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High Performance Computing Framework for Variable Selection on Genome-wide Association Studies

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High Performance Computing Framework for Variable Selection ...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiang Liu Xueling Liu Jing Diao Mengyao Zheng Jihe Li Dehui Wei Qipeng Xie Xia Li Linshan Jiang School of Computing National University of Singapore Group Technology Office National University Health System Department of Preventive Dentistry Peking University School and Hospital of Stomatology National Center for Stomatology & National Clinical Research Center for Oral Diseases Harvard T.H. Chan School of Public Health Harvard University School of Software and Microelectronics Peking University IoT Thrust Information Hub Hong Kong University of Science and Technology Guangzhou Department of Computer Science ETH Zurich Institute of Data Science National University of Singapore

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Variable selection for genome-wide association studies (GWAS) has been a major research focus for decades. With the exponential growth of biological and biomedical data in the era of big data, scientists are confronted with the challenge of extracting meaningful information from vast datasets while managing the inherent heterogeneity in bioinformatics. To date, there are no highly effective tools that support high-dimensional datasets and achieve robust variable selection performance, all while accounting for the non-i.i.d. features and structured relatedness among explanatory and response *** address these challenges, we introduce the first high-performance computing framework for variable selection in GWAS. Our framework integrates various state-of-the-art methods, allowing researchers to easily combine different techniques and fully explore their potential. Additionally, our approach employs novel optimization strategies to solve the problem efficiently, even for high-dimensional data with sparse characteristics. By processing the data holistically, the framework delivers comprehensive analysis and accurate linkage mapping associations. Designed for ease of use, the framework is implemented in Python and offers seamless deployment, making it accessible to a wide range of researchers.

关键词： Accuracy High dimensional data Input variables High performance computing Instruments Noise Genomics Feature extraction Bioinformatics Optimization

Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation

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arXiv 2023年

作者： Zheng, Tongya Wang, Xinchao Feng, Zunlei Song, Jie Hao, Yunzhi Song, Mingli Wang, Xingen Wang, Xinyu Chen, Chun The Big Graph Center College of Computer Science of Hangzhou City University China The College of Computer Science of Zhejiang University Hangzhou China The Department of Electrical and Computer Enigneering National University of Singapore Singapore The School of Software Technology Zhejiang University Hangzhou China The College of Computer Science Zhejiang University Hangzhou China The ZJU-Bangsun Joint Research Center The Shanghai Institute for Advanced Study Zhejiang University Hangzhou China

Temporal graphs exhibit dynamic interactions between nodes over continuous time, whose topologies evolve with time elapsing. The whole temporal neighborhood of nodes reveals the varying preferences of nodes. However, previous works usually generate dynamic representation with limited neighbors for simplicity, which results in both inferior performance and high latency of online inference. Therefore, in this paper, we propose a novel method of temporal graph convolution with the whole neighborhood, namely Temporal Aggregation and Propagation Graph Neural Networks (TAP-GNN). Specifically, we firstly analyze the computational complexity of the dynamic representation problem by unfolding the temporal graph in a message-passing paradigm. The expensive complexity motivates us to design the AP (aggregation and propagation) block, which significantly reduces the repeated computation of historical neighbors. The final TAP-GNN supports online inference in the graph stream scenario, which incorporates the temporal information into node embeddings with a temporal activation function and a projection layer besides several AP blocks. Experimental results on various real-life temporal networks show that our proposed TAP-GNN outperforms existing temporal graph methods by a large margin in terms of both predictive performance and online inference latency. Our code is available at https://***/doujiang-zheng/TAP-GNN. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery

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arXiv 2023年

作者： Liu, Jin Mao, Junbin Lin, Hanhe Kuang, Hulin Pan, Shirui Wu, Xusheng Xie, Shan Liu, Fei Pan, Yi Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of software Xinjiang University Wulumuqi830000 China Hunan Province Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Science and Engineering University of Dundee DundeeDD1 4HN United Kingdom School of Information and Communication Technology Griffith University Gold CoastQLD4215 Australia Shenzhen Health Development Research and Data Management Center Shenzhen518109 China

Graph learning-based multi-modal integration and classification is one of the most challenging tasks for disease prediction. To effectively offset the negative impact among modalities in the process of multi-modal integration and heterogeneous information extractions from graphs, we propose a novel method called Multi-modal Multi-Kernel Graph Learning (MMKGL). To solve the problem of negative impact among modalities, we propose a multi-modal graph embedding module to construct a multi-modal graph. Different from conventional methods that manually construct static graphs for all modalities, each modality generates a separate graph by adaptive learning, where a function graph and a supervision graph are introduced for optimization during the multi-graph fusion embedding process. We then propose a multi-kernel graph learning module to extract heterogeneous information from the multi-modal graph. The information in the multi-modal graph at different levels is aggregated by convolutional kernels with different receptive field sizes, followed by generating a cross-kernel discovery tensor for disease prediction. Our method is evaluated on the benchmark Autism Brain Imaging Data Exchange (ABIDE) dataset and outperforms the state-of-the-art methods. In addition, discriminative brain regions associated with autism are identified by our model, providing guidance for the study of autism pathology. The source code will be available at https://***/yutian0315/MMKGL. © 2023, CC BY-NC-ND.

