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2nd International Symposium on Benchmarking, Measuring, and Optimization, Bench 2019

作者： Wu, Wenqing Feng, Xiao Zhang, Wenli Chen, Mingyu State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Science Beijing China University of Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9783030495558

The network load generators are widely used by network researchers to analyze link bandwidth, evaluate network performance and test device capabilities. Data center and IoT networks are quickly evolving and we desire to get a load generator that can precisely generate flow-level workload with high-throughput. Often researchers choose software-based generators because of their flexibility and open-source nature. However, despite the emerging of different solutions, existing software-based flow-level generators have difficulty in generating millions of concurrent TCP connections or achieving one-microsecond precision of packet inter departure time (IDT) which can undermine the correctness of experiments. In this paper, we present a new network load generator, called Massive Client Connections (MCC). MCC is a client load generator which means it performs flow-level load simulation. We separate the control plane from the data plane and design a two-stage timer mechanism to get higher precision. To take full advantage of multicore processors, we utilize the shared-nothing multi-threaded model. Our evaluation demonstrates that MCC generates network load conforming to expected distribution with one-microsecond precision. Moreover, MCC shows definite scalability of throughput in multicore systems. And it is capable of generating more than three million concurrent TCP connections with ten CPU cores. © Springer Nature Switzerland AG 2020.

关键词： Scalability

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Variational Entanglement-Assisted Quantum Process Tomography with Arbitrary Ancillary Qubits

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Physical Review Letters 2022年第13期129卷 133601-133601页

作者： Shichuan Xue Yizhi Wang Junwei Zhan Yaxuan Wang Ru Zeng Jiangfang Ding Weixu Shi Yong Liu Yingwen Liu Anqi Huang Guangyao Huang Chunlin Yu Dongyang Wang Xiang Fu Xiaogang Qiang Ping Xu Mingtang Deng Xuejun Yang 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 National Innovation Institute of Defense Technology AMS Beijing 100071 China

Quantum process tomography is a pivotal technique in fully characterizing quantum dynamics. However, exponential scaling of the Hilbert space with the increasing system size extremely restrains its experimental implementations. Here, we put forward a more efficient, flexible, and error-mitigated method: variational entanglement-assisted quantum process tomography with arbitrary ancillary qubits. Numerically, we simulate up to eight-qubit quantum processes and show that this tomography with m ancillary qubits (0≤m≤n) alleviates the exponential costs on state preparation (from 4n to 2n−m), measurement settings (at least a 1 order of magnitude reduction), and data postprocessing (efficient and robust parameter optimization). Experimentally, we first demonstrate our method on a silicon photonic chip by rebuilding randomly generated one-qubit and two-qubit unitary quantum processes. Further using the error mitigation method, two-qubit quantum processes can be rebuilt with average gate fidelity enhanced from 92.38% to 95.56%. Our Letter provides an efficient and practical approach to process tomography on the noisy quantum computing platforms.

关键词： Integrated optics Quantum entanglement Quantum tomography Tomography Variational approach

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A Privacy Preserving and Byzantine Robust Collaborative Federated Learning Method Design

A Privacy Preserving and Byzantine Robust Collaborative Fede...

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IEEE International Conference on Communications (ICC)

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Changchuan Yin Christopher G. Brinton Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Collaborative federated learning (CFL) enables device cooperation in training shared machine learning models without reliance on a parameter server. However, the absence of a parameter server also impacts vulnerabilities associated with adversarial attacks, including privacy inference and Byzantine attacks. In this context, this paper introduces a novel CFL framework that enables each device to individually determine the subset of devices to transmit FL parameters to over the wireless network, based on its neighboring devices' location, current loss, and connection information, to achieve privacy protection and robust aggregation. This is formulated as an optimization problem whose goal is to minimize CFL training loss while satisfying the privacy preservation, robust aggregation, and transmission delay requirements. To solve this problem, a proximal policy optimization (PPO)-based reinforcement learning (RL) algorithm integrated with a graph neural network (GNN) is proposed. Compared to traditional algorithms that use global information with high computational complexity, the proposed GNN-RL method can be deployed on devices based on neighboring information with lower computational overhead. Simulation results show that the proposed algorithm can protect data privacy and increase identification accuracy by 15% compared to an algorithm in which devices are partially clustered for model aggregation.

