21 papers · ranked by Valyu relevance
Abdurakhmon Sadiev, Laurent Condat, Peter Richtárik
Composite optimization problems, formulated as the minimization of three functions, are ubiquitous in large-scale machine learning and signal processing. While state-of-the-art splitting methods such as Condat-Vũ (CV) [Condat, 2013, Vũ, 2013], Primal-Dual Davis-Yin (PDDY) [Salim et al., 2022b], and Primal-Dual Twice…
Bin Zhou, Liusheng Hou, Xingju Cai, Hailin Sun
In this paper, we introduce the G¨uler-type acceleration technique and utilize it to propose three acceleration algorithms: the G¨uler-type accelerated proximal gradient method (GPGM), the G¨uler-type accelerated linearized augmented Lagrangian method (GLALM) and the G¨uler-type accelerated linearized alternating…
Lingyi Chen, Haoran Tang, Hao Wu, Huihui Wu + 3 more
Numerical computation of the rate-distortion (RD) function is a key problem in RD theory. Thus far, efficient algorithms have been well studied for discrete sources, but for continuous sources, there is still lack of a rigorously developed solution. In this article, an integrated approach is conducted that bridges RD…
Anders Pitman, Cathy Yang, Yi Qiao
Next-generation sequencing now produces whole-genome data in hours, but downstream variant calling remains a multi-hour to multi-day bottleneck that excludes genomic analysis from time-critical clinical settings. GPU acceleration offers a natural path forward — variant calling is inherently parallelizable across…
Felix Kallenborn, Fawaz Dabbaghie, Martin Steinegger, Bertil Schmidt
Background The continually increasing volume of sequence data results in a growing demand for fast implementations of core algorithms. Computation of pairwise alignments based on dynamic programming is an important part in many bioinformatics pipelines and a major contributor to overall runtime due to the associated…
Mohammed Alaa Ala’anzy, Nurdaulet Tolendi, Baizhan Baubek, Abdulmohsen Algarni + 1 more
Sorting can be approached in two main ways: sequentially and in parallel. In sequential sorting, data is processed in a single-threaded manner, which can be slow for large datasets. However, parallel sorting divides the task across multiple processing units, enabling faster results by processing data simultaneously.…
Roberto Carrasco, Enzo Meneses, Héctor Ferrada, Cristóbal A. Navarro + 1 more
In recent years, applications such as real-time simulations, autonomous systems, and video games increasingly demand the processing of complex geometric models under stringent time constraints. Traditional geometric algorithms, including the convex hull, are subject to these challenges. A common approach to improve…
Joan Saurina-i-Ricos, Daniel Mas Montserrat, Alexander G. Ioannidis
Estimating genetic clusters from sequencing data is a fundamental task in population and medical genetics, enabling demographic inference and adjustment for population structure in association studies. ADMIXTURE, a widely used model-based clustering method, employs an accelerated Expectation–Maximization (EM) algorithm…
Zhejian Yu
Fast simulation of next-generation sequencing (NGS) data is vital for software development and benchmarking. Here we describe art_modern, an accelerated ART simulator that can simulate various NGS data. We accelerated ART using updated sampling algorithms, single-instruction multiple-data (SIMD) instruction-set…
Hector Prats, Weitian Li, Michail Stamatakis
Framework for Accelerating Graph-Theoretical Kinetic Monte Carlo Simulations Authors: Hector Prats, Weitian Li, Michail Stamatakis Kinetic Monte Carlo (KMC) simulations are a powerful tool for investigating catalytic reaction mechanisms, yet they often become intractably slow when certain fast, quasi-equilibrated…
Hassan Nassar, Rafik Youssef, Lars Bauer, Jörg Henkel
As the need for more computing power grows, traditional methods are hitting limits. To boost performance, we're expanding Central Processing Unit (CPU) capabilities and using specialized hardware accelerators. For example, mobile devices usually have cameras, video encoding, and audio accelerators. To perform the…
Agarwal, Aarush, Ruihua He, J. Kieseler + 2 more
We introduce FastGraph, a novel GPU-optimized k-nearest neighbor algorithm specifically designed to accelerate graph construction in low-dimensional spaces (2–10 dimensions), critical for high-performance graph neural networks. Our method employs a GPU-resident, bin-partitioned approach with full gradient-flow support…
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Data-driven approaches offer great potential for accelerating ab initio electronic structure calculations of molecules and materials but their transferability is often limited due to the vast amount of data needed for training, including when addressing the need to fine-tune universal models for each specific system to…
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This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
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The complete active space self-consistent field (CASSCF) method is essential for describing complex photochemical processes, but its application in ab initio molecular dynamics is often limited by the computational cost associated with four-center two-electron repulsion integrals (ERIs). We present the first…
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We describe a collaborative research project spanning the disciplines of quantum hardware, quantum algorithms, conventional computational chemistry, synthetic medicinal chemistry and life sciences. Our project seeks to demonstrate an impact of quantum computing on human health. It is one of several funded by Wellcome…
Joost de Jong, Mark Siertsema, Cemre Baykan, Elkan Akyürek
Humans and non-human animals adaptively boost their encoding speed when they expect limited sensory exposure time, so that they can capture essential information before it is gone. However, it is unclear how the brain implements this crucial adaptation. Using multivariate pattern analysis of human EEG data, we found…
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The Hidden Subgroup Problem (HSP) unifies several landmark quantum algorithms, yet systematic exploration of its variants and modern applications has slowed. This paper revives HSP-based algorithm design by examining new group structures with direct relevance to post-quantum cryptography, lattice problems, and…
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Mesoporous adsorbent materials offer a large volumetric capacity; however, cyclic adsorption/desorption processes in these systems often suffer from hysteresis and may require a significant pressure swing to access this capacity. To mitigate hysteresis, a proposed strategy is to include nucleation sites on the walls of…
Julia Golonka, Filip Krużel, Rosario Schiano Lo Moriello
Resource-constrained sensor nodes in Internet-of-Things (IoT) and embedded sensing applications frequently rely on low-cost microcontrollers, where even basic algorithmic choices directly impact latency, energy consumption, and memory footprint. This study evaluates six sorting algorithms-Bubble Sort, Insertion Sort…
Mahmudur Rahman Hera, David Koslicki, Conrado Martínez
With the surge in sequencing data generated from an ever-expanding range of biological studies, designing scalable computational techniques has become essential. One effective strategy to enable large-scale computation is to split long DNA or protein sequences into k-mers, and summarize large k-mer sets into compact…