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An integrated framework to develop domain-specific languages: Extended case study 1

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6th International Conference on Model-Driven engineering and Software Development, MODELSWARD 2018

作者： Zarrin, Bahram Baumeister, Hubert Sarjoughian, Hessam DTU Compute Technical University of Denmark Kgs Lyngby2800 Denmark School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States

ISBN: (数字)9783030110307

ISBN: (纸本)9783030110291

In this paper, we propose an integrated framework to formally specify the syntax and the semantics of domain-specific languages. We build this framework by integrating the Microsoft DSL Tools, a framework to develop graphical domain-specific languages, and an extension of the ForSpec, a logic-based specification language. The motivation for proposing this framework is the lack of a formal and rigorous approach by DSL Tools for semantics specifications. We combine the aforementioned technologies under the umbrella of Microsoft Visual Studio IDE to facilitate the development of graphical DSLs within a single development environment. We use the Microsoft DSL Tools to specify the metamodel and graphical notations for DSLs, and our extension of the ForSpec, offering better support for semantic specifications. As a case study, we develop a modeling language to design domain-specific flow-based languages. © 2019, Springer Nature Switzerland AG.

关键词： Specification languages

An Ising Machine Formulation for Design Updates in Topology Optimization of Flow Channels

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arXiv 2024年

作者： Suzuki, Yudai Aoki, Shiori Key, Fabian Endo, Katsuhiro Matsuda, Yoshiki Tanaka, Shu Behr, Marek Muramatsu, Mayu Quantum Computing Center Keio University Yokohama Kanagawa Japan Graduate School of Science and Technology Keio University Yokohama Kanagawa Japan Institute of Lightweight Design and Structural Biomechanics TU Wien Austria Tsukuba Ibaraki Japan Fixstars Corporation Tokyo Japan Green Computing System Research Organization Waseda University Tokyo Japan Department of Applied Physics and Physico-Informatics Keio University Yokohama Kanagawa Japan Keio University Tokyo Japan Keio University Tokyo Japan Computational Analysis of Technical Systems RWTH Aachen University Germany Department of Mechanical Engineering Keio University Yokohama Kanagawa Japan

Topology optimization is an essential tool in computational engineering, for example, to improve the design and efficiency of flow channels. At the same time, Ising machines, including digital or quantum annealers, have been used as efficient solvers for combinatorial optimization problems. Beyond combinatorial optimization, recent works have demonstrated applicability to other engineering tasks by tailoring corresponding problem formulations. In this study, we present a novel Ising machine formulation for computing design updates during topology optimization with the goal of minimizing dissipation energy in flow channels. We explore the potential of this approach to improve the efficiency and performance of the optimization process. To this end, we conduct experiments to study the impact of various factors within the novel formulation. Additionally, we compare it to a classical method using the number of optimization steps and the final values of the objective function as indicators of the time intensity of the optimization and the performance of the resulting designs, respectively. Our findings show that the proposed update strategy can accelerate the topology optimization process while producing comparable designs. However, it tends to be less exploratory, which may lead to lower performance of the designs. These results highlight the potential of incorporating Ising formulations for optimization tasks but also show their limitations when used to compute design updates in an iterative optimization process. In conclusion, this work provides an efficient alternative for design updates in topology optimization and enhances the understanding of integrating Ising machine formulations in engineering optimization. © 2024, CC BY-NC-ND.

关键词： Shape optimization

Cortical morphometry analysis based on worst transportation theory

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arXiv 2021年

作者： Zhang, Min An, Dongsheng Lei, Na Wu, Jianfeng Zhao, Tong Xu, Xiaoyin Wang, Yalin Gu, Xianfeng Zhejiang University China Department of Computer Science Stony Brook University Brookhaven United States DUT-RU ISE Dalian University of Technology China School of Computing Informatics and Decision Systems Engineering Arizona State University Arizona United States Université Côte d'Azur Inria Brigham and Women's Hospital Harvard Medical School

Biomarkers play an important role in early detection and intervention in Alzheimer's disease (AD). However, obtaining effective biomarkers for AD is still a big challenge. In this work, we propose to use the worst transportation cost as a univariate biomarker to index cortical morphometry for tracking AD progression. The worst transportation (WT) aims to find the least economical way to transport one measure to the other, which contrasts to the optimal transportation (OT) that finds the most economical way between measures. To compute the WT cost, we generalize the Brenier theorem for the OT map to the WT map, and show that the WT map is the gradient of a concave function satisfying the Monge-Ampere equation. We also develop an efficient algorithm to compute the WT map based on computational geometry. We apply the algorithm to analyze cortical shape difference between dementia due to AD and normal aging individuals. The experimental results reveal the effectiveness of our proposed method which yields better statistical performance than other competiting methods including the OT. © 2021, CC BY-NC-SA.

