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Mathematics : Publications since January 2017

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

Agarwal, Pankaj K.

  1. Agarwal, PK; Kaplan, H; Sharir, M, Union of hypercubes and 3D minkowski sums with random sizes, Leibniz International Proceedings in Informatics, Lipics, vol. 107 (July, 2018), ISBN 9783959770767 [doi]  [abs]
  2. Agarwal, PK; Kumar, N; Sintos, S; Suri, S, Range-max queries on uncertain data, Journal of Computer and System Sciences, vol. 94 (June, 2018), pp. 118-134 [doi]  [abs]
  3. Agarwal, PK; Arge, L; Staals, F, Improved dynamic geodesic nearest neighbor searching in a simple polygon, Leibniz International Proceedings in Informatics, Lipics, vol. 99 (June, 2018), pp. 41-414 [doi]  [abs]
  4. Agarwal, PK; Kumar, N; Sintos, S; Suri, S, Computing shortest paths in the plane with removable obstacles, Leibniz International Proceedings in Informatics, Lipics, vol. 101 (June, 2018), pp. 51-515, ISBN 9783959770682 [doi]  [abs]
  5. Agarwal, PK; Fox, K; Munagala, K; Nath, A; Pan, J; Taylor, E, Subtrajectory clustering: Models and algorithms, Proceedings of the Acm Sigact Sigmod Sigart Symposium on Principles of Database Systems (May, 2018), pp. 75-87, ISBN 9781450347068 [doi]  [abs]
  6. Agarwal, PK; Fox, K; Nath, A, Maintaining reeb graphs of triangulated 2-manifolds, Leibniz International Proceedings in Informatics, Lipics, vol. 93 (January, 2018), ISBN 9783959770552 [doi]  [abs]
  7. 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]
  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. Agarwal, PK; Rubin, N; Sharir, M, Approximate nearest neighbor search amid higher-dimensional flats, Leibniz International Proceedings in Informatics, Lipics, vol. 87 (September, 2017), ISBN 9783959770491 [doi]  [abs]
  10. Agarwal, PK; Kumar, N; Sintos, S; Suri, S, Efficient algorithms for k-regret minimizing sets, Leibniz International Proceedings in Informatics, Lipics, vol. 75 (August, 2017), ISBN 9783959770361 [doi]  [abs]
  11. Agarwal, PK; Fox, K; Panigrahi, D; Varadarajan, KR; Xiao, A, Faster algorithms for the geometric transportation problem, Leibniz International Proceedings in Informatics, Lipics, vol. 77 (June, 2017), pp. 71-716, ISBN 9783959770385 [doi]  [abs]
  12. 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]
  13. Wu, Y; Gao, J; Agarwal, PK; Yang, J, Finding diverse, high-value representatives on a surface of answers, Proceedings of the Vldb Endowment, vol. 10 no. 7 (January, 2017), pp. 793-804  [abs]
  14. 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]

Arlotto, Alessandro

  1. 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]
  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; Gurvich, I, Uniformly bounded regret in the multi-secretary problem (October, 2017)  [abs]

Bendich, Paul L

  1. Garagić, D; Peskoe, J; Liu, F; Claffey, MS; Bendich, P; Hineman, J; Borggren, N; Harer, J; Zulch, P; Rhodes, BJ, Upstream fusion of multiple sensing modalities using machine learning and topological analysis: An initial exploration, Ieee Aerospace Conference Proceedings, vol. 2018-March (June, 2018), pp. 1-8, ISBN 9781538620144 [doi]  [abs]
  2. Tralie, CJ; Smith, A; Borggren, N; Hineman, J; Bendich, P; Zulch, P; Harer, J, Geometric Cross-Modal Comparison of Heterogeneous Sensor Data, 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. Thompson, A; Calderbank, R, Sparse near-equiangular tight frames with applications in full duplex wireless communication, 2017 Ieee Global Conference on Signal and Information Processing, Globalsip 2017 Proceedings, vol. 2018-January (March, 2018), pp. 868-872, ISBN 9781509059904 [doi]  [abs]
  2. Mappouras, G; Vahid, A; Calderbank, R; Hower, DR; Sorin, DJ, Jenga: Efficient fault tolerance for stacked DRAM, Proceedings - 35th IEEE International Conference on Computer Design, ICCD 2017 (November, 2017), pp. 361-368, ISBN 9781538622544 [doi]  [abs]
  3. Kadhe, S; Calderbank, R, Rate optimal binary linear locally repairable codes with small availability, Ieee International Symposium on Information Theory Proceedings (August, 2017), pp. 166-170, ISBN 9781509040964 [doi]  [abs]
  4. Michelusi, N; Nokleby, M; Mitra, U; Calderbank, R, Multi-scale spectrum sensing in small-cell mm-wave cognitive wireless networks, Ieee International Conference on Communications (July, 2017), ISBN 9781467389990 [doi]  [abs]
  5. Cnaan-On, I; Harms, A; Krolik, JL; Calderbank, AR, Run-length limited codes for backscatter communication, 2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp) (June, 2017), pp. 6110-6114, ISBN 9781509041176 [doi]  [abs]
  6. 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]
  7. 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, Wcnc (May, 2017), ISBN 9781509041831 [doi]  [abs]
  8. 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]

