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arXiv 2024年

作者： Liu, Xinyue Wang, Jianyuan Leng, Biao Zhang, Shuo School of Computer Science and Engineering Beihang University Beijing China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer & Technology Beijing Jiaotong University Beijing China

knowledge distillation based on student-teacher network is one of the mainstream solution paradigms for the challenging unsupervised Anomaly Detection task, utilizing the difference in representation capabilities of the teacher and student networks to implement anomaly localization. However, over-generalization of the student network to the teacher network may lead to negligible differences in representation capabilities of anomaly, thus affecting the detection effectiveness. Existing methods address the possible over-generalization by using differentiated students and teachers from the structural perspective or explicitly expanding distilled information from the content perspective, which inevitably results in an increased likelihood of underfitting of the student network and poor anomaly detection capabilities in anomaly center or edge. In this paper, we propose Dual-Modeling Decouple Distillation (DMDD) for the unsupervised Anomaly Detection. In DMDD, a Decouple Student-Teacher Network is proposed to decouple the initial student features into normality and abnormality features. We further introduce Dual-Modeling Distillation based on normal-anomalous image pairs, fitting normality features of anomalous image and the teacher features of the corresponding normal image, widening the distance between abnormality features and the teacher features in anomalous regions. Synthesizing these two distillation ideas, we achieve anomaly detection which focuses on both edge and center of anomaly. Finally, a Multi-perception Segmentation Network is proposed to achieve focused anomaly map fusion based on multiple attention. Experimental results on MVTec AD show that DMDD surpasses SOTA localization performance of previous knowledge distillation-based methods, reaching 98.85% on pixel-level AUC and 96.13% on PRO. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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Aerial Secure Collab.rative Communications under Eavesdropper Collusion in Low-altitude Economy: A Generative Swarm Intelligent Approach

arXiv

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arXiv 2025年

作者： Li, Jiahui Sun, Geng Wu, Qingqing Liang, Shuang Wang, Jiacheng Niyato, Dusit Kim, Dong In College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Information Science and Technology Northeast Normal University Changchun130117 China Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

The rapid development of the low-altitude economy (LAE) has significantly increased the utilization of autonomous aerial vehicles (AAVs) in various applications, necessitating efficient and secure communication methods among AAV swarms. In this work, we aim to introduce distributed collab.rative beamforming (DCB) into AAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. Specifically, we consider a two-way DCB-enabled aerial communication between two AAV swarms and construct these swarms as two AAV virtual antenna arrays. Then, we minimize the two-way known secrecy capacity and maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers, respectively. Simultaneously, we also minimize the energy consumption of AAVs when constructing virtual antenna arrays. Due to the conflicting relationships between secure performance and energy efficiency, we consider these objectives by formulating a multi-objective optimization problem, which is NP-hard and with a large number of decision variables. Accordingly, we design a novel generative swarm intelligence (GenSI) framework to solve the problem with less overhead, which contains a conditional variational autoencoder (CVAE)-based generative method and a proposed powerful swarm intelligence algorithm. In this framework, CVAE can collect expert solutions obtained by the swarm intelligence algorithm in other environment states to explore characteristics and patterns, thereby directly generating high-quality initial solutions in new environment factors for the swarm intelligence algorithm to search solution space efficiently. Simulation results show that the proposed swarm intelligence algorithm outperforms other state-of-the-art baseline algorithms, and the GenSI can achieve similar optimization results by using far fewer iterations than the ordinary swarm intelligence algorithm. Experimental tests demonstrate that introducing the CVAE mechanism ach

关键词： Multiobjective optimization

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Effects of Urea Solution Concentration on Soil Hydraulic Properties and Water Infiltration Characteristics

SSRN

引用

SSRN 2023年

作者： Feng, Zheng-Jiang Nie, Wei-Bo Ma, Yun-Peng Li, Yu-Chen Ma, Xiao-Yi Zhu, Hong-Yan State Key Lab. of Eco-hydraulics in Northwest Arid Region Xi’an University of Technology Xi’an710048 China Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas Ministry of Education Northwest A&F University Yangling712100 China

