsic Gaussian processes on complex constrained domains, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 81 no. 3 (July, 2019), pp. 603-627 [doi]  [abs]
  7. Wang, L; Zhang, Z; Dunson, D, Symmetric Bilinear Regression for Signal Subgraph Estimation, Ieee Transactions on Signal Processing, vol. 67 no. 7 (April, 2019), pp. 1929-1940 [doi]  [abs]
  8. Zhang, Z; Descoteaux, M; Dunson, DB, Nonparametric Bayes Models of Fiber Curves Connecting Brain Regions, Journal of the American Statistical Association (January, 2019) [doi]  [abs]
  9. Norberg, A; Abrego, N; Blanchet, FG; Adler, FR; Anderson, BJ; Anttila, J; Araújo, MB; Dallas, T; Dunson, D; Elith, J; Foster, SD; Fox, R; Franklin, J; Godsoe, W; Guisan, A; O'Hara, B; Hill, NA; Holt, RD; Hui, FKC; Husby, M; Kålås, JA; Lehikoinen, A; Luoto, M; Mod, HK; Newell, G; Renner, I; Roslin, T; Soininen, J; Thuiller, W; Vanhatalo, J; Warton, D; White, M; Zimmermann, NE; Gravel, D; Ovaskainen, O, A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels, Ecological Monographs, vol. 89 no. 3 (January, 2019) [doi]  [abs]
  10. Wang, L; Zhang, Z; Dunson, D, Common and individual structure of brain networks, The Annals of Applied Statistics, vol. 13 no. 1 (January, 2019), pp. 85-112 [doi]  [abs]
  11. Li, M; Dunson, DB, Comparing and Weighting Imperfect Models Using D-Probabilities, Journal of the American Statistical Association (January, 2019) [doi]  [abs]
  12. Lin, L; Mu, N; Cheung, P; Dunson, D, Extrinsic Gaussian processes for regression and classification on manifolds, Bayesian Analysis, vol. 14 no. 3 (January, 2019), pp. 887-906 [doi]  [abs]
  13. Chae, M; Lin, L; Dunson, DB, Bayesian sparse linear regression with unknown symmetric error, Information and Inference, vol. 8 no. 3 (January, 2019), pp. 621-653 [doi]  [abs]
  14. Mukhopadhyay, M; Dunson, DB, Targeted Random Projection for Prediction From High-Dimensional Features, Journal of the American Statistical Association (January, 2019) [doi]  [abs]
  15. Badea, A; Wu, W; Shuff, J; Wang, M; Anderson, RJ; Qi, Y; Johnson, GA; Wilson, JG; Koudoro, S; Garyfallidis, E; Colton, CA; Dunson, DB, Identifying Vulnerable Brain Networks in Mouse Models of Genetic Risk Factors for Late Onset Alzheimer's Disease., Frontiers in Neuroinformatics, vol. 13 (2019), pp. 72 [doi]  [abs]

Durrett, Richard T.

  1. Cristali, I; Junge, M; Durrett, R, Poisson percolation on the oriented square lattice, Stochastic Processes and Their Applications, vol. 130 no. 2 (February, 2020), pp. 488-502 [doi]  [abs]
  2. Wang, Z; Durrett, R, Extrapolating weak selection in evolutionary games., Journal of Mathematical Biology, vol. 78 no. 1-2 (January, 2019), pp. 135-154 [doi]  [abs]
  3. Huo, R; Durrett, R, The Zealot voter model, The Annals of Applied Probability, vol. 29 no. 5 (January, 2019), pp. 3128-3154 [doi]  [abs]

Dym, Nadav

  1. Dym, N; Slutsky, R; Lipman, Y, Linear variational principle for Riemann mappings and discrete conformality., Proceedings of the National Academy of Sciences of the United States of America, vol. 116 no. 3 (January, 2019), pp. 732-737 [doi]  [abs]
  2. Dym, N, Spatial recurrence for ergodic fractal measures, Studia Mathematica, vol. 248 no. 1 (January, 2019), pp. 1-29 [doi]  [abs]
  3. Kushinsky, Y; Maron, H; Dym, N; Lipman, Y, Sinkhorn algorithm for lifted assignment problems, Siam Journal on Imaging Sciences, vol. 12 no. 2 (January, 2019), pp. 716-735, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  4. Dym, N; Kovalsky, SZ, Linearly Converging Quasi Branch and Bound Algorithms for Global Rigid Registration., Corr, vol. abs/1904.02204 (2019)
  5. Dym, N; Sober, B; Daubechies, I, Expression of Fractals Through Neural Network Functions., Corr, vol. abs/1905.11345 (2019)

Gao, Yuan   (search)

  1. Gao, Y, Global strong solution with BV derivatives to singular solid-on-solid model with exponential nonlinearity, Journal of Differential Equations, vol. 267 no. 7 (September, 2019), pp. 4429-4447 [doi]  [abs]
  2. Gao, Y; Liu, J-G; Lu, XY, Gradient flow approach to an exponential thin film equation: global existence and latent singularity, Esaim: Control, Optimisation and Calculus of Variations, vol. 25 (2019), pp. 49-49, E D P SCIENCES [doi]  [abs]

Getz, Jayce R.

  1. Getz, JR; Liu, B, A summation formula for triples of quadratic spaces, Advances in Mathematics, vol. 347 (April, 2019), pp. 150-191 [doi]  [abs]

Gu, Miao (Pam)

  1. Gu, M; Martin, G, Factorization Tests and Algorithms Arising from Counting Modular Forms and Automorphic Representations, Canadian Mathematical Bulletin, vol. 62 no. 1 (March, 2019), pp. 81-97, Canadian Mathematical Society [doi]  [abs]

Harer, John

  1. Tralie, CJ; Bendich, P; Harer, J, Multi-Scale Geometric Summaries for Similarity-Based Sensor Fusion, Ieee Aerospace Conference Proceedings, vol. 2019-March (March, 2019), ISBN 9781538668542 [doi]  [abs]

He, Siming

  1. He, S; Tadmor, E, Suppressing Chemotactic Blow-Up Through a Fast Splitting Scenario on the Plane, Archive for Rational Mechanics and Analysis, vol. 232 no. 2 (May, 2019), pp. 951-986, Springer Nature America, Inc [doi]  [abs]

Herschlag, Gregory J.

