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arXiv 2021年

作者： Pion-Tonachini, Luca Bouchard, Kristofer Martin, Hector Garcia Peisert, Sean Holtz, W. Bradley Aswani, Anil Dwivedi, Dipankar Wainwright, Haruko Pilania, Ghanshyam Nachman, Benjamin Marrone, Babetta L. Falco, Nicola Prabhat Arnold, Daniel Wolf-Yadlin, Alejandro Powers, Sarah Climer, Sharlee Jackson, Quinn Carlson, Ty Sohn, Michael Zwart, Petrus Kumar, Neeraj Justice, Amy Tomlin, Claire Jacobson, Daniel Micklem, Gos Gkoutos, Georgios V. Bickel, Peter J. Cazier, Jean-Baptiste Müller, Juliane Webb-Robertson, Bobbie-Jo Stevens, Rick Anderson, Mark Kreutz-Delgado, Ken Mahoney, Michael W. Brown, James B. Pattern Computer Inc. Friday HarborWA98250 United States Biosciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Helen Wils Neuroscience Institute Redwood Center for Theoretical Neuroscience Uc Berkeley BerkeleyCA94720 United States Doe Agile BioFoundry Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Joint BioEnergy Institute Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Bcam Basque Center for Applied Mathematics Bilbao48009 Spain Computer Science University of California Davis DavisCA95616 United States Cenic La MiradaCA90638 United States Health Informatics University of California Davis School of Medicine SacramentoCA95817 United States Berkeley Institute for Data Science University of California Berkeley BerkeleyCA94720 United States Industrial Engineering and Operations Research University of California Berkeley BerkeleyCA94720 United States Environmental & Earth Sciences Area Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Nuclear Engineering University of California Berkeley BerkeleyCA94720 United States Materials Science and Technology Division Los Alamos National Laboratory Los AlamosNM87545 United States Physics Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Bioscience Division Los Alamos National Laboratory Los AlamosNM87545 United States Earth and Environmental Sciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Energy Technologies Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computing and Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Industrial & Systems Engineering The University of Tennessee KnoxvilleTN37996 United States Department of Computer Science University of Missouri-Saint Louis St. LouisMO63121 United

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself. © 2021, CC BY.

关键词： Learning systems

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A preliminary empirical exploration of quality measurement for JavaScript solutions 6

A preliminary empirical exploration of quality measurement f...

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6th Workshop on Software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2017

作者： Kostanjevec, David Pušnik, Maja Heričko, Marjan Šumak, Boštjan Rakić, Gordana Budimac, Zoran Faculty of Electrical Engineering and Computer Science University of Maribor Smetanova 17 Maribor2000 Slovenia University of Novi Sad Faculty of Sciences Department of Mathematics and Informatics Trg Dositeja Obradovica 4 Novi Sad21000 Serbia

ISBN: (纸本)9788670313552

Contrary to the increasing popularity of JavaScript programming language in the field of web application development, the numerical expression of evidence about the quality of solutions developed in this language is still not reliable. Based on the preliminary literature review, which is the main subject of this paper, this area has not yet been fully explored. Measurement is done by application of general and object-oriented metrics, which can reflect only general characteristics of the solution, while the specifics related to the programming language are not expressible by existing metrics. Due to the popularity of the language and the increasing number of JavaScript projects, the idea is to determine appropriate metrics and approach to measurement for their application in practice. Finally, the measurement approach will be implemented in the SSQSA Framework to enable its application. The preliminary research presented in this paper was conducted during a student course of Empirical research methods at the University of Maribor and therefore is limited in number of included papers, depth of research, and analysis of its contents, which restricts us to preliminary conclusion only, but places the foundation and justifies the described forthcoming research. © Copyright 2017 by the paper's authors.

