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Mathematics : Publications since January 2017

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

Agarwal, Pankaj K.

  1. Agarwal, PK; Kumar, N; Sintos, S; Suri, S, Range-max queries on uncertain data, Journal of Computer and System Sciences (January, 2017) [doi]  [abs]
  2. Wu, Y; Gao, J; Agarwal, PK; Yang, J, Finding diverse, high-value representatives on a surface of answers, Proceedings of the VLDB Endowment International Conference on Very Large Data Bases, vol. 10 no. 7 (January, 2017), pp. 793-804  [abs]
  3. Garg, N; Sadiq, M; Agarwal, P, GOASREP: Goal oriented approach for software requirements elicitation and prioritization using analytic hierarchy process, Advances in Intelligent Systems and Computing, vol. 516 (January, 2017), pp. 281-287, ISBN 9789811031557 [doi]  [abs]
  4. Wu, Y; Agarwal, PK; Li, C; Yang, J; Yu, C, Computational Fact Checking through Query Perturbations, ACM Transactions on Database Systems, vol. 42 no. 1 (January, 2017), pp. 1-41 [doi]
  5. Agarwal, PK; Fox, K; Panigrahi, D; Varadarajan, KR; Xiao, A, Faster algorithms for the geometric transportation problem, LIPIcs, vol. 77 (June, 2017), pp. 71-716, ISBN 9783959770385 [doi]  [abs]
  6. Agarwal, PK; Kumar, N; Sintos, S; Suri, S, Efficient algorithms for k-regret minimizing sets, LIPIcs, vol. 75 (August, 2017), ISBN 9783959770361 [doi]  [abs]
  7. Agarwal, PK; Rubin, N; Sharir, M, Approximate nearest neighbor search amid higher-dimensional flats, LIPIcs, vol. 87 (September, 2017), ISBN 9783959770491 [doi]  [abs]
  8. Agarwal, PK; Har-Peled, S; Suri, S; Yıldız, H; Zhang, W, Convex Hulls Under Uncertainty, Algorithmica, vol. 79 no. 2 (October, 2017), pp. 340-367 [doi]
  9. Rav, M; Lowe, A; Agarwal, PK, Flood Risk Analysis on Terrains, GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, vol. 2017-November (November, 2017), ISBN 9781450354905 [doi]  [abs]
  10. Agarwal, PK; Fox, K; Nath, A, Maintaining reeb graphs of triangulated 2-manifolds, LIPIcs, vol. 93 (January, 2018), ISBN 9783959770552 [doi]  [abs]

Arlotto, Alessandro

  1. Arlotto, A; Gurvich, I, Uniformly bounded regret in the multi-secretary problem (October, 2017)  [abs]
  2. Arlotto, A; Frazelle, AE; Wei, Y, Strategic open routing in service networks, Management Science (2018), INFORMS
  3. Arlotto, A; Steele, JM, A central limit theorem for costs in Bulinskaya’s inventory management problem when deliveries face delays, Methodology and Computing in Applied Probability (2018) [doi]
  4. Arlotto, A; Wei, Y; Xie, X, An adaptive O(log n)-optimal policy for the online selection of a monotone subsequence from a random sample, Random Structures and Algorithms, vol. 52 no. 1 (January, 2018), pp. 41-53, Wiley [doi]  [abs]

Bendich, Paul L

  1. Tralie, CJ; Smith, A; Borggren, N; Hineman, J; Bendich, P; Zulch, P; Harer, J, Geometric Cross-Modal Comparison of Heterogeneous Sensor Data, Proceedings of the 39th IEEE Aerospace Conference (March, 2018)  [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, H; Roesch, H, Proof of a Null Geometry Penrose Conjecture, Notices of the American Mathematical Society., vol. 65 (February, 2018), American Mathematical Society

Bryant, Robert   (search)

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

Calderbank, Robert

  1. 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]
  2. Hadani, R; Rakib, S; Tsatsanis, M; Monk, A; Goldsmith, AJ; Molisch, AF; Calderbank, R, Orthogonal time frequency space modulation, IEEE Wireless Communications and Networking Conference (May, 2017), ISBN 9781509041831 [doi]  [abs]
  3. Wang, L; Chen, M; Rodrigues, M; Wilcox, D; Calderbank, R; Carin, L, Information-Theoretic Compressive Measurement Design., IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39 no. 6 (June, 2017), pp. 1150-1164 [doi]  [abs]
  4. Cnaan-On, I; Harms, A; Krolik, JL; Calderbank, AR, Run-length limited codes for backscatter communication, IEEE International Conference on Acoustics Speech and Signal Processing (June, 2017), pp. 6110-6114, ISBN 9781509041176 [doi]  [abs]

Cheng, Cheng

  1. Li, L; Cheng, C; Han, D; Sun, Q; Shi, G, Phase Retrieval From Multiple-Window Short-Time Fourier Measurements, IEEE Signal Processing Letters, vol. 24 no. 4 (April, 2017), pp. 372-376 [doi]
  2. Cheng, C; Jiang, Y; Sun, Q, Spatially distributed sampling and reconstruction, Applied and Computational Harmonic Analysis (August, 2017) [doi]

Cheng, Xiuyuan

  1. Cheng, X; Mishne, G; Steinerberger, S, The geometry of nodal sets and outlier detection, Journal of Number Theory (October, 2017) [doi]
  2. Lu, J; Lu, Y; Wang, X; Li, X; Linderman, GC; Wu, C; Cheng, X; Mu, L; Zhang, H; Liu, J; Su, M; Zhao, H; Spatz, ES; Spertus, JA; Masoudi, FA; Krumholz, HM; Jiang, L, Prevalence, awareness, treatment, and control of hypertension in China: data from 1·7 million adults in a population-based screening study (China PEACE Million Persons Project), The Lancet (November, 2017) [doi]

Daubechies, Ingrid

  1. Fodor, G; Cornelis, B; Yin, R; Dooms, A; Daubechies, I, Cradle Removal in X-Ray Images of Panel Paintings, Image Processing On Line, vol. 7 (2017), pp. 23-42 [doi]
  2. Cornelis, B; Yang, H; Goodfriend, A; Ocon, N; Lu, J; Daubechies, I, Removal of Canvas Patterns in Digital Acquisitions of Paintings., IEEE Transactions on Image Processing, vol. 26 no. 1 (January, 2017), pp. 160-171 [doi]  [abs]
  3. Voronin, S; Daubechies, I, An iteratively reweighted least squares algorithm for sparse regularization, in Contemporary Mathematics, vol. 693 (January, 2017), pp. 391-411 [doi]  [abs]
  4. Yin, R; Gao, T; Lu, YM; Daubechies, I, A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution Framelets, SIAM Journal on Imaging Sciences, vol. 10 no. 2 (January, 2017), pp. 711-750 [doi]
  5. 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]
  6. Alaifari, R; Daubechies, I; Grohs, P; Thakur, G, Reconstructing Real-Valued Functions from Unsigned Coefficients with Respect to Wavelet and Other Frames, Journal of Fourier Analysis and Applications, vol. 23 no. 6 (December, 2017), pp. 1480-1494 [doi]
  7. Xu, J; Yang, H; Daubechies, I, Recursive diffeomorphism-based regression for shape functions, SIAM Journal on Mathematical Analysis, vol. 50 no. 1 (January, 2018), pp. 5-32 [doi]  [abs]
  8. Gao, T; Yapuncich, GS; Daubechies, I; Mukherjee, S; Boyer, DM, Development and Assessment of Fully Automated and Globally Transitive Geometric Morphometric Methods, With Application to a Biological Comparative Dataset With High Interspecific Variation., The Anatomical Record : Advances in Integrative Anatomy and Evolutionary Biology, vol. 301 no. 4 (April, 2018), pp. 636-658 [doi]  [abs]

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]
  3. Peco, C; Chen, W; Liu, Y; Bandi, MM; Dolbow, JE; Fried, E, Influence of surface tension in the surfactant-driven fracture of closely-packed particulate monolayers., Soft Matter, vol. 13 no. 35 (September, 2017), pp. 5832-5841 [doi]  [abs]
  4. Zhang, Z; Jiang, W; Dolbow, JE; Spencer, BW, A modified moment-fitted integration scheme for X-FEM applications with history-dependent material data, Computational Mechanics (January, 2018), pp. 1-20 [doi]  [abs]

Dunson, David B.

