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Mathematics : Publications since January 2018

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

Agarwal, Pankaj K.

  1. Agarwal, PK; Fox, K; Salzman, O, An Efficient Algorithm for Computing High-Quality Paths amid Polygonal Obstacles, Acm Transactions on Algorithms, vol. 14 no. 4 (August, 2018), pp. 1-21, Association for Computing Machinery (ACM) [doi]  [abs]
  2. Agarwal, PK; Kaplan, H; Sharir, M, Union of hypercubes and 3D minkowski sums with random sizes, Leibniz International Proceedings in Informatics, Lipics, vol. 107 (July, 2018), ISBN 9783959770767 [doi]  [abs]
  3. Agarwal, PK; Kumar, N; Sintos, S; Suri, S, Range-max queries on uncertain data, Journal of Computer and System Sciences, vol. 94 (June, 2018), pp. 118-134, Elsevier BV [doi]  [abs]
  4. Agarwal, PK; Arge, L; Staals, F, Improved dynamic geodesic nearest neighbor searching in a simple polygon, Leibniz International Proceedings in Informatics, Lipics, vol. 99 (June, 2018), pp. 41-414 [doi]  [abs]
  5. Agarwal, PK; Kumar, N; Sintos, S; Suri, S, Computing shortest paths in the plane with removable obstacles, Leibniz International Proceedings in Informatics, Lipics, vol. 101 (June, 2018), pp. 51-515, ISBN 9783959770682 [doi]  [abs]
  6. Agarwal, PK; Fox, K; Nath, A; Sidiropoulos, A; Wang, Y, Computing the Gromov-Hausdorff Distance for Metric Trees, Acm Transactions on Algorithms, vol. 14 no. 2 (April, 2018), pp. 1-20, Association for Computing Machinery (ACM) [doi]
  7. Agarwal, PK; Fox, K; Nath, A, Maintaining reeb graphs of triangulated 2-manifolds, Leibniz International Proceedings in Informatics, Lipics, vol. 93 (January, 2018), ISBN 9783959770552 [doi]  [abs]
  8. Agarwal, PK; Fox, K; Munagala, K; Nath, A; Pan, J; Taylor, E, Subtrajectory Clustering, Proceedings of the 35th Acm Sigmod Sigact Sigai Symposium on Principles of Database Systems Sigmod/Pods '18 (2018), pp. 75-87, ACM Press, ISBN 9781450347068 [doi]  [abs]
  9. Lowe, A; Agarwal, PK, Flood-risk analysis on terrains under the multiflow-direction model, Proceedings of the 26th Acm Sigspatial International Conference on Advances in Geographic Information Systems Sigspatial '18 (2018), ACM Press, ISBN 9781450358897 [doi]

Arlotto, Alessandro

  1. Arlotto, A; Steele, JM, A Central Limit Theorem for Costs in Bulinskaya’s Inventory Management Problem When Deliveries Face Delays, Methodology and Computing in Applied Probability, vol. 20 no. 3 (September, 2018), pp. 839-854 [doi]  [abs]
  2. Arlotto, A; Wei, Y; Xie, X, An adaptive O(log n)-optimal policy for the online selection of a monotone subsequence from a random sample, Random Structures & Algorithms, vol. 52 no. 1 (January, 2018), pp. 41-53, Wiley [doi]  [abs]
  3. Arlotto, A; Xie, X, Logarithmic regret in the dynamic and stochastic knapsack problem., Corr, vol. abs/1809.02016 (2018)
  4. Arlotto, A; Frazelle, AE; Wei, Y, Strategic open routing in service networks, Management Science (2018), INFORMS

Autry, Eric A.

  1. Autry, EA; Bayliss, A; Volpert, VA, Biological control with nonlocal interactions, Mathematical Biosciences, vol. 301 (July, 2018), pp. 129-146 [doi]

Beckman, Erin M.

  1. Beckman, E; Dinan, E; Durrett, R; Huo, R; Junge, M, Asymptotic behavior of the Brownian frog model, Electronic Journal of Probability, vol. 23 (2018), Institute of Mathematical Statistics [doi]  [abs]

Bendich, Paul L

  1. Garagic, D; Peskoe, J; Liu, F; Claffey, MS; Bendich, P; Hineman, J; Borggren, N; Harer, J; Zulch, P; Rhodes, BJ, Upstream fusion of multiple sensing modalities using machine learning and topological analysis: An initial exploration, 2018 Ieee Aerospace Conference, vol. 2018-March (March, 2018), pp. 1-8, IEEE, ISBN 9781538620144 [doi]  [abs]
  2. Tralie, CJ; Smith, A; Borggren, N; Hineman, J; Bendich, P; Zulch, P; Harer, J, Geometric cross-modal comparison of heterogeneous sensor data, 2018 Ieee Aerospace Conference (March, 2018), IEEE [doi]  [abs]

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. Sormani, C; Bray, HL; Minicozzi, WP; Eichmair, M; Huang, L-H; Yau, S-T; Uhlenbeck, K; Kusner, R; Codá marques, F; Mese, C; Fraser, A, The Mathematics of Richard Schoen, Notices of the American Mathematical Society, vol. 65 no. 11 (December, 2018), pp. 1-1, American Mathematical Society (AMS) [doi]
  2. Bray, H; Roesch, H, Proof of a Null Geometry Penrose Conjecture, Notices of the American Mathematical Society., vol. 65 (February, 2018), American Mathematical Society

Cao, Yu

  1. Cao, Y; Lu, J, Stochastic dynamical low-rank approximation method, Journal of Computational Physics, vol. 372 (November, 2018), pp. 564-586, Elsevier BV [doi]  [abs]

