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2018 systems and information engineering Design Symposium, SIEDS 2018

作者： Pan, Huitong Gao, Sally Grant, Kennan Novicoff, Wendy Kang, Hyojung Data Science Institute University of Virginia United States Department of Public Health Sciences University of Virginia United States Department of Systems and Information Engineering University of Virginia United States

ISBN: (纸本)9781538663431

The misuse of opioids has become a national public health emergency in the United States. Successfully completing a program of treatment at a substance abuse treatment facility is associated with better long-term outcomes, but a significant portion of opioid use patients are discharged without successfully completing treatment. The objective of this study is to evaluate factors associated with treatment success. We used the Treatment Episodes datasets: Discharges (TEDS-D) dataset, a national sample of discharges from substance abuse treatment centers, and the National Survey of Substance Abuse Treatment Services (N-SSATS) dataset, an annual survey of all known substance use treatment facilities, for the years 2013 and 2014. A random slope, random intercept multilevel model was used to estimate the effects of patient- and state-level variables on treatment completion. We find that a patient is more likely to complete treatment if that patient is older, employed, has private insurance, waited less time before entering treatment, is not receiving medication-assisted treatment, and is in residential care. In addition, we find that patients living in with states with higher levels of Vivitrol adoption are more likely to complete treatment. Our findings could encourage policymakers and treatment facilities to consider prioritizing Vivitrol adoption. At the same time, our results demonstrate substantial variation of predictors across states and suggest the need for further research into the relationship between state contexts and the predictors of successful opioid use treatment. © 2018 IEEE.

关键词： Surveys

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Foundation models and intelligent decision-making: Progress, challenges, and perspectives

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Innovation 2025年第6期6卷 100948页

作者： Huang, Jincai Xu, Yongjun Wang, Qi Wang, Qi [Cheems] Liang, Xingxing Wang, Fei Zhang, Zhao Wei, Wei Zhang, Boxuan Huang, Libo Chang, Jingru Ma, Liantao Ma, Ting Liang, Yuxuan Zhang, Jie Guo, Jian Jiang, Xuhui Fan, Xinxin An, Zhulin Li, Tingting Li, Xuefei Shao, Zezhi Qian, Tangwen Sun, Tao Diao, Boyu Yang, Chuanguang Yu, Chenqing Wu, Yiqing Li, Mengxian Zhang, Haifeng Zeng, Yongcheng Zhang, Zhicheng Zhu, Zhengqiu Lv, Yiqin Li, Aming Chen, Xu An, Bo Xiao, Wei Bai, Chenguang Mao, Yuxing Yin, Zhigang Gui, Sheng Su, Wentao Zhu, Yinghao Gao, Junyi He, Xinyu Li, Yizhou Jin, Guangyin Ao, Xiang Zhai, Xuehao Tan, Haoran Yun, Lijun Shi, Hongquan Li, Jun Fan, Changjun Huang, Kuihua Harrison, Ewen Leung, Victor C.M. Qiu, Sihang Dong, Yanjie Zheng, Xiaolong Wang, Gang Zheng, Yu Wang, Yuanzhuo Guo, Jiafeng Wang, Lizhe Cheng, Xueqi Wang, Yaonan Yang, Shanlin Fu, Mengyin Fei, Aiguo Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Automation Tsinghua University Beijing100084 China Huazhong University of Science and Technology Wuhan430074 China School of Automation Beijing Institute of Technology Beijing100081 China School of Information Science and Engineering Dalian Polytechnic University Dalian116034 China National Engineering Research Center for Software Engineering Peking University Beijing100871 China Department of Oral Implantology Peking University School and Hospital of Stomatology Beijing100081 China Guangzhou511453 China College of Information and Electrical Engineering China Agricultural University Beijing100083 China IDEA Research International Digital Economy Academy Shenzhen518057 China Institute of Automation Chinese Academy of Sciences Beijing100190 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Science National University of Defense Technology Changsha410073 China Center for Systems and Control College of Engineering Peking University Beijing100871 China Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China College of Systems Engineering National University of Defense Technology Changsha410073 China Laboratory for Big Data and Decision National University of Defense Technology Changsha410073 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Electrical Engineering Chongqing University Chongqing400044 China Department of Mathematics The University of Hong Kong Hong Kong SAR999077 China School of Food Science and Technology Dalian Polytechnic University Dalian116034 China Centre for Medical Informatics University of Edinburgh EdinburghEH16 4UX United Kingdom Department of Stomatology Peking Union Medical College Hospital Chinese Academ