  1. Lin, L; Rao, V; Dunson, D, Bayesian nonparametric inference on the Stiefel manifold, Statistica Sinica (2017) [doi]
  2. Johndrow, JE; Bhattacharya, A; Dunson, DB, TENSOR DECOMPOSITIONS AND SPARSE LOG-LINEAR MODELS., Annals of statistics, vol. 45 no. 1 (January, 2017), pp. 1-38 [doi]  [abs]
  3. Lin, L; St Thomas, B; Zhu, H; Dunson, DB, Extrinsic local regression on manifold-valued data., Journal of the American Statistical Association, vol. 112 no. 519 (January, 2017), pp. 1261-1273 [doi]  [abs]
  4. 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]
  5. Abrego, N; Dunson, D; Halme, P; Salcedo, I; Ovaskainen, O, Wood-inhabiting fungi with tight associations with other species have declined as a response to forest management, Oikos, vol. 126 no. 2 (February, 2017) [doi]
  6. Durante, D; Paganin, S; Scarpa, B; Dunson, DB, Bayesian modelling of networks in complex business intelligence problems, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol. 66 no. 3 (April, 2017), pp. 555-580 [doi]
  7. 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]
  8. Tikhonov, G; Abrego, N; Dunson, D; Ovaskainen, O, Using joint species distribution models for evaluating how species-to-species associations depend on the environmental context, edited by Warton, D, Methods in Ecology and Evolution, vol. 8 no. 4 (April, 2017), pp. 443-452 [doi]
  9. Ovaskainen, O; Tikhonov, G; Norberg, A; Guillaume Blanchet, F; Duan, L; Dunson, D; Roslin, T; Abrego, N, How to make more out of community data? A conceptual framework and its implementation as models and software., Ecology Letters, vol. 20 no. 5 (May, 2017), pp. 561-576 [doi]  [abs]
  10. Ovaskainen, O; Tikhonov, G; Dunson, D; Grøtan, V; Engen, S; Sæther, B-E; Abrego, N, How are species interactions structured in species-rich communities? A new method for analysing time-series data., Proceedings of the Royal Society of London: Biological Sciences, vol. 284 no. 1855 (May, 2017), pp. 20170768-20170768 [doi]  [abs]
  11. Moffitt, AB; Ondrejka, SL; McKinney, M; Rempel, RE; Goodlad, JR; Teh, CH; Leppa, S; Mannisto, S; Kovanen, PE; Tse, E; Au-Yeung, RKH; Kwong, Y-L; Srivastava, G; Iqbal, J; Yu, J; Naresh, K; Villa, D; Gascoyne, RD; Said, J; Czader, MB; Chadburn, A; Richards, KL; Rajagopalan, D; Davis, NS; Smith, EC; Palus, BC; Tzeng, TJ; Healy, JA; Lugar, PL; Datta, J; Love, C; Levy, S; Dunson, DB; Zhuang, Y; Hsi, ED; Dave, SS, Enteropathy-associated T cell lymphoma subtypes are characterized by loss of function of SETD2., The Journal of Experimental Medicine, vol. 214 no. 5 (May, 2017), pp. 1371-1386 [doi]  [abs]
  12. Schaich Borg, J; Srivastava, S; Lin, L; Heffner, J; Dunson, D; Dzirasa, K; de Lecea, L, Rat intersubjective decisions are encoded by frequency-specific oscillatory contexts., Brain and Behavior, vol. 7 no. 6 (June, 2017), pp. e00710 [doi]  [abs]
  13. Zhu, B; Dunson, DB, Bayesian Functional Data Modeling for Heterogeneous Volatility, Bayesian Analysis, vol. 12 no. 2 (June, 2017), pp. 335-350 [doi]
  14. Wang, L; Durante, D; Jung, RE; Dunson, DB, Bayesian network-response regression., Bioinformatics, vol. 33 no. 12 (June, 2017), pp. 1859-1866 [doi]  [abs]
  15. Guhaniyogi, R; Qamar, S; Dunson, DB, Bayesian tensor regression, Journal of machine learning research : JMLR, vol. 18 (August, 2017), pp. 1-31  [abs]
  16. Li, C; Srivastava, S; Dunson, DB, Simple, scalable and accurate posterior interval estimation, Biometrika, vol. 104 no. 3 (September, 2017), pp. 665-680 [doi]  [abs]
  17. Lock, EF; Dunson, DB, Bayesian genome- and epigenome-wide association studies with gene level dependence., Biometrics, vol. 73 no. 3 (September, 2017), pp. 1018-1028 [doi]  [abs]
  18. Srivastava, S; Engelhardt, BE; Dunson, DB, Expandable factor analysis., Biometrika, vol. 104 no. 3 (September, 2017), pp. 649-663 [doi]  [abs]
  19. Reddy, A; Zhang, J; Davis, NS; Moffitt, AB; Love, CL; Waldrop, A; Leppa, S; Pasanen, A; Meriranta, L; Karjalainen-Lindsberg, M-L; Nørgaard, P; Pedersen, M; Gang, AO; Høgdall, E; Heavican, TB; Lone, W; Iqbal, J; Qin, Q; Li, G; Kim, SY; Healy, J; Richards, KL; Fedoriw, Y; Bernal-Mizrachi, L; Koff, JL; Staton, AD; Flowers, CR; Paltiel, O; Goldschmidt, N; Calaminici, M; Clear, A; Gribben, J; Nguyen, E; Czader, MB; Ondrejka, SL; Collie, A; Hsi, ED; Tse, E; Au-Yeung, RKH; Kwong, Y-L; Srivastava, G et al., Genetic and Functional Drivers of Diffuse Large B Cell Lymphoma., Cell, vol. 171 no. 2 (October, 2017), pp. 481-494.e15 [doi]  [abs]
  20. Durante, D; Dunson, DB; Vogelstein, JT, Rejoinder: Nonparametric Bayes Modeling of Populations of Networks, Journal of the American Statistical Association, vol. 112 no. 520 (October, 2017), pp. 1547-1552 [doi]
  21. Minsker, S; Srivastava, S; Lin, L; Dunson, DB, Robust and scalable bayes via a median of subset posterior measures, Journal of machine learning research : JMLR, vol. 18 (December, 2017), pp. 1-40  [abs]
  22. Wheeler, MW; Dunson, DB; Herring, AH, Bayesian Local Extremum Splines., Biometrika, vol. 104 no. 4 (December, 2017), pp. 939-952, Oxford University Press (OUP)  [abs]
  23. Shang, Y; Dunson, D; Song, J-S, Exploiting Big Data in Logistics Risk Assessment via Bayesian Nonparametrics, Operations Research, vol. 65 no. 6 (December, 2017), pp. 1574-1588 [doi]
  24. Dunson, DB, Statistics in the big data era: Failures of the machine, Statistics & Probability Letters (January, 2018) [doi]  [abs]
  25. Zhang, Z; Descoteaux, M; Zhang, J; Girard, G; Chamberland, M; Dunson, D; Srivastava, A; Zhu, H, Mapping population-based structural connectomes., NeuroImage, vol. 172 (January, 2018), pp. 130-145 [doi]  [abs]
  26. van den Boom, W; Schroeder, RA; Manning, MW; Setji, TL; Fiestan, G-O; Dunson, DB, Effect of A1C and Glucose on Postoperative Mortality in Noncardiac and Cardiac Surgeries., Diabetes Care, vol. 41 no. 4 (April, 2018), pp. 782-788 [doi]  [abs]

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. Nanda, M; Durrett, R, Spatial evolutionary games with weak selection, Proceedings of the National Academy of Sciences of USA, vol. 114 no. 23 (June, 2017), pp. 6046-6051 [doi]
  3. Huo, R; Durrett, R, Latent voter model on locally tree-like random graphs, Stochastic Processes and their Applications (August, 2017) [doi]
  4. Tomasetti, C; Durrett, R; Kimmel, M; Lambert, A; Parmigiani, G; Zauber, A; Vogelstein, B, Role of stem-cell divisions in cancer risk, Nature, vol. 548 no. 7666 (August, 2017), pp. E13-E14 [doi]
  5. Lopatkin, AJ; Meredith, HR; Srimani, JK; Pfeiffer, C; Durrett, R; You, L, Persistence and reversal of plasmid-mediated antibiotic resistance., Nature Communications, vol. 8 no. 1 (November, 2017), pp. 1689 [doi]  [abs]
  6. Gleeson, JP; Durrett, R, Temporal profiles of avalanches on networks, Nature Communications, vol. 8 no. 1 (December, 2017) [doi]

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]

Getz, Jayce R.

