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Navigation between initial and desired community states using shortcuts

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arXiv 2022年

作者： Blonder, Benjamin W. Lim, Michael H. Sunberg, Zachary Tomlin, Claire Department of Environmental Science Policy and Management University of California Berkeley BerkeleyCA United States Department of Electrical Engineering and Computer Science University of California Berkeley BerkeleyCA United States Aerospace Engineering Sciences Department University of Colorado Boulder BoulderCO United States

Ecological management problems often involve navigating from an initial to a desired community state. We ask whether navigation is possible without brute-force additions and deletions of species, using actions of varying costs: adding/deleting a small number of individuals of a species, changing the environment, and waiting. Navigation can yield direct paths (single sequence of actions) or shortcut paths (multiple sequences of actions with lower cost than a direct path). We ask (1) when is non-brute-force navigation possible?;(2) do shortcuts exist and what are their properties?;and (3) what heuristics predict shortcut existence? Using several empirical datasets, we show that (1) non-brute-force navigation is only possible between some state pairs, (2) shortcuts exist between many state pairs;and (3) changes in abundance and richness are the strongest predictors of shortcut existence, independent of dataset and algorithm choices. State diagrams thus unveil hidden strategies for efficiently shifting between states. © 2022, CC BY.

关键词： Navigation

Machine learning approaches and databases for prediction of drug-target interaction: a survey paper (vol 22, pg 247, 2021)

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BRIEFINGS IN BIOINFORMATICS 2021年第1期22卷 606-606页

作者： Bagherian, Maryam Sabeti, Elyas Wang, Kai Sartor, Maureen A. Nikolovska-Coleska, Zaneta Najarian, Kayvan Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI 48109 USA Michigan Institute for Data Science University of Michigan Ann Arbor MI 48109 USA Department of Biostatistics School of Public Health University of Michigan Ann Arbor MI 48109 USA Department of Pathology University of Michigan Ann Arbor MI 48109 USA Department of Emergency Medicine Medical School University of Michigan Ann Arbor MI 48109 USA Department of Electrical Engineering and Computer Science College of Engineering University of Michigan Ann Arbor MI 48109 USA

The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.

关键词： Machine learning drug–target interaction prediction DTI software DTI database

DOMINO: Domain-aware Loss for Deep Learning Calibration

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arXiv 2023年

作者： Stolte, Skylar E. Volle, Kyle Indahlastari, Aprinda Albizu, Alejandro Woods, Adam J. Brink, Kevin Hale, Matthew Fang, Ruogu United States Department of Mechanical and Aerospace Engineering Herbert Wertheim College of Engineering UF United States Center for Cognitive Aging and Memory McKnight Brain Institute UF United States Department of Clinical and Health Psychology College of Public Health and Health Professions UF United States Department of Neuroscience College of Medicine UF United States United States Air Force Research Laboratory Eglin Air Force Base Florida United States Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering UF United States

Deep learning has achieved the state-of-the-art performance across medical imaging tasks;however, model calibration is often not considered. Uncalibrated models are potentially dangerous in high-risk applications since the user does not know when they will fail. Therefore, this paper proposes a novel domain-aware loss function to calibrate deep learning models. The proposed loss function applies a class-wise penalty based on the similarity between classes within a given target domain. Thus, the approach improves the calibration while also ensuring that the model makes less risky errors even when incorrect. The code for this software is available at https://***/lab-smile/DOMINO. Copyright © 2023, The Authors. All rights reserved.

关键词： Medical imaging

GraphSAGE-Based Generative Adversarial Network for Short-Term Traffic Speed Prediction Problem

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GraphSAGE-Based Generative Adversarial Network for Short-Ter...

International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Han Zhao Ruikang Luo Bowen Yao Yiyi Wang Shaoqing Hu Rong Su School of Electrical and Electronic Engineering Nanyang Technological University Singapore Lee Luan Yew School of Public Policy National University of Singapore School of Media and Communication Coventry University School of Computer Science Beijing Universitiy of Posts and Telecommunications China

ISBN: (纸本)9781665476881

Traffic speed prediction is a significant branch of the intelligent transportation system (ITS). A good prediction could alleviate the non-recurring congestion on the road and provide a strong decision-making basis for traffic management and control. However, it is always a challenging research problem due to the complexity of the road network and the dynamics of traffic conditions. Many deep learning-based methods have been applied to the traffic prediction problem, which could extract both spatial and temporal information efficiently. However, for some dataset that suffers from data paucity problem, the generalization ability of the model is not good and the performance degrades. To tackle this problem, we proposed a novel graph-based generative adversarial network for the traffic speed prediction problem. We design a generative network to generate some fake traffic data and use a discriminative network to distinguish between real and fake targets. The generator consists of a GraphSAGE and LSTM model to learn the representation of spatial-temporal traffic data. Several experiments have been conducted on several real-world traffic datasets, demonstrating that our proposed model outperforms other baseline models. The experiment results illustrate the importance of utilizing GAN in the training process, which improves the generalization ability of the prediction model.

