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41st International Conference on Machine Learning, ICML 2024

作者： Che, Fengdi Xiao, Chenjun Mei, Jincheng Dai, Bo Gummadi, Ramki Ramirez, Oscar A. Harris, Christopher K. Rupam Mahmood, A. Schuurmans, Dale Department of Computing Science University of Alberta Canada School of Data Science The Chinese University of Hong Kong Shenzhen China Google DeepMind United Kingdom School of Computational Science and Engineering Georgia Tech United States Figure Uber United States CIFAR AI Amii

We prove that the combination of a target network and over-parameterized linear function approximation establishes a weaker convergence condition for bootstrapped value estimation in certain cases, even with off-policy data. Our condition is naturally satisfied for expected updates over the entire state-action space or learning with a batch of complete trajectories from episodic Markov decision processes. Notably, using only a target network or an over-parameterized model does not provide such a convergence guarantee. Additionally, we extend our results to learning with truncated trajectories, showing that convergence is achievable for all tasks with minor modifications, akin to value truncation for the final states in trajectories. Our primary result focuses on temporal difference estimation for prediction, providing high-probability value estimation error bounds and empirical analysis on Baird's counterexample and a Four-room task. Furthermore, we explore the control setting, demonstrating that similar convergence conditions apply to Q-learning. Copyright 2024 by the author(s)

关键词： Markov processes

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Evaluating Power System Stability in PV-Integrated Grid with AI-Based Solar Forecasting Model

Evaluating Power System Stability in PV-Integrated Grid with...

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IEEE Southeastcon

作者： Hiba Darwish Ahmad Harb Balakrishna Gokaraju Marwan Bikdash Computational Data Science and Engineering Department North Carolina A&T State University Greensboro USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

This research focuses on forecasting solar irradiance for the year 2025 by applying Artificial Neural Network (ANN) based on historical data collected over three years (2022–2024) for the humidity, earth temperature, and wind speed. The primary aim is to assess the potential output power of a photovoltaic (PV) system and its reliability in covering electrical loads. The developed forecasting model demonstrated promising accuracy, achieving accepted values for the Mean Squared Error (MSE). To evaluate the system's performance under adverse conditions, two fault scenarios were simulated by MATLAB-Simulink software: one where the load was supplied entirely by the grid (no PV system), and another where both the grid and the PV system supplied the load. Results indicated that the inclusion of the PV system introduced challenges in system recovery due to its lack of inertia, exacerbating instability during faults. To address this limitation, a battery energy storage system was integrated into the PV setup. This addition significantly improved the system's resilience and ensured smooth recovery once the fault is removed. The study underscores the importance of incorporating energy storage solutions to enhance the reliability and stability of PV-based power systems, offering critical insights for designing sustainable and robust energy infrastructures.

关键词： Training Solar irradiance Artificial neural networks Power system stability Predictive models Mathematical models Stability analysis Power system reliability Forecasting Sustainable development

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The SpliZ generalizes 'percent spliced in' to reveal regulated splicing at single-cell resolution (vol 19, pg 307, 2022)

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NATURE METHODS 2022年第5期19卷 628-628页

作者： Olivieri, Julia Eve Dehghannasiri, Roozbeh Salzman, Julia Department of Biomedical Data Science Stanford University Stanford CA USA Institute for Computational and Mathematical Engineering Stanford University Stanford CA USA Department of Biochemistry Stanford University Stanford CA USA

Detecting single-cell-regulated splicing from droplet-based technologies is challenging. Here, we introduce the splicing Z score (SpliZ), an annotation-free statistical method to detect regulated splicing in single-cell RNA sequencing. We applied the SpliZ to human lung cells, discovering hundreds of genes with cell-type-specific splicing patterns including ones with potential implications for basic and translational biology.

关键词： Software Statistical methods Transcriptomics Life sciences general Biological Techniques Biological Microscopy Biomedical engineering/Biotechnology Bioinformatics Proteomics

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Approaches to the Development of an Effective Regulatory Ecosystem and Policies for Big data Analytics in the Republic of Kazakhstan

Approaches to the Development of an Effective Regulatory Eco...

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2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Akatov, Kamil Yedilkhan, Didar Urnaliyev, Alibi Astana It University Department Of Computational And Data Science Nur-Sultan Kazakhstan Astana It University Department Of Computer Engineering Nur-Sultan Kazakhstan

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

This paper studies the Big data ecosystem and regulatory approaches for the technological issues used in Kazakhstan. Key points are considered in terms of the importance and necessity of managing data at various levels for decision-making and facilitating their introduction into economic circulation, including an analysis of international approaches and cases in the use of data by authorities and organizations. The main aim of this paper is to make analysis and prepare model for improving policies for the development of Big data policies included analytics at national level. © 2022 IEEE.

关键词： Information management

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Time Series Analysis and Forecasting of Air Quality Index of Dhaka City of Bangladesh

Time Series Analysis and Forecasting of Air Quality Index of...