  1. Getz, JR, Nonabelian fourier transforms for spherical representations, Pacific Journal of Mathematics, vol. 294 no. 2 (January, 2018), pp. 351-373, Mathematical Sciences Publishers [doi]  [abs]

Hain, Richard   (search)

  1. Hain, R, Deligne-Beilinson Cohomology of Affine Groups, in Hodge Theory and $L^2$-analysis, edited by Ji, L (July, 2015), International Press, ISBN 1571463518 [arXiv:1507.03144]  [abs]

Harer, John

  1. Hughes, ME; Abruzzi, KC; Allada, R; Anafi, R; Arpat, AB; Asher, G; Baldi, P; de Bekker, C; Bell-Pedersen, D; Blau, J; Brown, S; Ceriani, MF; Chen, Z; Chiu, JC; Cox, J; Crowell, AM; DeBruyne, JP; Dijk, D-J; DiTacchio, L; Doyle, FJ; Duffield, GE; Dunlap, JC; Eckel-Mahan, K; Esser, KA; FitzGerald, GA; Forger, DB; Francey, LJ; Fu, Y-H; Gachon, F; Gatfield, D; de Goede, P; Golden, SS; Green, C; Harer, J; Harmer, S; Haspel, J; Hastings, MH; Herzel, H; Herzog, ED; Hoffmann, C; Hong, C; Hughey, JJ et al., Guidelines for Genome-Scale Analysis of Biological Rhythms., Journal of Biological Rhythms, vol. 32 no. 5 (October, 2017), pp. 380-393 [doi]  [abs]
  2. Tralie, CJ; Smith, A; Borggren, N; Hineman, J; Bendich, P; Zulch, P; Harer, J, Geometric Cross-Modal Comparison of Heterogeneous Sensor Data, Proceedings of the 39th IEEE Aerospace Conference (March, 2018)  [abs]

Herschlag, Gregory J.

  1. Cao, Y; Feng, Y; Ryser, MD; Zhu, K; Herschlag, G; Cao, C; Marusak, K; Zauscher, S; You, L, Programmable assembly of pressure sensors using pattern-forming bacteria., Nature Biotechnology, vol. 35 no. 11 (November, 2017), pp. 1087-1093 [doi]  [abs]

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]

Junge, Matthew S

  1. Hoffman, C; Johnson, T; Junge, M, Recurrence and transience for the frog model on trees, Annals of Probability, vol. 45 no. 5 (September, 2017), pp. 2826-2854 [doi]

Kiselev, Alexander A.

  1. Kiselev, A; Yao, Y; Zlatoš, A, Local Regularity for the Modified SQG Patch Equation, Communications on Pure & Applied Mathematics, vol. 70 no. 7 (July, 2017), pp. 1253-1315 [doi]
  2. Choi, K; Hou, TY; Kiselev, A; Luo, G; Sverak, V; Yao, Y, On the Finite-Time Blowup of a One-Dimensional Model for the Three-Dimensional Axisymmetric Euler Equations, Communications on Pure & Applied Mathematics, vol. 70 no. 11 (November, 2017), pp. 2218-2243 [doi]
  3. Kiselev, A; Tan, C, Finite time blow up in the hyperbolic Boussinesq system, Advances in Mathematics, vol. 325 (February, 2018), pp. 34-55 [doi]  [abs]

Kovalsky, Shahar

  1. Aigerman, N; Kovalsky, SZ; Lipman, Y, Spherical orbifold tutte embeddings, ACM Transactions on Graphics, vol. 36 no. 4 (July, 2017), pp. 1-13 [doi]
  2. Shtengel, A; Poranne, R; Sorkine-Hornung, O; Kovalsky, SZ; Lipman, Y, Geometric optimization via composite majorization, ACM Transactions on Graphics, vol. 36 no. 4 (July, 2017), pp. 1-11 [doi]

Layton, Anita T.

  1. 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]
  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. Chen, Y; Fry, BC; Layton, AT, Modeling glucose metabolism and lactate production in the kidney., Mathematical Biosciences, vol. 289 (July, 2017), pp. 116-129 [doi]  [abs]
  4. Chen, Y; Sullivan, JC; Edwards, A; Layton, AT, Sex-specific computational models of the spontaneously hypertensive rat kidneys: factors affecting nitric oxide bioavailability., American Journal of Physiology: Renal Physiology, vol. 313 no. 2 (August, 2017), pp. F174-F183 [doi]  [abs]
  5. Layton, AT; Edwards, A; Vallon, V, Adaptive changes in GFR, tubular morphology, and transport in subtotal nephrectomized kidneys: modeling and analysis., American Journal of Physiology: Renal Physiology, vol. 313 no. 2 (August, 2017), pp. F199-F209 [doi]  [abs]
  6. Burt, T; Noveck, RJ; MacLeod, DB; Layton, AT; Rowland, M; Lappin, G, Intra-Target Microdosing (ITM): A Novel Drug Development Approach Aimed at Enabling Safer and Earlier Translation of Biological Insights Into Human Testing., Clinical and Translational Science, vol. 10 no. 5 (September, 2017), pp. 337-350 [doi]
  7. Sgouralis, I; Evans, RG; Layton, AT, Renal medullary and urinary oxygen tension during cardiopulmonary bypass in the rat., Mathematical Medicine and Biology: A Journal of the IMA, vol. 34 no. 3 (September, 2017), pp. 313-333 [doi]  [abs]
  8. Edwards, A; Layton, AT, Cell Volume Regulation in the Proximal Tubule of Rat Kidney : Proximal Tubule Cell Volume Regulation., Bulletin of Mathematical Biology, vol. 79 no. 11 (November, 2017), pp. 2512-2533 [doi]  [abs]

Levine, Adam S.

