In this paper,we develop an efcient and accurate procedure of electromagnetic multipole decomposition by using the Lebedev and Gaussian quadrature methods to perform the numerical ***,we briefy review the principles o...
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In this paper,we develop an efcient and accurate procedure of electromagnetic multipole decomposition by using the Lebedev and Gaussian quadrature methods to perform the numerical ***,we briefy review the principles of multipole decomposition,highlighting two numerical projection methods including surface and volume ***,we discuss the Lebedev and Gaussian quadrature methods,provide a detailed recipe to select the quadrature points and the corresponding weighting factor,and illustrate the integration accuracy and numerical efciency(that is,with very few sampling points)using a unit sphere surface and regular *** the demonstrations of an isotropic dielectric nanosphere,a symmetric scatterer,and an anisotropic nanosphere,we perform multipole decomposition and validate our numerical projection *** obtained results from our procedure are all consistent with those from Mie theory,symmetry constraints,and fnite element simulations.
This report presents the study results of remote access systems for FPGA development hardware during the period 2008-2021. The possibilities and development of these systems are considered. The study made it possible ...
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With the growing efficiency of the use of unlicensed spectrum,the challenge of ensuring spectrum security has become increasingly *** managers aim to accurately and efficiently detect and recognize anomaly behaviors i...
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With the growing efficiency of the use of unlicensed spectrum,the challenge of ensuring spectrum security has become increasingly *** managers aim to accurately and efficiently detect and recognize anomaly behaviors in the *** this study,we propose a novel framework for spectrum anomaly detection and localization by spectrum interpolation *** interpolation recovery refers to the recovery of the rest of the spectrum distribution based on a part of the spectrum distribution,which is achieved through a masked autoencoder(MAE)model with a core of multi-head self-attention(MHSA)*** spectrum interpolation recovery method restores the region where the masked abnormal signals are present,yielding anomaly-free results,with the difference between the restored and the masked representing the anomaly *** proposed method has been demonstrated to effectively reduce model-induced over-recovery of anomalous signals and dilute large-scale generation errors caused by anomalies,thereby improving the detection and localization performance of anomaly signals,and improving the area under the receiver operating characteristic curve(AUC)and the area under the precision-recall curve(AUPRC)by 0.0382(3.68%)and 0.1992(68.90%),*** a designed dataset containing 3 variables of interference-to-signal ratio(ISR),signal-to-noise ratio(SNR),and anomaly type,the total recall of anomaly detection and localization at a 5%false alarm rate reached 0.8799 and 0.5536,***,a comparative study among different methods demonstrates the effectiveness and rationality of the proposed method.
The influence of automation in the agriculture and construction industry plays a vital role in the development of the economic backbone of any country. The factors such as power, torque and speed are efficiently contr...
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Vapor nanobubble is generated on nanopillar plasmonic nanotransducer arrays using femtosecond laser. Its spatial-temporal dynamics is extracted and analyzed, facilitating the development of highly precise and efficien...
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We present an ultra-dense row of silicon microring resonators used as optical ultrasound sensors in a high-volume monolithic electronics-photonics CMOS platform, achieving an average (maximum) intrinsic sensitivity of...
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Short-term net load forecasting (STNLF) is of utmost importance for the efficient operation and control of residential buildings, with integrated photovoltaic (PV) systems. The intermittent PV generation increases the...
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An increasingly popular machine learning paradigm is to pretrain a neural network (NN) on many tasks offline, then adapt it to downstream tasks, often by re-training only the last linear layer of the network. This app...
An increasingly popular machine learning paradigm is to pretrain a neural network (NN) on many tasks offline, then adapt it to downstream tasks, often by re-training only the last linear layer of the network. This approach yields strong downstream performance in a variety of contexts, demonstrating that multitask pretraining leads to effective feature learning. Although several recent theoretical studies have shown that shallow NNs learn meaningful features when either (i) they are trained on a single task or (ii) they are linear, very little is known about the closer-to-practice case of nonlinear NNs trained on multiple tasks. In this work, we present the first results proving that feature learning occurs during training with a nonlinear model on multiple tasks. Our key insight is that multi-task pretraining induces a pseudocontrastive loss that favors representations that align points that typically have the same label across tasks. Using this observation, we show that when the tasks are binary classification tasks with labels depending on the projection of the data onto an r-dimensional subspace within the d > r-dimensional input space, a simple gradient-based multitask learning algorithm on a two-layer ReLU NN recovers this projection, allowing for generalization to downstream tasks with sample and neuron complexity independent of d. In contrast, we show that with high probability over the draw of a single task, training on this single task cannot guarantee to learn all r ground-truth features.
Due to the requirement of country development, geological surveys must be developed from shallow to deep. Correspondingly, the output power of transient electromagnetic method (TEM) transmitter devices, the necessary ...
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