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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

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Random Forest and Sobol Method Based data center PUE Prediction 12

Random Forest and Sobol Method Based Data Center PUE Predict...

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12th IEEE International Conference on Information and Communication Networks, ICICN 2024

作者： Yan, Longchuan Luan, Zhaofang He, Ling Jiang, Congfeng Liu, Junming Xu, Chuanqi Zhang, Liangbin State Grid Corporation of China Information & Telecommunication Branch Beijing China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Information Technology Center Hangzhou Dianzi University Hangzhou China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China

ISBN: (纸本)9798350355802

data centers are critical infrastructures that support a multitude of business loads such as big data, artificial intelligence, and cloud computing. While providing the necessary computing power, the problem of high energy consumption in data centers has become a major obstacle limiting their scale-up, reliability and quality of service. Improving the power usage effectiveness (PUE) of data centers is an important way to improve the energy efficiency of data centers. With the development of theories and methods of machine learning, modeling, analysis and prediction of data center energy consumption using machine learning techniques has become a feasible option. In this study, several accurate PUE prediction models are constructed using neural networks and machine learning techniques for the PUE of a small air-cooled data center in actual operation, among which the mean absolute error of the optimal model reaches 0.29%. In addition, this paper also uses Random Forest and Sobol methods to model and conduct sensitivity analyses of key factors affecting data center PUE, revealing the relationship between data center operating parameters and PUE, and finding that the cooling system exhibits different energy consumption patterns under different IT equipment power conditions, which has a decisive impact on PUE. Based on these sensitivity analyses, we argue that for small air-cooled data centers, improving internal insulation and humidity is an important strategy for optimizing PUE. © 2024 IEEE.

关键词： Sensitivity analysis

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Privacy-aware data Fusion and Prediction for Smart City Services in Edge Computing Environment 15

Privacy-aware Data Fusion and Prediction for Smart City Serv...

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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart data, Smartdata 2022

作者： Qi, Lianyong Chi, Xiaoxiao Zhou, Xiaokang Liu, Qi Dai, Fei Xu, Xiaolong Zhang, Xuyun School of Computer Science Qufu Normal University China Faculty of Data Science Shiga University Japan Riken Center for Advanced Intelligence Project Japan School of Computer and Software Nanjing University of Information Science and Technology China School of Big Data and Intelligence Engineering Southwest Forestry University China Department of Computing Macquarie University Australia

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

With fast development of Cyber Physical System, the variety and volume of data generated from different edge servers are fairly considerable. Mining and exploiting the data would definitely bring huge advantages. However, there are two major issues exposed in the process. First, utilizing the data with high efficiency is challenging in edge computing environment. Second, data from multiple edge servers often contain sensitive information such as user preferences, which will give rise to privacy breach risks when using the data. Locality-Sensitive Hashing and amplified technology are utilized for dealing with two problems. In this paper, we propose a novel privacy-protection approach named SRchain-LSH in edge computing environment which is based on amplified Locality-Sensitive Hashing technique. In order to verify high accuracy and efficiency of our proposal, large sets of experiments are conducted. According to experimental results, our proposal shows better performance than those of comparative methods in terms of MAE , RMSE and time costs. For our method, the best value of MAE is about 0.41 and time cost is about 0.1 seconds, which are more accurate and efficient than comparative methods. © 2022 IEEE.

关键词： Smart city

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LCGen: mining in low-certainty generation for view-consistent text-to-3D 24

LCGen: mining in low-certainty generation for view-consisten...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Zeng Tao Tong Yang Junxiong Lin Xinji Mai Haoran Wang Beining Wang Enyu Zhou Yan Wang Wenqiang Zhang Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China School of Computer Science Fudan University Shanghai China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China and Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai China

ISBN: (纸本)9798331314385

The Janus Problem is a common issue in SDS-based text-to-3D methods. Due to view encoding approach and 2D diffusion prior guidance, the 3D representation model tends to learn content with higher certainty from each perspective, leading to view inconsistency. In this work, we first model and analyze the problem, visualizing the specific causes of the Janus Problem, which are associated with discrete view encoding and shared priors in 2D lifting. Based on this, we further propose the LCGen method, which guides text-to-3D to obtain different priors with different certainty from various viewpoints, aiding in view-consistent generation. Experiments have proven that our LCGen method can be directly applied to different SDS-based text-to-3D methods, alleviating the Janus Problem without introducing additional information, increasing excessive training burden, or compromising the generation effect. Project page is https://***/zeng-tao/LCGen.

