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arXiv 2024年

作者： Moreno, Matthew Andres Yang, Connor Dolson, Emily Zaman, Luis Department of Ecology and Evolutionary Biology University of Michigan Ann Arbor United States Center for the Study of Complex Systems University of Michigan Ann Arbor United States Michigan Institute for Data Science University of Michigan Ann Arbor United States Undergraduate Research Opportunities Program University of Michigan Ann Arbor United States Department of Computer Science and Engineering Michigan State University East Lansing United States Program in Ecology Evolution and Behavior Michigan State University East Lansing United States

Continuing improvements in computing hardware are poised to transform capabilities for in silico modeling of cross-scale phenomena underlying major open questions in evolutionary biology and artificial life, such as transitions in individuality, eco-evolutionary dynamics, and rare evolutionary events. Emerging ML/AI-oriented hardware accelerators, like the 850,000 processor Cerebras Wafer Scale Engine (WSE), hold particular promise. However, many practical challenges remain in conducting informative evolution experiments that efficiently utilize these platforms’ large processor counts. Here, we focus on the problem of extracting phylogenetic information from agent-based evolution on the WSE platform. This goal drove significant refinements to decentralized in silico phylogenetic tracking, reported here. These improvements yield order-of-magnitude performance improvements. We also present an asynchronous island-based genetic algorithm (GA) framework for WSE hardware. Emulated and on-hardware GA benchmarks with a simple tracking-enabled agent model clock upwards of 1 million generations a minute for population sizes reaching 16 million agents. This pace enables quadrillions of agent replication events a day. We validate phylogenetic reconstructions from these trials and demonstrate their suitability for inference of underlying evolutionary conditions. In particular, we demonstrate extraction, from wafer-scale simulation, of clear phylometric signals that differentiate runs with adaptive dynamics enabled versus disabled. Together, these benchmark and validation trials reflect strong potential for highly scalable agent-based evolution simulation that is both efficient and observable. Developed capabilities will bring entirely new classes of previously intractable research questions within reach, benefiting further explorations within the evolutionary biology and artificial life communities across a variety of emerging high-performance computing platforms. Copyright © 20

关键词： Genetic algorithms

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Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using a Deep Learning Approach

Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using ...

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International Symposium on Applied Science 2024, ISAS 2024

作者： Raza, Aquib Ching, Congo Tak Shing Liu, Peng-Ta Phan, Thien-Luan Tran, Ngoc-Luan Nguyen, Ha Anh T. Rameez, Mohammad Li, Hung-Chung Linh, Dao Nguyen Phuong Van Hieu, Nguyen Tran, Trung Nghia National Taiwan University Taipei City10673 Taiwan Research Center for Information Technology Academia Sinica Taipei City11529 Taiwan Graduate Institute of Biomedical Engineering National Chung Hsing University Taichung402 Taiwan Department of Electrical Engineering National Chi Nan University Nantou County Taiwan Advanced Plant and Food Crop Biotechnology Center National Chung Hsing University Taichung402 Taiwan International Doctoral Program in Agriculture National Chung Hsing University Taichung402 Taiwan Fall Prevention Center Department of Physical Medicine & Rehabilitation Changhua Christian Hospital Changhua500 Taiwan Department of Physics and Electronic Engineering University of Science Vietnam National University - Ho Chi Minh City Ho Chi Minh City70000 Viet Nam Department of Computer Science University of York York HeslingtonYO10 5DD United Kingdom Undergraduate Program of Intellectual Creativity Engineering National Chung Hsing University Taichung402 Taiwan Ho Chi Minh City72409 Viet Nam HCMUT Vietnam National University Ho Chi Minh City Thu Duc Ho Chi Minh City71308 Viet Nam

This research introduces a deep learning framework that combines convolutional neural networks with autoencoders to improve the diagnostic accuracy of knee osteoarthritis. The study utilized a publicly available dataset from the Mendeley Data repository, where knee joint images were preprocessed using Contrast Limited Adaptive Histogram Equalization to enhance contrast and ensure consistent image sizing. Initially, an autoencoder was employed in an unsupervised manner to learn compressed representations of the data. These representations were then used to train a CNN to classify knee images into 'healthy' and 'osteoarthritic' categories. The model was trained using an 80:20 split for training and testing data, incorporating early stopping and data augmentation techniques to prevent overfitting and enhance generalization. Performance metrics such as accuracy, precision, recall, and the area under the Receiver Operating Characteristic curve (ROCAUC) were used to evaluate the model, resulting in a high test accuracy of 98.95%. The high precision and recall rates further confirmed the model's robustness in diagnosing KOA. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to identify critical regions in the images that influenced the model's decisions. Additionally, t-Distributed Stochastic Neighbor Embedding (t-SNE) was used to visualize the high-dimensional encoded features in a two-dimensional space, providing deeper insights into how the model differentiates between classes. The promising results of this deep learning approach suggest its potential for broader applications in medical imaging diagnostics. By providing healthcare professionals with a tool that enhances diagnostic accuracy and operational efficiency, this framework lays the foundation for developing scalable, AI-driven diagnostic systems for various complex medical conditions. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Joints (anatomy)

