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Design, Automation and Test in Europe Conference and Exhibition

作者： Longfei Luo Han Wang Dingcui Yu Yina Lv Liang Shi Software/Hardware Co-design Engineering Research Center Ministry of Education School of Computer Science and Technology East China Normal University Shanghai China Department of Computer Science City University of Hong Kong Hong Kong China

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

The pseudo-single-level-cell (pSLC) technique is widely adopted in high-density flash-based storage to mitigate the performance and endurance problem of high-density flash memory. Furthermore, prefetching schemes can compensate for performance differences among storage tiers. Existing prefetchers are implemented in the operating system (OS) or storage layers. However, OS layer prefetchers are conservative since it is a challenge to achieve both high accuracy and large coverage simultaneously. Storage layer prefetchers are sub-optimal due to the performance differences between pSLC and DRAM. In this paper, a cross-layer prefetching framework (CPF) is proposed to prefetch data selectively. The basic idea is that high-accuracy data will be prefetched to DRAM and large-coverage data will be prefetched to the pSLC flash in storage. To make it practical, an adaptive regulator is further designed to dynamically adjust the cross-layer prefetching to ensure accuracy and coverage. Evaluations show that CPF can improve read performance and reduce data transfer costs significantly.

关键词： Cross layer design Costs Regulators Adaptive systems Prefetching Operating systems Random access memory

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Multi-Signal Fusion of Social Diffusion Graph with Bi-Directional Semantic Consistency

Multi-Signal Fusion of Social Diffusion Graph with Bi-Direct...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Huacheng Li Chunhe Xia Tianbo Wang Wanshuang Lin Changnan Jiang Chen Chen Yuan Zhao Key Laboratory of Beijing Network Technology Beihang University Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University School of Cyber Science and Technology Beihang University Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai

Devising diffusion graph to learn user representations is a crucial step in studying information propagation prediction. However, previous works mainly focused on structural and temporal features. To better incorporate content features, we introduce the Backward Decomposition and Forward Preservation mechanisms. The former involves decomposing content features for initializing node signals in the diffusion graph, thus fusing user features with content features. The latter aims to maintain node features generated by graph encoder consistent with the original content features. A series of experiments demonstrate that our model outperforms state-of-the-art models, and both mechanisms significantly enhance the prediction performance. Furthermore, our methods enables the features generated by the diffusion graph to more effectively incorporate features from various semantic spaces, whether encoded by language models or generated by graph embedding algorithms.

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COUNTING RANDOM k-SAT NEAR THE SATISFIABILITY THRESHOLD

arXiv

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arXiv 2024年

作者： Chen, Zongchen Lonkar, Aditya Wang, Chunyang Yang, Kuan Yin, Yitong School of Computer Science Georgia Institute of Technology AtlantaGA United States State Key Laboratory for Novel Software Technology New Cornerstone Science Laboratory Nanjing University 163 Xianlin Avenue Jiangsu Province Nanjing210023 China John Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai China

We present efficient counting and sampling algorithms for random k-SAT when the clause density satisfies α ≤ 2k poly(k) . In particular, the exponential term 2k matches the satisfiability threshold Θ(2k) for the existence of a solution and the (conjectured) algorithmic threshold 2k (ln k)/k for efficiently finding a solution. Previously, the best-known counting and sampling algorithms required far more restricted densities α 2k/3 [HWY23]. Notably, our result goes beyond the lower bound d 2k/2 for worst-case k-SAT with bounded-degree d [BGG+19], showing that for counting and sampling, the average-case random k-SAT model is computationally much easier than the worst-case model. At the heart of our approach is a new refined analysis of the recent novel coupling procedure by [WY24], utilizing the structural properties of random constraint satisfaction problems (CSPs). Crucially, our analysis avoids reliance on the 2-tree structure used in prior works, which cannot extend beyond the worst-case threshold 2k/2. Instead, we employ a witness tree similar to that used in the analysis of the Moser-Tardos algorithm [MT10] for the Lovász Local lemma, which may be of independent interest. Our new analysis provides a universal framework for efficient counting and sampling for random atomic CSPs, including, for example, random hypergraph colorings. At the same time, it immediately implies as corollaries several structural and probabilistic properties of random CSPs that have been widely studied but rarely justified, including replica symmetry and non-reconstruction. Copyright © 2024, The Authors. All rights reserved.