Intelligent decision-making (IDM) is a cornerstone of artificial intelligence (AI) designed to automate or augment decision processes. Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps, such as AI agents and high-level reinforcement learning. Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision, language, and sensory data—into a cohesive decision-making process. Foundation models (FMs) have become pivotal in science and industry, transforming decision-making and research capabilities. Their large-scale, multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare, life sciences, and education. This survey examines IDM's evolution, advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains, highlighting the challenges and opportunities in building efficient, adaptive, and ethical decision systems. © 2025 The Authors

关键词： Reinforcement learning

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Text classification algorithms: A survey

arXiv

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arXiv 2019年

作者： Kowsari, Kamran Meimandi, Kiana Jafari Heidarysafa, Mojtaba Mendu, Sanjana Barnes, Laura Brown, Donald Department of Systems and Information Engineering University of Virginia CharlottesvilleVA22904 United States Sensing Systems for Health Lab University of Virginia CharlottesvilleVA22911 United States School of Data Science University of Virginia CharlottesvilleVA22904 United States

In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper understanding of machine learning methods to be able to accurately classify texts in many applications. Many machine learning approaches have achieved surpassing results in natural language processing. The success of these learning algorithms relies on their capacity to understand complex models and non-linear relationships within data. However, finding suitable structures, architectures, and techniques for text classification is a challenge for researchers. In this paper, a brief overview of text classification algorithms is discussed. This overview covers different text feature extractions, dimensionality reduction methods, existing algorithms and techniques, and evaluations methods. Finally, the limitations of each technique and their application in real-world problems are discussed. Copyright © 2019, The Authors. All rights reserved.

关键词： Text processing

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Scene Classification via Learning a Multi-Branch Convolutional Network

Scene Classification via Learning a Multi-Branch Convolution...

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IEEE International Conference on systems, Man and Cybernetics

作者： Xiaoyong Bian Chunfang Chen Yang Chen Xiongjun Fei Jingshan Tang Intelligent Information Processing and Real-time Industrial Systems Hubei Province Key Laboratory School of Computer Science and Technology Wuhan University of Science and Technology and Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan China School of Information Science and Engineering and Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and Technology Wuhan China School of Technology Michigan Technological University Michigan USA

Efficient and accurate classification of high-resolution scene remains a challenge of within-class diversity and between-class similarity due to rich image variations in viewpoint, object pose, spatial resolution and background. To address these issues, we propose a multi-branch convolutional neural network (MB-CNN), which focuses on tackling the problem of learning the appropriate representation of a high-resolution scene that is rich enough to discriminate between different semantic classes. First, pyramid scene parsing network (PSPNet) with minor modification is introduced to gather global object information. Then, an attention net is proposed to highlight transformation invariance and key regions for attention feature extraction. Finally, above two branches are fused with original input branch to learn consistently semantic class information and generate powerful predictions. Our approach achieves better performance favorably against state-of-the-arts on two publicly available scene datasets.

关键词： Conferences Cybernetics

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Women in ISIS propaganda: A natural language processing analysis of topics and emotions in a comparison with mainstream religious group

arXiv

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arXiv 2019年

作者： Heidarysafa, Mojtaba Kowsari, Kamran Odukoya, Tolu Potter, Philip Barnes, Laura E. Brown, Donald E. Department of Systems & Information Engineering University of Virginia CharlottesvilleVA United States School of Data Science University of Virginia CharlottesvilleVA United States Department of Politics University of Virginia CharlottesvilleVA United States

Online propaganda is central to the recruitment strategies of extremist groups and in recent years these efforts have increasingly extended to women. To investigate ISIS' approach to targeting women in their online propaganda and uncover implications for counterterrorism, we rely on text mining and natural language processing (NLP). Specifically, we extract articles published in Dabiq and Rumiyah (ISIS's online English language publications) to identify prominent topics. To identify similarities or differences between these texts and those produced by non-violent religious groups, we extend the analysis to articles from a Catholic forum dedicated to women. We also perform an emotional analysis of both of these resources to better understand the emotional components of propaganda. We rely on Depechemood (a lexical-base emotion analysis method) to detect emotions most likely to be evoked in readers of these materials. The findings indicate that the emotional appeal of ISIS and Catholic materials are similar. Copyright © 2019, The Authors. All rights reserved.

