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Gossip-based algorithm for economic dispatch of microgrids integrating isolated and grid-connected modes

Science China(Information Sciences) 2025年第3期68卷 258-271页

作者： Yanmeng ZHANG Yalin ZHANG Zhongxin LIU Zengqiang CHEN College of Artificial Intelligence Nankai University Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation

A gossip-based economic dispatch(ED) algorithm for microgrids is presented in this paper, designed to cope with communication link failures and enable smooth switching of microgrid operation modes. The algorithm is supported by the Push-Pull architecture, which allows its application to direct graphs and relaxes the initial conditions compared to many existing ED algorithms. Under an asynchronous communication network, where only one directed edge is activated at each moment,it has been shown that ED can be achieved with probability 1, provided that the communication graph is strongly ***, in a synchronous communication network, where each directed communication link is activated at each moment with a certain probability, ED is also achieved with probability 1 under the condition that the communication graph is strongly connected. This demonstrates that optimal consensus is reached under randomly switched communication networks as long as the expectation of communication graphs is strongly connected, a condition that is less stringent than the B-strongly connected requirement found in many other studies. The algorithm's use of a non-decreasing variable step size enables a transition from a sub-linear convergence rate, associated with a diminishing step size, to a linear convergence rate. This also lays the groundwork for future improvements in convergence rate through online step size optimization based on the communication topology. Finally,the algorithm's effectiveness and its potential application to anti-collusion are demonstrated through simulations.

关键词： distributed optimization economic dispatch gossip algorithms multi-agent systems consensus algorithm

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Role of the Thalamic Reticular Nucleus in Social Memory

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Neuroscience Bulletin 2025年第2期41卷 355-358页

作者： Xue Liu Jianing Zhu Junqiang Zheng Han Xu Department of Psychiatry of the Second Affiliated Hospital and School of Brain Science and Brain Medicine Zhejiang University School of MedicineHangzhou310058China Nanhu Brain-computer Interface Institute Hangzhou311100China College of Life Sciences Zhejiang UniversityHangzhou310058China Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-machine IntegrationState Key Laboratory of Brain-machine IntelligenceZhejiang UniversityHangzhou311121China NHC and CAMS Key Laboratory of Medical Neurobiology Zhejiang UniversityHangzhou310058China Lingang Laboratory Shanghai200031China

Social behaviors are crucial for gregarious animals,including *** order to exhibit appropriate behaviors in a complex social context,such as mating,aggression,avoidance,and cooperation,individuals need to remember their previous experiences with other members and accurately recognize them when they meet *** ability is called“social memory”[1].Many psychiatric disorders in humans,such as autism spectrum disorder,attention deficit hyperactivity disorder,and schizophrenia,are characterized by social memory *** with these disorders,along with corresponding animal models,often show defects associated with the thalamic reticular nucleus(TRN).The TRN,a thin layer of neurons surrounding the thalamus,mainly regulates and coordinates the transfer of information between the cortex and the thalamus,playing a role in higher brain functions such as consciousness,attention,and sensory ***,whether the TRN is involved in social memory remains unknown.

关键词： exhibit appropriate behaviors social behaviors mating social memory thalamic reticular nucleus gregarious animals autism spectrum disorderattention deficit hyperactivity disorderand psychiatric disorders

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Sex-Differential Neural Circuits and Behavioral Responses for Empathy

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Neuroscience Bulletin 2025年第1期41卷 192-194页

作者： Jingkai Fan Xinrong Wang Han Xu Department of Psychiatry of the Second Affiliated Hospital and School of Brain Science and Brain Medicine Zhejiang University School of MedicineHangzhou310058China Nanhu Brain-computer Interface Institute Hangzhou311100China Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-machine IntegrationState Key Laboratory of Brain-machine IntelligenceZhejiang UniversityHangzhou311121China NHC and CAMS Key Laboratory of Medical Neurobiology Zhejiang UniversityHangzhou310058China Lingang Laboratory Shanghai200031China

Empathy is one of the most important abilities for social *** a world that is largely socially constructed,when encountering altered affective states of others,particularly negative states,individuals often exhibit evolutionarily conserved empathic behaviors,such as mirroring,consolation,and helping[l,2].Appropriate empathic behaviors can foster the consolidation of social *** studies have suggested that both male and female rodents show some empathic behaviors when facing affective conspecifics[1].However,the role of sex in different empathic behaviors remains largely ***,there is a need to probe whether animals show sex-dependent empathic behavior and which neural circuits modulate these distinct behaviors.

