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Tonic and Event-Related Phasic Transcutaneous Auricular Vagus Nerve Stimulation Alters Pupil Responses in the Change-Detection Task

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Neuromodulation 2025年第4期28卷 652-662页

作者： Chen, Jingxin Ke, Yufeng Ni, Guangjian Liu, Shuang Ming, Dong Academy of Medical Engineering and Translational Medicine Tianjin International Joint Research Centre for Neural Engineering Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin University Tianjin China Department of Biomedical Engineering College of Precision Instrument and Optoelectronics Engineering Tianjin University Tianjin China Haihe Laboratory of Brain-computer Interaction and Human-machine Integration Tianjin China

Background: Transcutaneous auricular vagus nerve stimulation (taVNS) has emerged as a potential modulator of cognitive behavior that activates the locus coeruleus-noradrenaline (LC-NA) system. Previous studies explored both phasic and tonic taVNS by investigating their impact on LC-NA markers such as pupil dilation and heart rate variability (HRV). Objective: Inconsistencies persist in the identification of reliable markers for assessing the effects of taVNS on noradrenergic activity. Furthermore, it remains unclear whether the effects of taVNS extend beyond pure vagal nerve responses, particularly in specific cognitive domains such as working memory. In the present study, we investigated the effects of taVNS on working memory capacity and LC-NA markers using a change-detection task. Materials and Methods: Twenty-two healthy, right-handed university students participated in a sham-controlled, randomized cross-over experiment with four sessions. We applied two types of phasic and event-related stimulation (Pre-event and Event-synchronous), tonic stimulation (Pre-task), and sham stimulation across different sessions. Pupil size and electrocardiogram data were recorded during the tasks. Results: taVNS did not significantly modulate behavioral performance on the change-detection task, specifically working memory capacity. However, both tonic and event-related phasic taVNS significantly influenced the pupillary response during the task. In addition, the Pre-task condition of the taVNS affected the low-frequency parameter of HRV. Conclusions: Our findings suggest that tonic and event-related phasic taVNS may modulate noradrenergic activity, as evidenced by pupil responses and HRV changes during the change-detection task. This study provides new evidence regarding the impact of taVNS on cognitive tasks, thus supporting the development of noninvasive neuromodulation interventions. © 2025 International Neuromodulation Society

关键词： Heart rate variability (HRV) LC-NA pupil response pupillometry taVNS

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LabelEase: A Semi-Automatic Tool for Efficient and Accurate Trace Labeling in Microservices

LabelEase: A Semi-Automatic Tool for Efficient and Accurate ...

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International Symposium on Software Reliability Engineering (ISSRE)

作者： Shenglin Zhang Zeyu Che Zhongjie Pan Xiaohui Nie Yongqian Sun Lemeng Pan Dan Pei Nankai University Haihe Laboratory of Information Technology Application Innovation Computer Network Information Center Chinese Academy of Sciences Tianjin Key Laboratory of Software Experience and Human Computer Interaction Huawei Beijing National Research Center for Information Science and Technology Tsinghua University

ISBN: (数字)9798350353884

ISBN: (纸本)9798350353891

Trace data is crucial for system observability and maintainability within microservices architectures, and many operation algorithms depend heavily on trace data, including anomaly detection, root cause analysis, etc. However, the actual performance of these algorithms might be unsatisfactory due to the absence of high-quality labeled datasets for effective training and evaluation. Since billions of traces could be generated daily for large-scale microservices, labeling overhead is the main hurdle to obtaining high-quality trace *** this paper, we propose LabelEase, a novel semi-automatic trace labeling tool, which uses active learning techniques to achieve efficient and accurate trace labeling. For anomaly trace labeling, LabelEase clusters similar traces with a graph-based trace representation technique and selects a few representative traces for human labeling, avoiding labeling most of the traces. For root cause labeling, LabelEase aggregates the labeled anomalous traces and identifies the service’s failures for operators to label. Our systematic experiments on two large-scale datasets show that LabelEase achieves over 0.98 F 1 -score in anomaly trace labeling and 0.89 precision of failure detection in root cause labeling, LabelEase can reduce operators’ labeling overhead by more than 99.9%. To the best of our knowledge, we are the first to propose a semi-automatic trace labeling tool capable of achieving efficient and accurate trace labeling.

关键词： Training Location awareness Accuracy Costs Transfer learning Microservice architectures Manuals Data models Labeling Anomaly detection

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Computing Flow Experience Based on Physiological Signals: A Systematic Literature Review

Computing Flow Experience Based on Physiological Signals: A ...

