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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Zhixun Li Liang Wang Xin Sun Yifan Luo Yanqiao Zhu Dingshuo Chen Yingtao Luo Xiangxin Zhou Qiang Liu Shu Wu Jeffrey Xu Yu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences and Department of Automation University of Science and Technology of China Department of Automation University of Science and Technology of China School of Cyberspace Security Beijing University of Posts and Telecommunications Department of Computer Science University of California Los Angeles Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB.

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Loophole-free test of macroscopic realism via high-order correlations of measurement

arXiv

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arXiv 2024年

作者： Wang, Ping Chen, Chong Liao, Hao Vorobyov, Vadim V. Wrachtrup, Jörg Liu, Ren-Bao College of Education for the Future Beijing Normal University Zhuhai519087 China Department of Physics The Chinese University of Hong Kong New Territories Shatin Hong Kong Centre for Quantum Coherence The Chinese University of Hong Kong New Territories Shatin Hong Kong The Hong Kong Institute of Quantum Information Science and Technology The Chinese University of Hong Kong New Territories Shatin Hong Kong National Engineering Laboratory on Big Data System Computing Technology Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China 3rd Institute of Physics Research Center SCoPE and IQST University of Stuttgart Stuttgart70569 Germany New Cornerstone Science Laboratory The Chinese University of Hong Kong New Territories Shatin Hong Kong

Test of macroscopic realism (MR) is key to understanding the foundation of quantum mechanics. Due to the existence of the non-invasive measurability loophole and other interpretation loopholes, however, such test remains an open question. Here we propose a general inequality based on high-order correlations of measurements for a loophole-free test of MR at the weak signal limit. Importantly, the inequality is established using the statistics of raw data recorded by classical devices, without requiring a specific model for the measurement process, so its violation would falsify MR without the interpretation loophole. The non-invasive measurability loophole is also closed, since the weak signal limit can be verified solely by measurement data (using the relative scaling behaviors of different orders of correlations). We demonstrate that the inequality can be broken by a quantum spin model. The inequality proposed here provides an unambiguous test of the MR principle and is also useful to characterizing quantum coherence. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum theory

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

arXiv

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arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Deng, Pengchao Liu, Chongpei Sebe, Nicu Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China Department of Information Engineering and Computer Science University of Trento Trento38123 Italy School of Computing and Communications Lancaster University LA1 4YW United Kingdom Department of Computer Science The University of Western Australia WA6009 Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance. Our code will be made public at https://***/CNJianLiu/Diff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Augmented reality

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Local Walsh-average-based estimation and variable selection for single-index models

引用

science China Mathematics 2019年第10期62卷 1977-1996页

作者： Jing Yang Fang Lu Hu Yang Key Laboratory of High Performance Computing and StochasticInformation Processing(Ministry of Education of China) College of Mathematics and Computer ScienceHunan Normal UniversityChangsha410081China College of Mathematics and Statistics Chongqing UniversityChongqing401331China

We propose a robust estimation procedure based on local Walsh-average regression(LWR) for single-index models. Our novel method provides a root-n consistent estimate of the single-index parameter under some mild regularity conditions;the estimate of the unknown link function converges at the usual rate for the nonparametric estimation of a univariate covariate. We theoretically demonstrate that the new estimators show significant efficiency gain across a wide spectrum of non-normal error distributions and have almost no loss of efficiency for the normal error. Even in the worst case, the asymptotic relative efficiency(ARE) has a lower bound compared with the least squares(LS) estimates;the lower bounds of the AREs are 0.864 and 0.8896 for the single-index parameter and nonparametric function, respectively. Moreover, the ARE of the proposed LWR-based approach versus the ARE of the LS-based method has an expression that is closely related to the ARE of the signed-rank Wilcoxon test as compared with the t-test. In addition, to obtain a sparse estimate of the single-index parameter, we develop a variable selection procedure by combining the estimation method with smoothly clipped absolute deviation penalty;this procedure is shown to possess the oracle property. We also propose a Bayes information criterion(BIC)-type criterion for selecting the tuning parameter and further prove its ability to consistently identify the true model. We conduct some Monte Carlo simulations and a real data analysis to illustrate the finite sample performance of the proposed methods.

关键词： single-index models local Walsh-average regression asymptotic relative efficiency variable selection oracle property

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Styx: A Data-Oriented Mutation Framework to Improve the Robustness of DNN

Styx: A Data-Oriented Mutation Framework to Improve the Robu...

