Selected Online Publications

Monograph:    Probability Distributions in Quantum Statistical Mechanics (PDF)

Papers:  (Available as .pdf unless otherwise specified)

Software For Simulation of Wavelet Matrix Operations and Quantum Transforms (with Zhiguo Zhang)

BowSaw: inferring higher-order trait interactions associated with complex biological phenotypes (with D. Dimucci, and D. Segre), Frontiers in Molecular Biosciences, 17 June, 2021.  DOI: 10.3389/fmolb.2021.663532. [k10] https://doi.org/10.3389/fmolb.2021.663532

Analytic regularity and stochastic collocation of high dimensional Newton iterates, (with J. Castrillon), Advances in Computational Mathematics 46 (May 2020) 3, p. 42, doi:10.1007/s10444-020-09791-1, 2020.

Interpolatory filter banks and interpolatory wavelet packets (with Z. Zhang),  Journal of Computational and Applied Mathematics  374, (15 August 2020), 112755.  Doi:  10.1016/j.cam.2020.112755, 2020 . https://www.sciencedirect.com/science/article/pii/S0377042720300467

 

General methods for continuum limits of the quantum walk (with M. Manighalam), Quantum Information Processing 19, 379 (October 2020) doi: https://doi.org/10.1007/s11128-020-02880-6;    https://link.springer.com/article/10.1007/s11128-020-02880-6

 

Parallelization analysis of space-time bundles and applications in particle physics (with A. Levichev). In Proceedings of KONT-2019, Sobolev Institute of Mathematics, 385-392. Novosibirsk, 2019.  [d9] (2019)

 

Bifurcation Curves of Two-Dimensional Quantum Walks (with P. Kuklinski).  arXiv:1905.00057v1  [quant-ph]  30 Apr 2019 (https://arxiv.org/pdf/1905.00057.pdf)  [p11], 2020

 

The Marr conjecture and uniqueness of wavelet transforms (with B. Allen), Annals of Mathematical Sciences and Applications 3 (2), 473-528.  DOI: http://dx.doi.org/10.4310/AMSA.2018.v3.n2.a4  [s3] (2018) https://www.intlpress.com/site/pub/pages/journals/items/amsa/content/vols/0003/0002/a004/index.html

 

Absorption probabilities of quantum walks (with P. Kuklinski), Quantum Information Processing DOI: 17:263; https://doi.org/10.1007/s11128-018-2017-4 ,  October, 2018.

 

Transcription Factor-DNA Binding Via Machine Learning Ensembles (with D. DeLisi and Y. Fan), arXiv:1805.03771 [stat.ML] (https://arxiv.org/pdf/1805.03771.pdf).  [f7] (2018)

 

Wavelet sampling and generalization in neural networks (with Z. Zhang), Neurocomputing, 11-May, DOI: 10.1016/j.neucom.2017.04.054, May 11, 2017.

 

Use of the geometric mean as a statistic for the scale of coupled Gaussian distributions  (with K. Nelson and S. Umarov),  Physica A: Statistical Mechanics and its Applications 515, 1 February 2019, 248-257.

 

An application of spectral regularization to machine learning and cancer classification (with L. Raphael), Excursions in Harmonic Analysis 5, Norbert Wiener Center, 129, 2017.

 

Functional analytic regularization in machine learning (with Y. Fan and L. Raphael), preprint, 2018.

 

Dynamics of Lifespan Evolution (with R. Gryder), Proceedings of the First  International Conference on Complex Systems, Y. Bar-Yam, Ed. Cambridge,  MA, 2018.

 

Optimizing decision tree structures for spectral histopathology (SHP) (with X. Mu, S. Remiszewski, A. Ergin and M Diem), Analyst, DOI:10.1039/c8an01303a, https://pubs.rsc.org/en/content/articlehtml/2018/an/c8an01303a   [d10] (2018)

 

Differentiation and integration of machine learning feature vectors  (with X. Mu and A. Pavel), Machine Learning and Applications 16, IEEE, Washington, DOI: 10.1109/ICMLA.2016.010, 2016

 

Machine learning reveals missing edges and putative interaction mechanisms in microbial ecosystem networks (with D. Dimucci and D. Segre), MSystems,  September/October 2018 Volume 3 Issue 5 e00181.  18https://msystems.asm.org/content/3/5/e00181-18 .  https://doi.org/10.1128/mSystems.00181-18.  [h10]

 

On the average uncertainty for systems with nonlinear coupling (with K. Nelson and S. Umarov), Physica A, Oct. 27, 2016, DOI:  10.1016/j.physa.2016.09.046, 2016.

