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arXiv 2022年

作者： Eastman, Peter Behara, Pavan Kumar Dotson, David L. Galvelis, Raimondas Herr, John E. Horton, Josh T. Mao, Yuezhi Chodera, John D. Pritchard, Benjamin P. Wang, Yuanqing De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Department of Pharmaceutical Sciences University of California IrvineCA92697 United States The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Acellera Labs Doctor Trueta 183 Barcelona08005 Spain Department of Chemistry and Biochemistry University of Notre Dame Notre DameIN46556 United States School of Natural and Environmental Sciences Newcastle University Newcastle upon TyneNE1 7RU United Kingdom Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Molecular Sciences Software Institute Virginia Polytechnic Institute State University BlacksburgVA24060 United States Carrer Dr. Aiguader 88 Barcelona08003 Spain ICREA Passeig Lluis Companys 23 Barcelona08010 Spain Graduate Program in Physiology Biophysics and Systems Biology Weill Cornell Graduate School of Medical Sciences New YorkNY10065 United States

Machine learning potentials are an important tool for molecular simulation, but their development is held back by a shortage of high quality datasets to train them on. We describe the SPICE dataset, a new quantum chemistry dataset for training potentials relevant to simulating drug-like small molecules interacting with proteins. It contains over 1.1 million conformations for a diverse set of small molecules, dimers, dipeptides, and solvated amino acids. It includes 15 elements, charged and uncharged molecules, and a wide range of covalent and non-covalent interactions. It provides both forces and energies calculated at the ωB97M-D3(BJ)/def2-TZVPPD level of theory, along with other useful quantities such as multipole moments and bond orders. We train a set of machine learning potentials on it and demonstrate that they can achieve chemical accuracy across a broad region of chemical space. It can serve as a valuable resource for the creation of transferable, ready to use potential functions for use in molecular simulations. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum chemistry

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Review ofFish Vaccines: Health Management for Sustainable Aquaculture. Edited byPreetham Elumalai,Kim Thompson,Sreeja Lakshmi. Boca Raton, FL, USA: CRC Press,2023,262pp., eBook £108.00, ISBN 978-1-032-48341-2

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Journal of Fish Diseases 2023年第12期46卷

作者： Clara Artha Febriana Yunita Messe Graduate Program Faculty of Biology Gadjah Mada University Yogyakarta Indonesia Contribution: Conceptualization Investigation Methodology Data curation Writing - original draft Resources Formal analysis Project administration Supervision Validation Visualization Funding acquisition Software Contribution: Validation Formal analysis Writing - original draft

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An objectness score for accurate and fast detection during navigation

arXiv

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arXiv 2019年

作者： Choi, Hongsun Kang, Mincheul Kwon, Youngsun Yoon, Sung-eui Software Graduate Program KAIST Seoul06301 Korea Republic of School of Computing KAIST Daejeon34141 Korea Republic of

We propose a novel method utilizing an objectness score for maintaining the locations and classes of objects detected from Mask R-CNN during mobile robot navigation. The objectness score is defined to measure how well the detector identifies the locations and classes of objects during navigation. Specifically, it is designed to increase when there is sufficient distance between a detected object and the camera. During the navigation process, we transform the locations of objects in 3D world coordinates into 2D image coordinates through an affine projection and decide whether to retain the classes of detected objects using the objectness score. We conducted experiments to determine how well the locations and classes of detected objects are maintained at various angles and positions. Experimental results showed that our approach is efficient and robust, regardless of changing angles and distances. Copyright © 2019, The Authors. All rights reserved.

关键词： Navigation

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Technical debt and agile software development practices and processes: An industry practitioner survey

arXiv

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arXiv 2021年

作者： Holvitie, Johannes Licorish, Sherlock A. Spínola, Rodrigo O. Hyrynsalmi, Sami MacDonell, Stephen G. Mendes, Thiago S. Buchan, Jim Leppänen, Ville Turku Centre for Computer Science Software Development Laboratory Turku Finland University of Turku Department of Information Technology Turku Finland University of Otago Department of Information Science Otago Dunedin New Zealand Salvador University Graduate Program in Systems and Computer Bahia Salvador Brazil Federal University of Bahia Fraunhofer Project Center for Software and System Engineering Salvador Brazil Tampere University of Technology Pori Campus Pori Finland Federal Institute of Bahia Information Technology Department Bahia Santo Amaro Brazil Auckland University of Technology School of Engineering Computer and Mathematical Sciences Auckland New Zealand

