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Mathematics Faculty: Publications since January 2016

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

Abel, Michael

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

Agarwal, Pankaj K.

  1. Agarwal, PK; Fox, K; Salzman, O, An efficient algorithm for computing high-quality paths amid polygonal obstacles, Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms, vol. 2 (January, 2016), pp. 1179-1192, ISBN 9781510819672  [abs]
  2. Pan, J; Rao, V; Agarwal, PK; Gelfand, AE, Markov-modulated marked poisson processes for check-in data, 33rd International Conference on Machine Learning, ICML 2016, vol. 5 (January, 2016), pp. 3311-3320, ISBN 9781510829008  [abs]
  3. Yu, A; Agarwal, PK; Yang, J, Top-$k$ Preferences in High Dimensions, IEEE Transactions on Knowledge and Data Engineering, vol. 28 no. 2 (February, 2016), pp. 311-325, ISSN 1041-4347 [doi]
  4. Agarwal, PK; Fox, K; Pan, J; Ying, R, Approximating dynamic time warping and edit distance for a pair of point sequences, LIPIcs, vol. 51 (June, 2016), pp. 6.1-6.16 [doi]  [abs]
  5. Agarwal, PK; Kumar, N; Sintos, S; Suri, S, Range-max queries on uncertain data, Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, vol. 26-June-01-July-2016 (June, 2016), pp. 465-476, ISBN 9781450341912 [doi]  [abs]
  6. Agarwal, PK; Fox, K; Munagala, K; Nath, A, Parallel algorithms for constructing range and nearest-neighbor searching data structures, Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, vol. 26-June-01-July-2016 (June, 2016), pp. 429-440, ISBN 9781450341912 [doi]  [abs]
  7. Agarwal, PK; Aronov, B; Har-Peled, S; Phillips, JM; Yi, K; Zhang, W, Nearest-Neighbor Searching Under Uncertainty II, ACM Transactions on Algorithms, vol. 13 no. 1 (October, 2016), pp. 1-25 [doi]
  8. Ying, R; Pan, J; Fox, K; Agarwal, PK, A simple efficient approximation algorithm for dynamic time warping, GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems (October, 2016), ISBN 9781450345897 [doi]  [abs]
  9. Nath, A; Fox, K; Agarwal, PK; Munagala, K, Massively parallel algorithms for computing TIN DEMs and contour trees for large terrains, GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems (October, 2016), ISBN 9781450345897 [doi]  [abs]
  10. Agarwal, PK; Pan, J; Victor, W, An efficient algorithm for placing electric vehicle charging stations, LIPIcs, vol. 64 (December, 2016), pp. 7.1-7.12, ISBN 9783959770262 [doi]  [abs]
  11. 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]
  12. 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]
  13. Agarwal, PK; Kumar, N; Sintos, S; Suri, S, Efficient algorithms for k-regret minimizing sets, LIPIcs, vol. 75 (August, 2017), ISBN 9783959770361 [doi]  [abs]
  14. Agarwal, PK; Rubin, N; Sharir, M, Approximate nearest neighbor search amid higher-dimensional flats, LIPIcs, vol. 87 (September, 2017), ISBN 9783959770491 [doi]  [abs]
  15. 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]

Arlotto, Alessandro

  1. Arlotto, A; Steele, JM, Beardwood–Halton–Hammersley theorem for stationary ergodic sequences: a counterexample, The annals of applied probability : an official journal of the Institute of Mathematical Statistics, vol. 26 no. 4 (August, 2016), pp. 2141-2168 [doi]
  2. Arlotto, A; Mossel, E; Steele, JM, Quickest online selection of an increasing subsequence of specified size, Random Structures and Algorithms, vol. 49 no. 2 (September, 2016), pp. 235-252 [doi]
  3. Arlotto, A; Steele, JM, A central limit theorem for temporally nonhomogenous Markov chains with applications to dynamic programming, Mathematics of Operations Research, vol. 41 no. 4 (November, 2016), pp. 1448-1468 [doi]
  4. Arlotto, A; Gurvich, I, Uniformly bounded regret in the multi-secretary problem (October, 2017)  [abs]
  5. Arlotto, A; Frazelle, AE; Wei, Y, Strategic open routing in service networks, Management Science (2018), INFORMS
  6. 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]
  7. 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]

Beale, J. Thomas

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

Bendich, Paul L

  1. Paul Bendich, Ellen Gasparovic, John Harer, and Christopher J. Tralie, Scaffoldings and Spines: Organizing High-Dimensional Data Using Cover Trees, Local Principal Component Analysis, and Persistent Homology (2016) [1602.06245]
  2. Bendich, P; Marron, JS; Miller, E; Pieloch, A; Skwerer, S, Persistent Homology Analysis of Brain Artery Trees., The annals of applied statistics, vol. 10 no. 1 (January, 2016), pp. 198-218, ISSN 1932-6157 (to appear.) [repository], [doi]  [abs]
  3. Bendich, P; Gasparovic, E; Harer, J; Tralie, C, Geometric models for musical audio data, LIPIcs, vol. 51 (June, 2016), pp. 65.1-65.5, ISBN 9783959770095 [doi]  [abs]
  4. Bendich, P; Chin, SP; Clark, J; Desena, J; Harer, J; Munch, E; Newman, A; Porter, D; Rouse, D; Strawn, N; Watkins, A, Topological and statistical behavior classifiers for tracking applications, IEEE Transactions on Aerospace and Electronic Systems, vol. 52 no. 6 (December, 2016), pp. 2644-2661 [doi]  [abs]