关键词： Graph embeddings

Revolutionizing Modern Networks: Advances in AI, Machine Learning, and Blockchain for Quantum Satellites and UAV-based Communication

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arXiv 2023年

作者： Kumar, Adarsh Awad, Ali Ismail Sharma, Gaurav Krishnamurthi, Rajalakshmi Jain, Saurabh Srikanth, P. Sharma, Kriti Hedabou, Mustapha Sood, Surinder School of Computer Science University of Petroleum and Energy Studies Uttrakhand Dehradun India College of Information Technology United Arab Emirates University United Arab Emirates Department of Computer Science and Engineering Jaypee Institute of Information Technology Uttar Pradesh Noida India School of Engineering and Technology Sushant University Haryana Gurugram India DRDO Banglore India College of Computing Mohammed VI Polytechnic University Benguerir Morocco Department of Electrical Computer and Software Engineering University of Auckland New Zealand

Quantum communication is the most secure technique of transmitting data available today. Fiber communication lines and satellite-to-ground links have served as the basis for the most successful quantum networks that have been developed so far. Using a UAV, satellite or both for free-space quantum communication reduces the need for permanent ground connections and takes advantage of the lower loss limit in space, which makes it more efficient. This work surveys the recent development in Quantum Satellites and Quantum UAVs-based networks. Here, the importance of the latest technologies, including quantum artificial intelligence, blockchain quantum machine learning, quantum satellites and quantum UAVs, are explored from network perspectives. Further, this work discussed the role of satellite-based images and artificial intelligence. © 2023, CC BY.

关键词： Satellites

Understanding Older Adults' (Dis)Engagement with Design Materials 25

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Understanding Older Adults' (Dis)Engagement with Design Mate...

2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Pradhan, Alisha Jelen, Ben Thangaraj, Ramprabu Siek, Katie A. Jette, Shannon Lazar, Amanda Department of Informatics Ying Wu College of Computing New Jersey Institute of Technology NewarkNJ United States Computer Science and Software Engineering Rose-Hulman Institute of Technology Terre HauteIN United States Ying Wu College of Computing New Jersey Institute of Technology NewarkNJ United States Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States University of Maryland College ParkMD United States College of Information Studies University of Maryland College ParkMD United States

ISBN: (纸本)9798400713941

Design workshops are a popular approach to include older adults in the technology design process. However, formative design sessions with older adults have had unexpected outcomes such as the non-use of traditional design materials like craft-based prototyping supplies or disengagement from design activities. Analyzing the engagement of 32 older adults across two design workshops, this paper sheds insights on some of these outcomes. Contributing to a growing body of HCI research on understanding older adults' participation in design, we provide an understanding of how design materials can shape older adults' engagement in formative design activities. Our discussion furthers research on understanding who older adults design for and why, argues for a different understanding of creative expression, and offers considerations for choosing design materials. © 2025 Copyright held by the owner/author(s).

关键词： Design for manufacturability

Improving students’programming quality with the continuous inspection process:a social coding perspective

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Frontiers of computer science 2020年第5期14卷 43-60页

作者： Yao Lu Xinjun Mao Tao Wang Gang Yin Zude Li College of Computer National University of Defense TechnologyChangsha410073China Key Laboratory of Software Engineering for Complex Systems Changsha410073China School of Information Science and Engineering Central South UniversityChangsha410012China

College students majoring in computer science and software engineering need to master skills for high-quality ***,rich research has shown that both the teaching and learning of high-quality programming are challenging and deficient in most college education ***,the continuous inspection paradigm has been widely used by developers on social coding sites(e.g.,GitHub)as an important method to ensure the internal quality of massive code *** paper presents a case where continuous inspection is introduced into the classroom setting to improve students’programming *** the study,we first designed a specific continuous inspection process for students’collaborative projects and built an execution environment for the *** then conducted a controlled experiment with 48 students from the same course during two school years to evaluate how the process affects their programming *** results show that continuous inspection can help students in identifying their bad coding habits,mastering a set of good coding rules and significantly reducing the density of code quality issues introduced in the ***,we describe the lessons learned during the study and propose ideas to replicate and improve the process and its execution platform.

