12 papers · ranked by Valyu relevance
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Recent advances in machine learning force fields (MLFF) have significantly extended the reach of atomistic simulations. Continuous progress in this field requires reliable reference datasets, accurate MLFF architectures, and efficient active learning strategies to enable robust modeling of complex molecular and…
John Kruper, Ariel Rokem
Tractography based on diffusion-weighted MRI (dMRI) is the predominant in vivo method for mapping the brain’s white matter. However, it is also one of the most computationally demanding steps in neuroimaging data analysis-requiring the generation and filtering of millions of streamlines per subject. Over the past…
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.…
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…
Kecong Tang, Ardalan Naseri, Degui Zhi, Shaojie Zhang + 1 more
At each site indexed by k, the algorithm maintains a prefix array $P_{k}$, which encodes the co-lexicographic ordering of haplotypes based on their reversed prefixes up to the current site. Formally, let ${x_{i}}$, where $i=1$ to M, denote M haplotype sequences over N sites. At site k, the prefix array $P_{k}$ is…
Mohammad Abdur Rob, Md. Zakir Hossen, Md. Kamal Hossen, Md. Mithun Ali + 2 more
Sorting algorithms play a crucial role in computing, but most are designed with rigid structure that are only efficient under certain conditions. Although some sorting algorithms perform well in some circumstances, they do not perform well on some resistant platforms. This study introduces Wall-L Merge Sort, which…
Kevin Garner, Polykarpos Thomadakis, Nikos Chrisochoides
This paper presents a distributed memory method for anisotropic mesh adaptation that is designed to avoid the use of collective communication and global synchronization techniques. In the presented method, meshing functionality is separated from performance aspects by utilizing a separate entity for each - a multicore…
Renée A. Sirbu, Luciano Floridi
Biological computing (biocomputing) leverages biologically derived materials and processes, such as DNA and protein synthesis, to perform computational tasks. Biocomputing offers significant advantages over traditional silicon-based systems in terms of scalability, energy efficiency, computational flexibility, and…
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Background: Pharmaceutical batch scheduling in multi-reactor configurations presents complex optimization challenges under operational uncertainty, yet limited research addresses how parallel processing capacity affects heuristic performance and predictive modeling. Objectives: This study investigated scheduling…
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Early on in the emergence of virtual high-throughput screening (VHTS), it was recognized that for validation to be robust and reliable, decoys should match actives as closely as possible in as many aspects as possible. This has given rise to several generations of validation sets that address previously reported…
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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…
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Computational chemistry has entered a new era where machine learning (ML) models—particularly graph neural networks and machine learning force fields—routinely deliver quantum mechanical accuracy at classical speeds, scaling to millions of atoms and reshaping workflows in drug discovery, catalysis, and materials…