检索结果-内蒙古大学图书馆

Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Rucha Khot Teis Arets Joel Wester Franziska Burger Niels van Berkel Rens Brankaert Wijnand IJsselsteijn Minha Lee Industrial Design Eindhoven University of Technology Eindhoven Netherlands Human-Technology Interaction Eindhoven University of Technology Eindhoven Netherlands Department of Computer Science Aalborg University Aalborg Denmark Industrial Design Engineering University of Ghent Ghent Belgium Industrial Design Eindhoven University of Technology Eindhoven Netherlands Tranzo Tilburg University Tilburg Netherlands and Fontys School of Applied Sciences Eindhoven Netherlands Human Technology Interaction Eindhoven University of Technology Eindhoven Netherlands Industrial Design Eindhoven University of Technology Eindhoven Netherlands and Centrum Wiskunde & Informatica Amsterdam Netherlands

ISBN: (纸本)9798400713941

Intertemporal reflection, flexibly thinking forward and backward in time, is vital for one's future planning. Yet, cultivating intertemporal reflection about encountering difficult futures, e.g., developing a progressive cognitive condition like dementia, can be challenging. We assessed people's attitudes towards dementia following conversing with a chatbot presented as either neurotypical or simulating dementia symptoms. While neither the chatbot's presentation nor the framing of participants' future selves impacted attitudes toward dementia, it influenced participants' experiences. When framed as future selves, the chatbot evoked a strong emotional connection, leading to reflection on aging, particularly with the chatbot simulating dementia symptoms. Participants interacting with the chatbot framed as a stranger with simulated symptoms often felt frustrated, especially when they had a task-oriented *** can be promising tools for prompting reflections on challenging futures, such as dementia, although their effectiveness varies due to the tensions between simulated cognitive decline and expectations for effective communication. © 2025 Copyright held by the owner/author(s).

关键词： Dementia

来源：评论

学校读者我要写书评

暂无评论

Developing a Research center for Artificial Intelligence in Medicine

Mayo Clinic Proceedings: Digital Health

引用

Mayo Clinic Proceedings: Digital Health 2024年第4期2卷 677-686页

作者： Langlotz, Curtis P. Kim, Johanna Shah, Nigam Lungren, Matthew P. Larson, David B. Datta, Somalee Li, Fei Fei Montine, Thomas J. Harrington, Robert A. Gold, Garry E. Departments of Radiology Medicine and Biomedical Data Science Center for Artificial Intelligence in Medicine and Imaging Stanford University Stanford CA United States Center for Artificial Intelligence in Medicine and Imaging Stanford University Stanford CA United States Departments of Medicine and Biomedical Data Science Center for Artificial Intelligence in Medicine and Imaging Stanford Health Care Stanford University Stanford CA United States Department of Radiology Stanford University Stanford CA United States Department of Computer Science Institute for Human Centered Artificial Intelligence Stanford University Stanford CA United States Department of Pathology Stanford University Stanford CA United States Office of the Dean Weill Cornell Medicine New York NY United States Microsoft Redmond WA United States Research Information Technology Stanford University School of Medicine Stanford CA United States Department of Psychiatry and Behavioral Sciences Stanford University School of Medicine Stanford CA United States

Artificial intelligence (AI) and machine learning (ML) are driving innovation in biosciences and are already affecting key elements of medical scholarship and clinical care. Many schools of medicine are capitalizing on the promise of these new technologies by establishing academic units to catalyze and grow research and innovation in AI/ML. At Stanford University, we have developed a successful model for an AI/ML research center with support from academic leaders, clinical departments, extramural grants, and industry partners. The center for Artificial Intelligence in Medicine and Imaging uses the following 4 key tactics to support AI/ML research: project-based learning opportunities that build interdisciplinary collaboration;internal grant programs that catalyze extramural funding;infrastructure that facilitates the rapid creation of large multimodal AI-ready clinical data sets;and educational and open data programs that engage the broader research community. The center is based on the premise that foundational and applied research are not in tension but instead are complementary. Solving important biomedical problems with AI/ML requires high-quality foundational team science that incorporates the knowledge and expertise of clinicians, clinician scientists, computer scientists, and data scientists. As AI/ML becomes an essential component of research and clinical care, multidisciplinary centers of excellence in AI/ML will become a key part of the scholarly portfolio of academic medical centers and will provide a foundation for the responsible, ethical, and fair implementation of AI/ML systems. © 2024 The Authors