Elucidating the effect of fertigation on soil hydraulic parameters and soil water transport is fundamental to the design of farmland irrigation systems and their sustainable utilization. Few studies have focused on soil hydraulic parameters or water infiltration characteristics or how they are influenced by urea solution concentration. In this study, the clay loam and sandy loam in Yangling District of Shaanxi Province, China, were used as test soil, and experiments involving seven urea solution concentrations (0.2, 0.4, 0.6, 0.8, 1, 3, and 5 g/L) and a control treatment (0 g/L) were conducted to explore the influence of the various urea solution concentrations on soil hydraulic parameters and water infiltration characteristics. The results indicated that the cumulative infiltration and wetting front migration depth increased with urea solution concentration, as accurately estimated using the Kostiakov model and a power function, respectively. In addition, the coefficients of the Kostiakov model and the power function increased with urea solution concentration. The inhibition effect of solute potential due to urea solution concentration was far weaker than the improvement effect of urea solution concentration on soil pore structure. Treatment with multiple concentrations of urea solution resulted in an increase in the volume of macro pores in the soil but a reduction in the volume of small and micro pores in the soil, leading to increases in the saturated water content, saturated hydraulic conductivity, soil water diffusivity, and infiltration capacity and a reduction in the water-holding capacity of the soil. Our results increase the understanding of soil hydrological mechanisms and may be usefully applied for improving the management of fertigation. © 2023, The Authors. All rights reserved.

关键词： Infiltration

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Connecting Disconnected Agents in Multiagent Systems via Federated Control

Connecting Disconnected Agents in Multiagent Systems via Fed...

引用

European Control Conference (ECC)

作者： Rongrong Qian Zhisheng Duan Yuan Qi Tao Peng Wenbo Wang School of Artificial Intelligence Beijing University of Posts and Telecommunications (BUPT) Beijing China State Key Laboratory for Turbulence and Complex Systems College of Engineering Peking University Beijing China School of Electronic Engineering Beijing University of Posts and Telecommunications (BUPT) Beijing China Wireless Signal Processing and Network Lab (Key Lab. of Universal Wireless Communication Ministry of Education) Beijing University of Posts and Telecommunications (BUPT) Beijing China

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This study develops a control technique, called federated control, to connect disconnected agents in multiagent systems aided by control stations. Specifically, we first use a federated architecture to model multiagent systems with control stations. Based on this architecture, a federated-control procedure is proposed for the task of connecting disconnected agents, together with an initialization procedure. Then, how the federated-control procedure connects disconnected agents by incorporating simple global coordination (i.e., global averaging) is analyzed. We derive the close-form expression of the time sufficient to connect disconnected agents, which possesses interesting properties including early computability and delay independence. The convergence of the formation of multiagent systems and the velocities of all agents can also be proved.

关键词： Robot kinematics Oceans Computer architecture Control systems Space-air-ground integrated networks Path planning Delays

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Discover the Binding Domain of Transmembrane Proteins Based on Structural Universality

Discover the Binding Domain of Transmembrane Proteins Based ...

引用

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Bao, Yihang He, Fei Wang, Weixi Wang, Han Dong, Minglong Northeast Normal University Changchun School of Information Science and Technology China NE Norm. Univ. Key Lab. of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun School of Information Science and Technology China Nanyang Technological University Singapore School of Biological Sciences Singapore

ISBN: (纸本)9781665401265

Transmembrane proteins (TMPs) serve as drug targets for more than half of the drugs currently availab.e in the market. However, it had not been clearly explained how they realize their drug effects through multiple complex molecules bindings actions. Research into TMPs bindings and corresponding structural basis will provide key information for drug research and new drug development. In this study, we defined the binding domain of TMPs according to the binding region investigation of multiple conjugate types. A 3D deep learning model was architected to discover the structural universality inside those domains. The experimental results proved such binding domains existing on the surface of TMPs, and they are structural specific distinguishing to the surface regions without any binding activities. This work provides a new theoretical basis for TMPs binding research and can greatly boost the development of the drug industry. © 2021 IEEE.

关键词： Proteins

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Research on Tibetan medicine intelligent question answering system integrating confrontation training and reinforcement learning 21

Research on Tibetan medicine intelligent question answering ...

引用

2nd International Symposium on Artificial Intelligence for Medicine Sciences, ISAIMS 2021

作者： Dong, Andi Wang, Chao Tong, Pan Yang, Dan Yong, Cuo School of Information Science and Technology Key Lab. of Artificial Intelligence for Tibetan Information Technology in Tibet Autonomous Region Ministry of Education Engineering Research Center for Tibetan Information Technology Tibet University Lhasa Tibet China

ISBN: (纸本)9781450395588

In this study, a knowledge graph (KG) based Tibetan medicine intelligent question answering (QA) system model was proposed based on an adversarial learning generative network model, in an attempt to alleviate the scarcity of medical resources, promote the heritage and innovation of Tibetan medicine, and ease the shortage of Tibetan medical information. In this model, the simulated answers were generated via adversarial learning, and subsequently the reinforcement learning was applied for feedback-based optimization, with the ultimate aim of enhancing the accuracy rate of this model. Besides, a triple extraction method based on Tibetan features was proposed to construct a KG dialog set. Finally, this model was subjected to an experiment in Chinese and Tibetan datasets, with the results indicating that the accuracy of this intelligent QA model incorporating adversarial networks and reinforcement learning was higher than other models. © 2021 ACM.

