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39th Annual AAAI Conference on Artificial intelligence, AAAI 2025

作者： Zhou, Yangxuan Zhao, Sha Wang, Jiquan Jiang, Haiteng Li, Shijian Luo, Benyan Li, Tao Pan, Gang State Key Laboratory of Brain-machine Intelligence Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Neurobiology Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital Zhejiang University School of Medicine Hangzhou China MOE Frontier Science Center for Brain Science and Brain-machine Integration Zhejiang University Hangzhou China The First Affiliated Hospital College of Medicine Zhejiang University Hangzhou China

ISBN: (纸本)157735897X

Sleep staging is important for monitoring sleep quality and diagnosing sleep-related disorders. Recently, numerous deep learning-based models have been proposed for automatic sleep staging using polysomnography recordings. Most of them are trained and tested on the same labeled datasets which results in poor generalization to unseen target domains. However, they regard the subjects in the target domains as a whole and overlook the individual discrepancies, which limits the model’s generalization ability to new patients (i.e., unseen subjects) and plug-and-play applicability in clinics. To address this, we propose a novel Source-Free Unsupervised Individual Domain Adaptation (SF-UIDA) framework for sleep staging, leveraging sequential cross-view contrasting and pseudo-label based fine-tuning. It is actually a two-step subject-specific adaptation scheme, which enables the source model to effectively adapt to newly appeared unlabeled individual without access to the source data. It meets the practical needs in real-world scenarios, where the personalized customization can be plug-and-play applied to new ones. Our framework is applied to three classic sleep staging models and evaluated on three public sleep datasets, achieving the state-of-the-art performance. Copyright © 2025, Association for the Advancement of Artificial intelligence (***). All rights reserved.

关键词： Deep learning

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Spin-UP: Spin Light for Natural Light Uncalibrated Photometric Stereo

arXiv

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arXiv 2024年

作者： Li, Zongrui Lu, Zhan Yan, Haojie Shi, Boxin Pan, Gang Zheng, Qian Jiang, Xudong Lab Interdisciplinary Graduate Programme Nanyang Technological University Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore College of Computer Science and Technology Zhejiang University China The State Key Lab of Brain-Machine Intelligence Zhejiang University China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China National Engineering Research Center of Visual Technology School of Computer Science Peking University China

Natural Light Uncalibrated Photometric Stereo (NaUPS) relieves the strict environment and light assumptions in classical Uncalibrated Photometric Stereo (UPS) methods. However, due to the intrinsic ill-posedness and high-dimensional ambiguities, addressing NaUPS is still an open question. Existing works impose strong assumptions on the environment lights and objects' material, restricting the effectiveness in more general scenarios. Alternatively, some methods leverage supervised learning with intricate models while lacking interpretability, resulting in a biased estimation. In this work, we propose Spin Light Uncalibrated Photometric Stereo (Spin-UP), an unsupervised method to tackle NaUPS in various environment lights and objects. The proposed method uses a novel setup that captures the object's images on a rotatable platform, which mitigates NaUPS's ill-posedness by reducing unknowns and provides reliable priors to alleviate NaUPS's ambiguities. Leveraging neural inverse rendering and the proposed training strategies, Spin-UP recovers surface normals, environment light, and isotropic reflectance under complex natural light with low computational cost. Experiments have shown that Spin-UP outperforms other supervised/unsupervised NaUPS methods and achieves state-of-the-art performance on synthetic and real-world datasets. Codes and data are available at https://***/LMozart/CVPR2024-SpinUP. Copyright © 2024, The Authors. All rights reserved.

关键词： Photometry

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A Compact Dead-Zone-Free Multi-Channel Stimulation and Recording System for Precision Peripheral Nerve Modulation 46

A Compact Dead-Zone-Free Multi-Channel Stimulation and Recor...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Xing, Yanjie Chen, Weihuang Yu, Danlei Gao, Xiang Bao, Jieting Xu, Kedi Zhejiang China Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal Key Laboratory of Biomedical Engineering of Education Ministry Department of Biomedical Engineering Zhejiang University Hangzhou China Institute of Advanced Digital Technology and Instrument Zhejiang University Hangzhou310027 China Zhejiang Provincial Key Laboratory for Network Multimedia Technologies Zhejiang University Hangzhou310027 China Binjiang Institude of Zhejiang University Hangzhou310027 China Nanhu Brain-computer interface institute Hangzhou311100 China Zhejiang University Hangzhou China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China

ISBN: (纸本)9798350371499

Peripheral nerve modulation has a certain potential in motor recovery, but the majority of systems suffer from the drawback of dead time caused by switching channels to achieve multi-channel stimulation. As a response to this challenge, we have developed a peripheral nerve modulation device with 64-channel recording and 16-channel stimulation without dead time. Through modulation of the rat sciatic nerve, we identified the crucial role of initial phase and amplitude in the outcomes of dual-channel combined stimulation. This design not only enhanced neural activation capabilities with constant power supply but also achieves more precise neural activation and reduces unintentional activation. © 2024 IEEE.