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Fast Tracking of Maximum Power in a Shaded Photovoltaic System Using Ali Baba and the Forty Thieves (AFT) Algorithm

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Processes 2023年第10期11卷

作者： Rehman, Khalil Ur Sajid, Injila Lu, Shiue-Der Ahmad, Shafiq Liu, Hwa-Dong Bakhsh, Farhad Ilahi Tariq, Mohd Sarwar, Adil Lin, Chang-Hua Department of Electrical Engineering ZHCET Aligarh Muslim University Aligarh202002 India Department of Electrical Engineering National Chin-Yi University of Technology Taichung411 Taiwan Industrial Engineering Department College of Engineering King Saud University Riyadh11421 Saudi Arabia Undergraduate Program of Vehicle and Energy Engineering National Taiwan Normal University Taipei106 Taiwan Department of Electrical Engineering National Institute of Technology Srinagar190006 India Department of Electrical and Computer Engineering Florida International University MiamiFL33174 United States Department of Electrical Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

Photovoltaic (PV) generation systems that are partially shaded have a non-linear operating curve that is highly dependent on temperature and irradiance conditions. Shading from surrounding objects like clouds, trees, and buildings creates partial shading conditions (PSC) that can cause hot spot formation on PV panels. To prevent this, bypass diodes are installed in parallel across each panel, resulting in a global maximum power point (GMPP) and multiple local maximum power points (LMPPs) on the power-voltage (P-V) curve. Traditional methods for maximum power point tracking (MPPT), such as perturb and observe (P&O) and incremental conductance (INC), converge for LMPPs on the P-V curve, but metaheuristic algorithms can track the GMPP effectively. This paper proposes a new, efficient, and robust GMPP tracking technique based on a nature-inspired algorithm called Ali Baba and the Forty Thieves (AFT). Utilizing the AFT algorithm for MPPT in PV systems has several novel features and advantages, including its adaptability, exploration-exploitation balance, simplicity, efficiency, and innovative approach. These characteristics make AFT a promising choice for enhancing the efficiency of PV systems under varied circumstances. The performance of the proposed method in tracking the GMPP is evaluated using a simulation model under MATLAB/Simulink environment, the achieved simulation results are compared to particle swarm optimization (PSO). The proposed method is also tested in real-time using the Hardware-in-the-loop (HIL) emulator to validate the achieved simulation results. The findings indicate that the proposed AFT-based GMPP tracking method performs better under complex partial irradiance conditions than PSO. © 2023 by the authors.

关键词： Efficiency

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A Hybrid BRKGA Approach for the Two Stage Capacitated Facility Location Problem

A Hybrid BRKGA Approach for the Two Stage Capacitated Facili...

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Congress on Evolutionary Computation

作者： Gabriel Souto Igor Morais Liss Faulhaber Glaydston Mattos Ribeiro Pedro Henrique González Undergraduate Course in Computer Science CEFET/RJ Rio de Janeiro Brazil Graduate Program in Computer Science CEFET/RJ Rio de Janeiro Brazil Transport Engineering Program UFRJ Rio de Janeiro Brazil CEFET/RJ Rio de Janeiro Brazil

Finding optimal locations for installing factories and warehouses, fulfilling customers’ demand is the goal of the Two-Stage Capacitated Facility Location, which compounds a supply chain class of problems. Besides locating factories and warehouses, the problem’s objective is to minimize the operational costs: opening facilities and product flow, satisfying customer demand, and without disregarding the capacity of factories and warehouses. A Hybrid Biased Random-Key Genetic Algorithm (HBRKGA), combining BRKGA with Local Search and Local Branching, was proposed to solve the problem. The computational experiments showed that the method was reliable and stood out among other approaches to the problem.

