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Multilayer perceptron ensembles in a truly sparse training context

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Neural Computing and Applications 2025年

作者： van der Wal, Peter R. D. Strisciuglio, Nicola Azzopardi, George Mocanu, Decebal Constatin Information Systems Group Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Nijenborgh 9 Groningen9747 AG Netherlands Data Management and Biometrics Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Hallenweg 19 Enschede7522 NH Netherlands Department of Computer Science Faculty of Science Technology and Medicine University of Luxembourg Maison du Nombre 6 avenue de la Fonte Esch4364 Luxembourg

Ensemble learning for artificial neural networks (ANNs) is an effective method to enhance predictive performance. However, ANNs are computationally and memory intensive, and naively training multiple networks can lead to excessive training times and costs. An effective tool for improving ensemble efficiency is introducing topological sparsity. Even though several implementations of efficient ensembles have been proposed, none of them can provide actual benefits in terms of computational overhead as the sparsity is simulated using binary masks. In this paper, we address this issue by introducing a Truly Sparse Ensemble without binary masks and directly incorporate native sparsity. We also propose two algorithms for initializing new subnetworks within the ensemble, leveraging this native topological sparsity to enhance subnetwork diversity. We demonstrate the performance of the resulting models at high levels of sparsity on several datasets in terms of classification accuracy, floating point operations (FLOPs), and actual running time. The proposed methods outperform all baseline dense and truly sparse models on tabular data, successfully diversify the training trajectory of the subnetworks, and increase the topological distance between subnetworks after re-initialization. © The Author(s) 2025.

关键词： Digital arithmetic

Retraction notice to “Thermal management of a battery pack using a layer of phase change material around the batteries: Changes in the airflow through the battery” [J. Energy Storage 52 (2022) 104759]

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Journal of Energy Storage 2024年 92卷

作者： Mohammed N. Ajour Ahmad H. Milyani Nidal H. Abu-Hamdeh Turki AlQemlas Moath K. Khaled Arash Karimipour Center of Research Excellence in Renewable Energy and Power Systems Energy Efficiency Group Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Department of Mechanical Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia K. A. CARE Energy Research and Innovation Center King Abdulaziz University Jeddah 21589 Saudi Arabia Energy Efficiency Group Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia College of Engineering and Technology American University of the Middle East Kuwait Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Dipartimento di Ingegneria Astronautica Elettrica ed Energetica Sapienza Universit'a di Roma Via Eudossiana 18 Roma 00184 Italy

Revisiting Efficient Multi-Step Nonlinearity Compensation with Machine Learning: An Experimental Demonstration

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arXiv 2020年

作者： Oliari, Vinícius Goossens, Sebastiaan Häger, Christian Liga, Gabriele Bütler, Rick M. van den Hout, Menno van der Heide, Sjoerd Pfister, Henry D. Okonkwo, Chigo Alvarado, Alex Information and Communication Theory Lab Signal Processing Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Department of Electrical Engineering Chalmers University of Technology Gothenburg Sweden Department of Electrical and Computer Engineering Duke University Durham United States High Capacity Optical Transmission Lab Electro-Optical Communication Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands

Efficient nonlinearity compensation in fiber-optic communication systems is considered a key element to go beyond the "capacity crunch". One guiding principle for previous work on the design of practical nonlinearity compensation schemes is that fewer steps lead to better systems. In this paper, we challenge this assumption and show how to carefully design multi-step approaches that provide better performance–complexity tradeoffs than their few-step counterparts. We consider the recently proposed learned digital backpropagation (LDBP) approach, where the linear steps in the split-step method are re-interpreted as general linear functions, similar to the weight matrices in a deep neural network. Our main contribution lies in an experimental demonstration of this approach for a 25 Gbaud single-channel optical transmission system. It is shown how LDBP can be integrated into a coherent receiver DSP chain and successfully trained in the presence of various hardware impairments. Our results show that LDBP with limited complexity can achieve better performance than standard DBP by using very short, but jointly optimized, finite-impulse response filters in each step. This paper also provides an overview of recently proposed extensions of LDBP and we comment on potentially interesting avenues for future work. Copyright © 2020, The Authors. All rights reserved.

关键词： Light transmission

Fast, reliable, and secure drone communication: A comprehensive survey

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arXiv 2021年

作者： Hassija, Vikas Chamola, Vinay Agrawal, Adhar Goyal, Adit Luong, Nguyen Cong Niyato, Dusit Yu, F. Richard Guizani, Mohsen Department of Computer Science and IT Jaypee Institute of Information Technology Noida201304 India Department of Electrical and Electronics Engineering APPCAIR BITS-Pilani Pilani Campus 333031 India Faculty of Information Technology PHENIKAA University Hanoi12116 Viet Nam A&A Green Phoenix Group JSC No. 167 Hoang Ngan Trung Hoa Cau Giay Hanoi11313 Viet Nam School of Computer Science and Engineering Nanyang Technological University Singapore Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada Computer Science and Engineering Department Qatar University Qatar

