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Mathematics : Publications since January 2016

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

Abel, Michael

  1. HOMFLY-PT homology for general link diagrams and braidlike isotopy (June, 2016) [arxiv:1607.00314]
  2. with M. Hogancamp, Stable homology of torus links via categorified Young symmetrizers II: one-column partitions (February, 2016) [arXiv:1510.05330]

Agarwal, Pankaj K.

  1. Agarwal, PK; Har-Peled, S; Suri, S; Yıldız, H; Zhang, W, Convex Hulls Under Uncertainty, Algorithmica, vol. 79 no. 2 (October, 2017), pp. 340-367 [doi]
  2. Agarwal, PK; Rubin, N; Sharir, M, Approximate nearest neighbor search amid higher-dimensional flats, LIPIcs, vol. 87 (September, 2017), ISBN 9783959770491 [doi]  [abs]
  3. Agarwal, PK; Kumar, N; Sintos, S; Suri, S, Efficient algorithms for k-regret minimizing sets, LIPIcs, vol. 75 (August, 2017), ISBN 9783959770361 [doi]  [abs]
  4. Agarwal, PK; Fox, K; Panigrahi, D; Varadarajan, KR; Xiao, A, Faster algorithms for the geometric transportation problem, LIPIcs, vol. 77 (June, 2017), pp. 71-716, ISBN 9783959770385 [doi]  [abs]
  5. Agarwal, PK; Pan, J; Victor, W, An efficient algorithm for placing electric vehicle charging stations, LIPIcs, vol. 64 (December, 2016), pp. 7.1-7.12, ISBN 9783959770262 [doi]  [abs]
  6. Ying, R; Pan, J; Fox, K; Agarwal, PK, A simple efficient approximation algorithm for dynamic time warping, GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems (October, 2016), ISBN 9781450345897 [doi]  [abs]
  7. Nath, A; Fox, K; Agarwal, PK; Munagala, K, Massively parallel algorithms for computing TIN DEMs and contour trees for large terrains, GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems (October, 2016), ISBN 9781450345897 [doi]  [abs]
  8. Agarwal, PK; Aronov, B; Har-Peled, S; Phillips, JM; Yi, K; Zhang, W, Nearest-Neighbor Searching Under Uncertainty II, ACM Transactions on Algorithms, vol. 13 no. 1 (October, 2016), pp. 1-25 [doi]
  9. Agarwal, PK; Kumar, N; Sintos, S; Suri, S, Range-max queries on uncertain data, Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, vol. 26-June-01-July-2016 (June, 2016), pp. 465-476, ISBN 9781450341912 [doi]  [abs]
  10. Agarwal, PK; Fox, K; Munagala, K; Nath, A, Parallel algorithms for constructing range and nearest-neighbor searching data structures, Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, vol. 26-June-01-July-2016 (June, 2016), pp. 429-440, ISBN 9781450341912 [doi]  [abs]
  11. Agarwal, PK; Fox, K; Pan, J; Ying, R, Approximating dynamic time warping and edit distance for a pair of point sequences, LIPIcs, vol. 51 (June, 2016), pp. 6.1-6.16 [doi]  [abs]
  12. Yu, A; Agarwal, PK; Yang, J, Top-$k$ Preferences in High Dimensions, IEEE Transactions on Knowledge and Data Engineering, vol. 28 no. 2 (February, 2016), pp. 311-325, ISSN 1041-4347 [doi]
  13. Agarwal, PK; Fox, K; Salzman, O, An efficient algorithm for computing high-quality paths amid polygonal obstacles, Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms, vol. 2 (January, 2016), pp. 1179-1192, ISBN 9781510819672  [abs]
  14. Pan, J; Rao, V; Agarwal, PK; Gelfand, AE, Markov-modulated marked poisson processes for check-in data, 33rd International Conference on Machine Learning, ICML 2016, vol. 5 (January, 2016), pp. 3311-3320, ISBN 9781510829008  [abs]

Arlotto, Alessandro

  1. Arlotto, A; Steele, JM, A Central Limit Theorem for Temporally Nonhomogenous Markov Chains with Applications to Dynamic Programming, Mathematics of Operations Research, vol. 41 no. 4 (November, 2016), pp. 1448-1468 [doi]
  2. Arlotto, A; Mossel, E; Steele, JM, Quickest online selection of an increasing subsequence of specified size, Random Structures and Algorithms, vol. 49 no. 2 (September, 2016), pp. 235-252 [doi]

Beale, J. Thomas

  1. Beale, JT; Ying, W; Wilson, JR, A Simple Method for Computing Singular or Nearly Singular Integrals on Closed Surfaces, Communications in computational physics, vol. 20 no. 03 (September, 2016), pp. 733-753 [doi]

Bendich, Paul L

  1. Bendich, P; Chin, SP; Clark, J; Desena, J; Harer, J; Munch, E; Newman, A; Porter, D; Rouse, D; Strawn, N; Watkins, A, Topological and statistical behavior classifiers for tracking applications, IEEE Transactions on Aerospace and Electronic Systems, vol. 52 no. 6 (December, 2016), pp. 2644-2661 [doi]  [abs]
  2. Bendich, P; Gasparovic, E; Harer, J; Tralie, C, Geometric models for musical audio data, LIPIcs, vol. 51 (June, 2016), pp. 65.1-65.5, ISBN 9783959770095 [doi]  [abs]
  3. Bendich, P; Marron, JS; Miller, E; Pieloch, A; Skwerer, S, Persistent homology analysis of brain artery trees, Annals of Applied Statistics, vol. 10 no. 1 (January, 2016), pp. 19 pages, ISSN 1932-6157 (to appear.) [repository], [doi]  [abs]
  4. Paul Bendich, Ellen Gasparovic, John Harer, and Christopher J. Tralie, Scaffoldings and Spines: Organizing High-Dimensional Data Using Cover Trees, Local Principal Component Analysis, and Persistent Homology (2016) [1602.06245]

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, HL; Jauregui, JL; Mars, M, Time Flat Surfaces and the Monotonicity of the Spacetime Hawking Mass II, Annales Henri Poincaré, vol. 17 no. 6 (July 26, 2015), pp. 1457-1475, Springer Basel, ISSN 1424-0637 [arXiv:1402.3287 [math.DG]], [3287], [doi]  [abs]

Bryant, Robert   (search)

  1. Bryant, RL; Huang, L; Mo, X, On Finsler surfaces of constant flag curvature with a Killing field, Journal of Geometry and Physics, vol. 116 (June, 2017), pp. 345-357 [doi]

Calderbank, Robert

  1. Wang, L; Chen, M; Rodrigues, M; Wilcox, D; Calderbank, R; Carin, L, Information-Theoretic Compressive Measurement Design., IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39 no. 6 (June, 2017), pp. 1150-1164 [doi]  [abs]
  2. Hadani, R; Rakib, S; Tsatsanis, M; Monk, A; Goldsmith, AJ; Molisch, AF; Calderbank, R, Orthogonal time frequency space modulation, IEEE Wireless Communications and Networking Conference (May, 2017), ISBN 9781509041831 [doi]  [abs]
  3. Campbell, K; Carpenter, KLH; Espinosa, S; Hashemi, J; Qiu, Q; Tepper, M; Calderbank, R; Sapiro, G; Egger, HL; Baker, JP; Dawson, G, Use of a Digital Modified Checklist for Autism in Toddlers - Revised with Follow-up to Improve Quality of Screening for Autism., The Journal of Pediatrics, vol. 183 (April, 2017), pp. 133-139.e1 [doi]  [abs]
  4. Reboredo, H; Renna, F; Calderbank, R; Rodrigues, MRD, Bounds on the Number of Measurements for Reliable Compressive Classification, IEEE Transactions on Signal Processing, vol. 64 no. 22 (November, 2016), pp. 5778-5793 [doi]
  5. Thompson, A; Robles, FE; Wilson, JW; Deb, S; Calderbank, R; Warren, WS, Dual-wavelength pump-probe microscopy analysis of melanin composition., Scientific Reports, vol. 6 (November, 2016), pp. 36871 [doi]  [abs]
  6. Renna, F; Wang, L; Yuan, X; Yang, J; Reeves, G; Calderbank, R; Carin, L; Rodrigues, MRD, Classification and Reconstruction of High-Dimensional Signals From Low-Dimensional Features in the Presence of Side Information, IEEE Transactions on Information Theory, vol. 62 no. 11 (November, 2016), pp. 6459-6492 [doi]
  7. Kumar, S; Calderbank, R; Pfister, HD, Beyond double transitivity: Capacity-achieving cyclic codes on erasure channels, 2016 IEEE Information Theory Workshop, ITW 2016 (October, 2016), pp. 241-245, ISBN 9781509010905 [doi]  [abs]
  8. Mappouras, G; Vahid, A; Calderbank, R; Sorin, DJ, Methuselah flash: Rewriting codes for extra long storage lifetime, Proceedings - 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2016 (September, 2016), pp. 180-191, ISBN 9781467388917 [doi]  [abs]
  9. Vahid, A; Calderbank, R, Two-User Erasure Interference Channels With Local Delayed CSIT, IEEE Transactions on Information Theory, vol. 62 no. 9 (September, 2016), pp. 4910-4923 [doi]
  10. Nokleby, M; Beirami, A; Calderbank, R, Rate-distortion bounds on Bayes risk in supervised learning, IEEE International Symposium on Information Theory - Proceedings, vol. 2016-August (August, 2016), pp. 2099-2103, ISBN 9781509018062 [doi]  [abs]
  11. Vahid, A; Calderbank, R, When does spatial correlation add value to delayed channel state information?, IEEE International Symposium on Information Theory - Proceedings, vol. 2016-August (August, 2016), pp. 2624-2628, ISBN 9781509018062 [doi]  [abs]
  12. Sokolic, J; Renna, F; Calderbank, R; Rodrigues, MRD, Mismatch in the Classification of Linear Subspaces: Sufficient Conditions for Reliable Classification, IEEE Transactions on Signal Processing, vol. 64 no. 12 (June, 2016), pp. 3035-3050 [doi]
  13. Wang, L; Renna, F; Yuan, X; Rodrigues, M; Calderbank, R; Carin, L, A general framework for reconstruction and classification from compressive measurements with side information, IEEE International Conference on Acoustics Speech and Signal Processing, vol. 2016-May (May, 2016), pp. 4239-4243, ISBN 9781479999880 [doi]  [abs]
  14. Beirami, A; Calderbank, R; Christiansen, M; Duffy, K; Makhdoumi, A; Medard, M, A geometric perspective on guesswork, 2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015 (April, 2016), pp. 941-948, ISBN 9781509018239 [doi]  [abs]
  15. Vahid, A; Shomorony, I; Calderbank, R, Informational bottlenecks in two-unicast wireless networks with delayed CSIT, 2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015 (April, 2016), pp. 1256-1263, ISBN 9781509018239 [doi]  [abs]
  16. Huang, J; Qiu, Q; Calderbank, R, The Role of Principal Angles in Subspace Classification, IEEE Transactions on Signal Processing, vol. 64 no. 8 (April, 2016), pp. 1933-1945 [doi]
  17. Qiu, Q; Thompson, A; Calderbank, R; Sapiro, G, Data Representation Using the Weyl Transform, IEEE Transactions on Signal Processing, vol. 64 no. 7 (April, 2016), pp. 1844-1853 [doi]
  18. Goparaju, S; Rouayheb, SE; Calderbank, R, Can linear minimum storage regenerating codes be universally secure?, Conference Record of the Asilomar Conference on Signals, Systems and Computers, vol. 2016-February (February, 2016), pp. 549-553, ISBN 9781467385763 [doi]  [abs]
  19. Carpenter, KLH; Sprechmann, P; Calderbank, R; Sapiro, G; Egger, HL, Quantifying Risk for Anxiety Disorders in Preschool Children: A Machine Learning Approach., PloS one, vol. 11 no. 11 (January, 2016), pp. e0165524 [doi]  [abs]
  20. Thompson, A; Calderbank, R, Compressive imaging using fast transform coding, Proceedings of SPIE - The International Society for Optical Engineering, vol. 9992 (January, 2016), ISBN 9781510603882 [doi]  [abs]