关键词： Recording instruments

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Non-volatile optical switch based on Sb2Se3-loaded three-mode interference

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Optics Letters 2025年第12期50卷 4022-4025页

作者： Xinxin Zhao Xiaolong Liu Yujiao Tao Lei Yang Kai Xu Hongtao Lin Peipeng Xu Wei Zhang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo 315211 China Key Laboratory of Photoelectric Detecting Materials and Devices of Zhejiang Province Ningbo 315211 China 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 Hangzhou 310027 China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai 200050 China

In this Letter, we propose a non-volatile switch based on multimode interference in an Sb 2 Se 3 -loaded waveguide. By implementing low-loss phase control within a multimode interference coupler, this design effectively manages the self-imaging phase shift length during a single phase change cycle. The fabricated multiplexed optical switch exhibits impressive performance characteristics, including a low insertion loss of ∼ 1.5 dB, a compact length of ∼35 μm, and minimal cross talk below −6 dB over a 35 nm bandwidth for both the amorphous and crystalline states. The ability to reconfigure light routing via Sb 2 Se 3 phase transitions opens new avenues for optical field-programmable gate arrays and high-density photonic processors.

关键词： Crosstalk Effective refractive index Field programmable gate arrays Multimode interference Optical neural systems Phase shift

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Tracking functional changes in nonstationary signals with evolutionary ensemble Bayesian model for robust neural decoding 22

Tracking functional changes in nonstationary signals with ev...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Xinyun Zhu Yu Qi Gang Pan Yueming Wang Qiushi Academy for Advanced Studies Zhejiang University Hangzhou China and College of Computer Science and Technology Zhejiang University Hangzhou China and The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and the MOE Frontier Science Center for Brain Science and Brain-machine Integration Zhejiang University School of Medicine Hangzhou China and The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China and College of Computer Science and Technology Zhejiang University Hangzhou China Qiushi Academy for Advanced Studies Zhejiang University Hangzhou China and The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China

ISBN: (纸本)9781713871088

Neural signals are typical nonstationary data where the functional mapping between neural activities and the intentions (such as the velocity of movements) can occasionally change. Existing studies mostly use a fixed neural decoder, thus suffering from an unstable performance given neural functional changes. We propose a novel evolutionary ensemble framework (EvoEnsemble) to dynamically cope with changes in neural signals by evolving the decoder model accordingly. EvoEnsemble integrates evolutionary computation algorithms in a Bayesian framework where the fitness of models can be sequentially computed with their likelihoods according to the incoming data at each time slot, which enables online tracking of time-varying functions. Two strategies of evolve-at-changes and history-model-archive are designed to further improve efficiency and stability. Experiments with simulations and neural signals demonstrate that EvoEnsemble can track the changes in functions effectively thus improving the accuracy and robustness of neural decoding. The improvement is most significant in neural signals with functional changes.

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Darwin3: A large-scale neuromorphic chip with a Novel ISA and On-Chip Learning

arXiv

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arXiv 2023年

作者： Ma, De Jin, Xiaofei Sun, Shichun Li, Yitao Wu, Xundong Hu, Youneng Yang, Fangchao Tang, Huajin Zhu, Xiaolei Lin, Peng Pan, Gang College of Computer Science and Technology Zhejiang University Hangzhou310058 China Research Center for Intelligent Computing Hardware Zhejiang Lab Hangzhou311121 China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou310027 China MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University Hangzhou310027 China College of Micro-Nano College of Micro-Nano Electronics Zhejiang University Hangzhou311200 China

Spiking Neural Networks (SNNs) are gaining increasing attention for their biological plausibility and potential for improved computational efficiency. To match the high spatial-temporal dynamics in SNNs, neuromorphic chips are highly desired to execute SNNs in hardware-based neuron and synapse circuits directly. This paper presents a large-scale neuromorphic chip named Darwin3 with a novel instruction set architecture(ISA), which comprises 10 primary instructions and a few extended instructions. It supports flexible neuron model programming and local learning rule designs. The Darwin3 chip architecture is designed in a mesh of computing nodes with an innovative routing algorithm. We used a compression mechanism to represent synaptic connections, significantly reducing memory usage. The Darwin3 chip supports up to 2.35 million neurons, making it the largest of its kind in neuron scale. The experimental results showed that code density was improved up to 28.3x in Darwin3, and neuron core fan-in and fan-out were improved up to 4096x and 3072x by connection compression compared to the physical memory depth. Our Darwin3 chip also provided memory saving between 6.8X and 200.8X when mapping convolutional spiking neural networks (CSNN) onto the chip, demonstrating state-of-the-art performance in accuracy and latency compared to other neuromorphic chips. © 2023, CC BY.