Drone security is currently a major topic of discussion among researchers and industrialists. Although there are multiple applications of drones, if the security challenges are not anticipated and required architectural changes are not made, the upcoming drone applications will not be able to serve their actual purpose. Therefore, in this paper, we present a detailed review of the security-critical drone applications, and security-related challenges in drone communication such as DoS attacks, Man-in-the-middle attacks, De-Authentication attacks, and so on. Furthermore, as part of solution architectures, the use of Blockchain, Software Defined Networks (SDN), Machine Learning, and Fog/Edge computing are discussed as these are the most emerging technologies. Drones are highly resource-constrained devices and therefore it is not possible to deploy heavy security algorithms on board. Blockchain can be used to cryptographically store all the data that is sent to/from the drones, thereby saving it from tampering and eavesdropping. Various ML algorithms can be used to detect malicious drones in the network and to detect safe routes. Additionally, the SDN technology can be used to make the drone network reliable by allowing the controller to keep a close check on data traffic, and fog computing can be used to keep the computation capabilities closer to the drones without overloading them. © 2021, CC BY.

关键词： Fog computing

Reconfigurable Quantum Local Area Network Over Deployed Fiber

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PRX Quantum 2021年第4期2卷 040304-040304页

作者： Muneer Alshowkan Brian P. Williams Philip G. Evans Nageswara S.V. Rao Emma M. Simmerman Hsuan-Hao Lu Navin B. Lingaraju Andrew M. Weiner Claire E. Marvinney Yun-Yi Pai Benjamin J. Lawrie Nicholas A. Peters Joseph M. Lukens Quantum Information Science Group Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Autonomous and Complex Systems Group Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Department of Applied Physics Stanford University Stanford California 94305 USA School of Electrical and Computer Engineering and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA Materials Science and Technology Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA

Practical quantum networking architectures are crucial for scaling the connection of quantum resources. Yet quantum network testbeds have thus far underutilized the full capabilities of modern lightwave communications, such as flexible-grid bandwidth allocation. In this work, we implement flex-grid entanglement distribution in a deployed network for the first time, connecting nodes in three distinct campus buildings time synchronized via the Global Positioning System. We quantify the quality of the distributed polarization entanglement via log-negativity, which offers a generic metric of link performance in entangled bits per second. After demonstrating successful entanglement distribution for two allocations of our eight dynamically reconfigurable channels, we realize the first deployed fiber network demonstration of remote state preparation (RSP), a fundamental quantum communications protocol with utility for performing remote private “blind” quantum computing. We further demonstrate RSP not only at one location but over three nodes in three locations. In general, our results highlight an advanced paradigm for managing entanglement resources in quantum networks of ever-increasing complexity and service demands.

关键词： Nonlinear optics Quantum communication Quantum entanglement Quantum networks Quantum optics Timing & synchronization Bayesian methods

Corrections to “Evaluation of Machine Learning Approaches for Precision Farming in Smart Agriculture System: A Comprehensive Review”

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IEEE Access 2024年 12卷 118830-118830页

作者： Ghulam Mohyuddin Muhammad Adnan Khan Abdul Haseeb Shahzadi Mahpara Muhammad Waseem Ahmed Mohammed Saleh Department of Agriculture Ghazi University Dera Ghazi Khan Punjab Pakistan Department of Electrical Engineering HITEC University Taxila Punjab Pakistan LUMS Energy Institute Department of Computer Science Syed Babar Ali School of Science and Engineering Lahore University of Management Sciences Lahore Pakistan Department of Electronic Engineering International Renewable and Energy Systems Integration Research Group (IRESI) Maynooth University Kildare Maynooth Ireland Faculty of Engineering University of Aden Aden Yemen

Presents corrections to the paper, Evaluation of Machine Learning Approaches for Precision Farming in Smart Agriculture System: A Comprehensive Review.

关键词： Smart agriculture Reviews Machine learning Farming

Incremental Non-Gaussian Inference for SLAM Using Normalizing Flows

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arXiv 2021年

作者： Huang, Qiangqiang Pu, Can Khosoussi, Kasra Rosen, David M. Fourie, Dehann How, Jonathan P. Leonard, John J. MIT Computer Science and Artificial Intelligence Laboratory CambridgeMA02139 United States MIT Department of Nuclear Science and Engineering CambridgeMA02139 United States Robotics and Autonomous Systems Group DATA61 CSIRO BrisbaneQLD4069 Australia Northeastern University Department of Electrical and Computer Engineering BostonMA02115 United States NavAbility BostonMA02110 United States MIT Department of Aeronautical and Astronautical Engineering CambridgeMA02139 United States

This paper presents normalizing flows for incremental smoothing and mapping (NF-iSAM), a novel algorithm for inferring the full posterior distribution in SLAM problems with nonlinear measurement models and non-Gaussian factors. NF-iSAM exploits the expressive power of neural networks, and trains normalizing flows to model and sample the full posterior. By leveraging the Bayes tree, NF-iSAM enables efficient incremental updates similar to iSAM2, albeit in the more challenging non-Gaussian setting. We demonstrate the advantages of NF-iSAM over state-of-the-art point and distribution estimation algorithms using range-only SLAM problems with data association ambiguity. NF-iSAM presents superior accuracy in describing the posterior beliefs of continuous variables (e.g., position) and discrete variables (e.g., data association). Copyright © 2021, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

