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arXiv 2021年

作者： Markowitz, Spencer Snyder, Corey Eldar, Yonina C. Do, Minh N. Electrical and Computer Engineering Department University of Illinois Urbana Champaign United States Department of Mathematics and Computer Science Weizmann Institute of Science Rehovot Israel Signal Processing and Data Science Department of Electrical and Computer Engineering

Background foreground separation (BFS) is a popular computer vision problem where dynamic foreground objects are separated from the static background of a scene. Typically, this is performed using consumer cameras because of their low cost, human interpretability, and high resolution. Yet, cameras and the BFS algorithms that process their data have common failure modes due to lighting changes, highly reflective surfaces, and occlusion. One solution is to incorporate an additional sensor modality that provides robustness to such failure modes. In this paper, we explore the ability of a cost-effective radar system to augment the popular Robust PCA technique for BFS. We apply the emerging technique of algorithm unrolling to yield real-time computation, feedforward inference, and strong generalization in comparison with traditional deep learning methods. We benchmark on the RaDICaL dataset to demonstrate both quantitative improvements of incorporating radar data and qualitative improvements that confirm robustness to common failure modes of image-based methods. © 2021, CC BY.

关键词： Separation

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Automatic Textual Explanations of Concept Lattices

arXiv

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arXiv 2023年

作者： Hirth, Johannes Horn, Viktoria Stumme, Gerd Hanika, Tom Knowledge & Data Engineering Group University of Kassel Germany Partizipative IT-Gestaltung University of Kassel Germany Interdisciplinary Research Center for Information System Design University of Kassel Germany School of Library and Information Science Humboldt-Universität zu Berlin Berlin Germany Institute of Computer Science University of Hildesheim Germany

Lattices and their order diagrams are an essential tool for communicating knowledge and insights about data. This is in particular true when applying Formal Concept Analysis. Such representations, however, are difficult to comprehend by untrained users and in general in cases where lattices are large. We tackle this problem by automatically generating textual explanations for lattices using standard scales. Our method is based on the general notion of ordinal motifs in lattices for the special case of standard scales. We show the computational complexity of identifying a small number of standard scales that cover most of the lattice structure. For these, we provide textual explanation templates, which can be applied to any occurrence of a scale in any data domain. These templates are derived using principles from human-computer interaction and allow for a comprehensive textual explanation of lattices. We demonstrate our approach on the spices planner data set, which is a medium sized formal context comprised of fifty-six meals (objects) and thirty-seven spices (attributes). The resulting 531 formal concepts can be covered by means of about 100 standard *** Codes 06A15 03G10 68T30 68T27 Copyright © 2023, The Authors. All rights reserved.

关键词： Formal concept analysis

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Understanding and Improving Transfer Learning of Deep Models via Neural Collapse

arXiv

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arXiv 2022年

作者： Li, Xiao Liu, Sheng Zhou, Jinxin Lu, Xinyu Fernandez-Granda, Carlos Zhu, Zhihui Qu, Qing Department of Electrical Engineering and Computer Science University of Michigan United States Stanford University United States Center for Data Science New York University United States Courant Institute of Mathematical Sciences New York University United States Department of Computer Science & Engineering Ohio State University United States Language Technologies Institute Carnegie Mellon University United States

With the ever-increasing complexity of large-scale pre-trained models coupled with a shortage of labeled data for downstream training, transfer learning has become the primary approach in many fields, including natural language processing, computer vision, and multi-modal learning. Despite recent progress, the fine-tuning process for large-scale pre-trained models in vision still mostly relies on trial and error. This work investigates the relationship between neural collapse (NC) and transfer learning for classification problems. NC is an intriguing while prevalent phenomenon that has been recently discovered in terms of the final-layer features and linear classifiers of trained neural networks. Specifically, during the terminal phase of training, NC implies that the variability of the features within each class diminishes to zero, while the means of features between classes are maximally and equally distanced. In this work, we examine the NC attributes of pre-trained models on both downstream and source data for transfer learning, and we find strong correlation between feature collapse and downstream performance. In particular, we discovered a systematic pattern that emerges when linear probing pre-trained models on downstream training data: the more feature collapse of pre-trained models on downstream training data, the higher the transfer accuracy. Additionally, we also studied the relationship between NC and transfer accuracy on the source data. Moreover, these findings allow us to develop a principled, parameter-efficient fine-tuning method that employs skip-connection to induce the last-layer feature collapse on downstream data. Our proposed fine-tuning methods deliver good performances while reducing fine-tuning parameters by at least 90% and mitigating overfitting in situations especially when the downstream data is scarce. © 2022, CC BY.

关键词： Transfer learning

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Integration of a multi-rate electron density profile observer in the plasma control system of TCV 49

Integration of a multi-rate electron density profile observe...