Context: Contemporary software development is typically conducted in dynamic, resource-scarce environments that are prone to the accumulation of technical debt. While this general phenomenon is acknowledged, what remains unknown is how technical debt specifically manifests in and affects software processes, and how the software development techniques employed accommodate or mitigate the presence of this debt. Objectives: We sought to draw on practitioner insights and experiences in order to classify the effects of agile method use on technical debt management, given the popularity and perceived success of agile methods. We explore the breadth of practitioners' knowledge about technical debt;how technical debt is manifested across the software process;and the perceived effects of common agile software development practices and processes on technical debt. In doing so, we address a research gap in technical debt knowledge and provide novel and actionable managerial recommendations. Method: We designed, tested and executed a multi-national survey questionnaire to address our objectives, receiving 184 responses from practitioners in Brazil, Finland, and New Zealand. Results: Our findings indicate that: 1) Practitioners are aware of technical debt, although, there was under utilization of the concept, 2) Technical debt commonly resides in legacy systems, however, concrete instances of technical debt are hard to conceptualize which makes it problematic to manage, 3) Queried agile practices and processes help to reduce technical debt;in particular, techniques that verify and maintain the structure and clarity of implemented artifacts (e.g., Coding standards and Refactoring) positively affect technical debt management. Conclusions: The fact that technical debt instances tend to have characteristics in common means that a systematic approach to its management is feasible. However, notwithstanding the positive effects of some agile practices on technical debt management, comp

关键词： Legacy systems

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Understanding automated and human-based technical debt identification approaches-a two-phase study

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Journal of the Brazilian Computer Society 2019年第1期25卷 1页

作者： Spínola, Rodrigo O. Zazworka, Nico Vetro, Antonio Shull, Forrest Seaman, Carolyn Graduate Program in Systems and Computer Salvador University State University of Bahia SalvadorBahia Brazil Elsevier Frankfurt Germany Nexa Center for Internet & Society DAUIN Politecnico di Torino Torino Italy Carnegie Mellon University Software Engineering Institute ArlingtonVA United States Department of Information Systems University of Maryland Baltimore County BaltimoreMD United States

Context: The technical debt (TD) concept inspires the development of useful methods and tools that support TD identification and management. However, there is a lack of evidence on how different TD identification tools could be complementary and, also, how human-based identification compares with them. Objective: To understand how to effectively elicit TD from humans, to investigate several types of tools for TD identification, and to understand the developers’ point of view about TD indicators and items reported by tools. Method: We asked developers to identify TD items from a real software project. We also collected the output of three tools to automatically identify TD and compared the results in terms of their locations in the source code. Then, we collected developers’ opinions on the identification process through a focus group. Results: Aggregation seems to be an appropriate way to combine TD reported by developers. The tools used cannot help in identifying many important TD types, so involving humans is necessary. Developers reported that the tools would help them to identify TD faster or more accurately and that project priorities and current development activities are important to be considered together, along with the values of principal and interest, when deciding to pay off a debt. Conclusion: This work contributes to the TD landscape, which depicts an understanding between different TD types and how they are best discovered. © 2019, The Author(s).

关键词： software engineering

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Accelerating Real-Time Imaging for Radiotherapy: Leveraging Multi-GPU Training with PyTorch

Accelerating Real-Time Imaging for Radiotherapy: Leveraging ...

引用

International Conference on Machine Learning and Applications (ICMLA)

作者： Ruth Obe Brandt Kaufmann Kaelen Baird Sam Kadel Yasmin Soltani Mostafa Cham Matthias K. Gobbert Carlos A. Barajas Zhuoran Jiang Vijay R. Sharma Lei Ren Stephen W. Peterson Jerimy C. PoIf Dept. of Computer Science and of Software Engineering U. of Houston-Clear Lake USA Dept. of Mathematics and Statistics U. of San Francisco USA Dept. of Computer Science and of Mathematics Skidmore College USA Dept. of Computer Science and of Psychology Mount Holyoke College USA Dept. of Biomedical Engineering U. of Houston USA Dept. of Information Systems U. of Maryland Baltimore County Dept. of Mathematics and Statistics U. of Maryland Baltimore County Medical Physics Graduate Program Duke University USA Dept. of Radiation Oncology U. of Maryland School of Medicine USA Department of Radiation Oncology U. of Maryland School of Medicine USA Dept. of Physics U. of Cape Town South Africa H3D Inc. USA

Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors. This helps to mit-igate unnecessary radiation exposure in healthy tissues. Real-time imaging of prompt gamma rays with Compton cameras has been suggested to improve therapy efficacy. However, the camera's non-zero time resolution leads to incorrect interaction classifications and noisy images that are insufficient for accurately assessing proton delivery in patients. To address the challenges posed by the Compton camera's image quality, machine learning techniques are employed to classify and refine the generated data. These machine-learning techniques include recurrent and feedforward neural networks. A PyTorch model was designed to improve the data captured by the Compton camera. This decision was driven by PyTorch's flexibility, powerful capabilities in handling sequential data, and enhanced G PU usage. This accelerates the model's computations on large-scale radiotherapy data. Through hyperparameter tuning, the validation accuracy of our PyTorch model has been improved from an initial 7 % to over 60 %. Moreover, the PyTorch Distributed Data Parallelism strategy was used to train the RNN models on multiple G PU s, which significantly reduced the training time with a minor impact on model accuracy.

