24 papers · ranked by Valyu relevance
Matthew Parks, Xin Yu, Yuxing Peng, Jayant Bhambhani + 4 more
Accurate and scalable prediction of protein–ligand interactions remains a central challenge in computational drug discovery, especially when the binding site is unknown (i.e., blind docking). We present a high-throughput, end-to-end algorithm for virtual screening that combines DiffDock, a diffusion-based generative…
Rakbin Sung, Seongmi Woo, Dongmin Shin, Junil Kim + 2 more
The expression values of genes can be tracked through RNA sequencing. The process of bulk RNA sequencing involves the calculation of the mean expression value of a given gene across multiple cells. This results in low resolution for expression dynamics from each individual cell. Single-cell RNA sequencing (scRNA-seq)…
Roumaissa Ghlib, Rania Bouhadouza, Faicel Hnaien
Designing compact and efficient quantum circuits that are compatible with Noisy Intermediate-Scale Quantum (NISQ) hardware remains a central challenge in quantum computing. Most existing optimization approaches rely on fidelity-based fitness functions that require computing the full unitary matrix of the circuit.…
Zhiyang Chen, Hailong Yao, Xia Yin
Recent work has shown that machine-learned predictions can provably improve the performance of classic algorithms. In this work, we propose the first minimum-cost network flow algorithm augmented with a dual prediction. Our method is based on a classic minimum-cost flow algorithm, namely εrelaxation. We provide time…
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…
Amir Hossein Salehi Shayegan
In this work, we present a solution to the critical limitation of qubit capacity in near-term quantum hardware by giving a hybrid framework that integrates the spectral element method (SEM) with distributed quantum computing. Using domain decomposition techniques, the additive and multiplicative Schwarz methods, the…
Eunjin Oh, Hyeonjun Shin
In this paper, we study the problem of constructing a $(1/\varepsilon)$-well-separated pair decomposition (WSPD) for a point set of size $n$ in the Massively Parallel Computation (MPC) model, where multiple machines work in parallel and communicate in synchronous rounds. We present an $O(1)$-round MPC algorithm that…
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…
Florian Ingels, Léa Vandamme, Mathilde Girard, Clément Agret + 2 more
Modern sequencing continues to drive explosive growth of nucleotide sequence archives, pushing MinHash-derived sketching methods to their practical scalability limits. State-of-the-art tools such as Mash, Dashing2, and Bindash2 provide compact sketches and accurate similarity estimates for large collections, yet…
Sumesh Kumar, Joseph Zambreno, Ashfaq Khokhar, Shoaib Akram + 1 more
Improving the speed and efficiency of database search algorithms that deduce peptides from mass spectrometry (MS) data has been an active area of research for more than three decades. The significance of the need for faster database search methods has rapidly increased due to the growing interest in studying non-model…
Joshua Gould, Jose Sergio Hleap, Ping Wu, Takamasa Kudo + 12 more
Optical pooled screens (OPS) link pooled genetic perturbations to high-dimensional image-based phenotypes at scale, but their widespread adoption is hindered by computational bottlenecks in processing terabyte-scale, multimodal image data. We present SCALLOPS, a unified, modular, and cloud-native computational…
Xiao Fu, Yuanyuan Xu, Claudionor Ribeiro da Silva
The recent surge in digital agriculture has generated an emerging demand for scalable, resource-efficient solutions capable of handling both close-range images of agricultural products and high-scale remote-sensing images. Deep learning models have high accuracy, but they are expensive and lack the dynamism to be…
Authors not listed
This work establishes theoretical foundations for hierarchical quantum-classical algorithm design, where complex problems are decomposed across multiple spatial, temporal, or organizational scales with quantum and classical computation assigned to appropriate levels. We develop a mathematical framework that…
Bahman Arasteh, Seyed Salar Sefati, Huseyin Kusetogullari, Farzad Kiani + 3 more
Efficient task scheduling remains a key challenge in High-Performance Computing and Internet of Things (IoT) systems, where the sequential execution of nested loops often limits parallelism. This paper proposes a hybrid approach that dynamically parallelizes nested loops in heterogeneous IoT environments. The suggested…
William Won, Kartik Lakhotia, Madhu Kumar, Sudarshan Srinivasan + 1 more
Distributed machine learning has become increasingly important due to the massive scale of large-scale generative models. Both model parameters and data are distributed across many compute devices, which requires frequent collective communications to synchronize activations and parameter updates. Such collective…
Liangyan Li, Yangyi Liu, Yimo Ning, Stefano Rini + 2 more
We introduce the symmetric co-skewness moment (SCKM)-a third-order informational dissimilarity metric that consistently outperforms state-of-the-art client-selection heuristics in federated learning (FL) under heterogeneous data. Unlike similarity-driven schemes, SCKM minimizes redundancy by favoring clients with…
Lin, Honghao, Song, Zhao + 6 more
In the distributed monitoring model, a data stream over a universe of size n is distributed over k servers, who must continuously provide certain statistics of the overall dataset, while minimizing communication with a central coordinator. In such settings, the ability to efficiently collect a random sample from the…
Michael T. Goodrich, Vinesh Sridhar
Embedded systems and Internet of Things (IoT) applications motivate in-place parallel algorithms, which avoid allocating additional shared memory past the input. Work by Gu, Obeya, and Shun [APOCS '21] defines a family of PIP (parallel in-place) models and parallel algorithms that eschew auxiliary memory at high…
Thinh Nguyen Quang, Kosuke Matsuyama, Keisuke Shimizu, Hiroki Sugano + 9 more
This study proposes a new approach to optimizing the routing of Automated Guided Vehicles (AGVs) in large-scale logistics warehouses using Quantum Annealing. As logistics operations grow, efficient AGV routing becomes critical for ensuring safety, reliability, and throughput, particularly in high-density environments.…
Authors not listed
Free radical copolymerization, a crucial industrial technique for synthesizing copolymers from a diverse array of monomers with ease, has played a significant role in advancing human society. However, the journey from small-scale research and development to manufacturing has been hindered by a lack of research into…
Authors not listed
Self-driving laboratories (SDLs) promise accelerated scientific discovery and product development by closing the loop between robotic execution and AI/ML-driven decision making. In practice, however, SDL orchestration remains fragmented; workflows are typically encoded as laboratory-specific scripts or bespoke…
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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…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…