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arXiv 2023年

作者： Paul, Debarun Dey, Antara Banik, Amit Dutta Pal, Supratik Physics and Applied Mathematics Unit Indian Statistical Institute 203 B.T. Road Kolkata700108 India Technology Innovation Hub on Data Science Big Data Analytics and Data Curation Indian Statistical Institute 203 B.T. Road Kolkata700108 India

While the Z3 symmetric dark matter models have shown tremendous prospects in addressing a number of (astro-)particle physics problems, they can leave interesting imprints on cosmological observations as well. We consider two such promising models: semi-annihilating dark matter (SADM) and Co-SIMP 2 → 3 interaction, and investigate their effects on the global 21-cm signal. SADM alone cannot address the EDGES dip but can perform better with the aid of an excess radio background, whereas Co-SIMP can naturally explain the EDGES absorption feature by virtue of an intrinsic cooling effect without invoking any such excess radiation. Hence, the latter model turns out to be a rare model within the domain of CDM, that uses leptophilic interaction to achieve the EDGES dip. Further, keeping in mind the ongoing debate between EDGES and SARAS 3 on the global 21-cm signal, we demonstrate that our chosen models can still remain viable in this context, even if the EDGES data requires reassessment in future. We then extend our investigation to possible reflections on the Dark Ages, followed by a consistency check with the CMB and BAO observations via Planck 2018(+BAO) datasets. This work thus presents a compelling case of exploring these interesting particle physics models in the light of different cosmological observations. Copyright © 2023, The Authors. All rights reserved.

关键词： Dark Matter

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Differential Privacy-based Avatar Update in Cooperative Intelligent Transportation System Metaverse 10

Differential Privacy-based Avatar Update in Cooperative Inte...

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10th International Conference on Computer and Communications, ICCC 2024

作者： Khan, Salabat Luo, Fei Khan, Mansoor Ullah, Insaf Amin, Farhan Ullah, Shamsher Rahim, Mussadiq Abdul School of Computer and Enformation Engineering Qilu Institute of Technology Jinan China School of Computing and Information Technology Great Bay University Dongguan China School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China Institute for Analytics and Data Science University of Essex ColchesterCO4 3SQ United Kingdom School of Computer Science and Engineering Yeungnam University Korea Republic of Shenzhen University National Engineering Laboratory for Big Data Shenzhen China Dhahran31261 Saudi Arabia

Metaverse is the next generation and the successor to the Internet. Various sectors allocated capital to adapt the metaverse due to its inherent importance. Cooperative Intelligent Transportation Systems (C-ITS) is one of the important sectors that can benefit from metaverse adaptation and integration. The C-ITS and metaverse integration give rise to new use cases and cyber attacks. However, traditional Pseudonym Certificate (PCert) authentication based on Vehicular Public Key Infrastructure (VPKI) cannot meet the new use cases and thwart cyber attacks. We present a Differential Privacy (DP)-based PCert and avatar update model to ensure the privacy of end-devices (e.g., vehicles) in the C-ITS metaverse. The proposed DP-based model adds noise to avatar attributes (e.g., color, behavior) to prevent avatar linkage during the PCert update process. Our method ensures that PCerts and avatars cannot be linked to end-devices and the previous PCerts and avatars. We simulate the DP-based method to investigate it in terms of privacy, latency, color distortion, and adversarial success rate, and the results show that DP-based is effective and practical for avatar updates. © 2024 IEEE.

关键词： Advanced driver assistance systems

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Distributed bilevel optimization with communication compression 24

Distributed bilevel optimization with communication compress...

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Proceedings of the 41st International Conference on Machine Learning

作者： Yutong He Jie Hu Xinmeng Huang Songtao Lu Bin Wang Kun Yuan Peking University University of Pennsylvania IBM Research Zhejiang University Peking University and National Engineering Labratory for Big Data Analytics and Applications and AI for Science Institute Beijing China

Stochastic bilevel optimization tackles challenges involving nested optimization structures. Its fast-growing scale nowadays necessitates efficient distributed algorithms. In conventional distributed bilevel methods, each worker must transmit full-dimensional stochastic gradients to the server every iteration, leading to significant communication overhead and thus hindering efficiency and scalability. To resolve this issue, we introduce the first family of distributed bilevel algorithms with communication compression. The primary challenge in algorithmic development is mitigating bias in hypergradient estimation caused by the nested structure. We first propose C-SOBA, a simple yet effective approach with unbiased compression and provable linear speedup convergence. However, it relies on strong assumptions on bounded gradients. To address this limitation, we explore the use of moving average, error feedback, and multi-step compression in bilevel optimization, resulting in a series of advanced algorithms with relaxed assumptions and improved convergence properties. Numerical experiments show that our compressed bilevel algorithms can achieve 10× reduction in communication overhead without severe performance degradation.

