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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; 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]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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., The annals of applied statistics, vol. 10 no. 1 (January, 2016), pp. 198-218, 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]

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. 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]
  4. Daubechies, I; Defrise, M; Mol, CD, Sparsity-enforcing regularisation and ISTA revisited, Inverse Problems, vol. 32 no. 10 (October, 2016), pp. 104001-104001 [doi]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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. 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]
  2. 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]

Dunson, David B.

  1. 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]
  2. 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]
  3. 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]
  4. Lock, EF; Dunson, DB, Bayesian genome- and epigenome-wide association studies with gene level dependence., Biometrics (January, 2017) [doi]  [abs]
  5. Lin, L; Rao, V; Dunson, D, Bayesian nonparametric inference on the Stiefel manifold, Statistica Sinica (2017) [doi]
  6. 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]
  7. 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]
  8. 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]
  9. Guhaniyogi, R; Dunson, DB, Compressed Gaussian process for manifold regression, Journal of machine learning research : JMLR, vol. 17 (May, 2016)  [abs]
  10. 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]
  11. Yang, Y; Dunson, DB, Bayesian manifold regression, Annals of statistics, vol. 44 no. 2 (April, 2016), pp. 876-905 [doi]
  12. Zhou, J; Herring, AH; Bhattacharya, A; Olshan, AF; Dunson, DB, Nonparametric Bayes modeling for case control studies with many predictors., Biometrics, vol. 72 no. 1 (March, 2016), pp. 184-192 [doi]  [abs]
  13. 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]
  14. Kunihama, T; Dunson, DB, Nonparametric Bayes inference on conditional independence, Biometrika, vol. 103 no. 1 (March, 2016), pp. 35-47 [doi]
  15. 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]
  16. 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]
  17. 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]

Durrett, Richard T.

  1. Bessonov, M; Durrett, R, Phase transitions for a planar quadratic contact process, Advances in Applied Mathematics, vol. 87 (June, 2017), pp. 82-107 [doi]
  2. 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]
  3. 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]
  4. 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]
  5. 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
  4. Hain, R; Matsumoto, M, Universal Mixed Elliptic Motives (December, 2015) [arxiv:1512.03975]  [abs]
  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, 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]
  2. Benjamini, I; Foxall, E; Gurel-Gurevich, O; Junge, M; Kesten, H, Site recurrence for coalescing random walk, Electronic Communications in Probability, vol. 21 (2016) [doi]
  3. 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. Chen, Y; Fry, BC; Layton, AT, Modeling glucose metabolism and lactate production in the kidney., Mathematical Biosciences, vol. 289 (May, 2017), pp. 116-129 [doi]  [abs]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. 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 (June, 2016)  [abs]
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. 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]
  16. 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]
  17. 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]

Liu, Jian-Guo

  1. 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]
  2. 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]
  3. 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]
  4. W. Cong and J.-G. Liu, Uniform $L^\infty$ boundedness for a degenerate parabolic-parabolic Keller-Segel model, Discrete and Continuous Dynamical Systems - Series B, vol. 22 (2017), pp. 307-338
  5. 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
  6. Huang, H; Liu, J-G, A note on Monge–Ampère Keller–Segel equation, Applied Mathematics Letters, vol. 61 (November, 2016), pp. 26-34 [doi]
  7. 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]
  8. 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]
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. 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, vol. 162 (January, 2016), pp. 195-223, Springer, ISBN 9783319321424 [doi]  [abs]
  15. 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)
  16. J. Chen, J.-G. Liu and Z. Zhou, On a Schrodinger-Landau-Lifshitz system: Variational struc- ture and numerical methods, Multiscale Modeling and Simulation, vol. 14 (2016), pp. 1463-1487
  17. 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
  18. 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
  19. 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)
  20. H. Huang and J.-G. Liu, Error estimate of a random particle blob method for the Keller-Segel equation, Math. Comp. (2016)
  21. Y. Gao, J.-G. Liu, J. Lu, Continuum limit of a mesoscopic model of step motion on vicinal surfaces, J. Nonlinear Science (2016)
  22. 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. 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. 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]
  3. 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]
  4. Lu, J; Thicke, K, Orbital minimization method with ℓ 1 regularization, Journal of Computational Physics, vol. 336 (May, 2017), pp. 87-103 [doi]
  5. 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]
  6. 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]
  7. 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]
  8. Lu, J; Yang, H, Preconditioning Orbital Minimization Method for Planewave Discretization, Multiscale Modeling & Simulation, vol. 15 no. 1 (January, 2017), pp. 254-273 [doi]
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. 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]
  16. 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]
  17. Lai, R; Lu, J, Localized density matrix minimization and linear-scaling algorithms, Journal of Computational Physics, vol. 315 (June, 2016), pp. 194-210 [doi]
  18. Lu, J; Ying, L, Sparsifying preconditioner for soliton calculations, Journal of Computational Physics, vol. 315 (June, 2016), pp. 458-466 [doi]
  19. 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]
  20. 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]
  21. 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]