  1. Herschlag, G; Gounley, J; Roychowdhury, S; Draeger, EW; Randles, A, Multi-physics simulations of particle tracking in arterial geometries with a scalable moving window algorithm, Proceedings Ieee International Conference on Cluster Computing, Iccc, vol. 2019-September (September, 2019), ISBN 9781728147345 [doi]  [abs]
  2. Chin, A; Herschlag, G; Mattingly, J, The Signature of Gerrymandering in Rucho v. Common Cause, South Carolina Law Review, vol. 70 (2019)

Junge, Matthew S

  1. Cristali, I; Junge, M; Durrett, R, Poisson percolation on the oriented square lattice, Stochastic Processes and Their Applications (January, 2019) [doi]  [abs]
  2. Beckman, E; Frank, N; Jiang, Y; Junge, M; Tang, S, The frog model on trees with drift, Electronic Communications in Probability, vol. 24 (January, 2019) [doi]  [abs]
  3. Dygert, B; Kinzel, C; Junge, M; Raymond, A; Slivken, E; Zhu, J, The bullet problem with discrete speeds, Electronic Communications in Probability, vol. 24 (January, 2019) [doi]  [abs]

Kiselev, Alexander A.

  1. Kiselev, A; Li, C, Global regularity and fast small-scale formation for Euler patch equation in a smooth domain, Communications in Partial Differential Equations, vol. 44 no. 4 (April, 2019), pp. 279-308 [doi]  [abs]

Kovalsky, Shahar

  1. Shan, S; Kovalsky, SZ; Winchester, JM; Boyer, DM; Daubechies, I, ariaDNE: A robustly implemented algorithm for Dirichlet energy of the normal, Methods in Ecology and Evolution, vol. 10 no. 4 (April, 2019), pp. 541-552 [doi]  [abs]
  2. Dym, N; Kovalsky, SZ, Linearly Converging Quasi Branch and Bound Algorithms for Global Rigid Registration., Corr, vol. abs/1904.02204 (2019)
  3. Dov, D; Kovalsky, SZ; Cohen, J; Range, DE; Henao, R; Carin, L, Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images., edited by Doshi-Velez, F; Fackler, J; Jung, K; Kale, DC; Ranganath, R; Wallace, BC; Wiens, J, Mlhc, vol. 106 (2019), pp. 553-570, PMLR
  4. Dov, D; Kovalsky, SZ; Cohen, J; Range, DE; Henao, R; Carin, L, A Deep-Learning Algorithm for Thyroid Malignancy Prediction From Whole Slide Cytopathology Images., Corr, vol. abs/1904.12739 (2019)
  5. Dov, D; Kovalsky, SZ; Cohen, J; Range, D; Henao, R; Carin, L, Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images., Corr, vol. abs/1904.00839 (2019)

Layton, Anita T.

  1. Layton, AT, Solute and water transport along an inner medullary collecting duct undergoing peristaltic contractions., American Journal of Physiology. Renal Physiology, vol. 317 no. 3 (September, 2019), pp. F735-F742 [doi]  [abs]
  2. Layton, AT, Multiscale models of kidney function and diseases, Current Opinion in Biomedical Engineering, vol. 11 (September, 2019), pp. 1-8 [doi]  [abs]
  3. Ahmed, S; Hu, R; Leete, J; Layton, AT, Understanding sex differences in long-term blood pressure regulation: insights from experimental studies and computational modeling., American Journal of Physiology Heart and Circulatory Physiology, vol. 316 no. 5 (May, 2019), pp. H1113-H1123 [doi]  [abs]
  4. Fattah, H; Layton, A; Vallon, V, How Do Kidneys Adapt to a Deficit or Loss in Nephron Number?, Physiology (Bethesda, Md.), vol. 34 no. 3 (May, 2019), pp. 189-197 [doi]  [abs]
  5. Layton, AT, Optimizing SGLT inhibitor treatment for diabetes with chronic kidney diseases., Biological Cybernetics, vol. 113 no. 1-2 (April, 2019), pp. 139-148 [doi]  [abs]
  6. Layton, AT; Layton, HE, A computational model of epithelial solute and water transport along a human nephron., Plos Computational Biology, vol. 15 no. 2 (February, 2019), pp. e1006108 [doi]  [abs]
  7. Layton, AT; Sullivan, JC, Recent advances in sex differences in kidney function., American Journal of Physiology. Renal Physiology, vol. 316 no. 2 (February, 2019), pp. F328-F331 [doi]
  8. Layton, AT, Recent advances in renal epithelial transport., American Journal of Physiology. Renal Physiology, vol. 316 no. 2 (February, 2019), pp. F274-F276 [doi]
  9. Leete, J; Layton, AT, Sex-specific long-term blood pressure regulation: Modeling and analysis., Computers in Biology and Medicine, vol. 104 (January, 2019), pp. 139-148 [doi]  [abs]

Layton, Harold

  1. Layton, AT; Layton, HE, A computational model of epithelial solute and water transport along a human nephron., Plos Computational Biology, vol. 15 no. 2 (February, 2019), pp. e1006108 [doi]  [abs]

Leete, Jessica

  1. Ahmed, S; Hu, R; Leete, J; Layton, AT, Understanding sex differences in long-term blood pressure regulation: insights from experimental studies and computational modeling., American Journal of Physiology Heart and Circulatory Physiology, vol. 316 no. 5 (May, 2019), pp. H1113-H1123 [doi]  [abs]
  2. Leete, J; Layton, AT, Sex-specific long-term blood pressure regulation: Modeling and analysis., Computers in Biology and Medicine, vol. 104 (January, 2019), pp. 139-148 [doi]  [abs]

Levine, Adam S.