关键词： AI artificial intelligence AIMI center Stanford center for Artificial Intelligence in Medicine and Imaging CT computed tomography EHR electronic health record FDA Food and Drug Administration HAI Stanford Institute for human centered Artificial Intelligence ML machine learning STARR Stanford Research Repository

来源：评论

学校读者我要写书评

暂无评论

FerroVibe: Towards a Modular Tactile Device

FerroVibe: Towards a Modular Tactile Device

引用

Telepresence, IEEE Conference on

作者： Harsimran Singh Luis Perez Marcilla Premankur Banerjee Michael Rothammer Thomas Hulin The Institute of Robotics and Mechatronics German Aerospace Center (DLR) Munich Germany Centre for Tactile Internet with Human-in-the-Loop (CeTI) Cluster of Excellence at TU Dresden Dresden Germany Department of Computer Science University of Southern California USA Hapticlabs Germany

ISBN: (数字)9798331528249

ISBN: (纸本)9798331528256

This paper presents the proof of concept of the modular nature of ferrofluid based tactile device, FerroVibe. The modularity of FerroVibe allows for customization in degrees of freedom, force feedback, and vibrational cues by strategically positioning magnetic actuators around the central assembly containing a neodymium magnet and ferrofluid. Three distinct configurations of FerroVibe are introduced, each offering unique actuation mechanisms and physical characteristics that could be tailored to different application requirements. The paper showcases the proof of concept and details the working principle of each configuration, highlighting the strengths and limitations of each design. Initial comparative analysis is conducted to evaluate the trade-offs in terms of power consumption, speed, size, weight, and feedback fidelity. The results demonstrate that FerroVibe’s potential modular architecture provides a flexible and effective solution for a wide range of tactile feedback applications, making it a promising candidate for future advancements in haptic technology.

关键词： Ferrofluid Actuators Telepresence Power demand Neodymium Force feedback Tactile sensors Magnetosphere Assembly

来源：评论

学校读者我要写书评

暂无评论

Alteration of Cortical Somatosensory Feedback in Post-Stroke Movement Control 46

Alteration of Cortical Somatosensory Feedback in Post-Stroke...

引用

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

作者： Peng, Rita Huan-Ting Parmar, Nishaal Williamson, Jordan N. Sirpal, Parikshat Mulyana, Beni E. Rajabtabar Darvish, Mahmood Sung, Joohwan Sidorov, Evgeny V. Yang, Yuan Department of Bioengineering Grainger College of Engineering University of Illinois Urbana-Champaign UrbanaIL United States Clinical Imaging Research Center Stephenson Family Clinical Research Institute Carle Foundation Hospital UrbanaIL United States Department of Electrical and Computer Engineering University of Oklahoma TulsaOK74135 United States Department of Neurology University of Oklahoma Health Sciences Center Oklahoma CityOK73104 United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign UrbanaIL United States Department of Physical Therapy and Human Movement Sciences Northwestern University ChicagoIL United States

ISBN: (纸本)9798350371499

Sensory feedback is of vital importance in motor control, yet is rarely studied in diseases which frequently result in motor deficiency, such as hemiparetic stroke. This study employs the laterality index (LI) of somatosensory evoked potentials (SEPs) to investigate whether sensory feedback is altered in hemiparetic stroke during movements of the paretic arm, with a hemispheric shift from the lesioned hemisphere toward contralesional hemisphere. Through experimental design involving the isometric lifting of the paretic arms during tactile finger stimulation and the analysis of LI in SEPs P50 and N100, we found: 1) increased contralesional sensory activity in stroke participants when they are receiving sensory input in their paretic hand for both P50 and N100 and 2) the contralesional N100 activity is enhanced when stroke participants are performing an isometric arm lifting task. These results indicate a timedependent shifting of sensory feedback from the sensorimotor areas in the lesioned side to the contralesional side of the stroke brain. © 2024 IEEE.

