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arXiv 2023年

作者： Han, Hyeongrok Kim, Siwon Choi, Hyun-Soo Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Korea Republic of ZIOVISION Inc. Chuncheon Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of

Several recent studies have elucidated why knowledge distillation (KD) improves model performance. However, few have researched the other advantages of KD in addition to its improving model performance. In this study, we have attempted to show that KD enhances the interpretability as well as the accuracy of models. We measured the number of concept detectors identified in network dissection for a quantitative comparison of model interpretability. We attributed the improvement in interpretability to the class-similarity information transferred from the teacher to student models. First, we confirmed the transfer of class-similarity information from the teacher to student model via logit distillation. Then, we analyzed how class-similarity information affects model interpretability in terms of its presence or absence and degree of similarity information. We conducted various quantitative and qualitative experiments and examined the results on different datasets, different KD methods, and according to different measures of interpretability. Our research showed that KD models by large models could be used more reliably in various fields. © 2023, CC BY-NC-SA.

关键词： Distillation

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Synthetic Batik Pattern Generator Using Wasserstein Generative Adversarial Network with Gradient Penalty

Synthetic Batik Pattern Generator Using Wasserstein Generati...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Kus Andriadi Yaya Heryadi Lukas Wayan Suparta Ilvico Sonata Computer Science Department Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Batik is an Indonesian world cultural heritage. Batik consists of many kinds of patterns depending on where the batik comes from, Batik-making techniques continue to develop along with technology development. Among the batik making techniques that are widely used are hand-written, stamping, and printing. Batik motifs have been widely used as research material, especially in the field of artificial intelligence. The diverse appearance of batik motifs has attracted many researchers to carry out research on making synthetic batik patterns, one of which uses a Generative Adversarial Network. This paper presents a synthetic batik pattern model based on the Wasserstein Generative Adversarial Network with Gradient Penalty. This model has been proven to create new synthetic batik patterns quite well and almost identical with images provided in the dataset, with the notes if the dataset provided is large.

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Fq-Uwf: Unpaired Generative Image Enhancement for Fundus Quality Ultra-Widefield Retinal Images

SSRN

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SSRN 2024年

作者： Lee, Kang Geon Song, Su Jeong Lee, Soochahn Kim, Bo Hee Kong, Mingui Lee, Kyoung Mu Department of Electrical and Computer Engineering ASRI Seoul National University Seoul Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of Department of Ophthalmology Kangbuk Samsung Hospital Sungkyunkwan University School of Medicine Seoul Korea Republic of Sungkyunkwan University Suwon Korea Republic of School of Electrical Engineering Kookmin University Seoul Korea Republic of

Background and objectives :Ultra-widefield (UWF) retinal imaging stands as a pivotal modality for detecting major eye diseases such as diabetic retinopathy or retinal detachment. However, UWF exhibits a well-documented limitation in terms of low resolution and artifacts in the macular area, thereby constraining its clinical diagnostic accuracy, particularly for macular diseases like age-related macular *** supervised super-resolution techniques aim to address this limitation by enhancing the resolution of the macular region through the utilization of meticulously paired and aligned fundus image ground truth. However, obtaining such refined paired ground truth is a formidable *** :To tackle this issue, we propose an unpaired degradation-aware super-resolution technique for enhancing UWF retinal images. Our approach leverages recent advancements in deep learning, specifically employing generative adversarial networks and attention mechanisms. Results :Notably, our method excels in enhancing and super-resolving UWF images without relying on paired clean ground *** extensive experimentation and evaluation, we demonstrate that our approach not only produces visually pleasing results, but also establishes state-of-the-art performance in enhancing and super-resolving UWF retinal *** :We anticipate that our method will contribute to improving the accuracy of clinical assessments and treatments, ultimately leading to better patient outcomes. © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Cloud-Native Mobile Application Applied for Law and Regulations Harmonization

Cloud-Native Mobile Application Applied for Law and Regulati...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Muhammad Juan Al Qausar Yaya Heryadi Lukas Lukas Wayan Suparta Ilvico Sonata Computer Science Department Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Navigating the complex legal and regulatory landscape requires a sophisticated platform that is not only comprehensive but also user-friendly and enables seamless analysis and document comparison in the legal realm. To address this challenging requirement, this research is dedicated to the user experience (UX) design of state-of the-art cloud-native mobile applications specifically designed for legal and regulatory harmonization. The methodology used is based on design thinking principles and takes advantage of the benefits of cloud computing. These include, but are not limited to, scalability, reliability, and security. The result is a solution that is not only powerful but also cost-effective. The core of this application follows a user-centered design philosophy and involves potential users closely in the design process. A comprehensive evaluation is performed using the System Usability Scale (SUS) methodology to measure the effectiveness and usability of the application. The results of this comprehensive approach are convincing. The application has an impressive 95.5% usage rate, indicating strong user engagement. Furthermore, user satisfaction ratings are particularly high and transformative impact of this application in a complex environment of legal harmonization. Through its innovative design, seamless functionality, and user-centric approach, this application has proven to be not only a technical solution, but also a catalyst for positive change in the legal and regulatory harmonization process.

