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arXiv 2024年

作者： Okazaki, Naoaki Hattori, Kakeru Shota, Hirai Iida, Hiroki Ohi, Masanari Fujii, Kazuki Nakamura, Taishi Loem, Mengsay Yokota, Rio Mizuki, Sakae Department of Computer Science School of Computing Tokyo Institute of Technology Japan Global Scientific Information and Computing Center Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo152-8550 Japan

Open Japanese large language models (LLMs) have been trained on the Japanese portions of corpora such as CC-100, mC4, and OSCAR. However, these corpora were not created for the quality of Japanese texts. This study builds a large Japanese web corpus by extracting and refining text from the Common Crawl archive (21 snapshots of approximately 63.4 billion pages crawled between 2020 and 2023). This corpus consists of approximately 312.1 billion characters (approximately 173 million pages), which is the largest of all available training corpora for Japanese LLMs, surpassing CC-100 (approximately 25.8 billion characters), mC4 (approximately 239.7 billion characters) and OSCAR 23.10 (approximately 74 billion characters). To confirm the quality of the corpus, we performed continual pre-training on Llama 2 7B, 13B, 70B, Mistral 7B v0.1, and Mixtral 8x7B Instruct as base LLMs and gained consistent (6.6-8.1 points) improvements on Japanese benchmark datasets. We also demonstrate that the improvement on Llama 2 13B brought from the presented corpus was the largest among those from other existing corpora. © 2024, CC BY.

关键词： Benchmarking

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Error Interference in Quantum Simulation

arXiv

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arXiv 2024年

作者： Chen, Boyang Xu, Jue Zhao, Qi Yuan, Xiao Department of Computer Science and Technology Tsinghua University Beijing100084 China QICI Quantum Information and Computation Initiative School of Computing and Data Science The University of Hong Kong Pokfulam Road Hong Kong Center on Frontiers of Computing Studies Peking University Beijing100871 China School of Computer Science Peking University Beijing100871 China

Understanding algorithmic error accumulation in quantum simulation is crucial due to its fundamental significance and practical applications in simulating quantum many-body system dynamics. Conventional theories typically apply the triangle inequality to provide an upper bound for the error. However, these often yield overly conservative and inaccurate estimates as they neglect error interference — a phenomenon where errors in different segments can destructively interfere. Here, we introduce a novel method that directly estimates the long-time algorithmic errors with multiple segments, thereby establishing a comprehensive framework for characterizing algorithmic error interference. We identify the sufficient and necessary condition for strict error interference and introduce the concept of approximate error interference, which is more broadly applicable to scenarios such as power-law interaction models, the Fermi-Hubbard model, and higher-order Trotter formulas. Our work demonstrates significant improvements over prior ones and opens new avenues for error analysis in quantum simulation, offering potential advancements in both theoretical algorithm design and experimental implementation of Hamiltonian simulation. Copyright © 2024, The Authors. All rights reserved.

关键词： Hamiltonians

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Multi-View Incremental Learning with Structured Hebbian Plasticity for Enhanced Fusion Efficiency

arXiv

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arXiv 2024年

作者： Chen, Yuhong Song, Ailin Yin, Huifeng Zhong, Shuai Chen, Fuhai Xu, Qi Wang, Shiping Xu, Mingkun College of Computer and Data Science Fuzhou University Fuzhou China Guangdong Institute of Intelligence Science and Technology Hengqin Zhuhai China University of Electronic Science and Technology of China Chengdu China Center for Brain Inspired Computing Research Department of Precision Instrument Tsinghua University Beijing China School of Computer Science and Technology Dalian University of Technology Dalian China

