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arXiv 2022年

作者： Wang, Ziming Zhang, Yuhao Lian, Shuang Cui, Xiaoxin Yan, Rui Tang, Huajin College of Computer Science and Technology Zhejiang University Hangzhou310027 China Research Center for Intelligent Computing Hardware Zhejiang Lab Hangzhou311100 China School of Integrated Circuits Peking University Beijing100871 China College of Computer Science Zhejiang University of Technology Hangzhou310014 China State Key Laboratory of Brain-Machine Intelligence Zhejiang University Hangzhou310027 China

Spiking neural networks (SNNs) operating with asynchronous discrete events show higher energy efficiency with sparse computation. A popular approach for implementing deep SNNs is ANN-SNN conversion combining both efficient training of ANNs and efficient inference of SNNs. However, the accuracy loss is usually non-negligible, especially under few time steps, which restricts the applications of SNN on latencysensitive edge devices greatly. In this paper, we first identify that such performance degradation stems from the misrepresentation of the negative or overflow residual membrane potential in SNNs. Inspired by this, we decompose the conversion error into three parts: quantization error, clipping error, and residual membrane potential representation error. With such insights, we propose a two-stage conversion algorithm to minimize those errors respectively. Besides, We show each stage achieves significant performance gains in a complementary manner. By evaluating on challenging datasets including CIFAR-10, CIFAR- 100 and ImageNet, the proposed method demonstrates the stateof- the-art performance in terms of accuracy, latency and energy preservation. Furthermore, our method is evaluated using a more challenging object detection task, revealing notable gains in regression performance under ultra-low latency when compared to existing spike-based detection algorithms. Codes are available at https://***/Windere/snn-cvt-dual-phase. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Decoding Chinese phonemes from intracortical brain signals with hyperbolic-space neural representations

arXiv

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arXiv 2023年

作者： Tan, Xianhan Zhu, Junming Zhang, Jianmin Wang, Yueming Qi, Yu Qiushi Academy for Advanced Studies Zhejiang University Hangzhou310027 China College of Computer Science Zhejiang University Hangzhou310027 China State Key Lab of Brain-Machine Intelligence Hangzhou310027 China Second Affiliated Hospital of Zhejiang University School of Medicine Hangzhou310027 China Affiliated Mental Health Center Hangzhou Seventh People's Hospital Zhejiang University School of Medicine Hangzhou310027 China Moe Frontier Science Center for Brain Science and Brain-machine Integration Zhejiang University Hangzhou310027 China

Speech brain-computer interfaces (BCIs), which translate brain signals into spoken words or sentences, have shown significant potential for high-performance BCI communication. Phonemes are the fundamental units of pronunciation in most languages. While existing speech BCIs have largely focused on English, where words contain diverse compositions of phonemes, Chinese Mandarin is a monosyllabic language, with words typically consisting of a consonant and a vowel. This feature makes it feasible to develop high-performance Mandarin speech BCIs by decoding phonemes directly from neural signals. This study aimed to decode spoken Mandarin phonemes using intracortical neural signals. We observed that phonemes with similar pronunciations were often represented by inseparable neural patterns, leading to confusion in phoneme decoding. This finding suggests that the neural representation of spoken phonemes has a hierarchical structure. To account for this, we proposed learning the neural representation of phoneme pronunciation in a hyperbolic space, where the hierarchical structure could be more naturally optimized. Experiments with intracortical neural signals from a Chinese participant showed that the proposed model learned discriminative and interpretable hierarchical phoneme representations from neural signals, significantly improving Chinese phoneme decoding performance and achieving state-of-the-art. The findings demonstrate the feasibility of constructing high-performance Chinese speech BCIs based on phoneme decoding. Copyright © 2023, The Authors. All rights reserved.

关键词： brain computer interface

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Self-Stretchable Christmas-Tree-Shaped Ultraflexible Neural Probes 22

Self-Stretchable Christmas-Tree-Shaped Ultraflexible Neural ...

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22nd International Conference on Solid-state Sensors, Actuators and Microsystems, Transducers 2023

作者： Tian, Ye Zhou, Cunkai Zhang, Kuikui Yang, Huiran Chen, Zhaohan Zhou, Zhitao Wei, Xiaoling Tao, Tiger H. Sun, Liuyang 2020 X-Lab Shanghai Institute Of Microsystem And Information Technology Chinese Academy Of Sciences Shanghai China Schoool Of Graduate Study University Of Chinese Academy Of Sciences Beijing China College Of Electronics And Information Engineering Shanghai University Of Electric Power Shanghai China Institute Of Intelligent Manufacturing Nanjing Tech University Nanjing China State Key Laboratory Of Transducer Technology Shanghai Institute Of Microsystem And Information Technology Chinese Academy Of Sciences Shanghai China College Of Life Sciences Shanghai Normal University Shanghai China Center Of Materials Science And Optoelectronics Engineering University Of Chinese Academy Of Sciences Beijing China Center For Excellence In Brain Science And Intelligence Technology Chinese Academy Of Sciences Shanghai China Jiangxi Nanchang China Guangdong Institute Of Intelligence Science And Technology Zhuhai Guangdong Hengqin China Tianqiao And Chrissy Chen Institute For Translational Research Shanghai China

ISBN: (纸本)9784886864352

We devised a novel Christmas-tree structure for flexible neural probes, which features recording sites on side-branches that can be folded along the shank of the probe by temporary encapsulation before implantation. This design reduces the size of the implant and minimizes the surgical footprint. Upon implantation, the temporary encapsulation dissolves in vivo, leading to self-stretching of the branches, and an expansion of the lateral detecting range of the probe. We conducted in vitro experiment with our probe in 0.6% agar that mimics the brain tissue environment. The results demonstrate that the neural probe we have developed exhibits self-stretching ability and provides an increased lateral recording range without causing additional damage to the brain tissue. © 2023 IEEJ.