Cheng, Xiuyuan

  1. Cheng, X; Rachh, M; Steinerberger, S, On the diffusion geometry of graph Laplacians and applications, Applied and Computational Harmonic Analysis (April, 2018), Elsevier BV [doi]
  2. Cheng, X; Mishne, G; Steinerberger, S, The geometry of nodal sets and outlier detection, Journal of Number Theory, vol. 185 (April, 2018), pp. 48-64, Elsevier BV [doi]
  3. Qiu, Q; Cheng, X; Calderbank, AR; Sapiro, G, DCFNet: Deep Neural Network with Decomposed Convolutional Filters., edited by Dy, JG; Krause, A, Icml, vol. 80 (2018), pp. 4195-4204, JMLR.org

Dasgupta, Samit

  1. Dasgupta, S; Voight, J, Sylvester’s problem and mock Heegner points, Proceedings of the American Mathematical Society, vol. 146 no. 8 (March, 2018), pp. 3257-3273, American Mathematical Society (AMS) [doi]
  2. Dasgupta, S; Spieß, M, Partial zeta values, Gross's tower of fields conjecture, and Gross–Stark units, Journal of the European Mathematical Society, vol. 20 no. 11 (January, 2018), pp. 2643-2683, European Mathematical Publishing House [doi]  [abs]
  3. Samit Dasgupta, ; Mahesh Kakde, ; Kevin Ventullo,, On the Gross–Stark Conjecture, Annals of Mathematics, vol. 188 no. 3 (2018), pp. 833-833, Annals of Mathematics, Princeton U [doi]  [abs]

Daubechies, Ingrid

  1. Yin, R; Daubechies, I, Directional Wavelet Bases Constructions with Dyadic Quincunx Subsampling, Journal of Fourier Analysis and Applications, vol. 24 no. 3 (June, 2018), pp. 872-907, Springer Nature [doi]
  2. Gao, T; Yapuncich, GS; Daubechies, I; Mukherjee, S; Boyer, DM, Development and Assessment of Fully Automated and Globally Transitive Geometric Morphometric Methods, With Application to a Biological Comparative Dataset With High Interspecific Variation., Anatomical Record (Hoboken, N.J. : 2007), vol. 301 no. 4 (April, 2018), pp. 636-658 [doi]  [abs]
  3. Xu, J; Yang, H; Daubechies, I, Recursive Diffeomorphism-Based Regression for Shape Functions, Siam Journal on Mathematical Analysis, vol. 50 no. 1 (January, 2018), pp. 5-32, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  4. Alaifari, R; Daubechies, I; Grohs, P; Yin, R, Stable Phase Retrieval in Infinite Dimensions, Foundations of Computational Mathematics (January, 2018), Springer Nature America, Inc [doi]  [abs]

Dolbow, John E.

  1. 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]
  2. 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]
  3. Geelen, RJM; Liu, Y; Dolbow, JE; Rodríguez-Ferran, A, An optimization-based phase-field method for continuous-discontinuous crack propagation, International Journal for Numerical Methods in Engineering, vol. 116 no. 1 (October, 2018), pp. 1-20, WILEY [doi]
  4. Zhang, Z; Jiang, W; Dolbow, JE; Spencer, BW, A modified moment-fitted integration scheme for X-FEM applications with history-dependent material data, Computational Mechanics, vol. 62 no. 2 (August, 2018), pp. 233-252, Springer Nature [doi]  [abs]

Dunson, David B.   (search)

  1. Duan, LL; Johndrow, JE; Dunson, DB, Scaling up data augmentation MCMC via calibration, Journal of Machine Learning Research, vol. 19 (October, 2018)  [abs]
  2. Srivastava, S; Li, C; Dunson, DB, Scalable Bayes via barycenter in Wasserstein space, Journal of Machine Learning Research, vol. 19 (August, 2018), pp. 1-35  [abs]
  3. Guhaniyogi, R; Qamar, S; Dunson, DB, Bayesian Conditional Density Filtering, Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, vol. 27 no. 3 (July, 2018), pp. 657-672, Informa UK Limited [doi]
  4. van den Boom, W; Mao, C; Schroeder, RA; Dunson, DB, Extrema-weighted feature extraction for functional data., Bioinformatics (Oxford, England), vol. 34 no. 14 (July, 2018), pp. 2457-2464 [doi]  [abs]
  5. Shterev, ID; Dunson, DB; Chan, C; Sempowski, GD, Bayesian Multi-Plate High-Throughput Screening of Compounds., Scientific Reports, vol. 8 no. 1 (June, 2018), pp. 9551 [doi]  [abs]
  6. Johndrow, JE; Lum, K; Dunson, DB, Theoretical limits of microclustering for record linkage., Biometrika, vol. 105 no. 2 (June, 2018), pp. 431-446 [doi]  [abs]
  7. Miller, JW; Dunson, DB, Robust Bayesian Inference via Coarsening, Journal of the American Statistical Association (May, 2018), pp. 1-13, Informa UK Limited [doi]
  8. Dunson, DB, Statistics in the big data era: Failures of the machine, Statistics & Probability Letters, vol. 136 (May, 2018), pp. 4-9, Elsevier BV [doi]  [abs]
  9. Zhang, Z; Descoteaux, M; Zhang, J; Girard, G; Chamberland, M; Dunson, D; Srivastava, A; Zhu, H, Mapping population-based structural connectomes., Neuroimage, vol. 172 (May, 2018), pp. 130-145 [doi]  [abs]
  10. van den Boom, W; Schroeder, RA; Manning, MW; Setji, TL; Fiestan, G-O; Dunson, DB, Effect of A1C and Glucose on Postoperative Mortality in Noncardiac and Cardiac Surgeries., Diabetes Care, vol. 41 no. 4 (April, 2018), pp. 782-788 [doi]  [abs]
  11. Durante, D; Dunson, DB, Bayesian Inference and Testing of Group Differences in Brain Networks, Bayesian Analysis, vol. 13 no. 1 (March, 2018), pp. 29-58, Institute of Mathematical Statistics [doi]
  12. Sarkar, A; Chabout, J; Macopson, JJ; Jarvis, ED; Dunson, DB, Bayesian Semiparametric Mixed Effects Markov Models With Application to Vocalization Syntax, Journal of the American Statistical Association (January, 2018), pp. 1-13, Informa UK Limited [doi]  [abs]
  13. Bertrán, MA; Martínez, NL; Wang, Y; Dunson, D; Sapiro, G; Ringach, D, Active learning of cortical connectivity from two-photon imaging data., Plos One, vol. 13 no. 5 (January, 2018), pp. e0196527 [doi]  [abs]

Durrett, Richard T.