关键词： Quality control

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Mathematical models for social group behavior

Mathematical models for social group behavior

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2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017

作者： Eshghi, Soheil Williams, Grace-Rose Colombo, Gualtiero B. Turner, Liam D. Rand, David G. Whitaker, Roger M. Tassiulas, Leandros Electrical Engineering Department Yale University New Haven United States Porton Down United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Yale University New Haven United States

ISBN: (纸本)9781538604342

In this paper, we seek to identity how mathematical and economic analysis can be used to gain insights about the mutation of social groups. Group mutability has been studied in multiple domains, with insights generated on significant factors at differing scales. Mathematical modeling enables the simultaneous study of such phenomena, understanding interactions and generating hypotheses for experiments. In particular, we focus on group fracture, where individuals leave groups of which they are members. For example, this can be due to perceived differences with other group members due to norm related conflict (such as extreme actions by some members). Our aim is to consider simple mathematical models incorporating a selection of social and psychological theory which describes these phenomena as a way to understand their interplay, and describe the trade-offs and challenges. This will help a federation model the behavior of extremist groups, and determine not only when an intervention is necessary, but also the best course of action to take to induce the fracture of such groups. This paper is an exploratory investigation into methods of achieving this goal and evaluating the usefulness of the outputs to federations. © 2017 IEEE.

关键词： Economic analysis

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Magnetic-visual sensor fusion-based dense 3D reconstruction and localization for endoscopic capsule robots

arXiv

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arXiv 2018年

作者： Turan, Mehmet Almalioglu, Yasin Ornek, Evin Pinar Araujo, Helder Yanik, Mehmet Fatih Sitti, Metin Physical Intelligence Department Max Planck Institute for Intelligent Systems Germany Computer Science Department University of Oxford Oxford United Kingdom Informatics Department Technical University of Muenich Germany Institute for Systems and Robotics University of Coimbra Portugal Department of Information Technology and Electrical Engineering Zurich Switzerland

Reliable and real-time 3D reconstruction and localization functionality is a crucial prerequisite for the navigation of actively controlled capsule endoscopic robots as an emerging, minimally invasive diagnostic and therapeutic technology for use in the gastrointestinal (GI) tract. In this study, we propose a fully dense, non-rigidly deformable, strictly realtime, intraoperative map fusion approach for actively controlled endoscopic capsule robot applications which combines magnetic and vision-based localization, with non-rigid deformations based frame-to-model map fusion. The performance of the proposed method is demonstrated using four different ex-vivo porcine stomach models. Across different trajectories of varying speed and complexity, and four different endoscopic cameras, the root mean square surface reconstruction errors 1.58 to 2.17 cm. Copyright © 2018, The Authors. All rights reserved.

关键词： Robot applications

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The power of the patient voice: Learning indicators of treatment adherence from an online breast cancer forum 11

The power of the patient voice: Learning indicators of treat...

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11th International Conference on Web and Social Media, ICWSM 2017

作者： Yin, Zhijun Malin, Bradley Warner, Jeremy Hsueh, Pei-Yun Chen, Ching-Hua Department of Electrical Engineering and Computer Science School of Engineering Vanderbilt University United States Department of Biomedical Informatics School of Medicine Vanderbilt University United States Department of Medicine School of Medicine Vanderbilt University United States IBM Thomas J. Watson Research Center United States

ISBN: (纸本)9781577357889

Social media platforms have become popular online environments for patients seeking and sharing treatment experiences. These platforms enable us to move beyond traditional sources of clinical information for learning about a patient's long-term adherence to treatment. While adherence has been studied using data derived from medical records and structured surveys, these approaches are limited in that they are often 1) time consuming, 2) limited in scale, or 3) lack selfreported patient experiences. In this paper, we investigate treatment adherence through a patient's self-reported information in online discussion forums. Specifically, we consider hormonal therapy treatment adherence (HTA) for hormone receptor positive breast cancer, a disease subtype that comprises 75% of all breast cancer cases. We focus on the inferred emotions and personality traits from the posts created by the members of a large online breast cancer community. These factors have been neglected in traditional adherence research due to a lack of information. We study over 130,000 posts from the forum, spanning 10,000 patients over 9 years. We assess emotion and personality traits with respect to three types of adherence behaviors: 1) currently on a regimen, 2) an interruption (due to discontinuing, pausing, or switching a medication regimen before five years) and 3) the completion of a five-year protocol. We find statistically significant differences in emotions across adherence behaviors. We further show that specific personality traits, including self-discipline, are associated with HTA, but in the opposite direction than what traditional research studies have shown. Finally, we illustrate that there is potential for predicting future interruption behaviors based on an individual's posts. We anticipate that our methodology can be applied to study treatment adherence for other diseases using online self-reported information. © Copyright 2017, Association for the Advancement of Artificial Intellig