  1. Levine, AS; Zemke, I, Khovanov homology and ribbon concordances, Bulletin of the London Mathematical Society, vol. 51 no. 6 (December, 2019), pp. 1099-1103 [doi]  [abs]
  2. Levine, AS; Lidman, T, SIMPLY CONNECTED, SPINELESS 4-MANIFOLDS, Forum of Mathematics, Sigma (January, 2019) [doi]  [abs]
  3. Levine, AS, Indivisible, The Mathematical Intelligencer (January, 2019) [doi]

Li, Didong

  1. Didong Li and David B Dunson, Classification via local manifold approximation (2019) [arXiv:1903.00985]

Li, Yingzhou

  1. Wang, Z; Li, Y; Lu, J, Coordinate Descent Full Configuration Interaction., Journal of Chemical Theory and Computation, vol. 15 no. 6 (June, 2019), pp. 3558-3569 [doi]  [abs]
  2. Li, Y; Lu, J, Bold diagrammatic Monte Carlo in the lens of stochastic iterative methods, Transactions of Mathematics and Its Applications, vol. 3 no. 1 (February, 2019), pp. 1-17, Oxford University Press (OUP) [doi]  [abs]
  3. Li, Y; Lin, L, Globally constructed adaptive local basis set for spectral projectors of second order differential operators, Multiscale Modeling & Simulation, vol. 17 no. 1 (January, 2019), pp. 92-116, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  4. Yingzhou, LI; Jianfeng, LU; Wang, AZHE, Coordinatewise descent methods for leading eigenvalue problem, Siam Journal on Scientific Computing, vol. 41 no. 4 (January, 2019), pp. A2681-A2716, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  5. Wang, R; Li, Y; Mahoney, MW; Darve, E, Block Basis Factorization for Scalable Kernel Evaluation, Siam Journal on Matrix Analysis and Applications, vol. 40 no. 4 (January, 2019), pp. 1497-1526, Society for Industrial & Applied Mathematics (SIAM) [doi]

Liu, Jian-Guo

  1. Jin, S; Li, L; Liu, JG, Random Batch Methods (RBM) for interacting particle systems, Journal of Computational Physics, vol. 400 (January, 2020) [doi]  [abs]
  2. Li, L; Liu, JG; Yu, P, On the mean field limit for Brownian particles with Coulomb interaction in 3D, Journal of Mathematical Physics, vol. 60 no. 11 (November, 2019) [doi]  [abs] [high impact journal]
  3. Liu, JG; Pego, RL, On Local Singularities in Ideal Potential Flows with Free Surface, Chinese Annals of Mathematics, Series B, vol. 40 no. 6 (November, 2019), pp. 925-948 [doi]  [abs] [reputed journal]
  4. Liu, JG; Pego, RL; Pu, Y, Well-posedness and derivative blow-up for a dispersionless regularized shallow water system, Nonlinearity, vol. 32 no. 11 (October, 2019), pp. 4346-4376 [doi]  [abs] [high impact journal]
  5. Liu, JG; Pego, RL; Slepčev, D, Least action principles for incompressible flows and geodesics between shapes, Calculus of Variations and Partial Differential Equations, vol. 58 no. 5 (October, 2019) [doi]  [abs] [high impact journal]
  6. Lafata, KJ; Zhou, Z; Liu, J-G; Hong, J; Kelsey, CR; Yin, F-F, An Exploratory Radiomics Approach to Quantifying Pulmonary Function in CT Images., Scientific Reports, vol. 9 no. 1 (August, 2019), pp. 11509 [doi]  [abs] [high impact journal]
  7. Liu, JG; Tang, M; Wang, L; Zhou, Z, Analysis and computation of some tumor growth models with nutrient: From cell density models to free boundary dynamics, Discrete and Continuous Dynamical Systems Series B, vol. 24 no. 7 (July, 2019), pp. 3011-3035 [doi]  [abs] [high impact journal]
  8. Zhan, Q; Zhuang, M; Zhou, Z; Liu, J-G; Liu, QH, Complete-Q Model for Poro-Viscoelastic Media in Subsurface Sensing: Large-Scale Simulation With an Adaptive DG Algorithm, Ieee Transactions on Geoscience and Remote Sensing, vol. 57 no. 7 (July, 2019), pp. 4591-4599, Institute of Electrical and Electronics Engineers (IEEE) [doi] [high impact journal]
  9. Liu, JG; Niethammer, B; Pego, RL, Self-similar Spreading in a Merging-Splitting Model of Animal Group Size, Journal of Statistical Physics, vol. 175 no. 6 (June, 2019), pp. 1311-1330 [doi]  [abs] [high impact journal]
  10. Liu, JG; Lu, J; Margetis, D; Marzuola, JL, Asymmetry in crystal facet dynamics of homoepitaxy by a continuum model, Physica D: Nonlinear Phenomena, vol. 393 (June, 2019), pp. 54-67 [doi]  [abs] [high impact journal]
  11. Gao, Y; Li, L; Liu, JG, Patched peakon weak solutions of the modified Camassa–Holm equation, Physica D: Nonlinear Phenomena, vol. 390 (March, 2019), pp. 15-35 [doi]  [abs] [high impact journal]
  12. Zhan, Q; Zhuang, M; Fang, Y; Liu, J-G; Liu, QH, Green's function for anisotropic dispersive poroelastic media based on the Radon transform and eigenvector diagonalization., Proceedings. Mathematical, Physical, and Engineering Sciences, vol. 475 no. 2221 (January, 2019), pp. 20180610 [doi]  [abs] [high impact journal]
  13. Lafata, KJ; Hong, JC; Geng, R; Ackerson, BG; Liu, J-G; Zhou, Z; Torok, J; Kelsey, CR; Yin, F-F, Association of pre-treatment radiomic features with lung cancer recurrence following stereotactic body radiation therapy., Phys Med Biol, vol. 64 no. 2 (January, 2019), pp. 025007 [doi]  [abs] [high impact journal]
  14. Huang, H; Liu, JG; Lu, J, Learning interacting particle systems: Diffusion parameter estimation for aggregation equations, Mathematical Models and Methods in Applied Sciences, vol. 29 no. 1 (January, 2019), pp. 1-29 [doi]  [abs] [high impact journal]
  15. Frouvelle, A; Liu, JG, Long-Time Dynamics for a Simple Aggregation Equation on the Sphere, Springer Proceedings in Mathematics and Statistics, vol. 282 (January, 2019), pp. 457-479, ISBN 9783030150952 [doi]  [abs] [reputed journal]
  16. De Hoop, MV; Liu, JG; Markowich, PA; Ussembayev, NS, Plane-wave analysis of a hyperbolic system of equations with relaxation in ℝd, Communications in Mathematical Sciences, vol. 17 no. 1 (January, 2019), pp. 61-79 [doi]  [abs] [high impact journal]
  17. Liu, A; Liu, JG; Lu, Y, On the rate of convergence of empirical measure in ∞-Wasserstein distance for unbounded density function, Quarterly of Applied Mathematics, vol. 77 no. 4 (January, 2019), pp. 811-829 [doi]  [abs] [reputed journal]
  18. Li, L; Liu, JG, A discretization of Caputo derivatives with application to time fractional SDEs and gradient flows, Siam Journal on Numerical Analysis, vol. 57 no. 5 (January, 2019), pp. 2095-2120 [doi]  [abs] [high impact journal]
  19. Liu, JG; Strain, RM, Global stability for solutions to the exponential PDE describing epitaxial growth, Interfaces and Free Boundaries, vol. 21 no. 1 (January, 2019), pp. 61-86 [doi]  [abs] [high impact journal]
  20. Lafata, K; Zhou, Z; Liu, JG; Yin, FF, Data clustering based on Langevin annealing with a self-consistent potential, Quarterly of Applied Mathematics, vol. 77 no. 3 (January, 2019), pp. 591-613 [doi]  [abs] [reputed journal]
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  22. Gao, Y; Liu, J-G; Lu, XY, Gradient flow approach to an exponential thin film equation: global existence and latent singularity, Esaim: Control, Optimisation and Calculus of Variations, vol. 25 (2019), pp. 49-49, E D P SCIENCES [doi]  [abs] [high impact journal]