关键词： Trees (mathematics)

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Joint Clustering and Analyze Single Cell Multi-omics Data by scMOGAN

Joint Clustering and Analyze Single Cell Multi-omics Data by...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Congcong An Yiwen Chen Shanling Nie Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Center for Continuing and Lifelong Education National University of Singapore SG Faculty of Engineering The University of Sydney Australia

Advancements in single-cell multi-omics sequencing technologies have dramatically transformed the analysis of cellular states at single-cell resolution. Cluster analyses leveraging single-cell transcriptome and epigenome data have enabled the characterization of cellular states and the delineation of transcriptomic regulatory programs associated with cellular heterogeneity. However, the high dimensionality, sparsity, and heterogeneity inherent to multi-omics data present significant challenges to their clustering analysis. In this study, we introduced single-cell Multi-Omics Generative Adversarial Networks (scMOGAN), an unsupervised clustering model specifically tailored for single-cell transcriptome and epigenome data. scMOGAN accomplishes this by efficaciously modeling the omics data and mining potential features through neural networks, ultimately utilizing Gaussian mixture models for cell type identification. To validate and scrutinize the performance of scMOGAN, we performed analyses on two actual multi-omics datasets and one simulated dataset. The results demonstrate that scMOGAN surpasses other existing methods in terms of clustering performance, underscoring its efficacy in navigating the complexities of multi-omics data.

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PMMVar: Leveraging Multi-level Protein Structures for Enhanced Coding Variant Pathogenicity Prediction

PMMVar: Leveraging Multi-level Protein Structures for Enhanc...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yijia Chen Yiwen Chen Shanling Nie Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Center for Continuing and Lifelong Education National University of Singapore Singapore Faculty of Engineering The University of Sydney Australia

Genomic variants, which can disrupt cellular functions, present a challenge in distinguishing deleterious from benign instances. While assessing genome-wide functional impacts, many current algorithms neglect protein tertiary structure of coding region variants due to limitations in protein structural prediction. This study introduces PMMVar, an advanced multimodal deep convolutional network, which adeptly integrates protein tertiary structures with conservation properties from ESM-2, supplemented by other protein structural sequences. PMMVar achieves outstanding performance on the latest clinical variant datasets, NCBI ClinVar (2023), and the Mendelian variant dataset, surpassing existing benchmarks. Ablation analyses validate the significance of protein multi-level structures in enhancing the model’s accuracy. Overall, our findings spotlight the essential role of multi-level protein structures in pathogenicity predictions and their potential to discern deleterious genomic variants effectively.

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Satellite Federated Fine-Tuning for Foundation Models in Space Computing Power Networks

arXiv

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arXiv 2025年

作者： Zhu, Yan Zhu, Jingyang Wang, Ting Shi, Yuanming Jiang, Chunxiao Letaief, Khaled Ben MoE Engineering Research-Center of Software/Hardware Co-design Technology and Application The Shanghai Key Lab. of Trustworthy Computing East China Normal University Shanghai200062 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Advancements in artificial intelligence (AI) and low-earth orbit (LEO) satellites have promoted the application of large remote sensing foundation models for various downstream tasks. However, direct downloading of these models for fine-tuning on the ground is impeded by privacy concerns and limited bandwidth. Satellite federated learning (FL) offers a solution by enabling model fine-tuning directly on-board satellites and aggregating model updates without data downloading. Nevertheless, for large foundation models, the computational capacity of satellites is insufficient to support effective on-board fine-tuning in traditional satellite FL frameworks. To address these challenges, we propose a satellite-ground collaborative federated fine-tuning framework. The key of the framework lies in how to reasonably decompose and allocate model components to alleviate insufficient on-board computation capabilities. During fine-tuning, satellites exchange intermediate results with ground stations or other satellites for forward propagation and back propagation, which brings communication challenges due to the special communication topology of space transmission networks, such as intermittent satellite-ground communication, short duration of satellite-ground communication windows, and unstable inter-orbit inter-satellite links (ISLs). To reduce transmission delays, we further introduce tailored communication strategies that integrate both communication and computing resources. Specifically, we propose a parallel intra-orbit communication strategy, a topology-aware satellite-ground communication strategy, and a latency-minimalization inter-orbit communication strategy to reduce space communication costs. Simulation results demonstrate significant reductions in training time with improvements of approximately 33%. Copyright © 2025, The Authors. All rights reserved.

关键词： Satellite communication systems

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SAMMS: Multi-modality Deep Learning with the Foundation Model for the Prediction of Cancer Patient Survival

SAMMS: Multi-modality Deep Learning with the Foundation Mode...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wen Zhu Yiwen Chen Shanling Nie Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Center for Continuing and Lifelong Education National University of Singapore Singapore Faculty of Engineering The University of Sydney Australia

Cancer survival prediction is pivotal in tailoring individualized treatment strategies and guiding clinician decision-making. Yet, existing methodologies grapple with efficiently harnessing the intricate distribution of medical data spanning various modalities. In response, we present SAMMS, an advanced multi-omics multimodal deep learning framework tailored for survival prediction. SAMMS leverages the robust image segmentation model, "Segment Anything" to adeptly characterize pathological images. This prowess is further enhanced by integrating multi-omics data and clinical insights, facilitating holistic modeling across a diverse modal spectrum. The framework weaves a modality-specific subnetwork with a cross-modality common subnetwork, meticulously capturing intra-modality nuances and inter-modality correlations. SAMMS eclipsed its contemporaries by delivering remarkable performance on TCGA’s LGG and KIRC tumor datasets. A battery of analyses underscored SAMMS’s unparalleled capability to distill multifaceted insights from multimodal datasets, yielding richer and more integrative multimodal representations. Such strides promise significant advancements in cancer survival analytics, bolstering the precision and efficacy of patient-centric treatments, disease oversight, and clinical decision processes.