Cao, Yu

  1. Cao, Y; Lu, J, Stochastic dynamical low-rank approximation method, Journal of Computational Physics, vol. 372 (November, 2018), pp. 564-586 [doi]  [abs]
  2. 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), pp. 122105-122105 [doi]

Cheng, Cheng

  1. Cheng, C; Jiang, Y; Sun, Q, Spatially distributed sampling and reconstruction, Applied and Computational Harmonic Analysis (August, 2017) [doi]
  2. 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]

Cheng, Xiuyuan

  1. 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), Lancet (London, England), vol. 390 no. 10112 (December, 2017), pp. 2549-2558 [doi]
  2. Cheng, X; Mishne, G; Steinerberger, S, The geometry of nodal sets and outlier detection, Journal of Number Theory (October, 2017) [doi]

Daubechies, Ingrid

  1. Gao, T; Yapuncich, GS; Daubechies, I; Mukherjee, S; Boyer, DM, Development and Assessment of Fully Automated and Globally Transitive Geometric Morphometric Methods, With Application to a Biological Comparative Dataset With High Interspecific Variation., Anatomical Record (Hoboken, N.J. : 2007), vol. 301 no. 4 (April, 2018), pp. 636-658 [doi]  [abs]
  2. 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]
  3. 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]
  4. 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 : a Publication of the Ieee Signal Processing Society, vol. 26 no. 2 (February, 2017), pp. 751-764 [doi]
  5. 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 : a Publication of the Ieee Signal Processing Society, vol. 26 no. 1 (January, 2017), pp. 160-171 [doi]  [abs]
  6. 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]
  7. 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]
  8. 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]

Dolbow, John E.

  1. Zhang, Z; Jiang, W; Dolbow, JE; Spencer, BW, A modified moment-fitted integration scheme for X-FEM applications with history-dependent material data, Computational Mechanics, vol. 62 no. 2 (August, 2018), pp. 233-252 [doi]  [abs]
  2. 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]
  3. 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]
  4. Stershic, AJ; Dolbow, JE; Moës, N, The Thick Level-Set model for dynamic fragmentation, Engineering Fracture Mechanics, vol. 172 (March, 2017), pp. 39-60 [doi]

Dunson, David B.

  1. Shterev, ID; Dunson, DB; Chan, C; Sempowski, GD, Bayesian Multi-Plate High-Throughput Screening of Compounds., Scientific Reports, vol. 8 no. 1 (June, 2018), pp. 9551 [doi]  [abs]
  2. Johndrow, JE; Lum, K; Dunson, DB, Theoretical limits of microclustering for record linkage., Biometrika, vol. 105 no. 2 (June, 2018), pp. 431-446 [doi]  [abs]
  3. Zhang, Z; Descoteaux, M; Zhang, J; Girard, G; Chamberland, M; Dunson, D; Srivastava, A; Zhu, H, Mapping population-based structural connectomes., Neuroimage, vol. 172 (May, 2018), pp. 130-145 [doi]  [abs]
  4. 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]
  5. Sarkar, A; Chabout, J; Macopson, JJ; Jarvis, ED; Dunson, DB, Bayesian Semiparametric Mixed Effects Markov Models With Application to Vocalization Syntax, Journal of the American Statistical Association (January, 2018), pp. 1-13 [doi]  [abs]
  6. Dunson, DB, Statistics in the big data era: Failures of the machine, Statistics & Probability Letters (January, 2018) [doi]  [abs]
  7. Bertrán, MA; Martínez, NL; Wang, Y; Dunson, D; Sapiro, G; Ringach, D, Active learning of cortical connectivity from two-photon imaging data., Plos One, vol. 13 no. 5 (January, 2018), pp. e0196527 [doi]  [abs]
  8. Minsker, S; Srivastava, S; Lin, L; Dunson, DB, Robust and scalable bayes via a median of subset posterior measures, Journal of Machine Learning Research, vol. 18 (December, 2017), pp. 1-40  [abs]
  9. 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]
  10. 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]
  11. 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]
  12. Durante, D; Dunson, DB; Vogelstein, JT, Nonparametric Bayes Modeling of Populations of Networks, Journal of the American Statistical Association, vol. 112 no. 520 (October, 2017), pp. 1516-1530 [doi]  [abs]
  13. 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; Choi, WWL; Evens, AM; Pilichowska, M; Sengar, M; Reddy, N; Li, S; Chadburn, A; Gordon, LI; Jaffe, ES; Levy, S; Rempel, R; Tzeng, T; Happ, LE; Dave, T; Rajagopalan, D; Datta, J; Dunson, DB; Dave, SS, Genetic and Functional Drivers of Diffuse Large B Cell Lymphoma., Cell, vol. 171 no. 2 (October, 2017), pp. 481-494.e15 [doi]  [abs]
  14. 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]
  15. 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]
  16. Srivastava, S; Engelhardt, BE; Dunson, DB, Expandable factor analysis., Biometrika, vol. 104 no. 3 (September, 2017), pp. 649-663 [doi]  [abs]
  17. Guhaniyogi, R; Qamar, S; Dunson, DB, Bayesian tensor regression, Journal of Machine Learning Research, vol. 18 (August, 2017), pp. 1-31  [abs]
  18. 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]
  19. Zhu, B; Dunson, DB, Bayesian Functional Data Modeling for Heterogeneous Volatility, Bayesian Analysis, vol. 12 no. 2 (June, 2017), pp. 335-350 [doi]
  20. Wang, L; Durante, D; Jung, RE; Dunson, DB, Bayesian network-response regression., Bioinformatics (Oxford, England), vol. 33 no. 12 (June, 2017), pp. 1859-1866 [doi]  [abs]
  21. 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]
  22. 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 B: Biological Sciences, vol. 284 no. 1855 (May, 2017), pp. 20170768-20170768 [doi]  [abs]
  23. 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]
  24. 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]
  25. 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; Au-Yeung, RKH; Srivastava, G; Choi, WWL; Goodlad, JR; Aurer, I; Basic-Kinda, S; Gascoyne, RD; Davis, NS; Li, G; Zhang, J; Rajagopalan, D; Reddy, A; Love, C; Levy, S; Zhuang, Y; Datta, J; Dunson, DB; Davé, SS, The Genetic Basis of Hepatosplenic T-cell Lymphoma., Cancer Discovery, vol. 7 no. 4 (April, 2017), pp. 369-379 [doi]  [abs]
  26. 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]
  27. Dunson, DB, Toward Automated Prior Choice, Statistical Science, vol. 32 no. 1 (February, 2017), pp. 41-43 [doi]
  28. 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]
  29. Johndrow, JE; Bhattacharya, A; Dunson, DB, TENSOR DECOMPOSITIONS AND SPARSE LOG-LINEAR MODELS., The Annals of Statistics, vol. 45 no. 1 (January, 2017), pp. 1-38 [doi]  [abs]
  30. 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]
  31. Durante, D; Dunson, DB; Vogelstein, JT, Nonparametric Bayes Modeling of Populations of Networks, Journal of the American Statistical Association, vol. 112 no. 520 (2017), pp. 1516-1530 [doi]
  32. Lin, L; Rao, V; Dunson, D, Bayesian nonparametric inference on the Stiefel manifold, Statistica Sinica (2017) [doi]