Lu, Jianfeng

  1. Lu, J; Sachs, M; Steinerberger, S, Quadrature Points via Heat Kernel Repulsion, Constructive Approximation, vol. 51 no. 1 (February, 2020), pp. 27-48 [doi]  [abs]
  2. Chen, H; Li, Q; Lu, J, A numerical method for coupling the BGK model and Euler equations through the linearized Knudsen layer, Journal of Computational Physics, vol. 398 (December, 2019) [doi]  [abs]
  3. Lu, J; Sogge, CD; Steinerberger, S, Approximating pointwise products of Laplacian eigenfunctions, Journal of Functional Analysis, vol. 277 no. 9 (November, 2019), pp. 3271-3282 [doi]  [abs]
  4. Cao, Y; Lu, J; Lu, Y, Exponential Decay of Rényi Divergence Under Fokker–Planck Equations, Journal of Statistical Physics, vol. 176 no. 5 (September, 2019), pp. 1172-1184 [doi]  [abs]
  5. Wang, Z; Li, Y; Lu, J, Coordinate Descent Full Configuration Interaction., Journal of Chemical Theory and Computation, vol. 15 no. 6 (June, 2019), pp. 3558-3569 [doi]  [abs]
  6. Liu, JG; Lu, J; Margetis, D; Marzuola, JL, Asymmetry in crystal facet dynamics of homoepitaxy by a continuum model, Physica D: Nonlinear Phenomena, vol. 393 (June, 2019), pp. 54-67 [doi]  [abs]
  7. Cao, Y; Lu, J; Lu, Y, Gradient flow structure and exponential decay of the sandwiched Rényi divergence for primitive Lindblad equations with GNS-detailed balance, Journal of Mathematical Physics, vol. 60 no. 5 (May, 2019), pp. 052202-052202, AIP Publishing [doi]  [abs]
  8. Lin, L; Lu, J; Ying, L, Numerical methods for Kohn-Sham density functional theory, Acta Numerica, vol. 28 (May, 2019), pp. 405-539 [doi]  [abs]
  9. Yu, V; Dawson, W; Garcia, A; Havu, V; Hourahine, B; Huhn, W; Jacquelin, M; Jia, W; Keceli, M; Laasner, R; Li, Y; Lin, L; Lu, J; Roman, J; Vazquez-Mayagoitia, A; Yang, C; Blum, V, Large-scale benchmark of electronic structure solvers with the ELSI infrastructure, Abstracts of Papers of the American Chemical Society, vol. 257 (March, 2019), pp. 1 pages, AMER CHEMICAL SOC
  10. Khoo, Y; Lu, J; Ying, L, Solving for high-dimensional committor functions using artificial neural networks, Research in Mathematical Sciences, vol. 6 no. 1 (March, 2019), Springer Science and Business Media LLC [doi]
  11. Lu, J; Vanden-Eijnden, E, Methodological and Computational Aspects of Parallel Tempering Methods in the Infinite Swapping Limit, Journal of Statistical Physics, vol. 174 no. 3 (February, 2019), pp. 715-733 [doi]  [abs]
  12. Li, Y; Lu, J, Bold diagrammatic Monte Carlo in the lens of stochastic iterative methods, Transactions of Mathematics and Its Applications, vol. 3 no. 1 (February, 2019), pp. 1-17, Oxford University Press (OUP) [doi]  [abs]
  13. Martinsson, A; Lu, J; Leimkuhler, B; Vanden-Eijnden, E, The simulated tempering method in the infinite switch limit with adaptive weight learning, Journal of Statistical Mechanics: Theory and Experiment, vol. 2019 no. 1 (January, 2019), pp. 013207-013207, IOP Publishing [doi]  [abs]
  14. Lu, J; Lu, Y; Nolen, J, Scaling limit of the Stein variational gradient descent: The mean field regime, Siam Journal on Mathematical Analysis, vol. 51 no. 2 (January, 2019), pp. 648-671, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  15. Huang, H; Liu, JG; Lu, J, Learning interacting particle systems: Diffusion parameter estimation for aggregation equations, Mathematical Models and Methods in Applied Sciences, vol. 29 no. 1 (January, 2019), pp. 1-29 [doi]  [abs]
  16. Gauckler, L; Lu, J; Marzuola, JL; Rousset, F; Schratz, K, Trigonometric integrators for quasilinear wave equations, Mathematics of Computation, vol. 88 no. 316 (January, 2019), pp. 717-749, American Mathematical Society (AMS) [doi]  [abs]
  17. Yingzhou, LI; Jianfeng, LU; Wang, AZHE, Coordinatewise descent methods for leading eigenvalue problem, Siam Journal on Scientific Computing, vol. 41 no. 4 (January, 2019), pp. A2681-A2716, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  18. Agazzi, A; Lu, J, Temporal-difference learning for nonlinear value function approximation in the lazy training regime., Corr, vol. abs/1905.10917 (2019)

