检索结果-内蒙古大学图书馆

The Two Fundamental Shapes of Sleep Heart Rate Dynamics and Their Connection to Mental Health in College Students

Digital Biomarkers 2024年第1期8卷 120-131页

作者： Fudolig, Mikaela Irene Bloomfield, Laura S.P. Price, Matthew Bird, Yoshi M. Hidalgo, Johanna E. Kim, Julia N. Llorin, Jordan Lovato, Juniper McGinnis, Ellen W. McGinnis, Ryan S. Ricketts, Taylor Stanton, Kathryn Dodds, Peter Sheridan Danforth, Christopher M. Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science University of Vermont BurlingtonVT United States Department of Mathematics and Statistics University of Vermont BurlingtonVT United States Computational Story Lab MassMutual Center of Excellence for Complex Systems and Data Science University of Vermont BurlingtonVT United States Gund Institute for Environment University of Vermont BurlingtonVT United States Department of Psychological Science University of Vermont BurlingtonVT United States Project Lemurs University of Vermont BurlingtonVT United States Department of Electrical and Biomedical Engineering University of Vermont BurlingtonVT United States Department of Computer Science University of Vermont BurlingtonVT United States Wake Forest University School of Medicine Winston-SalemNC United States Rubenstein School of Environment and Natural Resources University of Vermont BurlingtonVT United States Santa Fe Institute Santa FeNM United States

Introduction: Wearable devices are rapidly improving our ability to observe health-related processes for extended durations in an unintrusive manner. In this study, we use wearable devices to understand how the shape of the heart rate curve during sleep relates to mental health. Methods: As part of the Lived Experiences Measured Using Rings Study (LEMURS), we collected heart rate measurements using the Oura ring (Gen3) for over 25,000 sleep periods and self-reported mental health indicators from roughly 600 first-year university students in the USA during the fall semester of 2022. Using clustering techniques, we find that the sleeping heart rate curves can be broadly separated into two categories that are mainly differentiated by how far along the sleep period the lowest heart rate is reached. Results: Sleep periods characterized by reaching the lowest heart rate later during sleep are also associated with shorter deep and REM sleep and longer light sleep, but not a difference in total sleep duration. Aggregating sleep periods at the individual level, we find that consistently reaching the lowest heart rate later during sleep is a significant predictor of (1) self-reported impairment due to anxiety or depression, (2) a prior mental health diagnosis, and (3) firsthand experience in traumatic events. This association is more pronounced among females. Conclusion: Our results show that the shape of the sleeping heart rate curve, which is only weakly correlated with descriptive statistics such as the average or the minimum heart rate, is a viable but mostly overlooked metric that can help quantify the relationship between sleep and mental health. © 2024 The Author(s). Published by S. Karger AG, Basel.

关键词： Wearable technology

来源：评论

学校读者我要写书评

暂无评论

Can Automated Metadata Extraction Make Scientific Data More Navigable?

Can Automated Metadata Extraction Make Scientific Data More ...

引用

IEEE International Conference on e-science and Grid Computing

作者： Tyler J. Skluzacek Kyle Chard Ian Foster Oak Ridge National Lab National Center for Computational Sciences Oak Ridge TN Department of Computer Science University of Chicago Chicago IL Data Science and Learning Division Argonne National Lab Lemont IL

FAIR principles require that scientific data be findable, discoverable, and reusable by users. To enable FAIRness, practioners of a science repository will often construct a rich, searchable index of metadata derived from the data. Unfortunately, manual metadata annotation methods do not scale to the many data files generated by many projects; and instead automated extraction systems are needed to scalably parse these files—often with nonstandard schema requiring specialized parsing strategies—and deposit representative metadata into a search index. In this work, we evaluate whether, and the extent to which, automatically extracted metadata make research repositories more navigable. We present a two-part user study conducted with scientists at two U.S. national laboratories from projects spanning spectroscopy and battery modeling. We constructed research indexes automatically by using the Xtract metadata extraction system. In the first part of our study, we learned about each user's role and identified key navigation concerns for scientists. We found that participants wished to navigate for purposes of discovery, retrieval, and organization. In the second part, participants completed simulated research data navigation tasks crafted to reflect real-world navigability concerns. We found that regardless of the interface used, participants consistently solved navigation tasks with high degrees of confidence and correctness, and significantly ( $1.2\mathrm{X}-50\times$ ) faster than via their alternative methods (e.g., manual directory scans or designing a customized navigational tool).