Durrett, Richard T.

  1. Wang, Z; Durrett, R, Extrapolating weak selection in evolutionary games., Journal of Mathematical Biology (July, 2018) [doi]  [abs]
  2. Gleeson, JP; Durrett, R, Temporal profiles of avalanches on networks, Nature Communications, vol. 8 no. 1 (December, 2017) [doi]
  3. 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]
  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. Huo, R; Durrett, R, Latent voter model on locally tree-like random graphs, Stochastic Processes and Their Applications (August, 2017) [doi]
  6. Nanda, M; Durrett, R, Spatial evolutionary games with weak selection, Proceedings of the National Academy of Sciences of the United States of America, vol. 114 no. 23 (June, 2017), pp. 6046-6051 [doi]
  7. 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, 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. Brown, F; Hain, R, Algebraic de Rham theory for weakly holomorphic modular forms of level one, Algebra & Number Theory, vol. 12 no. 3 (2018), pp. 723-750 [doi]
  2. 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. Garagić, D; Peskoe, J; Liu, F; Claffey, MS; Bendich, P; Hineman, J; Borggren, N; Harer, J; Zulch, P; Rhodes, BJ, Upstream fusion of multiple sensing modalities using machine learning and topological analysis: An initial exploration, Ieee Aerospace Conference Proceedings, vol. 2018-March (June, 2018), pp. 1-8, ISBN 9781538620144 [doi]  [abs]
  2. Tralie, CJ; Smith, A; Borggren, N; Hineman, J; Bendich, P; Zulch, P; Harer, J, Geometric Cross-Modal Comparison of Heterogeneous Sensor Data, Proceedings of the 39th IEEE Aerospace Conference (March, 2018)  [abs]
  3. 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]

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]
  2. Herschlag, G; Ravier, R; Mattingly, JC, Evaluating Partisan Gerrymandering in Wisconsin (September, 2017)  [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. Johnson, T; Junge, M, Stochastic orders and the frog model, Annales De L'Institut Henri Poincaré, Probabilités Et Statistiques, vol. 54 no. 2 (May, 2018), pp. 1013-1030 [doi]
  2. Hoffman, C; Johnson, T; Junge, M, Recurrence and transience for the frog model on trees, The Annals of Probability, vol. 45 no. 5 (September, 2017), pp. 2826-2854 [doi]

Kiselev, Alexander A.

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Kovalsky, Shahar

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Layton, Anita T.

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Levine, Adam S.