Bertozzi, Andrea L

  1. J. B. Greer and A. L. Bertozzi, H-1 solutions of a class of fourth order nonlinear equations for image processing, Discrete And Continuous Dynamical Systems, vol. 10 no. 1-2 (2004), pp. 349 -- 366

Bray, Hubert

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

Bryant, Robert   (search)

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

Calderbank, Robert

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

Daubechies, Ingrid

  1. Yin, R; Cornelis, B; Fodor, G; Ocon, N; Dunson, D; Daubechies, I, Removing Cradle Artifacts in X-Ray Images of Paintings, SIAM Journal on Imaging Sciences, vol. 9 no. 3 (January, 2016), pp. 1247-1272 [doi]
  2. Daubechies, I; Wang, YG; Wu, H-T, ConceFT: concentration of frequency and time via a multitapered synchrosqueezed transform., Philosophical Transactions A, vol. 374 no. 2065 (April, 2016), pp. 20150193, ISSN 1364-503X [doi]  [abs]
  3. Huang, NE; Daubechies, I; Hou, TY, Adaptive data analysis: theory and applications., Philosophical Transactions A, vol. 374 no. 2065 (April, 2016), pp. 20150207, ISSN 1364-503X [doi]
  4. Yin, R; Monson, E; Honig, E; Daubechies, I; Maggioni, M, Object recognition in art drawings: Transfer of a neural network, IEEE International Conference on Acoustics Speech and Signal Processing, vol. 2016-May (May, 2016), pp. 2299-2303, ISSN 1520-6149, ISBN 9781479999880 [doi]  [abs]
  5. Deligiannis, N; Mota, JFC; Cornelis, B; Rodrigues, MRD; Daubechies, I, X-ray image separation via coupled dictionary learning, Proceedings / ICIP ... International Conference on Image Processing, vol. 2016-August (August, 2016), pp. 3533-3537, ISBN 9781467399616 [doi]  [abs]
  6. O'Neal, WT; Wang, YG; Wu, H-T; Zhang, Z-M; Li, Y; Tereshchenko, LG; Estes, EH; Daubechies, I; Soliman, EZ, Electrocardiographic J Wave and Cardiovascular Outcomes in the General Population (from the Atherosclerosis Risk In Communities Study)., The American Journal of Cardiology, vol. 118 no. 6 (September, 2016), pp. 811-815 [doi]  [abs]
  7. Wu, H-T; Lewis, GF; Davila, MI; Daubechies, I; Porges, SW, Optimizing Estimates of Instantaneous Heart Rate from Pulse Wave Signals with the Synchrosqueezing Transform., Methods of information in medicine, vol. 55 no. 5 (October, 2016), pp. 463-472 [doi]  [abs]
  8. Daubechies, I; Defrise, M; Mol, CD, Sparsity-enforcing regularisation and ISTA revisited, Inverse Problems, vol. 32 no. 10 (October, 2016), pp. 104001-104001 [doi]
  9. 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]
  10. Cornelis, B; Yang, H; Goodfriend, A; Ocon, N; Lu, J; Daubechies, I, Removal of Canvas Patterns in Digital Acquisitions of Paintings., IEEE Transactions on Image Processing, vol. 26 no. 1 (January, 2017), pp. 160-171 [doi]  [abs]
  11. 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]
  12. 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]
  13. 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]
  14. 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]

Dolbow, John E.

  1. Spencer, BW; Jiang, W; Dolbow, JE; Peco, C, Pellet cladding mechanical interaction modeling using the extended finite element method, Top Fuel 2016: LWR Fuels with Enhanced Safety and Performance (January, 2016), pp. 929-938, ISBN 9780894487309  [abs]
  2. Stershic, AJ; Dolbow, JE; Moës, N, The Thick Level-Set model for dynamic fragmentation, Engineering Fracture Mechanics, vol. 172 (March, 2017), pp. 39-60 [doi]
  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. 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]

Dunson, David B.