关键词： Supply chains Evolutionary computation Search problems Production facilities Reliability Compounds Genetic algorithms

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Learner Action Patterns in the Problem-Solving Process Related to program Code Composition Based on Tracking System Activities

Learner Action Patterns in the Problem-Solving Process Relat...

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International Conference on Informatics and Computing (ICIC)

作者： Aulia Akhrian Syahidi Ahmad Afif Supianto Tsukasa Hirashima Yutaka Watanobe Smart City Information System Applied Undergraduate Program Politeknik Negeri Banjarmasin Banjarmasin City Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency (BRIN) Bandung City Indonesia Learning Engineering Laboratory Graduate School of Advanced Science and Engineering Hiroshima University Kagamiyama Higashihiroshima Japan Department of Computer Science and Engineering Division of Information Systems University of Aizu Aizu-Wakamatsu City Fukushima Japan

ISBN: (纸本)9798350345728

Learning activities are an indicator of the learner's desire to learn during the learning process. The pattern of learner action is related to learning activities. In this case, in extracting the learning process, it is necessary to collect a lot of data through analysis of the learning process. The purpose of this study is to recommend and report the performance of an activity tracking system equipped with visual artifacts as an educational data mining approach to analyze action patterns when learners complete the arrangement of program code lines in each programming problem. In this study, data were taken from activity recordings when learners used interactive learning media for a basic programming subject called TOLSYASUPI-EduMed which had problem-solving learning models embedded in their interactions. Learning Analytics is used as a method of this research and data relationships from learner actions during the learning process using the “if-then” rule. Log data is used for recording and detecting activities carried out by learners. We confirm that the results of the study are based on the findings of the data relationship that if the lines of program code are too long (more than five lines) then almost all learners experience a bottleneck condition. The completion time of each question and the number of iterations in completing the program code lines for each question have also been recorded and have a pattern of connection between the data. The information that has been obtained is then forwarded to teachers or stakeholders as key information to make appropriate feedback to learners based on the results as a means of analyzing and evaluating the programming learning process.

关键词： Visualization Codes Media Data models Recording Data mining Problem-solving

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Depression and Anxiety on Twitter During the COVID-19 Stay-At-Home Period in 7 Major U.S. Cities

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AJPM Focus 2023年第1期2卷 100062-100062页

作者： Levanti, Danielle Monastero, Rebecca N. Zamani, Mohammadzaman Eichstaedt, Johannes C. Giorgi, Salvatore Schwartz, H. Andrew Meliker, Jaymie R. Undergraduate Studies Cornell University Ithaca NY United States Renaissance School of Medicine Stony Brook University Stony Brook NY United States Department of Computer Science College of Engineering and Applied Sciences Stony Brook University Stony Brook NY United States Department of Psychology School of Humanities and Sciences Stanford University Palo Alto CA United States Department of Computer and Information Science University of Pennsylvania Philadelphia PA United States Program in Public Health Department of Family Population & Preventive Medicine Renaissance School of Medicine Stony Brook University Stony Brook NY United States

Introduction: Although surveys are a well-established instrument to capture the population prevalence of mental health at a moment in time, public Twitter is a continuously available data source that can provide a broader window into population mental health. We characterized the relationship between COVID-19 case counts, stay-at-home orders because of COVID-19, and anxiety and depression in 7 major U.S. cities utilizing Twitter data. Methods: We collected 18 million Tweets from January to September 2019 (baseline) and 2020 from 7 U.S. cities with large populations and varied COVID-19 response protocols: Atlanta, Chicago, Houston, Los Angeles, Miami, New York, and Phoenix. We applied machine learning‒based language prediction models for depression and anxiety validated in previous work with Twitter data. As an alternative public big data source, we explored Google Trends data using search query frequencies. A qualitative evaluation of trends is presented. Results: Twitter depression and anxiety scores were consistently elevated above their 2019 baselines across all the 7 locations. Twitter depression scores increased during the early phase of the pandemic, with a peak in early summer and a subsequent decline in late summer. The pattern of depression trends was aligned with national COVID-19 case trends rather than with trends in individual states. Anxiety was consistently and steadily elevated throughout the pandemic. Google search trends data showed noisy and inconsistent results. Conclusions: Our study shows the feasibility of using Twitter to capture trends of depression and anxiety during the COVID-19 public health crisis and suggests that social media data can supplement survey data to monitor long-term mental health trends. © 2022 The Author(s)

关键词： coronavirus mental health Social media stay-at-home order

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Investigating Case Learning Techniques for Agents to Play the Card Game of Truco

Investigating Case Learning Techniques for Agents to Play th...