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Hyperspectral Image Classification Using Custom Spectral Convolutional Neural Networks (CSCNNs)

Hyperspectral Image Classification Using Custom Spectral Con...

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International Conference on Information Science and Technology (ICIST)

作者： Stephanie Rouamba Nian Zhang Wagdy Mahmoud Lara Thompson Max Denis Tolessa Deksissa Department of Electrical and Computer Engineering University of the District of Columbia Washington D.C. USA Department of Mechanical Engineering Biomedical Engineering Program University of the District of Columbia Washington D.C. USA Department of Mechanical Engineering University of the District of Columbia Washington D.C. USA Water Resources Research Institute University of the District of Columbia Washington D.C. USA

ISBN: (数字)9798350353334

ISBN: (纸本)9798350353341

Custom spectral convolutional neural networks (CSCNNs) combine the strengths of convolutional neural networks with specialized spectral processing, resulting in improved classification accuracy by effectively capturing subtle variations in hyperspectral data. This paper proposes a CSCNNs based approach to classify hyperspectral images. The proposed method leverages the high-dimensional spectral data inherent in hyperspectral images, employing convolutional layers specifically designed to capture spectral-spatial features. By reducing dimensionality through principal component analysis and creating image patches, the model is trained to recognize complex patterns across different spectral bands. In addition, a comprehensive analysis of CSCNN performance is carried out, focusing on its architecture, key features, and benefits in computational efficiency and spectral representation. Experimental results on datasets such as Salinas-A, Pavia University (Pavia-U), and Indian Pines demonstrate that the CSCNN model surpasses traditional methods, achieving higher classification accuracy and more robust performance metrics like overall accuracy (OA), average accuracy (AA), and Kappa coefficient. The proposed model achieved an overall accuracy (OA) of 98.2%, 96.7%, and 92.5% for the Salinas-A, Pavia-U, and Indian Pines datasets, respectively. The model's average accuracy (AA) was 97.5%, 95.4%, and 89.8% for the same datasets. Furthermore, the model attained Kappa coefficients of 97.7%, 95.2%, and 89.1% for Salinas-A, Pavia-U, and Indian Pines, respectively.

关键词： Measurement Accuracy Computational modeling Predictive models Robustness Pattern recognition Convolutional neural networks Hyperspectral imaging Image classification Principal component analysis

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Data Acquisition for a Magnetic Levitation Vehicle

Data Acquisition for a Magnetic Levitation Vehicle

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IEEE Annual Southern Power Electronics Conference (SPEC)

作者： Gabriel Klajman Messer Elkin Ferney Rodriguez Velandia Richard Magdalena Stephan César Augusto Gonçalves Amado Program of Electrical Engineering (PEE) Federal University of Rio de Janeiro (UFRJ) Rio de Janeiro Brazil Department of Electrical Engineering (DEE) Federal University of Rio de Janeiro (UFRJ) Rio de Janeiro Brazil Department of Electronic and Computer Engineering (DEL) Federal University of Rio de Janeiro (UFRJ) Rio de Janeiro Brazil

The main objective of this paper is to discuss the data acquisition of the MagLev-Cobra magnetic levitation vehicle. The vehicle is located at the Technology Center of the Federal University of Rio de Janeiro. It was inaugurated in 2014 and is the first vehicle of its kind to use superconducting levitation in the world. As in other technological systems, it is extremely important to analyze the magnitudes related to its operation and efficiency. In this work, the main characteristics of the MagLev-Cobra are discussed, in addition to the analysis of experimental data of the magnitudes to be sensed.

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Bilevel optimization for machine learning: Algorithm design and convergence analysis

arXiv

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arXiv 2021年

作者： Ji, Kaiyi Graduate Program in Department of Electrical and Computer Engineering Ohio State University

Bilevel optimization has become a powerful framework in a variety of machine learning applications including signal processing, meta-learning, hyperparameter optimization, reinforcement learning and network architecture search. There are generally two classes of bilevel optimization formulations for modern machine learning: 1) problem-based bilevel optimization, whose inner-level problem is formulated as finding a minimizer of a given loss function;and 2) algorithm-based bilevel optimization, whose inner-level solution is an output of a fixed algorithm. For the first problem class, two popular types of gradient-based algorithms have been proposed to estimate the gradient of the outer-level objective (hypergradient) via approximate implicit differentiation (AID) and iterative differentiation (ITD). Algorithms for the second problem class include the popular model-agnostic meta-learning (MAML) and almost no inner loop (ANIL). Although bilevel optimization algorithms have been widely used, their convergence rate and fundamental limitations have not been well explored. Copyright © 2021, The Authors. All rights reserved.