The rapid evolution of multimedia technology has revolutionized human perception, paving the way for multi-view learning. However, traditional multi-view learning approaches are tailored for scenarios with fixed data views, falling short of emulating the intricate cognitive procedures of the human brain processing signals sequentially. Our cerebral architecture seamlessly integrates sequential data through intricate feed-forward and feedback mechanisms. In stark contrast, traditional methods struggle to generalize effectively when confronted with data spanning diverse domains, highlighting the need for innovative strategies that can mimic the brain’s adaptability and dynamic integration capabilities. In this paper, we propose a bio-neurologically inspired multiview incremental framework named MVIL aimed at emulating the brain’s fine-grained fusion of sequentially arriving views. MVIL lies two fundamental modules: structured Hebbian plasticity and synaptic partition learning. The structured Hebbian plasticity reshapes the structure of weights to express the high correlation between view representations, facilitating a fine-grained fusion of view representations. Moreover, synaptic partition learning is efficient in alleviating drastic changes in weights and also retaining old knowledge by inhibiting partial synapses. These modules bionically play a central role in reinforcing crucial associations between newly acquired information and existing knowledge repositories, thereby enhancing the network’s capacity for generalization. Experimental results on six benchmark datasets show MVIL’s effectiveness over state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Weakly-Supervised Multi-action Offline Reinforcement Learning for Intelligent Dosing of Epilepsy in Children 28th

Weakly-Supervised Multi-action Offline Reinforcement Learnin...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Li, Zhuo Shen, Yifei Xu, Ruiqing Yang, Yu Cao, Jiannong Wu, Linchun Wu, Qing School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Computer Science University College London London United Kingdom Donglian Information Technology Co. Ltd. Chengdu China Department of Pharmacy Children’s Hospital of Chongqing Medical University National Clinical Research Center for Child Health and Disorders Chongqing China

ISBN: (纸本)9783031306778

Epilepsy in childhood is a common neurological disorder in children. Most cases are benign childhood epilepsy, which can be controlled with medication by adaptive adjustment of the dosage of antiepileptic drugs (AEDs). Recently, reinforcement learning-based intelligent dosing has attracted increasing attention. In clinical practice, patients usually take more than one drug at a time, however, conventional reinforcement learning algorithms do not sufficiently address the combination of two or more active drugs. In this paper, we propose the multi-action offline reinforcement learning (MA-ORL) model to solve this problem. Concretely, MA-ORL inherits the basic framework of the actor-critic network. For the patient’s health and safety concerns, MA-ORL abandons the intrusive and high-risk "trial-and-error" interactions with the environment but directly learns from the offline clinical dataset in the initial phase until the model is sufficiently trained and ready to use. Besides, to choose multiple actions simultaneously, we replace the actor’s output in standard reinforcement learning with a 2D matrix indicating the mixed feature representation of all different actions, then multiply it with separate masks to obtain separated actions. In addition to reaching an optimal return (i.e., the reduction of seizure frequency), MA-ORL also emphasizes the accuracy of the recommended dosage. Therefore, we introduce weak supervision to the learning objective to restrict the range of the learning outcome. It guarantees the recommended dosage by MA-ORL is as close as the one prescribed by experienced physicians. We conduct extensive experiments on clinical medical records containing 245 cases of epilepsy in childhood. Experimental results show MA-ORL reaches the highest cumulative return among all baseline models. Moreover, the suggested amount of medication taken a day by MA-ORL is more accurate than any other benchmark. © 2023, The Author(s), under exclusive license to Springer Natu

关键词： Reinforcement learning

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Downstream-agnostic Adversarial Examples

Downstream-agnostic Adversarial Examples

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International Conference on computer Vision (ICCV)

作者： Ziqi Zhou Shengshan Hu Ruizhi Zhao Qian Wang Leo Yu Zhang Junhui Hou Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan University School of Information and Communication Technology Griffith University Department of Computer Science City University of Hong Kong School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab

Self-supervised learning usually uses a large amount of unlabeled data to pre-train an encoder which can be used as a general-purpose feature extractor, such that downstream users only need to perform fine-tuning operations to enjoy the benefit of "large model". Despite this promising prospect, the security of pre-trained encoder has not been thoroughly investigated yet, especially when the pre-trained encoder is publicly available for commercial *** this paper, we propose AdvEncoder, the first framework for generating downstream-agnostic universal adversarial examples based on the pre-trained encoder. AdvEncoder aims to construct a universal adversarial perturbation or patch for a set of natural images that can fool all the downstream tasks inheriting the victim pre-trained encoder. Unlike traditional adversarial example works, the pre-trained encoder only outputs feature vectors rather than classification labels. Therefore, we first exploit the high frequency component information of the image to guide the generation of adversarial examples. Then we design a generative attack framework to construct adversarial perturbations/patches by learning the distribution of the attack surrogate dataset to improve their attack success rates and transferability. Our results show that an attacker can successfully attack downstream tasks without knowing either the pre-training dataset or the downstream dataset. We also tailor four defenses for pre-trained encoders, the results of which further prove the attack ability of AdvEncoder. Our codes are available at: https://***/CGCL-codes/AdvEncoder.