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Li, Didong

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Li, Lei

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Li, Yingzhou

  1. Li, Y; Ying, L, Distributed-memory hierarchical interpolative factorization, Research in the Mathematical Sciences, vol. 4 no. 1 (December, 2017) [doi]
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Liu, Jian-Guo

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  16. 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]
  17. 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]
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  20. 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]
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  22. 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]
  23. 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]
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  26. 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]

Lu, Jianfeng

  1. Cao, Y; Lu, J, Stochastic dynamical low-rank approximation method, Journal of Computational Physics, vol. 372 (November, 2018), pp. 564-586 [doi]  [abs]
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  4. Cai, Z; Lu, J, A Quantum Kinetic Monte Carlo Method for Quantum Many-Body Spin Dynamics, Siam Journal on Scientific Computing, vol. 40 no. 3 (January, 2018), pp. B706-B722 [doi]
  5. 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, vol. 222 (January, 2018), pp. 267-285 [doi]
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  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. Lu, J; Zhou, Z, Path integral molecular dynamics with surface hopping for thermal equilibrium sampling of nonadiabatic systems., The Journal of Chemical Physics, vol. 146 no. 15 (April, 2017), pp. 154110 [doi]  [abs]
  20. 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]
  21. 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]
  22. 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]
  23. Lu, J; Yang, H, Preconditioning Orbital Minimization Method for Planewave Discretization, Multiscale Modeling & Simulation, vol. 15 no. 1 (January, 2017), pp. 254-273 [doi]
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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. Murphy, JM; Maggioni, M, Diffusion geometric methods for fusion of remotely sensed data, Smart Structures and Materials 2005: Active Materials: Behavior and Mechanics, vol. 10644 (January, 2018), ISBN 9781510617995 [doi]  [abs]
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Malen, Greg

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Mattingly, Jonathan C.

  1. Hairer, M; Mattingly, J, The strong Feller property for singular stochastic PDEs, Annales De L'Institut Henri Poincaré, Probabilités Et Statistiques, vol. 54 no. 3 (August, 2018), pp. 1314-1340 [doi]  [abs]
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Motta, Francis C.

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Mukherjee, Sayan

  1. 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]
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Nagy, Akos

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Ng, Lenhard L.

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Nolen, James H.

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Orizaga, Saulo

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Petters, Arlie O.

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

Pfister, Henry

  1. Hager, C; Pfister, HD, Approaching Miscorrection-Free Performance of Product Codes With Anchor Decoding, Ieee Transactions on Communications, vol. 66 no. 7 (July, 2018), pp. 2797-2808 [doi]
  2. Hager, C; Pfister, HD, Nonlinear interference mitigation via deep neural networks, 2018 Optical Fiber Communications Conference and Exposition, Ofc 2018 Proceedings (June, 2018), pp. 1-3, ISBN 9781943580385  [abs]
  3. Häger, C; Pfister, HD, Nonlinear interference mitigation via deep neural networks, Optics Infobase Conference Papers, vol. Part F84-OFC 2018 (January, 2018) [doi]  [abs]
  4. Rengaswamy, N; Calderbank, AR; Kadhe, S; Pfister, HD, Synthesis of Logical Clifford Operators via Symplectic Geometry., Corr, vol. abs/1803.06987 (2018)
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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]
  11. Häger, C; Pfister, HD, Miscorrection-free Decoding of Staircase Codes., CoRR, vol. abs/1709.06827 (2017)

Pierce, Lillian B.

  1. Pierce, LB; Yung, PL, A polynomial Carleson operator along the paraboloid, Revista Matematica Iberoamericana (2018), European Mathematical Society
  2. 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]
  3. 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]
  4. Pierce, LB, The Vinogradov Mean Value Theorem [after Wooley, and Bourgain, Demeter and Guth], Asterisque (July, 2017), Centre National de la Recherche Scientifique  [abs]
  5. 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 Fur Die Reine Und Angewandte Mathematik, vol. 2017 no. 727 (June, 2017), pp. 85-143 [doi]  [abs]
  6. 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]
  7. 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]
  8. Ellenberg, J; Pierce, LB; Wood, MM, On ℓ-torsion in class groups of number fields, Algebra & Number Theory, vol. 11 no. 8 (2017), pp. 1739-1778 [doi]  [abs]

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, The spin -function on for Siegel modular forms, Compositio Mathematica, vol. 153 no. 07 (July, 2017), pp. 1391-1432 [doi]
  2. 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]

Randles, Amanda

  1. Rafat, M; Stone, HA; Auguste, DT; Dabagh, M; Randles, A; Heller, M; Rabinov, JD, Impact of diversity of morphological characteristics and Reynolds number on local hemodynamics in basilar aneurysms, Aiche Journal, vol. 64 no. 7 (July, 2018), pp. 2792-2802 [doi]  [abs]
  2. Gounley, J; Vardhan, M; Randles, A, A framework for comparing vascular hemodynamics at different points in time, Computer Physics Communications (June, 2018) [doi]
  3. 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]
  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. 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]
  6. 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]
  7. 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, Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10076 (January, 2017), ISBN 9781510605930 [doi]  [abs]

Reed, Michael C.