  1. Canale, A; Dunson, DB, Multiscale Bernstein polynomials for densities, Statistica Sinica (2016) [doi]
  2. Chabout, J; Sarkar, A; Patel, SR; Radden, T; Dunson, DB; Fisher, SE; Jarvis, ED, A Foxp2 Mutation Implicated in Human Speech Deficits Alters Sequencing of Ultrasonic Vocalizations in Adult Male Mice., Frontiers in Behavioral Neuroscience, vol. 10 (January, 2016), pp. 197 [doi]  [abs]
  3. Yin, R; Cornelis, B; Fodor, G; Ocon, N; Dunson, D; Daubechies, I, Removing Cradle Artifacts in X-Ray Images of Paintings, SIAM Journal on Imaging Sciences, vol. 9 no. 3 (January, 2016), pp. 1247-1272 [doi]
  4. Wang, X; Dunson, D; Leng, C, No penalty no tears: Least squares in high-dimensional linear models, 33rd International Conference on Machine Learning, ICML 2016, vol. 4 (January, 2016), pp. 2685-2706, ISBN 9781510829008  [abs]
  5. Wang, X; Dunson, D; Leng, C, DECOrrelated feature space partitioning for distributed sparse regression, Advances in Neural Information Processing Systems (January, 2016), pp. 802-810  [abs]
  6. Van Den Boom, W; Dunson, D; Reeves, G, Quantifying uncertainty in variable selection with arbitrary matrices, 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015 (January, 2016), pp. 385-388, ISBN 9781479919635 [doi]  [abs]
  7. Zhou, J; Herring, AH; Bhattacharya, A; Olshan, AF; Dunson, DB; National Birth Defects Prevention Study, , Nonparametric Bayes modeling for case control studies with many predictors., Biometrics, vol. 72 no. 1 (March, 2016), pp. 184-192 [doi]  [abs]
  8. Tang, K; Dunson, DB; Su, Z; Liu, R; Zhang, J; Dong, J, Subspace segmentation by dense block and sparse representation., Neural Networks, vol. 75 (March, 2016), pp. 66-76 [doi]  [abs]
  9. Kunihama, T; Dunson, DB, Nonparametric Bayes inference on conditional independence, Biometrika, vol. 103 no. 1 (March, 2016), pp. 35-47 [doi]
  10. Yang, Y; Dunson, DB, Bayesian manifold regression, Annals of statistics, vol. 44 no. 2 (April, 2016), pp. 876-905 [doi]
  11. Yang, Y; Dunson, DB, Bayesian Conditional Tensor Factorizations for High-Dimensional Classification, Journal of the American Statistical Association, vol. 111 no. 514 (April, 2016), pp. 656-669 [doi]
  12. Kabisa, ST; Dunson, DB; Morris, JS, Online Variational Bayes Inference for High-Dimensional Correlated Data, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, vol. 25 no. 2 (April, 2016), pp. 426-444 [doi]
  13. Ovaskainen, O; Abrego, N; Halme, P; Dunson, D, Using latent variable models to identify large networks of species-to-species associations at different spatial scales, edited by Warton, D, Methods in Ecology and Evolution, vol. 7 no. 5 (May, 2016), pp. 549-555 [doi]
  14. Guhaniyogi, R; Dunson, DB, Compressed Gaussian process for manifold regression, Journal of machine learning research : JMLR, vol. 17 (May, 2016)  [abs]
  15. Kunihama, T; Herring, AH; Halpern, CT; Dunson, DB, Nonparametric Bayes modeling with sample survey weights, Statistics & Probability Letters, vol. 113 (June, 2016), pp. 41-48 [doi]
  16. Rao, V; Lin, L; Dunson, DB, Data augmentation for models based on rejection sampling., Biometrika, vol. 103 no. 2 (June, 2016), pp. 319-335 [doi]  [abs]
  17. Hultman, R; Mague, SD; Li, Q; Katz, BM; Michel, N; Lin, L; Wang, J; David, LK; Blount, C; Chandy, R; Carlson, D; Ulrich, K; Carin, L; Dunson, D; Kumar, S; Deisseroth, K; Moore, SD; Dzirasa, K, Dysregulation of Prefrontal Cortex-Mediated Slow-Evolving Limbic Dynamics Drives Stress-Induced Emotional Pathology., Neuron, vol. 91 no. 2 (July, 2016), pp. 439-452 [doi]  [abs]
  18. Li, D; Heyer, L; Jennings, VH; Smith, CA; Dunson, DB, Personalised estimation of a woman's most fertile days., European Journal of Contraception and Reproductive Health Care, vol. 21 no. 4 (August, 2016), pp. 323-328 [doi]  [abs]
  19. Durante, D; Dunson, DB; Vogelstein, JT, Nonparametric Bayes Modeling of Populations of Networks, Journal of the American Statistical Association (August, 2016), pp. 1-15 [doi]  [abs]
  20. Zhu, H; Strawn, N; Dunson, DB, Bayesian graphical models for multivariate functional data, Journal of machine learning research : JMLR, vol. 17 (October, 2016), pp. 1-27  [abs]
  21. Sarkar, A; Dunson, DB, Bayesian Nonparametric Modeling of Higher Order Markov Chains, Journal of the American Statistical Association, vol. 111 no. 516 (October, 2016), pp. 1791-1803 [doi]
  22. Bhattacharya, A; Dunson, DB; Pati, D; Pillai, NS, Sub-optimality of some continuous shrinkage priors, Stochastic Processes and their Applications, vol. 126 no. 12 (December, 2016), pp. 3828-3842 [doi]
  23. Durante, D; Dunson, DB, Locally adaptive dynamic networks, The annals of applied statistics, vol. 10 no. 4 (December, 2016), pp. 2203-2232 [doi]
  24. Datta, J; Dunson, DB, Bayesian inference on quasi-sparse count data, Biometrika, vol. 103 no. 4 (December, 2016), pp. 971-983 [doi]
  25. Lin, L; Rao, V; Dunson, D, Bayesian nonparametric inference on the Stiefel manifold, Statistica Sinica (2017) [doi]
  26. 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]
  27. Johndrow, JE; Bhattacharya, A; Dunson, DB, Tensor decompositions and sparse log-linear models, Annals of statistics, vol. 45 no. 1 (February, 2017), pp. 1-38 [doi]
  28. 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]
  29. 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]
  30. 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]
  31. 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]
  32. 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]
  33. 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]
  34. 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]
  35. Zhu, B; Dunson, DB, Bayesian Functional Data Modeling for Heterogeneous Volatility, Bayesian Analysis, vol. 12 no. 2 (June, 2017), pp. 335-350 [doi]
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Durrett, Richard T.

