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arXiv 2024年

作者： Bolusani, Suresh Besançon, Mathieu Bestuzheva, Ksenia Chmiela, Antonia Dionísio, João Donkiewicz, Tim van Doornmalen, Jasper Eifler, Leon Ghannam, Mohammed Gleixner, Ambros Graczyk, Christoph Halbig, Katrin Hedtke, Ivo Hoen, Alexander Hojny, Christopher van der Hulst, Rolf Kamp, Dominik Koch, Thorsten Kofler, Kevin Lentz, Jurgen Manns, Julian Mexi, Gioni Mühmer, Erik Pfetsch, Marc E. Schlösser, Franziska Serrano, Felipe Shinano, Yuji Turner, Mark Vigerske, Stefan Weninger, Dieter Xu, Liding Gaul, Oliver Zuse Institute Berlin Department AIS2T Takustr. 7 Berlin14195 Germany Université Grenoble Alpes Inria LIG Grenoble38000 France CMUP Department of Computer Science Faculty of Sciences University of Porto R Campo Alegre Porto4169-007 Portugal RWTH Aachen University Lehrstuhl für Operations Research Kackertstr. 7 Aachen52072 Germany Eindhoven University of Technology Department of Mathematics and Computer Science P.O. Box 513 Eindhoven5600 MB Netherlands Friedrich-Alexander Universität Erlangen-Nürnberg Department of Data Science Cauerstr. 11 Erlangen91058 Germany Schenker AG Global Data & AI Kruppstr. 4 Essen45128 Germany University of Twente Department of Discrete Mathematics and Mathematical Programming P.O. Box 217 Enschede7500 AE Netherlands University of Bayreuth Universitaetsstr. 30 Bayreuth95440 Germany Technische Universität Berlin Straße des 17. Juni 135 Berlin10623 Germany Zuse Institute Berlin Department A2IM Takustr. 7 Berlin14195 Germany DAGOPT Optimization Technologies GmbH Austria Technische Universität Darmstadt Fachbereich Mathematik Dolivostr. 15 Darmstadt64293 Germany Fair Isaac Germany GmbH Takustr. 7 Berlin14195 Germany COPT GmbH Berlin Germany GAMS Software GmbH c/o Zuse Institute Berlin Department AIS2T Takustr. 7 Berlin14195 Germany Friedrich-Alexander Universität Erlangen-Nürnberg Department of Mathematics Cauerstr. 11 Erlangen91058 Germany École polytechnique LIX CNRR Rue Honoré d’Estienne d’Orves. 1 Palaiseau9120 France

MSC Codes 90C05, 90C10, 90C11, 90C30, 90C90, 65Y05The SCIP Optimization Suite provides a collection of software packages for mathematical optimization, centered around the constraint integer programming (CIP) framework SCIP. This report discusses the enhancements and extensions included in SCIP Optimization Suite 9.0. The updates in SCIP 9.0 include improved symmetry handling, additions and improvements of nonlinear handlers and primal heuristics, a new cut generator and two new cut selection schemes, a new branching rule, a new LP interface, and several bugfixes. SCIP Optimization Suite 9.0 also features new Rust and C++ interfaces for SCIP, new Python interface for SoPlex, along with enhancements to existing interfaces. SCIP Optimization Suite 9.0 also includes new and improved features in the LP solver SoPlex, the presolving library PaPILO, the parallel framework UG, the decomposition framework GCG, and the SCIP extension SCIP-SDP. These additions and enhancements have resulted in an overall performance improvement of SCIP in terms of solving time, number of nodes in the branch-and-bound tree, as well as the reliability of the solver. Copyright © 2024, The Authors. All rights reserved.

关键词： Mixed-integer linear programming

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Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers

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Informatics in Medicine Unlocked 2023年 42卷

作者： Azim, Sayed Mehedi Sabab, Noor Hossain Nuri Noshadi, Iman Alinejad-Rokny, Hamid Sharma, Alok Shatabda, Swakkhar Dehzangi, Iman Center for Computational and Integrative Biology Rutgers University Camden 08102 NJ United States Department of Computer Science and Engineering United International University Plot 2 United City Madani Avenue BaddaDhaka 1212 Bangladesh Department of Bioengineering University of California Riverside 92507 CA United States BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering The University of New South Wales (UNSW Sydney) Sydney NSW 2052 Australia UNSW Data Science Hub UNSW Sydney Sydney NSW 2052 Australia Health Data Analytics Program AI-enabled Processes Research Centre Macquarie University Sydney 2109 Australia Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama 230-0045 Japan Department of Computer Science Rutgers University Camden 08102 NJ United States

