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arXiv 2023年

作者： Wu, Chengpei Lou, Yang Wang, Lin Li, Junli Li, Xiang Chen, Guanrong The College of Computer Science Sichuan Normal University Chengdu610066 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks. This kind of network robustness is typically measured by the time-consuming attack simulation, which returns a sequence of values that record the remaining connectivity and controllability after a sequence of node- or edge-removal attacks. For improvement, this paper develops an efficient framework for network robustness prediction, the spatial pyramid pooling convolutional neural network (SPP-CNN). The new framework installs a spatial pyramid pooling layer between the convolutional and fully-connected layers, overcoming the common mismatch issue in the CNN-based prediction approaches and extending its generalizability. Extensive experiments are carried out by comparing SPP-CNN with three state-of-the-art robustness predictors, namely a CNN-based and two graph neural networks-based frameworks. Synthetic and real-world networks, both directed and undirected, are investigated. Experimental results demonstrate that the proposed SPP-CNN achieves better prediction performances and better generalizability to unknown datasets, with significantly lower time-consumption, than its counterparts. © 2023, CC BY.

关键词： Complex networks

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Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization

arXiv

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arXiv 2023年

作者： Huang, Zhuo Zhu, Miaoxi Xia, Xiaobo Shen, Li Yu, Jun Gong, Chen Han, Bo Du, Bo Liu, Tongliang Syndey Ai Centre The University of Sydney Australia National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China Jd Explore Academy China Department of Automation University of Science and Technology of China China Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computer Science Hong Kong Baptist University Hong Kong

Robust generalization aims to tackle the most challenging data distributions which are rare in the training set and contain severe noises, i.e., photon-limited corruptions. Common solutions such as distributionally robust optimization (DRO) focus on the worst-case empirical risk to ensure low training error on the uncommon noisy distributions. However, due to the over-parameterized model being optimized on scarce worst-case data, DRO fails to produce a smooth loss landscape, thus struggling on generalizing well to the test set. Therefore, instead of focusing on the worst-case risk minimization, we propose SharpDRO by penalizing the sharpness of the worst-case distribution, which measures the loss changes around the neighbor of learning parameters. Through worst-case sharpness minimization, the proposed method successfully produces a flat loss curve on the corrupted distributions, thus achieving robust generalization. Moreover, by considering whether the distribution annotation is available, we apply SharpDRO to two problem settings and design a worst-case selection process for robust generalization. Theoretically, we show that SharpDRO has a great convergence guarantee. Experimentally, we simulate photon-limited corruptions using CIFAR10/100 and ImageNet30 datasets and show that SharpDRO exhibits a strong generalization ability against severe corruptions and exceeds well-known baseline methods with large performance gains. Copyright © 2023, The Authors. All rights reserved.

关键词： Optimization

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Dynamic Pricing based Near-Optimal Resource Allocation for Elastic Edge Offloading

arXiv

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arXiv 2024年

作者： Xia, Yun Xue, Hai Zhang, Di Mumtaz, Shahid Xu, Xiaolong Rodrigues, Joel J.P.C. School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Electrical and Information Engineering Zhengzhou University the Henan International Joint Laboratory of Intelligent Health Information System The National Telemedicine Center The National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China Department of Engineering Nottingham Trent University NottinghamNG1 4FQ United Kingdom The Department of Electronic Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of School of Software Nanjing University of Information Science and Technology Nanjing210044 China Amazonas State University AM Manaus Brazil

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC systems. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU’s utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 92.11%, increasing the final user utility function value by 0.24%, and decreasing the variance of results by 95.45%. Copyright © 2024, The Authors. All rights reserved.

关键词： Particle swarm optimization (PSO)

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Enabling surrogate-assisted evolutionary reinforcement learning via policy embedding

arXiv

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arXiv 2023年

作者： Tang, Lan Li, Xiaxi Zhang, Jinyuan Li, Guiying Yang, Peng Tang, Ke Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Faculty of engineering Shenzhen MSU-BIT university Shenzhen518172 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China

Evolutionary Reinforcement Learning (ERL) that applying Evolutionary Algorithms (EAs) to optimize the weight parameters of Deep Neural Network (DNN) based policies has been widely regarded as an alternative to traditional reinforcement learning methods. However, the evaluation of the iteratively generated population usually requires a large amount of computational time and can be prohibitively expensive, which may potentially restrict the applicability of ERL. Surrogate is often used to reduce the computational burden of evaluation in EAs. Unfortunately, in ERL, each individual of policy usually represents millions of weights parameters of DNN. This high-dimensional representation of policy has introduced a great challenge to the application of surrogates into ERL to speed up training. This paper proposes a PE-SAERL Framework to at the first time enable surrogate-assisted evolutionary reinforcement learning via policy embedding (PE). Empirical results on 5 Atari games show that the proposed method can perform more efficiently than the four state-of-the-art algorithms. The training process is accelerated up to 7x on tested games, comparing to its counterpart without the surrogate and PE. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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GSLB: the graph structure learning benchmark 23

GSLB: the graph structure learning benchmark

引用

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.

