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2024 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2024

作者： Mahmud, M. Hasan, M. Yeasin, M. Bidelman, G. Univ. Of Tennessee Health Science Center Div. Of General Internal Medicine MemphisTN United States Middle Tennessee State University Computational And Data Science MurfreesboroTN United States University Of Memphis Department Of Electrical And Computer Engineering TN United States Indiana University Language And Hearing Sciences Department Of Speech BloomingtonIN United States

ISBN: (纸本)9798350388572

Categorical perception (CP) is a perceptual phenomenon that refers to the tendency of humans to group speech sounds into discrete units. In this work, we used cortical event-related potential (ERP) signals, recorded during a speech identification task, as input to a Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) and machine learning (ML) classifiers to examine how neural signatures of the brain distinguish prototypical vs. ambiguous speech token categories. Particularly, we used extreme gradient boosting (XGBoost), support vector machine, and random forest classifiers. Our analysis shows that using source reconstructed whole-brain data, the XGBoost classifier yielded the best classification accuracy of 94.12% with (area under the curve (AUC) 94.13%, Precision 94.00%, F1-score 94.00%) in the 197-258 ms time window after the stimulus onset. We also identified 15 important brain regions that distinguished vowel classes with accuracy of 90.28% (AUC 90.17%, F1-score 90.00%, Precision 90.00%), 4% less than whole-brain data. Remarkably, out of these 15 critical brain regions, nine were from the left hemisphere, consistent with the left-brain dominance for language processing. The best speech decoding was obtained from early portions of the ERP (~180-320 ms), which corroborates notions that sensory encoding is critical for successful phonetic processing. © 2024 IEEE.

关键词： Hidden Markov models

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Performance improvement by enhancing spatial parallelism on FPGA for HPC applications

Performance improvement by enhancing spatial parallelism on ...

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IEEE International Conference on Cluster Computing Workshops and Posters (CLUSTER WORKSHOPS)

作者： Yuka Sano Taisuke Boku Mitsuhisa Sato Miwako Tsuji Norihisa Fujita Ryohei Kobayashi Degree Programs in Systems and Information Engineering University of Tsukuba Japan Center for Computational Sciences University of Tsukuba Japan Faculty of Health Data Science Juntendo University Japan Center for Computational Science RIKEN Japan

In today’s HPC systems, GPUs with high computational performance and memory bandwidth under relatively low power consumption are the leading players. However, GPU-based acceleration is designed to excel when utilizing many computation cores and performing SIMD/STMD manner of synchronized computation over a large number of uniform data array elements. Therefore, it may not fully exploit its computational performance in calculations with low parallelism, complex operations involving conditional branching, or parallel applications with frequent inter-node communication to interrupt continuous computing on GPU devices. One of the alternative solutions for accelerated computing is FPGA (Field Programmable Gate Array), especially with recent advancements in devices containing a large number of logic elements, high memory bandwidth, and even multiple channels of high-speed optical interconnection interfaces, reaching up to 100 Gbps for each. The performance of an FPGA is based on pipeline parallelism, enabling the computation stream to continue even with conditional branches.

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CoVID-19 Vaccination Certificate Supply Verification Based on Blockchain 2021

CoVID-19 Vaccination Certificate Supply Verification Based o...

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2021 4th International Conference on Blockchain Technology and Applications

作者： Yash Madhwal Yury Yanovich Ilya Chumakov Center for Computational andData-Intensive Science and Engineering Skolkovo Institute of Science and technology Russia Liebherr Russia

ISBN: (纸本)9781450387460

The emergence of the pandemic, Covid-19, impacted the worldwide economy. The daily scientific development helped in creating a vaccine against the virus. In order to limit the spread of the virus, government authorities are making it mandatory for vaccination certificates for people to run businesses or access public facilities like theaters, restaurants, etc. The government mandate of vaccine certificates compelled people to purchase fake certificates and fake entities to provide fake certificates. This paper provides an application-based blockchain solution for registering vaccinating authorities to vaccinate and provides relevant vaccine certificates that anyone can verify in no time. Additionally, analysis of data retrieving from the blockchain is provided because the data reading will be happening by thousands of authorities. The code for the prototype application is available on Github.

