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arXiv 2024年

作者： Jeon, Minkyu Raghu, Rishwanth Astore, Miro Woollard, Geoffrey Feathers, Ryan Kaz, Alkin Hanson, Sonya M. Cossio, Pilar Zhong, Ellen D. Department of Computer Science Princeton University PrincetonNJ United States Center for Computational Biology 3Center for Computational Mathematics Flatiron Institute New YorkNY United States Department of Computer Science University of British Columbia VancouverBC Canada

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining high-resolution 3D biomolecular structures from imaging data. Its unique ability to capture structural variability has spurred the development of heterogeneous reconstruction algorithms that can infer distributions of 3D structures from noisy, unlabeled imaging data. Despite the growing number of advanced methods, progress in the field is hindered by the lack of standardized benchmarks with ground truth information and reliable validation metrics. Here, we introduce CryoBench, a suite of datasets, metrics, and benchmarks for heterogeneous reconstruction in cryo-EM. CryoBench includes five datasets representing different sources of heterogeneity and degrees of difficulty. These include conformational heterogeneity generated from designed motions of antibody complexes or sampled from a molecular dynamics simulation, as well as compositional heterogeneity from mixtures of ribosome assembly states or 100 common complexes present in cells. We then analyze state-of-the-art heterogeneous reconstruction tools, including neural and non-neural methods, assess their sensitivity to noise, and propose new metrics for quantitative evaluation. We hope that CryoBench will be a foundational resource for accelerating algorithmic development and evaluation in the cryo-EM and machine learning communities. Project page: https://***. © 2024, CC BY.

关键词： High resolution electron microscopy

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A Scalable Real-Time Data Assimilation Framework for Predicting Turbulent Atmosphere Dynamics

A Scalable Real-Time Data Assimilation Framework for Predict...

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2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Yin, Junqi Liang, Siming Liu, Siyan Bao, Feng Chipilski, Hristo G. Lu, Dan Zhang, Guannan Oak Ridge National Laboratory National Center for Computational Science Oak RidgeTN United States Florida State University Department of Mathematics TallahasseeFL United States Oak Ridge National Laboratory Computational Science and Engineering Division Oak RidgeTN United States Florida State University Department of Scientific Computing TallahasseeFL United States Oak Ridge National Laboratory Computer Science and Mathematics Division Oak RidgeTN United States

ISBN: (纸本)9798350355543

AI-based foundation models like FourCastNet, GraphCast, ClimaX, and Pangu-Weather are revolutionizing weather and climate predictions but are not yet ready for operational use. Their limitation lies in the absence of a data assimilation system to incorporate real-time Earth system observations, crucial for accurately forecasting events like tropical cyclones and atmospheric rivers. To overcome these obstacles, we introduce a generic real-time data assimilation framework and demonstrate its end-to-end performance on the Frontier supercomputer. This framework comprises two primary modules: an ensemble score filter (EnSF), which significantly outperforms the state-of-the-art data assimilation method, e.g., the Local Ensemble Transform Kalman Filter (LETKF);and a vision transformer-based surrogate capable of real-time adaptation through the integration of observational data. We demonstrate both the strong and weak scaling of our framework up to 1024 GPUs on the Exascale supercomputer, Frontier. Our results not only illustrate the framework's exceptional scalability on high-performance computing systems, but also demonstrate the importance of supercomputers in real-time data assimilation for weather and climate predictions. Even though the proposed framework is tested only on a benchmark surface quasi-geostrophic (SQG) turbulence system, it has the potential to be combined with existing AI-based foundation models, making it suitable for future operational implementations. © 2024 IEEE.

