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arXiv 2023年

作者： Liu, Liu Liu, Xuanqing Hsieh, Cho-Jui Tao, Dacheng Institute of Artificial Intelligence State Key Lab of Software Development Environment Beihang University China University of California Los Angeles United States School of Computer Science Sydney AI Centre Faculty of Engineering the University of Sydney SydneyNSW2008 Australia

Trust-region (TR) and adaptive regularization using cubics (ARC) have proven to have some very appealing theoretical properties for non-convex optimization by concurrently computing function value, gradient, and Hessian matrix to obtain the next search direction and the adjusted parameters. Although stochastic approximations help largely reduce the computational cost, it is challenging to theoretically guarantee the convergence rate. In this paper, we explore a family of stochastic TR and ARC methods that can simultaneously provide inexact computations of the Hessian matrix, gradient, and function values. Our algorithms require much fewer propagations overhead per iteration than TR and ARC. We prove that the iteration complexity to achieve ǫ-approximate second-order optimality is of the same order as the exact computations demonstrated in previous studies. Additionally, the mild conditions on inexactness can be met by leveraging a random sampling technology in the finite-sum minimization problem. Numerical experiments with a non-convex problem support these findings and demonstrate that, with the same or a similar number of iterations, our algorithms require less computational overhead per iteration than current second-order methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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Hidden high-risky states identification from routine urban traffic

arXiv

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arXiv 2024年

作者： Liu, Shiyan Bai, Mingyang Guo, Shengmin Gao, Jianxi Sun, Huijun Gao, Ziyou Li, Daqing School of Reliability and Systems Engineering Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States School of Systems Science Beijing Jiaotong University Beijing100044 China

One of the core risk management tasks is to identify hidden high-risky states that may lead to system breakdown, which can provide valuable early warning knowledge. However, due to high dimensionality and nonlinear interaction embedded in large-scale complex systems like urban traffic, it remains challenging to identify hidden high-risky states from huge system state space where over 99% of possible system states are not yet visited in empirical data. Based on maximum entropy model, we infer the underlying interaction network from complicated dynamical processes of urban traffic, and construct system energy landscape. In this way, we can locate hidden high-risky states that have never been observed from real data. These states can serve as risk signals with high probability of entering hazardous minima in energy landscape, which lead to huge recovery cost. Our finding might provide insights for complex system risk management. Significance statement Hidden high-risky states are lethal system states with catastrophic consequences for large-scale complex systems. Existing methods for identifying hidden high-risky states face challenges in routine urban traffic, where over 99% of possible system states are not yet visited in empirical data. Based on large-scale traffic-speed data, we build a least-structured network entropy model that captures intricate traffic interactions. This model can generate the entire state space and help the identification of hidden high-risky states that may lead to extreme events. Our results suggest a new way to provide reliable early warning signals for system breakdown based on critical risk scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Large scale systems

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A dual plasmonic core-shell Pt/[TiN@TiO_(2)]catalyst for enhanced photothermal synergistic catalytic activity of VOCs abatement

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Nano Research 2022年第8期15卷 7071-7080页

作者： Anqi Li Qianpeng Zhang Shuaiqi Zhao Yanan Chong Peng Wu Yifei Li Xiaojing Jin Guangxu Chen Yongcai Qiu Shihe Yang Daiqi Ye Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control School of Environment and EnergySouth China University of TechnologyGuangzhou 510006China Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina Guangzhou HKUST Fok Ying Tung Research Institute NanshaGuangzhou 511458China State Key Laboratory of Luminescent Materials and Devices South China University of TechnologyGuangzhou 510640China Guangdong Key Lab of Nano-Micro Material Research School of Chemical Biology and Biotechnology Shenzhen Graduate SchoolPeking UniversityShenzhen 518055China

Volatile organic compounds(VOCs)are ubiquitous organic pollutants affecting atmospheric environment and human *** development of new efficient and environmentally friendly materials utilizing photothermal synergistic catalysis for purification of VOCs is still ***,we design and prepare a core–shell TiN@TiO_(2)nanostructure integrating with nanoscaled Pt(Pt/[TiN@TiO_(2)])by an attractive quenching *** strong light-harvesting capability of Pt and TiN components improve light-to-heat utilization efficiency by their intrinsic surface plasmon resonance *** TiO_(2)component upon the surface and the coexisting coupling effect of Pt0 and Pt2+enhance the photocatalytic effect of the *** a result,the catalytic performance is significantly improved with toluene(120 ppm)conversion of 100%under the gas hourly space velocity of 72,000 mL·g^(−1)·h^(−1)and light illumination of 500 mW·cm^(−2).The desired catalyst thus achieves highly efficient coupling effect of photocatalysis and light-to-heat conversion for promoting VOCs abatement.