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Fernandes de Oliveira, Goncalo M.

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Getz, Jayce R.

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Hahn, Heekyoung

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Hain, Richard   (search)

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Harer, John

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Herschlag, Gregory J.

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Hodel, Richard E.

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

Junge, Matthew S

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Kovalsky, Shahar

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Layton, Anita T.

  1. Xie, L; Layton, AT; Wang, N; Larson, PEZ; Zhang, JL; Lee, VS; Liu, C; Johnson, GA, Dynamic contrast-enhanced quantitative susceptibility mapping with ultrashort echo time MRI for evaluating renal function., American Journal of Physiology: Renal Physiology, vol. 310 no. 2 (2016), pp. F174-F182 [doi]  [abs]
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Levine, Adam S.

  1. LEVINE, ADAMSIMON, NONSURJECTIVE SATELLITE OPERATORS AND PIECEWISE-LINEAR CONCORDANCE, Forum of Mathematics, Sigma, vol. 4 (2016) [doi]
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Li, Lei

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Li, Yingzhou

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Liu, Jian-Guo

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Lu, Jianfeng

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  22. 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]
  23. 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]
  24. 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]
  25. Lu, J; Thicke, K, Cubic scaling algorithms for RPA correlation using interpolative separable density fitting, Journal of Computational Physics (September, 2017) [doi]
  26. 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]

Lu, Yulong

  1. Iglesias, M; Lu, Y; Stuart, A, A Bayesian level set method for geometric inverse problems, Interfaces and Free Boundaries, vol. 18 no. 2 (2016), pp. 181-217 [doi]
  2. 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. Ma, D, Period polynomial relations between formal double zeta values of odd weight, Mathematische Annalen, vol. 365 no. 1-2 (June, 2016), pp. 345-362 [doi]
  2. Ma, D, Inverse of some matrix related to double zeta values of odd weight, Journal of Number Theory, vol. 166 (September, 2016), pp. 166-180 [doi]

Maggioni, Mauro

  1. GL Davis and Mauro Maggioni and FJ Warner and FB Geshwind and AC Coppi and RA DeVerse and RR Coifman, Hyper-spectral Analysis of normal and malignant colon tissue microarray sections using a novel DMD system (2004) (Poster, Optical Imaging NIH workshop, to app. in proc..)
  2. Ronald R Coifman and Mauro Maggioni, Multiresolution Analysis associated to diffusion semigroups: construction and fast algorithms no. YALE/DCS/TR-1289 (2004)
  3. E Causevic and R~R Coifman and R Isenhart and A Jacquin and E~R John and M Maggioni and L~S Prichep and F~J Warner, QEEG-based classification with wavelet packets and microstate features for triage applications in the ER (2005)
  4. Maggioni, M; Minsker, S; Strawn, N, Multiscale dictionary learning: Non-asymptotic bounds and robustness, Journal of machine learning research : JMLR, vol. 17 (January, 2016), ISSN 1532-4435 (accepted for publication.) [arxiv:1401.5833]  [abs]
  5. Yin, R; Monson, E; Honig, E; Daubechies, I; Maggioni, M, Object recognition in art drawings: Transfer of a neural network, IEEE International Conference on Acoustics Speech and Signal Processing, vol. 2016-May (May, 2016), pp. 2299-2303, ISSN 1520-6149, ISBN 9781479999880 [doi]  [abs]
  6. Goetzmann, WN; Jones, PW; Maggioni, M; Walden, J, Beauty is in the bid of the beholder: An empirical basis for style, Research in Economics, vol. 70 no. 3 (September, 2016), pp. 388-402 [doi]
  7. Wang, Y; Chen, G; Maggioni, M, High-Dimensional Data Modeling Techniques for Detection of Chemical Plumes and Anomalies in Hyperspectral Images and Movies, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9 no. 9 (September, 2016), pp. 4316-4324, ISSN 1939-1404 [doi]  [abs]
  8. Liao, W; Maggioni, M; Vigogna, S, Learning adaptive multiscale approximations to data and functions near low-dimensional sets, 2016 IEEE Information Theory Workshop, ITW 2016 (October, 2016), pp. 226-230, ISBN 9781509010905 [doi]  [abs]
  9. 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]
  10. Crosskey, M; Maggioni, M, ATLAS: A Geometric Approach to Learning High-Dimensional Stochastic Systems Near Manifolds, Multiscale Modeling & Simulation, vol. 15 no. 1 (January, 2017), pp. 110-156 [doi]
  11. Bongini, M; Fornasier, M; Hansen, M; Maggioni, M, Inferring interaction rules from observations of evolutive systems I: The variational approach, Mathematical Models & Methods in Applied Sciences, vol. 27 no. 05 (May, 2017), pp. 909-951 [doi]
  12. Gerber, S; Maggioni, M, Multiscale strategies for computing optimal transport, Journal of machine learning research : JMLR, vol. 18 (August, 2017), pp. 1-32  [abs]
  13. 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. Hairer, M; Mattingly, J, The strong Feller property for singular stochastic PDEs (2016)  [abs]
  2. Tempkin, JOB; Koten, BV; Mattingly, JC; Dinner, AR; Weare, J, Trajectory stratification of stochastic dynamics (2016)  [abs]
  3. Glatt-Holtz, N; Mattingly, JC; Richards, G, On Unique Ergodicity in Nonlinear Stochastic Partial Differential Equations, Journal of Statistical Physics, vol. 166 no. 3-4 (February, 2017), pp. 618-649 [1512.04126v1], [doi]  [abs]
  4. Glatt-Holtz, NE; Herzog, DP; Mattingly, JC, Scaling and Saturation in Infinite-Dimensional Control Problems with Applications to Stochastic Partial Differential Equations (June, 2017)  [abs]
  5. Johndrow, JE; Mattingly, JC, Coupling and Decoupling to bound an approximating Markov Chain (July, 2017)  [abs]
  6. Bakhtin, Y; Hurth, T; Lawley, SD; Mattingly, JC, Smooth invariant densities for random switching on the torus (August, 2017)  [abs]
  7. Herschlag, G; Ravier, R; Mattingly, JC, Evaluating Partisan Gerrymandering in Wisconsin (September, 2017)  [abs]

