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Mathematics Faculty: Publications since January 2016

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

Abel, Michael

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

Agarwal, Pankaj K.

  1. 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]
  2. 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]
  3. 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]
  4. 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]
  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; 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]
  8. 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]
  9. 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]
  10. 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]

Arlotto, Alessandro

  1. 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]
  2. 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]

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. 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]
  2. 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]
  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]
  4. 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]

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. 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]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. Daubechies, I; Defrise, M; Mol, CD, Sparsity-enforcing regularisation and ISTA revisited, Inverse Problems, vol. 32 no. 10 (October, 2016), pp. 104001-104001 [doi]
  9. 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]
  10. 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]

Dolbow, John E.

  1. 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]
  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]

Dunson, David B.

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

Durrett, Richard T.

  1. 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]
  2. 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]
  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. 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]
  5. Bessonov, M; Durrett, R, Phase transitions for a planar quadratic contact process, Advances in Applied Mathematics, vol. 87 (June, 2017), pp. 82-107 [doi]

Fernandes de Oliveira, Goncalo M.

  1. 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]
  2. 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]
  3. Oliveira, G, G 2-Monopoles with Singularities (Examples), Letters in Mathematical Physics, vol. 106 no. 11 (November, 2016), pp. 1479-1497 [doi]
  4. Oliveira, G, Gerbes on G2 manifolds, Journal of Geometry and Physics, vol. 114 (April, 2017), pp. 570-580 [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. H. Hahn, On tensor thrid L-functions of automorphic representations of GL_n(A_F), Proc. Amer. Math. Soc. (2016)
  2. H. Hahn, On classical groups detected by the triple tensor product and the Littlewood-Richardson semigroup (2016)
  3. 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]
  4. 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]

Hain, Richard   (search)

  1. Hain, R; Matsumoto, M, Universal Mixed Elliptic Motives (December, 2015) [arxiv:1512.03975]  [abs]
  2. Hain, R, Deligne-Beilinson Cohomology of Affine Groups (July, 2015) [arXiv:1507.03144]  [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, 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]
  5. 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]

Harer, John

  1. 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]
  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; 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]

Hodel, Richard E.

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

Junge, Matthew S

  1. 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]
  2. 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]
  3. 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]

Layton, Anita T.

  1. 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]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. 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]
  16. 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]
  17. 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]

Liu, Jian-Guo

  1. 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
  2. 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)
  3. 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
  4. 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
  5. 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
  6. 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)
  7. H. Huang and J.-G. Liu, Error estimate of a random particle blob method for the Keller-Segel equation, Math. Comp. (2016)
  8. Y. Gao, J.-G. Liu, J. Lu, Continuum limit of a mesoscopic model of step motion on vicinal surfaces, J. Nonlinear Science (2016)
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. 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]
  16. Huang, H; Liu, J-G, A note on Monge–Ampère Keller–Segel equation, Applied Mathematics Letters, vol. 61 (November, 2016), pp. 26-34 [doi]
  17. 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]
  18. 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
  19. 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
  20. 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]
  21. 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]
  22. 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]

Lu, Jianfeng

  1. 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]
  2. 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]
  3. 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]
  4. Lai, R; Lu, J, Localized density matrix minimization and linear-scaling algorithms, Journal of Computational Physics, vol. 315 (June, 2016), pp. 194-210 [doi]
  5. Lu, J; Ying, L, Sparsifying preconditioner for soliton calculations, Journal of Computational Physics, vol. 315 (June, 2016), pp. 458-466 [doi]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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]
  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. 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. 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]
  15. 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]
  16. 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]
  17. 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]
  18. Lu, J; Thicke, K, Orbital minimization method with ℓ 1 regularization, Journal of Computational Physics, vol. 336 (May, 2017), pp. 87-103 [doi]
  19. 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]
  20. 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]
  21. 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]

Ma, Ding

  1. 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]
  2. 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]

Maggioni, Mauro

  1. 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..)
  2. Ronald R Coifman and Mauro Maggioni, Multiresolution Analysis associated to diffusion semigroups: construction and fast algorithms no. YALE/DCS/TR-1289 (2004)
  3. E Causevic and R~R Coifman and R Isenhart and A Jacquin and E~R John and M Maggioni and L~S Prichep and F~J Warner, QEEG-based classification with wavelet packets and microstate features for triage applications in the ER (2005)
  4. 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]
  5. 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]
  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. 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. 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]
  10. 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]
  11. 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]

Mattingly, Jonathan C.

  1. Hairer, M; Mattingly, J, The strong Feller property for singular stochastic PDEs (2016)  [abs]
  2. Tempkin, JOB; Koten, BV; Mattingly, JC; Dinner, AR; Weare, J, Trajectory stratification of stochastic dynamics (2016)  [abs]
  3. 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]

Miller, Ezra

  1. 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]
  2. 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]
  3. 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]
  4. 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]

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. 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]
  2. 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]
  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. 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]
  5. 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]

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, JH; Roquejoffre, J-M; Ryzhik, L, Refined long time asymptotics for Fisher-KPP fronts (2016)
  2. 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]
  3. 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]
  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. 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]
  6. 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]

Petters, Arlie O.

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

Pfister, Henry

  1. 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]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  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; 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]
  13. 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]
  14. 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]
  15. 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]
  16. 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]

Pierce, Lillian B.

  1. Guo, S; Pierce, LB; Roos, J; Yung, P, Polynomial Carleson operators along monomial curves in the plane, arXiv:1605.05812 [math] (May, 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. Ellenberg, J; Pierce, LB; Wood, MM, On $\ell$-torsion in class groups of number fields, arXiv:1606.06103 [math] (June, 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. 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
  2. Reed, MC; Nijhout, HF; Kurtz, T, Mathematical modeling of cell metabolism, in Encyclopedia of Applied and Computational Mathematics, edited by Engquist, B (2016), Springer
  3. 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]
  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. 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)
  7. Reed, MC; Lawley, S; Nijhout, HF, Spiracular fluttering increases oxygen uptake (2017)
  8. 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

Robles, Colleen M

  1. 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]
  2. Robles, C, Classification of horizontal s, Compositio Mathematica, vol. 152 no. 05 (May, 2016), pp. 918-954 [doi]
  3. 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]

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. 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]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  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. 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]
  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. 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]
  13. 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]
  14. 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]
  15. 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]

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. 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
  2. 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]
  3. 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]
  4. 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]

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. 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]
  2. 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]
  3. Smolka, LB; McLaughlin, CK; Witelski, TP, Oil capture from a water surface by a falling sphere, Colloids and Surfaces A: Physicochemical and Engineering Aspects, vol. 497 (2016), pp. 126-132, ISSN 0927-7757 [doi]
  4. 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]
  5. 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]

Yang, Haizhao   (search)

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Zhou, Zhennan

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