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8th International Building Physics Conference, IBPC 2021

作者： Hou, Tianfeng Roels, Staf Janssen, Hans KU Leuven Department of Civil Engineering Building Physics Section Leuven3001 Belgium Data Assimilation Research Team RIKEN Center for Computational Science Kobe Japan RIKEN Prediction Science Laboratory Kobe Japan RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program Kobe Japan

The capability of the proper orthogonal decomposition for the simulation of heat transfer in building components is investigated via three applications: linear heat transfer (not coupled to mass transfer), mildly non-linear heat transfer (coupled to air and moisture transfer (hygroscopic)) and highly non-linear heat transfer (coupled to moisture transfer (capillary)). It is shown that increasing non-linearity leads to an increasing number of required construction modes. To further investigate the reason for this degrading performance of POD, the singular values' decay progress from the different training snapshots is addressed in this paper. The results confirm that a fast decay of the singular values implies a high interrelation of the snapshots and a better performance of the POD method. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Heat transfer

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Personalized Stress Detection using a Lightweight Machine Learning Framework With Convenient Wrist-Worn Sensors

Personalized Stress Detection using a Lightweight Machine Le...

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IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

作者： M. Hasan S. Mithun M. Mahmud S. Baral K. Poudel Computational and Data Science Middle Tennessee State University Murfreesboro TN USA Dept. of Computer Science University of Memphis TN USA Dept. of Electrical and Computer Engineering University of Memphis TN USA Div. of General Internal Medicine Univ. of Tennessee Health Science Center Memphis TN USA Dept. of Computer Science Middle Tennessee State University Murfreesboro TN USA

ISBN: (数字)9798350388572

ISBN: (纸本)9798350388589

Over 70% Americans experience daily stress, making real-time, accurate stress detection crucial for timely intervention, and promoting physical and mental well-being. In this study, we detect stress using machine learning from publicly available wrist-worn sensor data. We examined both population and individual levels, focusing on simple, computation friendly statistical features for reduced processing to facilitate adoptions in real-life mHealth applications. Our framework demonstrates that stress detection achieves over 96% accuracy at both the population and individual levels, with the Extreme Gradient Boosting classifier achieving the highest predictive accuracy. We found significant variability between individuals and achieved an average accuracy of 99.63% with area under the curve (AUC) 99.23%, F1-score 99.00%, precision, and recall 99.22%. This approach offers a promising solution for real-life stress detection due to its high accuracy and simplicity.

关键词： Analytical models Accuracy Focusing Human factors Signal processing Predictive models Feature extraction Boosting Real-time systems Biology

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Investigation of High-Precision Laser Instrument for Distance and Displacement Measurements

Investigation of High-Precision Laser Instrument for Distanc...

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Laser science, LS 2021 - Part of Frontiers in Optics, FiO 2021

作者： Minin, Iurii Bulatov, Igor Korobov, Nikita Dubrov, Mstislav Fedorov, Maxim Skoltech Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Bolshoy Boulevard 30 bld.1 Moscow121205 Russia Laboratory of Artificial Media and Structures Fryazino Branch of Kotel'nikov Institute of Radio-Engineering and Electronics of RAS Vvedenskiy sq. 1 Moscow Region Fryazino141190 Russia Phystech School of Radio Engineering and Computer Technology Moscow Institute of Physics and Technology Institutskiy per. 9 Dolgoprudny Moscow Region 141701 Russia

The novel high-precision measurement method was proposed. The considered method possesses laser-interferometric precision, does not require to move any external mirrors over full measuring basis, and can be applied to... 详细信息

ISBN: (纸本)9781557528209

关键词： Laser interferometry

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BIM: Improving Graph Neural Networks with Balanced Influence Maximization

BIM: Improving Graph Neural Networks with Balanced Influence...

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International Conference on data engineering

作者： Wentao Zhang Xinyi Gao Ling Yang Meng Cao Ping Huang Jiulong Shan Hongzhi Yin Bin Cui Center for Machine Learning Research Peking University Institute of Advanced Algorithms Research Shanghai National Engineering Labratory for Big Data Analytics and Applications The University of Queensland Australia Key Lab of High Confidence Software Technologies Peking University Apple Inc. Institute of Computational Social Science Peking University Qingdao

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The imbalanced data classification problem has aroused lots of concerns from both academia and industry since data imbalance is a widespread phenomenon in many real-world scenarios. Although this problem has been well researched from the view of imbalanced class samples, we further argue that graph neural networks (GNNs) expose a unique source of imbalance from the influenced nodes of different classes of labeled nodes, i.e., labeled nodes are imbalanced in terms of the number of nodes they influenced during the influence propagation in GNNs. To tackle this previously unexplored influence-imbalance issue, we connect social influence maximization with the imbalanced node classification problem and propose balanced influence maximization (BIM). Specifically, BIM greedily assigns the pseudo label to the node which can maximize the number of influenced nodes in GNN training while making the influence of each class more balance. Experimental results on five public datasets demonstrate the effectiveness of our method in relieving the influence-imbalance issue. For example, when training a GCN with an imbalance ratio of 0.1, BIM significantly outperforms the most competitive baseline by 0.6% -9.8% in five public datasets in terms of the F1 score.