关键词： core-shell Pt/[TiN@TiO_(2)]catalyst volatile organic compounds(VOCs)abatement photothermal synergistic catalysis full spectrum sunlight utilization

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Adversarial Examples in the Physical World: A Survey

arXiv

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arXiv 2023年

作者： Wang, Jiakai Liu, Xianglong Hu, Jin Wang, Donghua Wu, Siyang Jiang, Tingsong Guo, Yuanfang Liu, Aishan Liu, Aishan Zhou, Jiantao Zhongguancun Laboratory Beijing China The College of Computer Science and Technology Zhejiang University Hangzhou China The State Key Lab of Software Development Environment Beihang University Beijing100191 China The Defense Innovation Institute Chinese Academy of Military Science Beijing China The Department of Computer and Information Science Faculty of Science and Technology University of Macau China

Deep neural networks (DNNs) have demonstrated high vulnerability to adversarial examples, raising broad security concerns about their applications. Besides the attacks in the digital world, the practical implications of adversarial examples in the physical world present significant challenges and safety concerns. However, current research on physical adversarial examples (PAEs) lacks a comprehensive understanding of their unique characteristics, leading to limited significance and understanding. In this paper, we address this gap by thoroughly examining the characteristics of PAEs within a practical workflow encompassing training, manufacturing, and re-sampling processes. By analyzing the links between physical adversarial attacks, we identify manufacturing and re-sampling as the primary sources of distinct attributes and particularities in PAEs. Leveraging this knowledge, we develop a comprehensive analysis and classification framework for PAEs based on their specific characteristics, covering over 100 studies on physical-world adversarial examples. Furthermore, we investigate defense strategies against PAEs and identify open challenges and opportunities for future research. We aim to provide a fresh, thorough, and systematic understanding of PAEs, thereby promoting the development of robust adversarial learning and its application in open-world scenarios. © 2023, CC BY.

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Simulation and Analysis of Back Siltation in a Navigation Channel Using MIKE 21

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Journal of Ocean University of China 2022年第4期21卷 893-902页

作者： ZHANG Kuncheng LI Qingjie ZHANG Jing SHI Hongyuan YU Jing GUO Xinchang DU Yonggang School of Marxism/Institute of Marine Development Ocean University of ChinaQingdao 266100China Guangxi Key Laboratory of Marine Environmental Science Guangxi Academy of SciencesGuangxi 530007China Yantai Marine Environment Monitoring Center Station State Oceanic AdministrationYantai 264006China Qingdao Ocean Shipping Mariners College Teaching and Research Department of Mathematics and Computer ScienceQingdao 266100China Civil Engineering College Ludong UniversityYantai 264025China College of Oceanic and Atmospheric Sciences/Institute of Marine Development Ocean University of ChinaQingdao266100China

The channel back-siltation problem has been restricting the development of channels,and its monitoring is limited by funds and natural ***,predicting the channel back-siltation situation in a timely and accurate manner is ***,a numerical simulation of the back-siltation problem in the sea area near the channel is of great significance to the maintenance of a *** this study,the back siltation of a deep-water channel in the Lanshan Port area of the Port of Rizhao after dredging is *** paper relies on the MIKE 21 software to establish the wave,tidal current,and sediment numerical models and uses measured data from two observation stations in the study area for *** this basis,taking one month as an example,the entire project channel was divided into five sections,and three observation points were set on each *** results show that the area with offshore siltation is located in the northerly direction of the artificial anti-wave *** occurred on the northern seabed in the sea a little farther from the *** occurred on the seabed surface far away from the shoreline,and with the increase in the distance from the shoreline,the amount of siltation in the south,center,and north became gradually closed,and the results can be used to guide actual engineering *** study will play a positive role in promoting the dredging project of Rizhao Lanshan Port.