关键词： Neurons

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Personalized Sleep Staging Leveraging Source-free Unsupervised Domain Adaptation

arXiv

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arXiv 2024年

作者： Zhou, Yangxuan Zhao, Sha Wang, Jiquan Jiang, Haiteng Li, Shijian Luo, Benyan Li, Tao Pan, Gang State Key Laboratory of Brain-machine Intelligence Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Neurobiology Affiliated Mental Health Center Hangzhou Seventh People’s Hospital Zhejiang University School of Medicine Hangzhou China MOE Frontier Science Center for Brain Science and Brain-machine Integration Zhejiang University Hangzhou China The First Affiliated Hospital College of Medicine Zhejiang University Hangzhou China

关键词： Sleep research

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Differentiable and Scalable Generative Adversarial Models for Data Imputation (Extended Abstract)

Differentiable and Scalable Generative Adversarial Models fo...

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International Conference on Data Engineering

作者： Yangyang Wu Jun Wang Xiaoye Miao Wenjia Wang Jianwei Yin Software College Zhejiang University Ningbo China Academy of Interdisciplinary Studies The Hong Kong University of Science and Technology Hong Kong China Center for Data Science Zhejiang University Hangzhou China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China Information Hub The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China College of Computer Science Zhejiang University Hangzhou China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The dramatically increasing volume of incomplete data makes the imputation models computationally infeasible in many real-life applications. In this paper, we propose an effective scalable imputation system named SCIS to significantly speed up the training of the differentiable generative adversarial imputation models under accuracy-guarantees for large-scale incomplete data. SCIS consists of two modules, differentiable imputation modeling (DIM) and sample size estimation (SSE). DIM leverages a new masking Sinkhorn divergence function to make an arbitrary generative adversarial imputation model differentiable, while for such a differentiable imputation model, SSE can estimate an appropriate sample size to ensure the user-specified imputation accuracy of the final model. Moreover, SCIS can also accelerate the autoencoder based imputation models. Extensive experiments upon several real-life large-scale datasets demonstrate that, our proposed system can accelerate the generative adversarial model training by 6.23x. Using around 1.27% samples, SCIS yields competitive accuracy with the state-of-the-art imputation methods in much shorter computation time.

关键词： Training Solid modeling Accuracy Computational modeling Estimation Data engineering Imputation

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Adaptive Positional Encoding for Bundle-Adjusting Neural Radiance Fields

Adaptive Positional Encoding for Bundle-Adjusting Neural Rad...

引用

International Conference on computer Vision (ICCV)

作者： Zelin Gao Weichen Dai Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province and School of Computer Science Hangzhou Dianzi University Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province

Neural Radiance Fields have shown great potential to synthesize novel views with only a few discrete image observations of the world. However, the requirement of accurate camera parameters to learn scene representations limits its further application. In this paper, we present adaptive positional encoding (APE) for bundle-adjusting neural radiance fields to reconstruct the neural radiance fields from unknown camera poses (or even intrinsics). Inspired by Fourier series regression, we investigate its relationship with the positional encoding method and therefore propose APE where all frequency bands are trainable. Furthermore, we introduce period-activated multilayer perceptrons (PMLPs) to construct the implicit network for the high-order scene representations and fine-grained gradients during backpropagation. Experimental results on public datasets demonstrate that the proposed method with APE and PMLPs can outperform the state-of-the-art methods in accurate camera poses and high-fidelity view synthesis.

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PMAE: PSEUDO MULTI-labEL ATTENTION ENSEMBLE

PMAE: PSEUDO MULTI-LABEL ATTENTION ENSEMBLE

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Wang, Xueman Du, Ling Li, Junbing School of Computer Science and Technology Tiangong University Tianjin 300387 China Tianjin Key Lab of Machine Learning College of Intelligence and Computing Tianjin University China

ISBN: (纸本)9781665438643

Multi-label classification has been recognized to be challenging since the complex correlations among labels and the combinatorial nature of the output label space. Motivated by the label dimension reduction and ensemble learning, in this paper, we propose a novel multi-label classification algorithm based on ensemble learning, termed PMAE: Pseudo Multi-label Attention Ensemble. Different from existing landmark-based methods which reduce the predicted label space by selecting a few real labels as landmarks in a "hard" manner, the proposed PMAE develops the encoder-decoder mechanism to effectively utilize the low-dimensional pseudo-label representation space in a "soft" manner, which can capture the underlying correlations among labels and lead to more robust performance in complex scenarios. Moreover, we conduct the feature embedding based on multiple attention networks integrated, then, exponent and hinge diversity regularization are introduced to ensure the diversity and complementarity of multiple learners. Experimental results on multiple benchmark datasets show the superiority of our proposed method over other state-of-the-arts. © 2021 IEEE

关键词： Complex networks

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