关键词：

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Merging synthetic and real embryo data for advanced AI predictions

arXiv

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arXiv 2024年

作者： Presacan, Oriana Dorobanţiu, Alexandru Thambawita, Vajira Riegler, Michael A. Stensen, Mette H. Iliceto, Mario Aldea, Alexandru C. Sharma, Akriti Faculty of Electronics Telecommunications and Information Technology National University of Science and Technology Politehnica Bucharest Bucharest061071 Romania Department of Computer Science and Electrical Engineering Lucian Blaga University of Sibiu Sibiu550024 Romania Department of Holistic Systems SimulaMet Stensberggata 27 Oslo0170 Norway Department of Computer Science Oslo Metropolitan University Oslo0167 Norway Volvat Spiren Oslo0176 Norway Faculty of Biotechnologies University of Agronomic Sciences and Veterinary Medicine Bucharest011464 Romania Department of Validation Intelligence for Autonomous Software Systems Simula Research Laboratory Oslo0164 Norway Cyber Security Simula Research Laboratory Oslo0164 Norway

Accurate embryo morphology assessment is essential in assisted reproductive technology for selecting the most viable embryo. Artificial intelligence has the potential to enhance this process. However, the limited availability of embryo data presents challenges for training deep learning models. To address this, we trained two generative models using two datasets—one we created and made publicly available, and one existing public dataset—to generate synthetic embryo images at various cell stages, including 2-cell, 4-cell, 8-cell, morula, and blastocyst. These were combined with real images to train classification models for embryo cell stage prediction. Our results demonstrate that incorporating synthetic images alongside real data improved classification performance, with the model achieving 97% accuracy compared to 94.5% when trained solely on real data. This trend remained consistent when tested on an external Blastocyst dataset from a different clinic. Notably, even when trained exclusively on synthetic data and tested on real data, the model achieved a high accuracy of 92%. Furthermore, combining synthetic data from both generative models yielded better classification results than using data from a single generative model. Four embryologists evaluated the fidelity of the synthetic images through a Turing test, during which they annotated inaccuracies and offered feedback. The analysis showed the diffusion model outperformed the generative adversarial network, deceiving embryologists 66.6% versus 25.3% and achieving lower Fréchet inception distance scores. © 2024, CC BY-SA.

关键词： Generative adversarial networks

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ItD: Large Language Models Can Teach Themselves Induction through Deduction

arXiv

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arXiv 2024年

作者： Sun, Wangtao Xu, Haotian Yu, Xuanqing Chen, Pei He, Shizhu Zhao, Jun Liu, Kang The Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China CAS Engineering Laboratory for Intelligent Industrial Vision Institute of Automation Chinese Academy of Sciences Beijing China Department of Computer Science and Engineering Texas A&M University United States Shanghai Artificial Intelligence Laboratory China Xiaohongshu Inc China

Although Large Language Models (LLMs) are showing impressive performance on a wide range of Natural Language Processing tasks, researchers have found that they still have limited ability to conduct induction. Recent works mainly adopt "post processes" paradigms to improve the performance of LLMs on induction (e.g., the hypothesis search & refinement methods), but their performance is still constrained by the inherent inductive capability of the LLMs. In this paper, we propose a novel framework, Induction through Deduction (ItD), to enable the LLMs to teach themselves induction through deduction. The ItD framework is composed of two main components: a Deductive Data Generation module to generate induction data and a Naive Bayesian Induction module to optimize the fine-tuning and decoding of LLMs. Our empirical results showcase the effectiveness of ItD on two induction benchmarks, achieving relative performance improvement of 36% and 10% compared with previous state-of-the-art, respectively. Our ablation study verifies the effectiveness of two key modules of ItD. We also verify the effectiveness of ItD across different LLMs and deductors. The data and code of this paper can be found at https://***/r/ItD-E844. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Denoising Reuse: Exploiting Inter-frame Motion Consistency for Efficient Video Generation

