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Towards Adversarial Robustness for Multi-Mode Data through Metric Learning

Sensors 2023年第13期23卷 6173页

作者： Khan, Sarwar Chen, Jun-Cheng Liao, Wen-Hung Chen, Chu-Song Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan Social Networks Human-Centered Computing Taiwan International Graduate Program Academia Sinica Taipei11529 Taiwan Department of Computer Science National Chengchi University Taipei11605 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei106319 Taiwan

Adversarial attacks have become one of the most serious security issues in widely used deep neural networks. Even though real-world datasets usually have large intra-variations or multiple modes, most adversarial defense methods, such as adversarial training, which is currently one of the most effective defense methods, mainly focus on the single-mode setting and thus fail to capture the full data representation to defend against adversarial attacks. To confront this challenge, we propose a novel multi-prototype metric learning regularization for adversarial training which can effectively enhance the defense capability of adversarial training by preventing the latent representation of the adversarial example changing a lot from its clean one. With extensive experiments on CIFAR10, CIFAR100, MNIST, and Tiny ImageNet, the evaluation results show the proposed method improves the performance of different state-of-the-art adversarial training methods without additional computational cost. Furthermore, besides Tiny ImageNet, in the multi-prototype CIFAR10 and CIFAR100 where we reorganize the whole datasets of CIFAR10 and CIFAR100 into two and ten classes, respectively, the proposed method outperforms the state-of-the-art approach by 2.22% and 1.65%, respectively. Furthermore, the proposed multi-prototype method also outperforms its single-prototype version and other commonly used deep metric learning approaches as regularization for adversarial training and thus further demonstrates its effectiveness. © 2023 by the authors.

关键词： Deep neural networks

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High-performance UV polarization sensitive photodetector for a graphene(2D)/GaN(3D) junction with a non-centrosymmetric electric field

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Photonics Research 2025年第6期13卷 1544-1553页

作者： Zou, Can Liu, Qing Zhang, Lu Tang, Xiao Li, Xiaohang Li, Shuti Gao, Fangliang Guangdong Engineering Research Center of Optoelectronic Functional Materials and Devices School of Electronic Science and Engineering School of Microelectronics South China Normal University Guangzhou510631 China Advanced Semiconductor Laboratory Electrical and Computer Engineering Program CEMSE Division King Abdullah University of Science and Technology Thuwal239556900 Saudi Arabia

This study pioneers a high-performance UV polarization-sensitive photodetector by ingeniously integrating non-centrosymmetric metal nanostructures into a graphene (Gr)∕Al2O3∕GaN heterojunction. Unlike conventional approaches constrained by graphene’s intrinsic isotropy or complex nanoscale patterning, our design introduces asymmetric metal architectures (E-/T-type) to artificially create directional anisotropy. These structures generate plasmon-enhanced localized electric fields that selectively amplify photogenerated carrier momentum under polarized UV light (325 nm), synergized with Fowler-Nordheim tunneling (FNT) across an atomically thin Al2O3 barrier. The result is a breakthrough in performance: a record anisotropy ratio of 115.5 (E-type, −2 V) and exceptional responsivity (97.7 A/W), surpassing existing graphene-based detectors by over an order of magnitude. Crucially, by systematically modulating metal geometry and density, we demonstrate a universal platform adaptable to diverse 2D/3D systems. This study provides a valuable reference for developing and practically applying photodetectors with higher anisotropy than ultraviolet polarization sensitivity. © 2025 Chinese Laser Press.

关键词： Nanoparticles

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Tensions in design and participation processes: An ethnographic approach to the design, building and evaluation of a collective intelligence model

Tensions in design and participation processes: An ethnograp...

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International Conference on computer Supported Cooperative Work in Design

作者： Ramon Chaves Claudia Motta Antonio Correia Jano de Souza Daniel Schneider Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research (NCE) Federal University of Rio de Janeiro Brazil UTAD INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal College of Information Science & Technology University of Nebraska at Omaha Omaha NE USA

This paper explores the challenges and strategies used to design, build and evaluate a collective intelligence (CI) model to support discussions about cities. Through an autoethnography of ideas and discussion practices in both online and offline contexts, this work explores the tensions between the chosen methodological approaches, including design science research (DSR) and participatory action research (PAR). Moreover, we also examine the challenges and pitfalls observed during the practical conduction of this research when involving participants in the process of empirically evaluating the proposed model. Finally, aspects related to the autoethnographic process itself as a reflective method are discussed alongside the consequences of desiring and seeking participation in the research process.