关键词：

来源：评论

学校读者我要写书评

暂无评论

Weakly-Supervised Learning via Multi-Lateral Decoder Branching for Tool Segmentation in Robot-Assisted Cardiovascular Catheterization

arXiv

引用

arXiv 2024年

作者： Omisore, Olatunji Mumini Akinyemi, Toluwanimi Nguyen, Anh Wang, Lei Research Center for Medical Robotics and Minimally Invasive Surgical Devices Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Smart Robotic Lab Department of Computer Science University of Liverpool United Kingdom

Robot-assisted catheterization has garnered a good attention for its potentials in treating cardiovascular diseases. However, advancing surgeon-robot collaboration still requires further research, particularly on task-specific automation. For instance, automated tool segmentation can assist surgeons in visualizing and tracking of endovascular tools during cardiac procedures. While learning-based models have demonstrated state-of-the-art segmentation performances, generating ground-truth labels for fully-supervised methods is both labor-intensive time consuming, and costly. In this study, we propose a weakly-supervised learning method with multi-lateral pseudo labeling for tool segmentation in cardiovascular angiogram datasets. The method utilizes a modified U-Net architecture featuring one encoder and multiple laterally branched decoders. The decoders generate diverse pseudo labels under different perturbations, augmenting available partial labels. The pseudo labels are self-generated using a mixed loss function with shared consistency across the decoders. The weakly-supervised model was trained end-to-end and validated using partially annotated angiogram data from three cardiovascular catheterization procedures. Validation results show that the model could perform closer to fully-supervised models. Also, the proposed weakly-supervised multi-lateral method outperforms three well known methods used for weakly-supervised learning, offering the highest segmentation performance across the three angiogram datasets. Furthermore, numerous ablation studies confirmed the model’s consistent performance under different parameters. Finally, the model was applied for tool segmentation in a robot-assisted catheterization experiments. The model enhanced visualization with high connectivity indices for guidewire and catheter, and a mean processing time of 35.26±11.29 ms per frame. This study provides a fast, stable and less expensive method for real-time tool segmentation and visua

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

The Fused Multiply-Add and Global Atmospheric Models: A Distributional Investigation into a Surprising Correctness Scenario

The Fused Multiply-Add and Global Atmospheric Models: A Dist...

引用

High Performance Computing, Networking, Storage and Analysis, SC-W: Workshops of the International Conference for

作者： Teo Price-Broncucia Allison H. Baker Michael Duda Department of Computer Science University of Colorado Boulder CO Computational and Information Systems Lab NSF National Center for Atmospheric Research Boulder CO Mesoscale and Microscale Meteorology Lab NSF National Center for Atmospheric Research Boulder CO

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

In a series of related works developing an ensemble consistency testing approach for multiple popular global climate models (GCMs), one test scenario has repeatedly stood out. Why does the use of the Fused Multiply-Add (FMA) operation result in model configurations getting flagged as failures, while changes to compiler choice, optimization level, processor type and number, etc. are passed as expected? This work explores the impacts of FMA on GCM simulation output from a distributional perspective and provides directions for future work to enable model developers and users to use numerical optimization techniques with confidence.

关键词： Analytical models Atmospheric modeling computational modeling High performance computing Conferences Numerical models Optimization Testing Meteorology

来源：评论

学校读者我要写书评

暂无评论

UC-NeRF: Uncertainty-aware Conditional Neural Radiance Fields from Endoscopic Sparse Views

arXiv

引用

arXiv 2024年

作者： Guo, Jiaxin Wang, Jiangliu Wei, Ruofeng Kang, Di Dou, Qi Liu, Yun-Hui CUHK T Stone Robotics Institute The Chinese University of Hong Kong Hong Kong The Department of Computer Science and Engineering The Chinese University of Hong Kong China Tencent AI Lab Shen Zhen China CUHK T Stone Robotics Institute The Chinese University of Hong Kong with Hong Kong Center for Logistics Robotics Hong Kong

Visualizing surgical scenes is crucial for revealing internal anatomical structures during minimally invasive procedures. Novel View Synthesis is a vital technique that offers geometry and appearance reconstruction, enhancing understanding, planning, and decision-making in surgical scenes. Despite the impressive achievements of Neural Radiance Field (NeRF), its direct application to surgical scenes produces unsatisfying results due to two challenges: endoscopic sparse views and significant photometric inconsistencies. In this paper, we propose uncertainty-aware conditional NeRF for novel view synthesis to tackle the severe shape-radiance ambiguity from sparse surgical views. The core of UC-NeRF is to incorporate the multi-view uncertainty estimation to condition the neural radiance field for modeling the severe photometric inconsistencies adaptively. Specifically, our UC-NeRF first builds a consistency learner in the form of multi-view stereo network, to establish the geometric correspondence from sparse views and generate uncertainty estimation and feature priors. In neural rendering, we design a base-adaptive NeRF network to exploit the uncertainty estimation for explicitly handling the photometric inconsistencies. Furthermore, an uncertainty-guided geometry distillation is employed to enhance geometry learning. Experiments on the SCARED and Hamlyn datasets demonstrate our superior performance in rendering appearance and geometry, consistently outperforming the current state-of-the-art approaches. Our code will be released at https://***/wrld/UC-NeRF. Copyright © 2024, The Authors. All rights reserved.