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2023 IEEE World AI IoT Congress, AIIoT 2023

作者： Sakib, Sheikh Rahmatulla Sara, Kamarun Nahar Hossain Rasel, Md. Tahmid Asif, Md. Masudul Islam Nahid, Md. Aynul Hasan Rahman, Md. Saifur Mridha, M.F. Islam, Ashraful Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh American International University-Bangladesh Department of Computer Science Dhaka Bangladesh Independent University Bangladesh Center for Computational and Data Sciences Dhaka Bangladesh

ISBN: (纸本)9798350337617

In Dhaka, the capital city of Bangladesh, various sources including vehicle emissions, industrial activities, brick kilns, building sites, and open rubbish burning contribute to the air pollution problem. To assess the air quality, the Air Quality Index (AQI) is utilized, which categorizes air quality based on pollutant concentration. In this study, we have built ARIMA, Auto-ARIMA, SARIMAX, and VAR models to predict the air quality of Dhaka. Unlike previous studies, we have utilized hourly air pollutants factors such as PM2.5, PM10, SO2, CO, NO2, and O3 to forecast air quality. Our novel approach enables us to predict the monthly and weekly air quality of Dhaka city. Our analysis reveals that the SARIMAX model, which takes into account seasonal patterns, trends, and external factors, is the most accurate in predicting Dhaka city's air quality. The model's prediction performance is assessed using statistical indicators such as mean absolute percentage error and root mean square error. The study highlights that the SARIMAX model could aid policymakers in evaluating the efficacy of air pollution control measures. © 2023 IEEE.

关键词： Air quality

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A Conceptual and computational Framework for Aspect-Based Collaborative Filtering Recommender Systems

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Journal of Computer and Communications 2023年第3期11卷 110-130页

作者： Samin Poudel Marwan Bikdash Department of Computational Data Science and Engineering North Carolina A & T University Greensboro North Carolina USA

Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspects of the items thus leading to more sophisticated and justifiable recommendations. However, most Collaborative Filtering (CF) techniques rely mainly on the overall preferences of users toward items only. And there is lack of conceptual and computational framework that enables an understandable aspect-based AI approach to recommending items to users. In this paper, we propose concepts and computational tools that can sharpen the logic of recommendations and that rely on users’ sentiments along various aspects of items. These concepts include: The sentiment of a user towards a specific aspect of a specific item, the emphasis that a given user places on a specific aspect in general, the popularity and controversy of an aspect among groups of users, clusters of users emphasizing a given aspect, clusters of items that are popular among a group of users and so forth. The framework introduced in this study is developed in terms of user emphasis, aspect popularity, aspect controversy, and users and items similarity. Towards this end, we introduce the Aspect-Based Collaborative Filtering Toolbox (ABCFT), where the tools are all developed based on the three-index sentiment tensor with the indices being the user, item, and aspect. The toolbox computes solutions to the questions alluded to above. We illustrate the methodology using a hotel review dataset having around 6000 users, 400 hotels and 6 aspects.

关键词： Recommender System Collaborative Filtering Aspect based recommendation Recommendation System Framework Aspect Sentiments

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Real-space methods for ab initio modeling of surfaces and interfaces under external potential bias

arXiv

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arXiv 2025年

作者： Ramakrishnan, Kartick Gautam, Gopalakrishnan Sai Motamarri, Phani Department of Computational and Data Sciences Indian Institute of Science Bangalore India Department of Materials Engineering Indian Institute of Science Bangalore India

Accurate ab initio modelling of surfaces and interfaces, especially under an applied external potential bias, is important for describing and characterizing various phenomena that occur in electronic, catalytic, and energy storage devices. Leveraging the ability of real-space density functional theory (DFT) codes to accommodate generic boundary conditions, we introduce two methods for applying an external potential bias that can be suitable for modelling surfaces and interfaces. In the first method, an external constant electric field is applied by modifying the DFT Hamiltonian via introduction of an auxiliary linear potential while solving the electrostatic potential arising in DFT using a Poisson equation with zero-Neumann boundary conditions. The second method directly enforces the desired external potential bias by imposing constraints on the electrostatic potential, thereby naturally mimicking experimental conditions. We describe the underlying DFT governing equations for the two setups within the real-space formalism employing finite-element discretization. First, we validate the constant electric field setup within real-space finite-element DFT (DFT-FE) with an equivalent approach using plane-wave DFT (i.e., using periodic boundary conditions) on three representative benchmark systems, namely La-terminated Li7La3Zr2O12, GaAs (111), and Al FCC (111) slabs. Subsequently, we present a comprehensive evaluation of the two setups in terms of the average ground-state properties, such as surface and adsorption energies. The methods developed in our work provide an attractive alternative to plane-wave DFT approaches in applying external potential bias that usually suffer from the periodic boundary conditions restrictions and poor scalability on parallel computing architectures. Our framework offers a robust approach for investigating surfaces and interfaces without any underlying assumptions or correction schemes while allowing for simulations of larger length scales

