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Optical and Quantum Electronics 2024年第12期56卷 1-1页

作者： Kavitha, V. P. Prabakar, D. Ranjith subramanian, S Balambigai, S. Department of ECE SRM Institute of Science and Technology Chennai India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Insitute of Science and Technology Kattankulathur India Department of Electronic and Communication Engineering Jeppiaar Engineering College Chennai India Department of ECE Kongu Engineering College Perundurai India

AI exposure predicts unemployment risk

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arXiv 2023年

作者： Frank, Morgan R. Ahn, Yong-Yeol Moro, Esteban Department of Informatics and Networked Systems University of Pittsburgh PittsburghPA15216 United States Digital Economy Lab Institute for Human-Centered Artificial Intelligence Stanford University StanfordCA94305 United States Media Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States Connection Science Massachusetts Institute of Technology CambridgeMA United States Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Indiana University Network Science Institute Indiana University BloomingtonIN United States Department of Mathematics & GISC Universidad Carlos III de Madrid Leganes28911 Spain Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States

Is artificial intelligence (AI) disrupting jobs and creating unemployment? Despite many attempts to quantify occupations' exposure to AI, inconsistent validation obfuscates the relative benefits of each approach. A lack of disaggregated labor outcome data, including unemployment data, further exacerbates the issue. Here, we assess which models of AI exposure predict job separations and unemployment risk using new occupation-level unemployment data by occupation from each US state's unemployment insurance office spanning 2010 through 2020. Although these AI exposure scores have been used by governments and industry, we find that individual AI exposure models are not predictive of unemployment rates, unemployment risk, or job separation rates. However, an ensemble of those models exhibits substantial predictive power suggesting that competing models may capture different aspects of AI exposure that collectively account for AI's variable impact across occupations, regions, and time. Our results also call for dynamic, context-aware, and validated methods for assessing AI exposure. Interactive visualizations for this study are available at https://***/~mrfrank/uiRiskDemo/. © 2023, CC BY.

关键词： Visualization

Topical Issue Scientific Machine Learning (1/2)

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GAMM Mitteilungen 2021年第1期44卷 e202100010-e202100010页

作者： Benner, Peter Klawonn, Axel Stoll, Martin Computational Methods in Systems and Control Theory Max Planck institute for Dynamics of Complex Technical Systems Magdeburg Germany Fakultät für Mathematik Otto-von-Guericke Universität Magdeburg Germany Department of Mathematics and Computer Science University of Cologne Germany Center for Data and Simulation Science University of Cologne Germany Chair of Scientific Computing Department of Mathematics TU Chemnitz Germany

Calibrating hypersonic turbulence flow models with the HIFiRE-1 experiment using data-driven machine-learned models

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arXiv 2021年

作者： Chowdhary, Kenny Hoang, Chi Lee, Kookjin Ray, Jaideep Extreme-Scale Data Science and Analytics Department Sandia National Laboratories LivermoreCA94550 United States School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States

In this paper we study the efficacy of combining machine-learning methods with projection-based model reduction techniques for creating data-driven surrogate models of computationally expensive, high-fidelity physics models. Such surrogate models are essential for many-query applications e.g., engineering design optimization and parameter estimation, where it is necessary to invoke the high-fidelity model sequentially, many times. Surrogate models are usually constructed for individual scalar quantities. However there are scenarios where a spatially varying field needs to be modeled as a function of the model's input parameters. We develop a method to do so, using projections to represent spatial variability while a machine-learned model captures the dependence of the model's response on the inputs. The method is demonstrated on modeling the heat flux and pressure on the surface of the HIFiRE-1 geometry in a Mach 7.16 turbulent flow. The surrogate model is then used to perform Bayesian estimation of freestream conditions and parameters of the SST (Shear Stress Transport) turbulence model embedded in the high-fidelity (Reynolds-Averaged Navier-Stokes) flow simulator, using wind-tunnel data. The paper provides the first-ever Bayesian calibration of a turbulence model for complex hypersonic turbulent flows. We find that the primary issues in estimating the SST model parameters are the limited information content of the heat flux and pressure measurements and the large model-form error encountered in a certain part of the flow. Copyright © 2021, The Authors. All rights reserved.