关键词： Network architecture

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Optoelectric Raman Nanosensors: Overcoming Intrinsic Limit in Nano-biosensing

Research Square

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Research Square 2024年

作者： Lian, Bin Li, Huaizhi Guo, Jianhe Fan, Donglei Emma Materials Science and Engineering Program Texas Materials Institute The University of Texas AustinTX78712 United States Walker Department of Mechanical Engineering Texas Materials Institute The University of Texas at Austin AustinTX78712 United States Chandra Family Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78712 United States

It is intrinsically difficult to detect low-concentration biomolecules with both ultra-sensitivity and high speed for early-stage disease diagnosis. The challenge originates from the small size of nanosensors, which enables ultra-sensitivity biosensing, while also substantially increases the detection time of dilute molecules. in this work, we report an original optoelectric sensing scheme, along with an innovative device design and a strategic fabrication approach to overcome such a challenge in biosensing. The proposed scheme exploits the profound optoelectric effect of semiconductor silicon nanowires, which can readily enrich trace-amounts of biomolecules at the point of laser and simultaneously detect Raman signals of focused molecules. The device design includes large arrays of silicon nanorods with electrodes integrated at their roots, and surface-distributed dense plasmonic silver nanoparticles for surface-enhanced Raman spectroscopy (SERS) detection. Operating at only -0.8 V in an electrochemical cell, these optoelectric nanosensors readily achieve a 150-fold signal enhancement, improving the detection limit of probing molecules, adenine, by five orders of magnitude, to 0.6 fM. The enhancement effect is robust, observed across concentrations from 1 μM to 1 fM. The working mechanism is general, not only for detecting small molecules, such as adenine, but also for large charged molecules, such as Vertebrate DNA. The underlying novel mechanism is unraveled by multiscale numerical simulations and calculations. This research, addressing an arduous bottleneck issues in nanobiosensing, is expected to inspire a new class of biochemical sensors, important for the practical detection of trace-amount biochemicals in solution, important for clinic relevant early-stage disease diagnostics. © 2024, CC BY.

关键词： Nanorods

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Corner cutting connects chiral colorimetry to net electric flux in lossless all-dielectric metasurfaces

arXiv

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arXiv 2024年

作者： Haddadin, Zaid Nguyen, Anna M.Y. Poulikakos, Lisa V. Department of Electrical & Computer Engineering UC San Diego La Jolla CA92093-0021 United States Department of Mechanical & Aerospace Engineering UC San Diego La Jolla CA92093-0021 United States Program of Materials Science & Engineering UC San Diego La Jolla CA92093-0021 United States

All-dielectric metasurfaces can produce structural colors, but the most advantageous design criteria are still being investigated. This work numerically studies how the two-dimensional shape of nanoparticles affects the colorimetric response under circularly polarized light (CPL) to develop a sensor distinguishing CPL orientations. Using lossless dielectric materials (silicon nitride on silicon dioxide), we achieve far-field dichroism by modifying oblong nanoparticles into L-shaped structures through corner cuts. This design suppresses one electric dipole under CPL illumination, leading to differential colorimetric responses. We link these responses to a decoupling effect in the near-field net electric flux. Our findings provide design guidelines for all-dielectric, lossless colorimetric sensors of chiral light. Copyright © 2024, The Authors. All rights reserved.

关键词： Silicon nitride

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Subwavelength-Engineered Antislot Photonic Crystals in a Silicon Photonics Foundry for On-chip Communications

Subwavelength-Engineered Antislot Photonic Crystals in a Sil...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Kellen P. Arnold Joshua A. Allen Sami I. Halimi Landen D. Ryder Francis O. Afzal Yusheng Bian Abdelsalam Aboketaf Takako Hirokawa Kevin Dezfulian Michal Rakowski Rod Augur Karen Nummy Sharon M. Weiss Interdisciplinary Materials Science Program Vanderbilt University Nashville TN USA Department of Electrical and Computer Engineering Vanderbilt University Nashville TN USA NASA Goddard Space Flight Center Greenbelt MD US GlobalFoundries Malta NY USA

We report scalable foundry fabrication and characterization of photonic crystal nanobeam waveguides incorporating subwavelength-scale dielectric antislot unit cells. This work enables enhanced light-matter interaction... 详细信息

ISBN: (纸本)9798350369311

关键词： Mach-Zehnder interferometers Optical device fabrication Photonic crystals Waveguide lasers Lasers and electrooptics Electro-optic modulators Silicon photonics Foundries System-on-chip Electro-optical waveguides

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