关键词： Training Industries data engineering Graph neural networks

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Key Research Areas for Building and Deploying a Common data Model for an intensive Medicine data Space in Europe and Beyond

Key Research Areas for Building and Deploying a Common Data ...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Parra-Calderón, Carlos Luis Rodríguez Mejías, Silvia Colpaert, Kirsten Balzer, Felix Delange, Boris Cuggia, Marc Delamarre, Denis Daniel, Christel Moinat, Maxim van Den Brand, Jan van Genderen, Michel E. Fleureng, Lucas Jungh, Christian Álvarez-Romero, Celia Computational Health Informatics Group Institute of Biomedicine of Seville IBIS Virgen del Rocío University Hospital CSIC University of Seville Spain Data Science Institute Ghent University Hospital Ghent Belgium Institute of Medical Informatics Charité - Universitätsmedizin Berlin Freie Universität Berlin Humboldt-Universität zu Berlin Germany CHU Rennes INSERM LTSI-UMR 1099 Univ Rennes Rennes35000 France Assistance Publique Hôpitaux de Paris Paris France Sorbonne Université Inserm Université Sorbonne Paris Nord LIMICS Paris France Erasmus MC University Medical Center Department of Adult Intensive Care Rotterdam Netherlands Dusseldorf Germany

ISBN: (纸本)9781643685335

Key Research Areas (KRAs) were identified to establish a semantic interoperability framework for intensive medicine data in Europe. These include assessing common data model value, ensuring smooth data interoperability, supporting data standardization for efficient dataset use, and defining anonymization requirements to balance data protection and innovation. © 2024 The Authors.

关键词： Artificial Intelligence Clinical Decision Support Common data Model data Space intensive Care Interoperability Standardization

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Kalman Filtering for Accurate and Fast Plant Growth Dynamics Assessment

Kalman Filtering for Accurate and Fast Plant Growth Dynamics...

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IEEE Instrumentation and Measurement Technology Conference

作者： Dmitrii Shadrin Tatiana Podladchikova George Ovchinnikov Artem Pavlov Mariia Pukalchik Andrey Somov Center for Computational and Data-Intensive Science and Engineering (CDISE) Space Center Skolkovo Institute of Science and Technology (SkolTech) Moscow Russia

ISBN: (数字)9781728144603

ISBN: (纸本)9781728144610

Artificial growth systems are the essential part of the precision agriculture. It allows solving many problems associated with the growing demand in the environmental friendly food production in the context of increasing world population. Accurate and reliable assessment of plant growth dynamics parameters is crucial for the future success of the whole growing system parameters optimization. In this research, we report on the implementation of the extended Kalman filtering method for insitu evaluation of plant growth dynamics parameters. We show the reliability and benefits of the proposed approach on the simulated and experimental data obtained from the IoT-based testbed. We demonstrate that our method serves as a robust and computationally cost-effective tool for the accurate assessment of the growing dynamics that, in turn, could be used for the further optimization of the whole plant cultivation process in artificial conditions.

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Deformable Phrase Level Attention: A Flexible Approach for Improving Ai Based Medical Coding

SSRN

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SSRN 2024年

作者： Metzner, Christoph Simon Gao, Shang Herrmannova, Drahomira Gounley, John Hanson, Heidi A. The Bredesen Center The University of Tennessee KnoxvilleTN37996 United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States TR Product Thomson Reuters EaganMN55123 United States Data Science Health Elsevier PhiladelphiaPA19103 United States

Objective: Improving the AI-driven automated medical encoding of clinical text plays a vital role in gathering information on the occurrence of diseases to improve population-level health. This work presents a novel attention mechanism designed to enhance text classification models and ensure appropriate classification of medical concepts in unstructured electronic health records. Materials and Methods: We developed a deformable, phrase-level attention mechanism to identify important lexical word-level and contextual phrase-level information from clinical text documents. We evaluated conventional and transformer-based deep learning models that we extended with our attention mechanism on the extraction of critical cancer information (e.g., site, subsite, laterality, histology, behavior) from 629,908 electronic pathology reports and on the automated medical encoding of 52,722 hospital discharge summaries. Results: Transformer-based models with the deformable, phrase-level attention mechanism achieved the best performance on the extraction of critical cancer information from pathology reports. Conventional- and transformer-based models show similar or better performance than their baseline counterparts on the automated medical encoding of clinical documents. Discussion: The addition of phrase-level information allowed models extended with our proposed method to outperform standard word-level attention. Our method showed favorable properties for the real-world application in terms of model robustness and phenotyping. These results indicate that our method is promising for automated data harmonization for common data models. Conclusion: This work proposes a novel deformable, phrase-level attention mechanism that enhances text classification models in the extraction of medical concepts from clinical text documents. We demonstrate strong performances on two clinical text datasets and showcase real-world deployability of our method. © 2024, The Authors. All rights reserved.