关键词： channel back siltation numerical simulation back silting analysis and prediction

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Attention-Based Knowledge-aware Multi-Interest Intelligent Model for Sequential Recommendation 19

Attention-Based Knowledge-aware Multi-Interest Intelligent M...

引用

2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Li, Yang Li, Qianmu Meng, Shunmei Hou, Jun Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China Wuyi University Intelligent Manufacturing Department Jiangmen China Nanjing University State Key Lab. for Novel Software Technology Nanjing China Nanjing Vocational University of Industry Technology School of Social Science Nanjing China

ISBN: (纸本)9798350346558

As a fundamental means of intelligent service, recommender systems have shown a trend of diversification. Sequential recommendation, a class of time-aware methods, aims to predict the user's next click. Multi-interest representation is a hot topic in recommender systems, but the research on how to apply multi-interest into sequences is still in the minority. And many methods will introduce knowledge graph to improve the representation of items, but how to consider both knowledge graph and user's multi-interest in sequential recommendation has become a temporary blank. In this paper, we propose an attentive sequential recommendation with knowledge-aware multi-interest module (AKAMI). Items are embedded from the sequence's structure and user's interests. Specifically, we represent items by stacking self-attention layers to mine the internal correlation of the sequence. Meanwhile, we segment the sequence via the common neighbors in the knowledge graph and cast the user's multi-interest based on the aggregation of subsequences. Multi-interests are integrated into item representation by the gating network adaptively. Comprehensive experiments on two real-world datasets show that the proposed model performs better than various representative methods under different evaluation metrics. Additionally, we also visually explain the model through a case study. © 2022 IEEE.

关键词： Recommender systems

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DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing

DeepRicci: Self-supervised Graph Structure-Feature Co-Refine...

引用

IEEE International Conference on Data Mining (ICDM)

作者： Li Sun Zhenhao Huang Hua Wu Junda Ye Hao Peng Zhengtao Yu Philip S. Yu School of Control and Computer Engineering North China Electric Power University Beijing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Department of Computer Science University of Illinois at Chicago IL USA

Graph Neural Networks (GNNs) have shown great power for learning and mining on graphs, and Graph Structure Learning (GSL) plays an important role in boosting GNNs with a refined graph. In the literature, most GSL solutions either primarily focus on structure refinement with task-specific supervision (i.e., node classification), or overlook the inherent weakness of GNNs themselves (e.g., over-squashing), resulting in suboptimal performance despite sophisticated designs. In light of these limitations, we propose to study self-supervised graph structure-feature co-refinement for effectively alleviating the issue of over-squashing in typical GNNs. In this paper, we take a fundamentally different perspective of the Ricci curvature in Riemannian geometry, in which we encounter the challenges of modeling, utilizing and computing Ricci curvature. To tackle these challenges, we present a self-supervised Riemannian model, DeepRicci. Specifically, we introduce a latent Riemannian space of heterogeneous curvatures to model various Ricci curvatures, and propose a gyrovector feature mapping to utilize Ricci curvature for typical GNNs. Thereafter, we refine node features by geometric contrastive learning among different geometric views, and simultaneously refine graph structure by backward Ricci flow based on a novel formulation of differentiable Ricci curvature. Finally, extensive experiments on public datasets show the superiority of DeepRicci, and the connection between backward Ricci flow and over-squashing. Codes of our work are given in https://***/RiemanGraph/.

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A Phase Estimation Algorithm for Quantum Speed-Up Multi-Party Computing

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computers, Materials & Continua 2021年第4期67卷 241-252页

作者： Wenbin Yu Hao Feng Yinsong Xu Na Yin Yadang Chen Zhi-Xin Yang Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Jiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Department of Computer Science and Engineering Michigan State UniversityEast Lansing48824MIUSA State Key Laboratory of Internet of Things for Smart City and Department of Electromechanical Engineering University of Macao999078Macao

Security and privacy issues have attracted the attention of researchers in the field of IoT as the information processing scale grows in sensor *** computing,theoretically known as an absolutely secure way to store and transmit information as well as a speed-up way to accelerate local or distributed classical algorithms that are hard to solve with polynomial complexity in computation or *** this paper,we focus on the phase estimation method that is crucial to the realization of a general multi-party computing model,which is able to be accelerated by quantum algorithms.A novel multi-party phase estimation algorithm and the related quantum circuit are proposed by using a distributed Oracle operator with *** proved theoretical communication complexity of this algorithm shows it can give the phase estimation before applying multi-party computing efficiently without increasing any additional ***,a practical problem of multi-party dating investigated shows it can make a successful estimation of the number of solution in advance with zero communication complexity by utilizing its special statistic *** simulations present the correctness,validity and efficiency of the proposed estimation method.