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Brazilian Symposium on Games and Digital Entertainment (SBGAMES)

作者： Ruan C. B. Moral Gustavo B. Paulus Joaquim V. C. Assunção Luis A. L. Silva Undergraduate Program in Computer Engineering Federal University of Santa Maria - UFSM Santa Maria RS Brazil Graduate Program in Computer Science Federal University of Santa Maria - UFSM Santa Maria RS Brazil Federal University of Santa Maria - UFSM Santa Maria RS Brazil

ISBN: (数字)9781728184326

ISBN: (纸本)9781728184333

Truco is a popular game in many regions of South America; however, unlike worldwide games, Truco still requires a competitive Artificial Intelligence. Due to the limited availability of Truco data and the stochastic and imperfect information characteristics of the game, creating competitive models for a card game like Truco is a challenging task. To approach this problem, this work investigates the generation of concrete Truco problem-solving experiences through alternative techniques of automatic case generation and active learning, aiming to learn with the retention of cases in case bases. From this, these case bases guide the playing actions of the implemented Truco bots permitting to assess the capabilities of each bot, all implemented with Case-Based Reasoning (CBR) techniques.

关键词： Games Artificial intelligence Problem-solving Cognition Intelligent agents Sports Real-time systems

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Time-limited search and optimization of the spatial positioning of agents in virtual environments

Time-limited search and optimization of the spatial position...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Marcos S. Morgenstern Felipe G. Pires Edison P. Freitas A. L. Luis Silva SIS-ASTROS Project Federal University of Santa Maria Santa Maria RS Brazil Graduate Program in Computer Science Federal University of Santa Maria Porto Alegre RS Brazil Undergraduate Program in Computer Engineering Federal University of Santa Maria Santa Maria RS Brazil Graduate Program in Computer Science Federal University of Rio Grande do Sul Porto Alegre RS Brazil

ISBN: (数字)9781728125473

ISBN: (纸本)9781728125480

Simulation systems are becoming a widely used tool for planning and staff training in different applications. To support the execution of agents' deployment tasks in virtual simulation systems, this work evaluates the effectiveness of search and optimization algorithms with different nature, namely, the Simulated Annealing and the Tabu Search algorithms, in the resolution of a constraint-based and time-limited optimization problem. Particularly, the modeled simulated application problem regards the spatial positioning (or distribution) of agents in tactical positions of a virtual terrain scenario. The work addresses how to model this problem and to solve it during simulation runtime, where strict processing time limits are considered. Experimental results show that the proposed approach permits to efficiently distribute the agents according to the required formation pattern, both in execution time and agent distribution error, with a significant optimization rate.

关键词： Optimization Computational modeling Search problems Task analysis Training Games Spatial resolution

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TAROGE-M: Antarctic High-Mountain Radio Antenna Array for Detecting Ultra-High Energy ANITA Anomalous Events 38

TAROGE-M: Antarctic High-Mountain Radio Antenna Array for De...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Wang, Shih-Hao Chen, Pisin Chen, Yaocheng Chen, Ying-Chih Choi, Taejin Ham, Young-Bae Hsu, Shih-Ying Huang, Jian-Jung Huang, MingHuey A. Jee, Geonhwa Jung, Jongil Kim, Jieun Kuo, Chung-Yun Kwon, Hyuck-Jin Lee, Changsup Leung, Chung-Hei Liu, Tsung-Che Nam, Jiwoo Shiao, Yu-Shao J. Shin, Bok-Kyun Su, Shih-Chieh Wang, Min-Zu Wang, Shih-Hao Wang, Yu-Hsin Anker, Astrid Barwick, Steven W. Besson, Dave Z. Bouma, Sjoerd Cataldo, Maddalena Gaswint, Geoffrey Glaser, Christian Hallmann, Steffen Hanson, Jordan C. Henrichs, Jakob Kleinfelder, Stuart A. Lahmann, Robert Meyers, Zachary S. Nelles, Anna Novikov, Alexander Paul, Manuel P. Pyras, Lilly Persichilli, Christopher Plaisier, Ilse Rice-Smith, Ryan Seikh, Mohammad F. H. Tatar, Joulien Welling, Christoph Zhao, Leshan Department of Physics National Taiwan University Taipei10617 Taiwan Leung Center for Cosmology and Particle Astrophysics National Taiwan University Taipei10617 Taiwan Kavli Institute for Particle Astrophysics and Cosmology SLAC National Accelerator Laboratory Stanford University StanfordCA94305 United States Division of Atmospheric Sciences Korea Polar Research Institute Incheon Korea Republic of Department of Polar Science Korea University of Science and Technology Daejeon Korea Republic of Department of Energy Engineering National United University Miaoli Taiwan Department of Astronomy Space Science and Geology Chungnam National University Daejeon34134 Korea Republic of Department of Physics and Astronomy University of Delaware NewarkDE19716 United States Department of Electrophysics National Yang Ming Chiao Tung University Hsinchu300 Taiwan Department of applied physics National Pingtung university Pingtung900 Taiwan Chung Cheng Institute of Technology National Defense University Taoyuan335 Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu300 Taiwan National Nano Device Laboratories Hsinchu300 Taiwan Ulsan National Institute of Science and Technology Ulsan44919 Korea Republic of Institute of Astronomy and Astrophysics Academia Sinica Astronomy-Mathematics Building No. 1 Sec. 4 Roosevelt Rd. Taipei10617 Taiwan Department of Physics and Astronomy University of California IrvineCA92697 United States Department of Physics and Astronomy University of Kansas LawrenceKS66045 United States DESY Zeuthen15738 Germany ECAP Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen91058 Germany Uppsala University Department of Physics and Astronomy UppsalaSE-752 37 Sweden Whittier College Department of Physics WhittierCA90602 United States Department of Electrical Engineering and Computer Science University of California IrvineCA92697 United States Resea

