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arXiv 2025年

作者： Li, Qiang Lin, Xinze Lv, Shenghao Huang, Faliang Li, Xiangju Shandong Women’s University School of Data and Computer Science Jinan250002 China Shandong University of Science and Technology College of Computer Science and Engineering Qiangdao266500 China Nanning Normal University Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning530100 China

Matching candidate news with user interests is crucial for personalized news recommendations. Most existing methods can represent a user’s reading interests through a single profile based on clicked news, which may not fully capture the diversity of user interests. Although some approaches incorporate candidate news or topic information, they remain insufficient because they neglect the multi-granularity relatedness between candidate news and user interests. To address this, this study proposed a multi-granularity candidate-aware user modeling framework that integrated user interest features across various levels of granularity. It consisted of two main components: candidate news encoding and user modeling. A news textual information extractor and a knowledge-enhanced entity information extractor can capture candidate news features, and word-level, entity-level, and news-level candidate-aware mechanisms can provide a comprehensive representation of user interests. Extensive experiments on a real-world dataset demonstrated that the proposed model could significantly outperform baseline models∗ Copyright © 2025, The Authors. All rights reserved.

关键词： Recommender systems

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Federated semi-supervised medical image classification via inter-client relation matching

arXiv

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arXiv 2021年

作者： Liu, Quande Yang, Hongzheng Dou, Qi Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Science and Engineering Beihang University China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Federated learning (FL) has emerged with increasing popularity to collaborate distributed medical institutions for training deep networks. However, despite existing FL algorithms only allow the supervised training setting, most hospitals in realistic usually cannot afford the intricate data labeling due to absence of budget or expertise. This paper studies a practical yet challenging FL problem, named Federated Semi-supervised Learning (FSSL), which aims to learn a federated model by jointly utilizing the data from both labeled and unlabeled clients (i.e., hospitals). We present a novel approach for this problem, which improves over traditional consistency regularization mechanism with a new inter-client relation matching scheme. The proposed learning scheme explicitly connects the learning across labeled and unlabeled clients by aligning their extracted disease relationships, thereby mitigating the deficiency of task knowledge at unlabeled clients and promoting discriminative information from unlabeled samples. We validate our method on two large-scale medical image classification datasets. The effectiveness of our method has been demonstrated with the clear improvements over state-of-the-arts as well as the thorough ablation analysis on both tasks4 © 2021, CC BY-NC-ND.

关键词： Hospitals

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

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

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

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

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

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 APP NL-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.

关键词： Proteins Rats Alzheimer's disease Engineering in medicine and biology Biomedical monitoring Hippocampus Monitoring

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Multimodal prototypical networks with Co-metric fusion for few-shot hyperspectral image classification

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Neurocomputing 2025年 648卷

作者： Yuhang Li Jinrong He Hanchi Liu Yurong Zhang Zhaokui Li College of Mathematics and Computer Science Yan'an University Yan’an 716000 China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning 530100 China School of Computer Shenyang Aerospace University Shenyang 110136 China

In the field of Hyperspectral image (HSI) classification, prototype-based network methods have achieved significant research progress. These methods utilize pixel-level information from images to construct central prototypes for each class, providing effective solutions for few-shot learning. However, traditional prototype networks have some inherent flaws; they primarily rely on a single image modality and fail to fully leverage the potential complementarity between different modalities, using only a single modality to generate class prototypes, which limits the model's performance in representing class prototypes and enhancing discriminative capabilities. And subtle inter-class differences are also a challenging task in cross-domain scenarios. To overcome these challenges, this study proposes an innovative Multimodal Prototypical Networks with Co-metric Fusion (MPCF). By integrating prototype information from both image and text modalities, MPCF significantly enhances the performance of few-shot learning. The method not only captures the spectral and spatial features of images to construct image prototypes but also extracts textual features from category descriptions to generate text prototypes. Furthermore, by integrating contrastive learning strategies with the Co-metric fusion mechanism, the method effectively harnesses the information from different modalities. This integration allows for the capture of category information across multiple dimensions, significantly boosting the model's discriminative power among various classes and enhancing its capacity to address few-shot learning scenarios. Experiments conducted on several public benchmark HSI datasets (Indian Pines-84.06 %, Houston-80.41 %, Salinas-92.63 %) demonstrate that MPCF exhibits excellent performance under few-shot and cross-domain conditions, achieving higher classification accuracy and stronger robustness compared to state-of-the-art methods. The related code will be made publicly available at t

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Theory of Mind Abilities Predict Robot's Gaze Effects on Object Preference

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IEEE Transactions on Affective Computing 2025年

作者： Manzi, Federico Ishikawa, Mitsuhiko Dio, Cinzia Di Itakura, Shoji Kanda, Takayuki Ishiguro, Hiroshi Massaro, Davide Marchetti, Antonella Università Cattolica del Sacro Cuore Research Unit on Theory of Mind Department of Psychology Milan Italy Università Cattolica del Sacro Cuore Research Unit in Psychology and Robotics in the Lifespan Department of Psychology Milan Italy IRCCS Fondazione Don Carlo Gnocchi Milan Italy Hitotsubashi University Hitotsubashi Institute for Advanced Study Japan Ritsumeikan University Kyoto Japan Kyoto University Department of Computer Science Human-Robot Interaction Laboratory Kyoto Japan IRC'HIL Keihana Science City Advanced Telecommunications Research Institute International Kyoto Japan Osaka University Department of Intelligent Systems Toyonaka Japan