引用

IEEE International Conference on Automated Software Engineering (ASE)

作者： Meixi Liu Weijiang Hong Weiyu Pan Chendong Feng Zhenbang Chen Ji Wang College of Computer National University of Defense Technology Changsha China State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China

ISBN: (数字)9781450367684

ISBN: (纸本)9781728172811

The robustness of deep neural network (DNN) is critical and challenging to ensure. In this paper, we propose a general data-oriented mutation framework, called Styx, to improve the robustness of DNN. Styx generates new training data by slightly mutating the training data. In this way, Styx ensures the DNN's accuracy on the test dataset while improving the adaptability to small perturbations, i.e., improving the robustness. We have instantiated Styx for image classification and proposed pixel-level mutation rules that are applicable to any image classification DNNs. We have applied Styx on several commonly used benchmarks and compared Styx with the representative adversarial training methods. The preliminary experimental results indicate the effectiveness of Styx.

关键词： Training Perturbation methods Neural networks Training data Robustness Image classification Software engineering

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MPI-SV: A Symbolic Verifier for MPI Programs

MPI-SV: A Symbolic Verifier for MPI Programs

引用

International Conference on Software Engineering - Companion, ICSE, Companion

作者： Zhenbang Chen Hengbiao Yu Xianjin Fu Ji Wang College of Computer National University of Defense Technology Changsha China State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China

ISBN: (数字)9781450371223

ISBN: (纸本)9781728165288

Message passing is the primary programming paradigm in high-performance computing. However, developing message passing programs is challenging due to the non-determinism caused by parallel execution and complex programming features such as non-deterministic communications and asynchrony. We present MPI-SV, a symbolic verifier for verifying the parallel C programs using message passing interface (MPI). MPI-SV combines symbolic execution and model checking in a synergistic manner to improve the scalability and enlarge the scope of verifiable properties. We have applied MPI-SV to real-world MPI C programs. The experimental results indicate that MPI-SV can, on average, achieve 19x speedups in verifying deadlock-freedom and 5x speedups in finding counter-examples. MPI-SV can be accessed at https://***, and the demonstration video is at https://***/zzCY0CPDNCw.

关键词： Model checking System recovery Message passing Tools Task analysis Scalability Generators

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Synthesizing Smart Solving Strategy for Symbolic Execution

Synthesizing Smart Solving Strategy for Symbolic Execution

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IEEE International Conference on Automated Software Engineering (ASE)

作者： Zehua Chen Zhenbang Chen Ziqi Shuai Yufeng Zhang Weiyu Pan College of Computer National University of Defense Technology Changsha China State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China

ISBN: (数字)9781450367684

ISBN: (纸本)9781728172811

Constraint solving is one of the challenges for symbolic execution. Modern SMT solvers allow users to customize the internal solving procedure by solving strategies. In this extended abstract, we report our recent progress in synthesizing a program-specific solving strategy for the symbolic execution of a program. We propose a two-stage procedure for symbolic execution. At the first stage, we synthesize a solving strategy by utilizing deep learning techniques. Then, the strategy will be used in the second stage to improve the performance of constraint solving. The preliminary experimental results indicate the promising of our method.

关键词： Deep learning Software engineering

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CPSketch: A ‘Couple’ Sketch-based Heavy Flow Detection Method

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Journal of Network and computer Applications 2025年

作者： Renpin Yao Yang Cao Yunhe Cui Yi Chen Chun Guo Guowei Shen Engineering Research Center of Text Computing & Cognitive Intelligence Guizhou University College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guizhou University