关键词： data assimilation Diffusion models foundation models generative AI high-performance computing uncertainty quantification

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A PEPS Plugin for TNQVM 4

A PEPS Plugin for TNQVM

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4th IEEE International Conference on Quantum Computing and Engineering, QCE 2023

作者： Chundury, Srikar Lietz, Justin Coello Perez, Eduardo Antonio Shehata, Amir Suh, In-Saeng Mueller, Frank North Carolina State University Department of Computer Science RaleighNC27695 United States Oak Ridge National Laboratory National Center for Computational Sciences Oak RidgeTN38830 United States

ISBN: (纸本)9798350343236

This work introduces an extension to the Tensor Network Quantum Virtual Machine (TNQVM) tool, enhancing the existing stack of ExaScale Tensor Network (ExaTN), ExaScale Accelerator (XACC), and TNQVM. It features a new plugin that enables efficient simulation of a Projected Entangled Pair State (PEPS), a 2D tensor network. To improve simulation efficiency for PEPS, we have implemented the snake boundary contraction algorithm. By integrating this capability into the existing stack, we enhance the overall functionality and versatility of the framework. We tested this new PEPS topology for a simple GHZ bell-pair generation quantum circuit and saw that its runtime is very close to that of the MPS topology. We estimate that the real potential of the PEPS topology becomes discernible when quantum circuits with multidimensional entanglement are simulated using tensor networks. In such cases, 1D tensor networks fail to represent or contract them efficiently. © 2023 IEEE.

关键词： Tensors

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DeeReCT-APA:Prediction of Alternative Polyadenylation Site Usage Through Deep Learning

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Genomics, Proteomics & Bioinformatics 2022年第3期20卷 483-495页

作者： Zhongxiao Li Yisheng Li Bin Zhang Yu Li Yongkang Long Juexiao Zhou Xudong Zou Min Zhang Yuhui Hu Wei Chen Xin Gao King Abdullah University of Science and Technology(KAUST) Computational Bioscience Research Center(CBRC)ComputerElectrical and Mathematical Sciences and Engineering(CEMSE)DivisionThuwal 23955-6900Saudi Arabia Department of Biology Southern University of Science and Technology(SUSTech)Shenzhen 518055China Cancer Science Institute of Singapore Singapore 117599Singapore

Alternative polyadenylation(APA)is a crucial step in post-transcriptional *** bioinformatic studies have mainly focused on the recognition of polyadenylation sites(PASs)in a given genomic sequence,which is a binary classification ***,computational methods for predicting the usage level of alternative PASs in the same gene have been ***,all of them cast the problem as a non-quantitative pairwise comparison task and do not take the competition among multiple PASs into *** address this,here we propose a deep learning architecture,Deep Regulatory Code and Tools for Alternative Polyadenylation(DeeReCT-APA),to quantitatively predict the usage of all alternative PASs of a given *** accommodate different genes with potentially different numbers of PASs,DeeReCT-APA treats the problem as a regression task with a variable-length *** on a convolutional neural network-long short-term memory(CNN-LSTM)architecture,DeeReCT-APA extracts sequence features with CNN layers,uses bidirectional LSTM to explicitly model the interactions among competing PASs,and outputs percentage scores representing the usage levels of all PASs of a *** addition to the fact that only our method can quantitatively predict the usage of all the PASs within a gene,we show that our method consistently outperforms other existing methods on three different tasks for which they are trained:pairwise comparison task,highest usage prediction task,and ranking ***,we demonstrate that our method can be used to predict the effect of genetic variations on APA patterns and sheds light on future mechanistic understanding in APA *** code and data are available at https://***/lzx325/DeeReCT-APA-repo.

关键词： Polyadenylation Gene regulation Sequence analysis Deep learning Bioinformatics

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On the Scalability of Computing Genomic Diversity Using SparkLeBLAST: A Feasibility Study

On the Scalability of Computing Genomic Diversity Using Spar...

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IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Ritvik Prabhu Bernard Moussad Karim Youssef Emil Vatai Wu-Chun Feng Department of Computer Science Virginia Tech USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory USA RIKEN Center for Computational Science Japan