  1. West, A; Best, J; Abdalla, A; Nijhout, F; Reed, M; Hashemi, P, Voltammetric evidence for discrete serotonin circuits, linked to specific reuptake domains, in the mouse medial prefrontal cortex., Neurochemistry International (July, 2018) [doi]  [abs]
  2. Duncan, W; Best, J; Golubitsky, M; Nijhout, HF; Reed, M, Homeostasis despite instability., Mathematical Biosciences, vol. 300 (March, 2018), pp. 130-137 [doi]  [abs]
  3. 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]
  4. Best, J; Nijhout, HF; Samaranayake, S; Hashemi, P; Reed, M, A mathematical model for histamine synthesis, release, and control in varicosities., Theoretical Biology & Medical Modelling, vol. 14 no. 1 (December, 2017), pp. 24 [doi]  [abs]
  5. 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]
  6. Nijhout, HF; Sadre-Marandi, F; Best, J; Reed, MC, Systems Biology of Phenotypic Robustness and Plasticity., Integrative and Comparative Biology, vol. 57 no. 2 (August, 2017), pp. 171-184 [doi]  [abs]
  7. 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)
  8. Reed, MC; Lawley, S; Nijhout, HF, Spiracular fluttering increases oxygen uptake (2017)
  9. 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
  10. 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
  11. 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

Robles, Colleen M

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

Rudin, Cynthia D.

  1. 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]
  2. 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]
  3. 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]
  4. 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]
  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, vol. 18 (August, 2017), pp. 1-37  [abs]
  6. Letham, B; Letham, PA; Rudin, C; Browne, EP, Erratum: "Prediction uncertainty and optimal experimental design for learning dynamical systems" [Chaos 26, 063110 (2016)]., Chaos (Woodbury, N.Y.), vol. 27 no. 6 (June, 2017), pp. 069901 [doi]
  7. 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]
  8. 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]
  9. 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, Icdm (January, 2017), pp. 1269-1274, ISBN 9781509054725 [doi]  [abs]
  10. Yang, H; Rudin, C; Seltzer, M, Scalable Bayesian rule lists, 34th International Conference on Machine Learning, Icml 2017, vol. 8 (January, 2017), pp. 5971-5980, ISBN 9781510855144  [abs]

Saper, Leslie

  1. Saper, L, ℒ-modules and micro-support, to appear in Annals of Mathematics (2018)
  2. 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]

Sapiro, Guillermo

  1. Bartesaghi, A; Aguerrebere, C; Falconieri, V; Banerjee, S; Earl, LA; Zhu, X; Grigorieff, N; Milne, JLS; Sapiro, G; Wu, X; Subramaniam, S, Atomic Resolution Cryo-EM Structure of β-Galactosidase., Structure (London, England : 1993), vol. 26 no. 6 (June, 2018), pp. 848-856.e3 [doi]  [abs]
  2. Giryes, R; Eldar, YC; Bronstein, AM; Sapiro, G, Tradeoffs Between Convergence Speed and Reconstruction Accuracy in Inverse Problems, Ieee Transactions on Signal Processing, vol. 66 no. 7 (April, 2018), pp. 1676-1690 [doi]  [abs]
  3. Campbell, K; Carpenter, KL; Hashemi, J; Espinosa, S; Marsan, S; Borg, JS; Chang, Z; Qiu, Q; Vermeer, S; Adler, E; Tepper, M; Egger, HL; Baker, JP; Sapiro, G; Dawson, G, Computer vision analysis captures atypical attention in toddlers with autism., Autism (March, 2018), pp. 1362361318766247 [doi]  [abs]
  4. 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]
  5. 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]
  6. Qiu, Q; Hashemi, J; Sapiro, G, Intelligent synthesis driven model calibration: framework and face recognition application, Proceedings 2017 Ieee International Conference on Computer Vision Workshops, Iccvw 2017, vol. 2018-January (January, 2018), pp. 2564-2572, ISBN 9781538610343 [doi]  [abs]
  7. Sokolić, J; Qiu, Q; Rodrigues, MRD; Sapiro, G, Learning to identify while failing to discriminate, Proceedings 2017 Ieee International Conference on Computer Vision Workshops, Iccvw 2017, vol. 2018-January (January, 2018), pp. 2537-2544, ISBN 9781538610343 [doi]  [abs]
  8. Bertrán, MA; Martínez, NL; Wang, Y; Dunson, D; Sapiro, G; Ringach, D, Active learning of cortical connectivity from two-photon imaging data., Plos One, vol. 13 no. 5 (January, 2018), pp. e0196527 [doi]  [abs]
  9. Asiedu, MN; Simhal, A; Lam, CT; Mueller, J; Chaudhary, U; Schmitt, JW; Sapiro, G; Ramanujam, N, Image processing and machine learning techniques to automate diagnosis of Lugol's iodine cervigrams for a low-cost point-of-care digital colposcope, Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10485 (January, 2018), ISBN 9781510614550 [doi]  [abs]
  10. Chiew, KS; Hashemi, J; Gans, LK; Lerebours, L; Clement, NJ; Vu, M-AT; Sapiro, G; Heller, NE; Adcock, RA, Motivational valence alters memory formation without altering exploration of a real-life spatial environment., Plos One, vol. 13 no. 3 (January, 2018), pp. e0193506 [doi]  [abs]
  11. Lezama, J; Qiu, Q; Sapiro, G, Not afraid of the dark: NIR-VIS face recognition via cross-spectral hallucination and low-rank embedding, Proceedings 30th Ieee Conference on Computer Vision and Pattern Recognition, Cvpr 2017, vol. 2017-January (November, 2017), pp. 6807-6816 [doi]  [abs]
  12. Ye, Q; Zhang, T; Ke, W; Qiu, Q; Chen, J; Sapiro, G; Zhang, B, Self-learning scene-specific pedestrian detectors using a progressive latent model, Proceedings 30th Ieee Conference on Computer Vision and Pattern Recognition, Cvpr 2017, vol. 2017-January (November, 2017), pp. 2057-2066 [doi]  [abs]
  13. Su, S; Delbracio, M; Wang, J; Sapiro, G; Heidrich, W; Wang, O, Deep video deblurring for hand-held cameras, Proceedings 30th Ieee Conference on Computer Vision and Pattern Recognition, Cvpr 2017, vol. 2017-January (November, 2017), pp. 237-246, ISBN 9781538604571 [doi]  [abs]
  14. Tepper, M; Sapiro, G, Nonnegative matrix underapproximation for robust multiple model fitting, Proceedings 30th Ieee Conference on Computer Vision and Pattern Recognition, Cvpr 2017, vol. 2017-January (November, 2017), pp. 655-663, ISBN 9781538604571 [doi]  [abs]
  15. 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]
  16. 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]
  17. 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]
  18. 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]
  19. 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]
  20. 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]
  21. 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]