NTIRE 2024 Quality Assessment of AI-Generated Content Challenge

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arXiv 2024年

作者： Liu, Xiaohong Min, Xiongkuo Zhai, Guangtao Li, Chunyi Kou, Tengchuan Sun, Wei Wu, Haoning Gao, Yixuan Cao, Yuqin Zhang, Zicheng Wu, Xiele Timofte, Radu Gankhuyag, Ganzorig Yoon, Kihwan Xu, Yifang Fan, Haotian Kong, Fangyuan Zhao, Shiling Dong, Weifeng Yin, Haibing Zhu, Li Wang, Zhiling Huang, Bingchen Peng, Fei Fu, Huiyuan Ming, Anlong Wang, Chuanming Ma, Huadong He, Shuai Dou, Zifei Chen, Shu Yu, Zihao Guan, Fengbin Motamednia, Hossein Hosseini-Benvidi, S. Farhad Mahmoudi-Aznaveh, Ahmad Mansouri, Azadeh Saha, Avinab Mishra, Sandeep Gupta, Shashank Sureddi, Rajesh Saha, Oindrila Celona, Luigi Bianco, Simone Napoletano, Paolo Schettini, Raimondo Yang, Junfeng Fu, Jing Zhang, Wei Cao, Wenzhi Liu, Limei Peng, Han Yuan, Weijun Li, Zhan Cheng, Yihang Deng, Yifan Zhang, Huacong Xie, Haiyi Wang, Chengwei Chen, Baoying Zeng, Jishen Yang, Jianquan Wang, Weigang Fang, Xi Lv, Xiaoxin Yan, Jun Li, Haohui Qu, Bowen Li, Yao Luo, Shuqing Wang, Shunzhou Gao, Wei Lu, Zihao Conde, Marcos V. Timofte, Radu Lu, Yiting Li, Xin Wang, Xinrui Guan, Fengbin Chen, Zhibo Liao, Ruling Ye, Yan Wang, Qiulin Li, Bing Zhou, Zhaokun Geng, Miao Chen, Rui Tao, Xin Liang, Xiaoyu Sun, Shangkun Fang, Xi Wang, Weigang Yan, Jun Ma, Xingyuan Li, Jiaze Yang, Mengduo Xu, Haoran Zhou, Jie Zhu, Shiding Yu, Bohan Zhi, Tianwu Zhang, Yabin Li, Yaohui Li, Yang Xu, Jingwen Liu, Jianzhao Liao, Yiting Li, Junlin Chen, Baoying Zeng, Jishen Xie, Haiyi Wang, Chengwei Yang, Jianquan Chen, Pengfei Xu, Xinrui Shen, Jiabin Duan, Zhichao Liu, Jiahe Yan, Qi Qu, Youran Zeng, Xiaohui Wang, Lele Liao, Renjie Shanghai Jiao Tong University China Nanyang Technological University Singapore ETH Zürich Switzerland University of Würzburg Germany Korea Electronics Technology Institute Korea Republic of ByteDance China Hangzhou Dianzi University China Sankuai China Beijing University of Posts and Telecommunications China Beijing Xiaomi Mobile Software Co. Ltd. China University of Science and Technology of China China High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Cyber Research Institute Shahid Beheshti University Tehran Iran University of Texas Austin United States University of Massachusetts Amher United States Department of Informatics Systems and Communication Xiangjiang Laboratory Hunan University of Technology and Business China Jinan University China Sun Yat-sen University China Alibaba Group China Cisco Systems Inc. United States DP Technology Ltd. Shopee Pte. Ltd Tongji University China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China University of Würzburg Germany Kuaishou Technology China Peking University China Zhejiang University China Bytedance Inc China Xidian University China University of British Columbia Canada University of Toronto Canada

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

Design and Implementation of a Lab Level Active Distribution AC Multi-Agent Testbed

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Design and Implementation of a Lab Level Active Distribution...

IEEE PES Asia-Pacific Power and Energy engineering Conference (APPEEC)

作者： Yufei Peng Kenneth Loparo Department of Electrical Computer and Systems Engineering Case Western Reserve University Cleveland USA China Energy Engineering Group Jiangsu Power Design Institute CO.LTD Nanjing China

ISBN: (数字)9781728157481

ISBN: (纸本)9781728157498

With the increasingly connection of distributed energy resources and IoT, distribution systems are becoming more heterogeneous and complex with bidirectional power flow. This requires autonomous control and management systems for the power distribution that can enable bidirectional power flow and communications of real-time data for both endusers and distribution system operators. This paper focuses on the development of a hardware test bed that includes a distribution network in conjunction with real-time communications, control and data exchange. This AC microgrid test bed has been built with lab-level voltages, and a multiagent control system using the VOLTTRON platform to study real-time control to manage demand and for energy price optimization. Some operational scenarios are presented in the paper to illustrate how this system work.

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