The use of therapeutic peptides for the treatment of cancer has received tremendous attention in recent years. Anticancer peptides (ACPs) are considered new anticancer drugs which have several advantages over chemistry-based drugs including high specificity, strong tumor penetration capacity, and low toxicity level for normal cells. Due to the rise of experimentally verified bioactive peptides, several in silico approaches became imperative for the investigation of the characteristics of ACPs. In this paper, we proposed a new machine learning tool named iACP-RF that uses a combination of several sequence-based features and an ensemble of three heterogeneously trained Random Forest classifiers to accurately predict anticancer peptides. Experimental results show that our proposed model achieves an accuracy of 75.9% which outperforms other state-of-the-art methods by a significant margin. We also achieve 0.52, 75.6%, and 76.2% in terms of Matthews Correlation Coefficient (MCC), Sensitivity, and Specificity, respectively. iACP-RF as a standalone tool and its source code are publicly available at: https://***/MLBC-lab/iACP-RF. © 2023 The Authors

关键词： Anticancer peptides Ensemble learning Feature extraction Heterogeneous classifiers Machine learning Random Forest

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Entanglement in quantum field theory via wavelet representations

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Physical Review D 2022年第3期106卷 036025-036025页

作者： Daniel J. George Yuval R. Sanders Mohsen Bagherimehrab Barry C. Sanders Gavin K. Brennen Department of Physics and Astronomy Macquarie University Sydney NSW 2109 Australia Sydney Quantum Academy Sydney NSW 2000 Australia ARC Centre of Excellence in Engineered Quantum Systems Macquarie University Sydney NSW 2109 Australia Centre for Quantum Software and Information University of Technology Sydney Sydney NSW 2007 Australia Institute for Quantum Science and Technology University of Calgary Calgary Alberta T2N 1N4 Canada Chemical Physics Theory Group Department of Chemistry University of Toronto Toronto Ontario M5G 1Z8 Canada Department of Computer Science University of Toronto Toronto Ontario M5S 2E4 Canada

Quantum field theory (QFT) describes nature using continuous fields, but physical properties of QFT are usually revealed in terms of measurements of observables at a finite resolution. We describe a multiscale representation of free scalar bosonic and Ising model fermionic QFTs using wavelets. Making use of the orthogonality and self-similarity of the wavelet basis functions, we demonstrate some well-known relations such as scale-dependent subsystem entanglement entropy and renormalization of correlations in the ground state. We also find some new applications of the wavelet transform as a compressed representation of ground states of QFTs which can be used to illustrate quantum phase transitions via fidelity overlap and holographic entanglement of purification.

关键词： Quantum entanglement Quantum information theory Quantum simulation Lattice field theory

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Enabling Research through the SCIP Optimization Suite 8.0

arXiv

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arXiv 2023年

作者： Bestuzheva, Ksenia Besançon, Mathieu Chen, Wei-Kun Chmiela, Antonia Donkiewicz, Tim van Doornmalen, Jasper Eifler, Leon Gaul, Oliver Gamrath, Gerald Gleixner, Ambros Gottwald, Leona Graczyk, Christoph Halbig, Katrin Hoen, Alexander Hojny, Christopher van der Hulst, Rolf Koch, Thorsten Lübbecke, Marco Maher, Stephen J. Matter, Frederic Mühmer, Erik Müller, Benjamin Pfetsch, Marc E. Rehfeldt, Daniel Schlein, Steffan Schlösser, Franziska Serrano, Felipe Shinano, Yuji Sofranac, Boro Turner, Mark Vigerske, Stefan Wegscheider, Fabian Wellner, Philipp Weninger, Dieter Witzig, Jakob Zuse Institute Berlin Department AIS2T Takustr. 7 Berlin14195 Germany ZIB-AIS2T Germany School of Mathematics and Statistics Beijing Institute of Technology Beijing100081 China RWTH Aachen University Lehrstuhl für Operations Research Kackertstr. 7 Aachen52072 Germany Eindhoven University of Technology Department of Mathematics and Computer Science Netherlands ZIB-AIS2T I2DAMO GmbH Englerallee 19 Berlin14195 Germany ZIB-AIS2T HTW Berlin Germany Friedrich-Alexander Universität Erlangen-Nürnberg Department of Data Science Cauerstr. 11 Erlangen91058 Germany University of Twente Department of Discrete Mathematics and Mathematical Programming P.O. Box 217 Enschede7500 AE Netherlands Technische Universität Berlin Software and Algorithms for Discrete Optimization Straße des 17. Juni 135 Berlin10623 Germany ZIB Department A2IM Takustr. 7 Berlin14195 Germany RWTH Aachen-Lehrstuhl für OR Germany University of Exeter College of Engineering Mathematics and Physical Sciences Exeter United Kingdom Technische Universität Darmstadt Fachbereich Mathematik Dolivostr. 15 Darmstadt64293 Germany TU Darmstadt Fachbereich Mathematik Germany ZIB-A2IM Germany TU Berlin Germany ZIB-A2IM Software and Algorithms for Discrete Optimization Institute of Mathematics TU Berlin Germany GAMS Software GmbH c/o ZIB-AIS2T Germany

