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arXiv 2023年

作者： Choi, Junggu Kim, Kion Park, Soohyun Hur, Juyoen Yang, Hyunjung Kim, Younghoon Lee, Hakbae Han, Sanghoon Yonsei Graduate program in Cognitive Science Yonsei University Seoul03722 Korea Republic of Korea Quantum Computing© Seoul06164 Korea Republic of Department of Psychology Yonsei University Seoul03722 Korea Republic of University College Yonsei University Seoul03722 Korea Republic of Deparment of Applied Statistics Yonsei University Seoul03722 Korea Republic of Department of Statistics and Data science Yonsei University Seoul03722 Korea Republic of

Diverse cases regarding the impact, with its related factors, of the COVID-19 pandemic on mental health have been reported in previous studies. College student groups have been frequently selected as the target population in previous studies because they are easily affected by pandemics. In this study, multivariable datasets were collected from 751 college students based on the complex relationships between various mental health factors. We utilized quantum annealing (QA)-based feature selection algorithms that were executed by commercial D-Wave quantum computers to determine the changes in the relative importance of the associated factors before and after the pandemic. Multivariable linear regression (MLR) and XGBoost models were also applied to validate the QA-based algorithms. Based on the experimental results, we confirm that QA-based algorithms have comparable capabilities in factor analysis research to the MLR models that have been widely used in previous studies. Furthermore, the performance of the QA-based algorithms was validated through the important factor results from the algorithms. Pandemic-related factors (e.g., confidence in the social system) and psychological factors (e.g., decision-making in uncertain situations) were more important in post-pandemic conditions. We believe that our study will serve as a reference for researchers studying similar topics. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiple linear regression

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Variational tensor network operator

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Physical Review Research 2022年第4期4卷 043153-043153页

作者： Yu-Hsueh Chen Ke Hsu Wei-Lin Tu Hyun-Yong Lee Ying-Jer Kao Center for Theoretical Physics and Department of Physics National Taiwan University Taipei 10607 Taiwan Division of Display and Semiconductor Physics Korea University Sejong 30019 Korea Department of Applied Physics Graduate School Korea University Sejong 30019 Korea Interdisciplinary Program in E·ICT-Culture-Sports Convergence Korea University Sejong 30019 Korea Center for Quantum Science and Technology National Taiwan University Taipei 10607 Taiwan National Center for High-Performance Computing Hsinchu City 30076 Taiwan

We propose a simple and generic construction of the variational tensor network operators to study the quantum spin systems by the synergy of ideas from the imaginary-time evolution and the variational optimization of trial wave functions. By applying these operators to simple initial states, accurate variational ground-state wave functions with extremely few parameters can be obtained. Furthermore, the framework can be applied to spontaneously study symmetry-breaking, symmetry-protected topological, and intrinsic topologically ordered phases, and we show that symmetries of the local tensors associated with these phases can emerge directly after the optimization without any gauge fixing. This provides a universal way to identify quantum phase transitions without prior knowledge of the system.

关键词： Magnetism Topological order Quantum many-body systems Tensor network methods

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K, T and D-like fuzzy Kripke models

K, T and D-like fuzzy Kripke models

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Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： Benjamin Bedregal Regivan H. N. Santiago Mario Benevides Petrúcio Viana Group of Logic Language Information Theory and Applications-LoLITA Department of Informatics and Applied Mathematics Federal University of Rio Grande do Norte Natal Rio Grande do Norte Brazil Program of Engineering of Systems and Computing-PESC Department of Computer Science Federal University of Rio de Janeiro Rio de Janeiro Rio de Janeiro Brazil Department of Analysis Fluminense Federal University (UFF) Niteroi Rio de Janeiro Brazil

The idea of a Kripke semantics endowed with possibility/plausibility information is not new; in fact there are different approaches for that; see: [6], [13], [16], [19]. This paper follows the approach found in [6], but whereas [6] provides a fixed interpretation for connectives into [0,1] here we provide a characterization of a fuzzy semantics for connectives in such a way that the resulting fuzzy frames: K, T and D, are described precisely by the set of statements which also describes, respectively, the usual modal systems K, T and D.