This study investigated the differences between human and robot gaze in influencing preference formation, and examined the role of Theory of Mind (ToM) abilities in this process. human eye gaze is one of the most important sources of information for social interaction and research has demonstrated its effectiveness in influencing people's preference. With increasing technological development, we will interact with robots that can exhibit gaze behavior and influence people's preference. It is unclear whether there are any differences between humans and robots in this process. The present study aimed to analyze the role of the gaze of a robot and a human in influencing the ascription of a preference to the gazer and the participants' preference. Furthermore, we examined the role of ToM abilities in preference formation. The results showed that the gaze has a greater effect on the gazer preference compared to participants' preference regardless of the agent (human or robot). In addition, ToM abilities predict both gazer and individual preferences in the robot's condition only even though different socio-cognitive mechanisms are involved. The study suggests that adults are cognitively able to process the gaze of a robot similar to a human, recognizing the underlying mental state. However, only for the robot, different cognitive mechanisms are involved in the gazer (i.e., perspective taking) and participants' preference formation (i.e., advanced ToM). © 2010-2012 IEEE.

关键词： Social robots

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Natural human Skin‑Inspired Wearable and Breathable Nanofiber‑based Sensors with Excellent Thermal Management Functionality

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Advanced Fiber Materials 2024年第6期6卷 1955-1968页

作者： Peng Wang Xiaodan Li Guifen Sun Guoqing Wang Qing Han Chuizhou Meng Zhonghe Wei Yang Li School of Mechanical Engineering University of JinanJinan 250022China School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China State Key Laboratory for Reliability and Intelligence of Electrical Equipment Engineering Research Center of Ministry of Education for Intelligent Rehabilitation Device and Detection TechnologyHebei Key Laboratory of Smart Sensing and Human‑Robot InteractionSchool of Mechanical EngineeringHebei University of TechnologyTianjin 300401China Shandong Provincial Key Laboratory of Network‑Based Intelligent Computing School of Information Science and EngineeringUniversity of JinanJinan 250022China Network Information Center Department of Computer ScienceQilu University of Technology(Shandong Academy of Sciences)Jinan 250353China School of Integrated Circuits Shandong UniversityJinan 250101China

Wearable sensors have been rapidly developed for application in various human monitoring ***,the wearing comfort and thermal properties of these devices have been largely ignored,and these characteristics urgently need to be ***,we develop a wearable and breathable nanofiber-based sensor with excellent thermal management functionality based on passive heat preservation and active Joule heating *** multifunctional device consists of a micropatterned carbon nanotube(CNT)/thermoplastic polyurethane(TPU)nanofiber electrode,a microporous ionic aerogel electrolyte and a microstructured Ag/TPU nanofiber *** to the presence of a supercapacitive sensing mechanism and the appli-cation of microstructuration,the sensor shows excellent sensing performance,with a sensitivity of 24.62 ***,due to the overall porous structure and hydrophobicity of TPU,the sensor shows good breathability(62 mm/s)and water repellency,with a water contact angle of 151.2°.In addition,effective passive heat preservation is achieved by combining CNTs with high solar absorption rates(85%)as the top layer facing the outside,aerogel with a low thermal conductivity(0.063 W m-1 k-1)as the middle layer for thermal insulation,and Ag with a high infrared reflectance rate as the bottom layer facing the *** warming,this material yields a higher temperature than ***,the active Joule heat-ing effect is realized by applying current through the bottom resistive electrode,which can quickly increase the temperature to supply controlled warming on *** proposed wearable and breathable sensor with tunable thermal properties is promising for monitoring and heat therapy applications in cold environments.

关键词： Wearable sensor Breathable Personal thermal management Electrospun nanofibers Joule heating

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Explaining the Black-box Smoothly - A Counterfactual Approach

arXiv

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arXiv 2021年

作者： Singla, Sumedha Eslami, Motahhare Pollack, Brian Wallace, Stephen Batmanghelich, Kayhan Computer Science Department The University of Pittsburgh PittsburghPA15206 United States School of Computer Science Human-Computer Interaction Institute Carnegie Mellon University United States Department of Biomedical Informatics The University of Pittsburgh PittsburghPA15206 United States University of Pittsburgh Medical School PittsburghPA15206 United States