Daubechies, Ingrid

  1. Deligiannis, N; Mota, JFC; Cornelis, B; Rodrigues, MRD; Daubechies, I, Multi-Modal Dictionary Learning for Image Separation With Application in Art Investigation, IEEE Transactions on Image Processing, vol. 26 no. 2 (February, 2017), pp. 751-764 [doi]
  2. Cornelis, B; Yang, H; Goodfriend, A; Ocon, N; Lu, J; Daubechies, I, Removal of Canvas Patterns in Digital Acquisitions of Paintings., IEEE Transactions on Image Processing, vol. 26 no. 1 (January, 2017), pp. 160-171 [doi]  [abs]
  3. Voronin, S; Daubechies, I, An iteratively reweighted least squares algorithm for sparse regularization, in Contemporary Mathematics, vol. 693 (January, 2017), pp. 391-411 [doi]  [abs]
  4. Yin, R; Gao, T; Lu, YM; Daubechies, I, A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution Framelets, SIAM Journal on Imaging Sciences, vol. 10 no. 2 (January, 2017), pp. 711-750 [doi]
  5. Fodor, G; Cornelis, B; Yin, R; Dooms, A; Daubechies, I, Cradle Removal in X-Ray Images of Panel Paintings, Image Processing On Line, vol. 7 (2017), pp. 23-42 [doi]
  6. Wu, H-T; Lewis, GF; Davila, MI; Daubechies, I; Porges, SW, Optimizing Estimates of Instantaneous Heart Rate from Pulse Wave Signals with the Synchrosqueezing Transform., Methods of information in medicine, vol. 55 no. 5 (October, 2016), pp. 463-472 [doi]  [abs]
  7. Daubechies, I; Defrise, M; Mol, CD, Sparsity-enforcing regularisation and ISTA revisited, Inverse Problems, vol. 32 no. 10 (October, 2016), pp. 104001-104001 [doi]
  8. O'Neal, WT; Wang, YG; Wu, H-T; Zhang, Z-M; Li, Y; Tereshchenko, LG; Estes, EH; Daubechies, I; Soliman, EZ, Electrocardiographic J Wave and Cardiovascular Outcomes in the General Population (from the Atherosclerosis Risk In Communities Study)., The American Journal of Cardiology, vol. 118 no. 6 (September, 2016), pp. 811-815 [doi]  [abs]
  9. Deligiannis, N; Mota, JFC; Cornelis, B; Rodrigues, MRD; Daubechies, I, X-ray image separation via coupled dictionary learning, Proceedings / ICIP ... International Conference on Image Processing, vol. 2016-August (August, 2016), pp. 3533-3537, ISBN 9781467399616 [doi]  [abs]
  10. Yin, R; Monson, E; Honig, E; Daubechies, I; Maggioni, M, Object recognition in art drawings: Transfer of a neural network, IEEE International Conference on Acoustics Speech and Signal Processing, vol. 2016-May (May, 2016), pp. 2299-2303, ISSN 1520-6149, ISBN 9781479999880 [doi]  [abs]
  11. Daubechies, I; Wang, YG; Wu, H-T, ConceFT: concentration of frequency and time via a multitapered synchrosqueezed transform., Philosophical Transactions A, vol. 374 no. 2065 (April, 2016), pp. 20150193, ISSN 1364-503X [doi]  [abs]
  12. Huang, NE; Daubechies, I; Hou, TY, Adaptive data analysis: theory and applications., Philosophical Transactions A, vol. 374 no. 2065 (April, 2016), pp. 20150207, ISSN 1364-503X [doi]
  13. Yin, R; Cornelis, B; Fodor, G; Ocon, N; Dunson, D; Daubechies, I, Removing Cradle Artifacts in X-Ray Images of Paintings, SIAM Journal on Imaging Sciences, vol. 9 no. 3 (January, 2016), pp. 1247-1272 [doi]

Dolbow, John E.

  1. Peco, C; Chen, W; Liu, Y; Bandi, MM; Dolbow, JE; Fried, E, Influence of surface tension in the surfactant-driven fracture of closely-packed particulate monolayers., Soft Matter, vol. 13 no. 35 (September, 2017), pp. 5832-5841 [doi]  [abs]
  2. Zhang, Z; Dolbow, JE, Remeshing strategies for large deformation problems with frictional contact and nearly incompressible materials, International Journal for Numerical Methods in Engineering, vol. 109 no. 9 (March, 2017), pp. 1289-1314 [doi]
  3. Stershic, AJ; Dolbow, JE; Moës, N, The Thick Level-Set model for dynamic fragmentation, Engineering Fracture Mechanics, vol. 172 (March, 2017), pp. 39-60 [doi]
  4. Spencer, BW; Jiang, W; Dolbow, JE; Peco, C, Pellet cladding mechanical interaction modeling using the extended finite element method, Top Fuel 2016: LWR Fuels with Enhanced Safety and Performance (January, 2016), pp. 929-938, ISBN 9780894487309  [abs]

Dunson, David B.

  1. Reddy, A; Zhang, J; Davis, NS; Moffitt, AB; Love, CL; Waldrop, A; Leppa, S; Pasanen, A; Meriranta, L; Karjalainen-Lindsberg, M-L; Nørgaard, P; Pedersen, M; Gang, AO; Høgdall, E; Heavican, TB; Lone, W; Iqbal, J; Qin, Q; Li, G; Kim, SY; Healy, J; Richards, KL; Fedoriw, Y; Bernal-Mizrachi, L; Koff, JL; Staton, AD; Flowers, CR; Paltiel, O; Goldschmidt, N; Calaminici, M; Clear, A; Gribben, J; Nguyen, E; Czader, MB; Ondrejka, SL; Collie, A; Hsi, ED; Tse, E; Au-Yeung, RKH; Kwong, Y-L; Srivastava, G et al., Genetic and Functional Drivers of Diffuse Large B Cell Lymphoma., Cell, vol. 171 no. 2 (October, 2017), pp. 481-494.e15 [doi]  [abs]
  2. Li, C; Srivastava, S; Dunson, DB, Simple, scalable and accurate posterior interval estimation, Biometrika, vol. 104 no. 3 (September, 2017), pp. 665-680 [doi]
  3. Lock, EF; Dunson, DB, Bayesian genome- and epigenome-wide association studies with gene level dependence., Biometrics, vol. 73 no. 3 (September, 2017), pp. 1018-1028 [doi]  [abs]
  4. Srivastava, S; Engelhardt, BE; Dunson, DB, Expandable factor analysis, Biometrika, vol. 104 no. 3 (September, 2017), pp. 649-663 [doi]
  5. Guhaniyogi, R; Qamar, S; Dunson, DB, Bayesian tensor regression, Journal of machine learning research : JMLR, vol. 18 (August, 2017), pp. 1-31  [abs]
  6. Schaich Borg, J; Srivastava, S; Lin, L; Heffner, J; Dunson, D; Dzirasa, K; de Lecea, L, Rat intersubjective decisions are encoded by frequency-specific oscillatory contexts., Brain and Behavior, vol. 7 no. 6 (June, 2017), pp. e00710 [doi]  [abs]
  7. Zhu, B; Dunson, DB, Bayesian Functional Data Modeling for Heterogeneous Volatility, Bayesian Analysis, vol. 12 no. 2 (June, 2017), pp. 335-350 [doi]
  8. Wang, L; Durante, D; Jung, RE; Dunson, DB, Bayesian network-response regression., Bioinformatics, vol. 33 no. 12 (June, 2017), pp. 1859-1866 [doi]  [abs]
  9. Moffitt, AB; Ondrejka, SL; McKinney, M; Rempel, RE; Goodlad, JR; Teh, CH; Leppa, S; Mannisto, S; Kovanen, PE; Tse, E; Au-Yeung, RKH; Kwong, Y-L; Srivastava, G; Iqbal, J; Yu, J; Naresh, K; Villa, D; Gascoyne, RD; Said, J; Czader, MB; Chadburn, A; Richards, KL; Rajagopalan, D; Davis, NS; Smith, EC; Palus, BC; Tzeng, TJ; Healy, JA; Lugar, PL; Datta, J; Love, C; Levy, S; Dunson, DB; Zhuang, Y; Hsi, ED; Dave, SS, Enteropathy-associated T cell lymphoma subtypes are characterized by loss of function of SETD2., The Journal of Experimental Medicine, vol. 214 no. 5 (May, 2017), pp. 1371-1386 [doi]  [abs]
  10. Durante, D; Paganin, S; Scarpa, B; Dunson, DB, Bayesian modelling of networks in complex business intelligence problems, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol. 66 no. 3 (April, 2017), pp. 555-580 [doi]
  11. McKinney, M; Moffitt, AB; Gaulard, P; Travert, M; De Leval, L; Nicolae, A; Raffeld, M; Jaffe, ES; Pittaluga, S; Xi, L; Heavican, T; Iqbal, J; Belhadj, K; Delfau-Larue, MH; Fataccioli, V; Czader, MB; Lossos, IS; Chapman-Fredricks, JR; Richards, KL; Fedoriw, Y; Ondrejka, SL; Hsi, ED; Low, L; Weisenburger, D; Chan, WC; Mehta-Shah, N; Horwitz, S; Bernal-Mizrachi, L; Flowers, CR; Beaven, AW; Parihar, M; Baseggio, L; Parrens, M; Moreau, A; Sujobert, P; Pilichowska, M; Evens, AM; Chadburn, A et al., The Genetic Basis of Hepatosplenic T-cell Lymphoma., Cancer Discovery, vol. 7 no. 4 (April, 2017), pp. 369-379 [doi]  [abs]
  12. Dunson, DB, Toward Automated Prior Choice, Statistical science : a review journal of the Institute of Mathematical Statistics, vol. 32 no. 1 (February, 2017), pp. 41-43 [doi]
  13. Johndrow, JE; Bhattacharya, A; Dunson, DB, Tensor decompositions and sparse log-linear models, Annals of statistics, vol. 45 no. 1 (February, 2017), pp. 1-38 [doi]
  14. Lin, L; Rao, V; Dunson, D, Bayesian nonparametric inference on the Stiefel manifold, Statistica Sinica (2017) [doi]
  15. Bhattacharya, A; Dunson, DB; Pati, D; Pillai, NS, Sub-optimality of some continuous shrinkage priors, Stochastic Processes and their Applications, vol. 126 no. 12 (December, 2016), pp. 3828-3842 [doi]
  16. Durante, D; Dunson, DB, Locally adaptive dynamic networks, The annals of applied statistics, vol. 10 no. 4 (December, 2016), pp. 2203-2232 [doi]
  17. Datta, J; Dunson, DB, Bayesian inference on quasi-sparse count data, Biometrika, vol. 103 no. 4 (December, 2016), pp. 971-983 [doi]
  18. Zhu, H; Strawn, N; Dunson, DB, Bayesian graphical models for multivariate functional data, Journal of machine learning research : JMLR, vol. 17 (October, 2016), pp. 1-27  [abs]
  19. Sarkar, A; Dunson, DB, Bayesian Nonparametric Modeling of Higher Order Markov Chains, Journal of the American Statistical Association, vol. 111 no. 516 (October, 2016), pp. 1791-1803 [doi]
  20. Durante, D; Dunson, DB; Vogelstein, JT, Nonparametric Bayes Modeling of Populations of Networks, Journal of the American Statistical Association (August, 2016), pp. 1-15 [doi]  [abs]
  21. Li, D; Heyer, L; Jennings, VH; Smith, CA; Dunson, DB, Personalised estimation of a woman's most fertile days., European Journal of Contraception and Reproductive Health Care, vol. 21 no. 4 (August, 2016), pp. 323-328 [doi]  [abs]
  22. Lin, L; St. Thomas, B; Zhu, H; Dunson, DB, Extrinsic Local Regression on Manifold-Valued Data, Journal of the American Statistical Association (July, 2016), pp. 1-13 [doi]
  23. Kunihama, T; Herring, AH; Halpern, CT; Dunson, DB, Nonparametric Bayes modeling with sample survey weights, Statistics & Probability Letters, vol. 113 (June, 2016), pp. 41-48 [doi]
  24. Rao, V; Lin, L; Dunson, DB, Data augmentation for models based on rejection sampling., Biometrika, vol. 103 no. 2 (June, 2016), pp. 319-335 [doi]  [abs]
  25. Guhaniyogi, R; Dunson, DB, Compressed Gaussian process for manifold regression, Journal of machine learning research : JMLR, vol. 17 (May, 2016)  [abs]
  26. Yang, Y; Dunson, DB, Bayesian Conditional Tensor Factorizations for High-Dimensional Classification, Journal of the American Statistical Association, vol. 111 no. 514 (April, 2016), pp. 656-669 [doi]
  27. Kabisa, ST; Dunson, DB; Morris, JS, Online Variational Bayes Inference for High-Dimensional Correlated Data, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, vol. 25 no. 2 (April, 2016), pp. 426-444 [doi]
  28. Yang, Y; Dunson, DB, Bayesian manifold regression, Annals of statistics, vol. 44 no. 2 (April, 2016), pp. 876-905 [doi]
  29. Zhou, J; Herring, AH; Bhattacharya, A; Olshan, AF; Dunson, DB; National Birth Defects Prevention Study, , Nonparametric Bayes modeling for case control studies with many predictors., Biometrics, vol. 72 no. 1 (March, 2016), pp. 184-192 [doi]  [abs]
  30. Tang, K; Dunson, DB; Su, Z; Liu, R; Zhang, J; Dong, J, Subspace segmentation by dense block and sparse representation., Neural Networks, vol. 75 (March, 2016), pp. 66-76 [doi]  [abs]
  31. Kunihama, T; Dunson, DB, Nonparametric Bayes inference on conditional independence, Biometrika, vol. 103 no. 1 (March, 2016), pp. 35-47 [doi]
  32. Van Den Boom, W; Dunson, D; Reeves, G, Quantifying uncertainty in variable selection with arbitrary matrices, 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015 (January, 2016), pp. 385-388, ISBN 9781479919635 [doi]  [abs]
  33. Chabout, J; Sarkar, A; Patel, SR; Radden, T; Dunson, DB; Fisher, SE; Jarvis, ED, A Foxp2 Mutation Implicated in Human Speech Deficits Alters Sequencing of Ultrasonic Vocalizations in Adult Male Mice., Frontiers in Behavioral Neuroscience, vol. 10 (January, 2016), pp. 197 [doi]  [abs]
  34. Yin, R; Cornelis, B; Fodor, G; Ocon, N; Dunson, D; Daubechies, I, Removing Cradle Artifacts in X-Ray Images of Paintings, SIAM Journal on Imaging Sciences, vol. 9 no. 3 (January, 2016), pp. 1247-1272 [doi]
  35. Wang, X; Dunson, D; Leng, C, No penalty no tears: Least squares in high-dimensional linear models, 33rd International Conference on Machine Learning, ICML 2016, vol. 4 (January, 2016), pp. 2685-2706, ISBN 9781510829008  [abs]
  36. Wang, X; Dunson, D; Leng, C, DECOrrelated feature space partitioning for distributed sparse regression, Advances in Neural Information Processing Systems (January, 2016), pp. 802-810  [abs]
  37. Canale, A; Dunson, DB, Multiscale Bernstein polynomials for densities, Statistica Sinica (2016) [doi]