Maggioni, Mauro

  1. Murphy, JM; Maggioni, M, Unsupervised Clustering and Active Learning of Hyperspectral Images with Nonlinear Diffusion, Ieee Transactions on Geoscience and Remote Sensing, vol. 57 no. 3 (March, 2019), pp. 1829-1845, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  2. Vogelstein, JT; Bridgeford, EW; Wang, Q; Priebe, CE; Maggioni, M; Shen, C, Discovering and deciphering relationships across disparate data modalities., Elife, vol. 8 (January, 2019) [doi]  [abs]
  3. 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, QEEG-based classification with wavelet packets and microstate features for triage applications in the ER (2005)
  4. GL Davis and Mauro Maggioni and FJ Warner and FB Geshwind and AC Coppi and RA DeVerse and RR Coifman, Hyper-spectral Analysis of normal and malignant colon tissue microarray sections using a novel DMD system (2004) (Poster, Optical Imaging NIH workshop, to app. in proc..)
  5. Ronald R Coifman and Mauro Maggioni, Multiresolution Analysis associated to diffusion semigroups: construction and fast algorithms no. YALE/DCS/TR-1289 (2004)

Mattingly, Jonathan C.   (search)

  1. Herzog, DP; Mattingly, JC, Ergodicity and Lyapunov Functions for Langevin Dynamics with Singular Potentials, Communications on Pure and Applied Mathematics, vol. 72 no. 10 (October, 2019), pp. 2231-2255, WILEY [doi]
  2. Chin, A; Herschlag, G; Mattingly, J, The Signature of Gerrymandering in Rucho v. Common Cause, South Carolina Law Review, vol. 70 (2019)

Miller, Ezra

  1. Katthän, L; Michałek, M; Miller, E, When is a Polynomial Ideal Binomial After an Ambient Automorphism?, Foundations of Computational Mathematics, vol. 19 no. 6 (December, 2019), pp. 1363-1385, Springer Nature America, Inc [doi]  [abs]

Mukherjee, Sayan

  1. Berchuck, SI; Mukherjee, S; Medeiros, FA, Estimating Rates of Progression and Predicting Future Visual Fields in Glaucoma Using a Deep Variational Autoencoder., Scientific Reports, vol. 9 no. 1 (December, 2019), pp. 18113 [doi]  [abs]
  2. Cakir, M; Mukherjee, S; Wood, KC, Label propagation defines signaling networks associated with recurrently mutated cancer genes., Scientific Reports, vol. 9 no. 1 (June, 2019), pp. 9401 [doi]  [abs]
  3. Gao, T; Brodzki, J; Mukherjee, S, The Geometry of Synchronization Problems and Learning Group Actions, Discrete & Computational Geometry (January, 2019) [doi]  [abs]
  4. Washburne, AD; Silverman, JD; Morton, JT; Becker, DJ; Crowley, D; Mukherjee, S; David, LA; Plowright, RK, Phylofactorization: a graph partitioning algorithm to identify phylogenetic scales of ecological data, Ecological Monographs (January, 2019) [doi]  [abs]
  5. Crawford, L; Monod, A; Chen, AX; Mukherjee, S; Rabadán, R, Predicting Clinical Outcomes in Glioblastoma: An Application of Topological and Functional Data Analysis, Journal of the American Statistical Association (January, 2019) [doi]  [abs]

Ng, Lenhard L.

  1. Chantraine, B; Ng, L; Sivek, S, Representations, sheaves and Legendrian (2,m) torus links, Journal of the London Mathematical Society, vol. 100 no. 1 (August, 2019), pp. 41-82, WILEY [doi]  [abs]

Nolen, James H.

  1. Nolen, J; Roquejoffre, J-M; Ryzhik, L, Refined long-time asymptotics for Fisher–KPP fronts, Communications in Contemporary Mathematics, vol. 21 no. 07 (November, 2019), pp. 1850072-1850072, World Scientific Pub Co Pte Lt [doi]  [abs]
  2. Henderson, NT; Pablo, M; Ghose, D; Clark-Cotton, MR; Zyla, TR; Nolen, J; Elston, TC; Lew, DJ, Ratiometric GPCR signaling enables directional sensing in yeast., Plos Biology, vol. 17 no. 10 (October, 2019), pp. e3000484 [doi]  [abs]
  3. Lu, J; Lu, Y; Nolen, J, Scaling limit of the Stein variational gradient descent: The mean field regime, Siam Journal on Mathematical Analysis, vol. 51 no. 2 (January, 2019), pp. 648-671 [doi]  [abs]

Petters, Arlie O.