关键词： Training Privacy Federated learning Simulation Collaboration Clustering algorithms Propagation losses

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On-chip multiphoton Greenberger-Horne Zeilinger state based on integrated frequency combs

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Frontiers of physics 2020年第6期15卷 33-41页

作者： Pingyu Zhu Qilin Zheng Shichuan Xue Chao Wu Xinyao Yu Yang Wang Yingwen Liu Xiaogang Qiang Junjie Wu Ping Xu Institute for Quantum Information and State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha 410073China National Innovation Institute of Defense Technology AMSBeijing 100071China National Laboratory of Solid State Micro structures and School of Physics Nanjing UniversityNanjing 210093China

One of the most important multipartite entangled states,Greenberger-Horne-Zeilinger state(GHZ),serves as a fundamental resource for quantum foundat ion test,quantum communication and quantum *** increase the number of entangled particles,significant experimental efforts should been invested due to the complexity of optical setup and the difficulty in maintaining the coherence condition for high-fidelity GHZ ***,we propose an ultra-integrated scalable on-chip GHZ state generation scheme based on frequency *** designing several microrings pumped by different lasers,multiple partially overlapped quantum frequency combs are generated to supply as the basis for on-chip polarization-encoded GHZ state with each qubit occupying a certain spectral *** even and odd numbers of GHZ states can be engineered with constant small number of integrated components and easily scaled up on the same chip by only adjusting one of the pumnp *** addition,we give the on-chip design of projection measurement for characterizing GHZ states and show the reconfigurability of the *** proposal is rather simple and feasible within the existing fabrication technologies and we believe it will boost the development of multiphoton technologies.

关键词： quantum information Greenberger-Horne Zeilinger state frequency comb

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Global-view based Task Migration for Deep Learning Processor

Global-view based Task Migration for Deep Learning Processor

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Jinyu Cheng Kai Zhao Yuanchao Xu College of Information Engineering Capital Normal University Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology CAS Beijing China

In order to cope with the deep neural network application characterized by big data and intensive computation and memory access, deep learning processors often adopt NUMA architecture and multi-core architecture to alleviate the bandwidth bottleneck and contention problems existing in a single storage node and improve the parallelism of the system. This design also increases the complexity of task scheduling, considering not only the data affinity but also the utilization of DLP. When the load of DLP is uneven, the existing local view scheduling will make forward view scheduling according to the status of tasks waiting in line, instead of following the principle of data affinity, and forcibly migrate tasks to DLP in other quadrants with lower load. It is observed that this strategy produces unnecessary task migration in heterogeneous programming model. The reason is that the load is uneven from local view but is even from global view. This unnecessary migration leads to bandwidth fluctuation and overall performance degradation. In order to overcome this problem, this paper proposes global view scheduling, which schedules according to the status of tasks waiting in line for execution and tasks waiting in line for scheduling. Lazy migration is adopted when DLP load is balanced from the global view, and eager migration is adopted immediately when DLP load is unbalanced from the global view. Experimental results show that this scheduling method can reduce unnecessary migration, alleviate bandwidth fluctuation and improve overall performance without reducing NPU utilization.

关键词： Deep learning Degradation Program processors Fluctuations Processor scheduling Bandwidth computer architecture

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Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacy

arXiv

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arXiv 2024年

作者： Guan, Zhenyu Kong, Xiangyu Zhong, Fangwei Wang, Yizhou Institute for Artificial Intelligence Peking University Beijing China Computer School Beijing Information Science & Technology University Beijing China School of Artificial Intelligence Beijing Normal University Beijing China Center on Frontiers of Computing Studies School of Computer Science Nat’l Eng. Research Center of Visual Technology State Key Lab of General Artificial Intelligence Peking University Beijing China State Key Laboratory of General Artificial Intelligence BIGAI Beijing China

Diplomacy is one of the most sophisticated activities in human society, involving complex interactions among multiple parties that require skills in social reasoning, negotiation, and long-term strategic planning. Previous AI agents have demonstrated their ability to handle multi-step games and large action spaces in multi-agent tasks. However, diplomacy involves a staggering magnitude of decision spaces, especially considering the negotiation stage required. While recent agents based on large language models (LLMs) have shown potential in various applications, they still struggle with extended planning periods in complex multi-agent settings. Leveraging recent technologies for LLM-based agents, we aim to explore AI’s potential to create a human-like agent capable of executing comprehensive multi-agent missions by integrating three fundamental capabilities: 1) strategic planning with memory and reflection;2) goal-oriented negotiation with social reasoning;and 3) augmenting memory through self-play games for self-evolution without human in the loop. Copyright © 2024, The Authors. All rights reserved.

关键词： Strategic planning

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A DoS-attack Resilient Control Approach for AIOT-enabled Systems

A DoS-attack Resilient Control Approach for AIOT-enabled Sys...

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Artificial Intelligence of Things and Systems (AIoTSys), International Conference on

作者： Xiaoya Cao Zhenya Chen Weisheng Liu Yuanlong Liu Wenting Wang Xin Wang Ming Yang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China State Grid Shandong Electric Power Company Jinan China State Grid Shandong Electric Power Research Institute Jinan China

This paper presents a novel controller design for dealing with the challenges posed by Denial-of-Service (DoS) attacks in the context of Artificial Intelligence of Things (AIOT). The proposed design employs a prediction approach that utilizes uniform quantization for both encoder and decoder, aiming to effectively mitigate data loss during data transmission in scenarios involving DoS attacks. Furthermore, the study investigates the application of dynamic system design as a means to enhance the security defense mechanisms of AIOT, consequently bolstering the overall resilience of the system. The effectiveness and practicality of the proposed approach are validated through comprehensive simulations, providing empirical evidence of its efficacy.

