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Memetic Search for Green Vehicle Routing Problem with Private Capacitated Refueling Stations

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arXiv 2025年

作者： Xu, Rui Fan, Xing Liu, Shengcai Chen, Wenjie Tang, Ke School of Business Hohai University Nanjing211100 China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China School of Information Management Central China Normal University Wuhan China

The green vehicle routing problem with private capacitated alternative fuel stations (GVRP-PCAFS) extends the traditional green vehicle routing problem by considering refueling stations limited capacity, where a limited number of vehicles can refuel simultaneously with additional vehicles must wait. This feature presents new challenges for route planning, as waiting times at stations must be managed while keeping route durations within limits and reducing total travel distance. This article presents METS, a novel memetic algorithm (MA) with separate constraint-based tour segmentation (SCTS) and efficient local search (ELS) for solving GVRP-PCAFS. METS combines global and local search effectively through three novelties. For global search, the SCTS strategy splits giant tours to generate diverse solutions, and the search process is guided by a comprehensive fitness evaluation function to dynamically control feasibility and diversity to produce solutions that are both diverse and near-feasible. For local search, ELS incorporates tailored move operators with constant-time move evaluation mechanisms, enabling efficient exploration of large solution neighborhoods. Experimental results demonstrate that METS discovers 31 new best-known solutions out of 40 instances in existing benchmark sets, achieving substantial improvements over current state-of-the-art methods. Additionally, a new large-scale benchmark set based on real-world logistics data is introduced to facilitate future research. Copyright © 2025, The Authors. All rights reserved.

关键词： Vehicle routing

Prognosis for patients with cognitive motor dissociation identified by brain-computer interface (vol 143, pg 1177, 2020)

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brain 2020年第8期143卷 1177-1189页

作者： Pan, Jiahui Xie, Qiuyou Qin, Pengmin Chen, Yan He, Yanbin Huang, Haiyun Wang, Fei Ni, Xiaoxiao Cichocki, Andrzej Yu, Ronghao Li, Yuanqing Center for Brain-Computer Interfaces and Brain Information Processing South China University of Technology Guangzhou China School of Software South China Normal University Guangzhou China Department of Rehabilitation Medicine Zhujiang Hospital Southern Medical University Guangzhou China Centre for Hyperbaric Oxygen and Neurorehabilitation Guangzhou General Hospital of Guangzhou Military Command Guangzhou China Centre for Studies of Psychological Applications Guangdong Key Laboratory of Mental Health and Cognitive Science School of Psychology South China Normal University Guangzhou China Centre for Hyperbaric Oxygen and Neurorehabilitation Guangzhou General Hospital of Guangzhou Military Command Guangzhou China Centre for Hyperbaric Oxygen and Neurorehabilitation Guangzhou General Hospital of Guangzhou Military Command Guangzhou China Department of Traumatic Brain Injury Rehabilitation and Severe Rehabilitation Guangdong Work Injury Rehabilitation Hospital Guangzhou China Center for Brain-Computer Interfaces and Brain Information Processing South China University of Technology Guangzhou China Center for Brain-Computer Interfaces and Brain Information Processing South China University of Technology Guangzhou China School of Software South China Normal University Guangzhou China Centre for Hyperbaric Oxygen and Neurorehabilitation Guangzhou General Hospital of Guangzhou Military Command Guangzhou China Skolkovo Institute of Science and Technology (Skoltech) Moscow 143026 Russia Nicolaus Copernicus University (UMK) Torun 87-100 Poland Centre for Hyperbaric Oxygen and Neurorehabilitation Guangzhou General Hospital of Guangzhou Military Command Guangzhou China Center for Brain-Computer Interfaces and Brain Information Processing South China University of Technology Guangzhou China Correspondence to: Yuanqing Li PhD Center for Brain-Computer Interfaces and Brain Information Processing South China University of Technology Guangzhou 510640 China E-mail: auyqli@scut.edu.c