关键词： Turbulence models

Stratified subsampling for regression analysis of big count response data

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AIP Conference Proceedings 2023年第1期2756卷

作者： Yeh-Ching Low Seng-Huat Ong Department of Computing and Information Systems School of Engineering and Technology Sunway University 47500 Selangor Malaysia Institute of Actuarial Science and Data Analytics UCSI University 56000 Kuala Lumpur Malaysia

Count frequency data are found in many applications such as traffic accident analysis, car insurance claims, hospital admissions records and adverse vaccine reaction data. In some cases, these data have high zero counts and/or heavy tails due to cases such as no claim or no incidence in insurance and accident data, respectively. In regression modelling, the data size can be formidable due to either large sample size and/or large number of covariates or predictors which leads to computational challenges. Although many computer engineering solutions are available through supercomputers and parallel computing, there exists limitations due to cost and accessibility. As such, statistical solutions have been considered to ameliorate the challenges posed by regression modelling of big data. In general, these statistical solutions are classified as divide and conquer approach, fine-to-coarse method and subsampling methods. To address the problem of large data size in count regression modelling, we propose a stratified subsampling strategy according to frequency classes with shrinkage leveraging for statistical inference. An attractive feature of this strategy is in its ability to preserve the characteristics of data like over dispersion, high zero counts and/or heavy-tailed. A Monte Carlo simulation study is conducted to investigate the performance of the proposed stratified subsampling method in regression modelling with big count data. The regression analysis will be illustrated using a family of mixed Poisson regression models which have been shown to be flexible in its ability to model count data with high zero counts and/or a long tail.

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Design and Comparison of Collaborative Filtering Technology for Product Suggestions in E-Commerce

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Design and Comparison of Collaborative Filtering Technology ...

Automation, computing and Renewable systems (ICACRS), International Conference on

作者： P. Rajasekar B. Mohanraj S. N. Padhi N. Sivakumar Lavanya J Charles Prabu V Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Information Technology Karpagam College of Engineering Coimbatore Tamil Nadu India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Panimalar Engineering College Chennai City Campus Chennai Tamil Nadu India Department of Fashion Designing SRM Institute of Science and Technology CSH Kattankulathur Campus Chennai Tamil Nadu India Department of Computer Science Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (纸本)9781665460859

E-commerce websites and internet purchasing have never been more necessary. Product recommendations are a key tactic employed by online buying platforms. This study compares two different filtering algorithms that are used in product recommendationson websites like Amazon and Flipkart. Reviews of electrical devices, user data, and timestamps were all gathered from Kaggle and put into a dataset. These specifics then undergo several preprocessing steps. The steps are data visualization, duplicate value analysis, missing value analysis, and datatype analysis. The exhibited data is then used to train and test the machine learning models. For this purpose, two machine learning models are developed using two different algorithms. The algorithms K-Nearest Neighbor(KNN) and Support Vector Decomposition (SVD) are employed. The models are then trained to acquire used in the prediction procedure after that. The best models for the prediction process are then determined by scrutinizing the results of both models. The Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE) values are located and compared because the findings of the analysis weren't *** erroneous data are plotted into a graph and evaluated to further demonstrate the point. As the SVD method is discovered to be the best of the group, the algorithm's greatest and worst predictions are discovered and examined. Even the poorest model constructed for the study's predictions turned out to be superior to several other prediction algorithms used for product suggestions. This algorithm can be deployed into the backend of any e-commerce website making it more effective.

关键词： Training Support vector machines Analytical models Machine learning algorithms data visualization Machine learning Predictive models

Advancing bioinformatics with large language models: components, applications and perspectives

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arXiv 2024年

作者： Liu, Jiajia Yang, Mengyuan Yu, Yankai Xu, Haixia Wang, Tiangang Li, Kang Zhou, Xiaobo Center for Computational Systems Medicine McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX77030 United States Department of Cell Biology and Genetics School of Basic Medical Sciences Xi’an Jiaotong University Health Science Center Xi’an China School of Computing and Artificial Intelligence Southwest Jiaotong University Sichuan Chengdu611756 China West China Biomedical Big Data Center West China Hospital Sichuan University Sichuan Chengdu610041 China McGovern Medical School The University of Texas Health Science Center at Houston HoustonTX77030 United States School of Dentistry The University of Texas Health Science Center at Houston HoustonTX77030 United States

Large language models (LLMs) are a class of artificial intelligence models based on deep learning, which have great performance in various tasks, especially in natural language processing (NLP). Large language models typically consist of artificial neural networks with numerous parameters, trained on large amounts of unlabeled input using self-supervised or semi-supervised learning. However, their potential for solving bioinformatics problems may even exceed their proficiency in modeling human language. In this review, we will provide a comprehensive overview of the essential components of large language models (LLMs) in bioinformatics, spanning genomics, transcriptomics, proteomics, drug discovery, and single-cell analysis. Key aspects covered include tokenization methods for diverse data types, the architecture of transformer models, the core attention mechanism, and the pre-training processes underlying these models. Additionally, we will introduce currently available foundation models and highlight their downstream applications across various bioinformatics domains. Finally, drawing from our experience, we will offer practical guidance for both LLM users and developers, emphasizing strategies to optimize their use and foster further innovation in the field. © 2024, CC BY.

