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21th International Florida Artificial Intelligence Research Society Conference, FLAIRS-21

作者： Niepert, Mathias Buckner, Cameron Allen, Colin Indiana University Department of Computer Science Indiana University Department of Philosophy Indiana University Department History and Philosophy of Science Program in Cognitive Science

ISBN: (纸本)9781577353652

The next generation of online reference works will require structured representations of their contents in order to support scholarly functions such as semantic search, automated generation of cross-references, tables of contents, and ontology-driven conceptual navigation. Many of these works can be expected to contain massive amounts of data and be updated dynamically, which limits the feasibility of "manually" coded ontologies to keep up with changes in content. However, relationships relevant to inferring an ontology can be recovered from statistical text processing, and these estimates can be verified with carefully-solicited expert feedback. In this paper, we explain a method by which we have used answer set programming on such expert feedback to dynamically populate and partially infer an ontology for a well-established, open-access reference work, the Stanford Encyclopedia of Philosophy. Copyright © 2008, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Ontology

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ESG Investing: A Statistically Valid Approach to Data-Driven Decision Making and the Impact of ESG Factors on Stock Returns and Risk

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IEEE Access 2024年 12卷 69434-69444页

作者： Teja, Kamurthi Ravi Liu, Chuan-Ming National Taipei University of Technology International Program of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

This study examines the impact of environmental, social, and governance (ESG) factors on economic investment from a statistical perspective, aiming to develop a tested investment strategy that capitalizes on the connection between ESG factors and financial performance. ESG investing: A statistically valid approach to data-driven decision-making (ESGI-SVADDM) investment strategy is based on a rigorous, statistically valid approach that utilizes data, math, statistics, and data science libraries to drive investment decisions, eliminating the need for personal opinions and subjectivity. The process includes establishing an investment thesis, formulating testable hypotheses (HPS), retrieving and refining relevant data, calculating relevant measures, and testing and validating all hypotheses. The study uses S&P 500 stock data and ESG data from Sustainalytics to test the hypotheses. The empirical tests conducted revealed a negative correlation between ESG risk and expected returns, as well as a positive trend in the relationship between ESG risk and the overall risk of stocks. Moreover, the study found that higher ESG risk scores are associated with lower returns for investors, and that adopting a strategy of investing in stocks with low ESG risk and shorting stocks with high ESG risk yields superior returns compared to the market portfolio. © 2013 IEEE.

关键词： Investments

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Trade Ease with Machine Learning and AWS

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IEEE Access 2023年 11卷 25893-25905页

Global trading is undergoing significant changes, necessitating modifications to the trading strategies. This study presents a newly developed cloud-based trading strategy that uses Amazon Web Services (AWS), machine learning (ML), and data science to automate trading tasks. The study begins by creating a machine learning trading strategy using a customized deep neural network (DNN). The strategy was then tested using a trading station before deployment on a cloud machine. The execution of the strategy resulted in a total profit of 42.74526 USD, with a test accuracy score of 87.45% and a training accuracy score of 89.15% over 6047 epochs. The roadmap provides a step-by-step overview of the entire process from strategy development to execution. In addition, this study offers insights into related issues and solutions that can enhance the effectiveness of trading strategies. Overall, this study contributes significantly to the field of cloud-based trading strategies and opens avenues for future research. © 2013 IEEE.

关键词： Deep neural networks

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A model-driven PBL application to support the authoring, delivery, and execution of PBL processes

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Research and Practice in Technology Enhanced Learning 2016年第1期11卷 1-24页

作者： Wang, Disi Samaka, Mohammed Miao, Yongwu Ali, Zeyad Hoppe, H. Ulrich Department of Computer Science and Applied Cognitive Science University of Duisburg-Essen Lotharstr. 63 Duisburg 47057 Germany Computer Science and Engineering Department Qatar University Doha Qatar

As problem-based learning (PBL) is becoming more and more popular, there is also a growing interest in developing and using technologies in the implementation of PBL. However, teachers may have difficulties to design and deliver a pedagogically well-designed and technically smoothly executable online or blended PBL process on their own because they lack appropriate expertise in learning theories and design methods as well as a deeper understanding of the potential affordances of the available technologies. From this premise, we are committed to developing and testing methods and tools to support the design and delivery of online or hybrid PBL processes with high flexibility and a low threshold of usage requirements. This paper presents a technical approach to develop a web-based PBL application that supports both authoring and run-time usage. In comparison with other tools and technical approaches, it is concluded that a combined use of a model-driven approach and semi-structured data management appears to be a promising approach to effectively and efficiently support the authoring, delivering, and execution of design-time and run-time PBL processes. © 2016, The Author(s).