TAROGE-M is a radio antenna array atop ∼ 2.7 km-high Mt. Melbourne in Antarctica for detecting ultra-high energy (UHE, E > 1017 eV) air showers in near-horizontal directions. Besides the detection of cosmic rays and Earth-skimming tau neutrinos, its primary goal is to reproduce the discovery and verify the origin of so-called ANITA anomalous events, having feature of upward-going UHE air showers but cannot be explained by tau neutrinos. The detection concept takes advantages of a high altitude for a broad view toward the horizon;strong and near-vertical geomagnetic field for enhancing the radio signal;and quiet radio environment in Antarctica. Its relatively simple design and high duty cycle make it easily to be extended and thus to achieve an exposure competitive with ANITA experiments within a few years. The first TAROGE-M station operating at 180-450 MHz frequencies deployed in 2020 detected seven UHE cosmic ray events within 25.3-day livetime. The events have a mean energy of ∼ 1 EeV and an estimated flux consistent with other experiments, and validate the station as an UHE particle detector. In 2022-2023 season, the system was upgraded for a robust long-term operation, with a more detailed calibration with a drone-borne pulser. We also discuss the planned upgrade, including the implementation of interferometric trigger, which is expected to increase the cosmic ray and tau neutrino acceptances by a factor of 10 at 0.1 EeV, corresponding to a lower energy threshold by a factor of 3. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic rays

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Application of factor analysis method to support the users acceptance model of ERP systems implementation

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Journal of Physics: Conference Series 2021年第1期1836卷

作者： W Sardjono J Sudirwan W Priatna G R Putra Information Systems Management Department BINUS Graduate Program -Master of Information Systems Management Bina Nusantara University Jakarta Indonesia – 11480 School of Information Systems Department BINUS Undergraduate Program Bina Nusantara University Jakarta Indonesia 11480 Informatics Engineering Engineering Bhayangkara University Jakarta Raya Indonesia Computer Science Study Program Faculty of Mathematics & Natural Sciences Pakuan University Bogor Indonesia

An e-Commerce company has been using an Enterprise Resource Planning (ERP) system for several years, but is still constrained in its implementation, this is reflected in the number of issue/change request tickets submitted by users who have not been able to reach ideal conditions. To overcome this problem, we need an appropriate reference model, the ERP system user acceptance model in companies. The study conducted factors that influence the level of acceptance of SAP ERP users in the XYZ group to then determine a model that is able to explain practically the level of ERP system user acceptance, which in turn can produce concrete proposals to increase ERP system user acceptance. Research is quantitative descriptive using factor analysis and regression analysis methods. The research method uses a questionnaire with a total of 107 valid respondents coming from 8 company names under the XYZ group in 6 Southeast Asian countries. The conclusion of the study is that there are 24 indicators and 4 factors that influence user acceptance of the SAP ERP system. These four factors are the low contribution of the ERP system, the quality of ERP systems and data, the development of ERP systems, and also the low quality of ERP system supporters. It is hoped that by optimizing the value of the 4 factors, the XYZ group can increase user acceptance of the SAP ERP system so that the system can provide benefits expected by the company to be competitive advantage.

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