关键词： Certificate Supply Smart Contract Blockchain Document Verification

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Improving the second-tier classification of methylmalonic acidemia patients using a machine learning ensemble method

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World Journal of Pediatrics 2024年第10期20卷 1090-1101页

作者： Zhi-Xing Zhu Georgi ZGenchev Yan-Min Wang Wei Ji Yong-Yong Ren Guo-Li Tian Sira Sriswasdi Hui Lu Shanghai Engineering Research Center for Big Datain Pediatric Precision Medicine Center for BiomedicalInformaticsShanghai Children’s HospitalSchoolof MedicineShanghai Jiao Tong UniversityShanghaiChina Center of Excellence in Computational Molecular Biology Faculty of MedicineChulalongkorn UniversityBangkokThailand Newborn Screening Center Shanghai Children’s HospitalSchool of MedicineShanghai Jiao Tong UniversityShanghaiChina SJTU-Yale Joint Center for Biostatistics and Data Science National Center for Translational MedicineShanghai JiaoTong UniversityShanghaiChina Center for Artifcial Intelligence in Medicine ResearchAfairsFaculty of MedicineChulalongkorn UniversityBangkokThailand State Key Laboratory of Microbial Metabolism JointInternational Research Laboratory of Metabolic&Developmental SciencesDepartment of Bioinformaticsand BiostatisticsSchool of Life Sciences and BiotechnologyShanghai Jiao Tong UniversityShanghaiChina

Introduction Methylmalonic acidemia(MMA)is a disorder of autosomal recessive inheritance,with an estimated prevalence of 1:50,***-tier clinical diagnostic tests often return many false positives[fve false positive(FP):one true positive(TP)].In this work,our goal was to refne a classifcation model that can minimize the number of false positives,currently an unmet need in the upstream diagnostics of *** We developed machine learning multivariable screening models for MMA with utility as a secondary-tier tool for false positives *** utilized mass spectrometry-based features consisting of 11 amino acids and 31 carnitines derived from dried blood samples of neonatal patients,followed by additional ratio feature *** selection strategies(selection by flter,recursive feature elimination,and learned vector quantization)were used to determine the input set for evaluating the performance of 14 classifcation models to identify a candidate model set for an ensemble model *** Our work identifed computational models that explore metabolic analytes to reduce the number of false positives without compromising *** best results[area under the receiver operating characteristic curve(AUROC)of 97%,sensitivity of 92%,and specifcity of 95%]were obtained utilizing an ensemble of the algorithms random forest,C5.0,sparse linear discriminant analysis,and autoencoder deep neural network stacked with the algorithm stochastic gradient boosting as the *** model achieved a good performance trade-of for a screening application with 6%false-positive rate(FPR)at 95%sensitivity,35%FPR at 99%sensitivity,and 39%FPR at 100%*** The classifcation results and approach of this research can be utilized by clinicians globally,to improve the overall discovery of MMA in pediatric *** improved method,when adjusted to 100%precision,can be used to further inform the diagnostic process journey of MMA and help r

关键词： Machine learning Methylmalonic acidemia Second-tier test Stacking

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Hybrid Deep Learning Framework for Classification of Kidney CT Images: Diagnosis of Stones, Cysts, and Tumors

arXiv

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arXiv 2025年

作者： Sharma, Kiran Uddin, Ziya Wadal, Adarsh Gupta, Dhruv School of Engineering & Technology BML Munjal University Gurugram Haryana-122413 India Center for Advanced Data and Computational Science BML Munjal University Gurugram Haryana-122413 India