关键词： Data management Graphical authoring tool Learning design Model-driven architecture (MDA) PBL PBL script Semi-structured data Web-based application

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Testing the Inference Accuracy of Accelerator-Friendly Approximate Phylogeny Tracking

Testing the Inference Accuracy of Accelerator-Friendly Appro...

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Computational Intelligence in Artificial Life and Cooperative Intelligent Systems (ALIFE-CIS), IEEE Symposium on

作者： Matthew Andres Moreno Anika Ranjan Emily Dolson Luis Zaman 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 and AI in Society 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

ISBN: (数字)9798331508371

ISBN: (纸本)9798331508388

computer simulations are an important tool for studying the mechanics of biological evolution. In particular, agent-based approaches provide an opportunity to collect high-quality records of ancestry relationships. Such phylogenies can provide insight into evolutionary dynamics within these simulations. Previous work generally tracks lineages directly, yielding an exact phylogenetic record of evolutionary history. However, challenges exist in scaling direct ancestrytracking approaches to highly-distributed, many-processor evolution in silico. An alternative approach is to estimate phylogenetic history via noncoding annotations on digital genomes, akin to how bioinformaticians build phylogenies by assessing genetic similarities between organisms. Recent work has extended this “hereditary stratigraphy” approach to support powerful hardware accelerator platforms, such as the Cerebras Wafer-Scale Engine. Although these second-generation “surface”-based hereditary stratigraphy algorithms have demonstrated order-ofmagnitude speedups over first-generation “column”-based algorithms, it remains unknown how they impact the accuracy of reconstructed phylogenies. To address this question, we assessed reconstruction accuracy under alternative configurations across a matrix of evolutionary conditions varying in selection pressure, spatial structure, and ecological dynamics. Encouragingly, we find that the second-generation approaches provide higher reconstruction quality across most surveyed conditions.

关键词： Surface reconstruction Accuracy Annotations Heuristic algorithms Computational modeling Biological system modeling Stratigraphy Phylogeny History Bars

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Testing the Inference Accuracy of Accelerator-Friendly Approximate Phylogeny Tracking

Testing the Inference Accuracy of Accelerator-Friendly Appro...

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2025 IEEE Symposium on Computational Intelligence in Artificial Life and Cooperative Intelligent Systems, ALIFE-CIS 2025

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

ISBN: (纸本)9798331508371

computer simulations are an important tool for studying the mechanics of biological evolution. In particular, agent-based approaches provide an opportunity to collect high-quality records of ancestry relationships. Such phylogenies can provide insight into evolutionary dynamics within these simulations. Previous work generally tracks lineages directly, yielding an exact phylogenetic record of evolutionary history. However, challenges exist in scaling direct ancestrytracking approaches to highly-distributed, many-processor evolution in silico. An alternative approach is to estimate phylogenetic history via noncoding annotations on digital genomes, akin to how bioinformaticians build phylogenies by assessing genetic similarities between organisms. Recent work has extended this 'hereditary stratigraphy' approach to support powerful hardware accelerator platforms, such as the Cerebras Wafer-Scale Engine. Although these second-generation 'surface'-based hereditary stratigraphy algorithms have demonstrated order-ofmagnitude speedups over first-generation 'column'-based algorithms, it remains unknown how they impact the accuracy of reconstructed phylogenies. To address this question, we assessed reconstruction accuracy under alternative configurations across a matrix of evolutionary conditions varying in selection pressure, spatial structure, and ecological dynamics. Encouragingly, we find that the second-generation approaches provide higher reconstruction quality across most surveyed conditions. © 2025 IEEE.

