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IMPROVED ANALYSIS OF SPARSE LINEAR REGRESSION IN LOCAL DIFFERENTIAL PRIVACY MODEL

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arXiv 2023年

作者： Zhu, Liyang Ding, Meng Aggarwal, Vaneet Xu, Jinhui Wang, Di Saudi Arabia Department of Computer Science and Engineering The State University of New York Buffalo United States School of IE ECE Purdue University United States Computational Bioscience Research Center Japan SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence Saudi Arabia

In this paper, we revisit the problem of sparse linear regression in the local differential privacy (LDP) model. Existing research in the non-interactive and sequentially local models has focused on obtaining the lower bounds for the case where the underlying parameter is 1-sparse, and extending such bounds to the more general k-sparse case has proven to be challenging. Moreover, it is unclear whether efficient non-interactive LDP (NLDP) algorithms exist. To address these issues, we first consider the problem in the ∊ non-interactive LDP model and provide a lower bound of (formula presented) on the 2-norm estimation error for sub-Gaussian data, where n is the sample size and d is the dimension of the space. We propose an innovative NLDP algorithm, the very first of its kind for the problem. As a remarkable outcome, this algorithm also yields a novel and_ highly efficient estimator as a valuable by-product. Our algorithm achieves an upper bound of (formula presented) for the estimation error when the data is sub-Gaussian, which can be further improved by a factor of O(√d) if the server has additional public but_ unlabeled data. For the sequentially interactive LDP model, we show a similar lower bound of (formula presented). As for the upper bound, we rectify a previous method and show that it is possible to achieve a bound of (formula presented). Our findings reveal fundamental differences between the non-private case, central DP model, and local DP model in the sparse linear regression problem. © 2023, CC BY.

关键词： Linear regression

Quantization of Bandlimited Graph Signals

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Quantization of Bandlimited Graph Signals

International Conference on Sampling Theory and Applications (SampTA)

作者： Felix Krahmer He Lyu Rayan Saab Anna Veselovska Rongrong Wang Department of Mathematics & Munich Data Science Institute Technical University of Munich and Munich Center for Machine Learning Garching/Munich Germany Department of Mathematics & Halicioglu Data Science Institute University of California San Diego San Diego USA Department of Computational Mathematics Science and Engineering & Department of Mathematics Michigan State University East Lansing USA

Graph models and graph-based signals are becoming increasingly important in machine learning, natural sciences, and modern signal processing. In this paper, we address the problem of quantizing bandlimited graph signals. We introduce two classes of noise-shaping algorithms for graph signals that differ in their sampling methodologies. We demonstrate that these algorithms can be efficiently used to construct quantized representatives of bandlimited graph-based signals with bounded amplitude. Moreover, for one of the algorithms, we provide theoretical guarantees on the relative error between the quantized representative and the true signal.

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Day-Ahead Forecasting of Solar Irradiance & PV Power Output Through Statistical Machine Learning Methods

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Day-Ahead Forecasting of Solar Irradiance & PV Power Output ...

2022 Saudi Arabia Smart Grid Conference, SASG 2022

作者： Kamarianakis, Yiannis Pantazis, Yannis Kalligiannaki, Evangelia Katsaounis, Theodoros D. Kotsovos, Konstantinos Gereige, Issam Abdullah, Marwan Jamal, Aqil Tzavaras, Athanasios Foundation for Research and Technology-Hellas Institute of Applied and Computational Mathematics Data Science Group Greece University of Crete Dept. of Mathematics and Applied Mathematics Greece Saudi Aramco RandD Center Carbon Management Division Dhahran Saudi Arabia Computer Electrical and Mathematical Sciences and Engineering Division Thuwal Saudi Arabia

ISBN: (纸本)9781665475891

Energy production from solar photovoltaic (PV) plants is unpredictable, mainly due to the stochastic formation and movement of clouds or aerosol - dust particles which scatter or disperse solar radiation. Accurate forecasts of PV output are essential to Distribution and Transportation System Operators as they assist efficient solar energy trading and management of electricity grids. This work evaluates an autoregressive, computationally-light KNN-regression scheme (TSFKNN) for hourly, day-ahead forecasts of solar irradiance and energy yield of various PV technologies. The model is being tested and validated using data measured in Thuwal, Saudi Arabia. The available measured records span a 60-month period. The developed forecasting models are designed for online systems and provide increased levels of accuracy and low computational cost. Several parametric and nonparametric specifications, coupled with conventional versus outlier-robust estimation procedures are tested, in order to derive an optimal month-specific daily profile (MDP). Current results demonstrate that including intraday variability to the monthly-based irradiance models achieve improved predictive accuracy between 10% and 25% on average. © 2022 IEEE.

