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4th International Workshop on Human Brain and Artificial intelligence, HBAI 2024

作者： Pan, Guandong Wu, Zhaobang Yang, Yaqian Wang, Xin Liu, Longzhao Zheng, Zhiming Tang, Shaoting School of Computer Science and Engineering Beihang University Beijing100191 China Institute of Artificial Intelligence Beihang University Beijing100191 China Key Laboratory of Mathematics Informatics and Behavioral Semantics Beihang University Beijing100191 China Institute of Medical Artificial Intelligence Binzhou Medical University Yantai264003 China Zhongguancun Laboratory Beijing100094 China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang University Beijing100191 China PengCheng Laboratory Shenzhen518055 China State Key Lab of Software Development Environment Beihang University Beijing100191 China

ISBN: (纸本)9789819640003

The need for automatic and high-quality emotion annotation is paramount in applications such as continuous emotion recognition and video highlight detection, yet achieving this through manual human annotations is challenging. Inspired by inter-subject correlation (ISC) utilized in neuroscience, this study introduces a novel Electroencephalography (EEG) based ISC methodology that leverages a single-electrode and feature-based dynamic approach. Our contributions are three folds: Firstly, we reidentify two potent emotion features suitable for classifying emotions-first-order difference (FD) an differential entropy (DE). Secondly, through the use of overall correlation analysis, we demonstrate the heterogeneous synchronized performance of electrodes. This performance aligns with neural emotion patterns established in prior studies, thus validating the effectiveness of our approach. Thirdly, by employing a sliding window correlation technique, we showcase the significant consistency of dynamic ISCs across various features or key electrodes in each analyzed film clip. Our findings indicate the method’s reliability in capturing consistent, dynamic shared neural synchrony among individuals, triggered by evocative film stimuli. This underscores the potential of our approach to serve as an indicator of continuous human emotion arousal. The implications of this research are significant for advancements in affective computing and the broader neuroscience field, suggesting a streamlined and effective tool for emotion analysis in real-world applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography (EEG) Emotion Annotation Inter-subject Correlation

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PCE-GAN: A Generative Adversarial Network for Point Cloud Attribute Quality Enhancement based on Optimal Transport

arXiv

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arXiv 2025年

作者： Guo, Tian Yuan, Hui Liu, Qi Su, Honglei Hamzaoui, Raouf Kwong, Sam School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Ji’nan250061 China School of Electronic Information Qingdao University Qingdao266071 China School of Engineering and Sustainable Development De Montfort University LeicesterLE1 9BH United Kingdom Department of Computing and Decision Science Lingnan University Hong Kong

Point cloud compression significantly reduces data volume but sacrifices reconstruction quality, highlighting the need for advanced quality enhancement techniques. Most existing approaches focus primarily on point-to-point fidelity, often neglecting the importance of perceptual quality as interpreted by the human visual system. To address this issue, we propose a generative adversarial network for point cloud quality enhancement (PCE-GAN), grounded in optimal transport theory, with the goal of simultaneously optimizing both data fidelity and perceptual quality. The generator consists of a local feature extraction (LFE) unit, a global spatial correlation (GSC) unit and a feature squeeze unit. The LFE unit uses dynamic graph construction and a graph attention mechanism to efficiently extract local features, placing greater emphasis on points with severe distortion. The GSC unit uses the geometry information of neighboring patches to construct an extended local neighborhood and introduces a transformer-style structure to capture long-range global correlations. The discriminator computes the deviation between the probability distributions of the enhanced point cloud and the original point cloud, guiding the generator to achieve high quality reconstruction. Experimental results show that the proposed method achieves state-of-the-art performance. Specifically, when applying PCE-GAN to the latest geometry-based point cloud compression (G-PCC) test model, it achieves an average BD-rate of -19.2% compared with the PredLift coding configuration and -18.3% compared with the RAHT coding configuration. Subjective comparisons show a significant improvement in texture clarity and color transitions, revealing finer details and more natural color gradients. Copyright © 2025, The Authors. All rights reserved.

