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arXiv 2024年

作者： Guruswami, Venkatesan Lin, Bingkai Ren, Xuandi Sun, Yican Wu, Kewen Simons Institute for the Theory of Computing Departments of EECS and Mathematics UC Berkeley United States State Key Laboratory for Novel Software Technology Nanjing University China Department of EECS UC Berkeley United States School of Computer Science Peking University China

The Parameterized Inapproximability Hypothesis (PIH), which is an analog of the PCP theorem in parameterized complexity, asserts that, there is a constant Ε > 0 such that for any computable function f : N → N, no f(k) · nO(1)-time algorithm can, on input a k-variable CSP instance with domain size n, find an assignment satisfying 1 − Ε fraction of the constraints. A recent work by Guruswami, Lin, Ren, Sun, and Wu (STOC’24) established PIH under the Exponential Time Hypothesis (ETH). In this work, we improve the quantitative aspects of PIH and prove (under ETH) that approximating sparse parameterized CSPs within a constant factor requires nk1−o(1) time. This immediately implies that, assuming ETH, finding a (k/2)-clique in an n-vertex graph with a k-clique requires nk1−o(1) time. We also prove almost optimal time lower bounds for approximating k-ExactCover and Max k-Coverage. Our proof follows the blueprint of the previous work to identify a "vector-structured" ETH-hard CSP whose satisfiability can be checked via an appropriate form of "parallel" PCP. Using further ideas in the reduction, we guarantee additional structures for constraints in the CSP. We then leverage this to design a parallel PCP of almost linear size based on Reed-Muller codes and derandomized low degree testing. Copyright © 2024, The Authors. All rights reserved.

关键词： Blueprints

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Ability of strong-pulse illumination to hack self-differencing avalanche photodiode detectors in a high-speed quantum-key-distribution system

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Physical Review A 2022年第3期106卷 033713-033713页

作者： Binwu Gao Zhihao Wu Weixu Shi Yingwen Liu Dongyang Wang Chunlin Yu Anqi Huang Junjie Wu Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha 410073 China China Greatwall Research Institute China Greatwall Technology Group Co. Ltd. Shenzhen 518057 China

Implementation of high-speed quantum key distribution (QKD) has become one of the major focuses in the field, which produces a high key-generation rate for applications. To achieve high-speed QKD, tailored techniques are developed and employed to quickly generate and detect quantum states. However, these techniques may introduce unique loopholes to compromise the security of QKD systems. In this paper, we investigate the loopholes of the self-differencing (SD) avalanche photodiode (APD) detector, typically used for high-speed detection in a QKD system, and demonstrate experimental testing of the SD APD detector under strong-pulse-illumination attack. This attack presents blinding stability and helps an eavesdropper to learn the secret key without introducing extra quantum bit rate error. Based on this testing, we propose a set of criteria for protecting SD APD detectors from the strong-pulse-illumination attack.

关键词： Quantum communication Quantum cryptography

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Communication Lower Bounds of Convolutions in CNNs 20

Communication Lower Bounds of Convolutions in CNNs

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32nd ACM Symposium on Parallelism in Algorithms and architectures, SPAA 2020

作者： Zhang, Xiaoyang Xiao, Junmin Tan, Guangming State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Science Beijing China

ISBN: (纸本)9781450369350

Convolution is the most time-consuming part in the computation of convolutional neural networks (CNNs). Due to the complex data dependency and the increase in the amount of model samples, the convolution suffers from high overhead on data movement. This work provides comprehensive analysis and methodologies to minimize the communication for the convolutions in CNNs. With an in-depth analysis on the I/O complexity theory under the red-blue pebble game model, we develop a general communication lower bound theory for a composite algorithm which consists of several different sub-computations. Based on the proposed theory, we establish the data movement lower bound results for three main convolution algorithms in CNNs, which are the direct convolution, the image2col method and Winograd algorithm. Furthermore, derived from I/O lower bound results, we design the near communication-optimal strategies respectively for the three main convolution algorithms by fully exploiting the data reuse. The deep analysis demonstrates that our designs are able to nearly reach the minimum communication in a two-level memory hierarchy. © 2020 Owner/Author.