Durrett, Richard T.

  1. Nanda, M; Durrett, R, Spatial evolutionary games with weak selection, Proceedings of the National Academy of Sciences of USA, vol. 114 no. 23 (June, 2017), pp. 6046-6051 [doi]
  2. Bessonov, M; Durrett, R, Phase transitions for a planar quadratic contact process, Advances in Applied Mathematics, vol. 87 (June, 2017), pp. 82-107 [doi]
  3. Durrett, R; Fan, W-TL, Genealogies in expanding populations, The annals of applied probability : an official journal of the Institute of Mathematical Statistics, vol. 26 no. 6 (December, 2016), pp. 3456-3490 [doi]
  4. Cox, JT; Durrett, R, Evolutionary games on the torus with weak selection, Stochastic Processes and their Applications, vol. 126 no. 8 (August, 2016), pp. 2388-2409 [doi]
  5. Ryser, MD; Worni, M; Turner, EL; Marks, JR; Durrett, R; Hwang, ES, Outcomes of Active Surveillance for Ductal Carcinoma in Situ: A Computational Risk Analysis., Journal of the National Cancer Institute, vol. 108 no. 5 (May, 2016) [doi]  [abs]
  6. Durrett, R; Foo, J; Leder, K, Spatial Moran models, II: cancer initiation in spatially structured tissue., Journal of Mathematical Biology, vol. 72 no. 5 (April, 2016), pp. 1369-1400, ISSN 0303-6812 [doi]  [abs]

Fernandes de Oliveira, Goncalo M.

  1. Oliveira, G, Gerbes on G2 manifolds, Journal of Geometry and Physics, vol. 114 (April, 2017), pp. 570-580 [doi]
  2. Oliveira, G, G 2-Monopoles with Singularities (Examples), Letters in Mathematical Physics, vol. 106 no. 11 (November, 2016), pp. 1479-1497 [doi]
  3. Oliveira, G, Monopoles on AC 3-manifolds, Journal of the London Mathematical Society, vol. 93 no. 3 (June, 2016), pp. 785-810, ISSN 0024-6107 [jlms.jdw017.abstract], [doi]
  4. Oliveira, G, Calabi–Yau Monopoles for the Stenzel Metric, Communications in Mathematical Physics, vol. 341 no. 2 (January, 2016), pp. 699-728, ISSN 0010-3616 [repository], [doi]

Getz, Jayce R.

  1. Getz, JR, A four-variable automorphic kernel function, Research in the Mathematical Sciences, vol. 3 no. 1 (December, 2016) [doi]

Hahn, Heekyoung

  1. Hahn, H, On Classical groups detected by the triple tensor product and the Littlewood–Richardson semigroup, Research in Number Theory, vol. 2 no. 1 (December, 2016), pp. 1-12 [doi]
  2. Hahn, H, On tensor third $L$-functions of automorphic representations of $GL_n(\mathbb {A}_F)$, Proceedings of the American Mathematical Society, vol. 144 no. 12 (May, 2016), pp. 5061-5069 [doi]
  3. H. Hahn, On tensor thrid L-functions of automorphic representations of GL_n(A_F), Proc. Amer. Math. Soc. (2016)
  4. H. Hahn, On classical groups detected by the triple tensor product and the Littlewood-Richardson semigroup (2016)

Hain, Richard   (search)

  1. Arapura, D; Dimca, A; Hain, R, On the fundamental groups of normal varieties, Communications in Contemporary Mathematics, vol. 18 no. 04 (August, 2016), pp. 1550065-1550065, ISSN 0219-1997 [doi]
  2. Hain, R, Notes on the Universal Elliptic KZB Equation, Pure and Applied Mathematics Quarterly, vol. 12 no. 2 (July, 2016), International Press [arXiv:1309.0580], [1309.0580v3]  [abs]
  3. Hain, R, The Hodge-de Rham theory of modular groups, in Recent Advances in Hodge Theory Period Domains, Algebraic Cycles, and Arithmetic, edited by Kerr, M; Pearlstein, G, vol. 427 (January, 2016), pp. 422-514, Cambridge University Press, ISBN 110754629X
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  5. Hain, R, Deligne-Beilinson Cohomology of Affine Groups (July, 2015) [arXiv:1507.03144]  [abs]

Harer, John

  1. Bendich, P; Chin, SP; Clark, J; Desena, J; Harer, J; Munch, E; Newman, A; Porter, D; Rouse, D; Strawn, N; Watkins, A, Topological and statistical behavior classifiers for tracking applications, IEEE Transactions on Aerospace and Electronic Systems, vol. 52 no. 6 (December, 2016), pp. 2644-2661 [doi]  [abs]
  2. McGoff, KA; Guo, X; Deckard, A; Kelliher, CM; Leman, AR; Francey, LJ; Hogenesch, JB; Haase, SB; Harer, JL, The Local Edge Machine: inference of dynamic models of gene regulation., Genome Biology: biology for the post-genomic era, vol. 17 no. 1 (October, 2016), pp. 214  [abs]
  3. Bendich, P; Gasparovic, E; Harer, J; Tralie, C, Geometric models for musical audio data, LIPIcs, vol. 51 (June, 2016), pp. 65.1-65.5, ISBN 9783959770095 [doi]  [abs]

Hodel, Richard E.

  1. with Donald W. Loveland, Richard E. Hodel, S.G. Sterrett, Three Views of Logic: Mathematics, Philosophy, Computer Science (2016)

Ji, Hangjie

  1. Y. Gao, H. Ji, J. Liu, T. P. Witelski, Global existence of solutions to a tear film model with locally elevated evaporation rates (2017) [arXiv:1701.00853]
  2. H. Ji, T. P. Witelski, Finite-time thin film rupture driven by generalized evaporative loss, Physica D: Nonlinear Phenomena (2016) [arXiv:1601.03625]

Junge, Matthew S

  1. Hoffman, C; Johnson, T; Junge, M, Recurrence and transience for the frog model on trees, Annals of Probability, vol. 45 no. 5 (September, 2017), pp. 2826-2854 [doi]
  2. Hoffman, C; Johnson, T; Junge, M, From transience to recurrence with Poisson tree frogs, The annals of applied probability : an official journal of the Institute of Mathematical Statistics, vol. 26 no. 3 (June, 2016), pp. 1620-1635 [doi]
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  4. Johnson, T; Junge, M, The critical density for the frog model is the degree of the tree, Electronic Communications in Probability, vol. 21 (2016) [doi]

Layton, Anita T.