  1. Ma, R; Durrett, R, A simple evolutionary game arising from the study of the role of IGF-II in pancreatic cancer, The Annals of Applied Probability, vol. 28 no. 5 (October, 2018), pp. 2896-2921, Institute of Mathematical Statistics [doi]  [abs]
  2. Wang, Z; Durrett, R, Extrapolating weak selection in evolutionary games., Journal of Mathematical Biology (July, 2018) [doi]  [abs]
  3. Talkington, A; Dantoin, C; Durrett, R, Ordinary Differential Equation Models for Adoptive Immunotherapy., Bulletin of Mathematical Biology, vol. 80 no. 5 (May, 2018), pp. 1059-1083 [doi]  [abs]
  4. Huo, R; Durrett, R, Latent voter model on locally tree-like random graphs, Stochastic Processes and Their Applications, vol. 128 no. 5 (May, 2018), pp. 1590-1614, Elsevier BV [doi]
  5. Beckman, E; Dinan, E; Durrett, R; Huo, R; Junge, M, Asymptotic behavior of the Brownian frog model, Electronic Journal of Probability, vol. 23 (2018), Institute of Mathematical Statistics [doi]  [abs]
  6. Basak, A; Durrett, R; Foxall, E, Diffusion limit for the partner model at the critical value, Electronic Journal of Probability, vol. 23 (2018), Institute of Mathematical Statistics [doi]  [abs]
  7. Cristali, I; Ranjan, V; Steinberg, J; Beckman, E; Durrett, R; Junge, M; Nolen, J, Block size in Geometric($p$)-biased permutations, Electronic Communications in Probability, vol. 23 (2018), pp. 1-10, Institute of Mathematical Statistics [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. Lazar, R; Dym, N; Kushinsky, Y; Huang, Z; Ju, T; Lipman, Y, Robust optimization for topological surface reconstruction, Acm Transactions on Graphics, vol. 37 no. 4 (July, 2018), pp. 1-10, Association for Computing Machinery (ACM) [doi]  [abs]

Freeman, Daniel

  1. Freeman, D; Odell, E; Sarı, B; Zheng, B, On spreading sequences and asymptotic structures, Transactions of the American Mathematical Society, vol. 370 no. 10 (April, 2018), pp. 6933-6953, American Mathematical Society (AMS) [doi]

Getz, Jayce R.

  1. Getz, JR, Secondary terms in asymptotics for the number of zeros of quadratic forms over number fields, Journal of the London Mathematical Society, vol. 98 no. 2 (October, 2018), pp. 275-305, WILEY [doi]
  2. Getz, J, Nonabelian Fourier transforms for spherical representations, Pacific Journal of Mathematics, vol. 294 no. 2 (2018), pp. 351-373, Mathematical Sciences Publishers [doi]  [abs]

Hahn, Heekyoung

  1. Hahn, H; Huh, J; Lim, E; Sohn, J, From partition identities to a combinatorial approach to explicit Satake inversions, Annals of Combinatorics, vol. 22 (June, 2018), pp. 543-562, Springer Verlag [doi]

Hain, Richard   (search)

  1. Brown, F; Hain, R, Algebraic de Rham theory for weakly holomorphic modular forms of level one, Algebra & Number Theory, vol. 12 no. 3 (2018), pp. 723-750 [doi]

Harer, John

  1. Tralie, CJ; Smith, A; Borggren, N; Hineman, J; Bendich, P; Zulch, P; Harer, J, Geometric cross-modal comparison of heterogeneous sensor data, 2018 Ieee Aerospace Conference (March, 2018), IEEE [doi]  [abs]
  2. Garagic, D; Peskoe, J; Liu, F; Claffey, MS; Bendich, P; Hineman, J; Borggren, N; Harer, J; Zulch, P; Rhodes, BJ, Upstream fusion of multiple sensing modalities using machine learning and topological analysis: An initial exploration, 2018 Ieee Aerospace Conference, vol. 2018-March (March, 2018), pp. 1-8, IEEE, ISBN 9781538620144 [doi]  [abs]

He, Siming

  1. He, S, Suppression of blow-up in parabolic–parabolic Patlak–Keller–Segel via strictly monotone shear flows, Nonlinearity, vol. 31 no. 8 (August, 2018), pp. 3651-3688, IOP Publishing [doi]
  2. He, S; Tadmor, E, Suppressing Chemotactic Blow-Up Through a Fast Splitting Scenario on the Plane, Archive for Rational Mechanics and Analysis (January, 2018), Springer Nature America, Inc [doi]  [abs]

Herschlag, Gregory J.

