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arXiv 2022年

作者： Grisci, Bruno Iochins Kuhn, Gabriela Colombelli, Felipe Matter, Vítor Kehl Lima, Leomar Heinen, Karine Pegoraro, Mauricio Borges, Marcio Rigo, Sandro José Barbosa, Jorge Luis Victória da Rosa Righi, Rodrigo da Costa, Cristiano André de Oliveira Ramos, Gabriel Software Innovation Laboratory - Softwarelab Graduate Program in Applied Computing Universidade do Vale do Rio dos Sinos Av. Unisinos 950 RS São Leopoldo93022-750 Brazil Institute of Informatics Federal University of Rio Grande do Sul Av. Bento Gonçalves 9500 RS Porto Alegre91501-970 Brazil Dell Technologies Brazil Av. Industrial Belgraf 400 Eldorado do Sul RS 92990-000 Brazil

Nowadays, data has become an invaluable asset to entities and companies, and keeping it secure represents a major challenge. Data centers are responsible for storing data provided by software applications. Nevertheless, the number of vulnerabilities has been increasing every day. Managing such vulnerabilities is essential for building a reliable and secure network environment. Releasing patches to fix security flaws in software is a common practice to handle these vulnerabilities. However, prioritization becomes crucial for organizations with an increasing number of vulnerabilities since time and resources to fix them are usually limited. This review intends to present a survey of vulnerability ranking techniques and promote a discussion on how multi-objective optimization could benefit the management of vulnerabilities risk prioritization. The state-of-the-art approaches for risk prioritization were reviewed, intending to develop an effective model for ranking vulnerabilities in data centers. The main contribution of this work is to point out multi-objective optimization as a not commonly explored but promising strategy to prioritize vulnerabilities, enabling better time management and increasing security. Copyright © 2022, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Entropy and Information Gain Analysis on Low Cost BCI for Motorbike Users to Prevent Accident

Entropy and Information Gain Analysis on Low Cost BCI for Mo...

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International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Rolly Maulana Awangga Syafrial Fachri Pane Dinda Majesty Asep Setiawan Moch Yusuf Asyhari Applied Bachelor Program of Informatics Engineering Politeknik Pos Indonesia Bandung Indonesia Magister Program of Informatics Engineering Universitas Islam Indonesia Yogyakarta Indonesia

ISBN: (数字)9781728151182

ISBN: (纸本)9781728151199

The phenomenon of mothers giving the wrong vehicle signal lights to turn left and right reminds the effect of giving the right signal lights to other road users. Weak human concentration while driving can cause errors when giving vehicle signal lights or not giving vehicle signal lights. Mistakes when giving a signal light a vehicle has a risk of an accident. The driver's brain wave data will show the results of the rider's brain thinking when turning. This study aims to analyze the concentration of the driver when driving. This research will obtain driver brain wave data when driving and analyze human brain waves while riding a motorcycle, especially when turning left and right, to reduce the risk of accidents while driving. The driver will use brain waves tool while driving, and the computer will detect the driver's brain waves, and then send brainwave data to the computer. Driver brain waves are analyzed using the formula entropy and gain. The results of the driver's brain waves become a decision whether the driver is allowed to drive on the highway based on the detected waves (alpha, beta, gamma, theta, and delta) to reduce the chance of an accident.

关键词： Electroencephalography Entropy Turning Tools Calibration Motorcycles

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Thermal Correction of Eu 3+ doped Na 2 O-Al 2 O 3 -P 2 O 5 Glasses System

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Materials Today: Proceedings 2021年 43卷 2490-2497页

作者： N. Chanthima J. Kaewkhao Y. Tariwong N. Kiwsakunkran N. Sangwaranatee N.W. Sangwaranatee Physics Program Faculty of Science and Technology Nakhon Pathom Rajabhat University Nakhon Pathom 73000 Thailand Center of Excellence in Glass Technology and Materials Science (CEGM) Nakhon Pathom Rajabhat University Nakhon Pathom 73000 Thailand Applied Physics Faculty of Science and Technology Suan Sunandha Rajabhat University Bangkok 10300 Thailand Informatics Mathematics Faculty of Science and Technology Suan Sunandha Rajabhat University Bangkok 10300 Thailand