  1. Baldwin, JA; Levine, AS; Sarkar, S, Khovanov homology and knot Floer homology for pointed links, Journal of Knot Theory & Its Ramifications, vol. 26 no. 02 (February, 2017), pp. 1740004-1740004 [doi]

Li, Lei

  1. Li, L; Liu, J-G, A note on deconvolution with completely monotone sequences and discrete fractional calculus, Quarterly of Applied Mathematics (2017), pp. 1-1 [doi]
  2. Li, L; Xu, X; Spagnolie, SE, A Locally Gradient-Preserving Reinitialization for Level Set Functions, Journal of Scientific Computing, vol. 71 no. 1 (April, 2017), pp. 274-302 [doi]
  3. Li, L; Liu, J-G; Lu, J, Fractional Stochastic Differential Equations Satisfying Fluctuation-Dissipation Theorem, Journal of Statistical Physics, vol. 169 no. 2 (October, 2017), pp. 316-339 [doi]

Li, Yingzhou

  1. Li, Y; Yang, H, Interpolative Butterfly Factorization, SIAM Journal on Scientific Computing, vol. 39 no. 2 (January, 2017), pp. A503-A531 [doi]
  2. Li, Y; Yang, H; Ying, L, Multidimensional butterfly factorization, Applied and Computational Harmonic Analysis (April, 2017) [doi]
  3. Zhang, L; Sun, L; Guan, Z; Lee, S; Li, Y; Deng, HD; Li, Y; Ahlborg, NL; Boloor, M; Melosh, NA; Chueh, WC, Quantifying and Elucidating Thermally Enhanced Minority Carrier Diffusion Length Using Radius-Controlled Rutile Nanowires, Nano Letters, vol. 17 no. 9 (September, 2017), pp. 5264-5272 [doi]
  4. Li, Y; Ying, L, Distributed-memory hierarchical interpolative factorization, Research in the Mathematical Sciences, vol. 4 no. 1 (December, 2017) [doi]

Liu, Jian-Guo

  1. Huang, H; Liu, J-G, Discrete-in-time random particle blob method for the Keller–Segel equation and convergence analysis, Communications in Mathematical Sciences, vol. 15 no. 7 (2017), pp. 1821-1842 [doi]  [abs]
  2. Degond, P; Herty, M; Liu, J-G, Mean-field games and model predictive control, Communications in Mathematical Sciences, vol. 15 no. 5 (2017), pp. 1403-1422 [doi]
  3. Li, L; Liu, J-G, A note on deconvolution with completely monotone sequences and discrete fractional calculus, Quarterly of Applied Mathematics (2017), pp. 1-1 [doi]
  4. Coquel, F; Jin, S; Liu, J-G; Wang, L, Entropic sub-cell shock capturing schemes via Jin-Xin relaxation and Glimm front sampling for scalar conservation laws, Mathematics of Computation (2017), pp. 1-1 [doi]
  5. Liu, J-G; Wang, L; Zhou, Z, Positivity-preserving and asymptotic preserving method for 2D Keller-Segal equations, Mathematics of Computation (2017), pp. 1-1 [doi]
  6. Gao, Y; Liu, J-G; Lu, J, Weak Solution of a Continuum Model For Vicinal Surface in The Attachment-Detachment-Limited Regime, SIAM Journal on Mathematical Analysis, vol. 49 no. 3 (January, 2017), pp. 1705-1731 [doi]
  7. Liu, J-G; Wang, J, A generalized Sz. Nagy inequality in higher dimensions and the critical thin film equation, Nonlinearity, vol. 30 no. 1 (2017), pp. 35-60 [doi]
  8. 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]
  9. Gao, Y; Liu, J-G, Global Convergence of a Sticky Particle Method for the Modified Camassa--Holm Equation, SIAM Journal on Mathematical Analysis, vol. 49 no. 2 (January, 2017), pp. 1267-1294 [doi]
  10. Liu, J-G; Xu, X, Analytical Validation of a Continuum Model for the Evolution of a Crystal Surface in Multiple Space Dimensions, SIAM Journal on Mathematical Analysis, vol. 49 no. 3 (January, 2017), pp. 2220-2245 [doi]
  11. 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]
  12. Huang, H; Liu, J-G, Error estimate of a random particle blob method for the Keller-Segel equation, Mathematics of Computation, vol. 86 no. 308 (February, 2017), pp. 2719-2744 [doi]
  13. 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]
  14. 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]
  15. Liu, J-G; Ma, Z; Zhou, Z, Explicit and Implicit TVD Schemes for Conservation Laws with Caputo Derivatives, Journal of Scientific Computing, vol. 72 no. 1 (July, 2017), pp. 291-313 [doi]
  16. 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]
  17. Li, L; Liu, J-G; Lu, J, Fractional Stochastic Differential Equations Satisfying Fluctuation-Dissipation Theorem, Journal of Statistical Physics, vol. 169 no. 2 (October, 2017), pp. 316-339 [doi]
  18. Li, L; Liu, JG, p-Euler equations and p-Navier-Stokes equations, Journal of Differential Equations (January, 2018) [doi]  [abs]
  19. Liu, J-G; Xu, X, Partial regularity of weak solutions to a PDE system with cubic nonlinearity, Journal of Differential Equations, vol. 264 no. 8 (April, 2018), pp. 5489-5526 [doi]

Lu, Jianfeng

  1. Li, XH; Lu, J, Quasi-nonlocal Coupling of Nonlocal Diffusions, SIAM Journal on Numerical Analysis, vol. 55 no. 5 (January, 2017), pp. 2394-2415 [doi]
  2. Lu, J; Yang, H, Preconditioning Orbital Minimization Method for Planewave Discretization, Multiscale Modeling & Simulation, vol. 15 no. 1 (January, 2017), pp. 254-273 [doi]
  3. Lin, L; Lu, J; Vanden-Eijnden, E, A Mathematical Theory of Optimal Milestoning (with a Detour via Exact Milestoning), Communications on Pure & Applied Mathematics (January, 2017) [doi]  [abs]
  4. 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]
  5. Li, Q; Lu, J, An asymptotic preserving method for transport equations with oscillatory scattering coefficients, Multiscale Modeling & Simulation, vol. 15 no. 4 (January, 2017), pp. 1694-1718 [doi]  [abs]
  6. Gao, Y; Liu, J-G; Lu, J, Weak Solution of a Continuum Model For Vicinal Surface in The Attachment-Detachment-Limited Regime, SIAM Journal on Mathematical Analysis, vol. 49 no. 3 (January, 2017), pp. 1705-1731 [doi]  [abs]
  7. 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]
  8. 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]
  9. 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]
  10. 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]
  11. Lu, J; Thicke, K, Orbital minimization method with ℓ 1 regularization, Journal of Computational Physics, vol. 336 (May, 2017), pp. 87-103 [doi]
  12. 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]
  13. 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]
  14. 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]
  15. Lu, J; Steinerberger, S, A variation on the Donsker-Varadhan inequality for the principal eigenvalue., Proceedings of the Royal Society of London: Mathematical, Physical and Engineering Sciences, vol. 473 no. 2204 (August, 2017), pp. 20160877 [doi]  [abs]
  16. Yu, VW-Z; Corsetti, F; García, A; Huhn, WP; Jacquelin, M; Jia, W; Lange, B; Lin, L; Lu, J; Mi, W; Seifitokaldani, A; Vázquez-Mayagoitia, Á; Yang, C; Yang, H; Blum, V, ELSI: A unified software interface for Kohn–Sham electronic structure solvers, Computer Physics Communications (September, 2017) [doi]
  17. Li, Q; Lu, J; Sun, W, A convergent method for linear half-space kinetic equations, ESAIM. Mathematical modelling and numerical analysis = ESAIM. Modelisation mathematique et analyse numerique : M=2AN, vol. 51 no. 5 (September, 2017), pp. 1583-1615 [doi]
  18. Li, L; Liu, J-G; Lu, J, Fractional Stochastic Differential Equations Satisfying Fluctuation-Dissipation Theorem, Journal of Statistical Physics, vol. 169 no. 2 (October, 2017), pp. 316-339 [doi]  [abs]
  19. Lu, J; Thicke, K, Cubic scaling algorithms for RPA correlation using interpolative separable density fitting, Journal of Computational Physics, vol. 351 (December, 2017), pp. 187-202 [doi]
  20. Cao, Y; Lu, J, Lindblad equation and its semiclassical limit of the Anderson-Holstein model, Journal of Mathematical Physics, vol. 58 no. 12 (December, 2017) [doi]  [abs]
  21. Dai, S; Li, B; Lu, J, Convergence of Phase-Field Free Energy and Boundary Force for Molecular Solvation, Archive for Rational Mechanics and Analysis, vol. 227 no. 1 (January, 2018), pp. 105-147 [doi]
  22. Lu, J; Zhou, Z, Accelerated sampling by infinite swapping of path integral molecular dynamics with surface hopping., Journal of Chemical Physics, vol. 148 no. 6 (February, 2018), pp. 064110 [doi]  [abs]
  23. Huang, Y; Lu, J; Ming, P, A Concurrent Global–Local Numerical Method for Multiscale PDEs, Journal of Scientific Computing (February, 2018), pp. 1-28 [doi]  [abs]