Medical image classification is a vital research area that utilizes advanced computational techniques to improve disease diagnosis and treatment planning. Deep learning models, especially Convolutional Neural Networks (CNNs), have transformed this field by providing automated and precise analysis of complex medical images. This study introduces a hybrid deep learning model that integrates a pre-trained ResNet101 with a custom CNN to classify kidney CT images into four categories: normal, stone, cyst, and tumor. The proposed model leverages feature fusion to enhance classification accuracy, achieving 99.73% training accuracy and 100% testing accuracy. Using a dataset of 12,446 CT images and advanced feature mapping techniques, the hybrid CNN model outperforms standalone ResNet101. This architecture delivers a robust and efficient solution for automated kidney disease diagnosis, providing improved precision, recall, and reduced testing time, making it highly suitable for clinical applications. © 2025, CC BY.

关键词： Convolutional neural networks

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NC-ALG: Graph-Based Active Learning under Noisy Crowd 40

NC-ALG: Graph-Based Active Learning under Noisy Crowd

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Zhang, Wentao Wang, Yexin You, Zhenbang Li, Yang Cao, Gang Yang, Zhi Cui, Bin Center for Machine Learning Research Peking University China Key Lab of High Confidence Software Technologies Peking University China Institute of Advanced Algorithms Research Shanghai China Institute of Computational Social Science Peking University Qingdao China National Engineering Labratory for Big Data Analytics and Applications China TEG Tencent Inc. Department of Data Platform China Beijing Academy of Artificial Intelligence China

ISBN: (纸本)9798350317152

Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of existing GNN-based Active Learning (AL) methods, they assume that the annotated labels are always correct, which is contradictory to the error-prone labeling process in a practical crowdsourcing environment. Besides, due to this impractical assumption, existing works only focus on optimizing the node selection in AL but neglect optimizing the labeling process. Therefore, we present NC-ALG, the first GNN-based AL framework that optimizes both the node selection and node labeling process under a noisy crowd. For node selection, NC-ALG introduces a new measurement to model influence reliability and an effective influence maximization objective to select nodes. For node labeling, NC-ALG significantly reduces the labeling cost by considering the model-predicted labels and the labels of mirror nodes. To the best of our knowledge, this is the first attempt to consider GNN-based AL under the practical noisy crowd. Empirical studies on public datasets demonstrate that NC-ALG significantly outperforms existing methods in terms labeling efficiency. Notably, it only takes NC-ALG one-third of the labeling budget that the competitive baseline GRAIN needs to achieve an accuracy of 70.7 % on PubMed. © 2024 IEEE.

关键词： Crowdsourcing

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Detecting lithium plating dynamics in a solid-state battery with operando X-ray computed tomography using machine learning

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npj computational Materials 2023年第1期9卷 1402-1410页

作者： Ying Huang David Perlmutter Andrea Fei-Huei Su Jerome Quenum Pavel Shevchenko Dilworth Y.Parkinson Iryna V.Zenyuk Daniela Ushizima Department of Materials Science&Engineering University of California IrvineIrvineCaliforniaUSA National Fuel Cell Research Center University of California IrvineIrvineCaliforniaUSA Applied Math and Computational Research Division Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA Department of Chemical&Biomolecular Engineering University of California IrvineIrvineCaliforniaUSA Department of Electrical Engineering and Computer Sciences College of EngineeringUniversity of California BerkeleyBerkeleyCaliforniaUSA Advanced Photon Source Argonne National Laboratory9700 South Cass AvenueLemontIllinoisUSA Advanced Light Source Lawrence Berkeley National LaboratoryBerkeleyCaliforniaUSA Berkeley Institute for Data Science University of California BerkeleyBerkeleyCaliforniaUSA

Operando X-ray micro-computed tomography(µCT)provides an opportunity to observe the evolution of Li structures inside pouch *** is an essential step to quantitatively analyzingµCT datasets but is challenging to achieve on operando Li-metal battery datasets due to the low X-ray attenuation of the Li metal and the sheer size of the ***,we report a computational approach,batteryNET,to train an Iterative Residual U-Net-based network to detect Li *** resulting semantic segmentation shows singular Li-related component changes,addressing diverse morphologies in the *** addition,visualizations of the dead Li are provided,including calculations about the volume and effective thickness of electrodes,deposited Li,and redeposited *** also report discoveries about the spatial relationships between these *** approach focuses on a method for analyzing battery performance,which brings insight that significantly benefits future Li-metal battery design and a semantic segmentation transferrable to other datasets.