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Incorporating Multi-armed Bandit with Local Search for MaxSAT

arXiv

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arXiv 2022年

作者： Zheng, Jiongzhi He, Kun Zhou, Jianrong Jin, Yan Li, Chu-Min Manyà, Felip School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hopcroft Center on Computing Science Huazhong University of Science and Technology Wuhan430074 China MIS University of Picardie Jules Verne Amiens80039 France CSIC Bellaterra08193 Spain

Partial MaxSAT (PMS) and Weighted PMS (WPMS) are two practical generalizations of the MaxSAT problem. In this paper, we propose a local search algorithm for these problems, called BandHS, which applies two multi-armed bandits to guide the search directions when escaping local optima. One bandit is combined with all the soft clauses to help the algorithm select to satisfy appropriate soft clauses, and the other bandit with all the literals in hard clauses to help the algorithm select appropriate literals to satisfy the hard clauses. These two bandits can improve the algorithm’s search ability in both feasible and infeasible solution spaces. We further propose an initialization method for (W)PMS that prioritizes both unit and binary clauses when producing the initial solutions. Extensive experiments demonstrate the excellent performance and generalization capability of our proposed methods, that greatly boost the state-of-the-art local search algorithm, SATLike3.0, and the state-of-the-art SAT-based incomplete solver, NuWLS-c. © 2022, CC BY.

关键词： Local search (optimization)

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Disentangled Cascaded Graph Convolution Networks for Multi-Behavior Recommendation

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ACM Transactions on Recommender Systems 2024年第4期2卷 1-27页

作者： Zhiyong Cheng Jianhua Dong Fan Liu Lei Zhu Xun Yang Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shandong Artificial Intelligence Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China School of Computing National University of Singapore Singapore Singapore School of Electronic and Information Engineering Tongji University Shanghai China School of Information Science and Technology University of Science and Technology of China Hefei China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei China and Hefei Comprehensive National Science Center Hefei China

Multi-behavioral recommender systems have emerged as a solution to address data sparsity and cold-start issues by incorporating auxiliary behaviors alongside target behaviors. However, existing models struggle to accurately capture varying user preferences across different behaviors and fail to account for diverse item preferences within behaviors. Various user preference factors (such as price or quality) entangled in the behavior may lead to sub-optimization problems. Furthermore, these models overlook the personalized nature of user behavioral preferences by employing uniform transformation networks for all users and items. To tackle these challenges, we propose the Disentangled Cascaded Graph Convolutional Network (Disen-CGCN), a novel multi-behavior recommendation model. Disen-CGCN employs disentangled representation techniques to effectively separate factors within user and item representations, ensuring their independence. In addition, it incorporates a multi-behavioral meta-network, enabling personalized feature transformation across user and item behaviors. Furthermore, an attention mechanism captures user preferences for different item factors within each behavior. By leveraging attention weights, we aggregate user and item embeddings separately for each behavior, computing preference scores that predict overall user preferences for items. Our evaluation of benchmark datasets demonstrates the superiority of Disen-CGCN over state-of-the-art models, showcasing an average performance improvement of 7.07% and 9.00% on respective datasets. These results highlight Disen-CGCN’s ability to effectively leverage multi-behavioral data, leading to more accurate recommendations.