关键词： Convolution

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High Quality Global Routing Algorithm Based on Multi-Stage Optimization

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Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of computer-Aided Design and computer Graphics 2024年第4期36卷 607-614页

作者： Genggeng, Liu Zhenyu, Pei Ning, Xu College of Computer and Data Science Fuzhou University Fuzhou350116 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Information Engineering Wuhan University of Technology Wuhan430070 China

Global routing is a crucial step in VLSI physical design. To address the problems that the low utilization rate of capacity and easy to fall into local optimum in the existing global routing algorithms, a high-quality global routing algorithm based on multi-stage optimization is proposed. First, the virtual capacity dynamic adjustment strategy is introduced in the pre-connection stage and the routing reorganization stage to improve the utilization rate of capacity. Then, in the routing reorganization stage, the routing subarea adaptive expansion strategy is proposed to narrow the search space and improve the routing efficiency. Finally, the A* algorithm heuristic search is combined with the global search capability of the particle swarm algorithm to avoid the algorithm to choose the shortest path in the routing process, and thus avoid the algorithm to fall into the local optimum. Experimental results comparing with existing global routing algorithms on ISPD benchmark circuits show that the routing results of proposed algorithm reduce the wirelength by 6.0% to 22.8%. © 2024 institute of computing technology. All rights reserved.

关键词： Routing algorithms

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Taking a Deep Breath: Enhancing Language Modeling of Large Language Models with Sentinel Tokens

arXiv

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arXiv 2024年

作者： Luo, Weiyao Zheng, Suncong Xia, Heming Wang, Weikang Lei, Yan Liu, Tianyu Chen, Shuang Sui, Zhifang State Key Laboratory of Multimedia Information Processing School of Computer Science Peking University China School of Software & Microelectronics Peking University China Tencent MLPD China Department of Computing The Hong Kong Polytechnic University Hong Kong Institute of Computing Technology Chinese Academy of Sciences China Alibaba Group China

Large language models (LLMs) have shown promising efficacy across various tasks, becoming powerful tools in numerous aspects of human life. However, Transformer-based LLMs suffer a performance degradation when modeling long-term contexts due to they discard some information to reduce computational overhead. In this work, we propose a simple yet effective method to enable LLMs to take a deep breath, encouraging them to summarize information contained within discrete text chunks. Specifically, we segment the text into multiple chunks and insert special token at the end of each chunk. We then modify the attention mask to integrate the chunk's information into the corresponding token. This facilitates LLMs to interpret information not only from historical individual tokens but also from the token, aggregating the chunk's semantic information. Experiments on language modeling and out-of-domain downstream tasks validate the superiority of our approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Brain-Inspired Efficient Pruning: Exploiting Criticality in Spiking Neural Networks

arXiv

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arXiv 2023年

作者： Chen, Shuo Liu, Boxiao Liu, Zeshi You, Haihang State Key Lab of Processors Institute of Computing Technology Chinese Academy of Science Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Zhongguancun Laboratory Beijing China Sensetime Research Beijing China

Spiking Neural Networks (SNNs) have gained significant attention due to the energy-efficient and multiplication-free characteristics. Despite these advantages, deploying large-scale SNNs on edge hardware is challenging due to limited resource availability. Network pruning offers a viable approach to compress the network scale and reduce hardware resource requirements for model deployment. However, existing SNN pruning methods cause high pruning costs and performance loss because they lack efficiency in processing the sparse spike representation of SNNs. In this paper, inspired by the critical brain hypothesis in neuroscience and the high biological plausibility of SNNs, we explore and leverage criticality to facilitate efficient pruning in deep SNNs. We firstly explain criticality in SNNs from the perspective of maximizing feature information entropy. Second, We propose a low-cost metric for assess neuron criticality in feature transmission and design a pruning-regeneration method that incorporates this criticality into the pruning process. Experimental results demonstrate that our method achieves higher performance than the current state-of-the-art (SOTA) method with up to 95.26% reduction of pruning cost. The criticality-based regeneration process efficiently selects potential structures and facilitates consistent feature representation. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost reduction

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LCD Codes and Self-orthogonal Codes in Finite Dihedral Group Algebras

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Chinese Annals of Mathematics,Series B 2021年第5期42卷 791-800页

作者： Yanyan GAO Qin YUE Yansheng WU Department of Mathematics and Physics Nanjing Institute of TechnologyNanjing 211167China State Key Laboratory of Cryptology P.O.Box 5159Beijing 100878China Department of Mathematics Nanjing University of Aeronautics and AstronauticsNanjing 211100China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China Shanghai Key Laboratory of TYustworthy Computing East China Normal UniversityShanghai 200062China

Let Fq be a finite field with order q and D2n be the dihedral group with 2n elements, and gcd(q, 2n) = 1. In this article, the authors give precise descriptions and enumerations of linear complementary dual(LCD) codes and self-orthogonal codes in the finite dihedral group algebras Fq[D2n]. Some numerical examples are also presented to illustrate the main results.