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2025 CHI Conference on human Factors in Computing Systems, CHI EA 2025

作者： Hong, Yichen Zhou, Chao Liu, Zelu Zifeng, Ni Liu, Juan Li, Xiangxian Yang, Chenglei Bian, Yulong Shandong University Weihai China Human Computer Interaction Technology and Intelligent Information Processing Laboratory Institute of Software Chinese Academy of Sciences Beijing China Shandong University School of Mechanical Electrical & Information Engineering Weihai China Shandong University Jinan China School of Software Shandong University Jinan China Shandong Key Laboratory of Intelligent Electronic Packaging Testing and Application Shandong University Shandong Weihai China Shandong Key Laboratory of Intelligent Electronic Packaging Testing and Application China

ISBN: (纸本)9798400713958

Flow experience is a key user experience in human-computer interaction (HCI). Compared with traditional subjective measurements, physiological signal-based methods (i.e. physiological computation of flow experience) can measure flow experience in a more objective and real-time way without interrupting the ongoing activity of the user. Despite scattered studies, a systematic review of research in this field is absent. Synthesizing key aspects of this field, including the conceptualization of flow experience, prevalent theoretical models, foundational research steps for experiment, computerized tasks for inducing flow, commonly used physiological signals, methods for analyzing flow state, and application in HCI. Finally, we discuss the potential of this field to propel HCI and user experience evaluation technologies, while projecting future research avenues. This paper is expected to help researchers grasp existing work in physiological signal-based flow computing and inspire subsequent explorations. © 2025 Copyright held by the owner/author(s).

关键词： Flow simulation

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Enhanced Fine-Tuning of Lightweight Domain-Specific Q&A Model Based on Large Language Models

arXiv

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arXiv 2024年

作者： Zhang, Shenglin Zhu, Pengtian Ma, Minghua Wang, Jiagang Sun, Yongqian Li, Dongwen Wang, Jingyu Guo, Qianying Hua, Xiaolei Zhu, Lin Pei, Dan Nankai University China Microsoft United States Tsinghua University China China Mobile Research Institute China Haihe Laboratory of Information Technology Application Innovation China Tianjin Key Laboratory of Software Experience and Human Computer Interaction China Beijing National Research Center for Information Science and Technology China

Large language models (LLMs) excel at general question-answering (Q&A) but often fall short in specialized domains due to a lack of domain-specific knowledge. Commercial companies face the dual challenges of privacy protection and resource constraints when involving LLMs for fine-tuning. This paper propose a novel framework, Self-Evolution, designed to address these issues by leveraging lightweight open-source LLMs through multiple iterative fine-tuning rounds. To enhance the efficiency of iterative fine-tuning, Self-Evolution employ a strategy that filters and reinforces the knowledge with higher value during the iterative process. We employed Self-Evolution on Qwen1.5-7B-Chat using 4,000 documents containing rich domain knowledge from China Mobile, achieving a performance score 174% higher on domain-specific question-answering evaluations than Qwen1.5-7B-Chat and even 22% higher than Qwen1.5-72B-Chat. Self-Evolution has been deployed in China Mobile's daily operation and maintenance for 117 days, and it improves the efficiency of locating alarms, fixing problems, and finding related reports, with an average efficiency improvement of over 18.6%. In addition, we release Self-Evolution framework code in https://***/Zero-Pointer/Self-Evolution. Copyright © 2024, The Authors. All rights reserved.

关键词： Data mining

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Raise Your Eyebrows Higher: Facilitating Emotional Communication in Social Virtual Reality Through Region-Specific Facial Expression Exaggeration 25

Raise Your Eyebrows Higher: Facilitating Emotional Communica...

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2025 CHI Conference on human Factors in Computing Systems, CHI 2025

作者： Wang, Xueyang Zhao, Sheng Wang, Yihe Han, Howard Ziyu Liu, Xinge Yi, Xin Tong, Xin Li, Hewu Institute for Network Sciences and Cyberspace Tsinghua University Beijing China Zhongguancun Laboratory Beijing China Weiyang College Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Human-Computer Interaction Institute Carnegie Mellon University pittsburghPA United States Beijing National Research Center for Information Science and Technology Beijing China Guangzhou China

ISBN: (纸本)9798400713941

While exaggerated facial expressions in cartoon avatars can enhance emotional communication in social virtual reality (VR), they risk triggering the uncanny valley effect. Our research reveals that this effect varies significantly across different emotions. In Study 1 (N=30), participants evaluated scaled facial expressions during simulated VR conversations. We found that expression exaggeration had opposing effects: it decreased facial realism for joy, surprise, and disgust due to overly dramatic mouth movements, while enhancing realism for fear, sadness, and anger - emotions that rely on upper facial expressions typically constrained by HMD pressure. Based on these findings, we developed a region-specific facial expression exaggeration strategy that enhances under-expressed upper facial features while maintaining natural lower facial movements. Study 2 (N=20) validated this approach, demonstrating enhanced emotional intensity and contagion for negative emotions while mitigating the uncanny valley effect. Our research provides practical guidelines for optimizing avatar-mediated emotional communication in social VR environments. © 2025 Copyright held by the owner/author(s).