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VQ-CTAP: Cross-Modal Fine-Grained Sequence Representation Learning for Speech Processing

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1849-1861页

作者： Chunyu Qiang Wang Geng Yi Zhao Ruibo Fu Tao Wang Cheng Gong Tianrui Wang Qiuyu Liu Jiangyan Yi Zhengqi Wen Chen Zhang Hao Che Longbiao Wang Jianwu Dang Jianhua Tao School of New Media and Communication Tianjin University Tianjin China Kuaishou Technology Company Ltd. Beijing China Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Institute of Automation Chinese Academy of Sciences Beijing China Department of Automation Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Migu Culture Technology Company Ltd. Beijing China Huiyan Technology (Tianjin) Company Ltd. Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Science Guangdong China

Deep learning has brought significant improvements to the field of cross-modal representation learning. For tasks such as text-to-speech (TTS), voice conversion (VC), and automatic speech recognition (ASR), a cross-modal fine-grained (frame-level) sequence representation is desired, emphasizing the semantic content of the text modality while de-emphasizing the paralinguistic information of the speech modality. We propose a method called “Vector Quantized Contrastive Token-Acoustic Pre-training (VQ-CTAP)”, which uses the cross-modal aligned sequence transcoder to bring text and speech into a joint multimodal space, learning how to connect text and speech at the frame level. The proposed VQ-CTAP is a paradigm for cross-modal sequence representation learning, offering a promising solution for fine-grained generation and recognition tasks in speech processing. The VQ-CTAP can be directly applied to VC and ASR tasks without fine-tuning or additional structures. We propose a sequence-aware semantic connector, which connects multiple frozen pre-trained modules for the TTS task, exhibiting a plug-and-play capability. We design a stepping optimization strategy to ensure effective model convergence by gradually injecting and adjusting the influence of various loss components. Furthermore, we propose a semantic-transfer-wise paralinguistic consistency loss to enhance representational capabilities, allowing the model to better generalize to unseen data and capture the nuances of paralinguistic information. In addition, VQ-CTAP achieves high-compression speech coding at a rate of 25 Hz from 24 kHz input waveforms, which is a 960-fold reduction in the sampling rate. The experimental results demonstrate that while VQ-CTAP outperforms baseline methods in TTS and VC tasks, its performance on ASR tasks is suboptimal.

关键词： Semantics Training Representation learning Speech recognition Decoding Acoustics Speech coding Speech processing Data mining Contrastive learning

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Electric-field-independent spin-orbit-coupling gap in h-BN-encapsulated bilayer graphene

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Physical Review Applied 2025年第4期23卷 044053-044053页

作者： Fang-Ming Jing Zhen-Xiong Shen Guo-Quan Qin Wei-Kang Zhang Ting Lin Ranran Cai Zhuo-Zhi Zhang Gang Cao Lixin He Xiang-Xiang Song Guo-Ping Guo CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei Anhui 230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou Jiangsu 215123 China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui 230026 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei Anhui 230026 China Origin Quantum Computing Company Limited Hefei Anhui 230088 China

The weak spin-orbit coupling (SOC) in bilayer graphene (BLG) is essential for encoding spin qubits while bringing technical challenges for extracting the opened small SOC gap ΔSO in experiments. Moreover, in addition to the intrinsic Kane-Mele term, extrinsic mechanisms also contribute to SOC in BLG, especially under experimental conditions including encapsulation of BLG with hexagonal boron nitride (h-BN) and applying an external out-of-plane electric displacement field D. Although measurements of ΔSO in h-BN-encapsulated BLG have been reported, the relatively large experimental variations and existing experimental controversy make it difficult to fully understand the physical origin of ΔSO. Here, we report a combined experimental and theoretical study on ΔSO in h-BN-encapsulated BLG. We use an averaging method to extract ΔSO in gate-defined single quantum dot devices. Under D fields as large as 0.57–0.90 V/nm, ΔSO ≈ 53.4–61.8 μeV is obtained from two devices. Benchmarked with values reported at lower D field regime, our results support a D-field-independent ΔSO. This behavior is confirmed by our first-principles calculations, based on which ΔSO is found to be independent of D field, regardless of different h-BN/BLG/h-BN stacking configurations. Our calculations also suggest a weak proximity effect from h-BN, indicating that SOC in h-BN-encapsulated BLG is dominated by the intrinsic Kane-Mele mechanism. Our results offer insightful understanding of SOC in BLG, which benefits SOC engineering and spin manipulations in BLG.