关键词： Training Roads Predictive models Generative adversarial networks Feature extraction Data models Generators

Can Personalized Medicine Coexist with Health Equity? Examining the Cost Barrier and Ethical Implications

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arXiv 2024年

作者： Francisco, Kishi Kobe Yee Apuhin, Andrane Estelle Carnicer Tan, Myles Joshua Toledo Byers, Mickael Cavanaugh Maravilla, Nicholle Mae Amor Tan Karim, Hezerul Abdul AlDahoul, Nouar Biology Program College of Arts and Science University of St. La Salle Negros Occidental Bacolod City Philippines Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL United States Department of Epidemiology College of Public Health & Health Professions College of Medicine University of Florida GainesvilleFL United States Department of Natural Sciences College of Arts and Science University of St. La Salle Negros Occidental Bacolod City Philippines Department of Chemical Engineering College of Engineering and Technology University of St. La Salle Negros Occidental Bacolod City Philippines Department of Electronics Engineering College of Engineering and Technology University of St. La Salle Negros Occidental Bacolod City Philippines Yo-Vivo Corporation Negros Occidental Bacolod City Philippines Department of Civil and Coastal Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL United States Faculty of Engineering Multimedia University Selangor Cyberjaya Malaysia Department of Computer Science Division of Science New York University Abu Dhabi Saadiyat Marina District Abu Dhabi United Arab Emirates

Personalized medicine (PM) promises to transform healthcare by providingtreatments tailored to individual genetic, environmental, and lifestylefactors. However, its high costs and infrastructure demands raise concernsabout exacerbating health disparities, especially between high-income countries(HICs) and low- and middle-income countries (LMICs). While HICs benefit fromadvanced PM applications through AI and genomics, LMICs often lack theresources necessary to adopt these innovations, leading to a wideninghealthcare divide. This paper explores the financial and ethical challenges ofPM implementation, with a focus on ensuring equitable access. It proposesstrategies for global collaboration, infrastructure development, and ethicalframeworks to support LMICs in adopting PM, aiming to prevent furtherdisparities in healthcare accessibility and outcomes. © 2024, CC BY.

关键词： Personalized medicine

Exploring the Structure of IoT Data: A Symbolic Analysis Perspective

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Shen, Yinghua Pedrycz, Witold Li, Wentao Xiao, Zhi Chen, Tianhua Hu, Xuan Chen, Yuan School of Economics and Business Administration Chongqing University Chongqing400044 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2G7 Canada College of Artificial Intelligence Southwest University Chongqing400715 China Department of Computer Science University of Huddersfield HuddersfieldHD1 3DH United Kingdom School of Public Policy and Administration Chongqing University Chongqing400044 China College of Management and Economics Tianjin University Tianjin300072 China

With the development of different kinds of techniques, especially the Internet of Things (IoT), a large amount of quantitative (either numeric or categorical) data have been generated, transmitted, and stored in the modern society. People hope to understand the interested phenomenon from the collected quantitative data by utilizing different data analysis methods. Exploring the structure of data (e.g., the cluster centers or prototypes) has always been a hot spot in the domain of data mining and knowledge discovery, yet it seems that the modeling and analyzing process still focus on a low-level abstraction of the data because normally, the structure found is only represented by some numeric data points. In this study, we highlight that a low-level abstraction may not be a user-friendly way for people to grasp the knowledge contained in the data. Instead, we explore the structure of the data from a perspective of symbolic analysis. Specifically, two modes of abstraction are proposed. In the vertical mode (i.e., values of each feature are abstracted), the numeric prototypes are characterized by the symbolic prototypes such that people could get rid of being stuck in minor details of each feature. In the horizontal mode (i.e., values of each prototype are abstracted), the linguistic summarization is used to describe all the features of each symbolic prototype such that people could immediately grasp the essential information conveyed in the symbolic prototype. We conduct comprehensive experimental studies on the publicly available data to illustrate the feasibility and validity of the proposed symbolic analysis process. © 2023 Yinghua Shen et al.

关键词： Internet of things

High-Resolution Hydrogen Molecule Detection Utilizing a Nanocandle-Based Optical Sensor

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High-Resolution Hydrogen Molecule Detection Utilizing a Nano...

Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Nu-Ri Park Min-Joong Kim Jongsu Lee Minah Seo Yong-Sang Ryu Myung-Ki Kim KU-KIST Graduate School of Converging Science and Technology Korea University Seoul Republic of Korea Sensor System Research Center Korea Institute of Science and Technology Seoul Republic of Korea Department of Electrical Engineering and Computer Science Northwestern University Evanston IL USA School of Biomedical Engineering Korea University Seoul Republic of Korea Interdisciplinary Program in Precision Public Health Korea University Seoul Republic of Korea

Our research introduces a novel method for safe hydrogen detection. We've developed an advanced nano-candle sensor, combining Pd with nano-candles, enabling accurate detection of low-concentration hydrogen (<3%... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Electric potential Accuracy Hydrogen Lasers and electrooptics Optical sensors Electro-optical waveguides

Evaluation Method of Ocean Healing Content on Stress Relieve Effects by Using Smart Watch and Questionnaire

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Journal of Coastal Research 2024年第SP1期116卷 493-497页

作者： Sunghoon Hong Hak Soo Lim Choong Gon Kim Jin Hyeok Park Min Gyu Kim Gi Young Chae Go Eun Sim Seon Woo Bak Ju Hwan Kim Oh Joon Kwon Coastal Disaster & Safety Research Department Korea Institute of Ocean Science and Technology Busan Republic of Korea Ocean Climate Response & Ecosystem Research Department Korea Institute of Ocean Science and Technology Busan Republic of Korea Ocean Policy Research Center Korea Institute of Ocean Science and Technology Busan Republic of Korea Department of Electrical Electronic and Computer Engineering University of Ulsan Ulsan Republic of Korea DM Studio Busan Republic of Korea

Hong, S.; Lim, H.S.; Kim, C.G.; Park, J.H.; Kim, M.G.; Chae, G.Y.; Sim, G.E.; Bak, S.W.; Kim, J.H., and Kwon, O.J., 2023. Evaluation method of ocean healing content on stress relieve effects by using smart watch and questionnaire. In: Lee, J.L.; Lee, H.; Min, B.I.; Chang, J.-I.; Cho, G.T.; Yoon, J.-S., and Lee, J. (eds.), Multidisciplinary Approaches to Coastal and Marine Management. Journal of Coastal Research, Special Issue No. 116, pp. 493-497. Charlotte (North Carolina), ISSN ***, research on ocean healing resources has been conducted widely to enhance physical and mental health conditions. Among the ocean healing resources, peaceful and beautiful marine sceneries are also regarded as natural resources that can relieve stress and body pain. In this paper, the positive effects of immersive ocean healing content on stress mitigation are investigated. The basic evaluation procedure with specific biometric data was also established. To investigate this effect, a realistic ocean healing content with stereoscopic visualization is developed and tested as a preliminary experiment. During the experiments, biometric data (blood pressure, heart rate, stress index) are acquired using a wearable Smart Watch (Galaxy Watch 5 Pro) before and after watching the ocean healing content. In addition, the questionnaire evaluation of variation of SRI (Stress Response Inventory) is included. The average stress index value decreased -15.3% after watching ocean healing content, whereas there were no significant changes in blood pressure and heart beat. In addition, the prominent enhancement of SRI value in all stress properties (tension, aggression, somatization, anger, depression, fatigue, frustration) was found. Even though the number of experiment participants was limited, this study presents the positive roles of ocean healing content that improves mental health promotion effects. Moreover, this ocean healing evaluation methodology can be utilized and reviewed as preli

关键词： immersive content marine scenery mental health Ocean healing wearable device linkage technology

Generalization in medical AI: a perspective on developing scalable models

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arXiv 2023年

作者： Behar, Joachim A. Levy, Jeremy Celi, Leo Anthony Faculty of Biomedical Engineering Technion Israel Institute of Technology Haifa Israel The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering Technion-IIT Haifa Israel Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States Department of Biostatistics Harvard TH Chan School of Public Health BostonMA United States Department of Medicine Beth Israel Deaconess Medical Center BostonMA United States

Over the past few years, research has witnessed the advancement of deep learning models trained on large datasets, some even encompassing millions of examples1,2. While these impressive performance on their hidden test sets, they often underperform when assessed on external datasets3–6. Recognizing the critical role of generalization in medical AI development, many prestigious journals now require reporting results both on the local hidden test set as well as on external datasets before considering a study for publication. Effectively, the field of medical AI has transitioned from the traditional usage of a single dataset that is split into train and test to a more comprehensive framework using multiple datasets, some of which are used for model development (source domain) and others for testing (target domains). However, this new experimental setting does not necessarily resolve the challenge of generalization. This is because of the variability encountered in intended use and specificities across hospital cultures making the idea of universally generalizable systems a myth7. Going even one step further Youssef et al.8 argued that patient data changes across time, geography, and facilities and thus these changes create significant volatility in the performance of a single fixed model. They thus motivate that that all models are local and that external validation should be replaced with recurring local validation. They thus argue for an MLOpsinspired paradigm of recurring local validation. On the other hand, the systematic, and a fortiori recurrent re-calibration, of models at the individual hospital level, although ideal, may be overoptimistic given the legal, regulatory and technical challenges that are involved. Re-calibration using transfer learning may not even be possible in some instances where reference labels of target domains are not available. In this perspective we establish a hierarchical three-level scale system reflecting the generalization level of a

关键词： Hospitals