关键词： Hamiltonians

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Building Consensus in Group Decision-Making with Intuitionistic Reciprocal Preference Relations: An Analysis of Various Protocols of Information Granularity Distribution

Building Consensus in Group Decision-Making with Intuitionis...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Gonzalez-Quesada, Juan Carlos Cabrerizo, Francisco Javier Herrera-Viedma, Enrique Pedrycz, Witold Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Department of Computer Science and Artificial Intelligence Granada18071 Spain University of Alberta Department of Electrical & Computer Engineering EdmontonABT6R 2V4 Canada

ISBN: (纸本)9781665410205

On the one hand, to model experts' preferences in group decision-making, intuitionistic reciprocal preference relations have widely been used because they allow for accommodating hesitation degrees, which are inherent to all decision-making processes. On the other hand, an optimization of information granularity distribution has recently been applied to establish consensus during group decision-making processes. Concretely, a symmetric and uniform distribution of information granularity has been considered for intuitionistic reciprocal preference relations. However, there exist other protocols of information granularity distribution that could be used. Therefore, we aim to analyze all the information granularity distribution protocols and determine their effectiveness in building consensus through intuitionistic reciprocal preference relations. The performance of the different protocols is discussed by conducting some numerical experiments that help provide insights into the effectiveness of the protocols to build consensus. © 2024 IEEE.

关键词： Information granules

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IoT-Enabled Precision Irrigation for Coconut Plantations: Enhancing Growth and Sustainability 8

IoT-Enabled Precision Irrigation for Coconut Plantations: En...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Reddy, M.Jaganmohan Ramu, V. Grace Shalini, T. Vincysahaba, R. Devaki, M. Athiraja, A. Telangana Secunderabad India Telangana Hanamkonda India Srm Institute of Science and Technology Kattankulathur School of Computing Department of Computational Intelligence Chengalpattu India St.Joseph College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Velammal College of Engineering and Technology Department of Eee Tamil Nadu Madurai India

ISBN: (纸本)9798350367904

In agriculture, precision irrigation plays a pivotal role in optimizing crop growth and resource utilization. In this study, must proposean Internet of Things based irrigation remote monitoring system tailored for enhancing coconut plant growth. The system integrates sensor technology, Internet of Things connectivity, and data analytics to provide real-time monitoring and control of irrigation parameters such as soilmoisture, temperature, and humidity. Leveraging a network of sensors deployed in the coconut plantation, the system collects and analyzes environmental data to optimize irrigation schedules and ensure optimal growing conditions for coconut plants. Through a user-friendly interface accessible via web or mobile applications, farmers can remotely monitor the status of their plantation and adjust irrigation settings as ***, the system incorporates predictive analytics capabilities to anticipate irrigation needs based on historical data and weather forecasts, thereby enhancing water efficiency and crop productivity. Field trials demonstrate the effectiveness of this Internet of Things based irrigation system in improving coconut plant growth while reducing water consumption and operational costs. This findings underscore the potential of Internet of Things technologies in revolutionizing agricultural practices, facilitating sustainable crop management, and enhancing food security in coconut-producing regions. © 2024 IEEE.

关键词： Precision agriculture

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Demystifying Disagreement-on-the-Line in High Dimensions 40

Demystifying Disagreement-on-the-Line in High Dimensions

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40th International Conference on Machine Learning, ICML 2023

作者： Lee, Donghwan Moniri, Behrad Huang, Xinmeng Dobriban, Edgar Hassani, Hamed Graduate Group in Applied Mathematics and Computational Science University of Pennsylvania PA United States Department of Electrical and Systems Engineering University of Pennsylvania PA United States Department of Statistics and Data Science University of Pennsylvania PA United States

Evaluating the performance of machine learning models under distribution shifts is challenging, especially when we only have unlabeled data from the shifted (target) domain, along with labeled data from the original (source) domain. Recent work suggests that the notion of disagreement, the degree to which two models trained with different randomness differ on the same input, is a key to tackling this problem. Experimentally, disagreement and prediction error have been shown to be strongly connected, which has been used to estimate model performance. Experiments have led to the discovery of the disagreement-on-the-line phenomenon, whereby the classification error under the target domain is often a linear function of the classification error under the source domain;and whenever this property holds, disagreement under the source and target domain follow the same linear relation. In this work, we develop a theoretical foundation for analyzing disagreement in high-dimensional random features regression;and study under what conditions the disagreement-on-the-line phenomenon occurs in our setting. Experiments on CIFAR-10-C, Tiny ImageNet-C, and Camelyon17 are consistent with our theory and support the universality of the theoretical findings. © 2023 Proceedings of Machine Learning Research. All rights reserved.

关键词： Errors

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