关键词： mirror largely behavior

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Multi-Level and Energy-Efficient Partial Computation Offloading in Heterogeneous Edge Intelligence

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IEEE Internet of Things Journal 2025年第11期12卷 14993-15007页

作者： Xu, Baoyu Ruan, Yancheng Qiu, Chenghu He, Shuibing Shu, Feng Kang, Xiaoyang Zhang, Lihua Academy for Engineering and Technology Shanghai200433 China Shanghai University School of Computer Engineering and Science Shanghai200444 China Zhejiang University College of Computer Science and Technology Hangzhou310027 China University of Sydney School of Electrical and Information Engineering Australia Fudan University Laboratory for Neural Interface and Brain Computer Interface MOE Frontiers Center for Brain Science Academy for Engineering and Technology Shanghai200433 China Fudan University Academy for Engineering and Technology Shanghai200433 China

Due to the diversity of edge devices and applications, edge systems are heterogeneous and have been applied in artificial intelligence fields such as smart factories and intelligent transportation, which is called heterogeneous edge intelligence. Many studies employ computation offloading to transfer processing data from resource-scarce edge devices to resource-rich edge servers. These studies primarily focus on the overall resource consumption of homogeneous edge systems, neglecting the system heterogeneity and the details of resource consumption. In this article, we construct a system model from a parallel perspective for the heterogeneous edge system with different processors, memory, and applications, which perceives the cost of energy and delay from three levels: system, application, and component. A hybrid metaheuristic algorithm combined with a greedy rule, hybrid mutation, and whale optimization algorithm (GHMWOA) is proposed to realize partial computation offloading. A partial offloading architecture of heterogeneous edge intelligence is proposed to validate our model and algorithm with real-world hardware and software. Experiment results not only show GHMWOA outperforms multiple classical optimization algorithms in minimizing energy consumption, but also discover on which system component energy consumption depends, and how properties of application and system influence the cost of energy. © 2014 IEEE.

关键词： Heuristic algorithms

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Learned distributed image compression with decoder side information

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Digital Communications and Networks 2025年第2期11卷 349-358页

作者： Yankai Yin Zhe Sun Peiying Ruan Ruidong Li Feng Duan Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai University Institute of Natural Sciences Kanazawa University Faculty of Health Data Science and Graduate School of Medicine Juntendo University NVIDIA AI Technology Center NVIDIA Japan

With the rapid development of digital communication and the widespread use of the Internet of Things, multi-view image compression has attracted increasing attention as a fundamental technology for image data communication. Multi-view image compression aims to improve compression efficiency by leveraging correlations between images. However, the requirement of synchronization and inter-image communication at the encoder side poses significant challenges, especially for constrained devices. In this study, we introduce a novel distributed image compression model based on the attention mechanism to address the challenges associated with the availability of side information only during decoding. Our model integrates an encoder network, a quantization module, and a decoder network, to ensure both high compression performance and high-quality image reconstruction. The encoder uses a deep Convolutional Neural Network(CNN) to extract high-level features from the input image, which then pass through the quantization module for further compression before undergoing lossless entropy coding. The decoder of our model consists of three main components that allow us to fully exploit the information within and between images on the decoder side. Specifically, we first introduce a channel-spatial attention module to capture and refine information within individual image feature maps. Second, we employ a semi-coupled convolution module to extract both shared and specific information in images. Finally, a cross-attention module is employed to fuse mutual information extracted from side information. The effectiveness of our model is validated on various datasets, including KITTI Stereo and Cityscapes. The results highlight the superior compression capabilities of our method, surpassing state-of-the-art techniques.