 

Real-time simulation of dissipation-driven quantum systems (with D. Banerjee, F. Hebenstreit, F.J. Jiang, U-J. Wiese) Proceedings of the International Conference on Lattice Field Theory, http://arxiv.org/abs/1510.08899 , 2015.

 

A Method for Interpolation Wavelet Construction Using Orthogonal Scaling Functions (with Z. Zhang), SPA 2016, Warsaw, 20-35, 2016.

Unique Recovery from Edge Information (with B. Allen), Sampling Theory and Applications 2015, IEEE, Washington, DC., 2015.

On Relating Interpolation Wavelets to Interpolation Scaling Functions in Multiresolution Analyses (with Z. Zhang),  Circuits, Systems & Signal Processing  34, June 2015, 1947-1976. DOI 10.1007/s00034-014-9937-8, 2015.

Real-time simulation of large open quantum spin systems driven by dissipation (with D. Banerjee, F.-J. Jiang and U.-J. Wiese) Phys. Rev. B (Rapid Communications) 90, 241104(R) Published 2 December. DOI: 10.1103/PhysRevB.90.241104, 2014.

Class discovery via bimodal feature selection in unsupervised settings (with J. Curtis), Machine Learning and Applications 14, IEEE, Washington, 348-351. [g9] (2015)

 

On the probabilistic continuous complexity conjecture  Posted 2012 arXiv:1212.1263

Ensemble machine methods for analysis of transcription factor and DNA interactions (with Y. Fan and C. DeLisi), preprint 2014.

Computational methods for analysis of transcriptional networks (with Y. Fan and C. DeLisi), Springer Handbook of Bioinformatics, Springer-Verlag, Berlin, 327-354, 2014.

Current trends in genome-wide association studies (with T. Yang and C. DeLisi), in Data Mining for Systems Biology, Hiroshi Mamitsuka and Minoru Kanehisa, eds., Springer-Verlag, 2012.

Pathway-based classification of cancer subtypes (with S. Kim and C. DeLisi), Biology Direct (2012), 7:21 doi:10.1186/1745-6150-7-21; Published: 3 July 2012.

On some integrated approaches to inference (with L. Plaskota), technical report (2011).

Empirical normalization for quadratic discriminant analysis and classifying cancer subtypes (with N. Nikolaev), Machine Learning and Applications 10 (2011), IEEE, Washington, 374-379.

Top scoring pairs for feature selection in machine learning with applications to cancer outcome prediction  (with P. Shi, S. Ray and Q. Zhu), BMC Bioinformatics, 12:375 (2011). DOI:10.1186/1471-2105-12-375

Combinations of newly confirmed glioma-associated loci link regions on chromosomes 1 and 9 to increased disease risk (with T. Yang and C. DeLisi), BMC Medical Genomics 4:63 doi:10.1186/1755-8794-4-63 (2011).

Regularization techniques for machine learning on graphs and networks with biological applications (with Y. Fan, S. Kim, L. Raphael, and C. DeLisi, Communications in Mathematical Analysis 8 (3; Special Volume in Honor of Peter Lax) (2010), 136-145.

Smoothing gene expression using biological networks (with Y. Fan, S. Kim,  and C. DeLisi), Machine Learning and Applications 9, IEEE, Washington DC. (2010)

A new phylogenetic diversity measure generalizing the Shannon index (with B. Allen and Y. Bar Yam,  American Naturalist 174 (2009),236-243.

 

Ensemble machine methods for DNA binding (with Y. Fan, and C. DeLisi), Machine Learning and Applications 7,  M. Wani, et al., eds.  IEEE, Washington (2008),709-716.  Algorithm available here.

 

Regulatory analysis for exploring human disease progression (with D. Holloway and C. DeLisi), Biology Direct 3:24, 2008. Algorithm available here.