关键词： Sensory feedback

来源：评论

学校读者我要写书评

暂无评论

Parts2Words: Learning Joint Embedding of Point Clouds and Texts by Bidirectional Matching Between Parts and Words

Parts2Words: Learning Joint Embedding of Point Clouds and Te...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Chuan Tang Xi Yang Bojian Wu Zhizhong Han Yi Chang School of Artificial Intelligence Jilin University China MoE Engineering Research Center of Knowledge-Driven Human-Machine Intelligence China Zhejiang University The Department of Computer Science Wayne State University USA

Shape-Text matching is an important task of high-level shape understanding. Current methods mainly represent a 3D shape as multiple 2D rendered views, which obviously can not be understood well due to the structural ambiguity caused by self-occlusion in the limited number of views. To resolve this issue, we directly represent 3D shapes as point clouds, and propose to learn joint embedding of point clouds and texts by bidirectional matching between parts from shapes and words from texts. Specifically, we first segment the point clouds into parts, and then leverage optimal transport method to match parts and words in an optimized feature space, where each part is represented by aggregating features of all points within it and each word is abstracted by its contextual information. We optimize the feature space in order to enlarge the similarities between the paired training samples, while simultaneously maximizing the margin between the unpaired ones. Experiments demonstrate that our method achieves a significant improvement in accuracy over the SOTAs on multi-modal retrieval tasks under the Text2Shape dataset. Codes are available at here.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Discovering Chunks in Neural Embeddings for Interpretability

arXiv

引用

arXiv 2025年

作者： Wu, Shuchen Alaniz, Stephan Schulz, Eric Akata, Zeynep Explainable Machine Learning Lab Helmholtz Munich Germany Max Planck Institute for Biological Cybernetics Germany Munich Center for Machine Learning Germany Department of Computer Science Technical University of Munich Germany Institute for Human-Centered AI Helmholtz Munich Germany

Understanding neural networks is challenging due to their high-dimensional, interacting components. Inspired by human cognition, which processes complex sensory data by chunking it into recurring entities, we propose leveraging this principle to interpret artificial neural population activities. Biological and artificial intelligence share the challenge of learning from structured, naturalistic data, and we hypothesize that the cognitive mechanism of chunking can provide insights into artificial systems. We first demonstrate this concept in recurrent neural networks (RNNs) trained on artificial sequences with imposed regularities, observing that their hidden states reflect these patterns, which can be extracted as a dictionary of chunks that influence network responses. Extending this to large language models (LLMs) like LLaMA, we identify similar recurring embedding states corresponding to concepts in the input, with perturbations to these states activating or inhibiting the associated concepts. By exploring methods to extract dictionaries of identifiable chunks across neural embeddings of varying complexity, our findings introduce a new framework for interpreting neural networks, framing their population activity as structured reflections of the data they process. © 2025, CC BY.

关键词： Network embeddings

来源：评论

学校读者我要写书评

暂无评论

Asynchronous stochastic gradient descent with decoupled backpropagation and layer-wise updates

arXiv

引用

arXiv 2024年

作者： Fokam, Cabrel Teguemne Nazeer, Khaleelulla Khan König, Lukas Kappel, David Subramoney, Anand Center for Cognitive Interaction Technology Universität Bielefeld Germany Institut für Neuroinformatik Ruhr Universität Bochum Germany Chair of Highly-Parallel VLSI Systems and Neuro-Microelectronics Technische Universität Dresden Germany Department of Computer Science Royal Holloway University of London United Kingdom

The increasing size of deep learning models has made distributed training across multiple devices essential. However, current methods such as distributed data-parallel training suffer from large communication and synchronization overheads when training across devices, leading to longer training times as a result of suboptimal hardware utilization. Asynchronous stochastic gradient descent (ASGD) methods can improve training speed, but are sensitive to delays due to both communication and differences throughput. Moreover, the backpropagation algorithm used within ASGD workers is bottlenecked by the interlocking between its forward and backward passes. Current methods also do not take advantage of the large differences in the computation required for the forward and backward passes. Therefore, we propose an extension to ASGD called Partial Decoupled ASGD (PD-ASGD) that addresses these issues. PD-ASGD uses separate threads for the forward and backward passes, decoupling the updates and allowing for a higher ratio of forward to backward threads than the usual 1:1 ratio, leading to higher throughput. PD-ASGD also performs layer-wise (partial) model updates concurrently across multiple threads. This reduces parameter staleness and consequently improves robustness to delays. Our approach yields close to state-of-the-art results while running up to 5.95× faster than synchronous data parallelism in the presence of delays, and up to 2.14× times faster than comparable ASGD algorithms by achieving higher model flops utilization. We mathematically describe the gradient bias introduced by our method, establish an upper bound, and prove *** Codes G.1.6 Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Automatic detection and classification algorithm of thyroid nodules in CT images based on YOLOv5s

Automatic detection and classification algorithm of thyroid ...