The SCIP Optimization Suite provides a collection of software packages for mathematical optimization centered around the constraint integer programming framework SCIP. The focus of this paper is on the role of the SCIP Optimization Suite in supporting research. SCIP's main design principles are discussed, followed by a presentation of the latest performance improvements and developments in version 8.0, which serve both as examples of SCIP's application as a research tool and as a platform for further developments. Further, the paper gives an overview of interfaces to other programming and modeling languages, new features that expand the possibilities for user interaction with the framework, and the latest developments in several extensions built upon *** Codes 90C05, 90C10, 90C11, 90C30, 90C90, 65Y05 Copyright © 2023, The Authors. All rights reserved.

关键词： Integer programming

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Interactive 3D Segmentation for Primary Gross Tumor Volume in Oropharyngeal Cancer

arXiv

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arXiv 2024年

作者： Saukkoriipi, Mikko Sahlsten, Jaakko Jaskari, Joel Orsmaa, Lotta Kangas, Jari Rasouli, Nastaran Raisamo, Roope Hirvonen, Jussi Mehtonen, Helena Järnstedt, Jorma Mäkitie, Antti Naser, Mohamed Fuller, Clifton Kann, Benjamin Kaski, Kimmo Department of Computer Science Aalto University School of Science Espoo Finland Faculty of Information Technology and Communication Sciences Computing Sciences University of Tampere Tampere Finland Department of Radiology Tampere University Faculty of Medicine and Health Technology Tampere University Hospital Tampere Finland Department of Otorhinolaryngology-Head and Neck Surgery Research Program in Systems Oncology Faculty of Medicine University of Helsinki Helsinki University Hospital Helsinki Finland Department of Radiation Oncology The University of Texas MD Anderson Cancer Center HoustonTX United States Artificial Intelligence in Medicine Program Mass General Brigham Harvard Medical School BostonMA United States Department of Radiation Oncology Dana-Farber Cancer Institute Brigham and Women's Hospital Harvard Medical School BostonMA United States The Alan Turing Institute British Library 96 Euston Rd LondonNW1 2DB United Kingdom

The main treatment modality for oropharyngeal cancer (OPC) is radiotherapy, where accurate segmentation of the primary gross tumor volume (GTVp) is essential. However, accurate GTVp segmentation is challenging due to significant interobserver variability and the time-consuming nature of manual annotation, while fully automated methods can occasionally fail. An interactive deep learning (DL) model offers the advantage of automatic high-performance segmentation with the flexibility for user correction when necessary. In this study, we examine interactive DL for GTVp segmentation in OPC. We implement state-of-the-art algorithms and propose a novel two-stage Interactive Click Refinement (2S-ICR) framework. Using the 2021 HEad and neCK TumOR (HECKTOR) dataset for development and an external dataset from The University of Texas MD Anderson Cancer Center for evaluation, the 2S-ICR framework achieves a Dice similarity coefficient of 0.713 ± 0.152 without user interaction and 0.824 ± 0.099 after five interactions, outperforming existing methods in both cases. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Experimental Investigation of Lord Kelvin’s Isentropic Cooling and Heating Expression in Tensile Bars for Two Engineering Alloys

arXiv

引用

arXiv 2023年

作者： Burns, S.J. Pratt, Christine E. Carrock, Joseph Gagnon, Jean-Philippe Sefkow, A.B. Materials Science Program College of Arts Science and Engineering University of Rochester RochesterNY14627 United States Department of Mechanical Engineering University of Rochester 236 Hopeman Hall RochesterNY14627 United States Field Application Engineering Telops GenevaNY14456 United States Systems Engineering Telops 100-2600 St-Jean-Baptiste Ave. QuébecQCG2E 6J5 Canada Laboratory for Laser Energetics University of Rochester 250 E. River Rd. RochesterNY14623 United States Department of Physics and Astronomy University of Rochester 206 Bausch and Lomb Hall RochesterNY14627 United States Department of Computer Science University of Rochester 2513 Wegmans Hall RochesterNY14627 United States