Stern, Mark A.

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

Tarokh, Vahid

  1. Ding, J; Shahrampour, S; Heal, K; Tarokh, V, Analysis of Multistate Autoregressive Models, Ieee Transactions on Signal Processing, vol. 66 no. 9 (May, 2018), pp. 2429-2440 [doi]
  2. Magnusson, S; Enyioha, C; Li, N; Fischione, C; Tarokh, V, Convergence of Limited Communication Gradient Methods, Ieee Transactions on Automatic Control, vol. 63 no. 5 (May, 2018), pp. 1356-1371 [doi]
  3. Soloveychik, I; Xiang, Y; Tarokh, V, Pseudo-Wigner Matrices, Ieee Transactions on Information Theory, vol. 64 no. 4 (April, 2018), pp. 3170-3178 [doi]
  4. Soloveychik, I; Xiang, Y; Tarokh, V, Symmetric Pseudo-Random Matrices, Ieee Transactions on Information Theory, vol. 64 no. 4 (April, 2018), pp. 3179-3196 [doi]
  5. Ding, J; Zhou, J; Tarokh, V, Optimal prediction of data with unknown abrupt change points, 2017 Ieee Global Conference on Signal and Information Processing, Globalsip 2017 Proceedings, vol. 2018-January (March, 2018), pp. 928-932, ISBN 9781509059904 [doi]  [abs]
  6. DIng, J; Xiang, Y; Shen, L; Tarokh, V, Detecting structural changes in dependent data, 2017 Ieee Global Conference on Signal and Information Processing, Globalsip 2017 Proceedings, vol. 2018-January (March, 2018), pp. 750-754, ISBN 9781509059904 [doi]  [abs]
  7. Han, Q; Ding, J; Airoldi, E; Tarokh, V, Modeling nonlinearity in multi-dimensional dependent data, 2017 Ieee Global Conference on Signal and Information Processing, Globalsip 2017 Proceedings, vol. 2018-January (March, 2018), pp. 206-210, ISBN 9781509059904 [doi]  [abs]
  8. Soloveychik, I; Xiang, Y; Tarokh, V, Explicit symmetric pseudo-random matrices, Ieee International Symposium on Information Theory Proceedings, vol. 2018-January (January, 2018), pp. 424-428, ISBN 9781509030972 [doi]  [abs]
  9. Shahrampour, S; Tarokh, V, Nonlinear sequential accepts and rejects for identification of top arms in stochastic bandits, 55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017, vol. 2018-January (January, 2018), pp. 228-235 [doi]  [abs]
  10. Soloveychik, I; Tarokh, V; Paulson, JA, On the spectral norms of pseudo-wigner and related matrices, 55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017, vol. 2018-January (January, 2018), pp. 61-66 [doi]  [abs]
  11. Enyioha, C; Magnusson, S; Heal, K; Li, N; Fischione, C; Tarokh, V, On Variability of Renewable Energy and Online Power Allocation, Ieee Transactions on Power Systems, vol. 33 no. 1 (January, 2018), pp. 451-462 [doi]
  12. Magnusson, S; Enyioha, C; Li, N; Fichione, C; Tarokh, V, Communication Complexity of Dual Decomposition Methods for Distributed Resource Allocation Optimization, Ieee Journal of Selected Topics in Signal Processing (2018), pp. 1-1 [doi]
  13. Han, Q; Ding, J; Airoldi, EM; Tarokh, V, SLANTS: Sequential Adaptive Nonlinear Modeling of Time Series, Ieee Transactions on Signal Processing, vol. 65 no. 19 (October, 2017), pp. 4994-5005 [doi]
  14. Ding, J; Xiang, Y; Shen, L; Tarokh, V, Multiple Change Point Analysis: Fast Implementation and Strong Consistency, Ieee Transactions on Signal Processing, vol. 65 no. 17 (September, 2017), pp. 4495-4510 [doi]
  15. Shahrampour, S; Noshad, M; Tarokh, V, On Sequential Elimination Algorithms for Best-Arm Identification in Multi-Armed Bandits, Ieee Transactions on Signal Processing, vol. 65 no. 16 (August, 2017), pp. 4281-4292 [doi]
  16. Deng, Z; Ding, J; Heal, K; Tarokh, V, The number of independent sets in hexagonal graphs, Ieee International Symposium on Information Theory Proceedings (August, 2017), pp. 2910-2914, ISBN 9781509040964 [doi]  [abs]
  17. Soloveychik, I; Xiang, Y; Tarokh, V, Pseudo-wigner matrices from dual BCH codes, Ieee International Symposium on Information Theory Proceedings (August, 2017), pp. 1381-1385, ISBN 9781509040964 [doi]  [abs]
  18. Jeong, S; Kang, J; Pahlavan, K; Tarokh, V, Fundamental Limits of TOA/DOA and Inertial Measurement Unit-Based Wireless Capsule Endoscopy Hybrid Localization, International Journal of Wireless Information Networks, vol. 24 no. 2 (June, 2017), pp. 169-179 [doi]
  19. Wei, L; Sarwate, AD; Corander, J; Hero, A; Tarokh, V, Analysis of a privacy-preserving PCA algorithm using random matrix theory, 2016 Ieee Global Conference on Signal and Information Processing, Globalsip 2016 Proceedings (April, 2017), pp. 1335-1339, ISBN 9781509045457 [doi]  [abs]
  20. Enyioha, C; Magnússon, S; Heal, K; Li, N; Fischione, C; Tarokh, V, Robustness analysis for an online decentralized descent power allocation algorithm, 2016 Information Theory and Applications Workshop, Ita 2016 (March, 2017), ISBN 9781509025299 [doi]  [abs]
  21. Kuiper, PK; Kolitz, SE; Tarokh, V, Base camp quality of life standardization and improvement, Proceedings International Carnahan Conference on Security Technology (January, 2017), ISBN 9781509010707 [doi]  [abs]
  22. Kurien, BG; Ashcom, JB; Shah, VN; Rachlin, Y; Tarokh, V, Robust interferometric imaging via prior-less phase recovery: redundant spacing calibration with generalized-closure phases, Monthly Notices of the Royal Astronomical Society, vol. 464 no. 2 (January, 2017), pp. 2356-2376 [doi]
  23. Jeong, S; Li, X; Yang, J; Li, Q; Tarokh, V, Dictionary learning and sparse coding-based denoising for high-resolution task functional connectivity MRI analysis, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10541 LNCS (January, 2017), pp. 45-52, ISBN 9783319673882 [doi]  [abs]
  24. Beirami, A; Razaviyayn, M; Shahrampour, S; Tarokh, V, On optimal generalizability in parametric learning, Advances in Neural Information Processing Systems, vol. 2017-December (January, 2017), pp. 3456-3466  [abs]
  25. Shahrampour, S; Noshad, M; Ding, J; Tarokh, V, Online Learning for Multimodal Data Fusion with Application to Object Recognition, Ieee Transactions on Circuits and Systems Ii: Express Briefs (2017), pp. 1-1 [doi]

