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4th Machine Learning for Health Symposium, ML4H 2024

作者： Zhu, Rui Yu, Jennifer Friend, Stephen H. Goodday, Sarah M. Wang, Bo Goldenberg, Anna Department of Computer Science University of Toronto Toronto Canada Hospital for Sick Children Toronto Canada Vector Institute Toronto Canada 4YouandMe SeattleWA United States Department of Psychiatry University of Oxford Oxford United Kingdom Department of Medical Biophysics University of Toronto Toronto Canada University Health Network Toronto Canada Department of Laboratory Medicine and Pathobiology University of Toronto Toronto Canada

Pregnancy is an intricate process involving substantial physiological changes that impact both maternal and fetal health. In this study, we demonstrate the ability to predict two common symptoms during the third trimester of pregnancy-edema and fatigue-using physiological measures from wearable devices and self-reported daily surveys. Our approach employs a Graph Neural Network (GNN) framework, enhanced with a modified weighted Cross-Entropy loss to improve prediction performance. The model achieved AUC scores of 77.27% for edema and 69.70% for fatigue. Additionally, we aligned data from self-reported pregnancy symptoms with clinical examinations to carefully select participant cohorts for our experiments. We also explored how various features identified by the GNN are linked to these symptoms, gaining deeper insights into the relationship between physiological measures and pregnancy symptoms. Our findings indicate that heart rate variability plays a significant role in predicting symptoms of edema and fatigue, and features related to low-intensity activity also have a notable impact. Some of our findings closely align with previous studies on pregnancy. Our research serves as a proof of concept that symptoms can be predicted using wearable data, which may enhance the immediate well-being of expectant mothers and potentially personalize the overall pregnancy experience. © 2024 R. Zhu, J. Yu, S.H. Friend, S.M. Goodday, B. Wang & A. Goldenberg.

关键词： Graph neural networks

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Critical (P5,dart)-Free Graphs

arXiv

引用

arXiv 2023年

作者： Xia, Wen Jooken, Jorik Goedgebeur, Jan Huang, Shenwei College of Computer Science Nankai University Tianjin300071 China Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin300071 China Department of Computer Science KU Leuven Campus Kulak-Kortrijk Kortrijk8500 Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent9000 Belgium

Given two graphs H1 and H2, a graph is (H1, H2)-free if it contains no induced subgraph isomorphic to H1 nor H2. A dart is the graph obtained from a diamond by adding a new vertex and making it adjacent to exactly one vertex with degree 3 in the diamond. In this paper, we show that there are finitely many k-vertex-critical (P5, dart)-free graphs for k ≥ 1. To prove these results, we use induction on k and perform a careful structural analysis via Strong Perfect Graph Theorem combined with the pigeonhole principle based on the properties of vertex-critical graphs. Moreover, for k ∈ {5, 6, 7} we characterize all k-vertex-critical (P5, dart)-free graphs using a computer generation algorithm. Our results imply the existence of a polynomial-time certifying algorithm to decide the k-colorability of (P5, dart)-free graphs for k ≥ 1 where the certificate is either a k-coloring or a (k+1)vertex-critical induced subgraph. © 2023, CC BY.

关键词： Graphic methods

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One algorithm for testing annulling of mixed trigonometric polynomial functions on boundary points 30

One algorithm for testing annulling of mixed trigonometric p...

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30th Telecommunications Forum, TELFOR 2022

作者： Malesevic, Branko Banjac, Bojan Sesum-Cavic, Vesna Korolija, Nenad University of Belgrade School of Electrical Engineering Department of Applied mathematics Belgrade Serbia University of Novi Sad Computer graphics chair Faculty of Technical sciences Novi Sad Serbia Digital innovation team Intens d.o.o. Novi Sad Serbia University of Belgrade Faculty of Civil Engineering Department for Geoinformatics Belgrade Serbia University of Belgrade School of Electrical Engineering Department for Computer science and information technology Belgrade Serbia

ISBN: (数字)9781665472739

ISBN: (纸本)9781665472739

This paper considers one problem of automated proving of MTP inequalities f(x) > 0 over [0, π/2] on boundary points. It suggests one algorithm for symbolic checking for f(0) = 0 or f(π/2) = 0 without use of numerical estimations. With stated approach it gives one contribution to theory and practice in this part of the field of automated proving. © 2022 IEEE.

