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IEEE International Symposium on Biomedical Imaging

作者： Qianru Sun Lianyu Wang Liying Zhang Peiliang Gong Yueying Zhou Daoqiang Zhang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education China School of Mathematics Science Liaocheng University Liaocheng China

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Attention is a cognitive process that is crucial in our daily lives, however, there is currently limited exploration of applying deep learning methods to investigate attentive states. This work introduces a novel Multi-scale Spatial-Temporal Information-enhanced Transformer (MSTI-Former) aimed at classifying different attentive states. We use the Multi-scale Temporal Information Enhanced (MTIE) module to extract temporal features and the spatial grouped convolution to extract spatial features. This network comprises three components: a spatial grouped convolution and the MTIE module to capture local information, a multi-head self-attention module to capture global information, and a classifier module to obtain classification results. Experimental results on a publicly available dataset show that our model achieves SOTA performance across 26 subjects, filling the gap in EEG-based attention state classification.

关键词： Measurement Visualization Convolution Biological system modeling Feature extraction brain modeling Transformers

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An Experimental Survey of Missing Data Imputation Algorithms (Extended Abstract)

An Experimental Survey of Missing Data Imputation Algorithms...

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International Conference on Data Engineering

作者： Xiaoye Miao Yangyang Wu Lu Chen Yunjun Gao Jianwei Yin Center for Data Science Zhejiang University Hangzhou China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China Software College Zhejiang University Ningbo China College of Computer Science Zhejiang University Hangzhou China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Due to the ubiquity of missing data, data imputation has received extensive attention in the past decades. It is a well-recognized problem impacting almost all fields of scientific study. Existing imputation algorithms differ in problem settings, model selection, and data evaluation. There is a lack of systematic comparison study among imputation algorithms. In this paper, we survey this interesting and evolving research topic by broadly reviewing and experimentally comparing the state-of-the-art missing data imputation algorithms. We analyze and categorize 19 imputation algorithms. Extensive experiments over 15 real-world benchmark datasets are conducted under various settings of data types, missing mechanisms, missing rates, dataset parameters, as well as the post-imputation prediction task. We shed light on a series of constructive insights on imputation algorithms to tackle missing data problem in real-life scenarios. Moreover, we put forward promising future directions for data imputation.

关键词： Surveys Systematics Benchmark testing Prediction algorithms Data engineering Imputation Data models

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Improving the Sparse Structure Learning of Spiking Neural Networks from the View of Compression Efficiency

arXiv

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arXiv 2025年

作者： Shen, Jiangrong Xu, Qi Pan, Gang Chen, Badong Faculty of Electronic and Information Engineering Xi’an Jiaotong University China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University China State Key Lab of Brain-Machine Intelligence Zhejiang University China National Key Lab of Human-Machine Hybrid Augmented Intelligence Xi’an Jiaotong University China School of Computer Science Dalian University of Technology China

The human brain utilizes spikes for information transmission and dynamically reorganizes its network structure to boost energy efficiency and cognitive capabilities throughout its lifespan. Drawing inspiration from this spike-based computation, Spiking Neural Networks (SNNs) have been developed to construct event-driven models that emulate this efficiency. Despite these advances, deep SNNs continue to suffer from over-parameterization during training and inference, a stark contrast to the brain’s ability to self-organize. Furthermore, existing sparse SNNs are challenged by maintaining optimal pruning levels due to a static pruning ratio, resulting in either under or over-pruning. In this paper, we propose a novel two-stage dynamic structure learning approach for deep SNNs, aimed at maintaining effective sparse training from scratch while optimizing compression efficiency. The first stage evaluates the compressibility of existing sparse subnetworks within SNNs using the PQ index, which facilitates an adaptive determination of the rewiring ratio for synaptic connections based on data compression insights. In the second stage, this rewiring ratio critically informs the dynamic synaptic connection rewiring process, including both pruning and regrowth. This approach significantly improves the exploration of sparse structures training in deep SNNs, adapting sparsity dynamically from the point view of compression efficiency. Our experiments demonstrate that this sparse training approach not only aligns with the performance of current deep SNNs models but also significantly improves the efficiency of compressing sparse SNNs. Crucially, it preserves the advantages of initiating training with sparse models and offers a promising solution for implementing Edge AI on neuromorphic hardware. © 2025, CC BY-NC-ND.

