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Earning Extra Performance From Restrictive Feedbacks

IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年第10期45卷 11753-11765页

作者： Li, Jing Pan, Yuangang Lyu, Yueming Yao, Yinghua Sui, Yulei Tsang, Ivor W. 138632 Singapore University of Technology Sydney The Australian Artificial Intelligence Institute UltimoNSW2007 Australia Southern University of Science and Technology The Guangdong Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China University of New South Wales The School of Computer Science and Engineering Sydney1466 Australia

Many machine learning applications encounter situations where model providers are required to further refine the previously trained model so as to gratify the specific need of local users. This problem is reduced to the standard model tuning paradigm if the target data is permissibly fed to the model. However, it is rather difficult in a wide range of practical cases where target data is not shared with model providers but commonly some evaluations about the model are accessible. In this paper, we formally set up a challenge named Earning eXtra PerformancE from restriCTive feEDdbacks (EXPECTED) to describe this form of model tuning problems. Concretely, EXPECTED admits a model provider to access the operational performance of the candidate model multiple times via feedback from a local user (or a group of users). The goal of the model provider is to eventually deliver a satisfactory model to the local user(s) by utilizing the feedbacks. Unlike existing model tuning methods where the target data is always ready for calculating model gradients, the model providers in EXPECTED only see some feedbacks which could be as simple as scalars, such as inference accuracy or usage rate. To enable tuning in this restrictive circumstance, we propose to characterize the geometry of the model performance with regard to model parameters through exploring the parameters' distribution. In particular, for deep models whose parameters distribute across multiple layers, a more query-efficient algorithm is further tailor-designed that conducts layerwise tuning with more attention to those layers which pay off better. Our theoretical analyses justify the proposed algorithms from the aspects of both efficacy and efficiency. Extensive experiments on different applications demonstrate that our work forges a sound solution to the EXPECTED problem, which establishes the foundation for future studies towards this direction. © 1979-2012 IEEE.

关键词： Efficiency

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Energy-Efficient Multi-Trip Routing for Municipal Solid Waste Collection by Contribution-Based Adaptive Particle Swarm Optimization

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Complex System Modeling and Simulation 2023年第3期3卷 202-219页

作者： Xiaoning Shen Hongli Pan Zhongpei Ge Wenyan Chen Liyan Song Shuo Wang Collaborative Innovation Center of Atmospheric Environment and Equipment Technology and also with the Jiangsu Key Laboratory of Big Data Analysis TechnologySchool of AutomationNanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation the Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China School of Computer Science University of BirminghamBirminghamB152TTUK

Waste collection is an important part of waste management *** costs and carbon emissions can be greatly reduced by proper vehicle ***,each vehicle can work again after achieving its capacity limit and unloading the *** this,an energy-efficient multi-trip vehicle routing model is established for municipal solid waste collection,which incorporates practical factors like the limited capacity,maximum working hours,and multiple trips of each *** both economy and environment,fixed costs,fuel costs,and carbon emission costs are minimized *** solve the formulated model effectively,contribution-based adaptive particle swarm optimization is *** strategies named greedy learning,multi-operator learning,exploring learning,and exploiting learning are specifically designed with their own searching *** assessing the contribution of each learning strategy during the process of evolution,an appropriate one is selected and assigned to each individual adaptively to improve the searching efficiency of the ***,an improved local search operator is performed on the trips with the largest number of waste sites so that both the exploiting ability and the convergence accuracy of the algorithm are *** of the proposed algorithm is tested on ten waste collection instances,which include one real-world case derived from the Green Ring Company of Jiangbei New District,Nanjing,China,and nine synthetic instances with increasing scales generated from the commonly-used capacitated vehicle routing problem benchmark *** with five state-of-the-art algorithms show that the proposed algorithm can obtain a solution with a higher accuracy for the constructed model.

关键词： municipal solid waste collection energy conservation multi-trip contribution particle swarm optimization

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A Region Enhanced Discrete Multi-Objective Fireworks Algorithm for Low-Carbon Vehicle Routing Problem

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Complex System Modeling and Simulation 2022年第2期2卷 142-155页

作者： Xiaoning Shen Jiaqi Lu Xuan You Liyan Song Zhongpei Ge Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Jiangsu Key Laboratory of Big Data Analysis TechnologySchool of AutomationNanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Institute of Trustworthy Autonomous Systems and also with the Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent ComputationDepartment of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China

A constrained multi-objective optimization model for the low-carbon vehicle routing problem(VRP)is established.A carbon emission measurement method considering various practical factors is *** minimizes both the total carbon emissions and the longest time consumed by the sub-tours,subject to the limited number of available *** to the characteristics of the model,a region enhanced discrete multi-objective fireworks algorithm is proposed.A partial mapping explosion operator,a hybrid mutation for adjusting the sub-tours,and an objective-driven extending search are designed,which aim to improve the convergence,diversity,and spread of the non-dominated solutions produced by the algorithm,*** low-carbon VRP instances with different scales are used to verify the effectiveness of the new ***,comparison results with four state-of-the-art algorithms indicate that the proposed algorithm has better performance of convergence and distribution on the low-carbon *** provides a promising scalability to the problem size.