关键词： Semantics Fuzzy sets Fuzzy logic Bismuth Electronic mail Uncertainty Reliability

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DP Compress: a Model Compression Scheme for Generating Efficient Deep Potential Models

arXiv

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arXiv 2021年

作者： Lu, Denghui Jiang, Wanrun Chen, Yixiao Zhang, Linfeng Jia, Weile Wang, Han Chen, Mohan HEDPS CAPT College of Engineering Peking University Beijing100871 China Songshan Lake Materials Laboratory Guangdong Dongguan523808 China Institute of Physics Chinese Academy of Sciences Beijing100190 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Beijing Institute of Big Data Research Beijing100871 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China

Machine-learning-based interatomic potential energy surface (PES) models are revolutionizing the field of molecular modeling. However, although much faster than electronic structure schemes, these models suffer from costly computations via deep neural networks to predict the energy and atomic forces, resulting in lower running efficiency as compared to the typical empirical force fields. Herein, we report a model compression scheme for boosting the performance of the Deep Potential (DP) model, a deep learning based PES model. This scheme, we call DP Compress, is an efficient post-processing step after the training of DP models (DP Train). DP Compress combines several DP-specific compression techniques, which typically speed up DP-based molecular dynamics simulations by an order of magnitude faster, and consume an order of magnitude less memory. We demonstrate that DP Compress is sufficiently accurate by testing a variety of physical properties of Cu, H2O, and Al-Cu-Mg systems. DP Compress applies to both CPU and GPU machines and is publicly available online. Copyright © 2021, The Authors. All rights reserved.

关键词： Molecular dynamics

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An ontology-based repository for a spontaneous social network

An ontology-based repository for a spontaneous social networ...

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International Conference on Computational Aspects of Social Networks, CASON

作者： Bryan Garber da Silva Cristiano André da Costa Dante Zaupa Gustavo Freitas Universidade do Vale do Rio dos Sinos - UNISINOS São Leopoldo Brazil Applied Computing Graduate Program - PIPCA Universidade do Vale do Rio dos Sinos - UNISINOS São Leopoldo Brazil CNPq Technological and Inovation Fellow Universidade do Vale do Rio dos Sinos – UNISINOS São Leopoldo Brazil Universidade do Vale do Rio dos Sinos – UNISINOS São Leopoldo Brazil

With the latest technological advancements and the popularization of mobile devices nowadays, software development has evolved towards mobility. One of the main uses of mobile devices is to access social networks. They are becoming important tools in people's life. In this context, the Mingle project has the objective of creating a spontaneous social networks that is mainly accessed by mobile devices, such as smartphones. Mingle uses the concepts of ubiquitous computing to create a notion of spontaneity, i.e, providing a social network adapted to the persons specific location. This article has the goal of presenting an ontology and the persistence model for managing the information inside a specific location (called “Cell” in our model). To evaluate our proposal, two technics were applied: lexical evaluation and application-level evaluation. The first assessment employs the Levenshtein edit distance and compares Mingle with other popular ontologies in the field, while the second one evaluates if we deal with all Mingle requirements.

关键词： Ontologies Social network services Prototypes Servers Knowledge based systems Mobile handsets Libraries

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A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging

A BCLC Staging System for Hepatocellular Carcinoma using Swi...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Shun-Cheng Chang Pochuang Wang Weichung Wang Tung-Hung Su Jia-Horng Kao Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Department of Computer Science and Information Engineering NTU Taipei Taiwan NTU Institute of Applied Mathematical Sciences Taipei Taiwan Department of Internal Medicine Division of Gastroenterology and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan NTU Center for Advanced Computing and Imaging in Biomedicine Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages.

关键词： Liver cancer Accuracy Computed tomography Biological system modeling Transformers Planning Biomedical imaging

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ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular ...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Shun-Cheng Chang Hsin-Pei Yu Yi-Hsien Hsieh Pochuang Wang Weichung Wang Tung-Hung Su Jia-Horng Kao Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Department of Computer Science and Information Engineering NTU Taipei Taiwan Institute of Applied Mathematical Sciences NTU Taipei Taiwan Department of Internal Medicine Division of Gastroenterology and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag.