  1. Herschlag, G; Lee, S; Vetter, JS; Randles, A, GPU Data Access on Complex Geometries for D3Q19 Lattice Boltzmann Method, 2018 Ieee International Parallel and Distributed Processing Symposium (Ipdps) (May, 2018), IEEE [doi]

Huo, Ran

  1. Huo, R; Durrett, R, Latent voter model on locally tree-like random graphs, Stochastic Processes and Their Applications, vol. 128 no. 5 (May, 2018), pp. 1590-1614, Elsevier BV [doi]
  2. Beckman, E; Dinan, E; Durrett, R; Huo, R; Junge, M, Asymptotic behavior of the Brownian frog model, Electronic Journal of Probability, vol. 23 (2018), Institute of Mathematical Statistics [doi]  [abs]

Junge, Matthew S

  1. BRITO, GERANDY; FOWLER, CHRISTOPHER; JUNGE, MATTHEW; LEVY, AVI, Ewens Sampling and Invariable Generation, Combinatorics, Probability and Computing, vol. 27 no. 06 (November, 2018), pp. 853-891, Cambridge University Press (CUP) [doi]  [abs]
  2. Johnson, T; Junge, M, Stochastic orders and the frog model, Annales De L'Institut Henri Poincaré, Probabilités Et Statistiques, vol. 54 no. 2 (May, 2018), pp. 1013-1030, Institute of Mathematical Statistics [doi]
  3. Foxall, E; Hutchcroft, T; Junge, M, Coalescing random walk on unimodular graphs, Electronic Communications in Probability, vol. 23 (2018), Institute of Mathematical Statistics [doi]  [abs]
  4. Beckman, E; Dinan, E; Durrett, R; Huo, R; Junge, M, Asymptotic behavior of the Brownian frog model, Electronic Journal of Probability, vol. 23 (2018), Institute of Mathematical Statistics [doi]  [abs]
  5. Cristali, I; Ranjan, V; Steinberg, J; Beckman, E; Durrett, R; Junge, M; Nolen, J, Block size in Geometric($p$)-biased permutations, Electronic Communications in Probability, vol. 23 (2018), Institute of Mathematical Statistics [doi]  [abs]

Kiselev, Alexander A.

  1. Do, T; Kiselev, A; Xu, X, Stability of Blowup for a 1D Model of Axisymmetric 3D Euler Equation, Journal of Nonlinear Science, vol. 28 no. 6 (December, 2018), pp. 2127-2152, Springer Nature America, Inc [doi]
  2. Kiselev, A, Special Issue Editorial: Small Scales and Singularity Formation in Fluid Dynamics, Journal of Nonlinear Science, vol. 28 no. 6 (December, 2018), pp. 2047-2050, Springer Nature America, Inc [doi]
  3. Do, T; Kiselev, A; Ryzhik, L; Tan, C, Global Regularity for the Fractional Euler Alignment System, Archive for Rational Mechanics and Analysis, vol. 228 no. 1 (April, 2018), pp. 1-37, Springer Nature [doi]
  4. Kiselev, A; Tan, C, Finite time blow up in the hyperbolic Boussinesq system, Advances in Mathematics, vol. 325 (February, 2018), pp. 34-55, Elsevier BV [doi]  [abs]
  5. Kiselev, A; Tan, C, Global Regularity for 1D Eulerian Dynamics with Singular Interaction Forces, Siam Journal on Mathematical Analysis, vol. 50 no. 6 (January, 2018), pp. 6208-6229, Society for Industrial & Applied Mathematics (SIAM) [doi]

Layton, Anita T.

  1. Li, Q; McDonough, AA; Layton, HE; Layton, AT, Functional implications of sexual dimorphism of transporter patterns along the rat proximal tubule: modeling and analysis., American Journal of Physiology. Renal Physiology, vol. 315 no. 3 (September, 2018), pp. F692-F700 [doi]  [abs]
  2. Wei, N; Gumz, ML; Layton, AT, Predicted effect of circadian clock modulation of NHE3 of a proximal tubule cell on sodium transport., American Journal of Physiology. Renal Physiology, vol. 315 no. 3 (September, 2018), pp. F665-F676 [doi]  [abs]
  3. Layton, AT; Vallon, V, Renal tubular solute transport and oxygen consumption: insights from computational models., Current Opinion in Nephrology and Hypertension, vol. 27 no. 5 (September, 2018), pp. 384-389 [doi]  [abs]
  4. Layton, AT, Sweet success? SGLT2 inhibitors and diabetes., American Journal of Physiology. Renal Physiology, vol. 314 no. 6 (June, 2018), pp. F1034-F1035 [doi]
  5. Leete, J; Gurley, S; Layton, A, Modeling Sex Differences in the Renin Angiotensin System and the Efficacy of Antihypertensive Therapies., Computers & Chemical Engineering, vol. 112 (April, 2018), pp. 253-264, Elsevier BV [doi]  [abs]
  6. Layton, AT; Edwards, A; Vallon, V, Renal potassium handling in rats with subtotal nephrectomy: modeling and analysis., American Journal of Physiology. Renal Physiology, vol. 314 no. 4 (April, 2018), pp. F643-F657 [doi]  [abs]
  7. Layton, AT; Vallon, V, Cardiovascular benefits of SGLT2 inhibition in diabetes and chronic kidney diseases., Acta Physiologica, vol. 222 no. 4 (April, 2018), pp. e13050 [doi]
  8. Wei, N; Layton, AT, Theoretical assessment of the Ca2+ oscillations in the afferent arteriole smooth muscle cell of the rat kidney, International Journal of Biomathematics, vol. 11 no. 03 (April, 2018), pp. 1850043-1850043, World Scientific Pub Co Pte Lt [doi]

Layton, Harold

  1. Li, Q; McDonough, AA; Layton, HE; Layton, AT, Functional implications of sexual dimorphism of transporter patterns along the rat proximal tubule: modeling and analysis., American Journal of Physiology. Renal Physiology, vol. 315 no. 3 (September, 2018), pp. F692-F700 [doi]  [abs]

Leete, Jessica C.