关键词： Forecasting

Building of Geographic Information System for Monitoring of Dams in the Republic of Kazakhstan

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Building of Geographic Information System for Monitoring of ...

International Conference on Information science and Communications Technologies (ICISCT)

作者： Balgaisha Mukanova Tolkyn Mirgalikyzy Kamila Tanyrbergenova Assemov Kambar Yermek Akhmetov Department of Computations and Data Science Astana IT University Astana Kazakhstan Department of Computational Science and Statistics Al Farabi Kazakh National University Almaty Kazakhstan Department of Computer Engineering Astana IT University Astana Kazakhstan RSE National Center on Complex Processing of Mineral Raw Materials of the Republic of Kazakhstan Almaty Kazakhstan

ISBN: (数字)9798331527921

ISBN: (纸本)9798331527938

We describe the structure and main components of the geographic information system devoted to collecting and monitoring dams and dikes in the Republic of Kazakhstan. We also propose the structure of the main tables of the relational database system, types of monitoring work, including drone flybys, and system functionality for different users. Methods of using available open-source data to identify unregistered dams are discussed.

关键词： Surveys Information science Dams Levee Relational databases Communications technology data mining Monitoring Geographic information systems Drones

A High-Order Multidomain Meshless Algorithm for Thermal Management of Batteries with Phase Change Materials

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SSRN

SSRN 2024年

作者： Bartwal, Naman Roy, Somnath Vanka, Surya Pratap Department of Mechanical Engineering Indian Institute of Technology Kharagpur West Bengal Kharagpur721302 India Center for Computational & Data Sciences Indian Institute of Technology Kharagpur West Bengal Kharagpur721302 India Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States

Thermal management of Li-ion batteries has attracted worldwide attention due to their widesp-read usage ranging from electronics to electric vehicles. To gain maximum performance from the battery pack, it is of utmost importance to limit the maximum temperature of Li-ion cells much below the safe working limit. In the present study, we use a high-order polyharmonic spline radial basis function (PHS-RBF) based multidomain meshless algorithm to study the distribution of temperature in the battery pack for discharge rate varying from 1C-5C. It is seen that for higher discharge rates (i.e. discharge rate $\geq$ 3C), active cooling method (air cooling) is insufficient to limit the working temperature of the battery pack to the safe operating limit. Hence, we cover each Li-ion cell with a layer of PCM. We study the effect of coolant inlet velocity and thickness of PCM on the temperature distribution within the battery pack. We present the variation of average and maximum temperature of batteries and the distribution of temperature in individual cells. The temperature distribution is not axisymmetric in most of the cells, especially for the cells located in the upstream portion of the battery pack. The highest temperatures are found in the cells located in the downstream portion of the battery pack. For effective battery pack management, the minimum PCM thickness required is determined to be 2 mm for a 3C discharge rate and 3 mm for a 4C discharge rate. However, at 5C, the PCM melts completely at all PCM thicknesses taken into consideration. For cases where Li-ion cells are covered by a layer of PCM, there is no significant change with the increase in coolant inlet velocity. © 2024, The Authors. All rights reserved.

关键词： Temperature control

EDSep: An Effective Diffusion-Based Method for Speech Source Separation

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arXiv 2025年

作者： Dong, Jinwei Wang, Xinsheng Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China Audio Speech and Language Processing Group School of Computer Science Northwestern Polytechnical University Xi’an China

关键词： Stochastic systems

Air Pollution Monitoring By Integrating Local and Global Information in Self-Adaptive Multiscale Transform Domain

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IEEE Transactions on Multimedia 2025年

作者： Gu, Ke Liu, Yuchen Liu, Hongyan Liu, Bo Qiao, Junfei Lin, Weisi Zhang, Wenjun Beijing University of Technology School of Information Science and Technology Engineering Research Center of Intelligent Perception and Autonomous Control of Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Massey University School of Mathematical and Computational Sciences New Zealand Nanyang Technological University College of Computing and Data Science Singapore Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai China