关键词： Endoscopy

来源：评论

学校读者我要写书评

暂无评论

Generalizable Audio Deepfake Detection via Latent Space Refinement and Augmentation

Generalizable Audio Deepfake Detection via Latent Space Refi...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wen Huang Yanmei Gu Zhiming Wang Huijia Zhu Yanmin Qian AI Institute Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Shanghai China SJTU Paris Elite Institute of Technology Ant Group Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Advances in speech synthesis technologies, like text-to-speech (TTS) and voice conversion (VC), have made detecting deepfake speech increasingly challenging. Spoofing countermeasures often struggle to generalize effectively, particularly when faced with unseen attacks. To address this, we propose a novel strategy that integrates Latent Space Refinement (LSR) and Latent Space Augmentation (LSA) to improve the generalization of deepfake detection systems. LSR introduces multiple learnable prototypes for the spoof class, refining the latent space to better capture the intricate variations within spoofed data. LSA further diversifies spoofed data representations by applying augmentation techniques directly in the latent space, enabling the model to learn a broader range of spoofing patterns. We evaluated our approach on four representative datasets, i.e. ASVspoof 2019 LA, ASVspoof 2021 LA and DF, and In-The-Wild. The results show that LSR and LSA perform well individually, and their integration achieves competitive results, matching or surpassing current state-of-the-art methods.

关键词： Deepfakes Refining Prototypes Data visualization Signal processing Robustness Data models Acoustics Text to speech

来源：评论

学校读者我要写书评

暂无评论

Robot Laughter: Does an appropriate laugh facilitate the robot’s humor performance?

Robot Laughter: Does an appropriate laugh facilitate the rob...

引用

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Heng Zhang Xiaoxuan Hei Junpei Zhong Adriana Tapus Computer Science and System Engineering Department (U2IS) Autonomous Systems and Robotics Lab ENSTA Paris Institut Polytechnique de Paris Paris France Department of Rehabilitation Sciences The Hong Kong Polytechnic University Hong Kong

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Laughter serves as a subtle social signal in human interaction, playing an essential role in expressing emotions and facilitating social connections. However, laughter comes in various forms and is usually accompanied by different non-verbal expressions, such as facial expressions and gestures. These accompanying factors can significantly influence the effect of laughter in diverse contexts, thus complicating the research on laughter, especially in understanding its role in social dynamics. Consequently, endowing robots with the ability to appropriately use laughter in interactions with humans is still a big challenge. Our current study focuses on the effect of robot laughter on robot humor expression. Our objective is to investigate whether and how two factors, the type of laughter and the robot laughter gesture, impact the overall humor performance. In this study, we selected four types of laughter (sarcastic, joyful, embarrassed, and relieved laughter) from a laughter corpus based on four specific types of jokes (Affiliative, Aggressive, Self-enhancing, and Self-defeating). For each type of laughter, we designed distinct robot gestures. During the humor performance, the robot NAO delivered jokes accompanied by matching or mismatching laughter, with or without corresponding gestures. To enhance the quantity and diversity of experimental data, we conducted an online survey utilizing recordings of the robot’s humor performance. The experimental findings indicate that when the robot’s laughter matches the type of humor in the joke, participants rate the humor performance significantly higher compared to situations where there is a mismatch. Additionally, the results confirm the positive impact of robot laughter gestures on humor performance.

关键词： Surveys Recording Robots

来源：评论

学校读者我要写书评

暂无评论

Surgical neural radiance fields from one image

引用

International journal of computer assisted radiology and surgery 2025年 1-5页

作者： Alberto Neri Maximilan Fehrentz Veronica Penza Leonardo S Mattos Nazim Haouchine Biomedical Robotics Lab Advanced Robotics Istituto Italiano di Tecnologia Genoa Italy. alberto.neri@iit.it. Department of Computer Science Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genoa Genova Italy. alberto.neri@iit.it. Harvard Medical School Brigham and Women's Hospital Boston USA. alberto.neri@iit.it. Technical University of Munich Munich Germany. Harvard Medical School Brigham and Women's Hospital Boston USA. Biomedical Robotics Lab Advanced Robotics Istituto Italiano di Tecnologia Genoa Italy.