Lu, Yulong

  1. Lu, Y; Stuart, A; Weber, H, Gaussian Approximations for Transition Paths in Brownian Dynamics, SIAM Journal on Mathematical Analysis, vol. 49 no. 4 (January, 2017), pp. 3005-3047 [doi]

Ma, Ding

  1. D. Ma, Period polynomial relations of binomial coefficients and binomial realization of formal double zeta space, International Journal of Number Theory, vol. 13 no. 03 (April, 2017), pp. 761-774 [doi]  [abs]

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. Tomita, TM; Maggioni, M; Vogelstein, JT, ROFLMAO: Robust oblique forests with linear MAtrix operations, Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017 (January, 2017), pp. 498-506, ISBN 9781611974874  [abs]
  5. Crosskey, M; Maggioni, M, ATLAS: A Geometric Approach to Learning High-Dimensional Stochastic Systems Near Manifolds, Multiscale Modeling & Simulation, vol. 15 no. 1 (January, 2017), pp. 110-156 [doi]
  6. 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]
  7. Gerber, S; Maggioni, M, Multiscale strategies for computing optimal transport, Journal of machine learning research : JMLR, vol. 18 (August, 2017), pp. 1-32  [abs]
  8. Wang, YG; Maggioni, M; Chen, G, Enhanced detection of chemical plumes in hyperspectral images and movies throughimproved backgroundmodeling, Proceedings of Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, vol. 2015-June (October, 2017), ISBN 9781467390156 [doi]  [abs]
  9. Little, AV; Maggioni, M; Rosasco, L, Multiscale geometric methods for data sets I: Multiscale SVD, noise and curvature, Applied and Computational Harmonic Analysis, vol. 43 no. 3 (November, 2017), pp. 504-567 [doi]

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. Glatt-Holtz, NE; Herzog, DP; Mattingly, JC, Scaling and Saturation in Infinite-Dimensional Control Problems with Applications to Stochastic Partial Differential Equations (June, 2017)  [abs]
  3. Johndrow, JE; Mattingly, JC, Coupling and Decoupling to bound an approximating Markov Chain (July, 2017)  [abs]
  4. Bakhtin, Y; Hurth, T; Lawley, SD; Mattingly, JC, Smooth invariant densities for random switching on the torus (August, 2017)  [abs]
  5. Herschlag, G; Ravier, R; Mattingly, JC, Evaluating Partisan Gerrymandering in Wisconsin (September, 2017)  [abs]

Motta, Francis C.

  1. Burris, CS; Motta, FC; Shipman, PD, An Unoriented Variation on de Bruijn Sequences, Graphs and Combinatorics, vol. 33 no. 4 (July, 2017), pp. 845-858 [doi]
  2. Cho, C-Y; Motta, FC; Kelliher, CM; Deckard, A; Haase, SB, Reconciling conflicting models for global control of cell-cycle transcription., Cell Cycle, vol. 16 no. 20 (October, 2017), pp. 1965-1978 [doi]  [abs]
  3. Motta, FC, Topological Data Analysis: Developments and Applications, in Advances in Nonlinear Geosciences, edited by Tsonis, A (November, 2017), pp. 369-391, Springer, ISBN 3319588958  [abs]

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. Gao, T; Yapuncich, GS; Daubechies, I; Mukherjee, S; Boyer, DM, Development and Assessment of Fully Automated and Globally Transitive Geometric Morphometric Methods, With Application to a Biological Comparative Dataset With High Interspecific Variation., The Anatomical Record : Advances in Integrative Anatomy and Evolutionary Biology (October, 2017) [doi]  [abs]
  3. Darnell, G; Georgiev, S; Mukherjee, S; Engelhardt, BE, Adaptive randomized dimension reduction on massive data, Journal of machine learning research : JMLR, vol. 18 (November, 2017)  [abs]
  4. Singleton, KR; Crawford, L; Tsui, E; Manchester, HE; Maertens, O; Liu, X; Liberti, MV; Magpusao, AN; Stein, EM; Tingley, JP; Frederick, DT; Boland, GM; Flaherty, KT; McCall, SJ; Krepler, C; Sproesser, K; Herlyn, M; Adams, DJ; Locasale, JW; Cichowski, K; Mukherjee, S; Wood, KC, Melanoma Therapeutic Strategies that Select against Resistance by Exploiting MYC-Driven Evolutionary Convergence., Cell Reports, vol. 21 no. 10 (December, 2017), pp. 2796-2812 [doi]  [abs]

Nagy, Akos

  1. Nagy, Á, The Berry Connection of the Ginzburg–Landau Vortices, Communications in Mathematical Physics, vol. 350 no. 1 (February, 2017), pp. 105-128 [doi]

Ng, Lenhard L.

  1. Ng, L; Rutherford, D; Shende, V; Sivek, S, The cardinality of the augmentation category of a Legendrian link, Mathematical Research Letters, vol. 24 no. 6 (2017), pp. 1845-1874
  2. Cieliebak, K; Ekholm, T; Latschev, J; Ng, L, Knot contact homology, string topology, and the cord algebra, vol. 4 (January, 2017), pp. 661-780 [doi]  [abs]
  3. Ekholm, T; Ng, L; Shende, V, A complete knot invariant from contact homology, Inventiones mathematicae, vol. 211 no. 3 (March, 2018), pp. 1149-1200 [doi]  [abs]

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. Mourrat, J-C; Nolen, J, Scaling limit of the corrector in stochastic homogenization, The annals of applied probability : an official journal of the Institute of Mathematical Statistics, vol. 27 no. 2 (April, 2017), pp. 944-959, Institute of Mathematical Statistics (IMS), ISSN 1050-5164 [arXiv:1502.07440], [1502.07440], [doi]  [abs]

Orizaga, Saulo

  1. Orizaga, S; Riahi, DN, Triad resonant wave interactions in electrically charged jets, Applied Mathematics and Mechanics, vol. 38 no. 8 (August, 2017), pp. 1127-1148 [doi]

Petters, Arlie O.

  1. A. O. Petters and M. C. Werner, Gravitational Lensing and Black Holes (Spring, 2017), Springer, in preparation

Pfister, Henry

  1. Jian, Y-Y; Pfister, HD; Narayanan, KR, Approaching Capacity at High Rates with Iterative Hard-Decision Decoding, IEEE Transactions on Information Theory (2017), pp. 1-1 [doi]
  2. Häger, C; Pfister, HD, Miscorrection-free Decoding of Staircase Codes., CoRR, vol. abs/1709.06827 (2017)
  3. 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]
  4. Sabag, O; Permuter, HH; Pfister, HD, A Single-Letter Upper Bound on the Feedback Capacity of Unifilar Finite-State Channels, IEEE Transactions on Information Theory, vol. 63 no. 3 (March, 2017), pp. 1392-1409 [doi]  [abs]
  5. Kudekar, S; Kumar, S; Mondelli, M; Pfister, HD; Sasoglu, E; Urbanke, RL, Reed–Muller Codes Achieve Capacity on Erasure Channels, IEEE Transactions on Information Theory, vol. 63 no. 7 (July, 2017), pp. 4298-4316 [doi]
  6. Hager, C; Pfister, HD; Graell i Amat, A; Brannstrom, F, Density Evolution for Deterministic Generalized Product Codes on the Binary Erasure Channel at High Rates, IEEE Transactions on Information Theory (July, 2017) [doi]
  7. Charbonneau, P; Li, YC; Pfister, HD; Yaida, S, Cycle-expansion method for the Lyapunov exponent, susceptibility, and higher moments., Physical review. E, vol. 96 no. 3-1 (September, 2017), pp. 032129 [doi]  [abs]

Pierce, Lillian B.