Cognitive motor dissociation describes a subset of patients with disorders of consciousness who show neuroimaging evidence of consciousness but no detectable command-following behaviours. Although essential for family counselling, decision-making, and the design of rehabilitation programmes, the prognosis for patients with cognitive motor dissociation remains under-investigated. The current study included 78 patients with disorders of consciousness who showed no detectable command-following behaviours. These patients included 45 patients with unresponsive wakefulness syndrome and 33 patients in a minimally conscious state, as diagnosed using the Coma Recovery Scale-Revised. Each patient underwent an EEG-based brain-computer interface experiment, in which he or she was instructed to perform an item-selection task (i.e. select a photograph or a number from two candidates). Patients who achieved statistically significant brain-computer interface accuracies were identified as cognitive motor dissociation. Two evaluations using the Coma Recovery Scale-Revised, one before the experiment and the other 3 months later, were carried out to measure the patients’ behavioural improvements. Among the 78 patients with disorders of consciousness, our results showed that within the unresponsive wakefulness syndrome patient group, 15 of 18 patients with cognitive motor dissociation (83.33%) regained consciousness, while only five of the other 27 unresponsive wakefulness syndrome patients without significant brain-computer interface accuracies (18.52%) regained consciousness. Furthermore, within the minimally conscious state patient group, 14 of 16 patients with cognitive motor dissociation (87.5%) showed improvements in their Coma Recovery Scale-Revised scores, whereas only four of the other 17 minimally conscious state patients without significant brain-computer interface accuracies (23.53%) had improved Coma Recovery Scale-Revised scores. Our results suggest that patients with cogn

关键词： cognitive motor dissociation brain-computer interface disorders of consciousness prognosis unresponsive wakefulness syndrome

Inverse design of artificial skins

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arXiv 2023年

作者： Liu, Zhiguang Cai, Minkun Hong, Shenda Shi, Junli Xie, Sai Liu, Chang Du, Huifeng Morin, James D. Li, Gang Liu, Wang Wang, Hong Tang, Ke Fang, Nicholas X. Guo, Chuan Fei Department of Materials Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States National Institute of Health Data Science Peking University Beijing100191 China Centers for Mechanical Engineering Research Education at MIT and SUSTech Southern University of Science and Technology Shenzhen518055 China Department of Mechanical Engineering The University of Hong Kong Hong Kong999077 Hong Kong Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Modern Mechanics University of Science and Technology of China Hefei231600 China

Mimicking the perceptual functions of human cutaneous mechanoreceptors, artificial skins or flexible pressure sensors can transduce tactile stimuli to quantitative electrical signals1-3. Conventional methods to design such devices follow a forward structure-to-property routine based on trial-and-error experiments/simulations, which take months or longer to determine one solution valid for one specific material. Target-oriented inverse design that shows far higher output efficiency has proven effective in other fields4,5, but is still absent for artificial skins because of the difficulties in acquiring big data. Here, we report a property-to-structure inverse design of artificial skins based on small dataset machine learning, exhibiting a comprehensive efficiency at least four orders of magnitude higher than the conventional routine. The inverse routine can predict hundreds of solutions that overcome the intrinsic signal saturation problem for linear response in hours, and the solutions are valid to a variety of materials. Our results demonstrate that the inverse design allowed by small dataset is an efficient and powerful tool to target multifarious applications of artificial skins, which can potentially advance the fields of intelligent robots, advanced healthcare, and human-machine interfaces. Copyright © 2023, The Authors. All rights reserved.

关键词： Intelligent robots

E-KAR : A Benchmark for Rationalizing Natural Language Analogical Reasoning

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arXiv 2022年

作者： Chen, Jiangjie Xu, Rui Fu, Ziquan Shi, Wei Li, Zhongqiao Zhang, Xinbo Sun, Changzhi Li, Lei Xiao, Yanghua Zhou, Hao Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China ByteDance AI Lab China Brain Technologies Inc. United States South China University of Technology China University of California Santa Barbara United States Fudan-Aishu Cognitive Intelligence Joint Research Center China

The ability to recognize analogies is fundamental to human cognition. Existing benchmarks to test word analogy do not reveal the underneath process of analogical reasoning of neural models. Holding the belief that models capable of reasoning should be right for the right reasons, we propose a first-of-its-kind Explainable Knowledge-intensive Analogical Reasoning benchmark (E-KAR). Our benchmark consists of 1,655 (in Chinese) and 1,251 (in English) problems sourced from the Civil Service Exams, which require intensive background knowledge to solve. More importantly, we design a free-text explanation scheme to explain whether an analogy should be drawn, and manually annotate them for each and every question and candidate answer. Empirical results suggest that this benchmark is very challenging for some state-of-the-art models for both explanation generation and analogical question answering tasks, which invites further research in this area. Project page of E-KAR can be found at https://***. Copyright © 2022, The Authors. All rights reserved.