关键词： Natural language processing systems

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Industrial topics in urban labor system

arXiv

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arXiv 2020年

作者： Park, Jaehyuk Frank, Morgan R. Sun, Lijun Youn, Hyejin Kellogg School of Management Northwestern University EvanstonIL United States Northwestern Institute on Complex Systems EvanstonIL United States Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Department of Informatics and Networked Systems School of Computing and Information University of Pittsburgh PittsburgePA United States Connection Science Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States Digital Economy Lab Institute for Human-Centered AI Stanford University StanfordCA United States Department of Civil Engineering McGill University MontrealQC Canada Department of Civil and Environmental Engineering Northwestern University EvanstonIL60208 United States

Categorization is an essential component for us to understand the world for ourselves and to communicate it collectively. It is therefore important to recognize that classification systems is not necessarily static, especially for economic systems, and even more so in urban areas where most innovation takes place and is implemented. Out-of-date classification systems would potentially limit further understanding of the current economy because things constantly change. Here, we develop an occupation-based classification system for the US labor economy, called industrial topics, that satisfy adaptability and representability. By leveraging the distributions of occupations across the US urban areas, we identify industrial topics — clusters of occupations based on their co-existence pattern. Industrial topics indicate the mechanisms under the systematic allocation of different occupations. Considering the densely connected occupations as an industrial topic, our approach characterizes regional economies by their topical composition. Unlike the existing survey-based top-down approach, our method provides timely information about the underlying structure of the regional economy, which is critical for policymakers and business leaders especially in our fast-changing economy. Copyright © 2020, The Authors. All rights reserved.

关键词： Employment

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Author Correction: Single-cell transcriptomics uncovers EGFR signaling-mediated gastric progenitor cell differentiation in stomach homeostasis

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Nature communications 2023年第1期14卷 6540页

作者： Hitomi Takada Yohei Sasagawa Mika Yoshimura Kaori Tanaka Yoshimi Iwayama Tetsutaro Hayashi Ayako Isomura-Matoba Itoshi Nikaido Akira Kurisaki Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan. Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan. Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan. Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Wako Saitama Japan. itoshi.nikaido@riken.jp. Department of Functional Genome Informatics Biological Data Science Medical Research Institute Tokyo Medical and Dental University Bunkyo Tokyo Japan. itoshi.nikaido@riken.jp. Master's/Doctoral Program in Life Science Innovation (Bioinformatics) Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan. itoshi.nikaido@riken.jp. Laboratory of Stem Cell Technologies Graduate School of Science and Technology Nara Institute of Science and Technology Takayama-cho Ikoma Nara Japan. akikuri@bs.naist.jp.

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Correction: The BCPM method: decoding breast cancer with machine learning

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BMC medical imaging 2024年第1期24卷 272页

作者： Badar Almarri Gaurav Gupta Ravinder Kumar Vandana Vandana Fatima Asiri Surbhi Bhatia Khan College of Computer Sciences and Information Technology King Faisal University Alhasa Saudi Arabia. baalmarri@kfu.edu.sa. Yogananda School of AI Computers and Data Sciences Shoolini University Solan 173212 Himachal Pradesh India. School of Bioengineering & Food Technology Shoolini University Solan 173212 Himachal Pradesh India. College of Computer Science Informatics and Computer Systems Department King Khalid University Abha Saudi Arabia. School of Science Engineering and Environment University of Salford Manchester UK. s.khan138@salford.ac.uk. University Centre for Research and Development Chandigarh University Punjab India. s.khan138@salford.ac.uk.