  1. 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; Ruberman, D, Heegaard Floer invariants in codimension one, Transactions of the American Mathematical Society (2018), pp. 1-1, American Mathematical Society (AMS) [doi]

Li, Yingzhou

  1. Li, Y; Yang, H; Ying, L, Multidimensional butterfly factorization, Applied and Computational Harmonic Analysis, vol. 44 no. 3 (May, 2018), pp. 737-758, Elsevier BV [doi]
  2. Wang, R; Li, Y; Darve, E, On the Numerical Rank of Radial Basis Function Kernels in High Dimensions, Siam Journal on Matrix Analysis and Applications, vol. 39 no. 4 (January, 2018), pp. 1810-1835, Society for Industrial & Applied Mathematics (SIAM) [doi]

Liu, Jian-Guo

  1. 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., Physics in Medicine and Biology, vol. 64 no. 2 (January, 2019), pp. 025007 [doi]  [abs]
  2. Feng, Y; Li, L; Liu, J-G; Xu, X, A note on one-dimensional time fractional ODEs, Applied Mathematics Letters, vol. 83 (September, 2018), pp. 87-94, Elsevier BV [doi]
  3. Li, L; Liu, J-G; Wang, L, Cauchy problems for Keller–Segel type time–space fractional diffusion equation, Journal of Differential Equations, vol. 265 no. 3 (August, 2018), pp. 1044-1096, Elsevier BV [doi]  [abs]
  4. Liu, J-G; Tang, M; Wang, L; Zhou, Z, An accurate front capturing scheme for tumor growth models with a free boundary limit, Journal of Computational Physics, vol. 364 (July, 2018), pp. 73-94, Elsevier BV [doi]  [abs]
  5. Chen, K; Li, Q; Liu, J-G, Online learning in optical tomography: a stochastic approach, Inverse Problems, vol. 34 no. 7 (July, 2018), pp. 075010-075010, IOP Publishing [doi]
  6. Gao, Y; Liu, J-G; Lu, XY; Xu, X, Maximal monotone operator theory and its applications to thin film equation in epitaxial growth on vicinal surface, Calculus of Variations and Partial Differential Equations, vol. 57 no. 2 (April, 2018), Springer Nature [doi]
  7. Liu, J-G; Xu, X, Partial regularity of weak solutions to a PDE system with cubic nonlinearity, Journal of Differential Equations, vol. 264 no. 8 (April, 2018), pp. 5489-5526, Elsevier BV [doi]  [abs]
  8. Li, L; Liu, J-G, p -Euler equations and p -Navier–Stokes equations, Journal of Differential Equations, vol. 264 no. 7 (April, 2018), pp. 4707-4748, Elsevier BV [doi]  [abs]
  9. Feng, Y; Li, L; Liu, JG, Semigroups of stochastic gradient descent and online principal component analysis: Properties and diffusion approximations, Communications in Mathematical Sciences, vol. 16 no. 3 (January, 2018), pp. 777-789 [doi]  [abs]
  10. Li, L; Liu, J-G, Some Compactness Criteria for Weak Solutions of Time Fractional PDEs, Siam Journal on Mathematical Analysis, vol. 50 no. 4 (January, 2018), pp. 3963-3995, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  11. Gao, Y; Li, L; Liu, J-G, A Dispersive Regularization for the Modified Camassa--Holm Equation, Siam Journal on Mathematical Analysis, vol. 50 no. 3 (January, 2018), pp. 2807-2838, Society for Industrial & Applied Mathematics (SIAM) [doi]
  12. Li, L; Liu, J-G, A Generalized Definition of Caputo Derivatives and Its Application to Fractional ODEs, Siam Journal on Mathematical Analysis, vol. 50 no. 3 (January, 2018), pp. 2867-2900, Society for Industrial & Applied Mathematics (SIAM) [doi]

Lu, Jianfeng

  1. Delgadillo, R; Lu, J; Yang, X, Frozen Gaussian approximation for high frequency wave propagation in periodic media, Asymptotic Analysis, vol. 110 no. 3-4 (November, 2018), pp. 113-135, IOS Press [doi]  [abs]
  2. Cao, Y; Lu, J, Stochastic dynamical low-rank approximation method, Journal of Computational Physics, vol. 372 (November, 2018), pp. 564-586, Elsevier BV [doi]  [abs]
  3. Chen, H; Lu, J; Ortner, C, Thermodynamic Limit of Crystal Defects with Finite Temperature Tight Binding, Archive for Rational Mechanics and Analysis, vol. 230 no. 2 (November, 2018), pp. 701-733, Springer Nature America, Inc [doi]
  4. Li, X; Liu, J; Lu, J; Zhou, X, Moderate deviation for random elliptic PDE with small noise, The Annals of Applied Probability, vol. 28 no. 5 (October, 2018), pp. 2781-2813, Institute of Mathematical Statistics [doi]  [abs]
  5. Barthel, T; Lu, J, Fundamental Limitations for Measurements in Quantum Many-Body Systems., Physical Review Letters, vol. 121 no. 8 (August, 2018), pp. 080406 [doi]  [abs]
  6. Huang, Y; Lu, J; Ming, P, A Concurrent Global–Local Numerical Method for Multiscale PDEs, Journal of Scientific Computing, vol. 76 no. 2 (August, 2018), pp. 1188-1215, Springer Nature [doi]  [abs]
  7. You, Z; Li, L; Lu, J; Ge, H, Integrated tempering enhanced sampling method as the infinite switching limit of simulated tempering., The Journal of Chemical Physics, vol. 149 no. 8 (August, 2018), pp. 084114 [doi]  [abs]
  8. Lin, L; Lu, J; Vanden-Eijnden, E, A Mathematical Theory of Optimal Milestoning (with a Detour via Exact Milestoning), Communications on Pure and Applied Mathematics, vol. 71 no. 6 (June, 2018), pp. 1149-1177, WILEY [doi]  [abs]
  9. Gauckler, L; Lu, J; Marzuola, JL; Rousset, F; Schratz, K, Trigonometric integrators for quasilinear wave equations, Mathematics of Computation, vol. 88 no. 316 (May, 2018), pp. 717-749, American Mathematical Society (AMS) [doi]
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Maggioni, Mauro

  1. Murphy, JM; Maggioni, M, Diffusion geometric methods for fusion of remotely sensed data, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery Xxiv, vol. 10644 (May, 2018), SPIE, ISBN 9781510617995 [doi]  [abs]
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  5. 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..)
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Malen, Greg

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Mattingly, Jonathan C.   (search)

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Miller, Ezra

  1. Katthän, L; Michałek, M; Miller, E, When is a Polynomial Ideal Binomial After an Ambient Automorphism?, Foundations of Computational Mathematics (January, 2018), Springer Nature America, Inc [doi]  [abs]

Mukherjee, Sayan

  1. Silverman, JD; Durand, HK; Bloom, RJ; Mukherjee, S; David, LA, Correction to: Dynamic linear models guide design and analysis of microbiota studies within artificial human guts., Microbiome, vol. 6 no. 1 (November, 2018), pp. 212 [doi]  [abs]
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Nagy, Akos

  1. Nagy, Á, Irreducible Ginzburg–Landau Fields in Dimension 2, The Journal of Geometric Analysis, vol. 28 no. 2 (April, 2018), pp. 1853-1868, Springer Nature [doi]

Ng, Lenhard L.