关键词： Edge computing security multi-party computing quantum algorithm phase estimation communication complexity

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Out-of-Distribution Detection in Long-Tailed Recognition with Calibrated Outlier Class Learning

arXiv

引用

arXiv 2023年

作者： Miao, Wenjun Pang, Guansong Li, Tianqi Bai, Xiao Zheng, Jin School of Computer Science and Engineering Beihang University China School of Computing and Information Systems Singapore Management University Singapore State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University China State Key Laboratory of Virtual Reality Technology and Systems Beihang University China

Existing out-of-distribution (OOD) methods have shown great success on balanced datasets but become ineffective in long-tailed recognition (LTR) scenarios where 1) OOD samples are often wrongly classified into head classes and/or 2) tail-class samples are treated as OOD samples. To address these issues, current studies fit a prior distribution of auxiliary/pseudo OOD data to the long-tailed in-distribution (ID) data. However, it is difficult to obtain such an accurate prior distribution given the unknowingness of real OOD samples and heavy class imbalance in LTR. A straightforward solution to avoid the requirement of this prior is to learn an outlier class to encapsulate the OOD samples. The main challenge is then to tackle the aforementioned confusion between OOD samples and head/tail-class samples when learning the outlier class. To this end, we introduce a novel calibrated outlier class learning (COCL) approach, in which 1) a debiased large margin learning method is introduced in the outlier class learning to distinguish OOD samples from both head and tail classes in the representation space and 2) an outlier-class-aware logit calibration method is defined to enhance the long-tailed classification confidence. Extensive empirical results on three popular benchmarks CIFAR10-LT, CIFAR100-LT, and ImageNet-LT demonstrate that COCL substantially outperforms state-of-the-art OOD detection methods in LTR while being able to improve the classification accuracy on ID data. Code is available at https://***/mala-lab/COCL. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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A Novel Hierarchical Discourse Model for Scientific Article and It's Efficient Top-K Resampling-based Text Classification Approach

A Novel Hierarchical Discourse Model for Scientific Article ...

引用

2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Gao, Min Chen, Chun-Hua Gao, Zhi-Han Chen, Wei-Long Ren, Yuan Kwong, Sam Zhan, Zhi-Hui South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information Guangzhou510006 China South China University of Technology School of Software Engineering Guangzhou510006 China South China University of Technology School of Environment and Energy Guangzhou510006 China City University of Hong Kong Department of Computer Science Hong Kong Hong Kong

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Scientific articles contain rich knowledge that can significantly assists scientific research, but it is difficult to precisely extract knowledge information due to the complexity of the discourse structure of scientific articles. To provide more accurate scientific research knowledge for researchers in a specific academic domain, it is necessary to study the discourse structure of domain scientific articles and to propose an automatic annotation approach to automatically annotate discourse information from articles. Unfortunately, few works have studied the discourse structure of domain scientific articles and the corresponding automatic discourse annotation. To fill this gap, we take scientific articles of the wastewater-based epidemiology domain as a case to study how to automatically and efficiently annotate discourse information. This paper has three contributions. Firstly, we propose a hierarchical discourse model with two layers to cover all potential discourses in various domain scientific articles. Specifically, the first layer defines four core discourse concepts to describe the main process of a scientific research which can be applied in all scientific articles in various domains. The second layer defines fine-granular domain-specific structure, which can accurately describe the entire research contents of a specific domain. Secondly, based on the proposed model, we build a corpus dataset of 100 annotated scientific articles in the wastewater-based epidemiology domain. Thirdly, based on the model and dataset, we propose a simple yet efficient Top-K resampling-based approach to train a more effective classifier for automatic annotation. Extensive experiments verify the effectiveness and efficiency of our proposed hierarchical discourse model and the Top-K resampling-based classification approach. © 2022 IEEE.

关键词： Classification (of information)

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