引用

International Conference on computer and Communications (ICCC)

作者： Ruifeng Guo Lei Xu Jiahui Guo Hongliang Wang 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 Department of Endocrinology and Metabolism The First Hospital of China Medical University Shenyang China

Thyroid nodular lesions are one of the most common diseases in the population. As the number of patients increases, it is clinically important to provide physicians with an accurate early aid to determine the benignity and malignancy of thyroid nodules in order to avoid unnecessary surgery and reduce patient burden. In this paper, we propose an improved Yolov5s-based target detection algorithm for benign and malignant thyroid nodules for thyroid nodule classification and identification. Replacing the backbone network of Yolov5s with ResNet18 to reduce the complexity of the model and the number of parameters; introducing the CBAM attention mechanism into the Yolov5s algorithm to improve the ability to extract features; Modify the spatial pyramid pooling module SPP to SimSPPF to improve the training speed of the algorithm; replace GIoU Loss with CIoU-Focal Loss as the loss function of the algorithm to improve the localization accuracy while increasing the regression rate of the bounding box. The experimental results show that the improved Yolov5s achieved an average accuracy of 92.9%, which is 2.9 percentage points higher than the average accuracy of the original algorithm, and can effectively improve the accuracy and efficiency of nodule detection.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Consistency Approximation: Incremental Feature Selection Based on Fuzzy Rough Set Theory

SSRN

引用

SSRN 2023年

作者： Zhao, Jie Wu, Daiyang Wu, JiaXin Ye, Wenhao Huang, Faliang Wang, Jiahai See-To, Wing-Kuen Eric Department of Data Science and Business Intelligence Guangdong University of Technology Guangzhou China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computing and Decision Sciences Faculty of Business Lingnan University Hong Kong

Fuzzy Rough Set Theory (FRST)-based feature selection has been widely used as a preprocessing step to handle dynamic and large datasets. However, large-scale or high-dimensional datasets remain intractable for FRST-based feature selection approaches due to high space complexity and unsatisfactory classification performance. To overcome these challenges, we propose a Consistency Approximation (CA)-based framework for incremental feature selection. By exploring CA, we introduce a novel significance measure and a tri-accelerator. The CA-based significance measure provides a mechanism for each sample in the universe to keep members with different class labels within its fuzzy neighbourhood as far as possible, while keeping members with the same label as close as possible. Furthermore, our tri-accelerator reduces the search space and decreases the computational space with a theoretical lower bound. The experimental results demonstrate the superiority of our proposed algorithm compared to state-of-the-art methods on efficiency and classification accuracy, especially for large-scale and high-dimensional datasets. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Incremental Feature Selection for Dynamic Incomplete Data Using Sub-Tolerance Relation

SSRN

引用

SSRN 2023年

作者： Zhao, Jie Ling, Yun Huang, Faliang Wang, Jiahai See-To, Eric Wing kuen Department of Data Science and Business Intelligence Guangdong University of Technology Guangzhou China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computing and Decision Sciences Faculty of Business Lingnan University Hong Kong

TRS theory is widely used for feature selection with incomplete data. However, the traditional positive region-based measure ignores uncertainty, resulting in redundant features and poor classification accuracy. In addition, the computation of the positive region incurs significant time and space costs. To address these limitations, we extend the tolerance class to a Subrelation Tolerance Class (STC) by decomposing the tolerance relation into two subrelations. STC transforms feature selection into a two-stage certainty measurement by partitioning the universe into certainty and uncertainty regions. By progressively filtering out certain regions, the computational space is reduced. Furthermore, we propose a new significance measure that incorporates both certain and uncertain information. This measure effectively reduces redundant features, resulting in improved computational efficiency and classification accuracy. Using STC and our measure, we develop an incremental feature selection algorithm for dealing with incomplete streaming data, which is validated through extensive experiments and comparisons. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：