关键词： vehicle routing problem carbon emission multi-objective optimization fireworks algorithm region enhanced

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Neural, cognitive and psychopathological signatures of a prosocial or delinquent peer environment during early adolescence

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Developmental Cognitive Neuroscience 2025年 73卷 101566页

作者： Liu, Yu Peng, Songjun Wu, Xinran Liu, Zhaowen Lian, Zhengxu Fan, Huaxin Kuang, Nanyu Gu, Xinrui Yang, Senyou Hu, Yechen Jiang, Xi Zhang, Yufeng Cheng, Wei Feng, Jianfeng Sahakian, Barbara J. Zhao, Xingming Robbins, Trevor W. Becker, Benjamin Zhang, Jie Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence Fudan University Ministry of Education Shanghai China School of Computer Science Northwestern Polytechnical University Shaanxi Xi'an China Sino-European School of Technology Shanghai University Shanghai China The Center of Psychosomatic Medicine Sichuan Provincial Center for Mental Health Sichuan Provincial People's Hospital University of Electronic Science and Technology of China Chengdu China Chinese language and literature Fudan University Shanghai China Department of Computer Science University of Warwick Coventry United Kingdom Behavioural and Clinical Neuroscience Institute University of Cambridge Cambridge United Kingdom Department of Psychiatry University of Cambridge School of Clinical Medicine Cambridge United Kingdom Department of Psychology University of Cambridge Cambridge United Kingdom State Key Laboratory of Brain and Cognitive Sciences The University of Hong Kong Hong Kong Department of Psychology The University of Hong Kong Hong Kong

Adolescence is a critical period for brain development, yet the impact of peer environments on brain structure, cognition, and psychopathology remains poorly understood. Here, we capitalized on data from 7806 adolescents (age = 12.02 ± 0.67) from the Adolescent brain Cognitive Development (ABCD) study, to determine associations between two distinct peer environments (proportion of prosocial or delinquent friends) and the structural and functional architecture of the brain, cognition, as well as behavioral and emotional dysregulation. A higher proportion of prosocial friends was associated with fewer behavioral problems and larger fronto-cingulate and striatal regions. In contrast, a higher proportion of delinquent friends was linked to increased behavioral problems, lower neurocognitive performance, and decreased functional connectivity in the default-mode and fronto-striato-limbic circuits, which spatially overlapped with external dopamine density maps. Moreover, the associations between prosocial friends and behaviors were mediated by brain volumes (e.g., pallidum), while the associations between delinquent friends and behaviors were primarily mediated by fronto-striato-limbic connectivity. Prosocial friends also attenuated the development of internalizing problems, whereas delinquent friends promoted externalizing symptoms. These findings underscore the profound influence of peer environments on adolescent brain development and mental health, highlighting the need for early interventions to promote resilience and healthy neuro-maturation. © 2025

关键词： Adolescent development Behavioral problems brain structure Functional connectivity Peer environments

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Sparse Point Clouds Assisted Learned Image Compression

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IEEE Transactions on Circuits and Systems for Video technology 2024年第5期35卷 4000-4010页

作者： Jiang, Yiheng Zhang, Haotian Li, Li Liu, Dong Li, Zhu University of Science and Technology of China MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition Hefei230027 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China University of Missouri Dept of Computer Science and Electrical Engineering Kansas City United States

In the field of autonomous driving, a variety of sensor data types exist, each representing different modalities of the same scene. Therefore, it is feasible to utilize data from other sensors to facilitate image compression. However, few techniques have explored the potential benefits of utilizing inter-modality correlations to enhance the image compression performance. In this paper, motivated by the recent success of learned image compression, we propose a new framework that uses sparse point clouds to assist in learned image compression in the autonomous driving scenario. We first project the 3D sparse point cloud onto a 2D plane, resulting in a sparse depth map. Utilizing this depth map, we proceed to predict camera images. Subsequently, we use these predicted images to extract multi-scale structural features. These features are then incorporated into learned image compression pipeline as additional information to improve the compression performance. Our proposed framework is compatible with various mainstream learned image compression models, and we validate our approach using different existing image compression methods. The experimental results show that incorporating point cloud assistance into the compression pipeline consistently enhances the performance. © 1991-2012 IEEE.