Solids when rapidly and elastically stressed change temperature, the effect proposed by Lord Kelvin is adiabatic thermo-elastic cooling or heating depending on the sign of the stress. A fast sensitive IR camera has measured temperature both decreasing and increasing. Temperature measurements made from the reversible, elastic part of the stress-strain curve during fast uniaxial tensile loading have been investigated. The isentropic temperature cooling from the loading curve is recovered by heating after the specimen fractures when the load is released. These measurements establish for the first time isentropic thermal recovery in two engineering alloys. The materials tested are an AISI 4340 steel and an aluminum 2024 alloy. Measurements of the isentropic thermo-elastic stress cooling are -0.61 K/GPa for steel and -1.7 K/GPa for aluminum alloy. The isentropic thermo-elastic stress heating is -1.16 K/GPa for steel and -1.6 K/GPa for aluminum alloy. These values are compared to Lord Kelvin’s theoretical thermodynamic prediction. Comparisons are also to equations of state tables. The experimental values are slightly below theoretical predictions. The isentropic, elastic part of the temperature is fully recoverable even after extensive plastic deformation upon fracture. Copyright © 2023, The Authors. All rights reserved.

关键词： Cooling

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DeepAmp: A convolutional neural network based tool for predicting protein AMPylation sites from binary profile representation

Research Square

引用

Research Square 2021年

作者： Azim, Sayed Mehedi Sharma, Alok Shatabda, Swakkhar Dehzangi, Abdollah Department of Computer Science and Engineering United International University Plot-2 United City Madani Avenue Badda Dhaka1212 Bangladesh Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama230-0045 Japan Department of Computer Science Rutgers University CamdenNJ08102 United States Center for Computational and Integrative Biology Rutgers University CamdenNJ08102 United States

AMPylation is an emerging post-translational modification that occurs on the hydroxyl group of threonine, serine, or tyrosine via a phosphodiester bond. AMPylators catalyze this process as covalent attachment of adenosine monophosphate to the amino acid side chain of a peptide. Recent studies have shown that this post-translational modification is directly responsible for regulation of neurodevelopment and neurodegeneration and also involved in many physiological processes. Despite the importance of this post-translational modification, there is no peptide sequence dataset available for conducting computational analysis. Therefore, so far, no computational approach has been proposed for predicting AMPylation. In this study, we introduce a new dataset of this distinct post-translational modification and develop a new machine learning tool using a deep convolutional neural network called DeepAmp to predict AMPylation sites in proteins. DeepAmp achieves 77.7%, 79.1%, 76.8%, and 0.55 in terms of Accuracy, Sensitivity, Specificity, and Matthews Correlation Coefficient (MCC) for AMPylation site prediction task, respectively. As the first machine learning model, DeepAmp demonstrate promising results which highlight its potential to solve this problem. Our presented dataset and DeepAmp as a standalone predictor are publicly available at https://***/MehediAzim/DeepAmp. © 2021, CC BY.

关键词： Peptides

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SEL-COVIDNET: An intelligent application for the diagnosis of COVID-19 from chest X-rays and CT-scans

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Informatics in Medicine Unlocked 2022年 32卷 101059页

作者： Smadi, Ahmad Al Abugabah, Ahed Al-smadi, Ahmad Mohammad Almotairi, Sultan School of Artificial Intelligence Xidian University No. 2 South Taibai Road Xian 710071 China College of Technological Innovation Zayed University Abu Dhabi Campus United Arab Emirates Department of Computer Science Al-Balqa Applied University Ajloun University College Jordan Faculty of Community College Majmaah University Al Majma'ah Saudi Arabia Department of Information Systems Faculty of Computer and Information Sciences Islamic university of Madinah 42351 Saudi Arabia