关键词： Digital communication Image compression Side information Channel-spatial attention module Cross-attention module

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Constructing A Theoretical Model to Bridge Neural Transition with a State Switch in Bipolar Disorder

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Neuroscience Bulletin 2025年第1期41卷 181-185页

作者： Xiaonan Guo Lizichen Chen Jianbo Lai Shaohua Hu Department of Psychiatry the First Affiliated HospitalZhejiang University School of MedicineHangzhou310003China Department of Engineering Mechanics Zhejiang UniversityHangzhou310027China Nanhu Brain-computer Interface Institute Hangzhou311100China The Zhejiang Key Laboratory of Precision Psychiatry Hangzhou310003China MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University School of MedicineHangzhou310003China Brain Research Institute of Zhejiang University Hangzhou310003China The State Key Lab of Brain-Machine Intelligence Zhejiang UniversityHangzhouChina Zhejiang Engineering Center for Mathematical Mental Health Hangzhou310003China

Reward or stress,which exists extensively,causes resilient emotional fluctuations under common ***,reward or stress is a typical trigger for manic or depressive episodes of bipolar disorder(BD),which is corroborated by psychological theory,biological findings,and psychosocial treatment approaches[1,2].During an episode of BD,the affective aberration can be persistent and switchable,accompanied by opposite constellations of cognitive and psychomotor *** by uncontrollable mood ranging in severity,duration,and polarity,to disentangle the pathophysiology mechanism of BD is to delineate the mystery of affective fluctuations driven by reward or stress.

关键词： bipolar Transition finding

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A Wireless Fully Implantable DBS Device for Closed-Loop Neuromodulation Applications

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IEEE Sensors Journal 2025年第9期25卷 15331-15344页

作者： Su, Kangyu Qiu, Zhang Xu, Kedi Xu, Jian Wang, Yueming Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang University MOE Frontier Science Center for Brain Science and Brain-Machine Integration Hangzhou310027 China Nanhu Brain-Computer Interface Institute Hangzhou311100 China Zhejiang University College of Biomedical Engineering and Instrument Science Hangzhou310027 China Zhejiang University School of Medicine Affiliated Mental Health Center Hangzhou Seventh People’s Hospital Hangzhou310013 China Zhejiang University State Key Laboratory of Brain-Machine Intelligence Hangzhou310027 China Zhejiang University College of Computer Science and Technology Hangzhou310027 China

Closed-loop deep brain stimulation (DBS) is an emerging technology in the field of neuromodulation, which can provide effective treatment methods for intractable neurological disorders. To address the faced challenges, this article presents a high-precision closed-loop DBS device based on a custom-designed neuromodulation integrated circuit (CNIC) fabricated in a 180-nm bipolar-CMOS-DMOS (BCD) process. Also, three new techniques have been developed to optimize system performance. First, to improve system stability, a frequency shaping & quick blanking amplifier (FSQBA)-based analog front-end is proposed to extend system dynamic range (DR). Second, both stimulation artifacts (SAs) suppression scheme and closed-loop control algorithm are developed to achieve real simultaneous recording and stimulation. Third, a redundant-crossfire (RXF) technique is designed to enhance current precision without consuming much extra area. Bench-top measurements show that the designed system features a wide DR of 112 dB, a short saturation recovery time of 100 μs, and a high stimulation current resolution of 13.1-bit. Besides, animal experiments demonstrate that the designed system is highly suitable for long-term implantation applications, thus providing accurate and reliable closed-loop control for neuromodulation therapies. © 2001-2012 IEEE.

关键词： CMOS integrated circuits

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Self-Attentive Spatio-Temporal Calibration for Precise Intermediate Layer Matching in ANN-to-SNN Distillation

arXiv

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arXiv 2025年

作者： Hong, Di Wang, Yueming The College of Computer Science and Technology Zhejiang University China Nanhu Brain-computer Interface Institute Hangzhou China The State Key Laboratory of Brain-Machine Intelligence Zhejiang University China