关键词： Rational functions

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GBEC: Geometry-Based Hand-Eye Calibration

GBEC: Geometry-Based Hand-Eye Calibration

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yihao Liu Jiaming Zhang Zhangcong She Amir Kheradmand Mehran Armand Department of Computer Science Johns Hopkins University Baltimore MD USA Department of Mechanical Engineering Johns Hopkins University Baltimore MD USA Department of Neurology and Department of Neuroscience Johns Hopkins University Baltimore MD USA Department of Mechanical Engineering Department of Computer Science Department of Orthopaedic Surgery Applied Physics Laboratory Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Hand-eye calibration is the problem of solving the transformation from the end-effector of a robot to the sensor attached to it. Commonly employed techniques, such as AXXB or AXZB formulations, rely on regression methods that require collecting pose data from different robot configurations, which can produce low accuracy and repeatability. However, the derived transformation should solely depend on the geometry of the end-effector and the sensor attachment. We propose Geometry-Based End-Effector Calibration (GBEC) that enhances the repeatability and accuracy of the derived transformation compared to traditional hand-eye calibrations. To demonstrate improvements, we apply the approach to two different robot-assisted procedures: Transcranial Magnetic Stimulation (TMS) and femoroplasty. We also discuss the generalizability of GBEC for camera-in-hand and marker-in-hand sensor mounting methods. In the experiments, we perform GBEC between the robot end-effector and an optical tracker’s rigid body marker attached to the TMS coil or femoroplasty drill guide. Previous research documents low repeatability and accuracy of the conventional methods for robot-assisted TMS hand-eye calibration. Applying GBEC to repeated calibrations, we obtain transformations with standard deviations of 0.37mm, 0.65mm, and 0.40mm (translation) along x, y, and z axes of the end-effector, respectively. The tool alignment experiments after using GBEC achieve a mean accuracy around 0.2mm in Euclidean distance. When compared to some existing methods, the proposed method relies solely on the geometry of the flange and the pose of the rigid-body marker, making it independent of workspace constraints or robot accuracy, without sacrificing the orthogonality of the rotation matrix. Our results validate the accuracy and applicability of the approach, providing a new and generalizable methodology for obtaining the transformation from the end-effector to a sensor.

关键词： Geometry Accuracy Transcranial magnetic stimulation Stimulated emission Flanges Robot sensing systems End effectors

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Meibomian glands segmentation in infrared images with limited annotation

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International Journal of Ophthalmology(English edition) 2024年第3期17卷 401-407页

作者： Jia-Wen Lin Ling-Jie Lin Feng Lu Tai-Chen Lai Jing Zou Lin-Ling Guo Zhi-Ming Lin Li Li College of Computer and Data Science Fuzhou UniversityFuzhou 350108Fujian ProvinceChina Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou UniversityFuzhou 350108Fujian ProvinceChina Shengli Clinical Medical College of Fujian Medical University Fuzhou 350001Fujian ProvinceChina Department of Ophthalmology Fujian Provincial Hospital South BranchFujian Provincial HospitalFuzhou 350001Fujian ProvinceChina

●AIM:To investigate a pioneering framework for the segmentation of meibomian glands(MGs),using limited annotations to reduce the workload on ophthalmologists and enhance the efficiency of clinical diagnosis.●METHODS:Totally 203 infrared meibomian images from 138 patients with dry eye disease,accompanied by corresponding annotations,were gathered for the study.A rectified scribble-supervised gland segmentation(RSSGS)model,incorporating temporal ensemble prediction,uncertainty estimation,and a transformation equivariance constraint,was introduced to address constraints imposed by limited supervision information inherent in scribble *** viability and efficacy of the proposed model were assessed based on accuracy,intersection over union(IoU),and dice coefficient.●RESULTS:Using manual labels as the gold standard,RSSGS demonstrated outcomes with an accuracy of 93.54%,a dice coefficient of 78.02%,and an IoU of 64.18%.Notably,these performance metrics exceed the current weakly supervised state-of-the-art methods by 0.76%,2.06%,and 2.69%,***,despite achieving a substantial 80%reduction in annotation costs,it only lags behind fully annotated methods by 0.72%,1.51%,and 2.04%.●CONCLUSION:An innovative automatic segmentation model is developed for MGs in infrared eyelid images,using scribble annotation for *** model maintains an exceptionally high level of segmentation accuracy while substantially reducing training *** holds substantial utility for calculating clinical parameters,thereby greatly enhancing the diagnostic efficiency of ophthalmologists in evaluating meibomian gland dysfunction.

