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arXiv 2022年

作者： Liew, Zi Qin Du, Hongyang Yang, Wei Lim, Bryan Xiong, Zehui Niyato, Dusit Miao, Chunyan Kim, Dong In Alibaba-NTU Singapore Joint Research Institute Nanyang Technological Univerity Singapore The School of Computer Science and Engineering The Energy Research Institute NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Nanyang Technological University Singapore639798 Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Semantic communication technologies enable wireless edge devices to communicate effectively by transmitting semantic meaning of data. Edge components, such as vehicles in next-generation intelligent transport systems, use well-trained semantic models to encode and decode semantic information extracted from raw and sensor data. However, the limitation in computing resources makes it difficult to support the training process of accurate semantic models on edge devices. As such, edge devices can buy the pretrained semantic models from semantic model providers, which is called "semantic model trading". Upon collecting semantic information with the semantic models, the edge devices can then sell the extracted semantic information, e.g., information about urban road conditions or traffic signs, to the interested buyers for profit, which is called "semantic information trading". To facilitate both types of the trades, effective incentive mechanisms should be designed. Thus, in this paper, we propose a hierarchical trading system to support both semantic model trading and semantic information trading jointly. The proposed incentive mechanism helps to maximize the revenue of semantic model providers in the semantic model trading, and effectively incentivizes model providers to participate in the development of semantic communication systems. For semantic information trading, our designed auction approach can support the trading between multiple semantic information sellers and buyers, while ensuring individual rationality, incentive compatibility, and budget balance, and moreover, allowing them achieve higher utilities than the baseline method. Copyright © 2022, The Authors. All rights reserved.

关键词： Commerce

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STEIN LATENT OPTIMIZATION FOR GENERATIVE ADVERSARIAL NETWORKS

arXiv

引用

arXiv 2021年

作者： Hwang, Uiwon Kim, Heeseung Jung, Dahuin Jang, Hyemi Lee, Hyungyu Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of AIIS ASRI INMC ISRC NSI Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of

Generative adversarial networks (GANs) with clustered latent spaces can perform conditional generation in a completely unsupervised manner. In the real world, the salient attributes of unlabeled data can be imbalanced. However, most of existing unsupervised conditional GANs cannot cluster attributes of these data in their latent spaces properly because they assume uniform distributions of the attributes. To address this problem, we theoretically derive Stein latent optimization that provides reparameterizable gradient estimations of the latent distribution parameters assuming a Gaussian mixture prior in a continuous latent space. Structurally, we introduce an encoder network and novel unsupervised conditional contrastive loss to ensure that data generated from a single mixture component represent a single attribute. We confirm that the proposed method, named Stein Latent Optimization for GANs (SLOGAN), successfully learns balanced or imbalanced attributes and achieves state-of-the-art unsupervised conditional generation performance even in the absence of attribute information (e.g., the imbalance ratio). Moreover, we demonstrate that the attributes to be learned can be manipulated using a small amount of probe data. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Low-Velocity-Favored Transition Radiation

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Physical Review Letters 2023年第11期131卷 113002-113002页

作者： Jialin Chen Ruoxi Chen Fuyang Tay Zheng Gong Hao Hu Yi Yang Xinyan Zhang Chan Wang Ido Kaminer Hongsheng Chen Baile Zhang Xiao Lin Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center College of Information Science & Electronic Engineering Zhejiang University Hangzhou 310027 China International Joint Innovation Center the Electromagnetics Academy at Zhejiang University Zhejiang University Haining 314400 China Department of Electrical and Computer Engineering Technion-Israel Institute of Technology Haifa 32000 Israel Department of Electrical and Computer Engineering Rice University Houston Texas 77005 USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston Texas 77005 USA School of Electrical and Electronic Engineering Nanyang Technological University Singapore 639798 Singapore Department of Physics University of Hong Kong Hong Kong 999077 China Key Lab of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Jinhua Institute of Zhejiang University Zhejiang University Jinhua 321099 China Shaoxing Institute of Zhejiang University Zhejiang University Shaoxing 312000 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore 637371 Singapore Centre for Disruptive Photonic Technologies Nanyang Technological University Singapore 637371 Singapore

When a charged particle penetrates through an optical interface, photon emissions emerge—a phenomenon known as transition radiation. Being paramount to fundamental physics, transition radiation has enabled many applications from high-energy particle identification to novel light sources. A rule of thumb in transition radiation is that the radiation intensity generally decreases with the decrease of particle velocity v; as a result, low-energy particles are not favored in practice. Here, we find that there exist situations where transition radiation from particles with extremely low velocities (e.g., v/c<10−3) exhibits comparable intensity as that from high-energy particles (e.g., v/c=0.999), where c is the light speed in free space. The comparable radiation intensity implies an extremely high photon extraction efficiency from low-energy particles, up to 8 orders of magnitude larger than that from high-energy particles. This exotic phenomenon of low-velocity-favored transition radiation originates from the interference of the excited Ferrell-Berreman modes in an ultrathin epsilon-near-zero slab. Our findings may provide a promising route toward the design of integrated light sources based on low-energy electrons and specialized detectors for beyond-standard-model particles.