  1. Pierce, LB; Turnage-Butterbaugh, CL; Wood, MM, An effective Chebotarev density theorem for families of number fields, with an application to $\ell$-torsion in class groups, (submitted) (2017)  [abs]
  2. Guo, S; Pierce, LB; Roos, J; Yung, P, Polynomial Carleson operators along monomial curves in the plane, Journal of Geometric Analysis (2017), Springer-Verlag  [abs]
  3. Ellenberg, J; Pierce, LB; Wood, MM, On ℓ-torsion in class groups of number fields, Algebra and Number Theory, vol. 11 no. 8 (2017), pp. 1739-1778 [doi]  [abs]
  4. Heath-Brown, DR; Pierce, LB; Heath-Brown, DR; Pierce, LB, Simultaneous integer values of pairs of quadratic formsSimultaneous integer values of pairs of quadratic forms, Journal für die Reine und Angewandte Mathematik (Crelle's Journal), vol. 2017 no. 727 (June, 2017), pp. 85-143 [doi]  [abs]
  5. Pierce, LB, The Vinogradov Mean Value Theorem [after Wooley, and Bourgain, Demeter and Guth], Asterisque (July, 2017), Centre National de la Recherche Scientifique  [abs]
  6. Carneiro, E; Madrid, J; Pierce, LB, Endpoint Sobolev and BV continuity for maximal operators, Journal of Functional Analysis, vol. 273 no. 10 (November, 2017), pp. 3262-3294 [doi]
  7. Heath-Brown, DR; Pierce, LB, Averages and moments associated to class numbers of imaginary quadratic fields, Compositio Mathematica, vol. 153 no. 11 (November, 2017), pp. 2287-2309 [doi]
  8. Pierce, LB; Yung, PL, A polynomial Carleson operator along the paraboloid, Revista Matematica Iberoamericana (2018), European Mathematical Society

Plesser, M. 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]

Pollack, Aaron

  1. Pollack, A; Shah, S, On the Rankin–Selberg integral of Kohnen and Skoruppa, Mathematical Research Letters, vol. 24 no. 1 (2017), pp. 173-222 [doi]
  2. Pollack, A, The spin -function on for Siegel modular forms, Compositio Mathematica, vol. 153 no. 07 (July, 2017), pp. 1391-1432 [doi]

Randles, Amanda

  1. Gounley, J; Draeger, EW; Randles, A, Numerical simulation of a compound capsule in a constricted microchannel., Procedia Computer Science, vol. 108 (January, 2017), pp. 175-184 [doi]  [abs]
  2. Laurence, TA; Ly, S; Fong, E; Shusteff, M; Randles, A; Gounley, J; Draeger, E, Using stroboscopic flow imaging to validate large-scale computational fluid dynamics simulations, Proceedings of SPIE, vol. 10076 (January, 2017), ISBN 9781510605930 [doi]  [abs]
  3. Dabagh, M; Jalali, P; Butler, PJ; Randles, A; Tarbell, JM, Mechanotransmission in endothelial cells subjected to oscillatory and multi-directional shear flow., Journal of the Royal Society Interface, vol. 14 no. 130 (May, 2017) [doi]  [abs]
  4. Gounley, J; Vardhan, M; Randles, A, A computational framework to assess the influence of changes in vascular geometry on blood flow, PASC 2017 - Proceedings of the Platform for Advanced Scientific Computing Conference (June, 2017), ISBN 9781450350624 [doi]  [abs]
  5. Randles, A; Frakes, DH; Leopold, JA, Computational Fluid Dynamics and Additive Manufacturing to Diagnose and Treat Cardiovascular Disease., Trends in Biotechnology, vol. 35 no. 11 (November, 2017), pp. 1049-1061 [doi]  [abs]
  6. Rafat, M; Stone, HA; Auguste, DT; Dabagh, M; Randles, A; Heller, M; Rabinov, JD, Impact of diversity of morphological characteristics and Reynolds number on local hemodynamics in basilar aneurysms, Aiche Journal (January, 2018) [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. Thanacoody, HKR; Nijhout, HF; Reed, MC; Thomas, S, Mathematical modeling of the effect of different intravenous acetylcysteine regimens on hepatic glutathione regeneration and hepatocyte death following simulated acetaminophen overdose, Clinical Toxicology, vol. 55 no. 7 (2017), pp. 753-753
  5. Thanacoody, HKR; Nijhout, HF; Reed, MC; Thomas, S, Mathematical modeling of the effect of late administration of a novel acetylcysteine regimen based on the SNAP trial on hepatic glutathione regeneration and hepatocyte death following simulated acetaminophen overdose, Clinical Toxicology, vol. 55 no. 7 (2017), pp. 753-754
  6. Nijhout, HF; Sadre-Marandi, F; Best, J; Reed, MC, Systems Biology of Phenotypic Robustness and Plasticity., Integrative and Comparative Biology (BioOne), vol. 57 no. 2 (August, 2017), pp. 171-184 [doi]  [abs]
  7. Reed, M; Best, J; Golubitsky, M; Stewart, I; Nijhout, HF, Analysis of Homeostatic Mechanisms in Biochemical Networks., Bulletin of Mathematical Biology, vol. 79 no. 11 (November, 2017), pp. 2534-2557 [doi]  [abs]
  8. Best, J; Nijhout, HF; Samaranayake, S; Hashemi, P; Reed, M, A mathematical model for histamine synthesis, release, and control in varicosities., Theoretical Biology and Medical Modelling, vol. 14 no. 1 (December, 2017), pp. 24 [doi]  [abs]
  9. Suppiramaniam, V; Bloemer, J; Reed, M; Bhattacharya, S, Neurotransmitter Receptors, in Comprehensive Toxicology: Third Edition, vol. 6-15 (December, 2017), pp. 174-201, ISBN 9780081006122 [doi]  [abs]
  10. Duncan, W; Best, J; Golubitsky, M; Nijhout, HF; Reed, M, Homeostasis despite instability., Mathematical Biosciences, vol. 300 (March, 2018), pp. 130-137 [doi]  [abs]

Robles, Colleen M

  1. Kerr, M; Robles, C, Classification of smooth horizontal Schubert varieties, European Journal of Mathematics, vol. 3 no. 2 (June, 2017), pp. 289-310 [doi]
  2. Kerr, M; Robles, C, Variations of Hodge structure and orbits in flag varieties, Advances in Mathematics, vol. 315 (July, 2017), pp. 27-87 [doi]  [abs]
  3. Robles, C, Characterization of Calabi–Yau variations of Hodge structure over tube domains by characteristic forms, Mathematische Annalen (September, 2017), pp. 1-25 [doi]  [abs]

Rudin, Cynthia D.