关键词： infrared meibomian glands images meibomian gland dysfunction meibomian glands segmentation weak supervision scribbled annotation

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Resolving Spurious Temporal Location Dependency for Video Corpus Moment Retrieval

Resolving Spurious Temporal Location Dependency for Video Co...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhang, Yishuo Zhang, Lanshan Zhang, Zhizhen Wang, Ziyi Xie, Xiaohui Wang, Wendong Beijing University of Posts and Telecommunications Beijing100876 China Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System and Network Culture Beijing100876 China Shenzhen International Graduate School Tsinghua University Shenzhen518057 China Tsinghua University Department of Computer Science and Technology Beijing100089 China

ISBN: (纸本)9781665410205

Video Corpus Moment Retrieval aims to retrieve the relevant video from a large corpus and localize the corresponding moment within the target video based on a specific query. Existing methods have achieved promising accuracy and efficiency through dedicated model designs. However, we suggest these methods overly exploit dataset biases instead of semantics. This will lead to exaggerated performances on biased datasets and implicit a significant deficiency in generalizability, which is an important metric yet not considered in existing studies. In this paper, we observe the degradation caused by a spurious dependency and design a model to mitigate this harm. Specifically, we generate an Out-Of-Distributed (OOD) test set from a widely used TV Retrieval dataset, revealing the existing models' erroneous dependency on the temporal locations of target moments. Therefore, we utilize a theoretical Structural Causal Model (SCM) to dig into the roots of this dependency by constructing causal paths for the models. Furthermore, we propose a concrete Clip Location Deconfounding Model (CLDM) to disentangle the confounded video features into the content part and the location confounder part, then produce results with causal intervention. Experiments show that CLDM significantly alleviates the impact brought by dataset biases thus providing advanced generalizability among existing works. © 2024 IEEE.

关键词： Video analysis

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Preserving Topological Feature with Sign-of-Determinant Predicates in Lossy Compression: A Case Study of Vector Field Critical Points 40

Preserving Topological Feature with Sign-of-Determinant Pred...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Xia, Mingze Di, Sheng Cappello, Franck Jiao, Pu Zhao, Kai Liu, Jinyang Wu, Xuan Liang, Xin Guo, Hanqi University of Kentucky Lexington United States Argonne National Laboratory Lemont United States Florida State University Tallahassee United States University of California Riverside Riverside United States The Ohio State University Columbus United States University of Kentucky Department of Computer Science LexingtonKY40506 United States

ISBN: (纸本)9798350317152

Lossy compression has been employed to reduce the unprecedented amount of data produced by today's large-scale scientific simulations and high-resolution instruments. To avoid loss of critical information, state-of-the-art scientific lossy compressors provide error controls on relatively simple metrics such as absolute error bound. However, preserving these metrics does not translate to the preservation of topological features, such as critical points in vector fields. To address this problem, we investigate how to effectively preserve the sign of determinant in error-controlled lossy compression, as it is an important quantity of interest used for the robust detection of many topological features. Our contribution is three-fold. (1) We develop a generic theory to derive the allowable perturbation for one row of a matrix while preserving its sign of the determinant. As a practical use-case, we apply this theory to preserve critical points in vector fields because critical point detection can be reduced to the result of the point-in-simplex test that purely relies on the sign of determinants. (2) We optimize this algorithm with a speculative compression scheme to allow for high compression ratios and efficiently parallelize it in distributed environments. (3) We perform solid experiments with real-world datasets, demonstrating that our method achieves up to 440% improvements in compression ratios over state-of-the-art lossy compressors when all critical points need to be preserved. Using the parallelization strategies, our method delivers up to 1.25 x and 4.38 x performance speedup in data writing and reading compared with the vanilla approach without compression. © 2024 IEEE.