PURPOSE:Neural radiance fields (NeRF) offer exceptional capabilities for 3D reconstruction and view synthesis, yet their reliance on extensive multi-view data limits their application in surgical intraoperative settings where only limited data are available. This work addresses this challenge by leveraging a single intraoperative image and preoperative data to train NeRF efficiently for surgical scenarios. METHODS:We leverage preoperative MRI data to define the set of camera viewpoints and images needed for robust and unobstructed training. Intraoperatively, the appearance of the surgical image is transferred to the pre-constructed training set through neural style transfer, specifically combining WTC and STROTSS to prevent over-stylization. This process enables the creation of a dataset for instant and fast single-image NeRF training. RESULTS:The method is evaluated with four clinical neurosurgical cases. Quantitative comparisons to NeRF models trained on real surgical microscope images demonstrate strong synthesis agreement, with similarity metrics indicating high reconstruction fidelity and stylistic alignment. When compared with ground truth, our method demonstrates high structural similarity, confirming good reconstruction quality and texture preservation. CONCLUSION:Our approach demonstrates the feasibility of single-image NeRF training in surgical settings, overcoming the limitations of traditional multi-view methods. By eliminating the dependency on a large multi-view dataset, our method offers a faster, more adaptable solution for generating accurate 3D reconstructions in real-time surgical scenarios.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Air Quality Forecasting in Bengaluru with Advanced Machine Learning Techniques

Revolutionizing Air Quality Forecasting in Bengaluru with Ad...

引用

Innovations in Communications, Electrical and computer Engineering (ICICEC), International Conference on

作者： A. Neeraja Ch. Mounika Begum K. Vigneswara Reddy T. V. Smitha N. Neelima Department of Computer Science Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Computational Science Lab Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Electronics and Communication Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

Exposure to air pollution can significantly affect one’s health, which can lead to itchy eyes, breathing issues, and hospitalisation. There are a variety of pollutants such as indoor, biological, and outdoor pollutants. This paper emphasizes estimating the quality of air concerning outdoor pollutants. This model aims to effectively predict the Air Quality Index (AQI) in major populated stations in Bengaluru city. Using data from five monitoring stations - Peenya, Bapuji Nagar, Silk Board, Hombegowda, and BTM Layout- collected between 2021 and 2023, machine learning models are developed to forecast AQI for 2024. The research involved data pre-processing, AQI calculation from key pollutants, and training of models for AQI prediction. Our findings indicate that the Random Forest algorithm outperformed the Decision Tree in both regression and classification tasks, providing more accurate predictions. Additionally, the study included a visual mapping of AQI categories using colour codes, revealing trends in air quality across different regions. This research serves as a foundation for future work, including expanding the model to predict AQI across India and integrating it with real-time data collection hardware to enhance prediction accuracy.

关键词： Training Visualization Technological innovation Accuracy Atmospheric modeling Urban areas Predictive models Data models Indexes Regression tree analysis

来源：评论

学校读者我要写书评

暂无评论

Optimised architecture design for an MEA power distribution system considering load profile and fault-tolerance

Optimised architecture design for an MEA power distribution ...

引用

International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC)

作者： Xin Wang Jason Atkin Seang Yeoh Serhiy Bozhko Department of Electrical and Electronic Engineering Faculty of Engineering University of Nottingham Nottingham UK Computational Optimisation and Learning Lab School of Computer Science University of Nottingham Nottingham UK

With the development of More Electric Aircraft (MEA), novel power distribution systems (PDSs) are required to meet significantly increased electrical load demands onboard. Nevertheless, newer designs must comply with the strict flight operational and safety requirements, while achieving optimisation aims, such as reducing aircraft weight and energy consumption. This paper proposes a two-stage optimisation design method for a novel PDS investigated for MEA. Two optimal power scheduling-based models for minimising power losses and PDS weight are formulated in Mixed-Integer Linear Programming (MILP). The first model considers varying load demands and is solved for all flight stages simultaneously in the first design stage for weight minimisation. The obtained architecture is further optimised for all faulty scenarios and selected typical fight stages simultaneously in the second design stage to improve the PDS reliability. Consequently, a low power loss, light weighted, and fault-tolerant PDS architecture is obtained. A case study is provided to exemplify the improvement in the PDS by adopting the proposed method.

关键词： Design methodology Atmospheric modeling Fault tolerant systems Redundancy Power distribution Transportation Reliability engineering

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：