Optical absorption, excitation, emission spectra and lasing potential of sodium aluminium phosphate (Na 2 O-Al 2 O 3 -P 2 O 5 ) glasses doped with Eu 3+ ions were studied and discussed. These glasses were synthesized by melt-quenching technique at 1200 °C. It was found that the emission spectra of glass samples exhibited an intense red (612 nm) emission band upon excitation with 394 nm. Judd-Ofelt (JO) intensity parameters (Ω λ=2,4,6 ), radiative transition probabilities (A R ), peak stimulated emission cross-sections ( σ (λ p )) and branching ratio ( β ) were calculated and compared between without and with thermal corrections. The evaluated JO values indicated that, this glass perform high potential for using as red laser medium application

关键词： Europium Judd-Ofelt analysis Phosphate glass Thermal corrections

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On the Parameter Combinations that Matter and on Those that do Not

arXiv

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arXiv 2021年

作者： Evangelou, Nikolaos Wichrowski, Noah J. Kevrekidis, George A. Dietrich, Felix Kooshkbaghi, Mahdi McFann, Sarah Kevrekidis, Ioannis G. Department of Chemical and Biomolecular Engineering Johns Hopkins University United States Department of Applied Mathematics and Statistics Johns Hopkins University United States Department of Mathematics and Statistics University of Massachusetts Amherst United States Department of Informatics Technical University of Munich Germany The Program in Applied and Computational Mathematics Princeton University United States Department of Chemical and Biological Engineering Princeton University United States Lewis-Sigler Institute for Integrative Genomics Princeton University United States

We present a data-driven approach to characterizing nonidentifiability of a model’s parameters and illustrate it through dynamic as well as steady kinetic models. By employing Diffusion Maps and their extensions, we discover the minimal combinations of parameters required to characterize the output behavior of a chemical system: a set of effective parameters for the model. Furthermore, we introduce and use a Conformal Autoencoder Neural Network technique, as well as a kernel-based Jointly Smooth Function technique, to disentangle the redundant parameter combinations that do not affect the output behavior from the ones that do. We discuss the interpretability of our data-driven effective parameters, and demonstrate the utility of the approach both for behavior prediction and parameter estimation. In the latter task, it becomes important to describe level sets in parameter space that are consistent with a particular output behavior. We validate our approach on a model of multisite phosphorylation, where a reduced set of effective parameters (nonlinear combinations of the physical ones) has previously been established *** Codes 37E99 (Primary), 68T07 (Secondary) Copyright © 2021, The Authors. All rights reserved.

关键词： Parameter estimation

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Employing texture features of chest x-ray images and machine learning in covid-19 detection and classification

Mendel

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Mendel 2021年第1期27卷 9-17页

作者： Alquran, Hiam Alsleti, Mohammad Alsharif, Roaa Qasmieh, Isam Abu Alqudah, Ali Mohammad Harun, Nor Hazlyna Binti Department of Biomedical Systems and Informatics Engineering Yarmouk University Irbid21163 Jordan The Institute of Biomedical Technology King Hussein Medical Center Royal Jordanian Medical Service Amman11855 Jordan College of applied medical Sciences Radiological Science Program King Saud University Jeddah21435 Saudi Arabia Data Science Research Lab School of Computing Universiti Utara Malaysia Sintok Kedah06010 Malaysia

The novel coronavirus (nCoV-19) was first detected in December 2019. It had spread worldwide and was declared coronavirus disease (COVID-19) pandemic by March 2020. Patients presented with a wide range of symptoms affecting multiple organ systems predominantly the lungs. Severe cases required intensive care unit (ICU) admissions while there were asymptomatic cases as well. Although early detection of the COVID-19 virus by Real-time reverse transcription-polymerase chain reaction (RT-PCR) is effective, it is not efficient;as there can be false negatives, it is time consuming and expensive. To increase the accuracy of in-vivo detection, radiological image-based methods like a simple chest X-ray (CXR) can be utilized. This reduces the false negatives as compared to solely using the RT-PCR technique. This paper employs various image processing techniques besides extracted texture features from the radiological images and feeds them to different artificial intelligence (AI) scenarios to distinguish between normal, pneumonia, and COVID-19 cases. The best scenario is then adopted to build an automated system that can segment the chest region from the acquired image, enhance the segmented region then extract the texture features, and finally, classify it into one of the three classes. The best overall accuracy achieved is 93.1% by exploiting Ensemble classifier. Utilizing radiological data to conform to a machine learning format reduces the detection time and increase the chances of survival. © 2021, Brno University of Technology. All rights reserved.

