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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Li, Junfan Wu, Zheshun Xu, Zenglin King, Irwin School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Pengcheng Lab China Institute Fudan University China Shanghai Academy of AI for Science China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

We consider online model selection with decentralized data over M clients, and study the necessity of collaboration among clients. Previous work proposed various federated algorithms without demonstrating their necessity, while we answer the question from a novel perspective of computational constraints. We prove lower bounds on the regret, and propose a federated algorithm and analyze the upper bound. Our results show (i) collaboration is unnecessary in the absence of computational constraints on clients;(ii) collaboration is necessary if the computational cost on each client is limited to o(K), where K is the number of candidate hypothesis spaces. We clarify the unnecessary nature of collaboration in previous federated algorithms for distributed online multi-kernel learning, and improve the regret bounds at a smaller computational and communication cost. Our algorithm relies on three new techniques including an improved Bernstein's inequality for martingale, a federated online mirror descent framework, and decoupling model selection and prediction, which might be of independent interest. © 2024 Neural information processing systems foundation. All rights reserved.

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Towards a Data-Centric Framework for Modelling, Managing and Mining BPM Processes over Pandemic Events

Towards a Data-Centric Framework for Modelling, Managing and...

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Workshops of the EDBT/ICDT 2025 Joint Conference, EDBT/ICDT-WS 2025

作者： Cuzzocrea, Alfredo Belmerabet, Islam Combi, Carlo Franconi, Enrico Terenziani, Paolo iDEA Lab University of Calabria Rende Italy Department of Computer Science University of Paris City Paris France Department of Computer Science University of Verona Verona Italy Faculty of Engineering Free University of Bozen-Bolzano Bolzano Italy DISIT Department University of Piemonte Orientale Alessandria Italy

The COVID-19 pandemic exposed significant shortcomings in the ability of modern healthcare information systems to manage and mitigate pandemics, especially when faced with unforeseen events. This paper addresses these shortcomings by introducing PROTECTION, a cutting-edge data-centric framework aimed at enhancing pandemic control and prevention, specifically focusing on BPM processes. PROTECTION offers a comprehensive approach to handling pandemic-related complexities. We provide an overview of PROTECTION's structure and main functions. Copyright © 2025 for this paper by its authors.

关键词： COVID-19

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Thermo-hydro-poro-mechanical responses of a reservoir-induced landslide tracked by high-resolution fiber optic sensing nerves

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Journal of Rock Mechanics and Geotechnical engineering 2024年第3期16卷 1018-1032页

作者： Xiao Ye Hong-Hu Zhu Gang Cheng Hua-Fu Pei Bin Shi Luca Schenato Alessandro Pasuto School of Earth Sciences and Engineering Nanjing UniversityNanjing210023China School of Computer Science North China Institute of Science and Technology(National Safety Training Center of Coal Mines)Beijing101601China State Key Lab of Coastal and Offshore Engineering Department of Geotechnical EngineeringDalian University of TechnologyDalian116024China National Research Council-Research Institute for Geo-Hydrological Protection(CNR-IRPI) Padova35127Italy Department of Information Engineering University of PadovaPadova35131Italy

Thermo-poro-mechanical responses along sliding zone/surface have been extensively ***,it has not been recognized that the potential contribution of other crucial engineering geological interfaces beyond the slip surface to progressive ***,we aim to investigate the subsurface multiphysics of reservoir landslides under two extreme hydrologic conditions(*** and dry),particularly within sliding *** on ultra-weak fiber Bragg grating(UWFBG)technology,we employ specialpurpose fiber optic sensing cables that can be implanted into boreholes as“nerves of the Earth”to collect data on soil temperature,water content,pore water pressure,and *** Xinpu landslide in the middle reach of the Three Gorges Reservoir Area in China was selected as a case study to establish a paradigm for in situ thermo-hydro-poro-mechanical *** UWFBG-based sensing cables were vertically buried in a 31 m-deep borehole at the foot of the landslide,with a resolution of 1 m except for the pressure *** reported field measurements covering the period 2021 and 2022 and produced the spatiotemporal profiles throughout the *** show that wet years are more likely to motivate landslide motions than dry *** annual thermally active layer of the landslide has a critical depth of roughly 9 m and might move downward in warmer *** dynamic groundwater table is located at depths of 9e15 m,where the peaked strain undergoes a periodical response of leap and withdrawal to annual hydrometeorological *** interface behaviors may support the interpretation of the contribution of reservoir regulation to slope stability,allowing us to correlate them to local damage events and potential global *** paper also offers a natural framework for interpreting thermo-hydro-poro-mechanical signatures from creeping reservoir bank slopes,which may form the basis for a landslide monitoring and early warning system.