关键词： Biostratigraphy

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Biomedical data and AI

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science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Brains Are Not computers

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IEEE SYSTEMS MAN AND CYBERNETICS MAGAZINE 2019年第1期5卷 21-25页

作者： Baars, Bernard Ilin, Roman Ruszinko, Miklos Tunstel, Edward Cognitive science University of California Los Angeles Computer science University of Memphis Tennessee Hungarian Academy of Sciences Budapest Electrical engineering University of New Mexico

In this article, we provide an overview of Dr. Robert Kozma's accomplishments, based on essays by leading experts in various areas related to his research, covering mathematical, computational, and cognitive aspects of his neurodynamics modeling and applications in biologically inspired autonomous systems and robotics. Specific contributions are given by Dr. Miklós Ruszinkó (the Rényi Institute of Mathematics, Hungarian Academy of sciences) on mathematical and graph theoretical aspects of brain modeling, detailing Dr. Kozma's work, which is now known as neuropercolation. This is followed by recollections from Dr. Roman Ilin [U.S. Air Force Research Laboratory, Wright Patterson Air Force Base (WPAFB)], a former student of Dr. Kozma's, on computational models of spatiotemporal neurodynamics, and the work Dr. Kozma has made jointly with the late, world-renowned Prof. Walter J. Freeman [University of California (UC), Berkeley] and his students in a field now called Freeman K Katchalsky-sets. Dr. Edward Tunstel [the United Technologies Robotics Center (UTRC), East Hartford, Connecticut] provides an overview of biologically inspired robotics and several unique applications Dr. Kozma has made in this field together with colleagues from NASA Robotics. Dr. Bernard Baars (Society of Brain-Mind sciences, La Jolla, California) contributes a cognitive scientist?s perspective on Dr. Kozma's work with an emphasis on implications of understanding and modeling consciousness. We believe this multiperspective overview provides an insight into the truly interdisciplinary character of Dr. Kozma's remarkable research achievements.

关键词： Kozma, Robert Brain modeling Robots Neurodynamics Computational modeling Mathemtical model Autonomous systems

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Rumination as a within-person indirect pathway between daily negative life events and suicidal ideation in recently hospitalized adults

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Journal of Psychiatric Research 2025年 188卷 153-161页

作者： Gemma T. Wallace Melanie L. Bozzay Leslie A. Brick Ivan W. Miller Emily Mower Provost Heather T. Schatten Department of Psychiatry and Human Behavior Alpert Medical School of Brown University 700 Butler Drive Providence RI 02906 United States Psychosocial Research Program Butler Hospital 345 Blackstone Blvd Providence RI 02906 United States The Ohio State University Wexner Medical Center 1960 Kenny Rd. Columbus OH 43215 United States Computer Science and Engineering University of Michigan 2260 Hayward St. Ann Arbor MI 48109 United States

Negative life events are often implicated as a near-term risk factor for suicidal ideation (SI); however ruminative processes may play a critical role in amplifying the distress following experiences of negative life events, ultimately leading to greater suicide risk. In the present work, we examined whether rumination indirectly impacted the association between negative events and SI intensity in a day-level ecological momentary assessment (EMA) study. Participants ( N = 107; M age = 35.9 years, 65% female, 81% white) completed an EMA protocol for 65 days following psychiatric hospitalization. We used dynamic structural equation modeling to test a day-level, within-persons model of direct and indirect effects between negative events, rumination, and SI intensity. After fitting the model across all people, we derived person-specific estimates to examine heterogeneity in the model parameters (i.e., while the indirect effect may be significant in the full sample, effect sizes may vary across individuals). Results indicated that the indirect effect of rumination was significant in the full sample (unstandardized estimate = 0.026) and represented 38.058% of the total effect. Moreover, results for the person-specific examination indicated that the indirect effect was present for nearly all participants who reported SI at any point in their EMA data. Thus, in addition to highlighting the critical role that rumination can play in SI, this study also highlights the value in conducting person-specific research to understand the complexity and heterogeneity of psychological processes involved in suicide risk.

关键词： Suicidal ideation Rumination Stressful event Psychiatric inpatient Ecological momentary assessment Post-discharge Heterogeneity

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Unobtrusive workstation farming without inconveniencing owners: Learning Backgammon with a genetic algorithm 1

Unobtrusive workstation farming without inconveniencing owne...

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1st IEEE computer Society International Workshop on Cluster Computing, IWCC 1999

作者： Darwen, P.J. Cognitive Science Research Group Department of Computer Science and Electrical Engineering University of Queensland Brisbane4072 Australia

ISBN: (纸本)0769503438

Most efforts at low-cost parallel computing assume a monopoly on the hardware being used. That all-or-nothing attitude ignores many machines dedicated to other activities, but which sit idle for 16 hours a day. However naive attempts to utilize idle machines can interfere with their primary purpose. This paper describes the successful effort to unobtrusively farm idle machines, for an artificial intelligence system using a genetic algorithm to learn the game Backgammon. It maintains owners' full access to their machines, without causing any detectable interference. © 1999 IEEE.

关键词： Genetic algorithms

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