  1. Aazami, AB; Keeton, CR; Petters, AO, Magnification cross sections for the elliptic umbilic caustic surface, Universe, vol. 5 no. 7 (July, 2019) [doi]  [abs]

Pfister, Henry

  1. Pfister, HD; Urbanke, RL, Near-Optimal Finite-Length Scaling for Polar Codes Over Large Alphabets, Ieee Transactions on Information Theory, vol. 65 no. 9 (September, 2019), pp. 5643-5655, Institute of Electrical and Electronics Engineers (IEEE) [doi]
  2. Carpi, F; Hager, C; Martalo, M; Raheli, R; Pfister, HD, Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding, 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton) (September, 2019), IEEE [doi]
  3. Rengaswamy, N; Calderbank, R; Pfister, HD, Unifying the Clifford hierarchy via symmetric matrices over rings, Physical Review A, vol. 100 no. 2 (August, 2019) [doi]  [abs]
  4. Lian, M; Carpi, F; Hager, C; Pfister, HD, Learned Belief-Propagation Decoding with Simple Scaling and SNR Adaptation, Ieee International Symposium on Information Theory Proceedings, vol. 2019-July (July, 2019), pp. 161-165 [doi]  [abs]
  5. Can, T; Rengaswamy, N; Calderbank, R; Pfister, HD, Kerdock Codes Determine Unitary 2-Designs, Ieee International Symposium on Information Theory Proceedings, vol. 2019-July (July, 2019), pp. 2908-2912 [doi]  [abs]
  6. Tal, I; Pfister, HD; Fazeli, A; Vardy, A, Polar Codes for the Deletion Channel: Weak and Strong Polarization, Ieee International Symposium on Information Theory Proceedings, vol. 2019-July (July, 2019), pp. 1362-1366 [doi]  [abs]
  7. Reeves, G; Pfister, HD, The Replica-Symmetric Prediction for Random Linear Estimation With Gaussian Matrices Is Exact, Ieee Transactions on Information Theory, vol. 65 no. 4 (April, 2019), pp. 2252-2283 [doi]  [abs]
  8. Schmidt, C; Pfister, HD; Zdeborová, L, Minimal sets to destroy the k-core in random networks., Physical Review. E, vol. 99 no. 2-1 (February, 2019), pp. 022310 [doi]  [abs]
  9. Yoo, I; Imani, MF; Sleasman, T; Pfister, HD; Smith, DR, Enhancing Capacity of Spatial Multiplexing Systems Using Reconfigurable Cavity-Backed Metasurface Antennas in Clustered MIMO Channels, Ieee Transactions on Communications, vol. 67 no. 2 (February, 2019), pp. 1070-1084, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  10. Sheikh, A; GraellAmat, A; Liva, G; Häger, C; Pfister, HD, On Low-Complexity Decoding of Product Codes for High-Throughput Fiber-Optic Systems, International Symposium on Turbo Codes and Iterative Information Processing, Istc, vol. 2018-December (January, 2019) [doi]  [abs]
  11. Lian, M; Häger, C; Pfister, HD, What can machine learning teach us about communications?, 2018 Ieee Information Theory Workshop, Itw 2018 (January, 2019) [doi]  [abs]
  12. Häger, C; Pfister, HD; Bütler, RM; Liga, G; Alvarado, A, Revisiting Multi-Step Nonlinearity Compensation with Machine Learning., Corr, vol. abs/1904.09807 (2019)

Pierce, Lillian B.

  1. Chruściel, PT; De Mesmay, A; Păun, M; Peyre, E; Barthe, F; Helfgott, HA; Kontsevich, M; Villani, C; Guillermou, S; Hernandez, D; Ma, X; Massot, P; Bergeron, N; Oesterlé, J; Pierce, LB; Rousset, F, Séminaire Bourbaki Volume 2016/2017 Exposés 1120-1135, Astérisque, vol. 407 (January, 2019), pp. 1-602 [doi]  [abs]

Rabinoff, Joseph D

  1. Dupuy, T; Katz, E; Rabinoff, J; Zureick-Brown, D, Total p-differentials on schemes over Z/p2, Journal of Algebra, vol. 524 (April, 2019), pp. 110-123 [doi]  [abs]

Randles, Amanda

  1. Vardhan, M; Gounley, J; Hegele, L; Draeger, EW; Randles, A, Moment representation in the lattice Boltzmann method on massively parallel hardware, International Conference for High Performance Computing, Networking, Storage and Analysis, Sc (November, 2019), ISBN 9781450362290 [doi]  [abs]
  2. Herschlag, G; Gounley, J; Roychowdhury, S; Draeger, EW; Randles, A, Multi-physics simulations of particle tracking in arterial geometries with a scalable moving window algorithm, Proceedings Ieee International Conference on Cluster Computing, Iccc, vol. 2019-September (September, 2019), ISBN 9781728147345 [doi]  [abs]
  3. Lee, S; Gounley, J; Randles, A; Vetter, JS, Performance portability study for massively parallel computational fluid dynamics application on scalable heterogeneous architectures, Journal of Parallel and Distributed Computing, vol. 129 (July, 2019), pp. 1-13 [doi]  [abs]
  4. Dabagh, M; Nair, P; Gounley, J; Frakes, D; Gonzalez, LF; Randles, A, Hemodynamic and morphological characteristics of a growing cerebral aneurysm., Neurosurgical Focus, vol. 47 no. 1 (July, 2019), pp. E13 [doi]  [abs]
  5. Vardhan, M; Gounley, J; Chen, SJ; Kahn, AM; Leopold, JA; Randles, A, The importance of side branches in modeling 3D hemodynamics from angiograms for patients with coronary artery disease., Scientific Reports, vol. 9 no. 1 (June, 2019), pp. 8854 [doi]  [abs]
  6. Feiger, B; Vardhan, M; Gounley, J; Mortensen, M; Nair, P; Chaudhury, R; Frakes, D; Randles, A, Suitability of lattice Boltzmann inlet and outlet boundary conditions for simulating flow in image-derived vasculature., International Journal for Numerical Methods in Biomedical Engineering, vol. 35 no. 6 (June, 2019), pp. e3198 [doi]  [abs]
  7. Grigoryan, B; Paulsen, SJ; Corbett, DC; Sazer, DW; Fortin, CL; Zaita, AJ; Greenfield, PT; Calafat, NJ; Gounley, JP; Ta, AH; Johansson, F; Randles, A; Rosenkrantz, JE; Louis-Rosenberg, JD; Galie, PA; Stevens, KR; Miller, JS, Multivascular networks and functional intravascular topologies within biocompatible hydrogels., Science (New York, N.Y.), vol. 364 no. 6439 (May, 2019), pp. 458-464 [doi]  [abs]
  8. Vardhan, M; Das, A; Gouruev, J; Randles, A, Computational fluid modeling to understand the role of anatomy in bifurcation lesion disease, Proceedings 25th Ieee International Conference on High Performance Computing Workshops, Hipcw 2018 (February, 2019), pp. 56-64, ISBN 9781728101149 [doi]  [abs]
  9. Gounley, J; Vardhan, M; Randles, A, A Framework for Comparing Vascular Hemodynamics at Different Points in Time., Computer Physics Communications, vol. 235 (February, 2019), pp. 1-8 [doi]  [abs]
  10. Dabagh, M; Randles, A, Role of deformable cancer cells on wall shear stress-associated-VEGF secretion by endothelium in microvasculature., Plos One, vol. 14 no. 2 (January, 2019), pp. e0211418 [doi]  [abs]
  11. Gounley, J; Draeger, EW; Oppelstrup, T; Krauss, WD; Gunnels, JA; Chaudhury, R; Nair, P; Frakes, D; Leopold, JA; Randles, A, Computing the ankle-brachial index with parallel computational fluid dynamics., Journal of Biomechanics, vol. 82 (January, 2019), pp. 28-37 [doi]  [abs]
  12. Gounley, J; Draeger, EW; Randles, A, Immersed Boundary Method Halo Exchange in a Hemodynamics Application, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11536 LNCS (January, 2019), pp. 441-455, ISBN 9783030227333 [doi]  [abs]