  1. Wang, T; Rudin, C; Velez-Doshi, F; Liu, Y; Klampfl, E; Macneille, P, Bayesian rule sets for interpretable classification, Proceedings / IEEE International Conference on Data Mining. IEEE International Conference on Data Mining (January, 2017), pp. 1269-1274, ISBN 9781509054725 [doi]  [abs]
  2. Ustun, B; Adler, LA; Rudin, C; Faraone, SV; Spencer, TJ; Berglund, P; Gruber, MJ; Kessler, RC, The World Health Organization Adult Attention-Deficit/Hyperactivity Disorder Self-Report Screening Scale for DSM-5., JAMA Psychiatry, vol. 74 no. 5 (May, 2017), pp. 520-526 [doi]  [abs]
  3. Letham, B; Letham, PA; Rudin, C; Browne, EP, Erratum: "Prediction uncertainty and optimal experimental design for learning dynamical systems" [Chaos 26, 063110 (2016)]., Chaos, vol. 27 no. 6 (June, 2017), pp. 069901 [doi]
  4. Zeng, J; Ustun, B; Rudin, C, Interpretable classification models for recidivism prediction, Journal of the Royal Statistical Society: Series A (Statistics in Society), vol. 180 no. 3 (June, 2017), pp. 689-722 [doi]
  5. Wang, T; Rudin, C; Doshi-Velez, F; Liu, Y; Klampfl, E; MacNeille, P, A Bayesian framework for learning rule sets for interpretable classification, Journal of machine learning research : JMLR, vol. 18 (August, 2017), pp. 1-37  [abs]
  6. Ustun, B; Rudin, C, Optimized risk scores, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. Part F129685 (August, 2017), pp. 1125-1134, ISBN 9781450348874 [doi]  [abs]
  7. Angelino, E; Larus-Stone, N; Alabi, D; Seltzer, M; Rudin, C, Learning certifiably optimal rule lists, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. Part F129685 (August, 2017), pp. 35-44, ISBN 9781450348874 [doi]  [abs]
  8. Struck, AF; Ustun, B; Ruiz, AR; Lee, JW; LaRoche, SM; Hirsch, LJ; Gilmore, EJ; Vlachy, J; Haider, HA; Rudin, C; Westover, MB, Association of an Electroencephalography-Based Risk Score With Seizure Probability in Hospitalized Patients., JAMA Neurology, vol. 74 no. 12 (December, 2017), pp. 1419-1424 [doi]  [abs]
  9. Vu, M-AT; Adalı, T; Ba, D; Buzsáki, G; Carlson, D; Heller, K; Liston, C; Rudin, C; Sohal, VS; Widge, AS; Mayberg, HS; Sapiro, G; Dzirasa, K, A Shared Vision for Machine Learning in Neuroscience., The Journal of neuroscience : the official journal of the Society for Neuroscience, vol. 38 no. 7 (February, 2018), pp. 1601-1607 [doi]  [abs]

Saper, Leslie

  1. Saper, L, Perverse sheaves and the reductive Borel-Serre compactification, in Hodge Theory and L²-analysis, edited by Ji, L, vol. 39 (2017), pp. 555-581, International Press  [abs]
  2. Saper, L, ℒ-modules and micro-support, to appear in Annals of Mathematics (2018)

Sapiro, Guillermo

  1. Gunalan, K; Chaturvedi, A; Howell, B; Duchin, Y; Lempka, SF; Patriat, R; Sapiro, G; Harel, N; McIntyre, CC, Creating and parameterizing patient-specific deep brain stimulation pathway-activation models using the hyperdirect pathway as an example., PloS one, vol. 12 no. 4 (January, 2017), pp. e0176132 [doi]  [abs]
  2. 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]
  3. Campbell, K; Carpenter, KLH; Espinosa, S; Hashemi, J; Qiu, Q; Tepper, M; Calderbank, R; Sapiro, G; Egger, HL; Baker, JP; Dawson, G, Use of a Digital Modified Checklist for Autism in Toddlers - Revised with Follow-up to Improve Quality of Screening for Autism., The Journal of Pediatrics, vol. 183 (April, 2017), pp. 133-139.e1 [doi]  [abs]
  4. Simhal, AK; Aguerrebere, C; Collman, F; Vogelstein, JT; Micheva, KD; Weinberg, RJ; Smith, SJ; Sapiro, G, Probabilistic fluorescence-based synapse detection., PLoS computational biology, vol. 13 no. 4 (April, 2017), pp. e1005493 [doi]  [abs]
  5. Sokolic, J; Giryes, R; Sapiro, G; Rodrigues, MRD, Robust Large Margin Deep Neural Networks, IEEE Transactions on Signal Processing, vol. 65 no. 16 (August, 2017), pp. 4265-4280 [doi]
  6. Sokolić, J; Giryes, R; Sapiro, G; Rodrigues, MRD, Generalization error of deep neural networks: Role of classification margin and data structure, 2017 12th International Conference on Sampling Theory and Applications, SampTA 2017 (September, 2017), pp. 147-151, ISBN 9781538615652 [doi]  [abs]
  7. Pisharady, PK; Sotiropoulos, SN; Sapiro, G; Lenglet, C, A Sparse Bayesian Learning Algorithm for White Matter Parameter Estimation from Compressed Multi-shell Diffusion MRI., Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, vol. 10433 (September, 2017), pp. 602-610, ISBN 9783319661810 [doi]  [abs]
  8. Giryes, R; Eldar, YC; Bronstein, A; Sapiro, G, Tradeoffs between Convergence Speed and Reconstruction Accuracy in Inverse Problems, IEEE Transactions on Signal Processing (January, 2018) [doi]  [abs]
  9. Vu, M-AT; Adalı, T; Ba, D; Buzsáki, G; Carlson, D; Heller, K; Liston, C; Rudin, C; Sohal, VS; Widge, AS; Mayberg, HS; Sapiro, G; Dzirasa, K, A Shared Vision for Machine Learning in Neuroscience., The Journal of neuroscience : the official journal of the Society for Neuroscience, vol. 38 no. 7 (February, 2018), pp. 1601-1607 [doi]  [abs]
  10. Pisharady, PK; Sotiropoulos, SN; Duarte-Carvajalino, JM; Sapiro, G; Lenglet, C, Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning., NeuroImage, vol. 167 (February, 2018), pp. 488-503 [doi]  [abs]

Stern, Mark A.

  1. "Nonlinear Harmonic Forms and Indefinite Bochner Formulas " in Hodge Theory and L^2-Analysis, vol. 39 (2017), Higher Education Press

Tralie, Christopher

  1. Tralie, CJ; Perea, JA, (Quasi)Periodicity Quantification in Video Data, Using Topology (April, 2017)  [abs]
  2. Tralie, CJ, Early MFCC And HPCP Fusion for Robust Cover Song Identification, 18th International Society for Music Information Retrieval (ISMIR) (October, 2017)  [abs]
  3. Tralie, C, Moebius Beats: The Twisted Spaces of Sliding Window Audio Novelty Functions with Rhythmic Subdivisions (November, 2017)  [abs]
  4. Tralie, CJ, Self-Similarity Based Time Warping (November, 2017)  [abs]
  5. Tralie, CJ; Smith, A; Borggren, N; Hineman, J; Bendich, P; Zulch, P; Harer, J, Geometric Cross-Modal Comparison of Heterogeneous Sensor Data, Proceedings of the 39th IEEE Aerospace Conference (March, 2018)  [abs]

Turnage-Butterbaugh, Caroline

  1. Pierce, LB; Turnage-Butterbaugh, CL; Wood, MM, An effective Chebotarev density theorem for families of number fields, with an application to $\ell$-torsion in class groups, (submitted) (2017)  [abs]
  2. Best, A; Dynes, P; Edelsbrunner, X; McDonald, B; Miller, SJ; Tor, K; Turnage-Butterbaugh, C; Weinstein, M, Benford Behavior of Generalized Zeckendorf Decompositions, Springer Proceedings in Mathematics and Statistics, vol. 220 (January, 2017), pp. 25-37, Springer [doi]  [abs]
  3. Conrey, JB; Turnage-Butterbaugh, CL, On r-gaps between zeros of the Riemann zeta-function, Bulletin of the London Mathematical Society (January, 2018) [doi]  [abs]

Venakides, Stephanos

  1. Kiehart, DP; Crawford, JM; Aristotelous, A; Venakides, S; Edwards, GS, Cell Sheet Morphogenesis: Dorsal Closure in Drosophila melanogaster as a Model System., Annual Review of Cell and Developmental Biology, vol. 33 (October, 2017), pp. 169-202 [doi]  [abs]
  2. Bruno, OP; Shipman, SP; Turc, C; Venakides, S, Three-dimensional quasi-periodic shifted Green function throughout the spectrum, including Wood anomalies, Proceedings of the Royal Society of London: Mathematical, Physical and Engineering Sciences, vol. 473 no. 2207 (November, 2017) [doi]  [abs]
  3. Perez-Arancibia, C; Shipman, S; Turc, C; Venakides, S, DDM solutions of quasiperiodic transmission problems in layered media via robust boundary integral equations at all frequencies (December, 2017)

Watson, Alexander

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

Witelski, Thomas P.   (search)