  1. Edwards, A; Layton, AT, Cell Volume Regulation in the Proximal Tubule of Rat Kidney : Proximal Tubule Cell Volume Regulation., Bulletin of Mathematical Biology (September, 2017) [doi]  [abs]
  2. Burt, T; Noveck, RJ; MacLeod, DB; Layton, AT; Rowland, M; Lappin, G, Intra-Target Microdosing (ITM): A Novel Drug Development Approach Aimed at Enabling Safer and Earlier Translation of Biological Insights Into Human Testing., Clinical and Translational Science, vol. 10 no. 5 (September, 2017), pp. 337-350 [doi]
  3. Sgouralis, I; Evans, RG; Layton, AT, Renal medullary and urinary oxygen tension during cardiopulmonary bypass in the rat., Mathematical Medicine and Biology: A Journal of the IMA, vol. 34 no. 3 (September, 2017), pp. 313-333 [doi]  [abs]
  4. Chen, Y; Sullivan, JC; Edwards, A; Layton, AT, Sex-specific computational models of the spontaneously hypertensive rat kidneys: factors affecting nitric oxide bioavailability., American Journal of Physiology: Renal Physiology, vol. 313 no. 2 (August, 2017), pp. F174-F183 [doi]  [abs]
  5. Layton, AT; Edwards, A; Vallon, V, Adaptive changes in GFR, tubular morphology, and transport in subtotal nephrectomized kidneys: modeling and analysis., American Journal of Physiology: Renal Physiology, vol. 313 no. 2 (August, 2017), pp. F199-F209 [doi]  [abs]
  6. Chen, Y; Fry, BC; Layton, AT, Modeling glucose metabolism and lactate production in the kidney., Mathematical Biosciences, vol. 289 (July, 2017), pp. 116-129 [doi]  [abs]
  7. Layton, AT, A new microscope for the kidney: mathematics., American Journal of Physiology: Renal Physiology, vol. 312 no. 4 (April, 2017), pp. F671-F672 [doi]
  8. Jiang, T; Li, Y; Layton, AT; Wang, W; Sun, Y; Li, M; Zhou, H; Yang, B, Generation and phenotypic analysis of mice lacking all urea transporters., Kidney international, vol. 91 no. 2 (February, 2017), pp. 338-351 [doi]  [abs]
  9. Layton, AT; Laghmani, K; Vallon, V; Edwards, A, Solute transport and oxygen consumption along the nephrons: effects of Na+ transport inhibitors., American Journal of Physiology: Renal Physiology, vol. 311 no. 6 (December, 2016), pp. F1217-F1229 [doi]  [abs]
  10. Layton, AT; Vallon, V; Edwards, A, A computational model for simulating solute transport and oxygen consumption along the nephrons., American Journal of Physiology: Renal Physiology, vol. 311 no. 6 (December, 2016), pp. F1378-F1390 [doi]  [abs]
  11. Sgouralis, I; Kett, MM; Ow, CPC; Abdelkader, A; Layton, AT; Gardiner, BS; Smith, DW; Lankadeva, YR; Evans, RG, Bladder urine oxygen tension for assessing renal medullary oxygenation in rabbits: experimental and modeling studies., American journal of physiology. Regulatory, integrative and comparative physiology, vol. 311 no. 3 (September, 2016), pp. R532-R544 [doi]  [abs]
  12. Layton, AT, Recent advances in renal hypoxia: insights from bench experiments and computer simulations., American Journal of Physiology: Renal Physiology, vol. 311 no. 1 (July, 2016), pp. F162-F165 [doi]  [abs]
  13. Layton, AT; Vallon, V; Edwards, A, Predicted consequences of diabetes and SGLT inhibition on transport and oxygen consumption along a rat nephron., American Journal of Physiology: Renal Physiology, vol. 310 no. 11 (June, 2016), pp. F1269-F1283 [doi]  [abs]
  14. Liu, R; Layton, AT, Modeling the effects of positive and negative feedback in kidney blood flow control., Mathematical Biosciences, vol. 276 (2016), pp. 8-18 [doi]  [abs]
  15. Chen, Y; Fry, BC; Layton, AT, Modeling Glucose Metabolism in the Kidney., Bulletin of Mathematical Biology, vol. 78 no. 6 (June, 2016), pp. 1318-1336 [doi]  [abs]
  16. Nganguia, H; Young, Y-N; Layton, AT; Lai, M-C; Hu, W-F, Electrohydrodynamics of a viscous drop with inertia., Physical review. E, vol. 93 no. 5 (May, 2016), pp. 053114 [doi]  [abs]
  17. Sgouralis, I; Maroulas, V; Layton, AT, Transfer Function Analysis of Dynamic Blood Flow Control in the Rat Kidney., Bulletin of Mathematical Biology, vol. 78 no. 5 (May, 2016), pp. 923-960 [doi]  [abs]
  18. Herschlag, G; Liu, J-G; Layton, AT, Fluid extraction across pumping and permeable walls in the viscous limit, Physics of Fluids, vol. 28 no. 4 (April, 2016), pp. 041902-041902 [doi]
  19. Sgouralis, I; Layton, AT, Conduction of feedback-mediated signal in a computational model of coupled nephrons., Mathematical Medicine and Biology: A Journal of the IMA, vol. 33 no. 1 (March, 2016), pp. 87-106 [doi]  [abs]
  20. Fry, BC; Edwards, A; Layton, AT, Impact of nitric-oxide-mediated vasodilation and oxidative stress on renal medullary oxygenation: a modeling study., American Journal of Physiology: Renal Physiology, vol. 310 no. 3 (2016), pp. F237-F247 [doi]  [abs]
  21. Xie, L; Layton, AT; Wang, N; Larson, PEZ; Zhang, JL; Lee, VS; Liu, C; Johnson, GA, Dynamic contrast-enhanced quantitative susceptibility mapping with ultrashort echo time MRI for evaluating renal function., American Journal of Physiology: Renal Physiology, vol. 310 no. 2 (2016), pp. F174-F182 [doi]  [abs]

Levine, Adam S.

  1. Baldwin, JA; Levine, AS; Sarkar, S, Khovanov homology and knot Floer homology for pointed links, Journal of Knot Theory & Its Ramifications, vol. 26 no. 02 (February, 2017), pp. 1740004-1740004 [doi]
  2. Greene, J; Levine, A, Strong Heegaard diagrams and strong L–spaces, Algebraic and Geometric Topology, vol. 16 no. 6 (December, 2016), pp. 3167-3208 [doi]
  3. Hedden, M; Levine, AS, Splicing knot complements and bordered Floer homology, Journal für die Reine und Angewandte Mathematik (Crelle's Journal), vol. 2016 no. 720 (January, 2016) [doi]
  4. LEVINE, ADAMSIMON, NONSURJECTIVE SATELLITE OPERATORS AND PIECEWISE-LINEAR CONCORDANCE, Forum of Mathematics, Sigma, vol. 4 (2016) [doi]

Li, Lei

  1. Li, L; Liu, J-G; Lu, J, Fractional Stochastic Differential Equations Satisfying Fluctuation-Dissipation Theorem, Journal of Statistical Physics, vol. 169 no. 2 (October, 2017), pp. 316-339 [doi]
  2. Li, L; Xu, X; Spagnolie, SE, A Locally Gradient-Preserving Reinitialization for Level Set Functions, Journal of Scientific Computing, vol. 71 no. 1 (April, 2017), pp. 274-302 [doi]

Li, Yingzhou

  1. Li, Y; Yang, H; Ying, L, Multidimensional butterfly factorization, Applied and Computational Harmonic Analysis (April, 2017) [doi]
  2. Li, Y; Yang, H, Interpolative Butterfly Factorization, SIAM Journal on Scientific Computing, vol. 39 no. 2 (January, 2017), pp. A503-A531 [doi]

Liu, Jian-Guo

  1. Li, L; Liu, J-G; Lu, J, Fractional Stochastic Differential Equations Satisfying Fluctuation-Dissipation Theorem, Journal of Statistical Physics, vol. 169 no. 2 (October, 2017), pp. 316-339 [doi]
  2. Liu, J-G; Ma, Z; Zhou, Z, Explicit and Implicit TVD Schemes for Conservation Laws with Caputo Derivatives, Journal of Scientific Computing, vol. 72 no. 1 (July, 2017), pp. 291-313 [doi]
  3. Gao, Y; Ji, H; Liu, J-G; Witelski, TP, Global existence of solutions to a tear film model with locally elevated evaporation rates, Physica D: Nonlinear Phenomena, vol. 350 (July, 2017), pp. 13-25 [doi]
  4. Gao, Y; Liu, J-G; Lu, J, Continuum Limit of a Mesoscopic Model with Elasticity of Step Motion on Vicinal Surfaces, Journal of Nonlinear Science, vol. 27 no. 3 (June, 2017), pp. 873-926 [doi]
  5. Degond, P; Liu, J-G; Pego, RL, Coagulation–Fragmentation Model for Animal Group-Size Statistics, Journal of Nonlinear Science, vol. 27 no. 2 (April, 2017), pp. 379-424 [doi]
  6. Huang, H; Liu, J-G, Error estimate of a random particle blob method for the Keller-Segel equation, Mathematics of Computation, vol. 86 no. 308 (February, 2017), pp. 2719-2744 [doi]
  7. Liu, J-G; Wang, J, Global existence for a thin film equation with subcritical mass, Discrete and Continuous Dynamical Systems - Series B, vol. 22 no. 4 (February, 2017), pp. 1461-1492 [doi]
  8. Gao, Y; Liu, J-G; Lu, J, Weak Solution of a Continuum Model For Vicinal Surface in The Attachment-Detachment-Limited Regime, SIAM Journal on Mathematical Analysis, vol. 49 no. 3 (January, 2017), pp. 1705-1731 [doi]
  9. Huang, H; Liu, JG, Discrete-in-time random particle blob method for the Keller-Segel equation and convergence analysis, Communications in Mathematical Sciences, vol. 15 no. 7 (January, 2017), pp. 1821-1842 [doi]  [abs]
  10. Degond, P; Liu, J-G; Merino-Aceituno, S; Tardiveau, T, Continuum dynamics of the intention field under weakly cohesive social interaction, Mathematical Models & Methods in Applied Sciences, vol. 27 no. 01 (January, 2017), pp. 159-182 [doi]
  11. Gao, Y; Liu, J-G, Global Convergence of a Sticky Particle Method for the Modified Camassa--Holm Equation, SIAM Journal on Mathematical Analysis, vol. 49 no. 2 (January, 2017), pp. 1267-1294 [doi]
  12. J.-G. Liu and J. Wang, A generalized Sz. Nagy inequality in higher dimensions and the critical thin film equation, Nonlinearity, vol. 30 (2017), pp. 35-60
  13. Liu, J-G; Cong, W, Uniform $L^{\infty}$ boundedness for a degenerate parabolic-parabolic Keller-Segel model, Discrete and Continuous Dynamical Systems - Series B, vol. 22 no. 2 (2017), pp. 307-338 [doi]
  14. Huang, H; Liu, J-G, A note on Monge–Ampère Keller–Segel equation, Applied Mathematics Letters, vol. 61 (November, 2016), pp. 26-34 [doi]
  15. Huang, H; Liu, J-G, Error estimates of the aggregation-diffusion splitting algorithms for the Keller-Segel equations, Discrete and Continuous Dynamical Systems - Series B, vol. 21 no. 10 (November, 2016), pp. 3463-3478 [doi]
  16. Liu, J-G; Huang, H, Well-posedness for the Keller-Segel equation with fractional Laplacian and the theory of propagation of chaos, Kinetic and Related Models, vol. 9 no. 4 (September, 2016), pp. 715-748 [doi]
  17. Liu, J-G; Cong, W, A degenerate $p$-Laplacian Keller-Segel model, Kinetic and Related Models, vol. 9 no. 4 (September, 2016), pp. 687-714 [doi]
  18. Liu, J-G; Wang, J, A Note on L ∞ $L^{\infty}$ -Bound and Uniqueness to a Degenerate Keller-Segel Model, Acta Applicandae Mathematicae, vol. 142 no. 1 (April, 2016), pp. 173-188, ISSN 0167-8019 [doi]
  19. Herschlag, G; Liu, J-G; Layton, AT, Fluid extraction across pumping and permeable walls in the viscous limit, Physics of Fluids, vol. 28 no. 4 (April, 2016), pp. 041902-041902, ISSN 1070-6631 [doi]
  20. Liu, J-G; Pego, RL, On generating functions of Hausdorff moment sequences, Transactions of the American Mathematical Society, vol. 368 no. 12 (February, 2016), pp. 8499-8518 [doi]
  21. Chen, J; Liu, J-G; Zhou, Z, On a Schrödinger--Landau--Lifshitz System: Variational Structure and Numerical Methods, Multiscale Modeling & Simulation, vol. 14 no. 4 (January, 2016), pp. 1463-1487 [doi]
  22. Liu, J-G; Xu, X, Existence Theorems for a Multidimensional Crystal Surface Model, SIAM Journal on Mathematical Analysis, vol. 48 no. 6 (January, 2016), pp. 3667-3687 [doi]
  23. Liu, JG; Zhang, Y, Convergence of diffusion-drift many particle systems in probability under a sobolev norm, Proceedings of Particle Systems and Partial Differential Equations - III, Springer Proceedings in Mathematics and Statistics, vol. 162 (January, 2016), pp. 195-223, Springer, ISBN 9783319321424 [doi]  [abs]
  24. J.-G. Liu and R. Yang, Propagation of chaos for large Brownian particle system with Coulomb interaction, Research in the Mathematical Sciences, vol. 3 no. 40 (2016)
  25. Y. Duan and J.-G. Liu, Error estimate of the particle method for the b-equation, Methods and Applications of Analysis, vol. 23 (2016), pp. 119-154
  26. J.-G. Liu and Y. Zhang, Convergence of stochastic interacting particle systems in probability under a Sobolev norm, Annals of Mathematical Sciences and Applications, vol. 1 (2016), pp. 251-299
  27. P. Degond, J.-G. Liu, S. Merino-Aceituno, T. Tardiveau, Continuum dynamics of the intention field under weakly cohesive social interactions, Math. Models Methods Appl. Sci. (2016)
  28. Y. Gao, J.-G. Liu, J. Lu, Continuum limit of a mesoscopic model of step motion on vicinal surfaces, J. Nonlinear Science (2016)
  29. J.-G. Liu and J. Wang, Refined hyper-contractivity and uniqueness for the Keller-Segel equations, Applied Math Letter, vol. 52 (2016), pp. 212-219