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Multi-Modal brain Network Fusion for Intelligent Diagnostic Devices

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Shengrong Zhu, Qi Sun, Liang Ma, Kai Ji, Yixin Qi, Shile Zhang, Daoqiang Nanjing University of Aeronautics and Astronautics College of Artificial Intelligence Nanjing211106 China University of Aeronautics and Astronautics Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Nanjing211106 China Jiangsu University of Science and Technology School of Computer Jiangsu Province Zhenjiang212100 China

Embedding multi-modal brain network analysis technology into consumer electronics, such as smart wearables, helps enable early intelligent diagnosis of brain diseases. Recent studies confirm that the functional-structural coupling in certain regions is more tightly correlated than in others. However, existing multi-modal methods often directly fuse functional brain networks (FBN) and structural brain networks (SBN), ignoring the regional heterogeneity between them. Additionally, identity information encoded in brain networks may interfere with disease diagnosis. In this paper, we develop a multi-modal brain network fusion method with regional heterogeneity constraints, and design a feature decoupling module to alleviate disease-irrelevant information. Specifically, we first divide FBN and SBN into multiple subnetworks, and introduce penalty weights to reduce the communication cost between cross-modal brain regions within the subnetwork while increasing the cost between different subnetworks. Then, under the regional heterogeneity constraints, we adopt optimal transport to simulate the transfer of brain region hubness from FBN to SBN, thereby effectively integrating the complex cross-modal interactions. Furthermore, we design a feature decoupling module to suppress ineffective features and enhance the discrimination between modality-specific features and multi-modal features. Experimental results show that the proposed method has promising performance and can identify multi-modal biomarkers for brain disease diagnosis. © 1975-2011 IEEE.

关键词： Wearable technology

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Chalcogenide integrated nanophotonics and its potential for quantum photonics

Chalcogenide integrated nanophotonics and its potential for ...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and Optical Communications, IPOC 2024

作者： Lei, Kunhao Xu, Kai Bao, Kangjian Weng, Jiawei Li, Lan Lin, Hongtao The State Key Lab of Brain-Machine Intelligence Key Laboratory of Micro-Nano Electronics and Smart System of Zhejiang Province College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China The Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province School of Engineering Westlake University Zhejiang Hangzhou310030 China Institute of Advanced Technology Westlake Institute for Advanced Study Zhejiang Hangzhou310024 China

Due to the high optical nonlinearity and nonvolatile phase-changing properties, chalcogenide integrated nanophotonics are emerging for entangling photon generation and nonvolatile quantum states operation. We will rev... 详细信息

ISBN: (纸本)9798350379266

关键词： Silicon photonics

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Multi-Modal Cross-Subject Emotion Feature Alignment and Recognition with EEG and Eye Movements

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IEEE Transactions on Affective Computing 2025年

作者： Zhu, Qi Zhu, Ting Fei, Lunke Zheng, Chuhang Shao, Wei Zhang, David Zhang, Daoqiang Nanjing University of Aeronautics and Astronautics College of Artificial Intelligence Nanjing211106 China Nanjing University of Aeronautics and Astronautics Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Nanjing211106 China Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China School of Data Science Shenzhen China

Multi-modal emotion recognition has attracted much attention in human-computer interaction, because it provides complementary information for the recognition model. However, the distribution drift among subjects and the heterogeneity of different modalities pose challenges to multi-modal emotion recognition, thereby limiting its practical application. Most of the current multi-modal emotion recognition methods are difficult to suppress above uncertainties in fusion. In this paper, we propose a cross-subject multi-modal emotion recognition framework, which jointly learns subject-independent representation and common feature between EEG and eye movements. Firstly, we design the dynamic adversarial domain adaptation for cross-subject distribution alignment, dynamically selecting source domains in training. Secondly, we simultaneously capture intra-modal and inter-modal emotion-related features by both self-attention and cross-attention mechanisms, thus obtaining the robust and complementary representation of emotional information. Then, two contrastive loss functions are imposed on above network to further reduce inter-modal heterogeneity, and mine higher-order semantic similarity between synchronously collected multi-modal data. Finally, we used the output of the softmax layer as the predicted value. The experimental results on several multi-modal emotion datasets with EEG and eye movements demonstrate that our method is significantly superior to the state-of-the-art emotion recognition approaches. © 2010-2012 IEEE.

关键词： Eye movements

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Enhancing SNN-based Spatio-Temporal Learning: A Benchmark Dataset and Cross-Modality Attention Model

arXiv

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arXiv 2024年

作者： Zhou, Shibo Yang, Bo Yuan, Mengwen Jiang, Runhao Yan, Rui Pan, Gang Tang, Huajin Research Center for Data Hub and Security Zhejiang Lab Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Research Center for High Efficiency Computing System Zhejiang Lab Hangzhou China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China

Spiking Neural Networks (SNNs), renowned for their low power consumption, brain-inspired architecture, and spatio-temporal representation capabilities, have garnered considerable attention in recent years. Similar to Artificial Neural Networks (ANNs), high-quality benchmark datasets are of great importance to the advances of SNNs. However, our analysis indicates that many prevalent neuromorphic datasets lack strong temporal correlation, preventing SNNs from fully exploiting their spatio-temporal representation capabilities. Meanwhile, the integration of event and frame modalities offers more comprehensive visual spatio-temporal information. Yet, the SNN-based cross-modality fusion remains underexplored. In this work, we present a neuromorphic dataset called DVS-SLR that can better exploit the inherent spatio-temporal properties of SNNs. Compared to existing datasets, it offers advantages in terms of higher temporal correlation, larger scale, and more varied scenarios. In addition, our neuromorphic dataset contains corresponding frame data, which can be used for developing SNN-based fusion methods. By virtue of the dual-modal feature of the dataset, we propose a Cross-Modality Attention (CMA) based fusion method. The CMA model efficiently utilizes the unique advantages of each modality, allowing for SNNs to learn both temporal and spatial attention scores from the spatio-temporal features of event and frame modalities, subsequently allocating these scores across modalities to enhance their synergy. Experimental results demonstrate that our method not only improves recognition accuracy but also ensures robustness across diverse scenarios. © 2024, CC0.