Tralie, Christopher

  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]
  2. Tralie, CJ; Perea, JA, (Quasi)Periodicity Quantification in Video Data, Using Topology, Siam Journal on Imaging Sciences, vol. 11 no. 2 (January, 2018), pp. 1049-1077 [doi]  [abs]
  3. Tralie, CJ, Self-Similarity Based Time Warping (November, 2017)  [abs]
  4. Tralie, C, Moebius Beats: The Twisted Spaces of Sliding Window Audio Novelty Functions with Rhythmic Subdivisions (November, 2017)  [abs]
  5. Tralie, CJ, Early MFCC And HPCP Fusion for Robust Cover Song Identification, 18th International Society for Music Information Retrieval (ISMIR) (October, 2017)  [abs]

Turnage-Butterbaugh, Caroline

  1. 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]
  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. 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]

Venakides, Stephanos

  1. Aristotelous, AC; Crawford, JM; Edwards, GS; Kiehart, DP; Venakides, S, Mathematical models of dorsal closure., Progress in Biophysics and Molecular Biology (May, 2018) [doi]  [abs]
  2. 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)
  3. Bruno, OP; Shipman, SP; Turc, C; Venakides, S, Three-dimensional quasi-periodic shifted Green function throughout the spectrum, including Wood anomalies, Proceedings. Mathematical, Physical, and Engineering Sciences, vol. 473 no. 2207 (November, 2017) [doi]  [abs]
  4. 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]

Viel, Shira

  1. Barnard, E; Meehan, E; Reading, N; Viel, S, Universal Geometric Coefficients for the Four-Punctured Sphere, Annals of Combinatorics, vol. 22 no. 1 (March, 2018), pp. 1-44 [doi]

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. Chiou, J-G; Ramirez, SA; Elston, TC; Witelski, TP; Schaeffer, DG; Lew, DJ, Principles that govern competition or co-existence in Rho-GTPase driven polarization., Plos Computational Biology, vol. 14 no. 4 (April, 2018), pp. e1006095 [doi]  [abs]
  2. Ji, H; Witelski, TP, Instability and dynamics of volatile thin films, Physical Review Fluids, vol. 3 no. 2 (February, 2018) [doi]  [abs]
  3. Gao, Y; Ji, H; Liu, J-G; Witelski, TP, Global existence of solutions to a tear film model with locally elevated evaporation rates, Physica D: Nonlinear Phenomena, vol. 350 (July, 2017), pp. 13-25 [doi]
  4. 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]