Lu, Jianfeng

  1. Li, L; Liu, J-G; Lu, J, Fractional Stochastic Differential Equations Satisfying Fluctuation-Dissipation Theorem, Journal of Statistical Physics, vol. 169 no. 2 (October, 2017), pp. 316-339 [doi]  [abs]
  2. Yu, VW-Z; Corsetti, F; García, A; Huhn, WP; Jacquelin, M; Jia, W; Lange, B; Lin, L; Lu, J; Mi, W; Seifitokaldani, A; Vázquez-Mayagoitia, Á; Yang, C; Yang, H; Blum, V, ELSI: A unified software interface for Kohn–Sham electronic structure solvers, Computer Physics Communications (September, 2017) [doi]
  3. Lu, J; Thicke, K, Cubic scaling algorithms for RPA correlation using interpolative separable density fitting, Journal of Computational Physics (September, 2017) [doi]
  4. Lu, J; Steinerberger, S, A variation on the Donsker-Varadhan inequality for the principal eigenvalue., Proceedings of the Royal Society of London: Mathematical, Physical and Engineering Sciences, vol. 473 no. 2204 (August, 2017), pp. 20160877 [doi]  [abs]
  5. Lu, J; Yang, H, A cubic scaling algorithm for excited states calculations in particle–particle random phase approximation, Journal of Computational Physics, vol. 340 (July, 2017), pp. 297-308 [doi]
  6. Gao, Y; Liu, J-G; Lu, J, Continuum Limit of a Mesoscopic Model with Elasticity of Step Motion on Vicinal Surfaces, Journal of Nonlinear Science, vol. 27 no. 3 (June, 2017), pp. 873-926 [doi]
  7. Li, C; Lu, J; Yang, W, On extending Kohn-Sham density functionals to systems with fractional number of electrons., Journal of Chemical Physics, vol. 146 no. 21 (June, 2017), pp. 214109 [doi]  [abs]
  8. Lu, J; Thicke, K, Orbital minimization method with ℓ 1 regularization, Journal of Computational Physics, vol. 336 (May, 2017), pp. 87-103 [doi]
  9. Lu, J; Zhou, Z, Path integral molecular dynamics with surface hopping for thermal equilibrium sampling of nonadiabatic systems., Journal of Chemical Physics, vol. 146 no. 15 (April, 2017), pp. 154110 [doi]  [abs]
  10. Watson, AB; Lu, J; Weinstein, MI, Wavepackets in inhomogeneous periodic media: Effective particle-field dynamics and Berry curvature, Journal of Mathematical Physics, vol. 58 no. 2 (February, 2017), pp. 021503-021503 [doi]
  11. Niu, X; Luo, T; Lu, J; Xiang, Y, Dislocation climb models from atomistic scheme to dislocation dynamics, Journal of the Mechanics and Physics of Solids, vol. 99 (February, 2017), pp. 242-258 [doi]
  12. Lu, J; Yang, H, Preconditioning Orbital Minimization Method for Planewave Discretization, Multiscale Modeling & Simulation, vol. 15 no. 1 (January, 2017), pp. 254-273 [doi]
  13. Li, Q; Lu, J; Sun, W, Validity and Regularization of Classical Half-Space Equations, Journal of Statistical Physics, vol. 166 no. 2 (January, 2017), pp. 398-433 [doi]
  14. Gao, Y; Liu, J-G; Lu, J, Weak Solution of a Continuum Model For Vicinal Surface in The Attachment-Detachment-Limited Regime, SIAM Journal on Mathematical Analysis, vol. 49 no. 3 (January, 2017), pp. 1705-1731 [doi]  [abs]
  15. Cornelis, B; Yang, H; Goodfriend, A; Ocon, N; Lu, J; Daubechies, I, Removal of Canvas Patterns in Digital Acquisitions of Paintings., IEEE Transactions on Image Processing, vol. 26 no. 1 (January, 2017), pp. 160-171 [doi]  [abs]
  16. Mendl, CB; Lu, J; Lukkarinen, J, Thermalization of oscillator chains with onsite anharmonicity and comparison with kinetic theory., Physical review. E, vol. 94 no. 6-1 (December, 2016), pp. 062104 [doi]  [abs]
  17. Li, Q; Lu, J; Sun, W, Half-space kinetic equations with general boundary conditions, Mathematics of Computation, vol. 86 no. 305 (October, 2016), pp. 1269-1301 [doi]
  18. Yu, T-Q; Lu, J; Abrams, CF; Vanden-Eijnden, E, Multiscale implementation of infinite-swap replica exchange molecular dynamics., Proceedings of the National Academy of Sciences of USA, vol. 113 no. 42 (October, 2016), pp. 11744-11749 [doi]  [abs]
  19. Lu, J; Zhou, Z, Improved sampling and validation of frozen Gaussian approximation with surface hopping algorithm for nonadiabatic dynamics., Journal of Chemical Physics, vol. 145 no. 12 (September, 2016), pp. 124109 [doi]  [abs]
  20. Li, X; Lu, J, Traction boundary conditions for molecular static simulations, Computer Methods in Applied Mechanics and Engineering, vol. 308 (August, 2016), pp. 310-329 [doi]
  21. Lin, L; Lu, J, Decay estimates of discretized Green’s functions for Schrödinger type operators, Science China Mathematics, vol. 59 no. 8 (August, 2016), pp. 1561-1578 [doi]
  22. Lai, R; Lu, J, Localized density matrix minimization and linear-scaling algorithms, Journal of Computational Physics, vol. 315 (June, 2016), pp. 194-210 [doi]
  23. Lu, J; Ying, L, Sparsifying preconditioner for soliton calculations, Journal of Computational Physics, vol. 315 (June, 2016), pp. 458-466 [doi]
  24. Lu, J; Wirth, B; Yang, H, Combining 2D synchrosqueezed wave packet transform with optimization for crystal image analysis, Journal of the Mechanics and Physics of Solids, vol. 89 (2016), pp. 194-210, ISSN 0022-5096 [arXiv:1501.06254], [repository], [doi]  [abs]
  25. Chen, J; Lu, J, Analysis of the divide-and-conquer method for electronic structure calculations, Mathematics of Computation, vol. 85 no. 302 (January, 2016), pp. 2919-2938 [doi]
  26. Delgadillo, R; Lu, J; Yang, X, Gauge-Invariant Frozen Gaussian Approximation Method for the Schrödinger Equation with Periodic Potentials, SIAM Journal on Scientific Computing, vol. 38 no. 4 (January, 2016), pp. A2440-A2463 [doi]

Lu, Yulong

  1. Lu, Y; Stuart, A; Weber, H, Gaussian Approximations for Transition Paths in Brownian Dynamics, SIAM Journal on Mathematical Analysis, vol. 49 no. 4 (January, 2017), pp. 3005-3047 [doi]
  2. Iglesias, M; Lu, Y; Stuart, A, A Bayesian level set method for geometric inverse problems, Interfaces and Free Boundaries, vol. 18 no. 2 (2016), pp. 181-217 [doi]

Ma, Ding

  1. Ma, D, Inverse of some matrix related to double zeta values of odd weight, Journal of Number Theory, vol. 166 (September, 2016), pp. 166-180 [doi]
  2. Ma, D, Period polynomial relations between formal double zeta values of odd weight, Mathematische Annalen, vol. 365 no. 1-2 (June, 2016), pp. 345-362 [doi]

Maggioni, Mauro

  1. Little, AV; Maggioni, M; Rosasco, L, Multiscale geometric methods for data sets I: Multiscale SVD, noise and curvature, Applied and Computational Harmonic Analysis, vol. 43 no. 3 (November, 2017), pp. 504-567 [doi]
  2. Gerber, S; Maggioni, M, Multiscale strategies for computing optimal transport, Journal of machine learning research : JMLR, vol. 18 (August, 2017), pp. 1-32  [abs]
  3. Bongini, M; Fornasier, M; Hansen, M; Maggioni, M, Inferring interaction rules from observations of evolutive systems I: The variational approach, Mathematical Models & Methods in Applied Sciences, vol. 27 no. 05 (May, 2017), pp. 909-951 [doi]
  4. Tomita, TM; Maggioni, M; Vogelstein, JT, ROFLMAO: Robust oblique forests with linear MAtrix operations, Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017 (January, 2017), pp. 498-506, ISBN 9781611974874  [abs]
  5. Crosskey, M; Maggioni, M, ATLAS: A Geometric Approach to Learning High-Dimensional Stochastic Systems Near Manifolds, Multiscale Modeling & Simulation, vol. 15 no. 1 (January, 2017), pp. 110-156 [doi]
  6. Liao, W; Maggioni, M; Vigogna, S, Learning adaptive multiscale approximations to data and functions near low-dimensional sets, 2016 IEEE Information Theory Workshop, ITW 2016 (October, 2016), pp. 226-230, ISBN 9781509010905 [doi]  [abs]
  7. Goetzmann, WN; Jones, PW; Maggioni, M; Walden, J, Beauty is in the bid of the beholder: An empirical basis for style, Research in Economics, vol. 70 no. 3 (September, 2016), pp. 388-402 [doi]
  8. Wang, Y; Chen, G; Maggioni, M, High-Dimensional Data Modeling Techniques for Detection of Chemical Plumes and Anomalies in Hyperspectral Images and Movies, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9 no. 9 (September, 2016), pp. 4316-4324, ISSN 1939-1404 [doi]  [abs]
  9. Yin, R; Monson, E; Honig, E; Daubechies, I; Maggioni, M, Object recognition in art drawings: Transfer of a neural network, IEEE International Conference on Acoustics Speech and Signal Processing, vol. 2016-May (May, 2016), pp. 2299-2303, ISSN 1520-6149, ISBN 9781479999880 [doi]  [abs]
  10. Maggioni, M; Minsker, S; Strawn, N, Multiscale dictionary learning: Non-asymptotic bounds and robustness, Journal of machine learning research : JMLR, vol. 17 (January, 2016), ISSN 1532-4435 (accepted for publication.) [arxiv:1401.5833]  [abs]
  11. 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)
  12. 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..)
  13. 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.