引用

49th EPS Conference on Plasma Physics, EPS 2023

作者： Pastore, F. Felici, F. Sauter, O. Bosman, T.O.S.J. Galperti, C. Vincent, B. Pau, A. Poels, Y. Ravensbergen, T. Vu, N.M.T. Lausanne Switzerland DIFFER - Dutch Institute for Fundamental Energy Research Energy Systems and Control Group Eindhoven Netherlands Eindhoven University of Technology Dept. of Mechanical Engineering Control Systems Technology Group Eindhoven Netherlands Eindhoven University of Technology Dept. of Mathematics and Computer Science Data Mining Group Eindhoven Netherlands ITER Organization Route de Vinon-sur-Verdon St. Paul Lez Durance13067 Cedex France

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Mitigating Voltage Fluctuations in Grid-Tied PV Systems: Power Quality Enhancement

Mitigating Voltage Fluctuations in Grid-Tied PV Systems: Pow...

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Computational Modelling, Simulation and Optimization (ICCMSO), International Conference on

作者： Sheena Tahira Khan Vilas Ramrao Joshi Deepak Bhardwaj Surender Kumar Sharma Shaikh Abdul Waheed Deepak K. Sharma Department of Electrical Engineering Poornima University Jaipur India Department of Computer Engineering ISBM College of Engineering Pune Maharashtra India Department of Mechanical Engineering Dr. Akhilesh Das Gupta Institute of Professional Studies New Delhi India GH Raisoni College of Engineering and Management Pune India Department of Artificial Intelligence and Data Science Savitribai Phule Pune University Pune Maharashtra

ISBN: (数字)9798350361391

ISBN: (纸本)9798350361407

This paper investigates voltage fluctuations in grid-tied Photovoltaic (PV) systems, employing advanced methodologies within a 10 kW experimental setup that incorporates SunTech STP330-24/Vfw panels, an Advanced Power Electronics Inverter, and real-time monitoring, supported by MAT-LAB/Simulink simulations. Robust data collection utilizes real-time monitoring, data loggers, and a power quality analyzer, with calibration against simulated outputs. The integration of smart inverters, advanced control algorithms, real-time monitoring, energy storage, grid-interactive power electronics, and Hardware in the Loop simulation showcases substantial enhancements in power quality, stability, and response times. Key findings include minimal voltage fluctuations (0.25 V), a power quality index of 0.92, and rapid response times (12 ms). Comparative analyses validate proposed methodologies, emphasizing implications for heightened reliability, potential widespread adoption of advanced PV technologies, and informed policymaking for cleaner energy. This study makes a significant contribution to renewable energy goals, with suggested avenues for future research focusing on adaptability, buffering enhancement, iterative testing, and economic feasibility assessments, ensuring the continuous evolution of resilient and eco-friendly energy solutions.

关键词： Photovoltaic systems Industries Voltage fluctuations Fluctuations Power quality Real-time systems Inverters

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Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study

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Journal of Medical Internet Research 2024年第1期26卷 e48443页

作者： Lee, You-Qian Chen, Ching-Tai Chen, Chien-Chang Lee, Chung-Hong Chen, Peitsz Wu, Chi-Shin Dai, Hong-Jie Dialogue System Technical Department Asustek Computer Inc Taipei Taiwan Intelligent System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan Center for Precision Health Research Asia University Taichung Taiwan Electromagnetic Sensing Control and AI Computing System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Knowledge Discovery and Data Mining Lab Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Chemical Engineering Feng Chia University Taichung Taiwan National Center for Geriatrics and Welfare Research National Health Research Institutes Zhunan Taiwan National Institute of Cancer Research National Health Research Institutes Tainan Taiwan School of Post-Baccalaureate Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan

Background: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. Objective: The aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. Methods: We compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models’ outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. Results: The developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of cod

关键词： ChatGPT code mixing deidentification electronic health record large language model pretrained language model

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Accustomed Frequency in 5G: Cognitive Broadcasting Technologies

Accustomed Frequency in 5G: Cognitive Broadcasting Technolog...

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2024 International Conference on Communication, Computing and Energy Efficient Technologies, I3CEET 2024

作者： Marasulov, M.B. Gangrade, Alka Kaur, Sumanpreet Shukla, Aasheesh Alabdeli, Haider Lanitha, B. Department of Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan Institute of Polymer Chemistry and Physics Tashkent Uzbekistan Western Caspian University Baku Azerbaijan Ies College of Technology Department of Computer Science & Engineering M.P. Bhopal462044 India Chandigarh Engineering College Chandigarh Group of Colleges Department of Computer Science Engineering Jhanjeri Punjab Mohali140307 India Gla University Department of Electronics and Communication Engineering Mathura India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Karpagam Academy of Higher Education Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798331541583

Traffic loads have dramatically grown as a result of the small-cell / Internet of Everything devices' increasing popularity. The present network has undergone a revolution with this expansion, transforming it into 5G wireless technology, which requires ultra-low latency, high data rates, and higher capacity. Maximizing the use of its bands and investigating the spectrum resource are two study topics that are prioritized to satisfy these needs. Nevertheless, obtaining an effective management plan is severely hampered by the spectrum resource's scarcity. The goal of this effort is to provide a thorough assessment of current 5G-enabling technologies and spectrum sharing technologies in relation to 5G development. SS surveys and associated research on SS methods pertinent to 5G networks will are evaluated, and SS techniques are categorized. Surveys and studies are categorized into one of the primary spectrum allocation methodologies based on the network architecture, spectrum access method, and spectrum allocation behavior. Additionally, a thorough analysis of cognitive radio technology in SS in relation to the deployment of 5G is carried out. To ensure a thorough analysis, talks are held about the problems and difficulties with the way SS and CR are currently being implemented, and resources for effective 5G development are offered. © 2024 IEEE.