关键词： Image compression

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Decoding Respiratory Signals from LFP in Olfactory Bulb based on ConvLSTM Framework 7

Decoding Respiratory Signals from LFP in Olfactory Bulb base...

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7th International Conference on Biological Information and Biomedical Engineering, BIBE 2024

作者： Xu, Haoze Yu, Bowen Yu, Chaonan Xu, Kedi Qiushi Academy for Advanced Studies Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal Key Laboratory for Biomedical Engineering of Ministry of Education School of Biomedical Engineering and Instrument Science Zhejiang University China Nanhu Brain-computer interface institute Hangzhou311100 China State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China

ISBN: (纸本)9783800762910

Respiratory signals are crucial for decoding olfactory neural circuits and understanding respiration-entrained local field potential (LFP) rhythms. Acquiring high-precision respiratory signals often requires additional equipment, increasing the experimental burden, particularly in free-moving paradigms. However, within the current neuroscience paradigm, we have not yet found a non-invasive method for obtaining the respiratory signals of laboratory animals. To minimize surgical trauma to experimental animals, this paper introduces a ConvLSTM-based decoding method, which uses the LFP from the olfactory bulb (OB) region of animals to accurately decode respiratory signals. After validating the effectiveness of the ConvLSTM method through comparative experiments, we further investigated the factors influencing the model's decoding performance. The Gamma frequency band exhibited the best decoding results, with a mean absolute error (MAE) of 0.16±0.09, which may be related to the causal direction from respiratory signals to Gamma rhythms. Additionally, the ConvLSTM model demonstrated strong trend prediction capabilities in multi-sequence point predictions and forward predictions, despite a slight decline in single-point prediction accuracy. Extensive validation on publicly available datasets indicates that the ConvLSTM-based respiratory decoding approach is highly applicable, offering a non-invasive alternative for respiratory signal acquisition in animal studies. © VDE VERLAG GMBH.

关键词： Noninvasive medical procedures

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Network intrusion detection methods based on deep learning

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Recent Patents on Engineering 2021年第4期15卷 11-19页

作者： Li, Xiangwen Zhang, Shuang The Engineering & Technical College of Chengdu University of Technology Leshan China Data Recovery Key Laboratory of Sichuan Province College of Computer Science & AI Neijiang Normal University Neijiang Sichuan China School of Life Science and Technology University of Electronic Science and Technology of China Chengdu Sichuan China High Field Magnetic Resonance Brain Imaging Laboratory of Sichuan Chengdu Sichuan China Key Laboratory for NeuroInformation of Ministry of Education Chengdu Sichuan China

To detect network attacks more effectively, this study uses Honeypot techniques to collect the latest network attack data and proposes network intrusion detection classification models, based on deep learning, combined with DNN and LSTM models. Experiments showed that the data set training models gave better results than the KDD CUP 99 training model’s detection rate and false positive rate. The DNN-LSTM intrusion detection algorithm, proposed in this study, gives better results than KDD CUP 99 training model. Compared to other algorithms, such as LeNet, DNNLSTM intrusion detection algorithm exhibits shorter classification test time along with better accuracy and recall rate of intrusion detection. © 2021 Bentham science Publishers.

关键词： Intrusion detection

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Defending Adversarial Examples by Negative Correlation Ensemble 7th

Defending Adversarial Examples by Negative Correlation Ense...

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7th International Conference on Data Mining and Big Data, DMBD 2022

作者： Luo, Wenjian Zhang, Hongwei Kong, Linghao Chen, Zhijian Tang, Ke School of Computer Science and Technology Harbin Institute of Technology Guangdong Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation School of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China

ISBN: (纸本)9789811989902

The security issues in DNNs, such as adversarial examples, have attracted much attention. Adversarial examples refer to the examples which are capable to induce the DNNs return incorrect predictions by introducing carefully designed perturbations. Obviously, adversarial examples bring great security risks to the real-world applications of deep learning. Recently, some defence approaches against adversarial examples have been proposed. However, the performance of these approaches are still limited. In this paper, we propose a new ensemble defence approach named the Negative Correlation Ensemble (NCEn), which achieves competitive results by making each member of the ensemble negatively correlated in gradient direction and gradient magnitude. NCEn can reduce the transferability of the adversarial samples among the members in ensemble. Extensive experiments have been conducted, and the results demonstrate that NCEn could improve the adversarial robustness of ensembles effectively. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Deep learning-based activity recognition and fine motor identification using 2D skeletons of cynomolgus monkeys