  1. 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]
  2. 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]
  3. Ji, H; Witelski, TP, Instability and dynamics of volatile thin films, vol. 3 no. 2 (February, 2018) [doi]  [abs]

Wu, Hau-Tieng

  1. Coifman, RR; Steinerberger, S; Wu, HT, Carrier frequencies, holomorphy. And unwinding, SIAM Journal on Mathematical Analysis, vol. 49 no. 6 (January, 2017), pp. 4838-4864 [doi]  [abs]
  2. Li, R; Frasch, MG; Wu, H-T, Efficient Fetal-Maternal ECG Signal Separation from Two Channel Maternal Abdominal ECG via Diffusion-Based Channel Selection., Frontiers in Physiology, vol. 8 (January, 2017), pp. 277 [doi]  [abs]
  3. Frasch, MG; Boylan, GB; Wu, H-T; Devane, D, Commentary: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial., Frontiers in Physiology, vol. 8 (January, 2017), pp. 721 [doi]
  4. Cicone, A; Wu, H-T, How Nonlinear-Type Time-Frequency Analysis Can Help in Sensing Instantaneous Heart Rate and Instantaneous Respiratory Rate from Photoplethysmography in a Reliable Way., Frontiers in Physiology, vol. 8 (January, 2017), pp. 701 [doi]  [abs]
  5. Liu, W-T; Wu, H-T; Juang, J-N; Wisniewski, A; Lee, H-C; Wu, D; Lo, Y-L, Prediction of the severity of obstructive sleep apnea by anthropometric features via support vector machine., PloS one, vol. 12 no. 5 (January, 2017), pp. e0176991 [doi]  [abs]
  6. Lin, Y-T; Wu, H-T, ConceFT for Time-Varying Heart Rate Variability Analysis as a Measure of Noxious Stimulation During General Anesthesia., IEEE Transactions on Biomedical Engineering, vol. 64 no. 1 (January, 2017), pp. 145-154 [doi]  [abs]
  7. Wu, H-T, Embedding Riemannian manifolds by the heat kernel of the connection Laplacian, Advances in Mathematics, vol. 304 (January, 2017), pp. 1055-1079 [doi]
  8. Herry, CL; Frasch, M; Seely, AJ; Wu, H-T, Heart beat classification from single-lead ECG using the synchrosqueezing transform., Physiological Measurement, vol. 38 no. 2 (February, 2017), pp. 171-187 [doi]  [abs]
  9. Wu, H-K; Ko, Y-S; Lin, Y-S; Wu, H-T; Tsai, T-H; Chang, H-H, The correlation between pulse diagnosis and constitution identification in traditional Chinese medicine., Complementary Therapies in Medicine, vol. 30 (February, 2017), pp. 107-112 [doi]  [abs]
  10. Sheu, Y-L; Hsu, L-Y; Chou, P-T; Wu, H-T, Entropy-based time-varying window width selection for nonlinear-type time–frequency analysis, International Journal of Data Science and Analytics, vol. 3 no. 4 (June, 2017), pp. 231-245 [doi]
  11. Malik, J; Reed, N; Wang, C-L; Wu, H-T, Single-lead f-wave extraction using diffusion geometry., Physiological Measurement, vol. 38 no. 7 (June, 2017), pp. 1310-1334 [doi]  [abs]
  12. Georgiou, A; Bello-Rivas, J; Gear, C; Wu, H-T; Chiavazzo, E; Kevrekidis, I, An Exploration Algorithm for Stochastic Simulators Driven by Energy Gradients, Entropy (Basel, Switzerland), vol. 19 no. 7 (July, 2017), pp. 294-294 [doi]
  13. Lin, T-Y; Fang, Y-F; Huang, S-H; Wang, T-Y; Kuo, C-H; Wu, H-T; Kuo, H-P; Lo, Y-L, Capnography monitoring the hypoventilation during the induction of bronchoscopic sedation: A randomized controlled trial., Scientific Reports, vol. 7 no. 1 (August, 2017), pp. 8685 [doi]  [abs]
  14. Chao, Y-S; Wu, H-T; Scutari, M; Chen, T-S; Wu, C-J; Durand, M; Boivin, A, A network perspective on patient experiences and health status: the Medical Expenditure Panel Survey 2004 to 2011., BMC Health Services Research, vol. 17 no. 1 (August, 2017), pp. 579 [doi]  [abs]
  15. Wu, H-K; Ko, Y-S; Lin, Y-S; Wu, H-T; Tsai, T-H; Chang, H-H, Corrigendum to "The correlation between pulse diagnosis and constitution identification in traditional Chinese medicine" [Complementary Ther. Med. 30 (2017) 107-112]., Complementary Therapies in Medicine, vol. 35 (December, 2017), pp. 145 [doi]
  16. Talmon, R; Wu, H-T, Latent common manifold learning with alternating diffusion: Analysis and applications, Applied and Computational Harmonic Analysis (January, 2018) [doi]
  17. Kowalski, M; Meynard, A; Wu, H-T, Convex Optimization approach to signals with fast varying instantaneous frequency, Applied and Computational Harmonic Analysis, vol. 44 no. 1 (January, 2018), pp. 89-122 [doi]
  18. Shen, C; Frasch, MG; Wu, HT; Herry, CL; Cao, M; Desrochers, A; Fecteau, G; Burns, P, Non-invasive acquisition of fetal ECG from the maternal xyphoid process: a feasibility study in pregnant sheep and a call for open data sets., Physiological Measurement, vol. 39 no. 3 (March, 2018), pp. 035005 [doi]  [abs]

Yang, Haizhao   (search)

  1. Cornelis, B; Yang, H; Goodfriend, A; Ocon, N; Lu, J; Daubechies, I, Removal of Canvas Patterns in Digital Acquisitions of Paintings., IEEE Transactions on Image Processing, vol. 26 no. 1 (January, 2017), pp. 160-171, Institute of Electrical and Electronics Engineers (IEEE), ISSN 1941-0042 [repository], [doi]  [abs]
  2. Lu, J; Yang, H, Preconditioning Orbital Minimization Method for Planewave Discretization, Multiscale Modeling & Simulation, vol. 15 no. 1 (January, 2017), pp. 254-273 [repository], [doi]  [abs]
  3. Li, Y; Yang, H, Interpolative Butterfly Factorization, SIAM Journal on Scientific Computing, vol. 39 no. 2 (January, 2017), pp. A503-A531 [1605.03616], [doi]
  4. Yang, H, Statistical analysis of synchrosqueezed transforms, Applied and Computational Harmonic Analysis (January, 2017), Elsevier, ISSN 1096-603X [repository], [doi]
  5. Li, Y; Yang, H; Ying, L, Multidimensional butterfly factorization, Applied and Computational Harmonic Analysis (April, 2017), Elsevier, ISSN 1096-603X [arXiv:1509.07925], [repository], [doi]
  6. 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]

Zhou, Zhennan

  1. Ma, Z; Zhang, Y; Zhou, Z, An improved semi-Lagrangian time splitting spectral method for the semi-classical Schrödinger equation with vector potentials using NUFFT, Applied Numerical Mathematics, vol. 111 (January, 2017), pp. 144-159 [doi]
  2. Lu, J; Zhou, Z, Path integral molecular dynamics with surface hopping for thermal equilibrium sampling of nonadiabatic systems., Journal of Chemical Physics, vol. 146 no. 15 (April, 2017), pp. 154110 [doi]  [abs]
  3. Liu, J-G; Ma, Z; Zhou, Z, Explicit and Implicit TVD Schemes for Conservation Laws with Caputo Derivatives, Journal of Scientific Computing, vol. 72 no. 1 (July, 2017), pp. 291-313 [doi]  [abs]

Zhu, Wei

  1. Zhu, W; Qiu, Q; Huang, J; Calderbank, AR; Sapiro, G; Daubechies, I, LDMNet: Low Dimensional Manifold Regularized Neural Networks., CoRR, vol. abs/1711.06246 (2017)

 

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