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Associative memories via predictive coding 21

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Associative memories via predictive coding

Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Tommaso Salvatori Yuhang Song Yujian Hong Lei Sha Simon Frieder Zhenghua Xu Rafal Bogacz Thomas Lukasiewicz Department of Computer Science University of Oxford UK Department of Computer Science University of Oxford UK and MRC Brain Network Dynamics Unit University of Oxford UK State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China MRC Brain Network Dynamics Unit University of Oxford UK

ISBN: (纸本)9781713845393

Associative memories in the brain receive and store patterns of activity registered by the sensory neurons, and are able to retrieve them when necessary. Due to their importance in human intelligence, computational models of associative memories have been developed for several decades now. In this paper, we present a novel neural model for realizing associative memories, which is based on a hierarchical generative network that receives external stimuli via sensory neurons. It is trained using predictive coding, an error-based learning algorithm inspired by information processing in the cortex. To test the model's capabilities, we perform multiple retrieval experiments from both corrupted and incomplete data points. In an extensive comparison, we show that this new model outperforms in retrieval accuracy and robustness popular associative memory models, such as autoencoders trained via backpropagation, and modern Hopfield networks. In particular, in completing partial data points, our model achieves remarkable results on natural image datasets, such as ImageNet, with a surprisingly high accuracy, even when only a tiny fraction of pixels of the original images is presented. Our model provides a plausible framework to study learning and retrieval of memories in the brain, as it closely mimics the behavior of the hippocampus as a memory index and generative model.

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Dendrite Net with Acceleration Module for Faster Nonlinear Mapping and System Identification

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arXiv 2020年

作者： Liu, Gang Pang, Yajing Yin, Shuai Niu, Xiaoke Wang, Jing Wan, Hong School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China Institute of Robotics and Intelligent Systems Xi'an Jiaotong University Xi'An710049 China Henan Provincial Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou450001 China

Nonlinear mapping is an essential and common demand in online systems, such as sensor systems and mobile phones. Accelerating nonlinear mapping will directly speed up online systems. Previously the authors of this paper proposed a Dendrite Net (DD) with enormously lower time complexity than the existing nonlinear mapping algorithms;however, there still are redundant calculations in DD. This paper presents a DD with an acceleration module (AC) to accelerate nonlinear mapping further. We conduct three experiments to verify whether DD with AC has lower time complexity while retaining DD's nonlinear mapping properties and system identification properties: The first experiment is the precision and identification of unary nonlinear mapping, reflecting the calculation performance using DD with AC for basic functions in online systems. The second experiment is the mapping precision and identification of the multi-input nonlinear system, reflecting the performance for designing online systems via DD with AC. Finally, this paper compares the time complexity of DD and DD with AC and analyzes the theoretical reasons through repeated experiments. Results: DD with AC retains DD's excellent mapping and identification properties and has lower time complexity. Significance: DD with AC can be used for most engineering systems, such as sensor systems, and will speed up computation in these online systems. The code of DD with AC is available on GitHub:Gang neuron. © 2020, CC BY-NC-SA.

关键词： Online systems

Multiobjective Bilevel Evolutionary Approach for Off-Grid Direction-of-Arrival Estimation

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arXiv 2021年

作者： Yan, Bai Zhao, Qi Zhang, Jin Zhang, J. Andrew Yao, Xin Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China School of Computer Science and Technology University of Science and Technology of China Hefei230027 China Global Big Data Technologies Centre University of Technology SydneyNSW2007 Australia

The source number identification is an essential step in direction-of-arrival (DOA) estimation. Existing methods may provide a wrong source number due to inferior statistical properties (in low SNR or limited snapshots) or modeling errors (caused by relaxing sparse penalties), especially in impulsive noise. To address this issue, we propose a novel idea of simultaneous source number identification and DOA estimation. We formulate a multiobjective off-grid DOA estimation model to realize this idea, by which the source number can be automatically identified together with DOA estimation. In particular, the source number is properly exploited by the l0 norm of impinging signals without relaxations, guaranteeing accuracy. Furthermore, we design a multiobjective bilevel evolutionary algorithm to solve the proposed model. The source number identification and sparse recovery are simultaneously optimized at the on-grid (lower) level. A forward search strategy is developed to further refine the grid at the off-grid (upper) level. This strategy does not need linear approximations and can eliminate the off-grid gap with low computational complexity. Simulation results demonstrate the outperformance of our method in terms of source number and root mean square error. Copyright © 2021, The Authors. All rights reserved.