关键词： brain computer interface Flexible probe Self-stretchable

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V-SWIR-IF: Visible and Short-Wave Infrared Image Fusion

V-SWIR-IF: Visible and Short-Wave Infrared Image Fusion

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computer Engineering and Intelligent Communications (ISCEIC), International Symposium on

作者： Hao Fang Gaomin Su Guanjie Xu Chuantong Cheng Brain Machine Fusion Intelligence Institute Suzhou China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou Jiangsu China State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences Beijing China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing China

This paper presents an algorithm for the fusion of visible light and short-wave infrared (SWIR) images. The algorithm is structured around three core processes: image registration, fusion, and color reconstruction. To register multimodal images, an intensity-based image registration method is employed, which utilizes Mattes mutual information as metrics to guide the 1+1 evolutionary algorithm optimizer. This step produces a registered SWIR image. The image fusion step entails separating the visible image into three images by separating the RGB channels. The registered SWIR image is then fused with each of the three images individually using the Discrete Wavelet Transform (DWT). Finally, the RGB channels of the three fused images are reconstructed to complete the fused image’s color information. Experimental results on our dataset of visible and SWIR images show that the proposed algorithm achieves good performance in terms of image fusion, with a fast fusion speed of approximately 1 seconds per pair on a common household computer.

关键词：

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DPA-2:a large atomic model as a multitask learner

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npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

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An Analytical Model for Normative Financial Texts and Research on Financial Strategies in an Intelligent Multi-factor Framework

An Analytical Model for Normative Financial Texts and Resear...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Wei Zhang Hanqing Zhu Zhigang Gao College of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province College of Computer Science and Technology Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9781665421751

How to analyze normative financial texts such as securities research reports and financial statements to dig out effective factors that affect the market is a focus of financial market research. After extracting effective factors, how to fully describe the relationship between factors and complex financial markets is another difficulty in financial market research. In this regard, this article proposed an new analysis model for normative financial texts, with the purpose of extracting effective factors from financial texts efficiently, comprehensively and automatically. In addition, we also proposed a unique intelligent multi-factor framework, through the dynamic allocation of weights for factors, mining the invisible influence of factors on the financial market.

关键词： Analytical models Big Data Market research Dynamic scheduling Security Resource management Data mining

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CEBRA Method: Decoding brain Activity for Advanced brain-computer Interface technology

CEBRA Method: Decoding Brain Activity for Advanced Brain-Com...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Jingcheng Yang Frank Kulwa Xuanwei Liu Yiqing Lu Yufa Fu Guanglin Li Yaping Huai Xin Zhang Yongcheng Li CAS Key Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences (CAS) Shenzhen China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Shenzhen Dapeng New District Nanao People’s Hospital Shenzhen China Department of Rehabilitation Medicine Shenzhen Longhua District Central Hospital Shenzhen China Brain Cognition and Brain-Computer Intelligence Integration Group Kunming University of Science and Technology Kunming China Shandong Zhongke Advanced Technology Co. Ltd. Jinan China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The emerging neurorehabilitation technology, brain-computer Interface (BCI), provides a novel prospect for stroke recovery. However, decoding brain activity during the movement present substantial challenges, and feature extraction is crutial to build a better decoder. In this study the CEBRA method were employed to extract features first based on electroencephalogram(EEG) data during Motor Execution(ME) and Motor lmagery (Ml) tasks for 20 participants (including 10 stroke patients). The results revealed that, in MI tasks, CEBRA-RF (Random Forests) achieved an average classification accuracy of 91.33%, with an average F1-score of 91.19%, and CEBRA-SVM (Support Vector machine) achieved an average classification accuracy of 91.32%, with an average F1-score of 90.83%. Compared to other conventional feature extraction methods, CEBRA shows significantly higher accuracy (t-test, p<0.01). However, in ME tasks, CEBRA-RF achieved an average classification accuracy of 75.67%, with an average F1-score of 75.39%, and CEBRA-SVM achieved an average classification accuracy of 76.13%, with an average F1-score of 75.80%. Nevertheless, no significant differences were observed compared to other feature extraction methods. These findings demonstrate the potential of CEBRA in decoding patients’ brain activity. The results of this study hold promise in addressing the current challenges of low decoding accuracy in BCI systems, offering a new approach for designing BCI-assisted rehabilitation systems for stroke patients.