关键词： COVID-19

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Towards a Visual-Language Foundation Model for Computational Pathology

arXiv

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arXiv 2023年

作者： Lu, Ming Y. Chen, Bowen Williamson, Drew F.K. Chen, Richard J. Liang, Ivy Ding, Tong Jaume, Guillaume Odintsov, Igor Zhang, Andrew Le, Long Phi Gerber, Georg Parwani, Anil V. Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Pathology Massachusetts General Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States CambridgeMA United States Health Sciences and Technology Harvard-MIT CambridgeMA United States Harvard John A. Paulson School of Engineering And Applied Sciences Harvard University CambridgeMA United States Department of Pathology Wexner Medical Center Ohio State University ColumbusOH United States Harvard Data Science Initiative Harvard University CambridgeMA United States

The accelerated adoption of digital pathology and advances in deep learning have enabled the development of powerful models for various pathology tasks across a diverse array of diseases and patient cohorts1–13. However, model training is often difficult due to label scarcity in the medical domain and the model’s usage is limited by the specific task and disease for which it is trained1–3, 14, 15. Additionally, most models in histopathology leverage only image data, a stark contrast to how humans teach each other and reason about histopathologic entities. We introduce CONtrastive learning from Captions for Histopathology (CONCH), a visual-language foundation model developed using diverse sources of histopathology images, biomedical text, and notably over 1.17 million image-caption pairs via task-agnostic pretraining. Evaluated on a suite of 13 diverse benchmarks, CONCH can be transferred to a wide range of downstream tasks involving either or both histopathology images and text, achieving state-of-the-art performance on histology image classification, segmentation, captioning, text-to-image and image-to-text retrieval. CONCH represents a substantial leap over concurrent visual-language pretrained systems for histopathology, with the potential to directly facilitate a wide array of machine learning-based workflows requiring minimal or no further supervised fine-tuning. © 2023, CC BY-NC-ND.

关键词： Pathology

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SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： de Franca, F.O. Virgolin, M. Kommenda, M. Majumder, M.S. Cranmer, M. Espada, G. Ingelse, L. Fonseca, A. Landajuela, M. Petersen, B. Glatt, R. Mundhenk, N. Lee, C.S. Hochhalter, J.D. Randall, D.L. Kamienny, P. Zhang, H. Dick, G. Simon, A. Burlacu, B. Kasak, Jaan Machado, Meera Wilstrup, Casper Cavaz, W.G.L. Federal University of ABC Santo Andre Brazil Evolutionary Intelligence group Centrum Wiskunde and Informatica Science Park Amsterdam Netherlands University of Applied Sciences Upper Austria Hagenberg Austria Computational Health Informatics Program Boston Children’s Hospital Harvard Medical School Boston USA Center for Computational Astrophysics Flatiron Institute and Department of Astrophysical Sciences of Princeton University USA LASIGE Faculdade de Ciências Universidade de Lisboa Lisboa Portugal Computational Engineering Division Lawrence Livermore National Laboratory Livermore USA Department of Mechanical Engineering University of Utah Utah USA Meta FAIR France Victoria University of Wellington School of Engineering and Computer Science New Zealand University of otago Department of Information Science New Zealand Institut für Angewandte Physik Universität TübingenMax Planck Institute for Intelligent Systems Tübingen Germany Abzu AI Orient Plads 1 Nordhavn Denmark

Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main promise of this approach is that it may return an interpretable model that can be insightful to users, while maintaining high accuracy. The current standard for benchmarking these algorithms is SRBench, which evaluates methods on hundreds of datasets that are a mix of real-world and simulated processes spanning multiple domains. At present, the ability of SRBench to evaluate interpretability is limited to measuring the size of expressions on real-world data, and the exactness of model forms on synthetic data. In practice, model size is only one of many factors used by subject experts to determine how interpretable a model truly is. Furthermore, SRBench does not characterize algorithm performance on specific, challenging sub-tasks of regression such as feature selection and evasion of local minima. In this work, we propose and evaluate an approach to benchmarking SR algorithms that addresses these limitations of SRBench by 1) incorporating expert evaluations of interpretability on a domain-specific task, and 2) evaluating algorithms over distinct properties of data science tasks. We evaluate 12 modern symbolic regression algorithms on these benchmarks and present an in-depth analysis of the results, discuss current challenges of symbolic regression algorithms and highlight possible improvements for the benchmark itself. Authors

关键词： Benchmarking

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Enhancing the National Cancer Database content using natural language processing and electronic health record data

Surgical Oncology Insight

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Surgical Oncology Insight 2024年第3期1卷 100058-100058页

作者： Stuart, Christina M. Fei, Yizhou Schulick, Richard D. Colborn, Kathryn L. Meguid, Robert A. Surgical Outcomes and Applied Research Program Department of Surgery University of Colorado School of Medicine Aurora CO USA Department of Biostatistics and Informatics Colorado School of Public Health Aurora CO USA Adult and Child Center for Health Outcomes Research and Delivery Science University of Colorado School of Medicine Aurora CO USA Department of Medicine University of Colorado School of Medicine Aurora CO USA

AbstractBackgroundThe prevalence of missing data in the National Cancer Database (NCDB) has marked implications on clinical care and research. The objective of this study was to enhance the NCDB by decreasing rates of missingness and adding new variables using automated statistical methodology. MethodsOne health system’s NCDB data from 2011–2021 was linked to electronic health record (EHR). Variables with frequent missingness and new clinically significant variables not yet included in the NCDB including patient Eastern Cooperative Oncology Group (ECOG) score, specific chemotherapy regimen, American Society of Anesthesiologists Physical Status Classification (ASA class), and discrete surgical procedure were identified in structured and unstructured EHR data. After automated incorporation of structured data from EHR, a natural language processing tool incorporating rule-based algorithms was designed to further extract variables from unstructured notes. Rates of missingness were compared between the original NCDB and the enhanced dataset, and example multivariable models were run to assess for altered model performance with reduced missingness and the addition of new clinically significant variables (chemotherapy regimen). ResultsA total of 6050 patients with NCDB records were linked to their EHR data. Prior to enhancement, rates of missingness for key variables ranged from 2.0% to 5.3%. Following dataset enhancement, missingness was significantly reduced, with relative missingness being reduced between 31.9% to 68.0%. Of the new variables added, 1367 (22.6%) of 6050 patients gained ECOG score, and 1099 (57.8%) of 1901 who received chemotherapy gained their chemotherapy regimen. Of 2989 who underwent surgery, 979 (32.8%) gained their procedure name and 621 (20.8%) gained ASA class. Comparison of the multivariable models demonstrated significant differences between the original NCDB and the enhanced dataset. Specifically, when replacing the binary predictor for chemoth

关键词： EHR Electronic health record Clinical care Clinical research

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Protein Folding as a Jamming Transition

PRX Life

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PRX Life 2025年第1期3卷 013018-013018页

作者： Alex T. Grigas Zhuoyi Liu Jack A. Logan Mark D. Shattuck Corey S. O'Hern Graduate Program in Computational Biology and Biomedical Informatics Yale University New Haven Connecticut 06520 USA Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven Connecticut 06520 USA Department of Mechanical Engineering Yale University New Haven Connecticut 06520 USA Benjamin Levich Institute and Physics Department The City College of New York New York New York 10031 USA Department of Physics Yale University New Haven Connecticut 06520 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA

Proteins fold to a specific functional conformation with a densely packed core that controls their stability. Despite their importance, we lack a quantitative explanation for why all protein cores, regardless of their overall fold, possess the same average packing fraction 〈ϕ〉≈0.55. However, important developments in the physics of jamming in particulate systems can shed light on the packing of protein cores. Here, we extend the framework of jamming to describe core packing in collapsed polymers, as well as in all-atom models of folded proteins. First, we show in a spherical bead-spring polymer model (with and without bond-angle constraints) that as the hydrophobic interactions increase relative to thermal fluctuations, a jamming-like transition occurs when the core packing fraction exceeds ϕc with the same power-law scaling behavior for the potential energy Vr, excess contact number ΔN, and characteristic frequency of the vibrational density of states ω* versus Δϕ=ϕ−ϕc as that for jammed particulate systems. Then, we develop an all-atom model for proteins and find that, above ϕc∼0.55, protein cores undergo a jamming-like transition, but with anomalous power-law scaling for Vr, ΔN, and ω* versus Δϕ. The all-atom protein model remains close to the native protein structure during jamming and accurately refolds from partially unfolded states.

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A Digital Photo Activity Intervention for Nursing Home Residents With Dementia and Their Carers: Mixed Methods Process Evaluation

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JMIR Formative Research 2025年 9卷 e56586页

作者： Tan, Josephine Rose Orejana Neal, David P. Vilmen, Maria Boersma, Petra Ettema, Teake P. Gobbens, Robbert J.J. Sikkes, Sietske A.M. Dröes, Rose-Marie Department of Psychiatry Amsterdam Public Health Research Institute Amsterdam UMC Location Vrije Universiteit Amsterdam Netherlands eHealth Living & Learning Lab Department of Medical Informatics Amsterdam UMC Amsterdam Netherlands Lectorate Health and Well-being of Frail Elderly People Faculty of Health Sports and Social Work Inholland University of Applied Sciences Amsterdam Netherlands Program Learning Improvement & Implementation Ben Sajet Centrum Amsterdam Netherlands Zonnehuisgroep Amstelland Amstelveen Netherlands Department of Family Medicine and Population Health Faculty of Medicine and Health Sciences University of Antwerp Antwerp Belgium Tranzo Tilburg University Tilburg Netherlands Department of Clinical Neuro- & Developmental Psychology Faculty of Behavioural and Movement Sciences Vrije Universiteit Amsterdam Amsterdam Netherlands Alzheimer Center Amsterdam Vrije Universiteit Amsterdam Amsterdam UMC Amsterdam Netherlands Amsterdam Neuroscience Neurodegeneration Amsterdam Netherlands

Background: Within the framework of a randomized controlled trial investigating the impact of a digital, psychosocial photo activity intervention for residents living with dementia in nursing homes and their informal and formal carers, a process evaluation was conducted to determine factors that affected the implementation of the intervention and potentially influenced the intervention outcomes. Objective: By tracing facilitators and barriers to implementation, the study also aimed to inform future implementation of the photo activity intervention. Methods: Following Medical Research Council guidance, mixed methods were used to investigate context, implementation, and mechanism-of-impact factors during the photo activity intervention via the Fotoscope web application versus a general conversation activity (control). Google Analytics was set up to gain insight into how the Fotoscope web application was used in practice. For quantitative data, descriptive statistics were calculated and differences between groups tested. For qualitative data, thematic analysis was performed. Results: In total, 163 semistructured interviews were conducted with residents (photo activity group: n=29, 17.8%;control: n=29, 17.8%), formal carers (photo activity group: n=23, 14.1%;control: n=27, 16.6%), and informal carers (photo activity group: n=28, 17.2%;control: n=27, 16.6%). Regarding contextual factors, a minority of formal carers in both groups (photo activity group: 4/18, 22%;control: 9/24, 38%) mentioned time and workload as barriers to implementing the intervention. Regarding implementation, 86% (25/29) of the residents in the intervention group felt that the digital photo activity worked well on a tablet. Informal carers from both groups wanted more intervention updates from formal carers. The majority of formal carers from both groups were satisfied with how the training and activities were implemented. Regarding the mechanisms of impact, residents in the photo activity group (27/

关键词： art dementia nursing home photos process evaluation psychosocial interventions social interaction

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