关键词： Reinforcement learning

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Deep Convolutional Neural Network Diabetic Entity Relationship Extraction Model Based on Enhanced Semantic Representation 21

Deep Convolutional Neural Network Diabetic Entity Relationsh...

引用

2nd International Symposium on Artificial Intelligence for Medicine Sciences, ISAIMS 2021

作者： Yu, Tao Yong, Cuo School of Information Science and Technology Key Lab. of Artificial Intelligence for Tibetan Information Technology in Tibet Autonomous Region Ministry of Education Engineering Research Center for Tibetan Information Technology Tibet University Lhasa Tibet China

ISBN: (纸本)9781450395588

Diabetes, as the number one chronic disease in China, plagues the lives of people of different age groups and causes great distress to people. This paper extracts the semantic relationships among diabetes entities through the entity relationship extraction technique, which helps to build the knowledge graph of diabetes domain. The experimental results show that the deep convolutional neural network (ESPDCNN) based on enhanced semantic representation proposed in this paper effectively improves the accuracy of the diabetic entity relationship extraction model. © 2021 ACM.

关键词： Semantics

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Hyperbolic Geometric Latent Diffusion Model for Graph Generation

arXiv

引用

arXiv 2024年

作者： Fu, Xingcheng Gao, Yisen Wei, Yuecen Sun, Qingyun Peng, Hao Li, Jianxin Li, Xianxian Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Topology

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Prompt Tuning Pushes Farther, Contrastive Learning Pulls Closer: A Two-Stage Approach to Mitigate Social Biases

arXiv

引用

arXiv 2023年

作者： Li, Yingji Du, Mengnan Wang, Xin Wang, Ying College of Computer Science and Technology Jilin University Changchun China Department of Data Science New Jersey Institute of Technology Newark United States School of Artificial Intelligence Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

As the representation capability of Pre-trained Language Models (PLMs) improve, there is growing concern that they will inherit social biases from unprocessed corpora. Most previous debiasing techniques used Counterfactual data Augmentation (CDA) to balance the training corpus. However, CDA slightly modifies the original corpus, limiting the representation distance between different demographic groups to a narrow range. As a result, the debiasing model easily fits the differences between counterfactual pairs, which affects its debiasing performance with limited text resources. In this paper, we propose an adversarial training-inspired two-stage debiasing model using Contrastive learning with Continuous Prompt Augmentation (named CCPA) to mitigate social biases in PLMs’ encoding. In the first stage, we propose a data augmentation method based on continuous prompt tuning to push farther the representation distance between sample pairs along different demographic groups. In the second stage, we utilize contrastive learning to pull closer the representation distance between the augmented sample pairs and then fine-tune PLMs’ parameters to get debiased encoding. Our approach guides the model to achieve stronger debiasing performance by adding difficulty to the training process. Extensive experiments show that CCPA outperforms baselines in terms of debiasing performance. Meanwhile, experimental results on the GLUE benchmark show that CCPA retains the language modeling capability of PLMs. Copyright © 2023, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Bridge the Gap between Supervised and Unsupervised Learning for Fine-Grained Classification

SSRN

引用

SSRN 2023年

作者： Wang, Jiabao Li, Yang Wei, Xiu-Shen Li, Hang Miao, Zhuang Zhang, Rui Army Engineering University of PLA Jiangsu Nanjing210007 China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing210094 China State Key Lab. for Novel Software Technology Nanjing University Jiangsu Nanjing210023 China

Unsupervised learning technology has caught up with or even surpassed supervised learning technology in general object classification (GOC) and person re-identification (re-ID). However, it is found that the unsupervised learning of fine-grained visual classification (FGVC) is more challenging than GOC and person re-ID. In order to bridge the gap between unsupervised and supervised learning for FGVC, we investigate the essential factors for the performance gap. Furthermore, we propose a simple, effective, and practical method, termed as UFCL, to alleviate the gap. Three key issues are concerned and improved: First, we introduce a robust and powerful backbone, ResNet50-IBN, which has an ability of domain adaptation when we transfer ImageNet pre-trained models to FGVC tasks. Next, we propose to introduce HDBSCAN instead of DBSCAN to do clustering, which can generate better clusters for adjacent categories with fewer hyper-parameters. Finally, we propose a weighted feature agent and its updating mechanism to do contrastive learning, which can improve the optimization process of learning the parameters of the network. The effectiveness of our UFCL is verified on four public FGVC datasets. Under the unsupervised FGVC setting, we achieve state-of-the-art results, and analyze the key factors and the important parameters to provide a practical guidance. © 2023, The Authors. All rights reserved.

关键词： Image classification

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