COVID-19 detection from medical imaging is a difficult challenge that has piqued the interest of experts worldwide. Chest X-rays and computed tomography (CT) scanning are the essential imaging modalities for diagnosing COVID-19. All researchers focus their efforts on developing viable methods and rapid treatment procedures for this pandemic. Fast and accurate automated detection approaches have been devised to alleviate the need for medical professionals. Deep Learning (DL) technologies have successfully recognized COVID-19 situations. This paper proposes a developed set of nine deep learning models for diagnosing COVID-19 based on transfer learning and implementation in a novel architecture (SEL-COVIDNET). We include a global average pooling layer, flattening, and two dense layers that are fully connected. The model's effectiveness is evaluated using balanced and unbalanced COVID-19 radiography datasets. After that, our model's performance is analyzed using six evaluation measures: accuracy, sensitivity, specificity, precision, F1-score, and Matthew's correlation coefficient (MCC). Experiments demonstrated that the proposed SEL-COVIDNET with tuned DenseNet121, InceptionResNetV2, and MobileNetV3Large models outperformed the results of comparative SOTA for multi-class classification (COVID-19 vs. No-finding vs. Pneumonia) in terms of accuracy (98.52%), specificity (98.5%), sensitivity (98.5%), precision (98.7%), F1-score (98.7%), and MCC (97.5%). For the COVID-19 vs. No-finding classification, our method had an accuracy of 99.77%, a specificity of 99.85%, a sensitivity of 99.85%, a precision of 99.55%, an F1-score of 99.7%, and an MCC of 99.4%. The proposed model offers an accurate approach for detecting COVID-19 patients, which aids in the containment of the COVID-19 pandemic. © 2022 The Author(s)

关键词： Classification COVID-19 CT-scans Deep Learning Pneumonia Transfer learning X-ray images

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Issues of organization of expertise and problems of expert assessments 23

Issues of organization of expertise and problems of expert a...

引用

23rd International Conference on Soft Computing and Measurement, SCM 2020

作者： Paderno, P.I. Burkov, E.A. Lavrov, E.A. Department of Information Systems Saint Petersburg Electrotechnical University «LETI» Professora Popova 5 St. Petersburg197376 Russia Department of Algorithmic Mathematics Saint Petersburg Electrotechnical University «LETI» Professora Popova 5 St. Petersburg197376 Russia Department of Computer Science Sumy State University Rymskogo-Korsakova 2 Sumy40007 Ukraine

In the course of preparation and carrying out of various expertises, in a number of cases methodological and organizational problems arise due to both the insufficiently high level of training of the working group members who develop the program and methodology of the expertise, and various restrictions, that could have a material impact on the credibility of the results. These restrictions generally are either financial in nature or are associated with a lack of sufficiently qualified and objective experts in the subject area. Objective: to develop a set of techniques that allow to expand the possibilities of using expert assessment methods in the preparation and carrying out of modern expertise in the presence of restrictions associated with external conditions. Methods: methods and approaches of system analysis, integration of individual expert assessments are used. Results: a comprehensive analysis of the problems faced by specialists in the organization of various expertises has been carried out. A number of techniques have been proposed that make it possible to significantly simplify the process of preparing and carrying out expertises but at the same time do not negatively affect the adequacy of the results obtained. Practical significance: the results obtained can be widely used in the course of various expertises, both in the process of selecting experts and in the process of direct obtaining and combining expert assessments. In addition, the use of obtained results in terms of different organizational and material restrictions will allow to expand the scope of possible applications of expert assessments. Prospects: the proposed approach and the set of techniques lay the methodological basis for creating a decision support system that will give specific recommendations to specialists involved in the development of program and methodology of the expertise at all stages of their activities. The introduction of such a system will greatly contribute to the simp

关键词： Decision support systems

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Fractal dimension and retinal pathology: A meta-analysis

arXiv

引用

arXiv 2021年

作者： Yu, Sam Lakshminarayanan, Vasudevan Department of Physics and Astronomy University of Waterloo ONN2L 3G1 Canada Theoretical and Experimental Epistemology Lab School of Optometry and Vision Science Department of Electrical and Computer Engineering University of Waterloo ONN2L 3G1 Canada Department of Systems Design Engineering University of Waterloo ONN2L 3G1 Canada

Due to the fractal nature of retinal blood vessels, the retinal fractal dimension is a natural parameter for researchers to explore and has garnered interest as a potential diagnostic tool. This review aims to summarize the current scientific evidence regarding the relationship between fractal dimension and retinal pathology and thus assess the clinical value of retinal fractal dimension. Following the PRISMA guidelines, a literature search for research articles was conducted in several internet databases (Embase, PubMed, Web of science, Scopus). This led to a result of 28 studies included in the final review, which were analyzed via meta-analysis to determine whether the fractal dimension changes significantly in retinal disease versus normal individuals. © 2021, CC BY.

关键词： Ophthalmology

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