  1. Ekholm, T; Ng, L; Shende, V, A complete knot invariant from contact homology, Inventiones Mathematicae, vol. 211 no. 3 (March, 2018), pp. 1149-1200, Springer Nature [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 (January, 2018), pp. 1850072-1850072, World Scientific Pub Co Pte Lt [doi]  [abs]
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Orizaga, Saulo

  1. Glasner, K; Orizaga, S, Multidimensional equilibria and their stability in copolymer–solvent mixtures, Physica D: Nonlinear Phenomena, vol. 373 (June, 2018), pp. 1-12, Elsevier BV [doi]  [abs]

Pfister, Henry

  1. Hager, C; Pfister, HD, Approaching Miscorrection-Free Performance of Product Codes With Anchor Decoding, Ieee Transactions on Communications, vol. 66 no. 7 (July, 2018), pp. 2797-2808, Institute of Electrical and Electronics Engineers (IEEE) [doi]
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Pierce, Lillian B.

  1. Pierce, LB; Yung, PL, A polynomial Carleson operator along the paraboloid, Revista Matemática Iberoamericana (2018), European Mathematical Society

Plesser, M. Ronen

  1. Bertolini, M; Plesser, MR, (0,2) hybrid models, Journal of High Energy Physics, vol. 2018 no. 9 (September, 2018), Springer Nature America, Inc [doi]  [abs]

Pollack, Aaron

  1. Pollack, A; Shah, S, The spin L-function on GSp(6) via a non-unique model, American Journal of Mathematics, vol. 140 no. 3 (2018), pp. 753-788, Johns Hopkins University Press
  2. Pollack, A, Unramified Godement-Jacquet theory for the spin similitude group, Journal of the Ramanujan Mathematical Society, vol. 33 no. 3 (2018), pp. 249-282, The Ramanujan Mathematical Society
  3. Pollack, A; Shah, S, Multivariate Rankin-Selberg integrals on GL(4) and GU(2,2), Canadian Mathematical Bulletin, vol. 61 no. 4 (2018), pp. 822-835, Canadian Mathematical Society
  4. Pollack, A, Lifting laws and arithmetic invariant theory, Cambridge Journal of Mathematics, vol. 6 no. 4 (2018), pp. 347-449

Randles, Amanda

  1. 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]
  2. 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]
  3. Hegele, LA; Scagliarini, A; Sbragaglia, M; Mattila, KK; Philippi, PC; Puleri, DF; Gounley, J; Randles, A, High-Reynolds-number turbulent cavity flow using the lattice Boltzmann method, Physical Review. E, vol. 98 no. 4 (October, 2018), American Physical Society (APS) [doi]  [abs]
  4. Rafat, M; Stone, HA; Auguste, DT; Dabagh, M; Randles, A; Heller, M; Rabinov, JD, Impact of diversity of morphological characteristics and Reynolds number on local hemodynamics in basilar aneurysms, Aiche Journal, vol. 64 no. 7 (July, 2018), pp. 2792-2802, WILEY [doi]  [abs]
  5. Herschlag, G; Lee, S; Vetter, JS; Randles, A, GPU Data Access on Complex Geometries for D3Q19 Lattice Boltzmann Method, 2018 Ieee International Parallel and Distributed Processing Symposium (Ipdps) (May, 2018), pp. 825-834, IEEE, ISBN 9781538643686 [doi]  [abs]

Reed, Michael C.

  1. Nijhout, HF; Best, JA; Reed, MC, Systems biology of robustness and homeostatic mechanisms., Wiley Interdisciplinary Reviews. Systems Biology and Medicine (October, 2018), pp. e1440 [doi]  [abs]
  2. Sadre-Marandi, F; Dahdoul, T; Reed, MC; Nijhout, HF, Sex differences in hepatic one-carbon metabolism., Bmc Systems Biology, vol. 12 no. 1 (October, 2018), pp. 89 [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 (July, 2018) [doi]  [abs]
  4. Duncan, W; Best, J; Golubitsky, M; Nijhout, HF; Reed, M, Homeostasis despite instability., Mathematical Biosciences, vol. 300 (March, 2018), pp. 130-137 [doi]  [abs]
  5. Suppiramaniam, V; Bloemer, J; Reed, M; Bhattacharya, S, Neurotransmitter Receptors, in Comprehensive Toxicology, vol. 6-15 (2018), pp. 174-201, Elsevier, ISBN 9780081006016 [doi]  [abs]

Robles, Colleen M

  1. Robles, C, Characterization of Calabi–Yau variations of Hodge structure over tube domains by characteristic forms, Mathematische Annalen, vol. 371 no. 3-4 (August, 2018), pp. 1229-1253, Springer Nature [doi]  [abs]

Rudin, Cynthia D.