Miller, Ezra

  1. Bendich, P; Marron, JS; Miller, E; Pieloch, A; Skwerer, S, Persistent Homology Analysis of Brain Artery Trees., The annals of applied statistics, vol. 10 no. 1 (January, 2016), pp. 198-218 [arXiv:1411.6652], [1411.6652v1]  [abs]
  2. Kahle, T; Miller, E; O’Neill, C, Irreducible decomposition of binomial ideals, Compositio Mathematica, vol. 152 no. 06 (June, 2016), pp. 1319-1332 [arXiv:1503.02607], [1503.02607], [doi]  [abs]
  3. Berenstein, A; Braverman, M; Miller, E; Retakh, V; Weitsman, J, Andrei Zelevinsky, 1953-2013, Advances in Mathematics, vol. 299 (August, 2016), pp. 601-604 [doi]
  4. Berenstein, A; Braverman, M; Miller, E; Retakh, V; Weitsman, J, Andrei Zelevinsky, 1953–2013, Advances in Mathematics, vol. 300 (September, 2016), pp. 1-4 [doi]

Motta, Francis C.

  1. with Francis C. Motta, ; Patrick D. Shipman, ; Bethany D. Springer, , Optimally Topologically Transitive Orbits in Discrete Dynamical Systems, American Mathematical Monthly, vol. 123 no. 2 (July, 2015), pp. 115-115 [doi]
  2. Burris, CS; Motta, FC; Shipman, PD, An Unoriented Variation on de Bruijn Sequences, Graphs and Combinatorics, vol. 33 no. 4 (July, 2017), pp. 845-858 [doi]
  3. 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]
  4. 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. Huang, B; Jarrett, NWD; Babu, S; Mukherjee, S; Yang, J, Cümülön: MatrixBased data analytics in the cloud with spot instances, in Proceedings of the VLDB Endowment, vol. 9 (January, 2016), pp. 156-167  [abs]
  2. Galinsky, KJ; Bhatia, G; Loh, P-R; Georgiev, S; Mukherjee, S; Patterson, NJ; Price, AL, Fast Principal-Component Analysis Reveals Convergent Evolution of ADH1B in Europe and East Asia., The American Journal of Human Genetics, vol. 98 no. 3 (March, 2016), pp. 456-472 [doi]  [abs]
  3. Zhao, S; Gao, C; Mukherjee, S; Engelhardt, BE, Bayesian group factor analysis with structured sparsity, Journal of machine learning research : JMLR, vol. 17 (April, 2016), pp. 1-47  [abs]
  4. Snyder-Mackler, N; Majoros, WH; Yuan, ML; Shaver, AO; Gordon, JB; Kopp, GH; Schlebusch, SA; Wall, JD; Alberts, SC; Mukherjee, S; Zhou, X; Tung, J, Efficient Genome-Wide Sequencing and Low-Coverage Pedigree Analysis from Noninvasively Collected Samples., Genetics, vol. 203 no. 2 (June, 2016), pp. 699-714 [doi]  [abs]
  5. Bobrowski, O; Mukherjee, S; Taylor, JE, Topological consistency via kernel estimation, Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability, vol. 23 no. 1 (February, 2017), pp. 288-328 [doi]
  6. 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]

Ng, Lenhard L.

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

Nolen, James H.