关键词： Self-supervised learning

Distributed and Localized Model Predictive Control. Part I: Synthesis and Implementation

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arXiv 2021年

作者： Alonso, Carmen Amo Li, Jing Shuang Anderson, James Matni, Nikolai Computing and Mathematical Sciences Department California Institute of Technology PasadenaCA United States Computing and Mathematical Sciences Department California Institute of Technology PasadenaCA United States Department of Electrical Engineering the Data Science Institute Columbia University New YorkNY United States Department of Electrical and Systems Engineering the University of Pennsylvania PhiladelphiaPA United States

The increasing presence of large-scale distributed systems highlights the need for scalable control strategies where only local communication is required. Moreover, in safety-critical systems it is imperative that such control strategies handle constraints in the presence of disturbances. In response to this need, we present the Distributed and Localized Model Predictive Control (DLMPC) algorithm for large-scale linear systems. DLMPC is a distributed closed-loop model predictive control (MPC) scheme wherein only local state and model information needs to be exchanged between subsystems for the computation and implementation of control actions. We use the System Level Synthesis (SLS) framework to reformulate the centralized MPC problem, and show that this allows us to naturally impose localized communication constraints between sub-controllers. The structure of the resulting problem can be exploited to develop an Alternating Direction Method of Multipliers (ADMM) based algorithm that allows for distributed and localized computation of closed-loop control policies. We demonstrate that computational complexity of the subproblems solved by each subsystem in DLMPC is independent of the size of the global system. DLMPC is the first MPC algorithm that allows for the scalable computation and implementation of distributed closed-loop control policies, and deals with additive disturbances. In our companion paper, we show that this approach enjoys recursive feasibility and asymptotic stability. Copyright © 2021, The Authors. All rights reserved.

关键词： Model predictive control

The exact asymptotic form of Bayesian generalization error in latent dirichlet allocation

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arXiv 2020年

作者： Hayashi, Naoki Department of Simulation & Mining NTT DATA Mathematical Systems Inc Japan Department of Mathematical and Computing Science Tokyo Institute of Technology Japan

Latent Dirichlet allocation (LDA) obtains essential information from data by using Bayesian inference. It is applied to knowledge discovery via dimension reducing and clustering in many fields. However, its generalization error had not been yet clarified since it is a singular statistical model where there is no one to one map from parameters to probability distributions. In this paper, we give the exact asymptotic form of its generalization error and marginal likelihood, by theoretical analysis of its learning coefficient using algebraic geometry. The theoretical result shows that the Bayesian generalization error in LDA is expressed in terms of that in matrix factorization and a penalty from the simplex restriction of LDA’s parameter *** Codes 62F15 (Primary) 62R01 (Secondary) Copyright © 2020, The Authors. All rights reserved.

关键词： Statistics

Interdisciplinary Papers Supported by Disciplinary Grants Garner Deep and Broad Scientific Impact

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arXiv 2023年

作者： Park, Minsu Maity, Suman Kalyan Wuchty, Stefan Wang, Dashun Division of Social Science New York University Abu Dhabi Abu Dhabi United Arab Emirates Center for Science of Science and Innovation Northwestern University EvanstonIL United States Northwestern Institute on Complex Systems Northwestern University EvanstonIL United States Kellogg School of Management Northwestern University EvanstonIL United States Department of Computer Science University of Miami Coral GablesFL United States Department of Biology University of Miami Coral GablesFL United States Sylvester Comprehensive Cancer Center University of Miami MiamiFL United States Institute of Data Science and Computing University of Miami MiamiFL United States McCormick School of Engineering Northwestern University EvanstonIL United States

Interdisciplinary research has emerged as a hotbed for innovation and a key approach to addressing complex societal challenges1-7. The increasing dominance of grant-supported research in shaping scientific advances8-16, coupled with growing interest in funding interdisciplinary work2,7,14,17-20, raises fundamental questions about the effectiveness of interdisciplinary grants in fostering high-impact interdisciplinary research outcomes. Here, we quantify the interdisciplinarity of both research grants and publications, capturing 350,000 grants from 164 funding agencies across 26 countries and 1.3 million papers that acknowledged their support from 1985 to 2009. Our analysis uncovers two seemingly contradictory patterns: Interdisciplinary grants tend to produce interdisciplinary papers, which are generally associated with high impact. However, compared to disciplinary grants, interdisciplinary grants on average yield fewer papers and interdisciplinary papers they support tend to have substantially reduced impact. We demonstrate that the key to explaining this paradox lies in the power of disciplinary grants in propelling high-impact interdisciplinary research. Specifically, our results show that highly interdisciplinary papers supported by deeply disciplinary grants garner disproportionately more citations, both within their core disciplines and from broader fields. Moreover, disciplinary grants, particularly when combined with other similar grants, are more effective in producing high-impact interdisciplinary research. Amidst the rapid rise of support for interdisciplinary work across the sciences, these results highlight the hitherto unknown role of disciplinary grants in driving crucial interdisciplinary advances, suggesting that interdisciplinary research requires deep disciplinary expertise and investments. © 2023, CC BY-NC-ND.

关键词： Paper