关键词： Diseases

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Semi-supervised deep metrics for image registration

arXiv

引用

arXiv 2018年

作者： Sedghi, Alireza Luo, Jie Mehrtash, Alireza Pieper, Steve Tempany, Clare M. Kapur, Tina Mousavi, Parvin Wells, William M. Medical Informatics Laboratory Queens University KingstonON Canada Radiology Department Brigham and Womens Hospital Harvard Medical School BostonMA United States Graduate School of Frontier Sciences University of Tokyo Japan Department of Electrical and Computer Engineering University of British Columbia VancouverBC Canada

Deep metrics have been shown effective as similarity measures in multi-modal image registration;however, the metrics are currently constructed from aligned image pairs in the training data. In this paper, we propose a strategy for learning such metrics from roughly aligned training data. Symmetrizing the data corrects bias in the metric that results from misalignment in the data (at the expense of increased variance), while random perturbations to the data, i.e. dithering, ensures that the metric has a single mode, and is amenable to registration by optimization. Evaluation is performed on the task of registration on separate unseen test image pairs. The results demonstrate the feasibility of learning a useful deep metric from substantially misaligned training data, in some cases the results are significantly better than from Mutual Information. Data augmentation via dithering is, therefore, an effective strategy for discharging the need for well-aligned training data;this brings deep metric registration from the realm of supervised to semi-supervised machine learning. Copyright © 2018, The Authors. All rights reserved.

关键词： Image registration

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A systematic approach to design product traceability in industry 4.0: Insights from the ceramic industry 26

A systematic approach to design product traceability in indu...

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26th International Conference on Information Systems Development: Advances in Methods, Tools and Management, ISD 2017

作者： Barata, João da Cunha, Paulo Rupino Gonnagar, Anand Subhashchandra Mendes, Mateus CTCV Technological Centre for Ceramics and Glass CISUC Department of Informatics Engineering University of Coimbra ESTGOH Polytechnic Institute of Coimbra ISMT Miguel Torga Institute Coimbra Coimbra Portugal ESTGOH Polytechnic Institute of Coimbra ISR Department of Electrical and Computer Engineering University of Coimbra Oliveira do Hospital Portugal

ISBN: (纸本)9789963228836

Departing from a case in the table and ornamental ceramics industry, we propose a comprehensive approach to design product traceability for Industry 4.0. Our design-science research approach includes a review of traceability technologies and participative enterprise modeling. We find benefits in combining Business Process Modeling Notation and Goal-oriented Requirement Language representations to (1) improve communication in complex Information System Development scenarios, (2) promote reflection by experts with different backgrounds, and (3) reach consensus in a solution that addresses the goals of multiple stakeholders. The resulting model combines technologies in different stages of product lifecycle. Depending on each stage and strategic intention, the identification code can be embedded in the product, transport, or package. Our contribution can assist managers in the creation of digital ecosystems to support traceability integration at (1) technological, (2) vertical, and (3) horizontal levels that are required in the fourth industrial revolution. © ISD 2017.

关键词： Industry 4.0

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A Mutual Debt Cut Algorithm for a Group of Countries

引用

IOP Conference Series: Materials Science and engineering 2018年第1期459卷

作者： S Fatouros P Papadopoulos N L Matiadou S Vassiliadis Department of Informatics and Computer Engineering University of West Attica Greece Department of Electrical and Electronics Engineering University of West Attica Greece

In the recent years the financial debate has focused on the national debt of the countries. These debts consist of different debts to banks, institutions and other countries. In the current work we investigate a method for a mutual national debt cut amongst a group of countries using as a deposit capital equal to the minimum of the debts within the group. The only condition is that all countries own and possess debt within the group. The deposit capital is circulated to repay the debts. At the end remains intact, it works in fact as a catalyst. The algorithm introduced in this paper is based on a simple transfer table. The advantage of the algorithm is that it is not actually based on financial resources. On the other hand depends on countries will for cooperation.