关键词： Group algebra Dihedral group LCD codes Self-orthogonal codes

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Dual Prototypes Contrastive Learning for Semi-Supervised Medical Image Segmentation

SSRN

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SSRN 2024年

作者： Yue, Tianai Xu, Rongtao Wu, Jingqian Yang, Wenjie Du, Shide Wang, Changwei Johns Hopkins University Baltimore21218 United States State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The University of Hong Kong Hong Kong999077 Hong Kong College of Computer and Big Data Fuzhou University Fuzhou350108 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan250014 China

Semi-supervised techniques for medical image segmentation have demonstrated potential, effectively training models using scarce labeled data alongside a wealth of unlabeled data. Therefore, semi-supervised medical image segmentation is one of the key issues in engineering applications of medical artificial intelligence. Consistency constraints based on prototype alignment provide an intuitively sensible way to discover valuable insights from unlabeled data that can motivate segmentation performance. In this work, we propose a dual prototypes contrastive network (DPC-Net) to motivate semi-supervised medical segmentation accuracy by imposing image-level global prototype constraints and pixel-level local prototype constraints. First, we introduce a Background-Separation Global Prototype Contrastive Learning technique that utilizes the natural mutual exclusivity of foreground and background to separate the inter-class distances and encourage the segmentation network to obtain segmentation results that are more complete and do not contain background regions. Second, we design a Cross-Consistent Local Prototype Contrastive Learning techniques to extend the perturbation consistency of the two networks to the prototype's localized response to the feature map, thereby shaping a more stable intra-class prototype space and producing accurate and robust pixel-level predictions. Finally, we comprehensively evaluate our method on mainstream semi-supervised medical image segmentation benchmarks and settings, and experimental results show that our DPC-Net outperforms current state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Building Agile Workflow Microservice System for HPC Applications Based on Fast-start OSv

Building Agile Workflow Microservice System for HPC Applicat...

引用

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Qiang Hu Lixian Ma En Shao Guangming Tan State Key Laboratory of Computer Architecture Institute of Computing Technology CAS Beijing China University of Chinese Academy of Science Beijing China

ISBN: (纸本)9781665408790

The advances of containers have significantly promoted the development of microservice architecture. This architecture splits a monolithic application into multiple independent components and the container orchestrator manages these components by the container in the cloud environment. The feasibility of deploying high performance computing(HPC) applications as microservices has been proven, but the existing container orchestrator incurs a large performance overhead as there is interference between different containers on the same physical host. In this paper, we design an agile workflow microservice system for HPC applications with fast-start OSv. We consider improving HPC workflow performance from two aspects: single OSv startup time optimization and workflow orchestration optimization. For single OSv startup time optimization, we design a fast-start OSv by analyzing the process of OSv startup and finding an optimization by modifying OSv source code. In this way, we get nearly 50% improvement of startup time. For workflow orchestration optimization, we propose four optimization techniques to speed up the execution of workflow by jointly considering OSv and workflow features, namely: node fusion, node merge, image preload, boot delay. Furthermore, we utilize our fast startup OSv to design an orchestration system for efficiently building an agile HPC workflow microservice by Kubevirt. Our experimental results optimization microservice system reduces the execution time by 30% compared with the original deployment with docker.

关键词： Cloud computing Conferences Buildings Microservice architectures computer architecture Interference Containers

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A COMPREHENSIVE DATASET AND AUTOMATED PIPELINE FOR NAILFOLD CAPILLARY ANALYSIS

arXiv

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arXiv 2023年

作者： Zhao, Linxi Tang, Jiankai Chen, Dongyu Liu, Xiaohong Zhou, Yong Shi, Yuanchun Wang, Guangyu Wang, Yuntao Key Laboratory of Pervasive Computing Ministry of Education Department of Computer Science and Technology Tsinghua University Beijing China UCL Cancer Institute University College London London United Kingdom Clinical Research Institute Shanghai General Hospital Shanghai China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

Nailfold capillaroscopy is widely used in assessing health conditions, highlighting the pressing need for an automated nailfold capillary analysis system. In this study, we present a pioneering effort in constructing a comprehensive nailfold capillary dataset—321 images, 219 videos from 68 subjects, with clinic reports and expert annotations—that serves as a crucial resource for training deep-learning models. Leveraging this dataset, we finetuned three deep learning models with expert annotations as supervised labels and integrated them into a novel end-to-end nailfold capillary analysis pipeline. This pipeline excels in automatically detecting and measuring a wide range of size factors, morphological features, and dynamic aspects of nailfold capillaries. We compared our outcomes with clinical reports. Experiment results showed that our automated pipeline achieves an average of sub-pixel level precision in measurements and 89.9% accuracy in identifying morphological abnormalities. These results underscore its potential for advancing quantitative medical research and enabling pervasive computing in healthcare. Our data and code are available at https://***/THU-CS-PI-LAB/ ANFC-Automated-Nailfold-Capillary. Copyright © 2023, The Authors. All rights reserved.

关键词： Pipelines

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