关键词： continuous inspection programming quality SonarQube

One-stage Low-resolution Text Recognition with High-resolution Knowledge Transfer

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arXiv 2023年

作者： Guo, Hang Dai, Tao Zhu, Mingyan Meng, Guanghao Chen, Bin Wang, Zhi Xia, Shu-Tao Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Tsinghua Shenzhen International Graduate School Tsinghua University Peng Cheng Laboratory Shenzhen China Department of Computer Science and Technology Harbin Institute of Technology Shenzhen China Tsinghua Shenzhen International Graduate School Tsinghua University Research Center of Artificial Intelligence Peng Cheng Laboratory Shenzhen China

Recognizing characters from low-resolution (LR) text images poses a significant challenge due to the information deficiency as well as the noise and blur in low-quality images. Current solutions for low-resolution text recognition (LTR) typically rely on a two-stage pipeline that involves super-resolution as the first stage followed by the second-stage recognition. Although this pipeline is straightforward and intuitive, it has to use an additional super-resolution network, which causes inefficiencies during training and testing. Moreover, the recognition accuracy of the second stage heavily depends on the reconstruction quality of the first stage, causing ineffectiveness. In this work, we attempt to address these challenges from a novel perspective: adapting the recognizer to low-resolution inputs by transferring the knowledge from the high-resolution. Guided by this idea, we propose an efficient and effective knowledge distillation framework to achieve multi-level knowledge transfer. Specifically, the visual focus loss is proposed to extract the character position knowledge with resolution gap reduction and character region focus, the semantic contrastive loss is employed to exploit the contextual semantic knowledge with contrastive learning, and the soft logits loss facilitates both local word-level and global sequence-level learning from the soft teacher label. Extensive experiments show that the proposed one-stage pipeline significantly outperforms super-resolution based two-stage frameworks in terms of effectiveness and efficiency, accompanied by favorable robustness. Code is available at https://***/csguoh/KD-LTR. © 2023, CC BY-NC-ND.

关键词： Distillation

Optimized Security Mechanism for publicly Secret Key Sharing over Cloud using Blockchain

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Optimized Security Mechanism for publicly Secret Key Sharing...

2024 International Symposium on Securing Next Generation Systems Using Future Artificial Intelligence Technologies, SNSFAIT 2024

作者： Byeon, Haewon Quraishi, Aadam Thuniki, Praveen Keshta, Ismail Soni, Mukesh Shabaz, Mohammad Department of AI and Software Inje University Gimhae50834 Korea Republic of Inje University Medical Big Data Research Center Gimhae50834 Korea Republic of Intervention Treatment Institute HoustonTX United States Sr Program Analyst Georgia CummingGA30040 United States Computer Science and Information Systems Department College of Applied Sciences Al Maarefa University Riyadh Saudi Arabia Dr. D. Y. Patii Vidyapeeth Pune Dr. D. Y. Patii School of Science & Technology Tathawade Pune India Model Institute of Engineering and Technology J&K Jammu India

A publicly verifiable key sharing mechanism based on threshold key sharing is provided to explore the security of users' private keys on the blockchain. Participating nodes check the key fragment after receiving it, effectively preventing it from being abused. The crucial sections of the nodes that participated in the critical splicing are made public during the critical recovery stage to prevent them from performing harmful things during the critical recovery stage. Add IDs to the nodes that participated in the crucial splicing during the key distribution stage;a dynamic threshold system is intended to track and update the status of malicious nodes in real time. When the node that possesses the crucial component fails, the owner of the critical component and the main node relocate a key element to the new participating nodes. To safeguard sensitive information. The experimental results show that this system has a key recovery rate of 80% and threshold qualities such as traceability, enforceability, and recoverability. © 2024 Copyright for this paper by its authors.

关键词： Blockchain Encryption Key sharing mechanism Privacy Secret key Security

A Complexity-Reduced Block-Selective Algebraic Multigrid Method for Implicitly Coupled Velocity-Pressure System on High Performance computers

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SSRN

SSRN 2023年

作者： Liang, Yuechao Guo, Xiao-Wei Li, Chao Yuan, Fan Song, Min Zhang, Qingyang Chen, Xinhai Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China School of Mathematics and Computational Sciences Xiangtan University Xiangtan411105 China College of Computer Science and Technology National University of Defense Technology Changsha410073 China

The implicitly coupled pressure-based algorithm is widely acknowledged for its superior convergence and robustness in solving incompressible flow problems. However, the increased expansion scale of equations and difficulty in solving the resulting equation system has limited the widespread use of the coupling algorithm. This paper presents an optimized block-SAMG method, referred to as Red-SAMG, to significantly reduce computational complexity and memory consumption. Our approach employs a parallel modified independent set algorithm, allowing each process to perform matrix coarsening individually. An aggressive coarsening strategy further reduces complexity to address larger-scale problems. We conducted numerical experiments on a serial two-dimensional backward-facing step case and a parallel three-dimensional tunnel between rod bundles in the reactor core problem. The results demonstrate that the Red-SAMG method with aggressive coarsening reduces solution time by 12\% on the backward-facing step case. For large-scale three-dimensional problems under the same parallel conditions, the Red-SAMG method shortens the solution time by 14\% compared to the existing SAMG method. The Red-SAMG method with aggressive coarsening further reduces solution time by 49\%. Our optimized Red-SAMG method effectively reduces the complexity of the linear system arising from the implicitly coupled algorithm. The aggressive coarsening strategy also represents a significant advancement in improving the computational efficiency of the SAMG method. © 2023, The Authors. All rights reserved.

关键词： Coarsening