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42nd Chinese Control Conference, CCC 2023

作者： Liu, Yaojun Liu, Xingwen Tashi, Nyima College of Electrical Engineering Southwest Minzu University Lab. of Complex Systems & Intelligent Control Key Lab. of Electronic Information of State Ethnic Affairs Commission Sichuan Chengdu610225 China Engineering Research Center for Tibetan Information Processing School of Information Science and Technology Tibet University Lhasa850000 China

ISBN: (纸本)9789887581543

Cooperative behavior has been of great concern in evolutionary game researches because of its important role in social life and natural evolution. Since memory can greatly influence the emergence of cooperative behavior, memory-based research on evolutionary games has proliferated in recent years. In the present paper, inspired by the well-known 'Stanford marshmallow experiment' (SME), we proposed a multi-memory mechanism in Snowdrift game (SDG) on spatial lattice networks whose core lies in what follows: Players are divided into two groups according to individual self-control;different memory lengths are set by group and by time in a single realization. The simulation results show that our proposed mechanism promotes cooperation level of the considered evolutionary game. More specifically, as memory length increases to an intermediate length, the cooperation level increases as well. For mechanism with short memory length, the effect of population structure is obvious and small values of adjusting factor can intensively facilitate high-frequency cooperative behavior. These findings may be helpful to understand the connection between population heterogeneity and the emergence of high cooperation. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Behavioral research

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Improved Cardinalized Probability Hypothesis Density Filter for Multitarget Tracking Using Formation Priors

Improved Cardinalized Probability Hypothesis Density Filter ...

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IET International Radar Conference 2023, IRC 2023

作者： Jiang, Qi Zhang, Jichuan Ni, Na Wang, Rui Hu, Cheng Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Advanced Technology Research Institute Beijing Institute of Technology Shandong Jinan China Beijing Key Laboratory of Real-time Information Processing Technology of Embedded Beijing100081 China

ISBN: (纸本)9781839539954

Effective observation of group targets (bird flocks, etc.) has significant importance in aviation safety and ecological research. Closely spaced group targets with swarm intelligence can form huge and complex linear formations. Tracking radar must feature a narrow beam and high bandwidth to discriminate different targets within the formation. However, there are two major problems in formation target tracking. First, the number of targets within the beam may fluctuate with the change of radar-target geometry;second, false measurements may be detected from the distributed Doppler spectra. This paper proposes a cardinalized probability hypothesis density (CPHD) filter for multitarget tracking using the formation priors form a scanning radar. The model of false measurements is optimized based on the experimental data of birds. The densities of new targets and the probability of target death are set according to the formation priors of group structure and radar-target geometry. Finally, the effectiveness of the proposed method is verified by experimental results. © The Institution of Engineering & Technology 2023.

关键词： Target tracking

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Protein Structure Prediction:Challenges,Advances,and the Shift of research Paradigms

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Genomics, Proteomics & Bioinformatics 2023年第5期21卷 913-925页

作者： Bin Huang Lupeng Kong Chao Wang Fusong Ju Qi Zhang Jianwei Zhu Tiansu Gong Haicang Zhang Chungong Yu Wei-Mou Zheng Dongbo Bu Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Changping Laboratory Beijing 102206China Microsoft Research AI4Science Beijing 100080China Huawei Noah’s Ark Lab Wuhan 430206China Zhongke Big Data Academy Zhengzhou 450046China Institute of Theoretical Physics Chinese Academy of SciencesBeijing 100190China

Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields,including biochemistry,medicine,physics,mathematics,and computer *** researchers adopt various research paradigms to attack the same structure prediction problem:biochemists and physicists attempt to reveal the principles governing protein folding;mathematicians,especially statisticians,usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure,while computer scientists formulate protein structure prediction as an optimization problem-finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native *** research paradigms fall into the two statistical modeling cultures proposed by Leo Breiman,namely,data modeling and algorithmic ***,we have also witnessed the great success of deep learning in protein structure *** this review,we present a survey of the efforts for protein structure *** compare the research paradigms adopted by researchers from different fields,with an emphasis on the shift of research paradigms in the era of deep *** short,the algorithmic modeling techniques,especially deep neural networks,have considerably improved the accuracy of protein structure prediction;however,theories interpreting the neural networks and knowledge on protein folding are still highly desired.