  1. Herschlag, G; Ravier, R; Mattingly, JC, Evaluating Partisan Gerrymandering in Wisconsin (September, 2017)  [abs]
  2. Bakhtin, Y; Hurth, T; Lawley, SD; Mattingly, JC, Smooth invariant densities for random switching on the torus (August, 2017)  [abs]
  3. Johndrow, JE; Mattingly, JC, Coupling and Decoupling to bound an approximating Markov Chain (July, 2017)  [abs]
  4. Glatt-Holtz, NE; Herzog, DP; Mattingly, JC, Scaling and Saturation in Infinite-Dimensional Control Problems with Applications to Stochastic Partial Differential Equations (June, 2017)  [abs]
  5. Glatt-Holtz, N; Mattingly, JC; Richards, G, On Unique Ergodicity in Nonlinear Stochastic Partial Differential Equations, Journal of Statistical Physics, vol. 166 no. 3-4 (February, 2017), pp. 618-649 [1512.04126v1], [doi]  [abs]
  6. Hairer, M; Mattingly, J, The strong Feller property for singular stochastic PDEs (2016)  [abs]
  7. Tempkin, JOB; Koten, BV; Mattingly, JC; Dinner, AR; Weare, J, Trajectory stratification of stochastic dynamics (2016)  [abs]

Miller, Ezra

  1. Berenstein, A; Braverman, M; Miller, E; Retakh, V; Weitsman, J, Andrei Zelevinsky, 1953–2013, Advances in Mathematics, vol. 300 (September, 2016), pp. 1-4 [doi]
  2. Berenstein, A; Braverman, M; Miller, E; Retakh, V; Weitsman, J, Andrei Zelevinsky, 1953-2013, Advances in Mathematics, vol. 299 (August, 2016), pp. 601-604 [doi]
  3. Kahle, T; Miller, E; O’Neill, C, Irreducible decomposition of binomial ideals, Compositio Mathematica, vol. 152 no. 06 (June, 2016), pp. 1319-1332 [arXiv:1503.02607], [1503.02607], [doi]  [abs]
  4. Bendich, P; Marron, JS; Miller, E; Pieloch, A; Skwerer, S, Persistent homology analysis of brain artery trees, Annals of Applied Statistics, vol. 10 no. 1 (January, 2016), pp. 19 pages [arXiv:1411.6652], [1411.6652v1]  [abs]

Motta, Francis C.

  1. Cho, C-Y; Motta, FC; Kelliher, CM; Deckard, A; Haase, SB, Reconciling conflicting models for global control of cell-cycle transcription., Cell Cycle, vol. 16 no. 20 (October, 2017), pp. 1965-1978 [doi]  [abs]
  2. Burris, CS; Motta, FC; Shipman, PD, An Unoriented Variation on de Bruijn Sequences, Graphs and Combinatorics, vol. 33 no. 4 (July, 2017), pp. 845-858 [doi]
  3. with Francis C. Motta, ; Patrick D. Shipman, ; Bethany D. Springer, , Optimally Topologically Transitive Orbits in Discrete Dynamical Systems, American Mathematical Monthly, vol. 123 no. 2 (July, 2015), pp. 115-115 [doi]

Mukherjee, Sayan

  1. Bobrowski, O; Mukherjee, S; Taylor, JE, Topological consistency via kernel estimation, Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability, vol. 23 no. 1 (February, 2017), pp. 288-328 [doi]
  2. Snyder-Mackler, N; Majoros, WH; Yuan, ML; Shaver, AO; Gordon, JB; Kopp, GH; Schlebusch, SA; Wall, JD; Alberts, SC; Mukherjee, S; Zhou, X; Tung, J, Efficient Genome-Wide Sequencing and Low-Coverage Pedigree Analysis from Noninvasively Collected Samples., Genetics, vol. 203 no. 2 (June, 2016), pp. 699-714 [doi]  [abs]
  3. Zhao, S; Gao, C; Mukherjee, S; Engelhardt, BE, Bayesian group factor analysis with structured sparsity, Journal of machine learning research : JMLR, vol. 17 (April, 2016), pp. 1-47  [abs]
  4. Galinsky, KJ; Bhatia, G; Loh, P-R; Georgiev, S; Mukherjee, S; Patterson, NJ; Price, AL, Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia., The American Journal of Human Genetics, vol. 98 no. 3 (March, 2016), pp. 456-472 [doi]  [abs]
  5. Huang, B; Jarrett, NWD; Babu, S; Mukherjee, S; Yang, J, Cümülön: MatrixBased data analytics in the cloud with spot instances, in Proceedings of the VLDB Endowment, vol. 9 (January, 2016), pp. 156-167  [abs]

Ng, Lenhard L.

  1. Cornwell, C; Ng, L; Sivek, S, Obstructions to Lagrangian concordance, Algebraic and Geometric Topology, vol. 16 no. 2 (April, 2016), pp. 797-824 [arXiv:1411.1364], [doi]

Nolen, James H.

  1. Mourrat, J-C; Nolen, J, Scaling limit of the corrector in stochastic homogenization, The annals of applied probability : an official journal of the Institute of Mathematical Statistics, vol. 27 no. 2 (April, 2017), pp. 944-959, Institute of Mathematical Statistics (IMS), ISSN 1050-5164 [arXiv:1502.07440], [1502.07440], [doi]  [abs]
  2. Nolen, J; Roquejoffre, J-M; Ryzhik, L, Convergence to a single wave in the Fisher-KPP equation, Chinese Annals of Mathematics - Series B, vol. 38 no. 2 (March, 2017), pp. 629-646 [1604.02994], [doi]
  3. Gloria, A; Nolen, J, A Quantitative Central Limit Theorem for the Effective Conductance on the Discrete Torus, Communications on Pure & Applied Mathematics, vol. 69 no. 12 (2015), pp. 2304-2348, ISSN 0010-3640 [cpa.21614], [doi]
  4. Nolen, J, Normal approximation for the net flux through a random conductor, Stochastic Partial Differential Equations: Analysis and Computations, vol. 4 no. 3 (2015), pp. 439-476, ISSN 2194-0401 [2186], [doi]
  5. Nolen, JH; Roquejoffre, J-M; Ryzhik, L, Refined long time asymptotics for Fisher-KPP fronts (2016)
  6. Hamel, F; Nolen, J; Roquejoffre, J-M; Ryzhik, L, The logarithmic delay of KPP fronts in a periodic medium, Journal of the European Mathematical Society, vol. 18 no. 3 (2015), pp. 465-505 [6173], [doi]

Orizaga, Saulo

  1. Orizaga, S; Riahi, DN, Triad resonant wave interactions in electrically charged jets, Applied Mathematics and Mechanics, vol. 38 no. 8 (August, 2017), pp. 1127-1148 [doi]
  2. Glasner, K; Orizaga, S, Improving the accuracy of convexity splitting methods for gradient flow equations, Journal of Computational Physics, vol. 315 (June, 2016), pp. 52-64 [doi]
  3. Orizaga, S; Glasner, K, Instability and reorientation of block copolymer microstructure by imposed electric fields., Physical review. E, vol. 93 no. 5 (May, 2016), pp. 052504 [doi]  [abs]

Pan, Yu

  1. Y. Pan, Exact Lagrangian fillings of Legendrian (2,n) torus links (July, 2016) [ArXiv: 1607.03167]
  2. Y. Pan, The augmentation category map induced by exact Lagrangian cobordisms. (June, 2016) [ArXiv 1606.05884]

Petters, Arlie O.

  1. A. O. Petters and M. C. Werner, Gravitational Lensing and Black Holes (Spring, 2017), Springer, in preparation
  2. A. O. Petters and X. Dong, An Introduction to Mathematical Finance: Understanding and Building Financial Intuition, SUMAT (Winter, 2016), Springer, in preparation

Pfister, Henry

  1. Charbonneau, P; Li, YC; Pfister, HD; Yaida, S, Cycle-expansion method for the Lyapunov exponent, susceptibility, and higher moments, Physical review. E, vol. 96 no. 3 (September, 2017) [doi]
  2. Kudekar, S; Kumar, S; Mondelli, M; Pfister, HD; Sasoglu, E; Urbanke, RL, Reed–Muller Codes Achieve Capacity on Erasure Channels, IEEE Transactions on Information Theory, vol. 63 no. 7 (July, 2017), pp. 4298-4316 [doi]
  3. Hager, C; Pfister, HD; Graell i Amat, A; Brannstrom, F, Density Evolution for Deterministic Generalized Product Codes on the Binary Erasure Channel at High Rates, IEEE Transactions on Information Theory (July, 2017) [doi]
  4. Sabag, O; Permuter, HH; Pfister, HD, A Single-Letter Upper Bound on the Feedback Capacity of Unifilar Finite-State Channels, IEEE Transactions on Information Theory, vol. 63 no. 3 (March, 2017), pp. 1392-1409 [doi]  [abs]
  5. Sabag, O; Permuter, HH; Pfister, HD, Single-letter bounds on the feedback capacity of unifilar finite-state channels, 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016 (January, 2017), ISBN 9781509021529 [doi]  [abs]
  6. Jian, Y-Y; Pfister, HD; Narayanan, KR, Approaching Capacity at High Rates with Iterative Hard-Decision Decoding, IEEE Transactions on Information Theory (2017), pp. 1-1 [doi]
  7. Kumar, S; Calderbank, R; Pfister, HD, Beyond double transitivity: Capacity-achieving cyclic codes on erasure channels, 2016 IEEE Information Theory Workshop, ITW 2016 (October, 2016), pp. 241-245, ISBN 9781509010905 [doi]  [abs]
  8. Hager, C; Amat, AGI; Pfister, HD; Brannstrom, F, Density evolution for deterministic generalized product codes with higher-order modulation, International Symposium on Turbo Codes and Iterative Information Processing, ISTC, vol. 2016-October (October, 2016), pp. 236-240, ISBN 9781509034017 [doi]  [abs]
  9. Sanatkar, MR; Pfister, HD, Increasing the rate of spatially-coupled codes via optimized irregular termination, International Symposium on Turbo Codes and Iterative Information Processing, ISTC, vol. 2016-October (October, 2016), pp. 31-35, ISBN 9781509034017 [doi]  [abs]
  10. Sabag, O; Permuter, HH; Pfister, HD, A single-letter upper bound on the feedback capacity of unifilar finite-state channels, IEEE International Symposium on Information Theory - Proceedings, vol. 2016-August (August, 2016), pp. 310-314, ISBN 9781509018062 [doi]  [abs]
  11. Pfister, HD; Urbanke, R, Near-optimal finite-length scaling for polar codes over large alphabets, IEEE International Symposium on Information Theory - Proceedings, vol. 2016-August (August, 2016), pp. 215-219, ISBN 9781509018062 [doi]  [abs]
  12. Reeves, G; Pfister, HD, The replica-symmetric prediction for compressed sensing with Gaussian matrices is exact, IEEE International Symposium on Information Theory - Proceedings, vol. 2016-August (August, 2016), pp. 665-669, ISBN 9781509018062 [doi]  [abs]
  13. Hager, C; Pfister, HD; Graell I Amat, A; Brannstrom, F, Deterministic and ensemble-based spatially-coupled product codes, IEEE International Symposium on Information Theory - Proceedings, vol. 2016-August (August, 2016), pp. 2114-2118, ISBN 9781509018062 [doi]  [abs]
  14. Kumar, S; Calderbank, R; Pfister, HD, Reed-muller codes achieve capacity on the quantum erasure channel, IEEE International Symposium on Information Theory - Proceedings, vol. 2016-August (August, 2016), pp. 1750-1754, ISBN 9781509018062 [doi]  [abs]
  15. Kudekar, S; Kumar, S; Mondelli, M; Pfister, HD; Urbankez, R, Comparing the bit-MAP and block-MAP decoding thresholds of reed-muller codes on BMS channels, IEEE International Symposium on Information Theory - Proceedings, vol. 2016-August (August, 2016), pp. 1755-1759, ISBN 9781509018062 [doi]  [abs]
  16. Hager, C; Pfister, HD; Amat, AG; Brannstrom, F, Density evolution and error floor analysis for staircase and braided codes, 2016 Optical Fiber Communications Conference and Exhibition, OFC 2016 (August, 2016), ISBN 9781943580071  [abs]
  17. Kudekar, S; Pfister, HD; Kumar, S; Şaşoǧlu, E; Mondelli, M; Urbanke, R, Reed-Muller codes achieve capacity on erasure channels, Proceedings of the Annual ACM Symposium on Theory of Computing, vol. 19-21-June-2016 (June, 2016), pp. 658-669, ISBN 9781450341325 [doi]  [abs]
  18. Kumar, S; Vem, A; Narayanan, K; Pfister, HD, Spatially-coupled codes for write-once memories, 2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015 (April, 2016), pp. 125-131, ISBN 9781509018239 [doi]  [abs]
  19. Lian, M; Pfister, HD, Belief-propagation reconstruction for compressed sensing: Quantization vs. Gaussian approximation, 2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015 (April, 2016), pp. 1106-1113, ISBN 9781509018239 [doi]  [abs]
  20. Kudekar, S; Kumar, S; Mondelli, M; Pfister, HD; Sasoglu, E; Urbanke, RL, Reed-Muller codes achieve capacity on erasure channels., edited by Wichs, D; Mansour, Y, STOC (2016), pp. 658-669, ACM, ISBN 978-1-4503-4132-5 [doi]

Pierce, Lillian B.

