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Digital Health (ICDH), IEEE International Conference on

作者： Shayan Fazeli Lionel Levine Mehrab Beikzadeh Baharan Mirzasoleiman Bita Zadeh Tara Peris Majid Sarrafzadeh Computer Science Department UCLA Los Angeles US Department of Psychology Chapman University Los Angeles US The Jane and Terry Semel Institute for Neuroscience and Human Behavior UCLA Los Angeles US

Recent advances in remote health monitoring systems have significantly benefited patients and played a crucial role in improving their quality of life. However, while physiological health-focused solutions have demonstrated increasing success and maturity, mental health-focused applications have seen comparatively limited success in spite of the fact that stress and anxiety disorders are among the most common issues people deal with in their daily lives. In the hopes of furthering progress in this domain through the development of a more robust analytic framework for the measurement of indicators of mental health, we propose a multi-modal semi-supervised framework for tracking physiological precursors of the stress response. Our methodology enables utilizing multi-modal data of differing domains and resolutions from wearable devices and leveraging them to map short-term episodes to semantically efficient embeddings for a given task. Additionally, we leverage an inter-modality contrastive objective, with the advantages of rendering our framework both modular and scalable. The focus on optimizing both local and global aspects of our embeddings via a hierarchical structure renders transferring knowledge and compatibility with other devices easier to achieve. In our pipeline, a task-specific pooling based on an attention mechanism, which estimates the contribution of each modality on an instance level, computes the final embeddings for observations. This additionally provides a thorough diagnostic insight into the data characteristics and highlights the importance of signals in the broader view of predicting episodes annotated per mental health status. We perform training experiments using a corpus of real-world data on perceived stress, and our results demonstrate the efficacy of the proposed approach in performance improvements.

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Self-supervised learning of a tailored Convolutional Auto Encoder for histopathological prostate grading 31

Self-supervised learning of a tailored Convolutional Auto En...

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Tabatabaei, Zahra Colomer, Adrián Engan, Kjersti Oliver, Javier Naranjo, Valery Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano Spain HUMAN-tech Universitat Politècnica de València Spain ValgrAI - Valencian Graduate School Research Network for Artificial Intelligence Spain Department of Electrical Engineering and Computer Science University of Stavanger Norway Department of Artificial Intelligence Tyris Tech S.L. Valencia Spain

ISBN: (纸本)9789464593600

According to GLOBOCAN 2020, prostate cancer is the second most common cancer in men worldwide and the fourth most prevalent cancer overall. For pathologists, grading prostate cancer is challenging, especially when discriminating between Grade 3 (G3) and Grade 4 (G4). This paper proposes a Self-Supervised Learning (SSL) framework to classify prostate histopathological images when labeled images are scarce. In particular, a tailored Convolutional Auto Encoder (CAE) is trained to reconstruct 128×128×3 patches of prostate cancer Whole Slide Images (WSIs) as a pretext task. The downstream task of the proposed SSL paradigm is the automatic grading of histopathological patches of prostate cancer. The presented framework reports promising results on the validation set, obtaining an overall accuracy of 83% and on the test set, achieving an overall accuracy value of 76% with F1-score of 77% in G4. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Grading

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kuo Guo Yifan Li Hao Chen Hong-Bin Shen Yang Yang Department of Computer Science and Engineering Shanghai Jiao Tong University and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Computational Biology Department School of Computer Science Carnegie Mellon University Pittsburgh PA USA

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function.

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Topological screen identifies hundreds of Cp190- and CTCF-dependent Drosophila chromatin insulator elements

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science Advances 2023年第5期9卷

作者： Kahn, Tatyana G. Savitsky, Mikhail Kuong, Chikuan Jacquier, Caroline Cavalli, Giacomo Chang, Jia-Ming Schwartz, Yuri B. Department of Molecular Biology Umeå University Umeå Sweden Department of Computer Science National Chengchi University Taipei City Taiwan Institute of Human Genetics UMR9002 CNRS Montpellier France