As the deadliest form of pollution, air pollution had a prolonged severe damage to the human health and life safety of nearly 99% of the world's population. Facing to the problem that billions of tons of pollutants discharged into the air per year by industrial activities, this paper proposed a novel image-based air pollution monitor (IAPM) by incorporating local and global information in the self-adaptive multiscale transform domain, so as to achieve the timely and effective leakage detection of typical air pollutants from a single image. To be specific, this paper first developed a screen-shaped module according to two significant findings in visual neuroscience, which include the high sensitivity of human eyes to horizontal and vertical stimuli and the center-surround inhibition, by designing and fusing the square module, horizontal strip module and vertical strip module parallelly for simulating the behaviour of human eyes to extract local features. Second, the learnable weights and proportional mapping were applied to incorporate the screen-shaped module and lightweight vision transformer as backbone, towards more richly exploiting and fusing local and global information just as the way a brain perceives external stimuli. Third, a new self-adaptive multiscale transform domain method was devised based on two motivations from the visual characteristics of multiscale perception and the brain characteristics of self-adaptive domain transform to modify the backbone by using the operations of pooling and pointwise convolution. Extensive experiments implemented on the datasets of carbon particulate matters and ethylene leakage confirmed the superior monitoring performance of the proposed IAPM model beyond the state-of-the-art (SOTA) peers by an accuracy gain of about 4%. Furthermore, the proposed IAPM model only required 0.089 GFLOPs and 0.15 million model parameters, remarkably outperforming SOTA competitors in computational efficiency and storage resources. © 1999-2

关键词： Information leakage

Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation

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arXiv 2023年

作者： Gu, Zhouhong Zhu, Xiaoxuan Ye, Haoning Zhang, Lin Wang, Jianchen Zhu, Yixin Jiang, Sihang Xiong, Zhuozhi Li, Zihan Wu, Weijie He, Qianyu Xu, Rui Huang, Wenhao Liu, Jingping Wang, Zili Wang, Shusen Zheng, Weiguo Feng, Hongwei Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China School of Information Science and Engineering East China University of Science and Technology China School of Data Science Fudan University China Fudan-Aishu Cognitive Intelligence Joint Research Center China Research Group of Computational and AI Communication Institute for Global Communications and Integrated Media Fudan University China

New Natural Langauge Process (NLP) benchmarks are urgently needed to align with the rapid development of large language models (LLMs). We present Xiezhi, the most comprehensive evaluation suite designed to assess holistic domain knowledge. Xiezhi comprises multiple-choice questions across 516 diverse disciplines ranging from 13 different subjects with 249,587 questions and accompanied by Xiezhi-Specialty with 14,041 questions and Xiezhi-Interdiscipline with 10,746 questions. We conduct evaluation of the 47 cutting-edge LLMs on Xiezhi. Results indicate that LLMs exceed average performance of humans in science, engineering, agronomy, medicine, and art, but fall short in economics, jurisprudence, pedagogy, literature, history, and management. © 2023, CC BY.

关键词： Domain Knowledge

Social network analysis of manga: similarities to real-world social networks and trends over decades

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arXiv 2023年

作者： Sugishita, Kashin Masuda, Naoki Department of Transdisciplinary Science and Engineering Tokyo Institute of Technology Tokyo152-8550 Japan Department of Mathematics State University of New York at Buffalo BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States Center for Computational Social Science Kobe University Kobe657-8501 Japan

Manga, Japanese comics, has been popular on a global scale. Social networks among characters, which are often called character networks, may be a significant contributor to their popularity. We collected data from 162 popular manga that span over 70 years and analyzed their character networks. First, we found that many of static and temporal properties of the character networks are similar to those of real human social networks. Second, the character networks of most manga are protagonist-centered such that a single protagonist interacts with the majority of other characters. Third, the character networks for manga mainly targeting boys have shifted to denser and less protagonist-centered networks and with fewer characters over decades. Manga mainly targeting girls showed the opposite trend except for the downward trend in the number of characters. The present study, which relies on manga data sampled on an unprecedented scale, paves the way for further population studies of character networks and other aspects of comics. Copyright © 2023, The Authors. All rights reserved.

关键词： Population statistics