  1. Rudin, C; Ertekin, Ş, Learning customized and optimized lists of rules with mathematical programming, Mathematical Programming Computation, vol. 10 no. 4 (December, 2018), pp. 659-702, Springer Nature America, Inc [doi]  [abs]
  2. Rudin, C; Ustun, B, Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice, Interfaces, vol. 48 no. 5 (October, 2018), pp. 449-466, Institute for Operations Research and the Management Sciences (INFORMS) [doi]  [abs]
  3. Vu, M-AT; Adalı, T; Ba, D; Buzsáki, G; Carlson, D; Heller, K; Liston, C; Rudin, C; Sohal, VS; Widge, AS; Mayberg, HS; Sapiro, G; Dzirasa, K, A Shared Vision for Machine Learning in Neuroscience., The Journal of Neuroscience : the Official Journal of the Society for Neuroscience, vol. 38 no. 7 (February, 2018), pp. 1601-1607 [doi]  [abs]
  4. Angelino, E; Larus-Stone, N; Alabi, D; Seltzer, M; Rudin, C, Learning certifiably optimal rule lists for categorical data, Journal of Machine Learning Research, vol. 18 (January, 2018), pp. 1-78  [abs]

Ryser, Marc D.

  1. 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]
  2. Ryser, MD; Yu, M; Grady, W; Siegmund, K; Shibata, D, Epigenetic Heterogeneity in Human Colorectal Tumors Reveals Preferential Conservation And Evidence of Immune Surveillance., Scientific Reports, vol. 8 no. 1 (November, 2018), pp. 17292 [doi]  [abs]
  3. Ryser, MD; Min, B-H; Siegmund, KD; Shibata, D, Spatial mutation patterns as markers of early colorectal tumor cell mobility., Proceedings of the National Academy of Sciences of the United States of America, vol. 115 no. 22 (May, 2018), pp. 5774-5779 [doi]  [abs]
  4. Role of Preoperative Variables in Reducing the Rate of Occult Invasive Disease for Women Considering Active Surveillance for Ductal Carcinoma In Situ., Jama Surgery, vol. 153 no. 3 (March, 2018), pp. 290-291 [doi]
  5. Ryser, MD; Horton, JK; Hwang, ES, How Low Can We Go-and Should We? Risk Reduction for Minimal-Volume DCIS., Annals of Surgical Oncology, vol. 25 no. 2 (February, 2018), pp. 354-355 [doi]
  6. Ryser, MD; Weaver, DL; Marks, JR; Hyslop, T; Hwang, ES, Quantifying the natural history and overtreatment rate of ductal carcinoma in situ, Cancer Research, vol. 78 no. 4 (February, 2018)
  7. Shen, Y; Dong, W; Gulati, R; Ryser, MD; Etzioni, R, Estimating the frequency of indolent breast cancer in screening trials., Statistical Methods in Medical Research (January, 2018), pp. 962280217754232 [doi]  [abs]

Saper, Leslie

  1. Saper, L, ℒ-modules and micro-support, to appear in Annals of Mathematics (2018)

Sapiro, Guillermo

  1. 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]
  2. Bovery, MDMJ; Dawson, G; Hashemi, J; Sapiro, G, A Scalable Off-the-Shelf Framework for Measuring Patterns of Attention in Young Children and its Application in Autism Spectrum Disorder, Ieee Transactions on Affective Computing (2019), pp. 1-1, Institute of Electrical and Electronics Engineers (IEEE) [doi]
  3. Dawson, G; Campbell, K; Hashemi, J; Lippmann, SJ; Smith, V; Carpenter, K; Egger, H; Espinosa, S; Vermeer, S; Baker, J; Sapiro, G, Atypical postural control can be detected via computer vision analysis in toddlers with autism spectrum disorder., Scientific Reports, vol. 8 no. 1 (November, 2018), pp. 17008 [doi]  [abs]
  4. Bartesaghi, A; Aguerrebere, C; Falconieri, V; Banerjee, S; Earl, LA; Zhu, X; Grigorieff, N; Milne, JLS; Sapiro, G; Wu, X; Subramaniam, S, Atomic Resolution Cryo-EM Structure of β-Galactosidase., Structure (London, England : 1993), vol. 26 no. 6 (June, 2018), pp. 848-856.e3 [doi]  [abs]
  5. Aguerrebere, C; Delbracio, M; Bartesaghi, A; Sapiro, G, A Practical Guide to Multi-Image Alignment, 2018 Ieee International Conference on Acoustics, Speech and Signal Processing (Icassp), vol. 2018-April (April, 2018), pp. 1927-1931, IEEE [doi]  [abs]
  6. Ahn, HK; Qiu, Q; Bosch, E; Thompson, A; Robles, FE; Sapiro, G; Warren, WS; Calderbank, R, Classifying Pump-Probe Images of Melanocytic Lesions Using the WEYL Transform, 2018 Ieee International Conference on Acoustics, Speech and Signal Processing (Icassp), vol. 2018-April (April, 2018), pp. 4209-4213, IEEE, ISBN 9781538646588 [doi]  [abs]
  7. Giryes, R; Eldar, YC; Bronstein, AM; Sapiro, G, The Learned Inexact Project Gradient Descent Algorithm, 2018 Ieee International Conference on Acoustics, Speech and Signal Processing (Icassp), vol. 2018-April (April, 2018), pp. 6767-6771, IEEE, ISBN 9781538646588 [doi]  [abs]
  8. Giryes, R; Eldar, YC; Bronstein, AM; Sapiro, G, Tradeoffs Between Convergence Speed and Reconstruction Accuracy in Inverse Problems, Ieee Transactions on Signal Processing, vol. 66 no. 7 (April, 2018), pp. 1676-1690, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  9. 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 (March, 2018), pp. 1362361318766247 [doi]  [abs]
  10. Sapiro, G; Schmitt, JW; Ramanujam, N; Chaudhary, U; Lam, CT; Mueller, J; Simhal, A; Asiedu, MN, Image processing and machine learning techniques to automate diagnosis of Lugol's iodine cervigrams for a low-cost point-of-care digital colposcope, Optics and Biophotonics in Low Resource Settings Iv, vol. 10485 (February, 2018), SPIE, ISBN 9781510614550 [doi]  [abs]
  11. Vu, M-AT; Adalı, T; Ba, D; Buzsáki, G; Carlson, D; Heller, K; Liston, C; Rudin, C; Sohal, VS; Widge, AS; Mayberg, HS; Sapiro, G; Dzirasa, K, A Shared Vision for Machine Learning in Neuroscience., The Journal of Neuroscience : the Official Journal of the Society for Neuroscience, vol. 38 no. 7 (February, 2018), pp. 1601-1607 [doi]  [abs]
  12. Pisharady, PK; Sotiropoulos, SN; Duarte-Carvajalino, JM; Sapiro, G; Lenglet, C, Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning., Neuroimage, vol. 167 (February, 2018), pp. 488-503 [doi]  [abs]
  13. Simhal, AK; Gong, B; Trimmer, JS; Weinberg, RJ; Smith, SJ; Sapiro, G; Micheva, KD, A Computational Synaptic Antibody Characterization Tool for Array Tomography., Frontiers in Neuroanatomy, vol. 12 (January, 2018), pp. 51 [doi]  [abs]
  14. Bertrán, MA; Martínez, NL; Wang, Y; Dunson, D; Sapiro, G; Ringach, D, Active learning of cortical connectivity from two-photon imaging data., Plos One, vol. 13 no. 5 (January, 2018), pp. e0196527 [doi]  [abs]
  15. Chiew, KS; Hashemi, J; Gans, LK; Lerebours, L; Clement, NJ; Vu, M-AT; Sapiro, G; Heller, NE; Adcock, RA, Motivational valence alters memory formation without altering exploration of a real-life spatial environment., Plos One, vol. 13 no. 3 (January, 2018), pp. e0193506 [doi]  [abs]
  16. Duchin, Y; Shamir, RR; Patriat, R; Kim, J; Vitek, JL; Sapiro, G; Harel, N, Patient-specific anatomical model for deep brain stimulation based on 7 Tesla MRI., Plos One, vol. 13 no. 8 (January, 2018), pp. e0201469 [doi]  [abs]
  17. Qiu, Q; Cheng, X; Calderbank, R; Sapiro, G, DCFNet: Deep Neural Network with Decomposed Convolutional Filters, 35th International Conference on Machine Learning, Icml 2018, vol. 9 (January, 2018), pp. 6687-6696  [abs]
  18. Hashemi, J; Dawson, G; Carpenter, KLH; Campbell, K; Qiu, Q; Espinosa, S; Marsan, S; Baker, JP; Egger, HL; Sapiro, G, Computer Vision Analysis for Quantification of Autism Risk Behaviors, Ieee Transactions on Affective Computing (2018), pp. 1-1, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  19. Qiu, Q; Lezama, J; Bronstein, A; Sapiro, G, ForestHash: Semantic Hashing with Shallow Random Forests and Tiny Convolutional Networks, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11206 LNCS (2018), pp. 442-459, Springer International Publishing [doi]  [abs]
  20. 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 Transactions on Bio Medical Engineering (2018), pp. 1-1, Institute of Electrical and Electronics Engineers (IEEE) [doi]