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Low-Loss, Nonvolatile Tuning of Bragg Structures with Sb2Se3

Low-Loss, Nonvolatile Tuning of Bragg Structures with Sb2Se3

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Conference on Lasers and Electro-Optics (CLEO)

作者： Nicholas A. Nobile Chuanyu Lian Hongyi Sun Yi-Siou Huang Brian Mills Cosmin Constantin Popescu Dennis Callahan Juejun Hu Carlos A. Ríos Ocampo Nathan Youngblood Electrical & Computer Engineering Department University of Pittsburgh Pittsburgh PA USA Department of Materials Science & Engineering University of Maryland College Park MD USA Institute for Research in Electronics and Applied Physics University of Maryland College Park MD USA Department of Materials Science & Engineering Massachusetts Institute of Technology Cambridge MA USA Draper Scholar Program The Charles Stark Draper Laboratory Inc Cambridge MA USA The Charles Stark Draper Laboratory Inc Cambridge MA USA

Sb 2 Se 3 is used to switch between broadband transparency and enhanced index contrast in two device types leveraging Bragg gratings for tunable stop-and pass-band functionalities. Experimental results highlight fabr... 详细信息

ISBN: (纸本)9798350369311

关键词： Bragg gratings Waveguide transitions Phase change materials Optical filters Optical switches Refractive index Fabry-Perot Indexes Passband Optical waveguides

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Mapping Tokenomics Arrangements to Expand the Digital Nomad Ecosystem

Mapping Tokenomics Arrangements to Expand the Digital Nomad ...

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International Conference on computer Supported Cooperative Work in Design

作者： Marcos Antonio de Almeida António Correia Jano Moreira de Souza Daniel Schneider Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal College of Information Science & Technology University of Nebraska at Omaha Omaha NE USA Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research NCE/UFRJ Rio de Janeiro Brazil

The Massification of remote work, in response to the COVID-19 pandemic, has been causing significant changes in productive and working arrangements, both for individuals, organizations, and society. At the level of personal life philosophy, for example, this transformation can be evidenced in the dissemination of digital nomadism values such as work/leisure balance among corporate workers. On the other hand, at the level of gig/crowd work platforms, the emergence of tensions may indicate the exhaustion of sociotechnical design models adopted by big techs. We show how evidence collected from digital nomads in empirical ethnography studies can inform HCI/CSCWD researchers of design-oriented strands to explore emerging opportunities in new creative digital crypto-economic ecosystems. Finally, we present a proposed research agenda to explore DNs’ activities in the crypto-economic ecosystem.

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AI in Industry: Real-World Applications and Case Studies

TechRxiv

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TechRxiv 2023年

作者： Rahmaniar, Wahyu Maarif, Alfian ul Haq, Qazi Mazhar Iskandar, Muchammad Edo Institute of Innovative Research Tokyo Institute of Technology Kanagawa226-8503 Japan Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55166 Indonesia International Bachelor Program in Informatics and Computer Science Yuan Ze University Taoyuan320315 Taiwan Ezy Industries Jakarta Indonesia

Artificial intelligence (AI) has advanced rapidly and is becoming a cornerstone technology that drives innovation and efficiency in various industries. This paper examines the real-world application of AI in multiple sectors, including healthcare, finance, agriculture, retail, energy, and automotive. Several case studies are described to understand better the practical applications, results, and challenges of implementing AI. While many industries have reaped enormous benefits from AI, inherent challenges include data privacy, the potential for bias, and the continuing demand for skilled labor. This comprehensive review aims to provide AI application insights to professionals and researchers. Thus, as AI grows, there may be challenges and avenues for future research. © 2023, CC BY.

关键词： Agriculture

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Picosecond optical-to-x-ray (Cd,Mg)Te radiation detectors

Picosecond optical-to-x-ray (Cd,Mg)Te radiation detectors

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Conference on Lasers and Electro-Optics (CLEO)

作者： J. Cheng I. Komissarov G. Chen D. Chakraborty S. Kutcher H. Chen S. Trivedi Roman Sobolewski Materials Science Graduate Program University of Rochester Rochester New York USA Laboratory for Laser Energetics University of Rochester Rochester New York USA Department of Electrical and Computer Engineering University of Rochester Rochester New York USA Brimrose Technology Corp Sparks Maryland USA

We developed a dual optical/x-ray ultrafast photodetector based on in-house grown Cd 0.97 Mg 0.03 Te single crystals. The detector is characterized by ~200 ps full-width-at-half-maximum, readout-electronics limited p... 详细信息

ISBN: (纸本)9781957171258

关键词： Femtosecond lasers Photodetectors Photon counting Pump probe spectroscopy Radiation detectors Ti:sapphire lasers

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Coherent Acoustic Phonons in Bi2Se3 Ultrathin Films Probed by Femtosecond, Optical Pump-Probe Spectroscopy

Coherent Acoustic Phonons in Bi2Se3 Ultrathin Films Probed b...