Drosophila insulators were the first DNA elements found to regulate gene expression by delimiting chromatin contacts. We still do not know how many of them exist and what impact they have on the Drosophila genome folding. Contrary to vertebrates, there is no evidence that fly insulators block cohesin-mediated chromatin loop extrusion. Therefore, their mechanism of action remains uncertain. To bridge these gaps, we mapped chromatin contacts in Drosophila cells lacking the key insulator proteins CTCF and Cp190. With this approach, we found hundreds of insulator elements. Their study indicates that Drosophila insulators play a minor role in the overall genome folding but affect chromatin contacts locally at many loci. Our observations argue that Cp190 promotes cobinding of other insulator proteins and that the model, where Drosophila insulators block chromatin contacts by forming loops, needs revision. Our insulator catalog provides an important resource to study mechanisms of genome folding. Copyright © 2023 The Authors.

关键词： Proteins

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Pre-training Multi-party Dialogue Models with Latent Discourse Inference

arXiv

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arXiv 2023年

作者： Li, Yiyang Huang, Xinting Bi, Wei Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China NLP Center Tencent AI-Lab Hong Kong

Multi-party dialogues are more difficult for models to understand than one-to-one two-party dialogues, since they involve multiple interlocutors, resulting in interweaving reply-to relations and information flows. To step over these obstacles, an effective way is to pre-train a model that understands the discourse structure of multi-party dialogues, namely, to whom each utterance is replying. However, due to the lack of explicitly annotated discourse labels in multi-party dialogue corpora, previous works fail to scale up the pre-training process by putting aside the unlabeled multi-party conversational data for nothing. To fully utilize the unlabeled data, we propose to treat the discourse structures as latent variables, then jointly infer them and pre-train the discourse-aware model by unsupervised latent variable inference methods. Experiments on multiple downstream tasks show that our pre-trained model outperforms strong baselines by large margins and achieves state-of-the-art (SOTA) results, justifying the effectiveness of our method. The official implementation of this paper is available at https://***/EricLee8/MPD_EMVI. © 2023, CC BY.

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Machine Learning for Measuring and Analyzing Online Social Communications 29

Machine Learning for Measuring and Analyzing Online Social C...

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29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021

作者： Bronk, Chris Lendasse, Amaury Lindner, Peggy Wallach, Dan S. Hammer, Barbara University of Houston Department of Information and Logistics Technology Houston United States Arcada University of Applied Sciences Risklab Helsinki Finland Rice University Department of Computer Science Houston United States Bielefeld University CITEC - Cognitive Interaction Technology Germany

ISBN: (纸本)9782875870827

In this paper, we propose a framework for application of a novel machine learning-based system for analyzing online social communications. As a example, we are targeting anti-Semitic graphical memes posted to social media. We presented very promising preliminary results on a Facebook dataset that consists of a total of 10000 labeled memes. We can conclude that machine learning will soon be able to successfully analyze and monitor complex social communications. © 2021 ESANN Intelligence and Machine Learning. All rights reserved.

关键词： Machine learning

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Isometric Vector Neuroanalyzer 2

Isometric Vector Neuroanalyzer

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2nd International Conference on Neural Networks and Neurotechnologies, NeuroNT 2021

作者： Safyannikov, Nikolay M. Bureneva, Olga I. Aleksanyan, Zoya A. Chepasov, Aleksandr E. Saint-Petersburg Electrotechnical University LETI Department of Computer Science and Engineering Saint Petersburg Russia Institute of the Human Brain Russian Academy of Sciences Saint Petersburg Russia

ISBN: (纸本)9781665445344

In the sphere of neurotechnology, hardware systems for obtaining and analyzing data on the features of central nervous system (CNS) functioning are of great importance. One of the directions is related to the development of methods and hardware for CNS state assessment by motor activity. The proposed report is about the isometric vector neuroanalyzer that allows estimating the functional state of the human central nervous system based on the analysis of involuntary movements of limbs occurring against the background of isometric efforts. The analyzer can be used for scientific research, in neurology, sports medicine, and in other areas where evaluation of the CNS condition is required. © 2021 IEEE.