关键词： Diseases

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Quantifying gender imbalance in East Asian academia: Research career and citation practice

arXiv

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arXiv 2023年

作者： Nakajima, Kazuki Liu, Ruodan Shudo, Kazuyuki Masuda, Naoki Department of Mathematical and Computing Science Tokyo Institute of Technology Meguro-ku Tokyo152-8552 Japan Department of Mathematics State University of New York at Buffalo Buffalo14260 United States Academic Center for Computing and Media Studies Kyoto University Sakyo-ku Kyoto606-8501 Japan Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo14260 United States Center for Computational Social Science Kobe University Kobe657-8501 Japan

Gender imbalance in academia has been confirmed in terms of a variety of indicators, and its magnitude often varies from country to country. Europe and North America, which cover a large fraction of research workforce in the world, have been the main geographical regions for research on gender imbalance in academia. However, the academia in East Asia, which accounts for a substantial fraction of research, may be exposed to strong gender imbalance because Asia has been facing persistent and stronger gender imbalance in society at large than Europe and North America. Here we use publication data between 1950 and 2020 to analyze gender imbalance in academia in China, Japan, and South Korea in terms of the number of researchers, their career, and citation practice. We found that, compared to the average of the other countries, gender imbalance is larger in these three East Asian countries in terms of the number of researchers and their citation practice and additionally in Japan in terms of research career. Moreover, we found that Japan has been exposed to the larger gender imbalance than China and South Korea in terms of research career and citation practice. Copyright © 2023, The Authors. All rights reserved.

关键词： Geographical regions

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Clinical Analogy Resolution Performance for Foundation Language Models

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ACM Transactions on Computing for Healthcare 2025年第2期6卷

作者： Villena, Fabián Quiroga, Tamara Dunstan, Jocelyn Department of Computer Science University of Chile Santiago Chile Millennium Institute for Foundation Research on Data Santiago Chile Center for Mathematical Modeling University of Chile Santiago Chile Department of Computer Science Pontificia Universidad Catolica de Chile Santiago Chile Institute for Mathematical and Computational Engineering Pontificia Universidad Catolica de Chile Santiago Chile

Using extensive data sources to create foundation language models has revolutionized the performance of deep learning-based architectures. This remarkable improvement has led to state-of-the-art results for various downstream NLP tasks, including clinical tasks. However, more research is needed to measure model performance intrinsically, especially in the clinical domain. We revisit the use of analogy questions as an effective method to measure the intrinsic performance of language models for the clinical domain in English. We tested multiple Transformer-based language models s over analogy questions constructed from the Unified Medical Language System (UMLS), a massive knowledge graph of clinical concepts. Our results show that large language models are significantly more performant for analogy resolution than small language models. Similarly, domain-specific language models perform better than general domain language models. We also found a correlation between intrinsic and extrinsic performance, validated through PubMedQA extrinsic task. Creating clinical-specific and language-specific language models is essential for advancing biomedical and clinical NLP and will ensure a valid application in clinical practice. Finally, given that our proposed intrinsic test is based on a term graph available in multiple languages, the dataset can be built to measure the performance of models in languages other than English. © 2025 Copyright held by the owner/author(s).

关键词： Foundation Models Clinical NLP Intrinsic Tests

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Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clustering

arXiv

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arXiv 2023年

作者： Dong, Yijun Miller, Kevin Lei, Qi Ward, Rachel Courant Institute of Mathematical Sciences New York University New YorkNY United States Oden Institute for Computational Engineering & Science University of Texas at Austin AustinTX United States Courant Institute of Mathematical Sciences Center of Data Science New York University New YorkNY United States

Despite the empirical success and practical significance of (relational) knowledge distillation that matches (the relations of) features between teacher and student models, the corresponding theoretical interpretations remain limited for various knowledge distillation paradigms. In this work, we take an initial step toward a theoretical understanding of relational knowledge distillation (RKD), with a focus on semi-supervised classification problems. We start by casting RKD as spectral clustering on a population-induced graph unveiled by a teacher model. Via a notion of clustering error that quantifies the discrepancy between the predicted and ground truth clusterings, we illustrate that RKD over the population provably leads to low clustering error. Moreover, we provide a sample complexity bound for RKD with limited unlabeled samples. For semi-supervised learning, we further demonstrate the label efficiency of RKD through a general framework of cluster-aware semi-supervised learning that assumes low clustering errors. Finally, by unifying data augmentation consistency regularization into this cluster-aware framework, we show that despite the common effect of learning accurate clusterings, RKD facilitates a "global" perspective through spectral clustering, whereas consistency regularization focuses on a "local" perspective via expansion. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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