关键词： Spatio-temporal data

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An allocation method of crowdsourcing tasks for protecting fairness of participants 23

An allocation method of crowdsourcing tasks for protecting f...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Xiao, Lei Gao, Zhigang Xu, Ruichao Wu, Bo Zheng, Leilei Zhao, Wei Cen, Weipeng He, Xuanzhang 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 Interdisciplinary Graduate School University of Yamanashi Kofu400-8511 Japan

ISBN: (纸本)9781665494571

With the increasingly powerful functions of mobile devices and the popularity of wireless networks, spatial crowdsourcing has become a new service model. This paper studies the task assignment problem in spatial crowdsourcing, which reduces the commuting cost of participants while ensuring the fairness of participants under the condition of task execution order constraints. This paper first proposes the Fairness Monte Carlo Tree Search (FMCTS) task allocation algorithm, ensuring that the maximum attendance difference of participants is below the set value while reducing the participants' commuting costs. After that, to optimize the efficiency of the algorithm, a FMCTS-Guided (FMCTSG) task allocation algorithm is proposed, which can reduce the running time of the algorithm. Finally, for non-emergency tasks, a Delayed FMCTS (DFMCTS) algorithm is proposed, which can further reduce the commuting cost of task participants. The experimental results show that the task allocation method proposed in this paper can effectively reduce the commuting cost of participants while ensuring the fairness of participants. © 2021 IEEE.

关键词： Crowdsourcing Costs Monte Carlo methods Smart cities Wireless networks Mobile handsets Resource management

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Online Query-Based Data Pricing with Time-Discounting Valuations

Online Query-Based Data Pricing with Time-Discounting Valuat...

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International Conference on Data Engineering

作者： Yicheng Fu Xiaoye Miao Huanhuan Peng Chongning Na Shuiguang Deng Jianwei Yin Center for Data Science Zhejiang University Hangzhou China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China PinTech Research Center Zhejiang Lab Hangzhou China College of Computer Science Zhejiang University Hangzhou China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Online data marketplaces emerge in diverse data-driven applications, where dynamically arriving consumers pur-chase the data at posted prices. The data value decays over time in many tasks, such as machine learning predictions and realtime systems. Existing query pricing methods do not consider the time-discounting data value. In this paper, we study the query feature-based data pricing problem with unknown time-discounting data valuation. We propose an effective online data pricing mechanism Pride to maximize the cumulative sales revenue. It leverages the powerful property of the ellipsoid method to efficiently solve online optimization via exploration and exploitation. Based on Thompson sampling, we present a novel non-stationary MAB algorithm Biased-TS to determine a suitable discount factor and attain the dynamic posted price. It is theoretically proved that, the regret upper bound order of Pride is dominated by the discretization error $O(\frac{T}{k})$ , where $K$ and $T$ are the numbers of discount candidates and total trading rounds, respectively. Biased-TS gets a sub-linear regret upper bound $O(K^{3}\sqrt{T\ln T}+K\exp\{4\sqrt{\ln T}\})$ . Extensive experiments using both synthetic and real datasets demonstrate that Pride yields around 90% of the optimal cumulative revenue, and it substantially outperforms the state-of-the-art methods.

关键词： Upper bound Heuristic algorithms Pricing machine learning Market research Data engineering Task analysis

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Spiking GS: Towards High-Accuracy and Low-Cost Surface Reconstruction via Spiking Neuron-based Gaussian Splatting

arXiv

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arXiv 2024年

作者： Zhang, Weixing Li, Zongrui Ma, De Tang, Huajin Jiang, Xudong Zheng, Qian Pan, Gang The State Key Lab of Brain-Machine Intelligence Zhejiang University China School of Software Technology Zhejiang University China Lab Nanyang Technological University Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore College of Computer Science and Technology Zhejiang University China

3D Gaussian Splatting is capable of reconstructing 3D scenes in minutes. Despite recent advances in improving surface reconstruction accuracy, the reconstructed results still exhibit bias and suffer from inefficiency in storage and training. This paper provides a different observation on the cause of the inefficiency and the reconstruction bias, which is attributed to the integration of the low-opacity parts (LOPs) of 3D Gaussians. We identify the source of LOPs and propose Spiking GS to reduce two types of LOPs by integrating spiking neurons into the Gaussian Splatting pipeline. Specifically, we introduce global and local full-precision integrate-and-fire spiking neurons to the opacity and representation function of flattened 3D Gaussians, respectively. Furthermore, we enhance the density control strategy with spiking neurons’ thresholds and a new cloning criterion. Our method can represent more accurate reconstructed surfaces at a lower cost. The code is available at https://***/zju-bmi-lab/SpikingGS. © 2024, CC BY-NC-ND.

关键词： Neurons

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