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Correction: Financial burden of catastrophic health expenditure on households with chronic diseases: financial ratio analysis

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BMC health services research 2022年第1期22卷 1368页

作者： Hyunwoo Jung Young Dae Kwon Jin-Won Noh Post Doc Department of Health Administration Graduate School·BK21 Graduate Program of Developing Glocal Experts in Health Policy and Management Yonsei University Wonju South Korea. Department of Humanities and Social Medicine College of Medicine and Catholic Institute for Healthcare Management The Catholic University of Korea Seoul South Korea. Division of Health Administration College of Software and Digital Healthcare Convergence Yonsei University Wonju South Korea. jinwon.noh@***.

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An Analysis of Machine Learning Classifiers in Breast Cancer Diagnosis

An Analysis of Machine Learning Classifiers in Breast Cancer...

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Conferencia Latinoamericana En Informatica (CLEI)

作者： Fabiano Teixeira João Luis Zeni Montenegro Cristiano André da Costa Rodrigo da Rosa Righi Software Innovation Laboratory - SOFTWARELAB Applied Computing Graduate Program Universidade do Vale do Rio dos Sinos - UNISINOS Av Unisinos 950 Sao Leopoldo 93022-750 Brazil

ISBN: (数字)9781728155746

ISBN: (纸本)9781728155753

In the field of assisted cancer diagnosis, it is expected that the involvement of machine learning in diseases will give doctors a second opinion and help them to make a faster / better determination. There are a huge number of studies in this area using traditional machine learning methods and in other cases, using deep learning for this purpose. This article aims to evaluate the predictive models of machine learning classification regarding the accuracy, objectivity, and reproducible of the diagnosis of malignant neoplasm with fine needle aspiration. Also, we seek to add one more class for testing in this database as recommended in previous studies. We present six different classification methods: Multilayer Perceptron, Decision Tree, Random Forest, Support Vector Machine and Deep Neural Network for evaluation. For this work, we used at University of Wisconsin Hospital database which is composed of thirty values which characterize the properties of the nucleus of the breast mass. As we showed in result sections, DNN classifier has a great performance in accuracy level (92%), indicating better results in relation to traditional models. Random forest 50 and 100 presented the best results for the ROC curve metric, considered an excellent prediction when compared to other previous studies published.

关键词： Machine learning Breast cancer Support vector machines Neural networks Forestry

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Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging

arXiv

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arXiv 2023年

作者： Stanley, Emma A.M. Souza, Raissa Winder, Anthony J. Gulve, Vedant Amador, Kimberly Wilms, Matthias Forkert, Nils D. Biomedical Engineering Graduate Program University of Calgary CalgaryABT2N 1N4 Canada Department of Radiology University of Calgary CalgaryABT2N 4N1 Canada Hotchkiss Brain Institute University of Calgary CalgaryABT2N 4N1 Canada Alberta Children’s Hospital Research Institute University of Calgary CalgaryABT2N 4N1 Canada Department of Pediatrics University of Calgary CalgaryABT2N 4N1 Canada Department of Community Health Sciences University of Calgary CalgaryABT2N 4N1 Canada Department of Clinical Neuroscience University of Calgary CalgaryABT2N 4N1 Canada Department of Electrical and Software Engineering University of Calgary CalgaryABT2N 1N4 Canada

Objective: Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of subgroup performance disparities. However, since not all sources of bias in real-world medical imaging data are easily identifiable, it is challenging to comprehensively assess their impacts. In this article, we introduce an analysis framework for systematically and objectively investigating the impact of biases in medical images on AI models. Materials and Methods: Our framework utilizes synthetic neuroimages with known disease effects and sources of bias. We evaluated the impact of bias effects and the efficacy of 3 bias mitigation strategies in counterfactual data scenarios on a convolutional neural network (CNN) classifier. Results: The analysis revealed that training a CNN model on the datasets containing bias effects resulted in expected subgroup performance disparities. Moreover, reweighing was the most successful bias mitigation strategy for this setup. Finally, we demonstrated that explainable AI methods can aid in investigating the manifestation of bias in the model using this framework. Discussion: The value of this framework is showcased in our findings on the impact of bias scenarios and efficacy of bias mitigation in a deep learning model pipeline. This systematic analysis can be easily expanded to conduct further controlled in silico trials in other investigations of bias in medical imaging AI. Conclusion: Our novel methodology for objectively studying bias in medical imaging AI can help support the development of clinical decision-support tools that are robust and responsible. © 2023, CC BY.

关键词： Convolutional neural networks

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

引用

arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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