  1. Nolen, JH; Roquejoffre, J-M; Ryzhik, L, Refined long time asymptotics for Fisher-KPP fronts (2016)
  2. Hamel, F; Nolen, J; Roquejoffre, J-M; Ryzhik, L, The logarithmic delay of KPP fronts in a periodic medium, Journal of the European Mathematical Society, vol. 18 no. 3 (2015), pp. 465-505 [6173], [doi]
  3. Nolen, J, Normal approximation for the net flux through a random conductor, Stochastic Partial Differential Equations: Analysis and Computations, vol. 4 no. 3 (2015), pp. 439-476, ISSN 2194-0401 [2186], [doi]
  4. Gloria, A; Nolen, J, A Quantitative Central Limit Theorem for the Effective Conductance on the Discrete Torus, Communications on Pure & Applied Mathematics, vol. 69 no. 12 (2015), pp. 2304-2348, ISSN 0010-3640 [cpa.21614], [doi]
  5. 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]
  6. 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; Glasner, K, Instability and reorientation of block copolymer microstructure by imposed electric fields., Physical review. E, vol. 93 no. 5 (May, 2016), pp. 052504 [doi]  [abs]
  2. Glasner, K; Orizaga, S, Improving the accuracy of convexity splitting methods for gradient flow equations, Journal of Computational Physics, vol. 315 (June, 2016), pp. 52-64 [doi]
  3. Orizaga, S; 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 X. Dong, An Introduction to Mathematical Finance: Understanding and Building Financial Intuition, SUMAT (Winter, 2016), Springer, in preparation
  2. A. O. Petters and M. C. Werner, Gravitational Lensing and Black Holes (Spring, 2017), Springer, in preparation

Pfister, Henry

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

Pierce, Lillian B.

  1. Pierce, LB; Schindler, D; Wood, MM, Representations of integers by systems of three quadratic forms, Proceedings of the London Mathematical Society, vol. 3 no. 113 (2016), pp. 289-344, London Mathematical Society [doi]  [abs]
  2. Pierce, LB, Burgess bounds for multi-dimensional short mixed character sums, Journal of Number Theory, vol. 163 (June, 2016), pp. 172-210 [doi]
  3. Guo, S; Pierce, LB; Roos, J; Yung, P, Polynomial Carleson operators along monomial curves in the plane (2017), Springer Verlag  [abs]
  4. 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]
  5. Heath-Brown, DR; Pierce, LB, Simultaneous integer values of pairs of quadratic forms, Journal für die Reine und Angewandte Mathematik (Crelle's Journal), vol. 2017 no. 727 (January, 2017) [doi]
  6. Pierce, LB, The Vinogradov Mean Value Theorem [after Wooley, and Bourgain, Demeter and Guth], Asterisque (July, 2017), Centre National de la Recherche Scientifique  [abs]
  7. 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) (September, 2017)  [abs]
  8. 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]
  9. 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]

Plesser, Ronen

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

Randles, Amanda

  1. Gounley, J; Chaudhury, R; Vardhan, M; Driscoll, M; Pathangey, G; Winarta, K; Ryan, J; Frakes, D; Randles, A, Does the degree of coarctation of the aorta influence wall shear stress focal heterogeneity?, Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, vol. 2016 (August, 2016), pp. 3429-3432, ISBN 9781457702204 [doi]  [abs]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]

Reed, Michael C.

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

Robles, Colleen M

  1. Robles, C, Characteristic cohomology of the infinitesimal period relation, Asian Journal of Mathematics, vol. 20 no. 4 (2016), pp. 725-758 [arXiv:1310.8154], [doi]
  2. Robles, C, Classification of horizontal s, Compositio Mathematica, vol. 152 no. 05 (May, 2016), pp. 918-954 [doi]
  3. Kerr, M; Robles, C, Classification of smooth horizontal Schubert varieties, European Journal of Mathematics, vol. 3 no. 2 (June, 2017), pp. 289-310 [doi]
  4. 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]
  5. 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]