Reed, Michael C.

  1. Abdalla, A; West, A; Jin, Y; Saylor, RA; Qiang, B; Peña, E; Linden, DJ; Nijhout, HF; Reed, MC; Best, J; Hashemi, P, Fast serotonin voltammetry as a versatile tool for mapping dynamic tissue architecture: I. Responses at carbon fibers describe local tissue physiology., Journal of Neurochemistry (August, 2019) [doi]  [abs]
  2. Nijhout, HF; Best, JA; Reed, MC, Systems biology of robustness and homeostatic mechanisms., Wiley Interdisciplinary Reviews. Systems Biology and Medicine, vol. 11 no. 3 (May, 2019), pp. e1440 [doi]  [abs]
  3. West, A; Best, J; Abdalla, A; Nijhout, HF; Reed, M; Hashemi, P, Voltammetric evidence for discrete serotonin circuits, linked to specific reuptake domains, in the mouse medial prefrontal cortex., Neurochemistry International, vol. 123 (February, 2019), pp. 50-58 [doi]  [abs]
  4. Saylor, RA; Hersey, M; West, A; Buchanan, AM; Berger, SN; Nijhout, HF; Reed, MC; Best, J; Hashemi, P, In vivo Hippocampal Serotonin Dynamics in Male and Female Mice: Determining Effects of Acute Escitalopram Using Fast Scan Cyclic Voltammetry., Frontiers in Neuroscience, vol. 13 (January, 2019), pp. 362 [doi]  [abs]
  5. Saylor, RA; Hersey, M; West, A; Buchanan, AM; Berger, SN; Nijhout, HF; Reed, MC; Best, J; Hashemi, P, Corrigendum: In vivo Hippocampal Serotonin Dynamics in Male and Female Mice: Determining Effects of Acute Escitalopram Using Fast Scan Cyclic Voltammetry., Frontiers in Neuroscience, vol. 13 (January, 2019), pp. 726 [doi]  [abs]

Rudin, Cynthia D.

  1. Wang, F; Rudin, C; Mccormick, TH; Gore, JL, Modeling recovery curves with application to prostatectomy., Biostatistics (Oxford, England), vol. 20 no. 4 (October, 2019), pp. 549-564 [doi]  [abs]
  2. Rudin, C; Shaposhnik, Y, Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation (May, 2019)
  3. Bravo, F; Rudin, C; Shaposhnik, Y; Yuan, Y, Simple Rules for Predicting Congestion Risk in Queueing Systems: Application to ICUs (May, 2019)
  4. Dieng, A; Liu, Y; Roy, S; Rudin, C; Volfovsky, A, Interpretable Almost-Exact Matching for Causal Inference., Proceedings of Machine Learning Research, vol. 89 (April, 2019), pp. 2445-2453  [abs]
  5. Ban, GY; Rudin, C, The big Data newsvendor: Practical insights from machine learning, Operations Research, vol. 67 no. 1 (January, 2019), pp. 90-108 [doi]  [abs]
  6. Usaid Awan, M; Liu, Y; Morucci, M; Roy, S; Rudin, C; Volfovsky, A, Interpretable almost-matching-exactly with instrumental variables, 35th Conference on Uncertainty in Artificial Intelligence, Uai 2019 (January, 2019)  [abs]
  7. Tracà, S; Rudin, C; Yan, W, Reducing exploration of dying arms in mortal bandits, 35th Conference on Uncertainty in Artificial Intelligence, Uai 2019 (January, 2019)  [abs]
  8. Rudin, C, Do Simpler Models Exist and How Can We Find Them?, Proceedings of the 25th Acm Sigkdd International Conference on Knowledge Discovery & Data Mining Kdd '19 (2019), ACM Press, ISBN 9781450362016 [doi]

Ryser, Marc D.