关键词： Protein folding Protein structure prediction Deep learning Transformer Language model

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Adaptively feature matching via joint transformational-spatial clustering

Adaptively feature matching via joint transformational-spati...

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作者： Wang, Linbo Tan, Li Fang, Xianyong Guo, Yanwen Wan, Shaohua MOE Key Laboratory of Intelligent Computing and Signal Processing School of Computer Science and Technology Anhui University Hefei China National Key Lab for Novel Software Technology Nanjing University Nanjing China School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan China

The transformational and spatial proximities are important cues for identifying inliers from an appearance based match set because correct matches generally stay close in input images and share similar local transformations. However, most existing approaches only check one type of them or both types consecutively with manually set thresholds, and thus their matching accuracy and flexibility in handling large-scale images are limited. In this paper, we present an efficient clustering based approach to identify match inliers with both proximities simultaneously. It first projects the putative matches into a joint transformational-spatial space, where mismatches tend to scatter all around while correct matches gather together. A mode-seeking process based on joint kernel density estimation is then proposed to obtain significant clusters in the joint space, where each cluster contains matches mapping the same object across images with high accuracy. Moreover, kernel bandwidths for measuring match proximities are adaptively set during density estimation, which enhances its applicability for matching different images. Experiments on three standard datasets show that the proposed approach delivers superior performance on a variety of feature matching tasks, including multi-object matching, duplicate object matching and object retrieval. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Clustering Density estimation Feature matching Mode-seeking

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A Category Agnostic Model for Visual Rearrangment

A Category Agnostic Model for Visual Rearrangment

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yuyi Liu Xinhang Song Weijie Li Xiaohan Wang Shuqiang Jiang Key Lab of Intelligent Information Processing Laboratory of the Chinese Academy of Sciences (CAS) Institute of Computing Technology Beijing University of Chinese Academy of Sciences Beijing

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

This paper presents a novel category agnostic model for visual rearrangement task, which can help an embodied agent to physically recover the shuffled scene configuration without any category concepts to the goal configuration. Previous methods usually follow a similar architecture, completing the rearrangement task by aligning the scene changes of the goal and shuffled configuration, according to the semantic scene graphs. However, constructing scene graphs requires the inference of category labels, which not only causes the accuracy drop of the entire task but also limits the application in real world scenario. In this paper, we delve deep into the essence of visual re-arrangement task and focus on the two most essential issues, scene change detection and scene change matching. We utilize the movement and the protrusion of point cloud to accurately identify the scene changes and match these changes depending on the similarity of category agnostic appearance feature. Moreover, to assist the agent to explore the environment more efficiently and comprehensively, we propose a closer-aligned-retrace exploration policy, aiming to observe more details of the scene at a closer distance. We conduct extensive experiments onAI2THOR Rearrangement Challenge based on RoomR dataset and a new multi-room multi-instance dataset MrMiR collected by us. The experimental results demonstrate the effectiveness of our proposed method.

关键词： Point cloud compression Visualization Computer vision Accuracy Semantics Feature extraction Pattern recognition

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research on Ground Clutter Polarization Suppression Algorithm Based on Prior information

Research on Ground Clutter Polarization Suppression Algorith...

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IET International Radar Conference 2023, IRC 2023

作者： Liu, Sheng Cai, Jiong Zhang, Jichuan Wang, Rui Hu, Cheng The Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China

ISBN: (纸本)9781839539954

Low-altitude unmanned aerial vehicle (UAV) targets are challenging to detect by radar due to their low flight altitude, which exposes them to strong interferences from complex ground clutter. To address this, this paper proposes a ground clutter polarization suppression method based on prior information, facilitating the effective detection of low-altitude UAV targets. Initially, the method establishes a clutter prior information database and a clutter map according to the actual radar application environment, classifying different types of clutter. This serves as the prior information for clutter, supporting detection in the context of complex ground clutter backgrounds. Subsequently, a linear polarization filtering algorithm optimized based on maximizing the signal-to-clutter ratio (SCR) is proposed for specific clutter environments. A strong clutter point is selected from the clutter map, and the effectiveness of the algorithm proposed in this paper is analyzed and verified using real-world radar data collected from field measurements. The results indicate that the proposed algorithm improves the average signal-to-clutter ratio by 15.39 dB compared to single polarization channels. This method provides a reference for complex ground clutter polarization suppression and polarization detection. © The Institution of Engineering & Technology 2023.