Saper, Leslie

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

Sapiro, Guillermo

  1. Carpenter, KLH; Sprechmann, P; Calderbank, R; Sapiro, G; Egger, HL, Quantifying Risk for Anxiety Disorders in Preschool Children: A Machine Learning Approach., PloS one, vol. 11 no. 11 (January, 2016), pp. e0165524 [doi]  [abs]
  2. Chang, Z; Qiu, Q; Sapiro, G, Synthesis-based low-cost gaze analysis, Communications in Computer and Information Science, vol. 618 (January, 2016), pp. 95-100, ISBN 9783319405414 [doi]  [abs]
  3. Lyzinski, V; Fishkind, DE; Fiori, M; Vogelstein, JT; Priebe, CE; Sapiro, G, Graph Matching: Relax at Your Own Risk., IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38 no. 1 (January, 2016), pp. 60-73 [doi]  [abs]
  4. Qiu, Q; Thompson, A; Calderbank, R; Sapiro, G, Data Representation Using the Weyl Transform, IEEE Transactions on Signal Processing, vol. 64 no. 7 (April, 2016), pp. 1844-1853 [doi]
  5. Tepper, M; Sapiro, G, Compressed Nonnegative Matrix Factorization Is Fast and Accurate, IEEE Transactions on Signal Processing, vol. 64 no. 9 (May, 2016), pp. 2269-2283 [doi]
  6. Tepper, M; Sapiro, G, A short-graph fourier transform via personalized pagerank vectors, IEEE International Conference on Acoustics Speech and Signal Processing, vol. 2016-May (May, 2016), pp. 4806-4810, ISBN 9781479999880 [doi]  [abs]
  7. Giryes, R; Sapiro, G; Bronstein, AM, Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?, IEEE Transactions on Signal Processing, vol. 64 no. 13 (July, 2016), pp. 3444-3457 [doi]
  8. Fiori, M; Muse, P; Tepper, M; Sapiro, G, Tell me where you are and i tell you where you are going: Estimation of dynamic mobility graphs, Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop, vol. 2016-September (September, 2016), ISBN 9781509021031 [doi]  [abs]
  9. Aguerrebere, C; Delbracio, M; Bartesaghi, A; Sapiro, G, Fundamental Limits in Multi-Image Alignment, IEEE Transactions on Signal Processing, vol. 64 no. 21 (November, 2016), pp. 5707-5722 [doi]
  10. Elhamifar, E; Sapiro, G; Sastry, SS, Dissimilarity-Based Sparse Subset Selection., IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38 no. 11 (November, 2016), pp. 2182-2197 [doi]  [abs]
  11. Lezama, J; Mukherjee, D; McNabb, RP; Sapiro, G; Kuo, AN; Farsiu, S, Segmentation guided registration of wide field-of-view retinal optical coherence tomography volumes., Biomedical Optics Express, vol. 7 no. 12 (December, 2016), pp. 4827-4846 [doi]  [abs]
  12. 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]
  13. Chen, J; Chang, Z; Qiu, Q; Li, X; Sapiro, G; Bronstein, A; Pietikäinen, M, RealSense = real heart rate: Illumination invariant heart rate estimation from videos, 2016 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016 (January, 2017), ISBN 9781467389105 [doi]  [abs]
  14. Campbell, K; Carpenter, KLH; Espinosa, S; Hashemi, J; Qiu, Q; Tepper, M; Calderbank, R; Sapiro, G; Egger, HL; Baker, JP; Dawson, G, Use of a Digital Modified Checklist for Autism in Toddlers - Revised with Follow-up to Improve Quality of Screening for Autism., The Journal of Pediatrics, vol. 183 (April, 2017), pp. 133-139.e1 [doi]  [abs]
  15. Simhal, AK; Aguerrebere, C; Collman, F; Vogelstein, JT; Micheva, KD; Weinberg, RJ; Smith, SJ; Sapiro, G, Probabilistic fluorescence-based synapse detection., PLoS computational biology, vol. 13 no. 4 (April, 2017), pp. e1005493 [doi]  [abs]
  16. Pisharady, PK; Sotiropoulos, SN; Duarte-Carvajalino, JM; Sapiro, G; Lenglet, C, Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning., NeuroImage (June, 2017) [doi]  [abs]
  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. 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]
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Smith, David A.

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

Stern, Mark A.

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

Tralie, Christopher

  1. Tralie, C, High Dimensional Geometry of Sliding Window Embeddings of Periodic Videos, Proceedings of the 32st International Symposium on Computational Geometry (SOCG) (June, 2016)  [abs]
  2. bendich, P; Gasparovic, E; harer, J; Tralie, C, Geometric Models for Musical Audio Data, Proceedings of the 32st International Symposium on Computational Geometry (SOCG) (June, 2016)
  3. Tralie, CJ, Early MFCC And HPCP Fusion for Robust Cover Song Identification, 18th International Society for Music Information Retrieval (ISMIR) (October, 2017)  [abs]
  4. 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. Best, A; Dynes, P; Edelsbrunner, X; McDonald, B; Miller, SJ; Tor, K; Turnage-Butterbaugh, C; Weinstein, M, Gaussian distribution of the number of summands in generalized Zeckendorf decomposition in small intervals, Integers, vol. 16 (2016), pp. 13 pages
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Venakides, Stephanos

  1. Komineas, S; Shipman, SP; Venakides, S, Lossless polariton solitons, Physica D: Nonlinear Phenomena, vol. 316 (February, 2016), pp. 43-56 [doi]  [abs]
  2. 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]

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)

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  2. Sanaei, P; Richardson, GW; Witelski, T; Cummings, LJ, Flow and fouling in a pleated membrane filter, Journal of Fluid Mechanics, vol. 795 (2016), pp. 36-59 [doi]
  3. Smolka, LB; McLaughlin, CK; Witelski, TP, Oil capture from a water surface by a falling sphere, Colloids and Surfaces A: Physicochemical and Engineering Aspects, vol. 497 (2016), pp. 126-132, ISSN 0927-7757 [doi]
  4. Ji, H; Witelski, TP, Finite-time thin film rupture driven by modified evaporative loss, Physica D: Nonlinear Phenomena, vol. 342 (March, 2017), pp. 1-15 [doi]
  5. Gao, Y; Ji, H; Liu, J-G; Witelski, TP, Global existence of solutions to a tear film model with locally elevated evaporation rates, Physica D: Nonlinear Phenomena, vol. 350 (July, 2017), pp. 13-25 [doi]