  1. Carneiro, E; Madrid, J; Pierce, LB, Endpoint Sobolev and BV continuity for maximal operators, Journal of Functional Analysis, vol. 273 no. 10 (November, 2017), pp. 3262-3294 [doi]
  2. Heath-Brown, DR; Pierce, LB, Averages and moments associated to class numbers of imaginary quadratic fields, Compositio Mathematica, vol. 153 no. 11 (November, 2017), pp. 2287-2309 [doi]
  3. Heath-Brown, DR; Pierce, LB, Simultaneous integer values of pairs of quadratic forms, Journal für die Reine und Angewandte Mathematik (Crelle's Journal), vol. 2017 no. 727 (January, 2017) [doi]
  4. Ellenberg, J; Pierce, LB; Wood, MM, On $\ell$-torsion in class groups of number fields, arXiv:1606.06103 [math] (June, 2016)  [abs]
  5. Pierce, LB, Burgess bounds for multi-dimensional short mixed character sums, Journal of Number Theory, vol. 163 (June, 2016), pp. 172-210 [doi]
  6. Guo, S; Pierce, LB; Roos, J; Yung, P, Polynomial Carleson operators along monomial curves in the plane, arXiv:1605.05812 [math] (May, 2016)  [abs]

Plesser, Ronen

  1. Jockers, H; Katz, S; Morrison, DR; Plesser, MR, SU(N) Transitions in M-Theory on Calabi–Yau Fourfolds and Background Fluxes, Communications in Mathematical Physics, vol. 351 no. 2 (April, 2017), pp. 837-871 [doi]

Randles, Amanda

  1. Randles, A; Frakes, DH; Leopold, JA, Computational Fluid Dynamics and Additive Manufacturing to Diagnose and Treat Cardiovascular Disease., Trends in Biotechnology (September, 2017) [doi]  [abs]
  2. Gounley, J; Vardhan, M; Randles, A, A computational framework to assess the influence of changes in vascular geometry on blood flow, PASC 2017 - Proceedings of the Platform for Advanced Scientific Computing Conference (June, 2017), ISBN 9781450350624 [doi]  [abs]
  3. Dabagh, M; Jalali, P; Butler, PJ; Randles, A; Tarbell, JM, Mechanotransmission in endothelial cells subjected to oscillatory and multi-directional shear flow., Journal of the Royal Society Interface, vol. 14 no. 130 (May, 2017) [doi]  [abs]
  4. Gounley, J; Draeger, EW; Randles, A, Numerical simulation of a compound capsule in a constricted microchannel., Procedia Computer Science, vol. 108 (January, 2017), pp. 175-184 [doi]  [abs]
  5. Laurence, TA; Ly, S; Fong, E; Shusteff, M; Randles, A; Gounley, J; Draeger, E, Using stroboscopic flow imaging to validate large-scale computational fluid dynamics simulations, Proceedings of SPIE, vol. 10076 (January, 2017), ISBN 9781510605930 [doi]  [abs]
  6. Gounley, J; Chaudhury, R; Vardhan, M; Driscoll, M; Pathangey, G; Winarta, K; Ryan, J; Frakes, D; Randles, A, Does the degree of coarctation of the aorta influence wall shear stress focal heterogeneity?, Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, vol. 2016 (August, 2016), pp. 3429-3432, ISBN 9781457702204 [doi]  [abs]

Reed, Michael C.

  1. Reed, M; Best, J; Golubitsky, M; Stewart, I; Nijhout, HF, Analysis of Homeostatic Mechanisms in Biochemical Networks., Bulletin of Mathematical Biology (September, 2017) [doi]  [abs]
  2. Reed, MC; Best, J; Nijhout, HF, Mathematical models of neuromodulation and implications for neurology and psychiatry, edited by Erdi, P; Battacharya, B; Cochran, A (2017)
  3. Reed, MC; Lawley, S; Nijhout, HF, Spiracular fluttering increases oxygen uptake (2017)
  4. Reed, MC; Best, J; Nijhout, HF, Mathematical models of neuromodulation and implications for neurology and psychiatry, in Computational Neurology and Psychiatry, edited by Erdi, P; Bhattacharya, B; Cochran, A (2017), Springer
  5. Samaranayake, S; Abdalla, A; Robke, R; Nijhout, HF; Reed, MC; Best, J; Hashemi, P, A voltammetric and mathematical analysis of histaminergic modulation of serotonin in the mouse hypothalamus., Journal of Neurochemistry, vol. 138 no. 3 (August, 2016), pp. 374-383 [doi]  [abs]
  6. Lawley, SD; Best, JA; Reed, MC, Neurotransmitter concentrations in the presence of neural switching in one dimension, Discrete and Continuous Dynamical Systems - Series B, vol. 21 no. 7 (August, 2016), pp. 2255-2273 [doi]
  7. Temamogullari, NE; Nijhout, HF; C Reed, M, Mathematical modeling of perifusion cell culture experiments on GnRH signaling., Mathematical Biosciences, vol. 276 (June, 2016), pp. 121-132 [doi]  [abs]
  8. Thanacoody, HKR; Nijhout, FH; Reed, MC; Thomas, SHL, Mathematical modelling of the effect of a high dose acetylcysteine regimen based on the SNAP trial on hepatic glutathione regeneration and hepatocyte death, Clinical Toxicology, vol. 54 no. 4 (2016), pp. 494-494
  9. Reed, MC; Nijhout, HF; Kurtz, T, Mathematical modeling of cell metabolism, in Encyclopedia of Applied and Computational Mathematics, edited by Engquist, B (2016), Springer

Robles, Colleen M

  1. Robles, C, Characterization of Calabi–Yau variations of Hodge structure over tube domains by characteristic forms, Mathematische Annalen (September, 2017), pp. 1-25 [doi]  [abs]
  2. Kerr, M; Robles, C, Variations of Hodge structure and orbits in flag varieties, Advances in Mathematics, vol. 315 (July, 2017), pp. 27-87 [doi]  [abs]
  3. Kerr, M; Robles, C, Classification of smooth horizontal Schubert varieties, European Journal of Mathematics, vol. 3 no. 2 (June, 2017), pp. 289-310 [doi]
  4. Robles, C, Classification of horizontal s, Compositio Mathematica, vol. 152 no. 05 (May, 2016), pp. 918-954 [doi]
  5. Robles, C, Characteristic cohomology of the infinitesimal period relation, Asian Journal of Mathematics, vol. 20 no. 4 (2016), pp. 725-758 [arXiv:1310.8154], [doi]

Saper, Leslie

  1. Saper, L, Perverse sheaves and the reductive Borel-Serre compactification, in Hodge Theory and L² Analysis (2017)  [abs]
  2. Saper, L, ℒ-modules and micro-support, to appear in Annals of Mathematics (2017)

Sapiro, Guillermo

  1. Sokolić, J; Giryes, R; Sapiro, G; Rodrigues, MRD, Generalization error of deep neural networks: Role of classification margin and data structure, 2017 12th International Conference on Sampling Theory and Applications, SampTA 2017 (September, 2017), pp. 147-151, ISBN 9781538615652 [doi]  [abs]
  2. Sokolic, J; Giryes, R; Sapiro, G; Rodrigues, MRD, Robust Large Margin Deep Neural Networks, IEEE Transactions on Signal Processing, vol. 65 no. 16 (August, 2017), pp. 4265-4280 [doi]
  3. 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 (June, 2017) [doi]  [abs]
  4. Simhal, AK; Aguerrebere, C; Collman, F; Vogelstein, JT; Micheva, KD; Weinberg, RJ; Smith, SJ; Sapiro, G, Probabilistic fluorescence-based synapse detection., PLoS computational biology, vol. 13 no. 4 (April, 2017), pp. e1005493 [doi]  [abs]
  5. Campbell, K; Carpenter, KLH; Espinosa, S; Hashemi, J; Qiu, Q; Tepper, M; Calderbank, R; Sapiro, G; Egger, HL; Baker, JP; Dawson, G, Use of a Digital Modified Checklist for Autism in Toddlers - Revised with Follow-up to Improve Quality of Screening for Autism., The Journal of Pediatrics, vol. 183 (April, 2017), pp. 133-139.e1 [doi]  [abs]
  6. Chen, J; Chang, Z; Qiu, Q; Li, X; Sapiro, G; Bronstein, A; Pietikäinen, M, RealSense = real heart rate: Illumination invariant heart rate estimation from videos, 2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016 (January, 2017), ISBN 9781467389105 [doi]  [abs]
  7. Pisharady, PK; Sotiropoulos, SN; Sapiro, G; Lenglet, C, A sparse bayesian learning algorithm for white matter parameter estimation from compressed multi-shell diffusion MRI, Lecture notes in computer science, vol. 10433 LNCS (January, 2017), pp. 602-610, ISBN 9783319661810 [doi]  [abs]
  8. Gunalan, K; Chaturvedi, A; Howell, B; Duchin, Y; Lempka, SF; Patriat, R; Sapiro, G; Harel, N; McIntyre, CC, Creating and parameterizing patient-specific deep brain stimulation pathway-activation models using the hyperdirect pathway as an example., PloS one, vol. 12 no. 4 (January, 2017), pp. e0176132 [doi]  [abs]
  9. Lezama, J; Mukherjee, D; McNabb, RP; Sapiro, G; Kuo, AN; Farsiu, S, Segmentation guided registration of wide field-of-view retinal optical coherence tomography volumes., Biomedical Optics Express, vol. 7 no. 12 (December, 2016), pp. 4827-4846 [doi]  [abs]
  10. Aguerrebere, C; Delbracio, M; Bartesaghi, A; Sapiro, G, Fundamental Limits in Multi-Image Alignment, IEEE Transactions on Signal Processing, vol. 64 no. 21 (November, 2016), pp. 5707-5722 [doi]
  11. Elhamifar, E; Sapiro, G; Sastry, SS, Dissimilarity-Based Sparse Subset Selection., IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38 no. 11 (November, 2016), pp. 2182-2197 [doi]  [abs]
  12. Fiori, M; Muse, P; Tepper, M; Sapiro, G, Tell me where you are and i tell you where you are going: Estimation of dynamic mobility graphs, Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop, vol. 2016-September (September, 2016), ISBN 9781509021031 [doi]  [abs]
  13. Giryes, R; Sapiro, G; Bronstein, AM, Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?, IEEE Transactions on Signal Processing, vol. 64 no. 13 (July, 2016), pp. 3444-3457 [doi]
  14. Tepper, M; Sapiro, G, A short-graph fourier transform via personalized pagerank vectors, IEEE International Conference on Acoustics Speech and Signal Processing, vol. 2016-May (May, 2016), pp. 4806-4810, ISBN 9781479999880 [doi]  [abs]
  15. Tepper, M; Sapiro, G, Compressed Nonnegative Matrix Factorization Is Fast and Accurate, IEEE Transactions on Signal Processing, vol. 64 no. 9 (May, 2016), pp. 2269-2283 [doi]
  16. Qiu, Q; Thompson, A; Calderbank, R; Sapiro, G, Data Representation Using the Weyl Transform, IEEE Transactions on Signal Processing, vol. 64 no. 7 (April, 2016), pp. 1844-1853 [doi]
  17. Carpenter, KLH; Sprechmann, P; Calderbank, R; Sapiro, G; Egger, HL, Quantifying Risk for Anxiety Disorders in Preschool Children: A Machine Learning Approach., PloS one, vol. 11 no. 11 (January, 2016), pp. e0165524 [doi]  [abs]
  18. Chang, Z; Qiu, Q; Sapiro, G, Synthesis-based low-cost gaze analysis, Communications in Computer and Information Science, vol. 618 (January, 2016), pp. 95-100, ISBN 9783319405414 [doi]  [abs]
  19. Lyzinski, V; Fishkind, DE; Fiori, M; Vogelstein, JT; Priebe, CE; Sapiro, G, Graph Matching: Relax at Your Own Risk., IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38 no. 1 (January, 2016), pp. 60-73 [doi]  [abs]

Smith, David A.