关键词： Cognitive radio

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Collaborative Defense with Multiple USVs and UAVs Based on Swarm Intelligence

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Journal of Shanghai Jiaotong university(science) 2020年第1期25卷 51-56页

作者： WU Xing LIU Yuan XIE Shaorong GUO Yike School of Computer Engineering and Science Shanghai UniversityShanghai 200444China Shanghai Institute for Advanced Communication and Data Science Shanghai 200444China Department of Computing Imperial College LondonLondon SW72AZUnited Kingdom

Modern defense systems are developing towards *** and automation,which include the collaborative defense system on the sea between multiple unmanned surface vehicles(USVs)and unmanned aerial vehicles(UAVs).UAVs can fly in high altitude and collect marine environment information on ***,UAVs can plan defense paths for USVs to intercept intruders with full-assignment or reassignment strategies aiming at maximum overall ***,we propose dynamic overlay reconnaissance algor计hm based on genetic idea(GI-DORA)to solve the problem of multi-UAV multi-station ***,we develop continuous particle swarm optimization based on obstaele dimension(OD-CPSO)to optimize defense path of USVs to intercept *** addition,under the designed defense constraints,we propose dispersed particle swarm optimization based on mutation and crossover(MC-DPSO)and real-time batch assignment algorithm(RTBA)in emergency for formulating combat defense mission assignment strategy in different ***,we illus trate the feasibility and effectiveness of the proposed met hods.

关键词： collaborative defense mission assignment path planning unmanned surface vehicles(USVs) unmanned aerial vehicles(UAVs)

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Discovering Conservation Laws using Optimal Transport and Manifold Learning

arXiv

引用

arXiv 2022年

作者： Lu, Peter Y. Dangovski, Rumen Soljačić, Marin Data Science Institute University of Chicago ChicagoIL60637 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

Conservation laws are key theoretical and practical tools for understanding, characterizing, and modeling nonlinear dynamical systems. However, for many complex systems, the corresponding conserved quantities are difficult to identify, making it hard to analyze their dynamics and build stable predictive models. Current approaches for discovering conservation laws often depend on detailed dynamical information or rely on black box parametric deep learning methods. We instead reformulate this task as a manifold learning problem and propose a non-parametric approach for discovering conserved quantities. We test this new approach on a variety of physical systems and demonstrate that our method is able to both identify the number of conserved quantities and extract their values. Using tools from optimal transport theory and manifold learning, our proposed method provides a direct geometric approach to identifying conservation laws that is both robust and interpretable without requiring an explicit model of the system nor accurate time information. © 2022, CC BY.

关键词： Nonlinear dynamical systems

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Ranking on Heterogeneous Manifold for Multimodal Information Retrieval

Ranking on Heterogeneous Manifold for Multimodal Information...

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IEEE International Conference on Big data and Cloud Computing (BdCloud)

作者： Zhenkun Jin Xingshi Wan Xin Nie Xinlei Zhou Yuanyuan Yi Gefei Zhou Wuhan Business University Wuhan Research Institute of Posts and Telecommunications Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Huazhong University of Science and Technology Wuhan China School of Journalism and Information Communication Huazhong University of Science and Technology Wuhan China School of Computer Science & Technology Huazhong University of Science and Technology Wuhan China

In light of recent advancements in Internet of Multimedia Things (IoMT) and 5G technology, both the variety and quantity of data have been rapidly increasing. Consequently, handling zero-shot cross-modal retrieval (ZSCMR) of unlabeled multi-modal data has emerged as a prominent research topic. Early models utilized generative adversarial networks and class embeddings to assist in building a common feature space. However, this approach resulted in a loss of reliability in the feature distributions of individual modalities. In this paper, we propose a method called Ranking on Heterogeneous Manifold (RHM) to address the aforementioned issue. Our model comprises three feature extractors, each dedicated to extracting features from text, images and multimodal data. Subsequently, the relationship structures derived from these feature extractors are employed to construct a heterogeneous manifold. A diffusion process is then conducted on this heterogeneous manifold, ultimately yielding the final ranking scores. Comprehensive experiments demonstrate that the proposed modal has the capability to acquire more effective knowledge representation, thus enhancing the effectiveness of ZSCMR.

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