Spiking Neural Networks (SNNs) are promising for low-power computation due to their event-driven mechanism but often suffer from lower accuracy compared to Artificial Neural Networks (ANNs). ANN-to-SNN knowledge distillation can improve SNN performance, but previous methods either focus solely on label information, missing valuable intermediate layer features, or use a layer-wise approach that neglects spatial and temporal semantic inconsistencies, leading to performance degradation. To address these limitations, we propose a novel method called self-attentive spatio-temporal calibration (SASTC). SASTC uses self-attention to identify semantically aligned layer pairs between ANN and SNN, both spatially and temporally. This enables the autonomous transfer of relevant semantic information. Extensive experiments show that SASTC outperforms existing methods, effectively solving the mismatching problem. Superior accuracy results include 95.12% on CIFAR-10, 79.40% on CIFAR-100 with 2 time steps, and 68.69% on ImageNet with 4 time steps for static datasets, and 97.92% on DVS-Gesture and 83.60% on DVS-CIFAR10 for neuromorphic datasets. This marks the first time SNNs have outperformed ANNs on both CIFAR-10 and CIFAR-100, shedding the new light on the potential applications of SNNs. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Visual numerical cognition in pigeons: conformity to the Weber–Fechner law

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Animal Cognition 2025年第1期28.0卷 1-13页

作者： Wu, Peng Zhu, Juncai He, Qingzhi Wang, Zhizhong Shi, Li School of Electrical and Information Engineering Zhengzhou University Zhengzhou 450001 China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou 450001 China Department of Automation Tsinghua University Beijing 100084 China

As representatives of a basal bird lineage, pigeons have exhibited remarkable visual numerical cognition, comparable even to that of monkeys. Nevertheless, whether visual numerical cognition in pigeons conforms to the Weber–Fechner law remains unknown. To address this, we designed a fully automated apparatus tailored for pigeons and used it to train them to perform a delayed match-to-numerosity task. The results showed that on a linear scale, pigeons represented smaller numerosities with higher precision and larger numerosities with lower precision, exhibiting a numerical magnitude effect. When the linear scale was compressed into a logarithmic scale, this magnitude effect was offset, resulting in similar representational characteristics across different numerosities. This finding suggests that the mental number line of pigeons is logarithmic rather than linear, consistent with the Weber–Fechner law. While biological brains seek precision in representing numerical information, they must also take computational load into account. This representational strategy may be the optimal outcome of the trade-off between computational precision and computational load that biological brains have achieved through long-term evolution. © The Author(s) 2025.

关键词： Birds Logarithm Mental number line Numerosity Pigeons

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Machine learning assisted screening of binary alloys for metal-based anode materials

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Journal of Energy Chemistry 2025年第5期104卷 62-68页

作者： Xingyue Shi Linming Zhou Yuhui Huang Chaohui Wu Yongjun Wu Juan Li Zijian Hong State Key Laboratory of Silicon and Advanced Semiconductor Materials School of Materials Science and EngineeringZhejiang UniversityHangzhou 310058ZhejiangChina Nanhu Brain-Computer Interface Institute Hangzhou 311100ZhejiangChina Zhejiang Key Laboratory of Advanced Solid State Energy Storage Technology and Applications Research Institute of Zhejiang University-TaizhouTaizhou 318000ZhejiangChina Inner Mongolia Electric Power Economic and Technological Research Institute Hohhot 010010Inner MongoliaChina College of Materials Science and Engineering Zhejiang University of TechnologyHangzhou 310014ZhejiangChina School of Engineering Hangzhou City UniversityHangzhou 310015ZhejiangChina Institute of Fundamental and Transdisciplinary Research Zhejiang UniversityHangzhou 311121China

Metal alloy anode materials with high specific capacity and low voltage have recently gained significant attention due to their excellent electrochemical performance and the ability to suppress dendrite ***,experimental investigations of metal alloys can be time-consuming and expensive,often requiring extensive experimental design and *** this study,we developed a machine learning model based on the Crystal Graph Convolutional Neural Network(CGCNN)to screen alloy anode materials for seven battery systems,including lithium(Li),sodium(Na),potassium(K),zinc(Zn),magnesium(Mg),calcium(Ca),and aluminum(Al).We utilized data with tens of thousands of alloy materials from the Materials Project(MP)and Automatic FLOW for Materials Discovery(AFLOW)*** any experimental voltage input,we identified over 30 alloy systems that have been experimentally validated with good ***,we predicted over 100 alloy anodes with low potential and high specific *** hope this work to spur further interest in employing advanced machine learning models for the design of battery materials.

关键词： CGCNN Alloy anodes Machine learning Metal-based batteries

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