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Empirical investigation of stochastic local search for maximum satisfiability

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Frontiers of computer Science 2019年第1期13卷 86-98页

作者： Yi CHU Chuan LUO Shaowei CAI Haihang YOU State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

The maximum satisfiability (MAX-SAT)problem is an important NP-hard problem in theory,and has a broad range of applications in *** local search (SLS)is becoming an increasingly popular method for solving ***,a powerful SLS algorithm called CCLS shows efficiency on solving random and crafted MAX-SAT ***,the performance of CCLS on solving industrial MAX-SAT instances lags far *** this paper,we focus on experimentally analyzing the performance of SLS algorithms for solving industrial MAXSAT ***,we conduct experiments to analyze why CCLS performs poor on industrial *** we propose a new strategy called additive BMS (Best from Multiple Selections)to ease the serious *** integrating CCLS and additive BMS,we develop a new SLS algorithm for MAXSAT called CCABMS,and related experiments indicate the efficiency of ***,we experimentally analyze the effectiveness of initialization methods on SLS algorithms for MAX-SAT,and combine an effective initialization method with CCABMS,resulting in an enhanced *** results show that our enhanced algorithm performs better than its state-of-the-art SLS competitors on a large number of industrial MAX-SAT instances.

关键词： empirical investigation stochastic local search maximum satisfiability industrial instances additive BMS

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The NeRF Signature: Codebook-Aided Watermarking for Neural Radiance Fields

arXiv

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arXiv 2025年

作者： Luo, Ziyuan Rocha, Anderson Shi, Boxin Guo, Qing Li, Haoliang Wan, Renjie Department of Computer Science Hong Kong Baptist University Hong Kong Institute of Computing University of Campinas Brazil State Key Laboratory of Multimedia Information Processing and National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China A*STAR Singapore Department of Electrical Engineering City University of Hong Kong Hong Kong

Neural Radiance Fields (NeRF) have been gaining attention as a significant form of 3D content representation. With the proliferation of NeRF-based creations, the need for copyright protection has emerged as a critical issue. Although some approaches have been proposed to embed digital watermarks into NeRF, they often neglect essential model-level considerations and incur substantial time overheads, resulting in reduced imperceptibility and robustness, along with user inconvenience. In this paper, we extend the previous criteria for image watermarking to the model level and propose NeRF Signature, a novel watermarking method for NeRF. We employ a Codebook-aided Signature Embedding (CSE) that does not alter the model structure, thereby maintaining imperceptibility and enhancing robustness at the model level. Furthermore, after optimization, any desired signatures can be embedded through the CSE, and no fine-tuning is required when NeRF owners want to use new binary signatures. Then, we introduce a joint pose-patch encryption watermarking strategy to hide signatures into patches rendered from a specific viewpoint for higher robustness. In addition, we explore a Complexity-Aware key Selection (CAKS) scheme to embed signatures in high visual complexity patches to enhance imperceptibility. The experimental results demonstrate that our method outperforms other baseline methods in terms of imperceptibility and robustness. The source code is available at: https://***/luo-ziyuan/NeRF_Signature. Copyright © 2025, The Authors. All rights reserved.

关键词： Image watermarking

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ISort: SSD Internal Sorting Algorithm for Big Data

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Mobile Information Systems 2022年第1期2022卷

作者： Liu, Yang Chen, Wenhan Ge, Xuran Chen, Zhiguang Ou, Yang Xiao, Nong Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Hunan Province Changsha City410000 China Sun Yat-sen University Guangzhou Province Guangzhou City510000 China Institute of Computing Technology College of Computer National University of Defense Technology Hunan Province Changsha City410000 China

As a basic algorithm for big data processing, external sorting suffers from massive read and write operations in the external memory. Recent works separate part of the data processing work from the host side to the solid state drive (SSD) to reduce data transmission. However, the internal memory of the SSD is limited, and undesirable data retention could occur during the merge phase. Therefore, to improve the efficiency of memory, we propose an algorithm named ISort. Specifically, we build an index table between the memory and the address. The index table determines the order of pages being read in the merge phase according to their minimum values, which are read into memory sequentially to reduce the data residing in memory and improve memory efficiency. Since the merge phase is performed inside the SSD, ISort can take advantage of the high IO bandwidth within the SSD to speed up the execution of the merge phase. We search for the optimal ratio of read and write channels by comparing the "specialized channel"and the "hybrid channel"for data of read and write performance because the utilization of the channel will directly influence performance. Experimental results show that ISort can maintain better data processing speed when SSD memory is limited, outperforming other robust algorithms. In addition, the algorithm's performance using the crossover strategy is better than that using the specialization strategy. © 2022 Yang Liu et al.

关键词： Big data

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