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Getting the most out of maths: How to coordinate mathematical modelling research to support a pandemic, lessons learnt from three initiatives that were part of the COVID-19 response in the UK

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Journal of Theoretical Biology 2023年 557卷 111332页

作者： Dangerfield, Ciara E. David Abrahams, I. Budd, Chris Butchers, Matt Cates, Michael E. Champneys, Alan R. Currie, Christine S.M. Enright, Jessica Gog, Julia R. Goriely, Alain Déirdre Hollingsworth, T. Hoyle, Rebecca B. INI Professional Services Isham, Valerie Jordan, Joanna Kaouri, Maha H. Kavoussanakis, Kostas Leeks, Jane Maini, Philip K. Marr, Christie Merritt, Clare Mollison, Denis Ray, Surajit Thompson, Robin N. Wakefield, Alexandra Wasley, Dawn Isaac Newton Institute to Mathematical Sciences University of Cambridge United Kingdom Joint UNIversities Pandemic and Epidemiological Research (JUNIPER) Consortium United Kingdom Department for Applied Mathematics and Theoretical Physics University of Cambridge United Kingdom Department of Mathematics University of Bath United Kingdom Department of Engineering Mathematics University of Bristol United Kingdom School of Mathematical Sciences University of Southampton United Kingdom School of Computing Science University of Glasgow United Kingdom Mathematical Institute University of Oxford United Kingdom Big Data Institute Li Ka Shing Centre for Health Information and Discovery University of Oxford United Kingdom Department of Statistical Science University College London United Kingdom Independent United Kingdom EPCC The University of Edinburgh United Kingdom Department of Actuarial Mathematics and Statistics Heriot-Watt University United Kingdom School of Mathematics and Statistics University of Glasgow United Kingdom Mathematics Institute University of Warwick United Kingdom Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research University of Warwick United Kingdom The Royal Society London United Kingdom International Centre for Mathematical Sciences University of Edinburgh & Heriot-Watt University United Kingdom

In March 2020 mathematics became a key part of the scientific advice to the UK government on the pandemic response to COVID-19. Mathematical and statistical modelling provided critical information on the spread of the virus and the potential impact of different interventions. The unprecedented scale of the challenge led the epidemiological modelling community in the UK to be pushed to its limits. At the same time, mathematical modellers across the country were keen to use their knowledge and skills to support the COVID-19 modelling effort. However, this sudden great interest in epidemiological modelling needed to be coordinated to provide much-needed support, and to limit the burden on epidemiological modellers already very stretched for time. In this paper we describe three initiatives set up in the UK in spring 2020 to coordinate the mathematical sciences research community in supporting mathematical modelling of COVID-19. Each initiative had different primary aims and worked to maximise synergies between the various projects. We reflect on the lessons learnt, highlighting the key roles of pre-existing research collaborations and focal centres of coordination in contributing to the success of these initiatives. We conclude with recommendations about important ways in which the scientific research community could be better prepared for future pandemics. This manuscript was submitted as part of a theme issue on “Modelling COVID-19 and Preparedness for Future Pandemics”. © 2022 The Author(s)

关键词： COVID-19 Knowledge exchange Mathematical modelling Research co-ordination

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The impact of peer influence on academic performance: A three-stage co-evolution model 39

The impact of peer influence on academic performance: A thre...

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39th International Conference on information systems, ICIS 2018

作者： Ding, Dan Phan, Tuan Q. Bhattacharya, Prasanta Lu, Xuesong Department of Information Systems and Analytics National University of Singapore Singapore117417 Singapore Singapore138632 Singapore School of Data Science and Engineering East China Normal University Shanghai200062 China

ISBN: (纸本)9780996683173

Estimating peer influence on academic performance is important to both social scientists and policy makers. The fundamental challenges in prior studies that assess peer influence using observational data are about (1) separating the effect of peer influence from homophily and (2) capturing network dynamics. In this paper, we build on related work and propose a three-stage co-evolution model to investigate the impact of peer influence on academic performance by controlling for multiple sources of homophily and network dynamics. Our preliminary analysis provides support for the existence of homophily and peer influence. In addition, we find evidences of asymmetric peer influence, i.e. influence is more significantly positive among higher ability students, than lower ability ones. Early findings from this study shed light on the possible underlying mechanisms of how networks evolve, and how students' behaviors are affected by their peers' performance and decisions, and vice versa. Our findings offer useful insights about the prevalence and role of peer influence to policy makers, educational researchers and teachers. © International Conference on information systems 2018, ICIS *** rights reserved.

关键词： information use

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