Search Results - sparse matrix algorithm for network evolution by testing~

A Deep Learning-Driven Fast Scanning Method for Micro-Computed Tomography Experiments on CMCs by Zhu, R.Q., Niu, G.H., Qu, Z.L., Wang, P.D., Fang, D.N.

“…Background In-situ micro-computed tomography (µCT) technology is an attractive approach to investigate the evolution process of damage inside ceramic matrix composites (CMCs…”
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Evolving an Improved Algorithm for Envelope Reduction Using a Hyper-Heuristic Approach by Koohestani, Behrooz, Poli, Riccardo

“…Large sparse symmetric matrices are typical characteristics of the linear systems found in various scientific and engineering disciplines, such as fluid mechanics, structural engineering, finite…”
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Fitting a geometric graph to a protein–protein interaction network by Higham, Desmond J., Rašajski, Marija, Pržulj, Nataša

“… Such a model would provide insights into the interplay between network structure and biological function as well as into evolution. Also, network (graph…”
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Searching a Bitstream in Linear Time for the Longest Substring of Any Given Density by Burton, Benjamin A.

“… (i) reformulating the problem as a constrained walk through a sparse matrix, and then (ii) developing a data structure for this sparse matrix that allows us to perform each step of the walk in amortised constant time…”
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incomplete enumeration algorithm for an exact test of Hardy-Weinberg proportions with multiple alleles by Maurer, H. P, Melchinger, A. E, Frisch, M

“…Testing of Hardy-Weinberg proportions (HWP) with asymptotic goodness-of-fit tests is problematic when the contingency table of observed genotype counts has sparse cells or the sample size is low, and exact procedures are to be…”
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Inference of computational models of tendon networks via sparse experimentation by Kurse, Manish Umesh

“…This dissertation presents novel computational methods to infer accurate functional models of musculoskeletal systems from minimal experimental data focussing on the tendon networks of the human fingers as an example…”
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Learning DFA: evolution versus evidence driven state merging by Lucas, S.M., Reynolds, T.J.

“… This paper presents a new method for evolving DFAs, where only the transition matrix is evolved, and the state labels are chosen to optimize the fit between final states and training set labels…”
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Stochastic Models in Reliability Engineering by Cui, Lirong, Frenkel, Ilia, Lisnianski, Anatoly

“…This book is a collective work by many leading scientists, analysts, mathematicians, and engineers who have been working at the front end of reliability science and engineering…”
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