关键词： Unmanned aerial vehicles (UAV)

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Common Targets Polarimetric Calibration Technique for Long Blind Range Radar

Common Targets Polarimetric Calibration Technique for Long B...

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IET International Radar Conference 2023, IRC 2023

作者： Tan, Lijia Li, Weidong Wang, Rui Wang, Jiangtao Li, Muyang Hu, Cheng The Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China

ISBN: (纸本)9781839539954

Many research indicates the importance of polarimetric information in radar systems for purposes such as detection, tracking, and parameter estimation. The calibration process is essential for obtaining precise polarimetric information. However, conducting calibration experiments for long blind range radars can be challenging in fields where ground clutter is prominent. For multi-pulse width switching radar, this paper introduces a polarimetric calibration algorithm by measuring common targets of short- and long-range waveforms. Firstly, we switch between various waveforms to measure the strong echo common targets in the overlapping detection range. The scattering matrices of common targets can be obtained by the short-range waveform calibrated by the conventional method. Then, the polarization error model is built based on the measured scattering matrices of long-range waveform, and the true values of the scattering matrices of common targets are used to estimate the polarization errors of long-range waveform. The effectiveness of the algorithm is validated through simulation results. © The Institution of Engineering & Technology 2023.

关键词： Polarimeters

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TagOOD: A Novel Approach to Out-of-Distribution Detection via Vision-Language Representations and Class Center Learning 24

TagOOD: A Novel Approach to Out-of-Distribution Detection vi...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Jinglun Zhou, Xinyu Jiang, Kaixun Hong, Lingyi Guo, Pinxue Chen, Zhaoyu Ge, Weifeng Zhang, Wenqiang Shanghai Engineering Research Center of Ai & Robotics Academy for Engineering & Technology Fudan University China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Engineering Research Center of Ai & Robotics Ministry of Education Academy for Engineering & Technology Fudan University China

ISBN: (纸本)9798400706868

Multimodal fusion, leveraging data like vision and language, is rapidly gaining traction. This enriched data representation improves performance across various tasks. Existing methods for out-of-distribution (OOD) detection, a critical area where AI models encounter unseen data in real-world scenarios, rely heavily on whole-image features. These image-level features can include irrelevant information that hinders the detection of OOD samples, ultimately limiting overall performance. In this paper, we propose TagOOD, a novel approach for OOD detection that leverages vision language representations to achieve label-free object feature decoupling from whole images. This decomposition enables a more focused analysis of object semantics, enhancing OOD detection performance. Subsequently, TagOOD trains a lightweight network on the extracted object features to learn representative class centers. These centers capture the central tendencies of IND object classes, minimizing the influence of irrelevant image features during OOD detection. Finally, our approach efficiently detects OOD samples by calculating distance-based metrics as OOD scores between learned centers and test samples. We conduct extensive experiments to evaluate TagOOD on several benchmark datasets and demonstrate its superior performance compared to existing OOD detection methods. This work presents a novel perspective for further exploration of multimodal information utilization in OOD detection, with potential applications across various tasks. Code is available at: https://***/Jarvisgivemeasuit/tagood. © 2024 ACM.

关键词： Semantics

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The Optimal Partition Number Estimation Algorithm Based on Variational Inference

The Optimal Partition Number Estimation Algorithm Based on V...

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IET International Radar Conference 2023, IRC 2023

作者： Zhang, Jichuan Jiang, Qi Xu, Liang Wang, Rui Hu, Cheng The Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China The Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China The Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing100081 China

ISBN: (纸本)9781839539954

The partition of individual targets into proper clusters is crucial in tracking group targets with complex spatial structures. However, most of the current clustering methods suffer from heavy computation burdens and unfitting a priori knowledge, resulting in the degradation of robustness. This paper proposes an optimal estimate of partition number based on the variational inference for group targets. By maximizing the variational evidence lower bound, which serves as the model-selected criterion, the optimal partition number can be estimated. The K-means++ algorithm is used to initialize the variation iteration. The proposed algorithm does not require a priori information and has low computation complexity, which benefits from the deterministic approximation character of variational inference. The effectiveness of the proposed method is verified by simulation results. © The Institution of Engineering & Technology 2023.

关键词： Consensus algorithm

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Contrastive Learning for Robust Android Malware Familial Classification

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IEEE Transactions on Dependable and Secure computing 2022年 1-14页

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China University of Texas at Dallas Dallas USA National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. IEEE

关键词： Semantics

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