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DiLiGenRT: A Photometric Stereo dataset with Quantified Roughness and Translucency

DiLiGenRT: A Photometric Stereo Dataset with Quantified Roug...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Heng Guo Jieji Ren Feishi Wang Boxin Shi Mingjun Ren Yasuyuki Matsushita School of Artificial Intelligence Beijing University of Posts and Telecommunications School of Mechanical Engineering Shanghai Jiao Tong University National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University AI Innovation Center School of Computer Science Peking University Graduate School of Information Science and Technology Osaka University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Photometric stereo faces challenges from non-Lambertian reflectance in real-world scenarios. Systematically measuring the reliability of photometric stereo methods in handling such complex reflectance necessitates a real-world dataset with quantitatively controlled reflectances. This paper introduces DiLiGenRT, the first real-world dataset for evaluating photometric stereo methods under quantified reflectances by manufacturing 54 hemispheres with varying degrees of two reflectance properties: Roughness and Transluency, Unlike qualitative and semantic labels, such as “diffuse” and “specular,” that have been used in previous datasets, our quantified dataset allows comprehensive and systematic benchmark evaluations. In addition, it facilitates selecting best-fit photometric stereo methods based on the quantitative reflectance properties. Our dataset and benchmark results are available at https://***/***.

关键词： Reflectivity computer vision Systematics Semantics Benchmark testing Pattern recognition Manufacturing

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Zero-shot event-intensity asymmetric stereo via visual prompting from image domain 24

Zero-shot event-intensity asymmetric stereo via visual promp...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Hanyue Lou Jinxiu Liang Minggui Teng Bin Fan Yong Xu Boxin Shi State Key Laboratory of Multimedia Information Processing and National Engineering Research Center of Visual Technology School of Computer Science Peking University National Key Laboratory of General AI School of Intelligence Science and Technology Peking University School of Computer Science and Engineering South China University of Technology

ISBN: (纸本)9798331314385

Event-intensity asymmetric stereo systems have emerged as a promising approach for robust 3D perception in dynamic and challenging environments by integrating event cameras with frame-based sensors in different views. However, existing methods often suffer from overfitting and poor generalization due to limited dataset sizes and lack of scene diversity in the event domain. To address these issues, we propose a zero-shot framework that utilizes monocular depth estimation and stereo matching models pretrained on diverse image datasets. Our approach introduces a visual prompting technique to align the representations of frames and events, allowing the use of off-the-shelf stereo models without additional training. Furthermore, we introduce a monocular cue-guided disparity refinement module to improve robustness across static and dynamic regions by incorporating monocular depth information from foundation models. Extensive experiments on real-world datasets demonstrate the superior zero-shot evaluation performance and enhanced generalization ability of our method compared to existing approaches.

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Patch Target Guided Dual-Branch Deep Multiple Instance Learning for 3D MRI Analysis

Patch Target Guided Dual-Branch Deep Multiple Instance Learn...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Dai, Mingzhe Shi, Xiaoshuang Zhu, Xiaofeng Pan, Tingrui Li, Kang University of Science and Technology of China School of Software Engineering Hefei China West China Hospital of Sichuan University West China Biomedical Big Data Center Chengdu China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China Sichuan Artificial Intelligence Research Institute Yibin China University of Science and Technology of China Suzhou Institute for Advanced Research Suzhou China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (纸本)9798350386226

Deep multiple instance learning (MIL) has attracted considerable attention in medical image analysis, since it only requires image-level labels for model training without using fine-grained (or patch) annotations. Unfortunately, MIL-based methods might lose some significant patch features. Although pseudo-label-based methods, which assign a pre-defined label to each patch, can explore more patch-level features, they might bring label noise and make the patch-level features lose diversity, thereby possibly restricting the model performance. To overcome this issue, we propose a novel gradient-based patch target generation (PTG) module to dynamically produce a feature vector for each patch as its target. Additionally, based on the PTG module, we propose a patch-target guided dual-branch deep MIL framework for 3D MRI data analysis, where both the two branches consist of a CNN model to extract patch-level features, an attention module to interpret the significance of patches, and a bag-level classifier, while the second branch also contains the PTG module to generate patch targets of patches. Moreover, the two branches are alternatively updated in our framework, resulting in a bi-level optimization problem, and thus we design a bi-level optimization algorithm to solve our proposed objective function. Extensive experiments demonstrate the superior classification and interpretation performance of the proposed framework over recent state-of-the-art methods. Codes are available at https://***/daimz1213/mil2024. © 2024 IEEE.

关键词： Image annotation

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Providing a labeled statements dataset to enhance the trans-compilation-based tools 4

Providing a labeled statements dataset to enhance the trans-...