关键词： battery lithium state

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Beyond pixels:Learning from multimodal hyperspectral superpixels for land cover classification

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science China(Technological sciences) 2022年第4期65卷 802-808页

作者： HONG DanFeng WU Xin YAO Jing ZHU XiaoXiang Key Laboratory of Computational Optical Imaging Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China Remote Sensing Technology Institute German Aerospace Center(DLR)Wessling 82234Germany Data Science in Earth Observation Technical University of MunichMunich 80333Germany

Despite tons of advanced classification models that have recently been developed for the land cover mapping task,the monotonicity of a single remote sensing data source,such as only using hyperspectral data or multispectral data,hinders the classification accuracy from being further improved and tends to meet the performance *** this reason,we develop a novel superpixel-based subspace learning model,called Supace,by jointly learning multimodal feature representations from HS and MS superpixels for more accurate LCC *** can learn a common subspace across multimodal RS data,where the diverse and complementary information from different modalities can be better combined,being capable of enhancing the discriminative ability of to-be-learned features in a more effective *** better capture semantic information of objects in the feature learning process,superpixels that beyond pixels are regarded as the study object in our Supace for *** experiments have been conducted on two popular hyperspectral and multispectral datasets,demonstrating the superiority of the proposed Supace in the land cover classification task compared with several well-known baselines related to multimodal remote sensing image feature learning.

关键词： classification hyperspectral image land cover multimodal multispectral image remote sensing subspace learning superpixels

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

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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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Photoionization process of the hydrogenlike carbon ion embedded in warm and hot dense plasmas

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Physical Review E 2024年第5期109卷 055205-055205页

作者： S. M. Lu F. Y. Zhou L. Y. Xie Y. L. Ma G. P. Zhao X. Gao Y. Wu J. G. Wang Institute of Modern Physics National Key Laboratory of Computational Physics Key Laboratory of Atomic and Molecular Physics and Functional Materials of Gansu Province and College of Physics and Electronic Engineering College of Data Science School of Physics and Information Technology Center for Applied Physics and Technology College of Engineering

Complicated many-body interactions between ions and surrounding particles exist in warm and hot dense plasmas. It will significantly alter the atomic structures and dynamic properties of the embedded ions. Recently, the atomic-state-dependent (ASD) screening model has been proposed and shown to be valid for investigating the screening effect in warm and hot dense plasmas over a wide range of electron densities and temperatures. By employing the ASD model, we investigate the photoionization process for the hydrogenlike carbon ion embedded in warm and hot dense plasmas with corresponding Coulomb coupling parameter ranges of 0.05 ≤ Γ ≤ 1.16, where Γ characterizes the ratio of the average potential to thermal energy. It is found that there are stronger plasma screening effects on the ionization energy and photoionization cross section due to the negative-energy electron distributions considered in the ASD model compared to those considering only free electrons. The present results from the ASD model show reasonable agreement with the classical Debye-Hückel (DH) model in weakly coupled plasmas. However, significant deviations of the ionization energy and cross section between these two models are observed in moderately and strongly coupled plasmas, due to the approximate treatment of the plasma-electron density distribution of the DH model. In the region of low photoelectron energies, the positions of the shape resonance peaks of the cross sections obtained from the ASD model differ significantly from those of the DH model due to the different screening effects.

关键词： Atomic & molecular collisions Single- and few-photon ionization & excitation Hot dense plasma Ions

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