关键词： Reservoir landslide Thermo-hydro-poro-mechanical response Ultra-weak fiber bragg grating(UWFBG) subsurface evolution engineering geological interface Geotechnical monitoring

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IoT Systems and Battery-Based Energy Sources

IoT Systems and Battery-Based Energy Sources

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Artificial Intelligence and Smart Communication (AISC), International Conference on

作者： Komal Sai Sajja Biswajit Bhowmik Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal BRICS Laboratory Mangalore Bharat

The Internet of Things (IoT) has rapidly expanded over the past few years and has begun taking over many aspects of our lives, including hospitals, houses, roadways, and much more. Due to its expansion, IoT may become the future technology in the following years. IoT is a collection of real-world items equipped with electronics, software, networks, and sensors to collect and exchange data. To power IoT devices, there must be a proper understanding of the energy consumption of thesensors and the kind of energy sources to be used. This paper provides state-of-the-art battery-powered IoT systems. First, the study discusses IoT’s architecture and various applications. The study then extends IoT energy resources. Next, the study examines the use of batteries in IoT systems, their advantages and disadvantages, followed by unravelling the ambient energy harvesting mechanism. Finally, we present related works on battery-based IoT systems.

关键词： Energy consumption Energy resources Hospitals computer architecture Supercapacitors Software Batteries

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ACM-MILP: Adaptive Constraint Modification via Grouping and Selection for Hardness-Preserving MILP Instance Generation 41

ACM-MILP: Adaptive Constraint Modification via Grouping and ...

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41st International Conference on Machine Learning, ICML 2024

作者： Guo, Ziao Li, Yang Liu, Chang Ouyang, Wenli Yan, Junchi School of Artificial Intelligence Department of Computer Science and Engineering MoE Lab of AI Shanghai Jiao Tong University Shanghai China AI Lab Lenovo Research Beijing China

Data plays a pivotal role in the development of both classic and learning-based methods for Mixed-Integer Linear Programming (MILP). However, the scarcity of data in real-world applications underscores the necessity for MILP instance generation methods. Currently, these methods primarily rely on iterating random single-constraint modifications, disregarding the underlying problem structure with constraint interrelations, thereby leading to compromised quality and solvability. In this paper, we propose ACM-MILP, a framework for MILP instance generation, to achieve adaptive constraint modification and constraint interrelation modeling. It employs an adaptive constraint selection mechanism based on probability estimation within the latent space to preserve instance characteristics. Meanwhile, it detects and groups strongly related constraints through community detection, enabling collective modifications that account for constraint dependencies. Experimental results show significant improvements in problem-solving hardness similarity under our framework. Additionally, in the downstream task, we showcase the efficacy of our generated instances for hyperparameter tuning. Source code is available: https://***/Thinklab-SJTU/ACM-MILP. Copyright 2024 by the author(s)

关键词： Mixed-integer linear programming

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MILP-FBGen: LP/MILP Instance Generation with Feasibility/Boundedness 41

MILP-FBGen: LP/MILP Instance Generation with Feasibility/Bou...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhang, Yahong Fan, Chenchen Chen, Donghui Li, Congrui Ouyang, Wenli Zhu, Mingda Yan, Junchi AI Lab Lenovo Research Beijing China School of Artificial Intelligence Department of Computer Science and Engineering MoE Lab of AI Shanghai Jiao Tong University Shanghai China