关键词： Support vector machines brain Accuracy Stroke (medical condition) Feature extraction Motors brain-computer interfaces Neurorehabilitation Decoding Paralysis

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Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning

arXiv

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arXiv 2025年

作者： Xu, Qi Zhu, Junyang Zhou, Dongdong Chen, Hao Liu, Yang Shen, Jiangrong Zhang, Qiang School of Computer Science and Technology Dalian University of Technology Dalian China Faculty of Electronic and Information Engineering Xi’an Jiaotong University China National Key Lab of Human-Machine Hybrid Augmented Intelligence Xi’an Jiaotong University China State Key Lab of Brain-Machine Intelligence Zhejiang University China

Deep neural networks (DNNs) excel in computer vision tasks, especially, few-shot learning (FSL), which is increasingly important for generalizing from limited examples. However, DNNs are computationally expensive with scalability issues in real world. Spiking Neural Networks (SNNs), with their event-driven nature and low energy consumption, are particularly efficient in processing sparse and dynamic data, though they still encounter difficulties in capturing complex spatiotemporal features and performing accurate cross-class comparisons. To further enhance the performance and efficiency of SNNs in few-shot learning, we propose a few-shot learning framework based on SNNs, which combines a self-feature extractor module and a cross-feature contrastive module to refine feature representation and reduce power consumption. We apply the combination of temporal efficient training loss and InfoNCE loss to optimize the temporal dynamics of spike trains and enhance the discriminative power. Experimental results show that the proposed FSL-SNN significantly improves the classification performance on the neuromorphic dataset N-Omniglot, and also achieves competitive performance to ANNs on static datasets such as CUB and miniImageNet with low power consumption. © 2025, CC BY-NC-SA.

关键词： Deep neural networks

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Toward multi-target self-organizing pursuit in a partially observable Markov game

arXiv

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arXiv 2022年

作者： Sun, Lijun Chang, Yu-Cheng Lyu, Chao Shi, Ye Shi, Yuhui Lin, Chin-Teng Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology China Centre for Artificial Intelligence CIBCI Lab Faculty of Engineering and Information Technology University of Technology Sydney Australia College of Computer and Information Science Southwest University China School of Information Science and Technology ShanghaiTech University China

The multiple-target self-organizing pursuit (SOP) problem has wide applications and has been considered a challenging self-organization game for distributed systems, in which intelligent agents cooperatively pursue multiple dynamic targets with partial observations. This work proposes a framework for decentralized multi-agent systems to improve the implicit coordination capabilities in search and pursuit. We model a self-organizing system as a partially observable Markov game (POMG) featured by large-scale, decentralization, partial observation, and noncommunication. The proposed distributed algorithm-fuzzy self-organizing cooperative coevolution (FSC2) is then leveraged to resolve the three challenges in multi-target SOP: distributed self-organizing search (SOS), distributed task allocation, and distributed single-target pursuit. FSC2 includes a coordinated multi-agent deep reinforcement learning (MARL) method that enables homogeneous agents to learn natural SOS patterns. Additionally, we propose a fuzzy-based distributed task allocation method, which locally decomposes multi-target SOP into several single-target pursuit problems. The cooperative coevolution principle is employed to coordinate distributed pursuers for each single-target pursuit problem. Therefore, the uncertainties of inherent partial observation and distributed decision-making in the POMG can be alleviated. The experimental results demonstrate that by decomposing the SOP task, FSC2 achieves superior performance compared with other implicit coordination policies fully trained by general MARL algorithms. The scalability of FSC2 is proved that up to 2048 FSC2 agents perform efficient multi-target SOP with almost 100% capture rates. Empirical analyses and ablation studies verify the interpretability, rationality, and effectiveness of component algorithms in FSC2. Copyright © 2022, The Authors. All rights reserved.

关键词： Intelligent agents

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Extraction Method for Constructive Proposals based on Online Comments

Extraction Method for Constructive Proposals based on Online...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Weipeng Cen Zhigang Gao Ruichao Xu Bo Wu Leilei Zheng Wei Zhao Lei Xiao Xuanzhang He College of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province Hangzhou China School of Computer Science Tokyo University of Technology Hachioji Japan

ISBN: (纸本)9781665421751

In the flipped classroom teaching, it transfers the traditional teaching process from in-class to out-of-class, which is transforming the traditional education mode. In order to make good use of students' feedback data, and improve the quality of the online course and the effect of class teaching, this paper proposes an Extraction Method for constructive Proposals based on online Comments (EMPC). First, EMPC obtains the target words of course comments through BERT (Bidirectional Encoder Representations from Transformers) and the target filter algorithm, then it inputs target words into Bi-LSTM (Bi-directional Long Short-Term Memory) module with attention mechanism to obtain the opinion words. After all the target-opinion pairs are obtained, the algorithm based on k-means is used to extract constructive proposals. We conducted experiments by using MOOC (Massive Open Online Course) courses comment data, the experimental results show that EMPC achieves a high accuracy in both target word extraction and opinion word extraction, and it can get the proposals mentioned by students most frequently in order to improve the flipped classroom teaching.

关键词： computer aided instruction Electronic learning Education Bit error rate Memory modules Filtering algorithms Transformers

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