关键词： Atom or molecule scattering from surfaces Atomic & molecular collisions Cherenkov radiation Crystal optics Electron beams & optics Light-matter interaction Optical sources & detectors Optical transient phenomena Plasmonics

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Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

arXiv

引用

arXiv 2023年

作者： Du, Hongyang Zhang, Ruichen Liu, Yinqiu Wang, Jiacheng Lin, Yijing Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Cui, Shuguang Ai, Bo Zhou, Haibo Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Shenzhen China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing210093 China The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. © 2023, CC BY.

关键词： Reinforcement learning

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Noise-robust deep spiking neural networks with temporal information

arXiv

引用

arXiv 2021年

作者： Park, Seongsik Lee, Dongjin Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Institute of New Media and Communications Seoul National University Seoul08826 Korea Republic of Asri and Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of

Spiking neural networks (SNNs) have emerged as energy-efficient neural networks with temporal information. SNNs have shown a superior efficiency on neuromorphic devices, but the devices are susceptible to noise, which hinders them from being applied in real-world applications. Several studies have increased noise robustness, but most of them considered neither deep SNNs nor temporal information. In this paper, we investigate the effect of noise on deep SNNs with various neural coding methods and present a noise-robust deep SNN with temporal information. With the proposed methods, we have achieved a deep SNN that is efficient and robust to spike deletion and jitter. © 2021, CC BY.

关键词： Deep neural networks

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Multifunctional metaoptics based on bilayer metasurfaces

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Light(Science & Applications) 2019年第1期8卷 466-474页

作者： You Zhou Ivan I.Kravchenko Hao Wang Hanyu Zheng Gong Gu Jason Valentine Interdisciplinary Materials Science Program Vanderbilt UniversityNashvilleTN 37212USA Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN 37831USA Min H.Kao Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN 37996USA Department of Electric Engineering and Computer Science Vanderbilt UniversityNashvilleTN 37212USA Department of Mechanical Engineering Vanderbilt UniversityNashvilleTN 37212USA

Optical metasurfaces have become versatile platforms for manipulating the phase,amplitude,and polarization of light.A platform for achieving independent control over each of these properties,however,remains elusive due to the limited engineering space available when using a single-layer *** instance,multiwavelength metasurfaces suffer from performance limitations due to space filling constraints,while control over phase and amplitude can be achieved,but only for a single ***,we explore bilayer dielectric metasurfaces to expand the design space for *** ability to independently control the geometry and function of each layer enables the development of multifunctional metaoptics in which two or more optical properties are independently *** a proof of concept,we demonstrate multiwavelength holograms,multiwavelength waveplates,and polarization-insensitive 3D holograms based on phase and amplitude *** proposed architecture opens a new avenue for designing complex flat optics with a wide variety of functionalities.

关键词： surface. optics independently

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Training energy-efficient deep spiking neural networks with time-to-first-spike coding

arXiv

引用

arXiv 2021年

作者： Park, Seongsik Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Institute of New Media and Communications Seoul National University Seoul08826 Korea Republic of ASRI and Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of

The tremendous energy consumption of deep neural networks (DNNs) has become a serious problem in deep learning. Spiking neural networks (SNNs), which mimic the operations in the human brain, have been studied as prominent energy-efficient neural networks. Due to their event-driven and spatiotemporally sparse operations, SNNs show possibilities for energy-efficient processing. To unlock their potential, deep SNNs have adopted temporal coding such as time-to-first-spike (TTFS) coding, which represents the information between neurons by the first spike time. With TTFS coding, each neuron generates one spike at most, which leads to a significant improvement in energy efficiency. Several studies have successfully introduced TTFS coding in deep SNNs, but they showed restricted efficiency improvement owing to the lack of consideration for efficiency during training. To address the aforementioned issue, this paper presents training methods for energy-efficient deep SNNs with TTFS coding. We introduce a surrogate DNN model to train the deep SNN in a feasible time and analyze the effect of the temporal kernel on training performance and efficiency. Based on the investigation, we propose stochastically relaxed activation and initial value-based regularization for the temporal kernel parameters. In addition, to reduce the number of spikes even further, we present temporal kernel-aware batch normalization. With the proposed methods, we could achieve comparable training results with significantly reduced spikes, which could lead to energy-efficient deep SNNs. © 2021, CC BY.