Machine learning (ML) has been actively adopted in Linear Programming (LP) and Mixed-Integer Linear Programming (MILP), whose potential is hindered by instance *** synthetic instance generation methods often fall short in closely mirroring the distribution of original datasets or ensuring the feasibility and boundedness of the generated data - a critical requirement for obtaining reliable supervised labels in model *** this paper, we present a diffusion-based LP/MILP instance generative framework called *** strikes a balance between structural similarity and novelty while maintaining feasibility/boundedness via a meticulously designed structure-preserving generation module and a feasibility/boundedness-constrained sampling *** method shows superiority on two fronts: 1) preservation of key properties (hardness, feasibility, and boundedness) of LP/MILP instances, and 2) enhanced performance on downstream *** studies show two-fold superiority that our method ensures higher distributional similarity and 100% feasibility in both easy and hard datasets, surpassing current state-of-the-art techniques. Copyright 2024 by the author(s)

关键词： Mixed-integer linear programming

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Analysis of Heat Transfer of the Nanofluid Al2O3/H2O Flow Across a Stretching Sheet under the Effects of Thermal Radiation

Analysis of Heat Transfer of the Nanofluid Al2O3/H2O Flow Ac...

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2024 International Conference on Thermofluids and Manufacturing science, ICTMS 2024

作者： Krishnendu, S. Nagaraja, K.V. Shali, S. Department of Computer Science Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Computational Science Lab Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

Efficient heat transfer is very crucial for the various industries in electrical, nuclear power, aerospace and automobile fields. By exhibiting improved thermal conductivity, nanofluids have emerged as an alternative for enhanced thermal and energy efficiency applications. The present study examines the movement of nano fluid across a surface that extend when heat radiation and magnetic fields are present. Using appropriate similarity variables, governing partial differential equations are converted to nonlinear ordinary differential equations. engineering factors which are discussed include Nusselt number and skin friction coefficient. Numerical analysis is optimized using the response surface methodology. The Response Surface Methodology (RSM) with face-centred Central Composite Design is utilized to calculate heat flux transfer at the stretched sheet. The interaction of heat radiation, magnetic field parameter, and solid volume fraction are investigated in the present work. It is worth noting that the local Nusselt number responds positively to the magnetic field parameter and the solid volume fraction, whereas the skin friction coefficient responds negatively. © Published under licence by IOP Publishing Ltd.

关键词： Skin friction

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Near-Linear Time Approximation Algorithms for k-means with Outliers 41

Near-Linear Time Approximation Algorithms for k-means with O...

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41st International Conference on Machine Learning, ICML 2024

作者： Huang, Junyu Feng, Qilong Huang, Ziyun Xu, Jinhui Wang, Jianxin School of Computer Science and Engineering Central South University Changsha410083 China Xiangjiang Laboratory Changsha410205 China Department of Computer Science and Software Engineering Penn State Erie The Behrend College United States Department of Computer Science and Engineering State University of New York BuffaloNY United States The Hunan Provincial Key Lab of Bioinformatics Central South University Changsha410083 China

The k-means with outliers problem is one of the most extensively studied clustering problems in the field of machine learning, where the goal is to discard up to z outliers and identify a minimum k-means clustering on the remaining data points. Most previous results for this problem have running time dependent on the aspect ratio ∆ (the ratio between the maximum and the minimum pairwise distances) to achieve fast approximations. To address the issue of aspect ratio dependency on the running time, we propose sampling-based algorithms with almost linear running time in the data size, where a crucial component of our approach is an algorithm called Fast-Sampling. Fast-Sampling algorithm can find inliers that well approximate the optimal clustering centers without relying on a guess for the optimal clustering costs, where a 4-approximate solution can be obtained in time O(ndk log log n/∊2) with O(k/∊) centers opened and (1 + ∊)z outliers discarded. To reduce the number of centers opened, we propose a center reduction algorithm, where an O(1/∊)-approximate solution can be obtained in time O(ndk log log n/∊2 + dpoly(k, 1/∊) log(n∆)) with (1 + ∊)z outliers discarded and exactly k centers opened. Empirical experiments suggest that our proposed sampling-based algorithms outperform state-of-the-art algorithms for the k-means with outliers problem. Copyright 2024 by the author(s)