  1. Ryser, MD; Weaver, DL; Zhao, F; Worni, M; Grimm, LJ; Gulati, R; Etzioni, R; Hyslop, T; Lee, SJ; Hwang, ES, Cancer Outcomes in DCIS Patients Without Locoregional Treatment., J Natl Cancer Inst, vol. 111 no. 9 (September, 2019), pp. 952-960 [doi]  [abs]
  2. Ryser, MD; Hendrix, LH; Worni, M; Liu, Y; Hyslop, T; Hwang, ES, Incidence of Ductal Carcinoma In Situ in the United States, 2000-2014., Cancer Epidemiol Biomarkers Prev, vol. 28 no. 8 (August, 2019), pp. 1316-1323 [doi]  [abs]
  3. Grimm, LJ; Miller, MM; Thomas, SM; Liu, Y; Lo, JY; Hwang, ES; Hyslop, T; Ryser, MD, Growth Dynamics of Mammographic Calcifications: Differentiating Ductal Carcinoma in Situ from Benign Breast Disease., Radiology, vol. 292 no. 1 (July, 2019), pp. 77-83 [doi]  [abs]
  4. Shen, Y; Dong, W; Gulati, R; Ryser, MD; Etzioni, R, Estimating the frequency of indolent breast cancer in screening trials., Stat Methods Med Res, vol. 28 no. 4 (April, 2019), pp. 1261-1271 [doi]  [abs]
  5. Ryser, MD; Gulati, R; Eisenberg, MC; Shen, Y; Hwang, ES; Etzioni, RB, Identification of the Fraction of Indolent Tumors and Associated Overdiagnosis in Breast Cancer Screening Trials., American Journal of Epidemiology, vol. 188 no. 1 (January, 2019), pp. 197-205 [doi]  [abs]

Sapiro, Guillermo

  1. Simhal, AK; Zuo, Y; Perez, MM; Madison, DV; Sapiro, G; Micheva, KD, Multifaceted Changes in Synaptic Composition and Astrocytic Involvement in a Mouse Model of Fragile X Syndrome., Scientific Reports, vol. 9 no. 1 (September, 2019), pp. 13855 [doi]  [abs]
  2. Martinez, N; Bertran, M; Sapiro, G; Wu, HT, Non-Contact Photoplethysmogram and Instantaneous Heart Rate Estimation from Infrared Face Video, Proceedings International Conference on Image Processing, Icip, vol. 2019-September (September, 2019), pp. 2020-2024, ISBN 9781538662496 [doi]  [abs]
  3. Asiedu, MN; Simhal, A; Chaudhary, U; Mueller, JL; Lam, CT; Schmitt, JW; Venegas, G; Sapiro, G; Ramanujam, N, Development of Algorithms for Automated Detection of Cervical Pre-Cancers With a Low-Cost, Point-of-Care, Pocket Colposcope., Ieee Trans Biomed Eng, vol. 66 no. 8 (August, 2019), pp. 2306-2318, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  4. Dawson, G; Sapiro, G, Potential for Digital Behavioral Measurement Tools to Transform the Detection and Diagnosis of Autism Spectrum Disorder., Jama Pediatr, vol. 173 no. 4 (April, 2019), pp. 305-306 [doi]
  5. Campbell, K; Carpenter, KL; Hashemi, J; Espinosa, S; Marsan, S; Borg, JS; Chang, Z; Qiu, Q; Vermeer, S; Adler, E; Tepper, M; Egger, HL; Baker, JP; Sapiro, G; Dawson, G, Computer vision analysis captures atypical attention in toddlers with autism., Autism, vol. 23 no. 3 (April, 2019), pp. 619-628 [doi]  [abs]
  6. Shamir, RR; Duchin, Y; Kim, J; Patriat, R; Marmor, O; Bergman, H; Vitek, JL; Sapiro, G; Bick, A; Eliahou, R; Eitan, R; Israel, Z; Harel, N, Microelectrode Recordings Validate the Clinical Visualization of Subthalamic-Nucleus Based on 7T Magnetic Resonance Imaging and Machine Learning for Deep Brain Stimulation Surgery., Neurosurgery, vol. 84 no. 3 (March, 2019), pp. 749-757 [doi]  [abs]
  7. Sapiro, G; Hashemi, J; Dawson, G, Computer vision and behavioral phenotyping: an autism case study, Current Opinion in Biomedical Engineering, vol. 9 (March, 2019), pp. 14-20 [doi]  [abs]
  8. Kim, J; Duchin, Y; Shamir, RR; Patriat, R; Vitek, J; Harel, N; Sapiro, G, Automatic localization of the subthalamic nucleus on patient-specific clinical MRI by incorporating 7 T MRI and machine learning: Application in deep brain stimulation., Human Brain Mapping, vol. 40 no. 2 (February, 2019), pp. 679-698 [doi]  [abs]
  9. Azami, H; Arnold, SE; Sanei, S; Chang, Z; Sapiro, G; Escudero, J; Gupta, AS, Multiscale fluctuation-based dispersion entropy and its applications to neurological diseases, Ieee Access, vol. 7 (January, 2019), pp. 68718-68733 [doi]  [abs]
  10. Cheng, X; Qiu, Q; Calderbank, R; Sapiro, G, RoTDCF: Decomposition of convolutional filters for rotation-equivariant deep networks, 7th International Conference on Learning Representations, Iclr 2019 (January, 2019)  [abs]
  11. Fellous, J-M; Sapiro, G; Rossi, A; Mayberg, H; Ferrante, M, Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation., Frontiers in Neuroscience, vol. 13 (2019), pp. 1346 [doi]  [abs]

Schoen, Chadmark L.

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Shan, Shan

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Sober, Barak

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Sorribes Rodriguez, Inmaculada C

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Stern, Mark A.

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Tarokh, Vahid

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Vafaee, Faramarz

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Venakides, Stephanos

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Wang, Lihan

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Wang, Min

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Wang, Zhe

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Wickelgren, Kirsten G.

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Witelski, Thomas P.   (search)

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Wong, Jeffrey T

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Wu, Hau-Tieng

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Wu, Nan

  1. Wu, N; Zhu, Z, An Upper Bound for the Smallest Area of a Minimal Surface in Manifolds of Dimension Four, The Journal of Geometric Analysis (January, 2019) [doi]  [abs]

 

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