关键词： Virtualization

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Towards Unified Robustness Against Both Backdoor and Adversarial Attacks

arXiv

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arXiv 2024年

作者： Niu, Zhenxing Sun, Yuyao Miao, Qiguang Jin, Rong Hua, Gang The Key Laboratory of Smart Human-Computer Interaction Wearable Technology of Shaanxi Province Xi-dian University China DAMO Academy Alibaba Group Beijing China Wormpex AI Research BellevueWA98004 United States

Deep Neural Networks (DNNs) are known to be vulnerable to both backdoor and adversarial attacks. In the literature, these two types of attacks are commonly treated as distinct robustness problems and solved separately, since they belong to training-time and inference-time attacks respectively. However, this paper revealed that there is an intriguing connection between them: (1) planting a backdoor into a model will significantly affect the model's adversarial examples;(2) for an infected model, its adversarial examples have similar features as the triggered images. Based on these observations, a novel Progressive Unified Defense (PUD) algorithm is proposed to defend against backdoor and adversarial attacks simultaneously. Specifically, our PUD has a progressive model purification scheme to jointly erase backdoors and enhance the model's adversarial robustness. At the early stage, the adversarial examples of infected models are utilized to erase backdoors. With the backdoor gradually erased, our model purification can naturally turn into a stage to boost the model's robustness against adversarial attacks. Besides, our PUD algorithm can effectively identify poisoned images, which allows the initial extra dataset not to be completely clean. Extensive experimental results show that, our discovered connection between backdoor and adversarial attacks is ubiquitous, no matter what type of backdoor attack. The proposed PUD outperforms the state-of-the-art backdoor defense, including the model repairing-based and data filtering-based methods. Besides, it also has the ability to compete with the most advanced adversarial defense methods. The code is available here. Copyright © 2024, The Authors. All rights reserved.

关键词： Purification

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Impairments in Spatial Representations of Place Cells and Neural rhythms In APP knock-in rat model 46

Impairments in Spatial Representations of Place Cells and Ne...

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

作者： Wang, Xueling Wang, Yimeng Pang, Keliang Lu, Bai Tian, Yutao Zheng, Chenguang Fudan University Aging and Longevity Institute Institute of Biological Science Zhongshan Hospital Shanghai China Tsinghua University-Peking University Joint Center for Life Sciences Tsinghua University School of Pharmaceutical Sciences IDG/McGovern Institute for Brain Research Beijing China Tianjin University Academy of Medical Engineering and Translational Medicine Medical College Tianjin China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin China

ISBN: (纸本)9798350371499

Alzheimer's disease (AD) is a neurodegenerative disease that severely impacts spatial memory. Place cells in the hippocampus play an important role in spatial memory. However, neuronal activity in the hippocampus is difficult to monitor and study in human AD patients. Thus, it is critical to have an animal model that resembles closely human AD. The newly developed APPNL-G-F rat model, which is a knock-in rat line of the amyloid precursor protein (APP) with Swedish-Beyreuther/Iberian-Arctic mutations, presents a unique opportunity. In this study, we investigated how hippocampal place cells and neural rhythms were impaired in APP rats. Place cells of APP rats showed a less defined spatial tunning and disrupted remapping. In addition, APP rats exhibited decreased power of theta rhythm and slow gamma rhythm. These results suggest that the impairment of spatial memory in APP rats may be caused by the damage of place cells' ability to represent spatial location and abnormal neural rhythm in the hippocampus. © 2024 IEEE.

关键词： Rats

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Simulating the Dilution of Sulfuric Acid on Mixed Reality Platform 8

Simulating the Dilution of Sulfuric Acid on Mixed Reality Pl...