关键词： Neurodegenerative diseases

Critical avalanches of Susceptible-Infected-Susceptible dynamics in finite networks

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arXiv 2023年

作者： Notarmuzi, Daniele Flammini, Alessandro Castellano, Claudio Radicchi, Filippo Institut für Theoretische Physik TU Wien Wiedner Hauptstraße 8-10 WienA-1040 Austria Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Via dei Taurini 19 RomeI-00185 Italy Centro Ricerche Enrico Fermi Piazza del Viminale 1 Rome00184 Italy

We investigate the avalanche temporal statistics of the Susceptible-Infected-Susceptible (SIS) model when the dynamics is critical and takes place on finite random networks. By considering numerical simulations on annealed topologies we show that the survival probability always exhibits three distinct dynamical regimes. Size-dependent crossover timescales separating them scale differently for homogeneous and for heterogeneous networks. The phenomenology can be qualitatively understood based on known features of the SIS dynamics on networks. A fully quantitative approach based on Langevin theory is shown to perfectly reproduce the results for homogeneous networks, while failing in the heterogeneous case. The analysis is extended to quenched random networks, which behave in agreement with the annealed case for strongly homogeneous and strongly heterogeneous networks. © 2023, CC BY.

关键词： Heterogeneous networks

Fair Patient Model: Mitigating Bias in the Patient Representation Learned from the Electronic Health Records

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arXiv 2023年

作者： Sivarajkumar, Sonish Huang, Yufei Wang, Yanshan Intelligent Systems Program School of Computing and Information University of Pittsburgh PittsburghPA United States Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA United States Department of Health Information Management University of Pittsburgh PittsburghPA United States Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Clinical and Translational Science Institute University of Pittsburgh PittsburghPA United States

Objective: To pre-train fair and unbiased patient representations from Electronic Health Records (EHRs) using a novel weighted loss function that reduces bias and improves fairness in deep representation learning models. Methods: We defined a new loss function, called weighted loss function, in the deep representation learning model to balance the importance of different groups of patients and features. We applied the proposed model, called Fair Patient Model (FPM), to a sample of 34,739 patients from the MIMIC-III dataset and learned patient representations for four clinical outcome prediction tasks. Results: FPM outperformed the baseline models in terms of three fairness metrics: demographic parity, equality of opportunity difference, and equalized odds ratio. FPM also achieved comparable predictive performance with the baselines, with an average accuracy of 0.7912. Feature analysis revealed that FPM captured more information from clinical features than the baselines. Conclusion: FPM is a novel method to pre-train fair and unbiased patient representations from EHR data using a weighted loss function. The learned representations can be used for various downstream tasks in healthcare and can be extended to other domains where bias and fairness are important. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

The Temporal Structural Pattern in Scientific Collaborative Behavior from the Perspective of Complex Network 23rd

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The Temporal Structural Pattern in Scientific Collaborative...

23rd International Symposium on Knowledge and systems Sciences, KSS 2024

作者： Zholdoshbaeva, Elina Zhang, Shuang Liu, Feifan Xia, Haoxiang Institute of Systems Engineering Dalian University of Technology No. 2 Linggong Road Liaoning Dalian116024 China Center for Big Data and Intelligent Decision-Making Dalian University of Technology Linggong Road Liaoning Dalian116024 China School of Business East China University of Science and Technology 130 Meilong Road Xuhui District Shanghai200237 China Institute for Advanced Intelligence Dalian University of Technology Linggong Road Liaoning Dalian116024 China Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education of China Linggong Road Liaoning Dalian116024 China

ISBN: (纸本)9789819601776

The rapid advancement of science has underscored the importance of collaboration among scientists. While many studies have explored the structure of collaboration networks, there is a lack of comprehensive quantitative research on the temporal patterns of these collaborations. This study addresses this gap by analyzing temporal scientific collaboration networks in physics, spanning from 1960 to 2021, using complex network theories and methods. By constructing five-year temporal collaborative networks and employing metrics such as network diameter, shortest path, and clustering coefficient, this research reveals an increase in connectivity and small-world characteristics over time. Additionally, community detection highlights a shift towards specialized collaboration patterns and increased cross-community interactions. The study also uncovers growing centrality and social stratification within the research system, with the "rich club" phenomenon indicating strong and dynamic collaborations among highly connected scientists. Temporal analysis shows that while ultra-high connectivity scientists engage in fluctuating relationships, medium-high connectivity scientists maintain more stable collaborations. These findings provide insights into the evolution of collaborative behaviors and inform science policy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collaborative learning