Wong, Jeffrey T

  1. Mavromoustaki, A; Wang, L; Wong, J; Bertozzi, AL, Surface tension effects for particle settling and resuspension in viscous thin films, Nonlinearity, vol. 31 no. 7 (July, 2018), pp. 3151-3173 [doi]  [abs]

Wu, Hau-Tieng

  1. Katz, O; Talmon, R; Lo, Y-L; Wu, H-T, Alternating diffusion maps for multimodal data fusion, Information Fusion, vol. 45 (January, 2019), pp. 346-360 [doi]
  2. Wu, H-T; Wu, J-C; Huang, P-C; Lin, T-Y; Wang, T-Y; Huang, Y-H; Lo, Y-L, Phenotype-Based and Self-Learning Inter-Individual Sleep Apnea Screening With a Level IV-Like Monitoring System, Frontiers in Physiology, vol. 9 (July, 2018) [doi]
  3. Wu, HT; Liu, YW, Analyzing transient-evoked otoacoustic emissions by concentration of frequency and time, The Journal of the Acoustical Society of America, vol. 144 no. 1 (July, 2018), pp. 448-466 [doi]  [abs]
  4. Liu, TC; Wu, HT; Chen, YH; Fang, TY; Wang, PC; Liu, YW, Analysis of click-evoked otoacoustic emissions by concentration of frequency and time: Preliminary results from normal hearing and Ménière's disease ears, Aip Conference Proceedings, vol. 1965 (May, 2018), ISBN 9780735416703 [doi]  [abs]
  5. Wu, H-T; Soliman, EZ, A new approach for analysis of heart rate variability and QT variability in long-term ECG recording., Biomedical Engineering Online, vol. 17 no. 1 (May, 2018), pp. 54 [doi]  [abs]
  6. Frasch, MG; Lobmaier, SM; Stampalija, T; Desplats, P; Pallarés, ME; Pastor, V; Brocco, MA; Wu, H-T; Schulkin, J; Herry, CL; Seely, AJE; Metz, GAS; Louzoun, Y; Antonelli, MC, Non-invasive biomarkers of fetal brain development reflecting prenatal stress: An integrative multi-scale multi-species perspective on data collection and analysis, Neuroscience and Biobehavioral Reviews (May, 2018) [doi]
  7. Zhang, J-T; Cheng, M-Y; Wu, H-T; Zhou, B, A new test for functional one-way ANOVA with applications to ischemic heart screening, Computational Statistics & Data Analysis (May, 2018) [doi]
  8. 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]
  9. Talmon, R; Wu, H-T, Latent common manifold learning with alternating diffusion: Analysis and applications, Applied and Computational Harmonic Analysis (January, 2018) [doi]
  10. 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]
  11. Tan, C; Zhang, L; Wu, H-T, A Novel Blaschke Unwinding Adaptive Fourier Decomposition based Signal Compression Algorithm with Application on ECG Signals, Ieee Journal of Biomedical and Health Informatics (2018), pp. 1-1 [doi]  [abs]
  12. 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]
  13. 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]
  14. 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]
  15. 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, vol. 19 no. 7 (July, 2017), pp. 294-294 [doi]
  16. 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]
  17. 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]
  18. 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]
  19. 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]
  20. 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]
  21. 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]
  22. 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]
  23. 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]
  24. 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]
  25. 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 Bio Medical Engineering, vol. 64 no. 1 (January, 2017), pp. 145-154 [doi]  [abs]
  26. 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]

Yang, Haizhao   (search)

  1. Lu, J; Yang, H, A cubic scaling algorithm for excited states calculations in particle–particle random phase approximation, Journal of Computational Physics, vol. 340 (July, 2017), pp. 297-308 [doi]
  2. Li, Y; Yang, H; Ying, L, Multidimensional butterfly factorization, Applied and Computational Harmonic Analysis (April, 2017), Elsevier, ISSN 1096-603X [arXiv:1509.07925], [repository], [doi]
  3. Cornelis, B; Yang, H; Goodfriend, A; Ocon, N; Lu, J; Daubechies, I, Removal of Canvas Patterns in Digital Acquisitions of Paintings., IEEE Transactions on Image Processing, vol. 26 no. 1 (January, 2017), pp. 160-171, Institute of Electrical and Electronics Engineers (IEEE), ISSN 1941-0042 [repository], [doi]  [abs]
  4. 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]
  5. Li, Y; Yang, H, Interpolative Butterfly Factorization, SIAM Journal on Scientific Computing, vol. 39 no. 2 (January, 2017), pp. A503-A531 [1605.03616], [doi]
  6. Yang, H, Statistical analysis of synchrosqueezed transforms, Applied and Computational Harmonic Analysis (January, 2017), Elsevier, ISSN 1096-603X [repository], [doi]

Yao, Dong

  1. van der Hoorn, P, Average nearest neighbor degrees in scale-free networks, Internet Mathematics (January, 2018) [doi]

Zhou, Zhennan

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

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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