关键词： Spin orbit coupling

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Contrast of multi-resolution analysis approach to transhumeral phantom motion decoding

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CAAI Transactions on intelligence Technology 2021年第3期6卷 360-375页

作者： Ejay Nsugbe Oluwarotimi William Samuel Mojisola Grace Asogbon Guanglin Li Independent Scholar Key Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences(CAS)Shenzhen Institutes of Advanced TechnologyShenzhenChina

In signal processing,multiresolution decomposition techniques allow for the separation of an acquired signal into sub levels,where the optimal level within the signal minimises redundancy,uncertainties,and contains the information required for the characterisation of the sensed *** the area of physiological signal processing for prosthesis control,scenarios where a signal decomposition analysis are required:the wavelet decomposition(WD)has been seen to be the favoured time-frequency approach for the decomposition of non-stationary *** a research perspective,the WD in certain cases has allowed for a more accurate motion intent decoding process following feature extraction and *** this,there is yet to be a widespread adaptation of the WD in a practical setting due to perceived computational ***,for neuro-muscular(electromyography)and brainwave(electroencephalography)signals acquired from a transhumeral amputee,a computationally efficient time domain signal decom-position method based on a series of heuristics was applied to process the acquired signals before feature *** results showed an improvement in motion intent decoding prowess for the proposed time-domain-based signal decomposition across four different classifiers for both the neuromuscular and brain wave signals when compared to the WD and the raw signal.

关键词： motion approach analysis

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Dual radio tomographic imaging with Bayesian compressive sensing

Dual radio tomographic imaging with Bayesian compressive sen...

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Chinese Intelligent Systems Conference, CISC 2017

作者： Shang, Baolin Tan, Jiaju Guo, Xuemei Wang, Guoli Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Ministry of Education Guangzhou China Tianjin China

ISBN: (纸本)9789811064982

A common difficulty in device-free localization (DFL) using received signal strength (RSS) is that the uninformative and redundant RSS data in wireless sensor network (WSN) usually degrades the localization performance. This paper develops a dual radio tomographic imaging (RTI) to keep data efficiency in sparse signal recovery as well as eliminating the redundant measurements. In addition to Tikhonov regularization, we also utilize a robust loss function in sparse Bayesian learning for RTI to handle with the outlier data. Moreover, by taking advantage of proposed spatial continuous filter in position estimation, our dual RTI achieves lower localization errors in DFL system. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Efficiency

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The Temporal Structural Pattern in Scientific Collaborative Behavior from the Perspective of Complex Network 23rd

The Temporal Structural Pattern in Scientific Collaborative...

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23rd International Symposium on Knowledge and Systems Sciences, KSS 2024

作者： Zholdoshbaeva, Elina Zhang, Shuang Liu, Feifan Xia, Haoxiang Institute of Systems Engineering Dalian University of Technology No. 2 Linggong Road Liaoning Dalian116024 China Center for Big Data and Intelligent Decision-Making Dalian University of Technology Linggong Road Liaoning Dalian116024 China School of Business East China University of Science and Technology 130 Meilong Road Xuhui District Shanghai200237 China Institute for Advanced Intelligence Dalian University of Technology Linggong Road Liaoning Dalian116024 China Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education of China Linggong Road Liaoning Dalian116024 China

ISBN: (纸本)9789819601776

The rapid advancement of science has underscored the importance of collaboration among scientists. While many studies have explored the structure of collaboration networks, there is a lack of comprehensive quantitative research on the temporal patterns of these collaborations. This study addresses this gap by analyzing temporal scientific collaboration networks in physics, spanning from 1960 to 2021, using complex network theories and methods. By constructing five-year temporal collaborative networks and employing metrics such as network diameter, shortest path, and clustering coefficient, this research reveals an increase in connectivity and small-world characteristics over time. Additionally, community detection highlights a shift towards specialized collaboration patterns and increased cross-community interactions. The study also uncovers growing centrality and social stratification within the research system, with the "rich club" phenomenon indicating strong and dynamic collaborations among highly connected scientists. Temporal analysis shows that while ultra-high connectivity scientists engage in fluctuating relationships, medium-high connectivity scientists maintain more stable collaborations. These findings provide insights into the evolution of collaborative behaviors and inform science policy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collaborative learning

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Quantum speedup of twin support vector machines

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Science China(Information Sciences) 2020年第8期63卷 272-274页

作者： Zekun YE Lvzhou LI Haozhen SITU Yuyi WANG School of Data and Computer Science Sun Yat-Sen University Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing (Sun Yat-Sen University) College of Mathematics and Informatics South China Agricultural University

Dear editor,machine learning enables agents or computers to learn from data and improves the performance of specific tasks without requiring explicit programming. As the amount of available data continues to grow, existing machine learning algorithms cannot satisfy the efficiency requirements of various practical applications, and several methods have been

关键词： quantum computing quantum machine learning quantum algorithms twin support vector machines exponential speedup