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4th Novel Intelligent and Leading Emerging sciences Conference, NILES 2022

作者： Soliman, Abdelrahman M. Radwan, Moataz-Bellah A. Eida, Hussain E. Mahmoud, Amira T. Mohammed, Youmna E. Asal, Walaa M. Yousef, Ahmed H. School of Information Technology and Computer Science Nile University Giza Egypt Center for Informatics Science Nile University Giza Egypt Ain Shams University Faculty of Engineering Cairo Egypt Banha University Faculty of Computer and AI Banha Egypt

ISBN: (数字)9781665452410

ISBN: (纸本)9781665452410

Nowadays Mobile Applications are a necessity as everyone is depending on them in their everyday tasks. We use them for communication, entertainment, and utilities. Every day new devices are introduced to the market. The diversity in these devices resulted in many platforms like Android and iOS. These different mobile platforms used by different companies and manufacturers made it challenging for mobile developersto build an application or a system that can be deployed on different platforms. Developers will be required to go through multiple development cycles to develop the application for each of the targeted platforms. To solve these issues, cross-platform frameworks emerged as a solution that would help the developers to write one codebase and deploy it on multiple platforms at the same time. Even though cross-platform frameworks reduced the time and cost needed to develop an application, but they did not provide the native feel and performance of the native applications. It was also challenging for developers to integrate cross-platform applications with a pre-existing system and to maintain the system. Some approaches were introduced to solve these issues by allowing the developer to write one codebase and generate the native code for each platform. One of the recently presented approaches is the trans-compilation approach that depends on the parser code to recognize the source code and generate the corresponding code for it in the target language. Even though the trans-compiler-based approaches were effective in providing an application that combines the advantages of the cross-platform and native development, it faced some problems in dealing with statements that is not expressed in the language's grammar rules, eternal APIs, and APIs that require code definition, like Firebase. In this paper, our aim is to provide a solution that can be integrated with the trans-compilation approach to solve the previously mentioned issues to enhance the conversion success

关键词： data mining

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Fast Sampling of Diffusion Models for Accelerated MRI using Dual Manifold Constraints

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Qiao, Lihong Wang, Rongxuan Shu, Yucheng Li, Baobin Li, Weisheng Gao, Xinbo Cai, Zhanchuan Chongqing University of Posts and Telecom munications Chongqing Big Data Collaborative Innovation Center Chongqing400065 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing101408 China Macau University of Science and Technology School of Computer Science and Engineering Macau China

Diffusion models show great potential in solving inverse problems, including MRI reconstruction. With its unique characteristics, medical imaging demands both efficiency and accuracy in the reconstruction process. However, existing MRI reconstruction methods based on diffusion models often fall short of fully leveraging the available measurements during sampling. Consequently, these methods suffer from compromised reconstruction quality and elevated bias, especially when dealing with large acceleration factors. In response to these challenges, we propose Dual Manifold Constraints (DMC), a fast MRI reconstruction method based on diffusion models. We treat the sampling process as a combination of denoising and adding noise processes, and we constrain these two processes using both pristine measurements and their noisy counterparts to adapt to the geometry of diffusion. It's worth noting that we propose a method to estimate the noisy measurement that satisfies the sub-sampling process to maintain the current data manifold when performing data consistency constraints. Experimental results show that our method outperforms the latest diffusion-based methods regarding both reconstruction speed and accuracy, and exhibits strong out-of-distribution generalization performance. © 1991-2012 IEEE.

关键词： Inverse problems

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FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning

arXiv

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arXiv 2023年

作者： Li, Anran Peng, Hongyi Zhang, Lan Huang, Jiahui Guo, Qing Yu, Han Liu, Yang The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Computer Science and Technology University of Science and Technology of China China The Center for Frontier AI Research A*STAR Singapore

Vertical Federated Learning (VFL) enables multiple data owners, each holding a different subset of features about largely overlapping sets of data sample(s), to jointly train a useful global model. Feature selection (FS) is important to VFL. It is still an open research problem as existing FS works designed for VFL either assumes prior knowledge on the number of noisy features or prior knowledge on the post-training threshold of useful features to be selected, making them unsuitable for practical applications. To bridge this gap, we propose the Federated Stochastic Dual-Gate based Feature Selection (FedSDG-FS) approach. It consists of a Gaussian stochastic dual-gate to efficiently approximate the probability of a feature being selected, with privacy protection through Partially Homomorphic Encryption without a trusted third-party. To reduce overhead, we propose a feature importance initialization method based on Gini impurity, which can accomplish its goals with only two parameter transmissions between the server and the clients. Extensive experiments on both synthetic and real-world datasets show that FedSDG-FS significantly outperforms existing approaches in terms of achieving accurate selection of high-quality features as well as building global models with improved performance. © 2023, CC BY.

关键词： Stochastic systems

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