关键词： Neurophysiology

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PaCaNet: A Study on CycleGAN with Transfer Learning for Diversifying Fused Chinese Painting and Calligraphy

arXiv

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arXiv 2023年

作者： Yang, Zuhao Xu, Yang Yuan, Yisheng Bai, Huajun Pang, Wei Hu, Yeqi Luo, Zhang Wang, Yue Yuan, Yingfang School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computer Science San Diego State University United States Graphic Design Department Rhode Island School of Design United States Seafog AI China School of Mathematical and Computer Sciences Heriot-Watt University United Kingdom College of Information Science and Engineering Ocean University of China China Graduate School of Advanced Integrated Studies in Human Survivability Kyoto University Japan

AI-Generated Content (AIGC) has recently gained a surge in popularity, powered by its high efficiency and consistency in production, and its capability of being customized and diversified. The cross-modality nature of the representation learning mechanism in most AIGC technology allows for more freedom and flexibility in exploring new types of art that would be impossible in the past. Inspired by the pictogram subset of Chinese characters, we proposed PaCaNet, a CycleGAN-based pipeline for producing novel artworks that fuse two different art types, traditional Chinese painting and calligraphy. In an effort to produce stable and diversified output, we adopted three main technical innovations: (1) Using one-shot learning to increase the creativity of pre-trained models and diversify the content of the fused images. (2) Controlling the preference over generated Chinese calligraphy by freezing randomly sampled parameters in pre-trained models. (3) Using a regularization method to encourage the models to produce images similar to Chinese paintings. Furthermore, we conducted a systematic study to explore the performance of PaCaNet in diversifying fused Chinese painting and calligraphy, which showed satisfying results. In conclusion, we provide a new direction of creating arts by fusing the visual information in paintings and the stroke features in Chinese calligraphy. Our approach creates a unique aesthetic experience rooted in the origination of Chinese hieroglyph characters. It is also a unique opportunity to delve deeper into traditional artwork and, in doing so, to create a meaningful impact on preserving and revitalizing traditional heritage. © 2023, CC BY-NC-SA.

关键词： Transfer learning

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Developing Digital Therapeutics: The University Health Network Experience

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Journal of Commercial Biotechnology 2022年第1期27卷 7-11页

作者： Pham, Quynh Cafazzo, Joseph The Centre for Global eHealth Innovation Techna Institute University Health Network Canada Healthcare Human Factors Techna Institute University of Toronto Institute of Health Policy Canada Dalla Lana School of Public Health University of Toronto Canada Telfer School of Management University of Ottawa Canada Institute of Biomedical Engineering University of Toronto Department of Computer Science Canada

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Coach, Data Analyst, and Protector: Exploring Data Practices of Collegiate Coaching Staff 25

Coach, Data Analyst, and Protector: Exploring Data Practices...

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2025 CHI Conference on human Factors in Computing Systems, CHI 2025

作者： Brewer, Mollie Childs, Kevin Thomas, Spencer Wilkins, Celeste Boyer, Kristy Elizabeth Nichols, Jennifer A. Butler, Kevin R.B. Beatty, Garrett F. Ferris, Daniel P. Department of Computer & Information Science & Engineering University of Florida Gainesville United States University Athletic Association GainesvilleFL United States J. Crayton Pruitt Family Dept. of Biomedical Engineering University of Florida GainesvilleFL United States College of Health & Human Performance University of Florida GainesvilleFL United States

ISBN: (纸本)9798400713941

A rapidly emerging research community at the intersection of sport and human-computer interaction (SportsHCI) explores how technology can support physically active humans, such as athletes. At highly competitive levels, coaching staff play a central role in the athlete experience by using data to enhance performance, reduce injuries, and foster team success. However, little is known about the practices and needs of these coaching staff. We conducted five focus groups with 17 collegiate coaching staff across three women's teams and two men's teams at an elite U.S. university. Our findings show that coaching staff selectively use data with the goal of balancing performance goals, athlete emotional well-being, and privacy. This paper contributes design recommendations to support coaching staff in operating across the data life cycle through gathering, sharing, deciding, acting, and assessing data as they aim to support team success and foster the well-being of student-athletes. © 2025 Copyright held by the owner/author(s).

关键词： SportsHCI

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