关键词： Errors

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Performance Optimization for Vehicular Cooperative Sensing: A Graph Attention Based Reinforcement Learning Approach

Performance Optimization for Vehicular Cooperative Sensing: ...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Xuefei Chen, Mingzhe Liu, Danpu Zhang, Zhilong Quek, Tony Q. S. Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States Information Systems Technology and Design Pillar Singapore University of Technology and Design 487372 Singapore

ISBN: (纸本)9798350351255

In this paper, the problem of collaborative vehicle sensing is investigated. In the considered model, a set of cooperative vehicles provide sensing information to sensing request vehicles with limited sensing and communication resources. A base station (BS) determines the subset of sensing request vehicles that each cooperative vehicle will serve and the sub-regions that each cooperative vehicle will detect. We formulate an optimization problem aiming to maximize the number of successfully detected sub-regions of sensing request vehicles while satisfying the cooperative sensing energy requirement by jointly determining the cooperative vehicle association and the sensing sub-region selection. To solve this problem, we propose a graph attention based reinforcement learning (RL) algorithm that can generate the graph information vectors based on the correlation between each cooperative vehicle and each sensing request vehicle. Using the learned graph information, the joint cooperative vehicle association and sensing sub-region selection strategy will be determined. Simulation results show that the proposed scheme can improve the number of successfully detected sub-regions of sensing request vehicles by up to 12.5% compared to the conventional RL algorithm without using graph attention networks (GANs). © 2024 IEEE.

关键词： Reinforcement learning

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Discovery of association rules between factors affecting user satisfaction in software project by FP-GROWTH Algorithm 18

Discovery of association rules between factors affecting use...

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18th International Conference on Electrical Engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2021

作者： Kaewbanjong, Katawut Intakosum, Sarun King Mongkut's Institute of Technology Ladkrabang Software System Engineering Laboratory Department of Computer Science Faculty of Science Bangkok Thailand

ISBN: (纸本)9780738111278

The main objective of this work was to find the association rules between factors affecting user satisfaction in software project by using an association rule discovery technique. Data from 191 software projects were collected and association rules between 15 Selected factors were discovered by FP-growth algorithm. Primarily, 281 association rules were discovered. Rules that were not directly related to user satisfaction were filtered out by simple criteria. In the end, 11 final association rules that passed those criteria were obtained with an average confidence value of 65.30%. These rules can incorporated in the planning and administration of a software project to gain utmost user satisfaction © 2021 IEEE.

关键词： Association rules

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Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms

arXiv

引用

arXiv 2023年

作者： Lau, Tim Tsz-Kit Liu, Han Pock, Thomas Department of Statistics and Data Science Northwestern University EvanstonIL60208 United States The University of Chicago Booth School of Business ChicagoIL60637 United States Department of Computer Science Northwestern University EvanstonIL60208 United States Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria

We study the problem of approximate sampling from non-log-concave distributions, e.g., Gaussian mixtures, which is often challenging even in low dimensions due to their multimodality. We focus on performing this task via Markov chain Monte Carlo (MCMC) methods derived from discretizations of the overdamped Langevin diffusions, which are commonly known as Langevin Monte Carlo algorithms. Furthermore, we are also interested in two nonsmooth cases for which a large class of proximal MCMC methods have been developed: (i) a nonsmooth prior is considered with a Gaussian mixture likelihood;(ii) a Laplacian mixture distribution. Such nonsmooth and non-log-concave sampling tasks arise from a wide range of applications to Bayesian inference and imaging inverse problems such as image deconvolution. We perform numerical simulations to compare the performance of most commonly used Langevin Monte Carlo algorithms. © 2023, CC BY.

关键词： Inverse problems

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