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Wang, Chenyu Yan, Shuo Chen, Yixuan Wang, Xianwei Wang, Yujiang Dong, Mingzhi Yang, Xiaochen Li, Dongsheng Zhu, Rui Clifton, David A. Dick, Robert P. Lv, Qin Yang, Fan Lu, Tun Gu, Ning Shang, Li Fudan University School of Computer Science Shanghai China Fudan University Shanghai Key Laboratory of Data Science Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China Oxford Suzhou Centre for Advanced Research Suzhou China University of Bath Bath United Kingdom University of Glasgow School of Mathematics and Statistics United Kingdom Microsoft Research Asia Shanghai China University of London Bayes Business School City United Kingdom University of Oxford Department of Engineering Science Oxford United Kingdom University of Michigan Department of Electrical Engineering and Computer Science College of Engineering Ann ArborMI United States University of Colorado Boulder BoulderCO United States

Denoising-based diffusion models have attained impressive image synthesis;however, their applications on videos can lead to unaffordable computational costs due to the per-frame denoising operations. In pursuit of efficient video generation, we present a Diffusion Reuse MOtion (Dr. Mo) network to accelerate the video-based denoising process. Our crucial observation is that the latent representations in early denoising steps between adjacent video frames exhibit high consistencies with motion clues. Inspired by the discovery, we propose to accelerate the video denoising process by incorporating lightweight, learnable motion features. Specifically, Dr. Mo will only compute all denoising steps for base frames. For a non-based frame, Dr. Mo will propagate the pre-computed based latents of a particular step with interframe motions to obtain a fast estimation of its coarse-grained latent representation, from which the denoising will continue to obtain more sensitive and fine-grained representations. On top of this, Dr. Mo employs a meta-network named Denoising Step Selector (DSS) to dynamically determine the step to perform motion-based propagations for each frame, ensuring the correct transformation of multi-granularity visual features. Extensive evaluations on video generation and editing tasks indicate that Dr. Mo delivers widely applicable acceleration for diffusion-based video generations while effectively retaining the visual quality and style. Video generation and visualization results can be found at https://***. © 1991-2012 IEEE.

关键词： HTTP

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Reliability-Aware Network Slicing based on Multi-Objective Optimization

Reliability-Aware Network Slicing based on Multi-Objective O...

引用

Congress on Evolutionary Computation

作者： Qiqi Xia Jin Wang Chengqiang Huang Xin Yao Research Institute of Trustworthy Autonomous Systems (RITAS) Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Guangdong Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Reliability Technology Lab Huawei Technologies Co. Ltd. Shenzhen China Department of Computing and Decision Sciences Lingnan University Hong Kong SAR China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Network slicing is an essential technology in 5G and the forthcoming 6G networks. It aims to embed multiple virtual networks, i.e., network slices, on top of a shared substrate network to meet diverse service requirements. While a considerable body of existing research strives to maximize overall profits by meeting the resource demands of the network slices, optimizing their reliability is frequently overlooked. In this paper, we formalize the network slicing problem as a multi-objective optimization problem that aims to maximize total profits and reliability of network slices. To tackle this problem, we propose a new multi-objective optimization approach that improves over the state-of-the-art algorithm, which can achieve good approximate Pareto front results balancing total profits and reliability of network slices. The performance of our proposed method is evaluated on both artificial and real-world network topologies. Experimental results demonstrate the superior performance of our proposed method compared to the baseline algorithm, outperforming the latter in 92% of instances in terms of the Hypervolume (HV) metric.

关键词： Measurement 6G mobile communication Costs Network topology Network slicing Evolutionary computation Approximation algorithms

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Equalizer zero-determinant strategy in discounted repeated Stackelberg asymmetric game

arXiv

引用

arXiv 2023年

作者： Zhaoyang, Cheng Guanpu, Chen Yiguang, Hong Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai210201 China

This paper focuses on the performance of equalizer zero-determinant (ZD) strategies in discounted repeated Stackerberg asymmetric games. In the leader-follower adversarial scenario, the strong Stackelberg equilibrium (SSE) deriving from the opponents’ best response (BR), is technically the optimal strategy for the leader. However, computing an SSE strategy may be difficult since it needs to solve a mixed-integer program and has exponential complexity in the number of states. To this end, we propose to adopt an equalizer ZD strategy, which can unilaterally restrict the opponent’s expected utility. We first study the existence of an equalizer ZD strategy with one-to-one situations, and analyze an upper bound of its performance with the baseline SSE strategy. Then we turn to multi-player models, where there exists one player adopting an equalizer ZD strategy. We give bounds of the sum of opponents’ utilities, and compare it with the SSE strategy. Finally, we give simulations on unmanned aerial vehicles (UAVs) and the moving target defense (MTD) to verify the effectiveness of our approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Equalizers

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