ISBN: (数字)9798350387131

ISBN: (纸本)9798350387148

Studying the genomic diversity of viruses can help us understand how viruses evolve and how that evolution can impact human health. Rather than use a laborious and tedious wet-lab approach to conduct a genomic diversity study, we take a computational approach, using the ubiquitous NCBI BLAST and our parallel and distributed SparkLeBLAST, across 53 patients (~40,000,000 query sequences) on Fugaku, the world's fastest homogeneous supercomputer with 158,976 nodes, where each code contains a 48-core A64FX processor and 32 GB RAM. To project how long BLAST and SparkLeBLAST would take to complete a genomic diversity study of COVID-19, we first perform a feasibility study on a subset of 50 query sequences from a single COVID-19 patient to identify bottlenecks in sequence alignment processing. We then create a model using Amdahl's law to project the run times of NCBI BLAST and SparkLeBLAST on supercomputing systems like Fugaku. Based on the data from this 50-sequence feasibility study, our model predicts that NCBI BLAST, when running on all the cores of the Fugaku supercomputer, would take approximately 26.7 years to complete the full-scale study. In contrast, SparkLeBLAST, using both our query and database segmentation, would reduce the execution time to 0.026 years (i.e., 22.9 hours) – resulting in more than a 10,000× speedup over using the ubiquitous NCBI BLAST.

关键词： COVID-19 Databases computer viruses Scientific computing Scalability Genomics Predictive models Supercomputers Bioinformatics Optimization

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Complete Classification of Hadamard Products of Codimension 1 Subcodes of Reed–Muller Codes

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Moscow University computational Mathematics and Cybernetics 2024年第1期48卷 65-78页

作者： Chizhov, I. V. Department of Computational Mathematics and Cybernetics Moscow State University Moscow Russia Federal Research Center of Computer Science and Control Russian Academy of Sciences Moscow Russia

The author constructs a complete classification of linear codes that are obtained from different types of codimension $$1$$ subcodes of Reed–Muller codes using the Hadamard product operation.

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An efficient and resilience linear prefix approach for mining maximal frequent itemset using clustering

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Journal of Safety science and Resilience 2025年第1期6卷 93-104页

作者： M.Sinthuja S.Pravinthraja B K Dhanalakshmi H L Gururaj Vinayakumar Ravi G Jyothish Lal Department of ISE M.S.Ramaiah Institute of Technology BengaluruVisvesvaraya Technological UniversityBelagaviKarnataka590018India Department of CSE Presidency UniversityBengaluruIndia Department of Computer Science and Engineering BMS institute of Technology&ManagementBengaluruVisvesvaraya Technological UniversityBelagaviKarnataka590018India Department of Information Technology Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipalIndia Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia Center for Computational Engineering and Networking(CEN) Amrita School of EngineeringAmrita Vishwa VidyapeethamCoimbatoreIndia

The numerous volumes of data generated every day necessitate the deployment of new technologies capable of dealing with massive amounts of data *** is the case with Association Rules,a tool for unsupervised data mining that extracts information in the form of IF-THEN *** various approaches for extracting frequent itemset(prior step before mining association rules)in extremely large databases have been presented,the high computational cost and shortage of memory remain key issues to be addressed while processing enormous *** objective of this research is to discover frequent itemset by using clustering for preprocessing and adopting the linear prefix tree algorithm for mining the maximal frequent *** performance of the proposed CL-LP-MAX-tree was evaluated by comparing it with the existing FP-max *** was performed with the three different standard datasets to record evidence to prove that the proposed CL-LP-MAX-tree algorithm outperform the existing FP-max algorithm in terms of runtime and memory consumption.

关键词： Clustering Data mining Frequent itemset mining Linear prefix tree Maximal frequent itemset mining

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A Scalable Training-Free Diffusion Model for Uncertainty Quantification

A Scalable Training-Free Diffusion Model for Uncertainty Qua...

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2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Muhammad Rafid, Ali Haisam Yin, Junqi Geng, Yuwei Liang, Siming Bao, Feng Ju, Lili Zhang, Guannan Virginia Polytechnic Institute State University Department of Computer Science BlacksburgVA United States National Center of Computational Science Oak Ridge National Laboratory Oak RidgeTN United States University of South Carolina Department of Mathematics ColumbiaSC United States Florida State University Department of Mathematics TallahasseeFL United States Oak Ridge National Laboratory Computer Science and Math Division Oak RidgeTN United States