关键词： K-means clustering

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Revolutionizing Air Quality Forecasting in Bengaluru with Advanced Machine Learning Techniques 1

Revolutionizing Air Quality Forecasting in Bengaluru with Ad...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Neeraja, A. Begum, Ch. Mounika Reddy, K. Vigneswara Smitha, T.V. Neelima, N. Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Computer Science Bengaluru India Amrita Vishwa Vidyapeetham Computational Science Lab Amrita School of Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics and Communication Bengaluru India

ISBN: (纸本)9798350376517

Exposure to air pollution can significantly affect one's health, which can lead to itchy eyes, breathing issues, and hospitalisation. There are a variety of pollutants such as indoor, biological, and outdoor pollutants. This paper emphasizes estimating the quality of air concerning outdoor pollutants. This model aims to effectively predict the Air Quality Index (AQI) in major populated stations in Bengaluru city. Using data from five monitoring stations - Peenya, Bapuji Nagar, Silk Board, Hombegowda, and BTM Layout- collected between 2021 and 2023, machine learning models are developed to forecast AQI for 2024. The research involved data pre-processing, AQI calculation from key pollutants, and training of models for AQI prediction. Our findings indicate that the Random Forest algorithm outperformed the Decision Tree in both regression and classification tasks, providing more accurate predictions. Additionally, the study included a visual mapping of AQI categories using colour codes, revealing trends in air quality across different regions. This research serves as a foundation for future work, including expanding the model to predict AQI across India and integrating it with real-time data collection hardware to enhance prediction accuracy. © 2024 IEEE.

关键词： Indoor air pollution

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Linear Time Approximation Algorithm for Column Subset Selection with Local Search 38

Linear Time Approximation Algorithm for Column Subset Select...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zou, Yuanbin Huang, Ziyun Xu, Jinhui Wang, Jianxin Feng, Qilong School of Computer Science and Engineering Central South University Changsha410083 China Xiangjiang Laboratory Changsha410205 China Department of Computer Science and Software Engineering Penn State Erie The Behrend College United States Department of Computer Science and Engineering State University of New York BuffaloNY United States The Hunan Provincial Key Lab of Bioinformatics Central South University Changsha410083 China

The Column Subset Selection (CSS) problem has been widely studied in dimensionality reduction and feature selection. The goal of the CSS problem is to output a submatrix S, consisting of k columns from an n × d input matrix A that minimizes the residual error A − SS†A2F, where S† is the Moore-Penrose inverse matrix of S. Many previous approximation algorithms have non-linear running times in both n and d, while the existing linear-time algorithms have a relatively larger approximation ratios. Additionally, the local search algorithms in existing results for solving the CSS problem are heuristic. To achieve linear running time while maintaining better approximation using a local search strategy, we propose a local search-based approximation algorithm for the CSS problem with exactly k columns selected. A key challenge in achieving linear running time with the local search strategy is how to avoid exhaustive enumerations of candidate columns for constructing swap pairs in each local search step. To address this issue, we propose a two-step mixed sampling method that reduces the number of enumerations for swap pair construction from O(dk) to k in linear time. Although the two-step mixed sampling method reduces the search space of local search strategy, bounding the residual error after swaps is a non-trivial task. To estimate the changes in residual error after swaps, we propose a matched swap pair construction method to bound the approximation loss, ensuring a constant probability of loss reduction in each local search step. In expectation, these techniques enable us to obtain the local search algorithm for the CSS problem with theoretical guarantees, where a 53(k + 1)-approximate solution can be obtained in linear running time O(ndk4 log k). Empirical experiments show that our proposed algorithm achieves better quality and time compared to previous algorithms on both small and large datasets. Moreover, it is at least 10 times faster than state-of-the-art algorithms a

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