关键词： Multi-Behavior Recommendation disentangled representation learning graph convolutional network multi-behavior recommendation multi-task learning

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Symbolic Reasoning About Quantum Circuits in Coq

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Journal of computer science & technology 2021年第6期36卷 1291-1306页

作者： Wen-Jun Shi Qin-Xiang Cao Yu-Xin Deng Han-Ru Jiang Yuan Feng Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai 200062China John Hopcroft Center of Computer Science Shanghai Jiao Tong UniversityShanghai 200240China Yanqi Lake Beijing Institute of Mathematical Sciences and Applications Beijing 101408China Centre for Quantum Software and Information University of Technology SydneySydney NSW 2007Australia

A quantum circuit is a computational unit that transforms an input quantum state to an output state.A natural way to reason about its behavior is to compute explicitly the unitary matrix implemented by ***,when the number of qubits increases,the matrix dimension grows exponentially and the computation becomes *** this paper,we propose a symbolic approach to reasoning about quantum *** is based on a small set of laws involving some basic manipulations on vectors and *** symbolic reasoning scales better than the explicit one and is well suited to be automated in Coq,as demonstrated with some typical examples.

关键词： quantum circuit symbolic reasoning Dirac notation Coq

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Verify All Traffic: Towards Zero-Trust In-Network Intrusion Detection against Multipath Routing

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2155-2171页

作者： Zhao, Ziming Li, Zhaoxuan Xie, Xiaofei Liu, Zhipeng Li, Tingting Yu, Jiongchi Zhang, Fan Chen, Binbin School of Software Technology Zhejiang University Ningbo315100 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China ZJU-Hangzhou Global Scientific and Technological Innovation Center 311200 China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province 310027 China Jiaxing Research Institute Zhejiang University 314000 China Zhengzhou Xinda Institute of Advanced Technology Zhengzhou450001 China Singapore State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security UCAS Beijing100049 China School of Computing and Information Systems Singapore Management University Singapore188065 Singapore Advanced Digital Sciences Center Singapore138632 Singapore Singapore University of Technology and Design SingaporeB96049 Singapore

With the popularity of encryption protocols, machine learning (ML)-based traffic analysis technologies have attracted widespread attention. To adapt to modern high-speed bandwidth, recent research is dedicated to advancing zero-trust intrusion detection by offloading feature extraction and model inference into the network dataplane. Especially, with the rise of programmable switches, achieving line-speed ML inference becomes promising. However, existing research only considers a single switch node as a relay to conduct evaluation. This is far from real-world deployments involving multiple switches (given that zero-trust security assumes that threats can originate from anywhere, including within the network), particularly the multi-path routing phenomenon that exists in practice. In this paper, we reveal practical challenges in the context of enabling line-speed model inference in the network dataplane. Furthermore, we propose FCPlane, the forwarding and computing integrated dataplane for zero-trust intrusion detection that aims to enable efficient load balancing while providing reliable traffic analysis results, even against multipath routing. The core idea is to reconcile forwarding and computation to the flowlet level, for which a tailor-made Markov chain model is designed. Based on two public traffic datasets, we evaluate seven state-of-the-art in-network traffic analysis models deployed in four types of topologies (three with multipath routing and one without) to explore performance impact and demonstrate the effectiveness of our proposal. © 1983-2012 IEEE.

关键词： Network intrusion

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ACGraph: Accelerating Streaming Graph Processing via Dependence Hierarchy

ACGraph: Accelerating Streaming Graph Processing via Depende...

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Design Automation Conference

作者： Zihan Jiang Fubing Mao Yapu Guo Xu Liu Haikun Liu Xiaofei Liao Hai Jin Wei Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Electronic and Computer Engineering HKUST Hong Kong

Streaming graph processing needs to timely evaluate continuous queries. Prior systems suffer from massive redundant computations due to the irregular order of processing vertices influenced by updates. To address this issue, we propose ACGraph, a novel streaming graph processing approach for monotonic graph algorithms. It maintains dependence trees during runtime, and makes affected vertices processed in a top-to-bottom order in the hierarchy of the dependence trees, thus normalizing the state propagation order and coalescing of multiple propagation to the same vertices. Experimental results show that ACGraph reduces the number of updates by 50% on average, and achieves the speedup of 1.75~7.43× over state-of-the-art systems.

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