关键词： Visualization Accuracy Three-dimensional displays Liver Transformers Planning Convolutional neural networks Context modeling Biomedical imaging Tumors

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programming Variational Quantum Circuits with Quantum-Train Agent

arXiv

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arXiv 2024年

作者： Liu, Chen-Yu Chen, Samuel Yen-Chi Chen, Kuan-Cheng Huang, Wei-Jia Chang, Yen-Jui Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Research Institute Taipei Taiwan Wells Fargo New YorkNY United States Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Imperial College London London United Kingdom Quantum Information Center Chung Yuan Christian University Taoyuan City Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City Taiwan

In this study, the Quantum-Train Quantum Fast Weight programmer (QT-QFWP) framework is proposed, which facilitates the efficient and scalable programming of variational quantum circuits (VQCs) by leveraging quantum-driven parameter updates for the classical slow programmer that controls the fast programmer VQC model. This approach offers a significant advantage over conventional hybrid quantum-classical models by optimizing both quantum and classical parameter management. The framework has been benchmarked across several time-series prediction tasks, including Damped Simple Harmonic Motion (SHM), NARMA5, and Simulated Gravitational Waves (GW), demonstrating its ability to reduce parameters by roughly 70-90% compared to Quantum Long Short-term Memory (QLSTM) and Quantum Fast Weight programmer (QFWP) without compromising accuracy. The results show that QT-QFWP outperforms related models in both efficiency and predictive accuracy, providing a pathway toward more practical and cost-effective quantum machine learning applications. This innovation is particularly promising for near-term quantum systems, where limited qubit resources and gate fidelities pose significant constraints on model complexity. QT-QFWP enhances the feasibility of deploying VQCs in time-sensitive applications and broadens the scope of quantum computing in machine learning domains. Copyright © 2024, The Authors. All rights reserved.

关键词： Long short-term memory

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Pipel: Exploiting resource reorganisation to optimise performance of pipeline-structured applications in the cloud

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International Journal of Computational Systems Engineering 2019年第1期5卷 1-17页

作者： Meyer, Vinicius Rodrigues, Vinicius Facco da Rosa Righi, Rodrigo da Costa, Cristiano André Galante, Guilherme Both, Cristiano Bonato Applied Computing Graduate Program Unisinos University Av. Unisinos 950 - Cristo Rei Rio Grande do Sul São Leopoldo Brazil Western Paraná State University UNIOESTE R. Universitária 2069 - Jardim Universitário Paraná Cascavel Brazil Federal University of Health Sciences of Porto Alegre - UFCSPA R. Sarmento Leite 245 - Centro Histórico Rio Grande do Sul Porto Alegre Brazil

Workflow has become a standard for many scientific applications that are characterized by a collection of processing elements. Particularly, a pipeline application is a type of workflow that receives a set of tasks, which must pass through all processing elements in a linear fashion. However, the strategy of using a fixed number of resources can cause under- or over-provisioning situations, besides not fitting irregular demands. In this context, our idea is to deploy the pipeline application in the cloud, so executing it with a feature that differentiates cloud from other distributed systems: resource elasticity. Thus, we propose Pipel: a reactive elasticity model that uses lower and upper load thresholds and the CPU metric to on-the-fly select the most appropriated number of compute nodes for each stage along the pipeline execution. The results were promising, presenting an average gain of 38% in the application time when comparing non-elastic and elastic executions. © 2019 Inderscience Enterprises Ltd.

关键词： Pipelines

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5th International Conference on Variable Neighborhood Search (ICVNS'17)

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Electronic Notes in Discrete Mathematics 2018年 66卷 1-5页

作者： Vitor Nazário Coelho Haroldo Gambini Santos Igor Machado Coelho Puca Huachi Vaz Penna Thays A. Oliveira Marcone Jamilson Freitas Souza Angelo Sifaleras Institute of Computer Science Universidade Federal Fluminense Niterói Brazil Grupo da Causa Humana Ouro Preto Brazil Department of Computing Universidade Federal de Ouro Preto Ouro Preto Brazil Institute of Mathematics Statitics Universidade Estadual do Rio de Janeiro Rio de Janeiro Brazil Graduate Program of the Department of Engineering and Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain Department of Applied Informatics School of Information Sciences University of Macedonia 156 Egnatia Str. Thessaloniki 54636 Greece

This volume presents selected, peer-reviewed, short papers that were accepted for presentation in the 5th International Conference on Variable Neighborhood Search (ICVNS'17) which was held in Ouro Preto, Brazil, d... 详细信息

关键词： Metaheuristics Variable Neighborhood Search Discrete Optimization Nonlinear optimization

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