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Zoological Research 2023年第5期44卷 967-980页

作者： Chuxi Li Zifan Xiao Yerong Li Zhinan Chen Xun Ji Yiqun Liu Shufei Feng Zhen Zhang Kaiming Zhang Jianfeng Feng Trevor W.Robbins Shisheng Xiong Yongchang Chen Xiao Xiao School of Information Science and Technology Micro Nano System Center Fudan UniversityShanghai 200433China Department of Anesthesiology Huashan HospitalKey Laboratory of Computational Neuroscience and Brain-Inspired IntelligenceMinistry of EducationBehavioral and Cognitive Neuroscience CenterInstitute of Science and Technology for Brain-Inspired IntelligenceMOE Frontiers Center for Brain ScienceFudan UniversityShanghai 200433China Kuang Yaming Honors School Nanjing UniversityNanjingJiangsu 210023China Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China State Key Laboratory of Primate Biomedical Research Institute of Primate Translational MedicineKunming University of Science and TechnologyKunmingYunnan 650500China New Vision World LLC. Aliso ViejoCalifornia 92656USA Behavioural and Clinical Neuroscience Institute University of CambridgeCambridgeCB21TNUK

Video-based action recognition is becoming a vital tool in clinical research and neuroscientific study for disorder detection and ***,action recognition currently used in non-human primate(NHP)research relies heavily on intense manual labor and lacks standardized *** this work,we established two standard benchmark datasets of NHPs in the laboratory:Monkeyin Lab(Mi L),which includes 13 categories of actions and postures,and MiL2D,which includes sequences of two-dimensional(2D)skeleton ***,based on recent methodological advances in deep learning and skeleton visualization,we introduced the Monkey Monitor Kit(Mon Kit)toolbox for automatic action recognition,posture estimation,and identification of fine motor activity in *** the datasets and Mon Kit,we evaluated the daily behaviors of wild-type cynomolgus monkeys within their home cages and experimental environments and compared these observations with the behaviors exhibited by cynomolgus monkeys possessing mutations in the MECP2 gene as a disease model of Rett syndrome(RTT).Mon Kit was used to assess motor function,stereotyped behaviors,and depressive phenotypes,with the outcomes compared with human manual *** Kit established consistent criteria for identifying behavior in NHPs with high accuracy and efficiency,thus providing a novel and comprehensive tool for assessing phenotypic behavior in monkeys.

关键词： Action recognition Fine motor identification Two-stream deep model 2D skeleton Non-human primates Rett syndrome

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Rethinking the Membrane Dynamics and Optimization Objectives of Spiking Neural Networks 38

Rethinking the Membrane Dynamics and Optimization Objectives...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Shen, Hangchi Zheng, Qian Wang, Huamin Pan, Gang College of Artificial Intelligence Southwest University China Chongqing Key Laboratory of Brain Inspired Computing and Intelligent Chips China The State Key Lab of Brain-Machine Intelligence Zhejiang University China College of Computer Science and Technology Zhejiang University China

Despite spiking neural networks (SNNs) have demonstrated notable energy efficiency across various fields, the limited firing patterns of spiking neurons within fixed time steps restrict the expression of information, which impedes further improvement of SNN performance. In addition, current implementations of SNNs typically consider the firing rate or average membrane potential of the last layer as the output, lacking exploration of other possibilities. In this paper, we identify that the limited spike patterns of spiking neurons stem from the initial membrane potential (IMP), which is set to 0. By adjusting the IMP, the spiking neurons can generate additional firing patterns and pattern mappings. Furthermore, we find that in static tasks, the accuracy of SNNs at each time step increases as the membrane potential evolves from zero. This observation inspires us to propose a learnable IMP, which can accelerate the evolution of membrane potential and enables higher performance within a limited number of time steps. Additionally, we introduce the last time step (LTS) approach to accelerate convergence in static tasks, and we propose a label smooth temporal efficient training (TET) loss to mitigate the conflicts between optimization objective and regularization term in the vanilla TET. Our methods improve the accuracy by 4.05% on ImageNet compared to baseline and achieve state-of-the-art performance of 87.80% on CIFAR10-DVS and 87.86% on N-Caltech101. The code is available at https://***/StephenTaylor1998/IMP-SNN. © 2024 Neural information processing systems foundation. All rights reserved.

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