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Evaluation of MRI to ultrasound registration methods for brain shift correction: The CuRIOUS2018 Challenge

arXiv

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arXiv 2019年

作者： Xiao, Yiming Rivaz, Hassan Chabanas, Matthieu Fortin, Maryse Machado, Ines Ou, Yangming Heinrich, Mattias P. Schnabel, Julia A. Zhong, Xia Maier, Andreas Wein, Wolfgang Shams, Roozbeh Kadoury, Samuel Drobny, David Modat, Marc Reinertsen, Ingerid Robarts Research Institute Western University LondonON Canada PERFORM Centre Department of Electrical and Computer Engineering Concordia University Montreal Canada University of Grenoble Aples Grenoble Institute of Technology Grenoble France Department of Health Kinesiology & Applied Physiology Concordia University Montreal Canada Department of Radiology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Pediatrics and Radiology Boston Children’s Hospital Harvard Medical School BostonMA United States Institute of Medical Informatics University of Luebeck Germany School of Biomedical Engineering and Imaging Sciences King’s College London United Kingdom Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Martensstr. 3 Erlangen91058 Germany Institute of Biomedical Engineering Polytechnique Montreal CHUM Research Centre Wellcome/EPSRC Cenre for Interventional and Surgical Sciences University of College London United Kingdom School of Biomedical Engineering & Imaging Sciences King’s College London King’s Health Partners St Thomas’ Hospital LondonSE1 7EH United Kingdom ImFusion GmbH Munich Germany SINTEF TrondheimNO-7465 Norway

In brain tumor surgery, the quality and safety of the procedure can be impacted by intra-operative tissue deformation, called brain shift. Brain shift can move the surgical targets and other vital structures such as blood vessels, thus invalidating the pre-surgical plan. Intra-operative ultrasound (iUS) is a convenient and cost-effective imaging tool to track brain shift and tumor resection. Accurate image registration techniques that update pre-surgical MRI based on iUS are crucial but challenging. The MICCAI Challenge 2018 for Correction of Brain shift with Intra-Operative UltraSound (CuRIOUS2018) provided a public platform to benchmark MRI-iUS registration algorithms on newly released clinical datasets. In this work, we present the data, setup, evaluation, and results of CuRIOUS 2018, which received 6 fully automated algorithms from leading academic and industrial research groups. All algorithms were first trained with the public RESECT database, and then ranked based on test dataset of 10 additional cases with identical data curation and annotation protocols as the RESECT database. The article compares the results of all participating teams and discusses the insights gained from the challenge, as well as future work. Copyright © 2019, The Authors. All rights reserved.

关键词： Tumors

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The impact of parallel programming on faster image filtering

The impact of parallel programming on faster image filtering

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Federated Conference on computer Science and Information Systems

作者： Kamil Ksiazek Zbigniew Marszalek Giacomo Capizzi Christian Napoli Dawid Polap Marcin Wozniak Institute of Mathematics Silesian University of Technology Department Electrical Electronics and Informatics Engineering University of Catania Department of Mathematics and Computer Science University of Catania

ISBN: (纸本)9781538623718

Parallel programming is a field of science with a great potential nowadays due to the development of advanced computers architectures. Appropriate usage of this tool can be therefore highly beneficial in multimedia applications and significantly decreases the time of calculations. In this article, we analyze how the speed of calculations is influenced by the usage of parallel algorithms in image filtering processes. We present a method based on multithreading and the division of the image for rectangles. The filter is applied parallel on each part of the image. Results show that in some cases our proposition can bring over 90% benefit when compared to the classical approach.

关键词： Parallel programming Image filtering Laplaclan Multithreading

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