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. 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]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. Little, AV; Maggioni, M; Rosasco, L, Multiscale geometric methods for data sets I: Multiscale SVD, noise and curvature, Applied and Computational Harmonic Analysis (March, 2016) [doi]
  8. 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]
  9. 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)
  10. 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..)
  11. 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. 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]
  2. Hairer, M; Mattingly, J, The strong Feller property for singular stochastic PDEs (2016)  [abs]
  3. 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., The annals of applied statistics, vol. 10 no. 1 (January, 2016), pp. 198-218 [arXiv:1411.6652], [1411.6652v1]  [abs]

Motta, Francis C.

  1. 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. 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]
  2. 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]
  3. 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]
  4. Nolen, JH; Roquejoffre, J-M; Ryzhik, L, Refined long time asymptotics for Fisher-KPP fronts (2016)
  5. 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]
  6. Nolen, J; Mourrat, J-C, Scaling limit of the corrector in stochastic homogenization, The annals of applied probability : an official journal of the Institute of Mathematical Statistics (2016), Institute of Mathematical Statistics (IMS), ISSN 1050-5164 [arXiv:1502.07440], [1502.07440]

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. 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]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. Sabag, O; Permuter, HH; Pfister, HD, A single-letter upper bound on the feedback capacity of unifilar finite-state channels, vol. 2016-August (August, 2016), pp. 310-314, ISBN 9781509018062 [doi]  [abs]
  7. Pfister, HD; Urbanke, R, Near-optimal finite-length scaling for polar codes over large alphabets, vol. 2016-August (August, 2016), pp. 215-219, ISBN 9781509018062 [doi]  [abs]
  8. Reeves, G; Pfister, HD, The replica-symmetric prediction for compressed sensing with Gaussian matrices is exact, vol. 2016-August (August, 2016), pp. 665-669, ISBN 9781509018062 [doi]  [abs]
  9. Hager, C; Pfister, HD; Graell I Amat, A; Brannstrom, F, Deterministic and ensemble-based spatially-coupled product codes, vol. 2016-August (August, 2016), pp. 2114-2118, ISBN 9781509018062 [doi]  [abs]
  10. Kumar, S; Calderbank, R; Pfister, HD, Reed-muller codes achieve capacity on the quantum erasure channel, vol. 2016-August (August, 2016), pp. 1750-1754, ISBN 9781509018062 [doi]  [abs]
  11. 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, vol. 2016-August (August, 2016), pp. 1755-1759, ISBN 9781509018062 [doi]  [abs]
  12. 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]
  13. 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]
  14. 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]
  15. 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]
  16. 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. Ellenberg, J; Pierce, LB; Wood, MM, On $\ell$-torsion in class groups of number fields, arXiv:1606.06103 [math] (June, 2016)  [abs]
  2. Pierce, LB, Burgess bounds for multi-dimensional short mixed character sums, Journal of Number Theory, vol. 163 (June, 2016), pp. 172-210 [doi]
  3. 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. 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, MC; Best, J; Nijhout, HF, Mathematical models of neuromodulation and implications for neurology and psychiatry, edited by Erdi, P; Battacharya, B; Cochran, A (2017)
  2. Reed, MC; Lawley, S; Nijhout, HF, Spiracular fluttering increases oxygen uptake (2017)
  3. 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
  4. 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]
  5. 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]
  6. 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]
  7. 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
  8. 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. 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]
  2. Robles, C, Classification of horizontal s, Compositio Mathematica, vol. 152 no. 05 (May, 2016), pp. 918-954 [doi]
  3. 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. 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]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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]
  11. 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]
  12. Huang, J; Qiu, Q; Calderbank, R; Sapiro, G, Geometry-aware deep transform, Proceedings / IEEE International Conference on Computer Vision. IEEE International Conference on Computer Vision, vol. 11-18-December-2015 (February, 2016), pp. 4139-4147, ISBN 9781467383912 [doi]  [abs]
  13. 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]
  14. 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]
  15. 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. Komineas, S; Shipman, SP; Venakides, S, Lossless polariton solitons, Physica D: Nonlinear Phenomena, vol. 316 (February, 2016), pp. 43-56 [doi]  [abs]

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]
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Yang, Haizhao   (search)

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Zhou, Zhennan

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