Sober, Barak

  1. Shaus, A; Sober, B; Tzang, O; Ioffe, Z; Cheshnovsky, O; Finkelstein, I; Piasetzky, E, Raman Binary Mapping of Iron Age Ostracon in an Unknown Material Composition and High-Fluorescence Setting-A Proof of Concept, Archaeometry (January, 2018), WILEY [doi]  [abs]

Stern, Mark A.

  1. Lipnowski, M; Stern, M, Geometry of the Smallest 1-form Laplacian Eigenvalue on Hyperbolic Manifolds, Geometrical and Functional Analysis Gafa, vol. 28 no. 6 (December, 2018), pp. 1717-1755, Springer Nature [doi]  [abs]

Stubbs, Kevin

  1. Czaja, W; Manning, B; Murphy, JM; Stubbs, K, Discrete directional Gabor frames, Applied and Computational Harmonic Analysis, vol. 45 no. 1 (July, 2018), pp. 1-21, Elsevier BV [doi]

Tarokh, Vahid

  1. Ding, J; Tarokh, V; Yang, Y, Model Selection Techniques: An Overview, Ieee Signal Processing Magazine, vol. 35 no. 6 (November, 2018), pp. 16-34, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  2. Shahrampour, S; Noshad, M; Ding, J; Tarokh, V, Online Learning for Multimodal Data Fusion With Application to Object Recognition, Ieee Transactions on Circuits and Systems Ii: Express Briefs, vol. 65 no. 9 (September, 2018), pp. 1259-1263, Institute of Electrical and Electronics Engineers (IEEE) [doi]
  3. Magnusson, S; Enyioha, C; Li, N; Fischione, C; Tarokh, V, Communication Complexity of Dual Decomposition Methods for Distributed Resource Allocation Optimization, Ieee Journal of Selected Topics in Signal Processing, vol. 12 no. 4 (August, 2018), pp. 717-732, Institute of Electrical and Electronics Engineers (IEEE) [doi]
  4. Banerjee, T; Whipps, G; Gurram, P; Tarokh, V, Sequential Event Detection Using Multimodal Data in Nonstationary Environments, 2018 21st International Conference on Information Fusion (Fusion) (July, 2018), pp. 1940-1947, IEEE [doi]  [abs]
  5. Banerjee, T; Choi, J; Pesaran, B; Ba, D; Tarokh, V, Classification of Local Field Potentials using Gaussian Sequence Model, 2018 Ieee Statistical Signal Processing Workshop (Ssp) (June, 2018), pp. 218-222, IEEE [doi]  [abs]
  6. Ding, J; Shahrampour, S; Heal, K; Tarokh, V, Analysis of Multistate Autoregressive Models, Ieee Transactions on Signal Processing, vol. 66 no. 9 (May, 2018), pp. 2429-2440, Institute of Electrical and Electronics Engineers (IEEE) [doi]
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