关键词： Energy efficiency

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Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks

引用

Nucleic acids research 2023年第13期51卷 6578-6592页

作者： Yongjian Yang Guanxun Li Yan Zhong Qian Xu Bo-Jia Chen Yu-Te Lin Robert S Chapkin James J Cai Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA. Department of Statistics Texas A&M University College Station TX 77843 USA. Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University 3663 North Zhongshan Road Shanghai 200062 China. Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA. Graduate Institute of Microbiology and Public Health College of Veterinary Medicine National Chung Hsing University Taichung 402 Taiwan. Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan. Program in Integrative & Complex Diseases Department of Nutrition Texas A&M University College Station TX 77843 USA. Interdisciplinary Program of Genetics Texas A&M University College Station TX 77843 USA.

In this paper, we introduce Gene Knockout Inference (GenKI), a virtual knockout (KO) tool for gene function prediction using single-cell RNA sequencing (scRNA-seq) data in the absence of KO samples when only wild-type (WT) samples are available. Without using any information from real KO samples, GenKI is designed to capture shifting patterns in gene regulation caused by the KO perturbation in an unsupervised manner and provide a robust and scalable framework for gene function studies. To achieve this goal, GenKI adapts a variational graph autoencoder (VGAE) model to learn latent representations of genes and interactions between genes from the input WT scRNA-seq data and a derived single-cell gene regulatory network (scGRN). The virtual KO data is then generated by computationally removing all edges of the KO gene-the gene to be knocked out for functional study-from the scGRN. The differences between WT and virtual KO data are discerned by using their corresponding latent parameters derived from the trained VGAE model. Our simulations show that GenKI accurately approximates the perturbation profiles upon gene KO and outperforms the state-of-the-art under a series of evaluation conditions. Using publicly available scRNA-seq data sets, we demonstrate that GenKI recapitulates discoveries of real-animal KO experiments and accurately predicts cell type-specific functions of KO genes. Thus, GenKI provides an in-silico alternative to KO experiments that may partially replace the need for genetically modified animals or other genetically perturbed systems.

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Robust end-to-end focal liver lesion detection using unregistered multiphase computed tomography images

arXiv

引用

arXiv 2021年

作者： Lee, Sang-Gil Kim, Eunji Bae, Jae Seok Kim, Jung Hoon Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of Department of Radiology Seoul National University College of Medicine Seoul Korea Republic of Institute of Radiation Medicine Seoul National University Medical Research Center Seoul Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of

The computer-aided diagnosis of focal liver lesions (FLLs) can help improve workflow and enable correct diagnoses;FLL detection is the first step in such a computer-aided diagnosis. Despite the recent success of deep-learning-based approaches in detecting FLLs, current methods are not sufficiently robust for assessing misaligned multiphase data. By introducing an attention-guided multiphase alignment in feature space, this study presents a fully automated, end-to-end learning framework for detecting FLLs from multiphase computed tomography (CT) images. Our method is robust to misaligned multiphase images owing to its complete learning-based approach, which reduces the sensitivity of the model’s performance to the quality of registration and enables a standalone deployment of the model in clinical practice. Evaluation on a large-scale dataset with 280 patients confirmed that our method outperformed previous state-of-the-art methods and significantly reduced the performance degradation for detecting FLLs using misaligned multiphase CT images. The robustness of the proposed method can enhance the clinical adoption of the deep-learning-based computer-aided detection system. Copyright © 2021, The Authors. All rights reserved.

关键词： Object detection

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UNTANGLING HIGH-ORDER MESHES BASED ON SIGNED ANGLES 28

UNTANGLING HIGH-ORDER MESHES BASED ON SIGNED ANGLES

引用

28th International Meshing Roundtable, IMR 2019

作者： Stees, Mike Dotzel, Myra Shontz, Suzanne M. Department of Electrical Engineering and Computer Science Information and Telecommunication Technology Center University of Kansas 1520 W. 15th Street LawrenceKS66045 United States Department of Electrical Engineering and Computer Science Department of Mathematics Information and Telecommunication Technology Center University of Kansas 1520 W. 15th Street LawrenceKS66045 United States Department of Electrical Engineering and Computer Science Bioengineering Program Information and Telecommunication Technology Center University of Kansas 1520 W. 15th Street LawrenceKS66045 United States

ISBN: (纸本)9781733489003

One challenge in the generation of high-order meshes is that mesh tangling can occur as a consequence of moving the new boundary nodes to the true curved boundary. In this paper, we propose a new optimization-based method that uses signed angles to untangle invalid second- and third-order triangular meshes. Our proposed method consists of two passes. In the first pass, we loop over each high-order interior edge node and minimize an objective function based on the signed angles of the pair of triangles that share the node. In the second pass, we loop over face nodes and move them to the mean of the high-order nodes of the triangle to which the face node belongs. We present several numerical examples in two dimensions with second- and third-order elements that demonstrate the capabilities of our method for untangling invalid meshes. © 2019 Proceedings of the 28th International Meshing Roundtable, IMR 2019. All rights reserved.

关键词： Numerical methods

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