Search · four archives
Search · four archives
6 papers · ranked by Valyu relevance
Jingwen Ren, Mark JP Chaisson
It is computationally challenging to detect variation by aligning long reads from single-molecule sequencing (SMS) instruments, or megabase-scale contigs from SMS assemblies. One approach to efficiently align long sequences is sparse dynamic programming (SDP), where exact matches are found between the sequence and the…
Qi Wang, Zhaoqian Liu, Bo Yan, Wen-Chi Chou + 3 more
Alternative transcription units (ATUs) are dynamically encoded under different conditions or environmental stimuli in bacterial genomes, and genome-scale identification of ATUs is essential for studying the emergence of human diseases caused by bacterial organisms. However, it is unrealistic to identify all ATUs using…
Abbas Kazemipour, Behtash Babadi, Min Wu, Kaspar Podgorski + 1 more
We consider the problem of optimizing general convex objective functions with nonnegativity constraints. Using the Karush-Kuhn-Tucker (KKT) conditions for the nonnegativity constraints we will derive fast multiplicative update rules for several problems of interest in signal processing, including non-negative…
Thomas C. Keaty, Paul A. Jensen
Gapsplit generates random samples from convex and non-convex constraint-based models. Gapsplit targets under-sampled regions of the solution space for uniform coverage. Python and Matlab source code are freely available at http://jensenlab.net/tools. pjens@illinois.edu
Peter L. Bartlett, Chris Junchi Li, Jingfeng Wu, Bin Yu
In the field of optimization, developing accelerated methods for solving minimax and fixed-point problems remains a fundamental challenge. This paper presents a novel family of dual accelerated algorithms that achieve optimal convergence rates for both minimax and fixed-point problems. By exploring new anchoring…
Hiroshi Higashi
Human perception is not a series of isolated snapshots; our recent past continuously shapes what we currently see, a phenomenon known as serial dependence. While convolutional neural networks (CNNs) excel as models of vision, they are typically static and fail to capture such dynamic, history-dependent effects. Here…