  1. Smith, DA; Fey, JT, Algebra as Part of an Integrated High School Curriculum, in And the Rest is Just Algebra, edited by Stewart, S (October 6, 2016), pp. 119-129, Springer, ISBN 3319450530  [abs]

Stern, Mark A.

  1. Sergey A. Cherkis, Andres Larrain-Hubach, Mark Stern, Instantons on multi-Taub-NUT Spaces I: Asymptotic Form and Index Theorem, arXiv:1608.00018 (August, 2016)  [abs]

Turnage-Butterbaugh, Caroline

  1. Mackall, B; Miller, SJ; Rapti, C; Turnage-Butterbaugh, C; Winsor, K, Some Results in the Theory of Low-lying Zeros, in Families of Automorphic Forms and the Trace Formula (September, 2016), Springer, ISBN 3319414240  [abs]
  2. Bui, HM; Heap, WP; Turnage-Butterbaugh, CL, GAPS BETWEEN ZEROS OF DEDEKIND ZETA-FUNCTIONS OF QUADRATIC NUMBER FIELDS. II, Quarterly Journal of Mathematics, vol. 67 no. 3 (September, 2016), pp. 467-482 [doi]
  3. Barrett, O; Firk, F; Miller, SJ; Turnage-Butterbaugh, C, From Quantum Systems to L-Functions: Pair Correlation Statistics and Beyond, in Open Problems in Mathematics, edited by John Nash Jr., Michael Th. Rassias (August, 2016), pp. 123-171, Springer, ISBN 3319321625 [arXiv:1505.07481]
  4. Best, A; Dynes, P; Edelsbrunner, X; McDonald, B; Miller, SJ; Tor, K; Turnage-Butterbaugh, C; Weinstein, M, Gaussian distribution of the number of summands in generalized Zeckendorf decomposition in small intervals, Integers, vol. 16 (2016), pp. 13 pages

Venakides, Stephanos

  1. Kiehart, DP; Crawford, JM; Aristotelous, A; Venakides, S; Edwards, GS, Cell Sheet Morphogenesis: Dorsal Closure in Drosophila melanogaster as a Model System., Annual Review of Cell and Developmental Biology, vol. 33 (October, 2017), pp. 169-202 [doi]  [abs]
  2. Komineas, S; Shipman, SP; Venakides, S, Lossless polariton solitons, Physica D: Nonlinear Phenomena, vol. 316 (February, 2016), pp. 43-56 [doi]  [abs]

Watson, Alexander

  1. Watson, AB; Lu, J; Weinstein, MI, Wavepackets in inhomogeneous periodic media: Effective particle-field dynamics and Berry curvature, Journal of Mathematical Physics, vol. 58 no. 2 (February, 2017), pp. 021503-021503 [doi]

Witelski, Thomas P.   (search)

  1. Gao, Y; Ji, H; Liu, J-G; Witelski, TP, Global existence of solutions to a tear film model with locally elevated evaporation rates, Physica D: Nonlinear Phenomena, vol. 350 (July, 2017), pp. 13-25 [doi]
  2. Ji, H; Witelski, TP, Finite-time thin film rupture driven by modified evaporative loss, Physica D: Nonlinear Phenomena, vol. 342 (March, 2017), pp. 1-15 [doi]
  3. George, C; Virgin, LN; Witelski, T, Experimental study of regular and chaotic transients in a non-smooth system, International Journal of Non-Linear Mechanics, vol. 81 (2016), pp. 55-64 [doi]
  4. Sanaei, P; Richardson, GW; Witelski, T; Cummings, LJ, Flow and fouling in a pleated membrane filter, Journal of Fluid Mechanics, vol. 795 (2016), pp. 36-59 [doi]
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Wong, Jeffrey T

  1. Wong, JT; Bertozzi, AL, A conservation law model for bidensity suspensions on an incline, Physica D: Nonlinear Phenomena, vol. 330 (September, 2016), pp. 47-57 [doi]

Wu, Hau-Tieng

  1. Lin, T-Y; Fang, Y-F; Huang, S-H; Wang, T-Y; Kuo, C-H; Wu, H-T; Kuo, H-P; Lo, Y-L, Capnography monitoring the hypoventilation during the induction of bronchoscopic sedation: A randomized controlled trial., Scientific Reports, vol. 7 no. 1 (August, 2017), pp. 8685 [doi]  [abs]
  2. Malik, J; Reed, N; Wang, C-L; Wu, H-T, Single-lead f-wave extraction using diffusion geometry, Physiological Measurement, vol. 38 no. 7 (July, 2017), pp. 1310-1334 [doi]
  3. Georgiou, A; Bello-Rivas, J; Gear, C; Wu, H-T; Chiavazzo, E; Kevrekidis, I, An Exploration Algorithm for Stochastic Simulators Driven by Energy Gradients, Entropy (Basel, Switzerland), vol. 19 no. 7 (July, 2017), pp. 294-294 [doi]
  4. Sheu, Y-L; Hsu, L-Y; Chou, P-T; Wu, H-T, Entropy-based time-varying window width selection for nonlinear-type time–frequency analysis, International Journal of Data Science and Analytics, vol. 3 no. 4 (June, 2017), pp. 231-245 [doi]
  5. Li, R; Frasch, MG; Wu, H-T, Efficient Fetal-Maternal ECG Signal Separation from Two Channel Maternal Abdominal ECG via Diffusion-Based Channel Selection, Frontiers in Physiology, vol. 8 (May, 2017) [doi]
  6. Herry, CL; Frasch, M; Seely, AJ; Wu, H-T, Heart beat classification from single-lead ECG using the synchrosqueezing transform., Physiological Measurement, vol. 38 no. 2 (February, 2017), pp. 171-187 [doi]  [abs]
  7. Lin, Y-T; Wu, H-T, ConceFT for Time-Varying Heart Rate Variability Analysis as a Measure of Noxious Stimulation During General Anesthesia., IEEE Transactions on Biomedical Engineering, vol. 64 no. 1 (January, 2017), pp. 145-154 [doi]  [abs]
  8. Wu, H-T, Embedding Riemannian manifolds by the heat kernel of the connection Laplacian, Advances in Mathematics, vol. 304 (January, 2017), pp. 1055-1079 [doi]
  9. Wu, C-H; Wang, T-D; Hsieh, C-H; Huang, S-H; Lin, J-W; Hsu, S-C; Wu, H-T; Wu, Y-M; Liu, T-M, Imaging Cytometry of Human Leukocytes with Third Harmonic Generation Microscopy, Scientific Reports, vol. 6 no. 1 (December, 2016) [doi]
  10. Marchesini, S; Tu, Y-C; Wu, H-T, Alternating projection, ptychographic imaging and phase synchronization, Applied and Computational Harmonic Analysis, vol. 41 no. 3 (November, 2016), pp. 815-851 [doi]
  11. Wu, H-T; Lewis, GF; Davila, MI; Daubechies, I; Porges, SW, Optimizing Estimates of Instantaneous Heart Rate from Pulse Wave Signals with the Synchrosqueezing Transform., Methods of information in medicine, vol. 55 no. 5 (October, 2016), pp. 463-472 [doi]  [abs]
  12. Lin, Y-T; Flandrin, P; Wu, H-T, When Interpolation-Induced Reflection Artifact Meets Time-Frequency Analysis., IEEE Transactions on Biomedical Engineering, vol. 63 no. 10 (October, 2016), pp. 2133-2141 [doi]  [abs]
  13. O'Neal, WT; Wang, YG; Wu, H-T; Zhang, Z-M; Li, Y; Tereshchenko, LG; Estes, EH; Daubechies, I; Soliman, EZ, Electrocardiographic J Wave and Cardiovascular Outcomes in the General Population (from the Atherosclerosis Risk In Communities Study)., The American Journal of Cardiology, vol. 118 no. 6 (September, 2016), pp. 811-815 [doi]  [abs]
  14. Daubechies, I; Wang, YG; Wu, H-T, ConceFT: concentration of frequency and time via a multitapered synchrosqueezed transform., Philosophical Transactions A, vol. 374 no. 2065 (April, 2016), pp. 20150193 [doi]  [abs]
  15. Kowalski, M; Meynard, A; Wu, H-T, Convex Optimization approach to signals with fast varying instantaneous frequency, Applied and Computational Harmonic Analysis (April, 2016) [doi]
  16. El Karoui, N; Wu, H-T, Graph connection Laplacian methods can be made robust to noise, Annals of statistics, vol. 44 no. 1 (February, 2016), pp. 346-372 [doi]
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Yang, Haizhao   (search)

  1. Lu, J; Yang, H, A cubic scaling algorithm for excited states calculations in particle–particle random phase approximation, Journal of Computational Physics, vol. 340 (July, 2017), pp. 297-308 [doi]
  2. Li, Y; Yang, H; Ying, L, Multidimensional butterfly factorization, Applied and Computational Harmonic Analysis (April, 2017), Elsevier, ISSN 1096-603X [arXiv:1509.07925], [repository], [doi]
  3. Cornelis, B; Yang, H; Goodfriend, A; Ocon, N; Lu, J; Daubechies, I, Removal of Canvas Patterns in Digital Acquisitions of Paintings., IEEE Transactions on Image Processing, vol. 26 no. 1 (January, 2017), pp. 160-171, Institute of Electrical and Electronics Engineers (IEEE), ISSN 1941-0042 [repository], [doi]  [abs]
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Zhou, Zhennan

  1. Liu, J-G; Ma, Z; Zhou, Z, Explicit and Implicit TVD Schemes for Conservation Laws with Caputo Derivatives, Journal of Scientific Computing, vol. 72 no. 1 (July, 2017), pp. 291-313 [doi]  [abs]
  2. Lu, J; Zhou, Z, Path integral molecular dynamics with surface hopping for thermal equilibrium sampling of nonadiabatic systems., Journal of Chemical Physics, vol. 146 no. 15 (April, 2017), pp. 154110 [doi]  [abs]
  3. Ma, Z; Zhang, Y; Zhou, Z, An improved semi-Lagrangian time splitting spectral method for the semi-classical Schrödinger equation with vector potentials using NUFFT, Applied Numerical Mathematics, vol. 111 (January, 2017), pp. 144-159 [doi]
  4. Jin, S; Sparber, C; Zhou, Z, On the classical limit of a time-dependent self-consistent field system: Analysis and computation, Kinetic and Related Models, vol. 10 no. 1 (November, 2016), pp. 263-298 [doi]
  5. Chen, J; Liu, J-G; Zhou, Z, On a Schrödinger--Landau--Lifshitz System: Variational Structure and Numerical Methods, Multiscale Modeling & Simulation, vol. 14 no. 4 (January, 2016), pp. 1463-1487 [doi]

 

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