Wong, Jeffrey T

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

Wu, Hau-Tieng

  1. Herry, CL; Cortes, M; Wu, H-T; Durosier, LD; Cao, M; Burns, P; Desrochers, A; Fecteau, G; Seely, AJE; Frasch, MG, Temporal Patterns in Sheep Fetal Heart Rate Variability Correlate to Systemic Cytokine Inflammatory Response: A Methodological Exploration of Monitoring Potential Using Complex Signals Bioinformatics., PloS one, vol. 11 no. 4 (January, 2016), pp. e0153515 [doi]  [abs]
  2. Wu, H-T; Wu, H-K; Wang, C-L; Yang, Y-L; Wu, W-H; Tsai, T-H; Chang, H-H, Modeling the Pulse Signal by Wave-Shape Function and Analyzing by Synchrosqueezing Transform., PloS one, vol. 11 no. 6 (January, 2016), pp. e0157135 [doi]  [abs]
  3. El Karoui, N; Wu, H-T, Graph connection Laplacian methods can be made robust to noise, Annals of statistics, vol. 44 no. 1 (February, 2016), pp. 346-372 [doi]
  4. Daubechies, I; Wang, YG; Wu, H-T, ConceFT: concentration of frequency and time via a multitapered synchrosqueezed transform., Philosophical Transactions A, vol. 374 no. 2065 (April, 2016), pp. 20150193 [doi]  [abs]
  5. Chui, CK; Lin, Y-T; Wu, H-T, Real-time dynamics acquisition from irregular samples — With application to anesthesia evaluation, Analysis & Applications, vol. 14 no. 04 (July, 2016), pp. 537-590 [doi]
  6. O'Neal, WT; Wang, YG; Wu, H-T; Zhang, Z-M; Li, Y; Tereshchenko, LG; Estes, EH; Daubechies, I; Soliman, EZ, Electrocardiographic J Wave and Cardiovascular Outcomes in the General Population (from the Atherosclerosis Risk In Communities Study)., The American Journal of Cardiology, vol. 118 no. 6 (September, 2016), pp. 811-815 [doi]  [abs]
  7. Wu, H-T; Lewis, GF; Davila, MI; Daubechies, I; Porges, SW, Optimizing Estimates of Instantaneous Heart Rate from Pulse Wave Signals with the Synchrosqueezing Transform., Methods of information in medicine, vol. 55 no. 5 (October, 2016), pp. 463-472 [doi]  [abs]
  8. Lin, Y-T; Flandrin, P; Wu, H-T, When Interpolation-Induced Reflection Artifact Meets Time-Frequency Analysis., IEEE Transactions on Biomedical Engineering, vol. 63 no. 10 (October, 2016), pp. 2133-2141 [doi]  [abs]
  9. Marchesini, S; Tu, Y-C; Wu, H-T, Alternating projection, ptychographic imaging and phase synchronization, Applied and Computational Harmonic Analysis, vol. 41 no. 3 (November, 2016), pp. 815-851 [doi]
  10. Wu, C-H; Wang, T-D; Hsieh, C-H; Huang, S-H; Lin, J-W; Hsu, S-C; Wu, H-T; Wu, Y-M; Liu, T-M, Imaging Cytometry of Human Leukocytes with Third Harmonic Generation Microscopy, Scientific Reports, vol. 6 no. 1 (December, 2016) [doi]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. 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]
  16. Li, R; Frasch, MG; Wu, H-T, Efficient Fetal-Maternal ECG Signal Separation from Two Channel Maternal Abdominal ECG via Diffusion-Based Channel Selection, Frontiers in Physiology, vol. 8 (May, 2017) [doi]
  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. Malik, J; Reed, N; Wang, C-L; Wu, H-T, Single-lead f-wave extraction using diffusion geometry, Physiological Measurement, vol. 38 no. 7 (July, 2017), pp. 1310-1334 [doi]
  19. 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]
  20. 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]
  21. 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 (September, 2017) [doi]
  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 (September, 2017) [doi]
  23. 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 (October, 2017) [doi]
  24. 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 (December, 2017) [doi]
  25. 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]

Yang, Haizhao   (search)

  1. Lu, J; Wirth, B; Yang, H, Combining 2D synchrosqueezed wave packet transform with optimization for crystal image analysis, edited by Bhattacharya, K, Journal of the Mechanics and Physics of Solids, vol. 89 (January, 2015), pp. 194-210, Elsevier, ISSN 0022-5096 [repository], [doi]  [abs]
  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, Institute of Electrical and Electronics Engineers (IEEE), ISSN 1941-0042 [repository], [doi]  [abs]
  3. 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]
  4. Li, Y; Yang, H, Interpolative Butterfly Factorization, SIAM Journal on Scientific Computing, vol. 39 no. 2 (January, 2017), pp. A503-A531 [1605.03616], [doi]
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  7. 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. Chen, J; Liu, J-G; Zhou, Z, On a Schrödinger--Landau--Lifshitz System: Variational Structure and Numerical Methods, Multiscale Modeling & Simulation, vol. 14 no. 4 (January, 2016), pp. 1463-1487 [doi]
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  3. Ma, Z; Zhang, Y; Zhou, Z, An improved semi-Lagrangian time splitting spectral method for the semi-classical Schrödinger equation with vector potentials using NUFFT, Applied Numerical Mathematics, vol. 111 (January, 2017), pp. 144-159 [doi]
  4. 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]
  5. 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]

 

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