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DeepRisk:A deep learning approach for genome-wide assessment of common disease risk

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Fundamental Research 2024年第4期4卷 752-760页

作者： Jiajie Peng Zhijie Bao Jingyi Lia Ruijiang Han Yuxian Wang Lu Han Jinghao Peng Tao Wang Jianye Hao Zhongyu Wei Xuequn Shang AI for Science Interdisciplinary Research Center Schoolof Computer ScienceNorthwestern Polyechnical UniversityXi'an 129China Key Laboratoryof Bg Data Sorage and Management Northwestern Polytechnical UniversityMinisty of Industy and Information TechnologyX'an 129Chin College of Intelligence and Computing Tianjin UniversityTianjin 300072China School of Data Science Fudan UniversityShanghai 200433China Research and Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518000China

The potential for being able to identify individuals at high disease risk solely based on genotype data has garnered significant *** widely applied,traditional polygenic risk scoring methods fall short,as they are built on additive models that fail to capture the intricate associations among single nucleotide polymorphisms(SNPs).This presents a limitation,as genetic diseases often arise from complex interactions between multiple *** address this challenge,we developed DeepRisk,a biological knowledge-driven deep learning method for modeling these complex,nonlinear associations among SNPs,to provide a more effective method for scoring the risk of common diseases with genome-wide genotype *** demonstrated that DeepRisk outperforms existing PRs-based methods in identifying individuals at high risk for four common diseases:Alzheimer's disease,inflammatory bowel disease,type 2diabetes,and breast cancer.

关键词： Disease risk prediction Deep learning Polygenic risk score Common disease risk Disease prevention

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Research on the Application of computer Vision technology and Optimization of Space Design in a Smart Home Environment

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Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Peng, Mengzhou Kang, Xiaoran Zhang, Xun Zhao, Chenxu Network and Information Management Center Beijing Technology and Business University Beijing102488 China Software Development Center Information Management Department Air China Limited Beijing101318 China School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing102488 China CO. LTD Ningbo315336 China

This study aims to explore the application of computer vision technology in smart home environments and its influence on space design optimization. Through the comprehensive application of image recognition and processing technology, the behavior patterns of family members, the placement status of household items, and the real-time changes in the indoor environment in smart home systems are deeply analyzed. The experimental results show that computer vision technology can effectively improve smart home systems' perception and analysis ability, providing data support for automatically adjusting indoor environments and providing personalized service. At the same time, this study also focuses on the optimization strategy of space design based on computer vision technology and puts forward a series of humanized design schemes through reasonable arrangement of cameras and sensors, real-time analysis of user behavior data and environmental parameters, aiming at improving living comfort and achieving efficient use of energy. This study reveals the internal relationship between computer vision technology and space design, which complement each other and jointly promote the intelligent and humanized development of smart homes. © 2024 Mengzhou Peng et al., published by Sciendo.

关键词： Smart homes

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Broadband high efficiency polarization grating with continuously varying periodicity based on liquid crystal polymer

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Journal of Molecular Liquids 2025年 426卷

作者： Zhao, Yadi Wang, Jianguo Chen, Yi Wang, Fajia Wang, Jundong Zhu, Xiangyu Hong, Ruijin Tao, Chunxian Zhang, Dawei Lian, Yafei Zhao, Yuan-an Research and Development Center of Thin Film Optics Shanghai Institute of Optics and Fine Mechanics No.390 Qinghe Road Jiading District Shanghai201800 China Key Laboratory of Materials for High Power Laser Chinese Academy of Sciences Shanghai201800 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

Liquid crystal polarization gratings (LCPGs) exhibit unique optical characteristics, making them widely applicable in various optical fields such as optical field manipulation, beam deflection, and laser radar. Consequently, improving their achromatic performance holds significant importance. In this work, we demonstrate a broadband, high diffraction efficiency, and varying periodicity liquid crystal polarization grating fabricated through the photoalignment technique. A spherical wave is employed for pattern exposure within the interference optical path, with the surface anchoring periodicity varying from 2.56um to 12.04um. Subsequently, a double-layer twisted structure is introduced to construct a high diffraction efficiency PG, which attains an average diffraction efficiency exceeding 98 % within the 520–700 nm spectral range. The structural and diffraction properties of the grating are thoroughly investigated. The manufacturing process for this grating structure is both straightforward and efficient. This work has significant implications for the application and advancement of LCPGs in achromatic performance and contributes substantially to the fields of optical field manipulation and beam deflection. © 2025

关键词： Diffraction efficiency

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