We propose a BlackBox Counterfactual Explainer, designed to explain image classification models for medical applications. Classical approaches (e.g., saliency maps) that assess feature importance do not explain how imaging features in important anatomical regions are relevant to the classification decision. Such reasoning is crucial for transparent decision-making in healthcare applications. Our framework explains the decision for a target class by gradually exaggerating the semantic effect of the class in a query image. We adopted a Generative Adversarial Network (GAN) to generate a progressive set of perturbations to a query image, such that the classification decision changes from its original class to its negation. Our proposed loss function preserves essential details (e.g., support devices) in the generated images. We used counterfactual explanations from our framework to audit a classifier trained on a chest x-ray dataset with multiple labels. Clinical evaluation of model explanations is a challenging task. We proposed clinically-relevant quantitative metrics such as cardiothoracic ratio and the score of a healthy costophrenic recess to evaluate our explanations. We used these metrics to quantify the counterfactual changes between the populations with negative and positive decisions for a diagnosis by the given classifier. We conducted a human-grounded experiment with diagnostic radiology residents to compare different styles of explanations (no explanation, saliency map, cycleGAN explanation, and our counterfactual explanation) by evaluating different aspects of explanations: (1) understandability, (2) classifier's decision justification, (3) visual quality, (d) identity preservation, and (5) overall helpfulness of an explanation to the users. Our results show that our counterfactual explanation was the only explanation method that significantly improved the users' understanding of the classifier's decision compared to the no-explanation baseline. Our metric

关键词： Medical applications

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Mixed reality based respiratory liver tumor puncture navigation

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Computational Visual Media 2019年第4期5卷 363-374页

作者： Ruotong Li Weixin Si Xiangyun Liao Qiong Wang Reinhard Klein Pheng-Ann Heng Institute of Computer Science II University of Bonn53115 BonnGermany Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesChina Department of Computer Science and Engineering Chinese University of Hong KongHong KongChina

This paper presents a novel mixed reality based navigation system for accurate respiratory liver tumor punctures in radiofrequency ablation(RFA).Our system contains an optical see-through head-mounted display device(OST-HMD),Microsoft Holo Lens for perfectly overlaying the virtual information on the patient,and a optical tracking system NDI Polaris for calibrating the surgical utilities in the surgical *** with traditional navigation method with CT,our system aligns the virtual guidance information and real patient and real-timely updates the view of virtual guidance via a position tracking *** addition,to alleviate the difficulty during needle placement induced by respiratory motion,we reconstruct the patientspecific respiratory liver motion through statistical motion model to assist doctors precisely puncture liver *** proposed system has been experimentally validated on vivo pigs with an accurate real-time registration approximately 5-mm mean FRE and TRE,which has the potential to be applied in clinical RFA guidance.

关键词： mixed reality human computer interaction statistical motion model

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Towards expert gaze modeling and recognition of a user’s attention in realtime

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Procedia computer science 2020年 176卷 2020-2029页

作者： Nora Castner Lea Geßler David Geisler Fabian Hüttig Enkelejda Kasneci Human-Computer Interaction University of Tübingen Germany Institute of Computer Science University of Tübingen Germany Department of Prosthodontics University Hospital Tübingen Germany

One of the appealing areas of expertise research is devoted to measuring the effectiveness of training programs for novices. With recent progress in eye tracking, gaze-based interaction systems recognize a user’s attention and can direct it accordingly. Moreover, dynamic visualization of an expert gaze model facilitates novice training by guiding the gaze to relevant areas. In addition, the system should be aware of realtime attention to remove an overlay that could occlude relevant information. We use an implementation of subtle gaze direction (SGD) and the simplified scanpath of a dentist to train naive participants in finding anomalies in dental radiographs. We were able to effectively direct user gaze to relevant image features without occluding the area when attention was recognized. Additionally, participants reported that the intervention was helpful for image inspection. The results of the model intervention show minimal improvements in anomaly detection, which is expected of naive subjects. We advocate that the system has the potential to be highly effective for advanced students and trainees with a certain foundation of conceptual knowledge.

关键词： Eye Tracking Gaze-based interaction Attention Guiding Expertise Learning

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MLM-Net: Streamlined Multi-Lane Detection Network with Spatio-temporal Memory for Video Instance Lane Detection

MLM-Net: Streamlined Multi-Lane Detection Network with Spati...

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International Conference on Intelligent Transportation

作者： Xiaoqin Wang Yunfei Yin Faliang Huang Xianjian Bao School of Mechanical and Vehicle Engineering Chongqing University Chongqing P. R. China School of Computer Science Chongqing University Chongqing P. R. China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning P. R. China School of Maharishi University of Management Fairfield IOWA USA

Lane detection is a crucial task in autonomous driving as it helps vehicles maintain stable and efficient driving in complex traffic environments. Image-based and video-based lane detection are two common methods, with video-based lane detection being more effective in handling complex scenarios such as lane occlusion and wear. However, most existing video-based lane detection methods suffer from information redundancy, where the same key frames of the same lane are repeatedly calculated during the detection of multiple lanes. Additionally, the spatio-temporal sequence information of video-based lanes is not fully utilized. To address these challenges, MLM-Net is proposed as a streamlined multilane network that facilitates the sharing of spatiotemporal information. The Global Memory Feature (GMF) module is redesigned to mitigate information redundancy across multiple lanes, while the Local Spatio-Temporal Feature (LSTF) module is introduced to enhance the utilization of spatiotemporal consistency information. Additionally, the Enhanced Global Memory Feature (EGMF) module is introduced to extract enhanced features and handle challenging detection scenarios. Experimental results demonstrate that our method achieves state-of-the-art performance for video instance lane detection on the VIL-100 dataset.

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