One of the core challenges in network measurement for large-scale networks is the accurate and efficient identification of heavy flows. This task has grown increasingly complex due to limited memory resources and the dynamic nature of network traffic patterns. Most current heavy flow detection methods fail to fully consider the mutual collisions between large flows and their sparse distribution, which are caused by the combined effects of hash collisions and the heavy-tailed distribution of network traffic, resulting in an inefficient use of limited memory resources. Additionally, existing approaches uniformly treat all flows. As a result, when the network contains many small flows, heavy flows are frequently and erroneously replaced. To address these issues, we propose CPSketch, a novel method that combines sketch with ‘couple’ buckets to enhance heavy flows detection accuracy. CPSketch optimizes memory utilization by leveraging cold buckets to store more heavy flows. Specifically, it accurately estimates flow sizes by monitoring the number of matched packets for candidate flows in real-time. For small flows, CPSketch rapidly identifies and evicts them based on flow ratios. For other flows, CPSketch calculates replacement probabilities using multidimensional statistical information. To protect heavy flows that have been incorrectly evicted, CPSketch employs a global hash function to provide additional storage opportunities and dynamically constructs couple buckets for these heavy flows. Moreover, CPSketch extends the selection range for minimum flows by leveraging couple buckets in the flow replacement policy, effectively mitigating the impact of hash limitations and improving memory efficiency. Experimental results from trace-driven simulations and Open vSwitch (OVS) tests demonstrate that CPSketch outperforms existing methods in accuracy, even under constrained memory and high traffic loads. It achieves improvements of up to 14.74% in F1-score while reducing the

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A Scalable Test Problem Generator for Sequential Transfer Optimization

arXiv

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arXiv 2023年

作者： Xue, Xiaoming Yang, Cuie Feng, Liang Zhang, Kai Song, Linqi Tan, Kay Chen The Department of Computer Science City University of Hong Kong Hong Kong The City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The College of Computer Science Chongqing University Chongqing400044 China The Civil Engineering School Qingdao University of Technology Qingdao266520 China The Department of Computing Hong Kong Polytechnic University Hong Kong

Sequential transfer optimization (STO), which aims to improve the optimization performance on a task of interest by exploiting the knowledge captured from several previously-solved optimization tasks stored in a database, has been gaining increasing research attention over the years. However, despite the remarkable advances in algorithm design, the development of a systematic benchmark suite for comprehensive comparisons of STO algorithms received far less attention. Existing test problems are either simply generated by assembling other benchmark functions or extended from specific practical problems with limited scalability. The relationships between the optimal solutions of the source and target tasks in these problems are also often manually configured, limiting their ability to model different similarity relationships presented in real-world problems. Consequently, the good performance achieved by an algorithm on these problems might be biased and hard to be generalized to other problems. In light of the above, in this study, we first introduce four concepts for characterizing STO problems and present an important problem feature, namely similarity distribution, which quantitatively delineates the relationship between the optima of the source and target tasks. Then, we present the general design guidelines of STO problems and a particular STO problem generator with good scalability. Specifically, the similarity distribution of a problem can be easily customized, enabling a continuous spectrum of representation of the diverse similarity relationships of real-world problems. Lastly, a benchmark suite with 12 STO problems featured by a variety of customized similarity relationships is developed using the proposed generator, which would serve as an arena for STO algorithms and provide more comprehensive evaluation results. The source code of the problem generator is available at htt s://ithub com/Xmin Hsueh/STOP-G. Copyright © 2023, The Authors. All rights reserved.

关键词： Scalability

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Two Birds With One Stone: Enhancing Communication and Sensing via Multi-Functional RIS

arXiv

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arXiv 2024年

作者： Ni, Wanli Wang, Wen Zheng, Ailing Wang, Peng You, Changsheng Eldar, Yonina C. Niyato, Dusit Schober, Robert The Department of Electronic Engineering Tsinghua University Beijing100084 China The Pervasive Communications Center Purple Mountain Laboratories Nanjing211111 China The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China The School of Electrical and Electronic Engineering North China Electric Power University Beijing102206 China The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China The Faculty of Mathematics and Computer Science Weizmann Institute of Science Israel The College of Computing and Data Science Nanyang Technological University Singapore Erlangen91054 Germany

In this article, we propose new network architectures that integrate multi-functional reconfigurable intelligent surfaces (MF-RISs) into 6G networks to enhance both communication and sensing capabilities. Firstly, we elaborate how to leverage MF-RISs for improving communication performance in different communication modes including unicast, mulitcast, and broadcast and for different multi-access schemes. Next, we emphasize synergistic benefits of integrating MF-RISs with wireless sensing, enabling more accurate and efficient target detection in 6G networks. Furthermore, we present two schemes that utilize MF-RISs to enhance the performance of integrated sensing and communication (ISAC). We also study multi-objective optimization to achieve the optimal trade-off between communication and sensing performance. Finally, we present numerical results to show the performance improvements offered by MF-RISs compared to conventional RISs in ISAC. We also outline key research directions for MF-RIS under the ambition of 6G. Copyright © 2024, The Authors. All rights reserved.

关键词： Reconfigurable architectures

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