关键词： Impulse noise

Slow and weak attractor computation embedded in fast and strong E-I balanced neural dynamics 23

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Slow and weak attractor computation embedded in fast and str...

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Xiaohan Lin Liyuan Li Boxin Shi Tiejun Huang Yuanyuan Mi Si Wu Nat'l Key Lab for Multimedia Information Processing Nat'l Eng. Lab of Visual Technology School of Computer Science Peking University and Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies School of Psychological and Cognitive Sciences Beijing Key Laboratory of Behavior and Mental Health IDG/McGovern Institute for Brain Research Center of Quantitative Biology Peking University Department of Mathematics Peking University and Institute of Neuroinformatics University of Zurich Nat'l Key Lab for Multimedia Information Processing Nat'l Eng. Lab of Visual Technology School of Computer Science Peking University and Institute for Artificial Intelligence Peking University Department of Psychology Tsinghua University Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies School of Psychological and Cognitive Sciences Beijing Key Laboratory of Behavior and Mental Health IDG/McGovern Institute for Brain Research Center of Quantitative Biology Peking University

Attractor networks require neuronal connections to be highly structured in order to maintain attractor states that represent information, while excitation and inhibition balanced networks (E-INNs) require neuronal connections to be random and sparse to generate irregular neuronal firing. Despite being regarded as canonical models of neural circuits, both types of networks are usually studied independently, and it remains unclear how they coexist in the brain, given their very different structural demands. In this study, we investigate the compatibility of continuous attractor neural networks (CANNs) and E-INNs. In line with recent experimental data, we find that a neural circuit can exhibit both the traits of CANNs and E-INNs when the neuronal synapses consist of two sets: one set is strong and fast for irregular firing, and the other set is weak and slow for attractor dynamics. In addition, both simulations and theoretical analysis reveal that the network exhibits enhanced performance compared to the case of using only one set of synapses, with accelerated convergence of attractor states and retained E-I balanced condition for localized input. We hope that this study provides insight into how structured neural computations are realized by irregular firings of neurons.

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Recommendation for Cardiac Magnetic Resonance Imaging-Based Phenotypic Study:Imaging Part

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Phenomics 2021年第4期1卷 151-170页

作者： Chengyan Wang Yan Li Jun Lv Jianhua Jin Xumei Hu Xutong Kuang Weibo Chen He Wang Human Phenome Institute Fudan University825 Zhangheng RoadPudong New DistrictShanghai 201203China Department of Radiology Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina School of Computer and Control Engineering Yantai UniversityYantaiChina School of Data Science Fudan UniversityShanghaiChina Philips Healthcare.Co. ShanghaiChina Institute of Science and Technology for Brain-Inspired Intelligence Fudan University220 Handan RoadYangpu DistrictShanghai 200433China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence(Fudan University) Ministry of EducationShanghaiChina

Cardiac magnetic resonance(CMR)imaging provides important biomarkers for the early diagnosis of many cardiovascular diseases and has been reported to reveal phenome-wide associations of cardiac/aortic structure and functionality in popula-tion ***,due to the complexity of operation and variations among manufactural vendors,magnetic field strengths,coils,sequences,scan parameters,and image analysis approaches,CMR is rarely used in large cohort *** guidelines mainly focused on the diagnosis of cardiovascular diseases,which did not aim to basic *** purpose of this study was to propose a recommendation for CMR based phenotype measurements for cohort *** classify the imaging sequences of CMR into three categories according to the importance and universality of correspond-ing measurable *** acquisition time and repeatability of the phenotypic measurement were also taken into consideration during the *** other guidelines,this recommendation focused on quantitative measurement of large amount of phenotypes from CMR.

关键词： Cardiac imaging Magnetic resonance imaging Phenotype Standardization