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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Cheng, Jing Niherysh, Kiryl A. Francis, Peter Chakraborty, Debamitra Lombardi, Floriana Erts, Donats Prischepa, Serghej L. Laszcz, Adam Komissarov, Ivan V. Sobolewski, Roman Materials Science Graduate Program University of Rochester RochesterNY14627 United States Laboratory for Laser Energetics University of Rochester RochesterNY14623-1299 United States Department of Microtechnology and Nanoscience Chalmers University of Technology Gothenburg Sweden Institute of Chemical Physics University of Latvia Riga Latvia Department of Electrical and Computer Engineering University of Rochester RochesterNY14627 United States Dipartimento di Fisica "E.R. Caianiello" Università degli Studi di Salerno Sa FiscianoI-84084 Italy Lukasiewicz Research Network Institute of Microelectronics and Photonics WarszawaPL-02668 Poland

ISBN: (纸本)9781957171395

The dynamics of coherent acoustic phonons in the Bi2Se3 layered crystal system are investigated. The findings reveal that the frequency evolution of breathing modes with the number of the material quintuple layers and the dispersion relation both can be explained by a modified chain model with a nonuniform coupling force. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Ultrathin films

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Medical Image Segmentation by Improved Nested Unet 2nd

Medical Image Segmentation by Improved Nested Unet

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2nd International Conference on Intelligent Systems and Data Science, ISDS 2024

作者： Tran, Song-Toan Le, Minh-Hai Nguyen, Thai-Son Nghi, Vinh-Khanh Nguyen, Thanh-Nguyen School of Engineering and Technology Tra Vinh University Tra Vinh87000 Viet Nam Resource Development Institute Tra Vinh University Tra Vinh87000 Viet Nam PhD program in Computer Science Tra Vinh University Tra Vinh87000 Viet Nam Facilities Management Department Tien Giang University Tien Giang84000 Viet Nam

ISBN: (纸本)9789819796120

Deep learning is revolutionizing the field of medical image segmentation. The U-shaped (Unet) model, with its encoder-decoder architecture and skip connections, has become the dominant architecture for this task. However, variations like Nested-Unet are necessary to address the challenge of adapting network size for datasets of varying scales. This study proposes a novel model based on a multilayer Unet architecture that leverages all output features within nodes of a Nested Unet network. We evaluate the proposed model on four key medical image datasets: electron microscopy (EM), nuclei, polyps (CVC-ClinicDB), and skin lesions. The model achieves impressive Dice scores: 94.08% for EM, 92.68% for nuclei, 87.20% for skin lesions, and 93.02% for the CVC-ClinicDB dataset. These outstanding results demonstrate the superior performance of our proposed model compared to recent advancements in the field. Remarkably, the model achieves this performance while maintaining a size that is half that of a traditional Unet model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Images

Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Im...

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International Conference on engineering technology and their Applications (IICETA)

作者： Mohammad Alslatie Hiam Alquran Wan Azani Mustafa Isam Abu-Qasmieh Ali Mohammad Alqudah Ahmed Alkhayyat King Hussein Medical Center Royal Jordanian Medical Service The Institute of Biomedical Technology Amman Jordan Biomedical Systems and Medical Informatics Engineering Yarmouk University Irbid Jordan Faculty of Electrical Engineering Technology Univerisiti Malaysia Perlis (UniMAP) Arau Perlis Malaysia Department of Electrical and Computer Engineering Biomedical Engineering Program University of Manitoba Canada Faculty of Engineering The Islamic University Najaf Iraq

ISBN: (数字)9781665472159

ISBN: (纸本)9781665472166

The state of the art of artificial intelligence (AI) for various medical imaging applications leads to enhanced accuracy, analysis, visualization, and interpretation of chest Xray (CXR) images for diagnosis. Many diseases are diagnosed based on CXR images. In this paper, two types of abnormalities are diagnosed based on AI techniques. The two classes are atelectasis and cardiomegaly. The acquired images are segmented to localize the chest region and then enhanced using gray-level transformation methods. The enhanced images are passed to two pretrained convolutional neural networks (CNNs): shuffle and mobile net. The transfer learning approach is utilized in this stage. The automated features are extracted from the last fully connected layer. Each CNN deserves to have the two most representative features for the two classes. These four features are passed to support the vector machine classifier. The training accuracy reached 100% and the test accuracy was 96.7%. The proposed method can be extended to be a milestone in the classification of all heart-lung diseases that can be diagnosed using chest X-ray images.

关键词： Image segmentation Visualization Transfer learning Feature extraction Convolutional neural networks Artificial intelligence X-ray imaging

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