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Fast reconstruction and optical-sectioning three-dimensional structured illumination microscopy (vol 6, 100757, 2025)

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INNOVATION 2025年第2期6卷 100757页

作者： Cao, Ruijie Li, Yaning Wang, Wenyi Fu, Yunzhe Bu, Xiaoyu Saimi, Dilizhatai Sun, Jing Ge, Xichuan Jiang, Shan Pei, Yuru Gao, Baoxiang Chen, Zhixing Li, Meiqi Xi, Peng Department of Biomedical Engineering College of Future Technology Peking University Beijing 100871 China National Biomedical Imaging Center College of Future Technology Peking University Beijing 100871 China China Academy of Space Technology Beijing Institute of Space Mechanics and Electricity Beijing 100094 China Airy Technologies Co. Ltd. Beijing 100081 China Key Laboratory of Analytical Science and Technology of Hebei Province College of Chemistry and Materials Science Hebei University Baoding 071002 China College of Future Technology Institute of Molecular Medicine National Biomedical Imaging Center Beijing Key Laboratory of Cardiometabolic Molecular Medicine Peking University Beijing 100871 China Institute of Biomedical Engineering Beijing Institute of Collaborative Innovation Beijing China Key Laboratory of Machine Perception (MOE) Department of Machine Intelligence Peking University Beijing 100871 China Peking-Tsinghua Center for Life Science Academy for Advanced Interdisciplinary Studies Peking University Beijing 100871 China School of Life Sciences Peking University Beijing 100871 China

Three-dimensional structured illumination microscopy (3DSIM) is a popular method for observing subcellular/cellular structures or animal/plant tissues with gentle phototoxicity and 3D super-resolution. However, its time-consuming reconstruction process poses challenges for high-throughput imaging and real-time observation. Moreover, traditional 3DSIM typically requires more than six z layers for successful reconstruction and is susceptible to defocused backgrounds. This poses a great gap between single-layer 2DSIM and 6-layer 3DSIM, and limits the observation of thicker samples. To address these limitations, we developed FO-3DSIM, a novel method that integrates spatial-domain reconstruction with optical-sectioning SIM. FO-3DSIM enhances reconstruction speed by up to 855.7 times with superior performance with limited z layers and under high defocused backgrounds. It retains the high-fidelity, low-photon reconstruction capabilities of our previously proposed Open-3DSIM. Utilizing fast reconstruction and optical sectioning, we achieved large field-of-view (FOV) 3D super-resolution imaging of mouse kidney actin, covering a region of 0.453 mm × 0.453 mm × 2.75 μm within 23 min of acquisition and 13 min of reconstruction. Near real-time performance was demonstrated in live actin imaging with FO-3DSIM. Our approach reduces photodamage through limited z layer reconstruction, allowing the observation of ER tubes with just three layers. We anticipate that FO-3DSIM will pave the way for near real-time, large FOV 6D imaging, encompassing xyz super-resolution, multi-color, long-term, and polarization imaging with less photodamage, removed defocused backgrounds, and reduced reconstruction time.

关键词： three-dimensional structure illumination optical sectioning reconstruction speed large field of view imaging real-time observation

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Movement Control and Attitude Adjustment of Climbing Robot on Flexible Surfaces

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2018年第3期65卷 2618-2628页

作者： Wu, Xinyu Liu, Jia Zhou, Yimin Lv, Qin Hu, Chaofang CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Electrical and Information Engineering Tianjin University Tianjin China

In this paper, a series of control algorithms have been designed for the climbing cloth robot, "Clothbot" to climb vertically. The developed algorithm consists of three parts: a sliding mode control with integral action (SMC with integral action), a linear quadratic regulator, and dead reckoning for semi-autonomous navigation. First, the SMC with integral action is developed for high-precision position tracking and speed tracking of the permanent magnet direct current (PMDC) motor installed on the robot. The error between the expected velocity and the actual velocity of the PMDC motor is adopted to design the sliding surface. In order to compensate the fluctuation of the load torque due to the varied friction during climbing, an observer is formulated to estimate the load torque in real-time. Second, the LQR is employed for attitude adjustment by an active torque so as to prevent the robot from falling or being jammed during the movement. Third, due to the onboard-limited energy, the robot is just equippedwith semi-autonomous navigation named dead reckoning carrying on primary localization and primary path planning. Experiments are performed to verify the efficacy of the proposed control algorithms.

关键词： Climbing robot flexible surface linear quadratic regulator load observer sliding mode control with integral action

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