Brain multigraph prediction using topology-aware adversarial graph neural network

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arXiv 2021年

作者： Bessadok, Alaa Mahjoub, Mohamed Ali Rekik, Islem BASIRA Lab Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Higher Institute of Informatics and Communication Technologies University of Sousse Tunisia National Engineering School of Sousse University of Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia School of Science and Engineering Computing University of Dundee United Kingdom

Brain graphs (i.e, connectomes) constructed from medical scans such as magnetic resonance imaging (MRI) have become increasingly important tools to characterize the abnormal changes in the human brain. Due to the high acquisition cost and processing time of multimodal MRI, existing deep learning frameworks based on Generative Adversarial Network (GAN) focused on predicting the missing multimodal medical images from a few existing modalities. While brain graphs help better understand how a particular disorder can change the connectional facets of the brain, synthesizing a target brain multigraph (i.e, multiple brain graphs) from a single source brain graph is strikingly lacking. Additionally, existing graph generation works mainly learn one model for each target domain which limits their scalability in jointly predicting multiple target domains. Besides, while they consider the global topological scale of a graph (i.e., graph connectivity structure), they overlook the local topology at the node scale (e.g., how central a node is in the graph). To address these limitations, we introduce topology-aware graph GAN architecture (topoGAN), which jointly predicts multiple brain graphs from a single brain graph while preserving the topological structure of each target graph. Its three key innovations are: (i) designing a novel graph adversarial auto-encoder for predicting multiple brain graphs from a single one, (ii) clustering the encoded source graphs in order to handle the mode collapse issue of GAN and proposing a cluster-specific decoder, (iii) introducing a topological loss to force the prediction of topologically sound target brain graphs. The experimental results using five target domains demonstrated the outperformance of our method in brain multigraph prediction from a single graph in comparison with baseline approaches. © 2021, CC BY-NC-SA.

关键词： Graphic methods

Guidelines for reporting cell types: The MIRACL standard

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arXiv 2022年

作者： Lubiana, Tiago Roncaglia, Paola Mungall, Christopher J. Quardokus, Ellen M. Fortriede, Joshua D. Osumi-Sutherland, David Diehl, Alexander D. School of Pharmaceutical Sciences University of São Paulo SP São Paulo Brazil Ronin Institute United States Wellcome Genome Campus Hinxton Cambridge CB10 1SD United Kingdom Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Intelligent Systems Engineering Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center CincinnatiOH45224 United States Department of Biomedical Informatics University at Buffalo Jacobs School of Medicine and Biomedical Sciences BuffaloNY14203 United States

Cell types are at the root of modern biology, and describing them is a core task of the Human Cell Atlas project. Surprisingly, there are no standards for reporting new cell types, leading to a gap between classes mentioned in biomedical literature and the Cell Ontology, the primary registry of cell types. Here we introduce the Minimal Information Reporting About a CelL (MIRACL) standard, a guideline for describing cell types alongside scientific articles. In a MIRACL sheet, authors organize a label, a diagnostic description, a taxon, an anatomical structure, and a parent cell class for each cell type of interest. The MIRACL standard bridges the gap between wet-lab researchers and ontologists, facilitating the integration of biomedical knowledge into ontologies and artificial intelligence systems. © 2022, CC BY.

关键词： Cells

Topological descriptors for Parkinson⇔s disease classification and regression analysis

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arXiv 2020年

作者： Nawar, Afra Rahman, Farhan Krishnamurthi, Narayanan Som, Anirudh Turaga, Pavan School of Electrical Computer and Energy Engineering Arizona State University School of Computing Informatics and Decision Systems Engineering Arizona State University Edson College of Nursing and Health Innovation Arizona State University School of Arts Media and Engineering Arizona State University

At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson’s disease classification and severity assessment. An automated, stable, and accurate method to evaluate Parkinson’s would be significant in streamlining diagnoses of patients and providing families more time for corrective measures. We propose a methodology which incorporates TDA into analyzing Parkinson’s disease postural shifts data through the representation of persistence images. Studying the topology of a system has proven to be invariant to small changes in data and has been shown to perform well in discrimination tasks. The contributions of the paper are twofold. We propose a method to 1) classify healthy patients from those afflicted by disease and 2) diagnose the severity of disease. We explore the use of the proposed method in an application involving a Parkinson’s disease dataset comprised of healthy-elderly, healthy-young and Parkinson’s disease patients. Our code is available at https://***/itsmeafra/Sublevel-Set-TDA. Copyright © 2020, The Authors. All rights reserved.

关键词： Hospital data processing