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big OLAP data Cube Compression Algorithms in Column-Oriented Cloud/Edge data Infrastructures

Big OLAP Data Cube Compression Algorithms in Column-Oriented...

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IEEE International Conference on Multimedia big data (bigMM)

作者： Alfredo Cuzzocrea The Big Data Engineering and Analytics Laboratory University of Calabria Rende Italy Dept. of Computer Science University of Paris City Paris France

This paper provides a brief overview on the innovative problem of devising and implementing big OLAP data cube compression algorithms in column-oriented Cloud/Edge data infrastructures, an emerging need for next-gener...

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Parkinson's Disease Detection Using data Mining Models: A Comparative Study

Parkinson's Disease Detection Using Data Mining Models: A Co...

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data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Reem Jamal Majed Rashid Moahmmed Al-Heddi Ahmed M. Zeki Big Data Science and Analytics College of Science University of Bahrain Sakhir Bahrain Department of Information System College of Information Technology University of Bahrain Sakhir Bahrain

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

data Mining techniques have emerged in the healthcare industry over the past years in determining the occurrences and development of diseases. This paper aim is to conduct a comparative study between three data Mining models in predicting Parkinson’s Disease using biological voice measurements dataset. The three models that are evaluated are: Random Forest (RF), K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). data oversampling using Synthetic Minority Oversampling Technique (SMOTE) method was used to balance the data and feature selection was applied to select the most relevant attributes. The CRISP-DM framework is used to perform the analysis using Weka machine learning tool. The results showed that the KNN model obtained the best accuracy of 94.11% on balanced dataset.

关键词： Support vector machines Industries Radio frequency Analytical models Biological system modeling Nearest neighbor methods Feature extraction

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Time-Series Cross-Validation Parallel Programming using MPI

Time-Series Cross-Validation Parallel Programming using MPI

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2021 International Conference on data analytics for Business and Industry, ICDABI 2021

作者： Mohammed, Abdulrahim Khedr, Ahmed Alhaj, Duaa Al Khalifa, Reem Alqaddoumi, Abdulla University of Bahrain Big Data Science and Analytics College of Science Sakhir Bahrain University of Bahrain College of Information Technology Department of Computer Science Sakhir Bahrain

ISBN: (纸本)9781665416566

Time-series data has a natural chronological arrangement with modeling and cross-validation techniques, highly dependent on sequential processing of data, which challenges its parallelization. Since the running time of the modeling algorithm largely consists of a main operation of solving the modeling algorithm several times with various training and testing dataset sizes, it is the prime target for optimizing the running time. This research presents a parallel implementation of time-series cross-validation on a rolling basis that involves using a subset of the dataset for training purposes, expanding each dataset with each run to obtain test accuracy. The evaluation is conducted using parallel speedup, parallel efficiency, and computational time. The proposed algorithm distributes the task of modeling among several processors to run independently and obtain results. The computational time recorded ranged from 15.47 seconds at best with five processors to 28.33 seconds at worst when done with a single processor. The parallel system speedup was found to be sub-linear with an efficiency decreasing with the increase in the number of processors. © 2021 IEEE.

关键词： Time series

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AEFNet: Adaptive Scale Feature Based on Elastic-and-Funnel Neural Network for Healthcare Representation

AEFNet: Adaptive Scale Feature Based on Elastic-and-Funnel N...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Liu, Yang Wu, Jialun Wei, Yuhua Mao, Bing Li, Chen Gong, Tieliang Xi'An Jiaotong University School of Computer Science and Technology National Engineering Lab for Big Data Analytics Shaanxi Xi'an710049 China

ISBN: (纸本)9781665401265

Healthcare Representation learning has been a key element to achieving state-of-the-art performance on healthcare prediction. Recent advances based Electronic Healthcare Records(EHRs) are mostly devoted to extracting temporal progression patterns with temporal model and their variants. Although these works have shown excellent performances in healthcare prediction, the unified temporal pattern may not be suitable for individuals in all healthcare conditions. Moreover, some studies ususally introduce complex Deep Neural Networks models and medical prior knowledge to get compact representation, causing great computational burden. In this paper, we propose a general health care representation model, named AEFNet. We only leverage three simple convolution operations and a set of up and down sampling to ensure performance and model complexity equally, which achieves adaptively extract distinct individual key feature in a light manner. AEFNet can shrink and refine highly suitable scale information adaptively and comletely. Breaking traditional fixed convolution scale or multi-scale, AEFNet achieves scale adaptively to extract the most significant information and context relationship. Finally, We validate our method on the public dataset MIMIC-III, and the evaluation results indicate that our method can significantly outperform other remarkable baseline models. © 2021 IEEE.

