In federated learning (FL), the communication constraint between the remote users and the Parameter Server (PS) is a crucial bottleneck. This paper proposes M22, a rate-distortion inspired approach to model update com...
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In this paper, we propose a novel volumetric video caching and rendering approach for an edge-assisted extended reality (XR) system to enhance user quality of experience (QoE). Particularly, user QoE consists of visua...
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The rapidly developing perovskite solar cells(PSCs) provide a new and promising choice of thin film solar cells due to their attractive *** the commercialized thin film solar cells,CuInGaSe(CIGS)cells demonstrate high...
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The rapidly developing perovskite solar cells(PSCs) provide a new and promising choice of thin film solar cells due to their attractive *** the commercialized thin film solar cells,CuInGaSe(CIGS)cells demonstrate higher efficiency than amorphous Si photovoltaic devices and lower toxicity than CdTe ***,a wide variety of studies have been conducted on ***,we elucidate CIGS materials and perovskites as absorber in thin film solar cells in terms of structure and optoelectronic ***,a comparison of PSCs and commercialized CIGS cells is made from the point of view of fabrication process,stability,and *** addition,the integration of CIGS devices and PSCs is elaborated based on tandem ***,prospects for the advancement of CIGS cells and PSCs are discussed.
Far-feld chemical microscopy providing molecular electronic or vibrational fingerprint information opens a new window for the study of three-dimensional biological,material,and chemical *** microscopy provides a nonde...
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Far-feld chemical microscopy providing molecular electronic or vibrational fingerprint information opens a new window for the study of three-dimensional biological,material,and chemical *** microscopy provides a nondestructive way of chemical identification without exterior ***,the diffraction limit of optics hindered it from discovering more details under the resolution *** development of super-resolution techniques gives enlightenment to open this door behind far-field chemical ***,we review recent advances that have pushed the boundary of far-field chemical microscopy in terms of spatial *** further highlight applications in biomedical research,material characterization,environmental study,cultural heritage conservation,and integrated chip inspection.
Quantification is the task of estimating the class distribution of a given dataset. This paper presents an Enhanced Distribution Matching method that can directly quantify multiclass datasets. Our method utilizes inte...
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In this study, we present a deep learning-based ghost holography approach to recover occlusion-obscured image details, using a neural network trained on varied datasets, markedly enhancing image reconstruction in comp...
Following the ideas put forth by industry 4.0, flexible manufacturing systems that make use of robots, sensors and artificial intelligence are gaining more and more relevance. While vision systems are fundamental for ...
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It is challenging to integrate high levels of renewable sources into power distributions systems due to their intermittent nature. Microgrids provide a feasible framework for accommodating higher levels of renewable e...
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While recognizing the significance of data in machine learning, we focus on addressing the challenge of concept drift, particularly in dynamic data streams. We propose an innovative incremental decision tree algorithm...
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While recognizing the significance of data in machine learning, we focus on addressing the challenge of concept drift, particularly in dynamic data streams. We propose an innovative incremental decision tree algorithm tailored for learning regression trees and model trees from evolving data streams. Vital to ensuring the quality and accuracy of predictive models is addressing this challenge. In this context, we present a novel solution: an incremental decision tree algorithm tailored for learning regression trees and model trees from time-varying data streams. Our algorithm is designed to operate at high speeds, effectively accommodating the influx of data at any scale, including scenarios with potentially unlimited data. Key innovations of our approach include a probabilistic defined sampling strategy that enhances the learning process and an advanced automatic method capable of handling non-stationary data distributions. However, the primary innovation lies in our methodology for detecting concept drift. Unlike conventional methods that reactively respond to increased errors, we introduce a proactive approach: monitoring the quality of individual subtrees by tracking their error evolution. This method allows us to detect changes in the objective function promptly, leading to timely adaptations in the model structure. Through extensive experimentation and evaluation, we demonstrate the effectiveness of our proposed algorithm in terms of prediction accuracy, model size, and change detection capabilities. Representing a significant advancement in the field of machine learning, particularly in addressing the challenge of concept drift in data streams, the proposed algorithm offers a competitive alternative to existing flow classifiers. Showcasing superior performance in terms of precision, recall, Fisher measure, and scalability, it underscores its potential to enhance decision-making processes across various domains by adapting swiftly to changing data patterns and m
This paper proposes a distributed algorithm to drive all agents in a heterogeneous multi-agent system (MAS) to the geometric mean of their initial conditions. It is defined as the GM-consensus problem. The proposed GM...
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