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8th International Conference on Virtual Reality, ICVR 2022

作者： Pan, Zhipeng Miao, Jinda Cai, Ning Luo, Tianren Zhang, Mingmin Pan, Zhigeng Research Institute of Virtual Reality and Intelligent Systems Hangzhou Normal University Hangzhou China Zhejiang University College of Computer Science and Technology Hangzhou China Institute of Software Chinese Academy of Sciences State Key Laboratory of Computer Science and Beijing Key Lab of Human-Computer Interaction Beijing China Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing China

ISBN: (数字)9781665479110

ISBN: (纸本)9781665479110

Augmented reality technology has been widely used in experimental education. Augmented reality provides virtual-real integration, real-Time interaction and three-dimensional immersion, which provides a new development direction for simulating the teaching environment and promoting learning interaction. We have developed an MR experiment of diluting concentrated sulfuric acid, which helps students learn experimental manipulations and scientific concepts before conducting real chemical experiments. Virtual experiments can avoid the risks of real chemical experiments and reduce the waste of chemical materials. At the same time, we correctly render the occlusion relationship between the hand, the beaker and the virtual liquid, providing a realistic experimental effect. The tracking of multiple cameras allows students to interact more naturally. We compared the availability and students' attitudes of three virtual experiments about diluting concentrated sulfuric acid. The results show that the usability of the MR experimental system reaches the level of user satisfaction. The realistic visual effects and natural interaction method of the MR experiment have been recognized by the students. © 2022 IEEE.

关键词： Students

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Biogas Fermentation Monitoring research Based on Borderline-SMOTE Algorithm and Stacking Ensemble Learning

Biogas Fermentation Monitoring Research Based on Borderline-...

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Information and Communication Technology (ICTech), International Conference of

作者： Pengfei Xiu Lijun Fu Yukun Sun Hongliang Wang Xuerao Li Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shandong University Big Data Technology and Cognitive Intelligence Laboratory University of Chinese Academy of Sciences Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (数字)9798350376258

ISBN: (纸本)9798350376265

Anaerobic fermentation is the most important link to gas production, this paper solves the current problem with the help of the current complete machine learning technology, combining perception and intelligent processing, intelligent processing of information, and derive a decision-making basis for its control or management [1]. Aiming at the current problems of the anaerobic fermentation process in biogas engineering, the system design was carried out, and the anaerobic fermentation process of biogas and its main influencing factors were investigated, and the monitoring parameters of the system were selected to be temperature, PH value, liquid level and redox potential [2]. Previous studies are mainly based on a single prediction model, which leads to poor generalization ability of the model. In this study, unbalanced samples are handled by Borderline-SMOTE algorithm. Based on the Stacking integrated learning approach, a common single prediction model is used to construct a combined prediction model. The accuracy, precision, recall, F1 score and area under the ROC curve are used to evaluate the advantages and disadvantages of the combined prediction model over the single prediction model.

关键词： Accuracy Biological system modeling Stacking Process control Production Predictive models Prediction algorithms Biogas Fermentation Monitoring

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STSE-xLSTM: A Deep Learning Framework for Automated Seizure Detection in Long Video Sequences Using Spatio-Temporal and Attention Mechanisms

STSE-xLSTM: A Deep Learning Framework for Automated Seizure ...

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International Conference on computer and Communications (ICCC)

作者： Lihui Ding Hongliang Wang Lijun Fu Shenyang Institute of Computing Technology University of Chinese Academy of Sciences Shenyang China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Laboratory of Big Data and Artificial Intelligence Technology Shandong University Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

A detailed analysis of seizure semiology, the symptoms and signs that occur during a seizure, is crucial for the management of epilepsy patients. The inter-rater reliability of qualitative visual analysis is often low, highlighting the need for automatic and quantitative analysis of video-recorded seizures for objective assessment. To enhance evaluation accuracy and consistency, computer-aided video analysis of seizures has emerged as a natural direction of development. In the field of medical applications, deep learning and computer vision methods have driven significant advancements. However, analyzing video data presents challenges due to high dimensionality, variations in lighting, posture, and video quality. Additionally, the duration of seizures can range from seconds to minutes. To effectively extract features from seizures, we propose a novel deep learning model, STSE-xLSTM, designed to learn deep representations of arbitrarily long seizure videos. This model integrates a Spatio-Temporal Convolutional Network (STCNN) for feature extraction, a Squeeze-and-Excitation (SE) attention mechanism to enhance relevant features, and an extended Long Short-Term Memory network (xLSTM) to process long video sequences. The SE mechanism adaptively recalibrates channel weights, improving recognition of minority classes and reducing information redundancy. The xLSTM introduces scalar and matrix memory units, optimizing memory capacity and multi-scale feature extraction, thereby enhancing detection accuracy. When evaluated on the GESTURES dataset, our model outperformed state-of-the-art epilepsy detection methods in classification performance. Key contributions include the integration of STCNN, SE attention mechanism, and xLSTM to address data imbalance issues and improve the handling of complex sequential data in epilepsy detection.

关键词： Deep learning Adaptation models Visualization Attention mechanisms Accuracy Video sequences Epilepsy Feature extraction Reliability Long short term memory

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