关键词： Forecasting

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A thorough investigation of the prospects of eLISA in addressing the Hubble tension: Fisher Forecast, MCMC and Machine Learning

arXiv

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arXiv 2023年

作者： Shah, Rahul Bhaumik, Arko Mukherjee, Purba Pal, Supratik Physics and Applied Mathematics Unit Indian Statistical Institute 203 B.T. Road Kolkata700 108 India Technology Innovation Hub on Data Science Big Data Analytics and Data Curation Indian Statistical Institute 203 B.T. Road Kolkata700 108 India

We carry out an in-depth analysis of the capability of the upcoming space-based gravitational wave mission eLISA in addressing the Hubble tension, with a primary focus on observations at intermediate redshifts (3 0) at the sub-percent level. MCMC/GP results predict reduced tensions for models/fiducials which are currently harder to reconcile with direct measurements of H0, whereas no significant change occurs for models/fiducials at lesser tensions with the latter. This feature warrants further investigation in this direction. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Parallel Branch-and-Bound-Comparing Serial and OpenMP Implementations in a Color Sorting Game

Parallel Branch-and-Bound-Comparing Serial and OpenMP Implem...

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2021 International Conference on data analytics for Business and Industry, ICDABI 2021

作者： Khalid, Aisha Abdulrahman, Hajar Khedr, Ahmed Alqaddoumi, Abdulla University of Bahrain Big Data Science and Analytics College of Science Sakhir Bahrain University of Bahrain College of Information Technology Department of Computer Science Sakhir Bahrain

ISBN: (纸本)9781665416566

Branch-and-Bound algorithm is very diverse in its applications, one of which is the gaming industry, especially for complicated games that need optimization in their search for a solution from a large search space. For a thorough study, the Branch-and-Bound algorithm will be implemented in a serial and parallel (using Open Multi-Processing) approach to compare time required to solve the game. Results showed that for easy levels, the serial program performed better, while for difficult levels (with more flasks and colors) the parallel program outperformed the serial program with a speed up of more than 1,000x. Future work can replicate the algorithm on different types of games and explore different approaches such as Backtracking in a parallel approach. © 2021 IEEE.

关键词： Branch and bound method

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A MATHEMATICS-INSPIRED LEARNING-TO-OPTIMIZE FRAMEWORK FOR DECENTRALIZED OPTIMIZATION

arXiv

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arXiv 2024年

作者： He, Yutong Shang, Qiulin Huang, Xinmeng Liu, Jialin Yuan, Kun Peking University China University of Pennsylvania United States OrlandoFL United States National Engineering Labratory for Big Data Analytics and Applications AI for Science Institute Beijing China

Most decentralized optimization algorithms are handcrafted. While endowed with strong theoretical guarantees, these algorithms generally target a broad class of problems, thereby not being adaptive or customized to specific problem features. This paper studies data-driven decentralized algorithms trained to exploit problem features to boost convergence. Existing learning-to-optimize methods typically suffer from poor generalization or prohibitively vast search spaces. In addition, the vast search space of communicating choices and final goal to reach the global solution via limited neighboring communication cast more challenges in decentralized settings. To resolve these challenges, this paper first derives the necessary conditions that successful decentralized algorithmic rules need to satisfy to achieve both optimality and consensus. Based on these conditions, we propose a novel Mathematics-inspired Learning-to-optimize framework for Decentralized optimization (MiLoDo). Empirical results demonstrate that MiLoDo-trained algorithms outperform handcrafted algorithms and exhibit strong generalizations. Algorithms learned via MiLoDo in 100 iterations perform robustly when running 100,000 iterations during inferences. Moreover, MiLoDo-trained algorithms on synthetic datasets perform well on problems involving real data, higher dimensions, and different loss functions. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization algorithms

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