 

Building transcription factor classifiers and discovering relevant biological features,  (with D. Holloway and C. DeLisi), BiologyDirect 3:22, 30 May 2008. Algorithm available here.

 

SVMMotif:  A machine learning motif algorithm (with Y. Fan, D.Holloway and C. DeLisi), International Conference on Machine Learning and Applications 6, 573-580, IEEE, Washington, 2007. Algorithm available here.

 

Learning methods for DNA binding in computational biology (with D. Holloway, et al.) International Joint Conference on Neural Networks,  20, IEEE, Los Alamitos 1605, 2007.

 

Machine learning for regulatory analysis and transcription factor target prediction in yeast (with D. Holloway and C. DeLisi), Systems and Synthetic Biology 1 (2006), 25-46.                                                                                                                                                                                                                                            

 

Approximating functions in reproducing kernel Hilbert spaces via statistical learning theory (with L. Raphael), in Splines and Wavelets, G. Chen and M.J. Lai, eds, (2006) 270-286

Machine learning methods for transcription data integration (with D. Holloway and C.DeLisi),IBM Journal of Research and Development 50(2006), 631-644 (Abstract only - Journal link is here)

Information-based nonlinear approximation:  An average case setting (with L. Plaskota),  J. Complexity 21 (2005),211-228.

Extending Girosi's approximation estimates for functions in Sobolev spaces via statistical learning theory (with L. Raphael and D. Williams), J. Analysis and Applications 3 No. 2 (2005), 67-90.

Statistical likelihood representations of prior knowledge in machine learning (with L, Plaskota and A. Przybyszewski), Artificial Intelligence and Applications, M.H. Hamza, Ed., Innsbruck (2005), 467-472.

Integrating genomic data to predict transcription factor binding  (with D. Holloway and C. DeLisi), Genome Informatics 16 (2005), 83-94.

Machine learning and statistical MAP methods (with L. Plaskota and A. Przybyszewski), Intelligent Information Processing, Springer, Berlin (2005), 441-445.

Complexity of predictive neural networks (with L. Plaskota) Proceedings of International Conference on Complexity,  Y. Bar-Yam, Ed., Cambridge, MA (2003)

Sup-norm convergence rates of wavelet expansions in Besov Spaces (with L. Raphael), in Applicable Mathematics (2002), 193-203.

Pointwise wavelet convergence in Besov and uniformly local Sobolev Spaces  (with L. Raphael), J. Contemporary Math. Analysis 36 (2002), 51-68.

Complexity of neural network approximation with limited information:  a worst-case approach (with L. Plaskota), J. Complexity 17 (2001), 345-365.

Convergence rates of multiscale and wavelet expansions (with L. Raphael), in Wavelet Transforms and Time-FrequencySignal Analysis, American Mathematical Society CBMS Volume, L.Debnath, Ed. (2001), 37-65.

A characterization of wavelet convergence in Sobolev spaces (with L. Raphael), Applicable Analysis 78 (2001), 271-324.

Complexity of regularization RBF networks (with L. Plaskota), in Proceedings of International Joint Congress on Neural Networks,INNS, Washington (2001), 342-346.

Review of Complexity and Information, (by J.F. Traub and A.G. Werschulz), Bull. Amer. Math Soc. 37 (2000), 199-204.

Information complexity of neural networks (with L. Plaskota), Neural Networks 13 (2000),365-376.

Oscillation criteria for delay equations (with Y.Sficas and I. P. Stavroulakis), Proc. Am. Math. Soc. 128 (2000), 2989-2997.

Neural networks, radial basis functions, and complexity (with L. Plaskota), Proceedings of Bialowieza Conference on Statistical Physics, 1997, 122-145.

Exact smoothing properties of Schrodinger semigroups (with A. Gulisashvili), American J. Math. 118 (1996), 1215-1248

Pointwise convergence of wavelet expansions (with S. Kelly and L. Raphael), Bull. Amer. Math. Soc. 30 (1994), 87-94

Local convergence of wavelet expansions (with S. Kelly and L. Raphael), J. Functional Anal. 126 (1994), 102-138

 

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