ISBN: (纸本)9798350355543

Generative artificial intelligence extends beyond its success in image/text synthesis, proving itself a powerful uncertainty quantification (UQ) technique through its capability to sample from complex high-dimensional probability distributions. However, existing methods often require a complicated training process, which greatly hinders their applications to real-world UQ problems, especially in dynamic UQ tasks where the target probability distribution evolves rapidly with time. To alleviate this challenge, we have developed a scalable, training-free score-based diffusion model for high-dimensional sampling. We incorporate a parallel-in-time method into our diffusion model to use a large number of GPUs to solve the backward stochastic differential equation and generate new samples of the target distribution. Moreover, we also distribute the computation of the large matrix subtraction used by the training-free score estimator onto multiple GPUs available across all nodes. Compared to existing methods, our approach completely avoids training the score function, making it capable of adapting to rapid changes in the target probability distribution. We showcase the remarkable strong and weak scaling capabilities of the proposed method on the Frontier supercomputer, as well as its uncertainty reduction capability in hurricane predictions when coupled with AI-based foundation models. © 2024 IEEE.

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Predicting Phosphoglycerylation with Transformer Features and Deep Learning

Predicting Phosphoglycerylation with Transformer Features an...

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2023 IEEE Asia-Pacific Conference on computer science and Data Engineering, CSDE 2023

作者： Chandra, Abel Sharma, Alok Dehzangi, Iman Tsunoda, Tatsuhiko Sattar, Abdul Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Rutgers University Department of Computer Science CamdenNJ United States Center for Computational and Integrative Biology Rutgers University Camden United States Riken Center for Integrative Medical Sciences Laboratory for Medical Science Mathematics Yokohama Japan School of Science The University of Tokyo Laboratory for Medical Science Mathematics Department of Biological Sciences Tokyo Japan Graduate School of Frontier Sciences The University of Tokyo Laboratory for Medical Science Mathematics Department of Computational Biology and Medical Sciences Tokyo Japan

ISBN: (纸本)9798350341072

Understanding protein sequences can advance treatments for various diseases. However experimentally obtaining this information is laborious, time-consuming, and expensive. Traditional machine learning techniques, like support vector machine, random forest and logistic regression, offer potential to fast-track this process but are sometimes limited by data complexity. Deep learning algorithms, in contrast, tend to yield higher performance. In this study, we employed a convolutional neural network to predict protein phosphoglycerylation. Features were extracted from pre-trained transformer models and compared with conventional features, such as evolutionary information and physicochemical/biochemical properties. Our results indicate significant performance improvements across all feature types, with the combination of transformerbased features and the convolutional neural network being especially effective. This methodology holds potential for other protein property prediction tasks. Our software and datasets used in this study are publicly available at https://***/abelavit/***. © 2023 IEEE.

关键词： Convolutional neural networks

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Hardware Acceleration for HPS Algorithms in Two and Three Dimensions

arXiv

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arXiv 2025年

作者： Melia, Owen Fortunato, Daniel Hoskins, Jeremy Willett, Rebecca Department of Computer Science University of Chicago United States Center for Computational Mathematics Flatiron Institute United States Center for Computational Biology Flatiron Institute United States Data Science Institute University of Chicago United States Computational and Applied Mathematics Department of Statistics University of Chicago United States

We provide a flexible, open-source framework for hardware acceleration, namely massively-parallel execution on general-purpose graphics processing units (GPUs), applied to the hierarchical Poincaré–Steklov (HPS) family of algorithms for building fast direct solvers for linear elliptic partial differential equations. To take full advantage of the power of hardware acceleration, we propose two variants of HPS algorithms to improve performance on two- and three-dimensional problems. In the two-dimensional setting, we introduce a novel recomputation strategy that minimizes costly data transfers to and from the GPU;in three dimensions, we modify and extend the adaptive discretization technique of Geldermans and Gillman [2019] to greatly reduce peak memory usage. We provide an open-source implementation of these methods written in JAX, a high-level accelerated linear algebra package, which allows for the first integration of a high-order fast direct solver with automatic differentiation tools. We conclude with extensive numerical examples showing our methods are fast and accurate on two- and three-dimensional problems. © 2025, CC BY.

关键词： Graphics processing unit

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