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arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Shen, Xuemin 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 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 The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Artificial Intelligence-Generated Content (AIGC) refers to the use of AI to automate the information creation process while fulfilling the personalized requirements of users. However, due to the instability of AIGC models, e.g., the stochastic nature of diffusion models, the quality and accuracy of the generated content can vary significantly. In wireless edge networks, the transmission of incorrectly generated content may unnecessarily consume network resources. Thus, a dynamic AIGC service provider (ASP) selection scheme is required to enable users to connect to the most suited ASP, improving the users’ satisfaction and quality of generated content. In this article, we first review the AIGC techniques and their applications in wireless networks. We then present the AIGC-as-a-service (AaaS) concept and discuss the challenges in deploying AaaS at the edge networks. Yet, it is essential to have performance metrics to evaluate the accuracy of AIGC services. Thus, we introduce several image-based perceived quality evaluation metrics. Then, we propose a general and effective model to illustrate the relationship between computational resources and user-perceived quality evaluation metrics. To achieve efficient AaaS and maximize the quality of generated content in wireless edge networks, we propose a deep reinforcement learning-enabled algorithm for the optimal ASP selection. Simulation results show that the proposed algorithm can provide a higher quality of generated content to users and achieve fewer crashed tasks by comparing with four benchmarks, i.e., overloading-avoidance, random, round-robin policies, and the upper-bound schemes. © 2023, CC BY.

关键词： Reinforcement learning

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Porous silicon on paper sensors: Factors affecting biosensing performance 17

Porous silicon on paper sensors: Factors affecting biosensin...

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Frontiers in Biological Detection: From Nanosensors to Systems XVII 2025

作者： An, Huijin Laibinis, Paul E. Locke, Andrea K. Weiss, Sharon M. Interdisciplinary Material Science Program Vanderbilt University NashvilleTN37235 United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37235 United States Department of Chemical and Biomolecular Engineering Vanderbilt University NashvilleTN37235 United States Department of Chemistry Vanderbilt University NashvilleTN37235 United States Department of Biomedical Engineering Vanderbilt University NashvilleTN37235 United States

ISBN: (纸本)9781510684249

A paper-based biosensor integrating a functionalized porous silicon (PSi) membrane as the active sensing element for quantifiable protein detection has been developed. For similar short-time exposures to an analyte, improved molecular transport in PSi membranes when on paper leads to larger signal changes compared to traditional PSi films that remain on a silicon substrate. In this work, we discuss controlling the incubation time of the analyte and the overall testing time of the sensor by incorporating different combinations of wicking and absorbent paper beneath the PSi membrane. With this control, the PSi-on-paper sensor platform has the potential to serve as an effective low-cost rapid diagnostic test with highly sensitive, quantitative readout for a wide range of analytes. © 2025 SPIE.

关键词： Porous silicon

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Annotating TSSs in Multiple Cell Types Based on DNA Sequence and RNA-seq Data via DeeReCT-TSS

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Genomics, Proteomics & Bioinformatics 2022年第5期20卷 959-973页

作者： Juexiao Zhou Bin Zhang Haoyang Li Longxi Zhou Zhongxiao Li Yongkang Long Wenkai Han Mengran Wang Huanhuan Cui Jingjing Li Wei Chen Xin Gao Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Department of Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Gene Regulation and Systems Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Academy for Advanced Interdisciplinary Studies Southern University of Science and TechnologyShenzhen 518055China

The accurate annotation of transcription start sites(TSSs)and their usage are critical for the mechanistic understanding of gene regulation in different biological *** fulfill this,specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner,and various computational tools have also been developed for in silico prediction of TSSs solely based on genomic *** of these computational tools cast the problem as a binary classification task on a balanced dataset,thus resulting in drastic false positive predictions when applied on the genome ***,we present Dee Re CT-TSS,a deep learningbased method that is capable of identifying TSSs across the whole genome based on both DNA sequence and conventional RNA sequencing *** show that by effectively incorporating these two sources of information,Dee Re CT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell ***,we develop a meta-learning-based extension for simultaneous TSS annotations on 10 cell types,which enables the identification of cell type-specific ***,we demonstrate the high precision of DeeReCT-TSS on two independent datasets by correlating our predicted TSSs with experimentally defined TSS chromatin *** source code for Dee Re CT-TSS is available at https://github.-com/Joshua Chou2018/Dee Re CT-TSS_release and https://***/biocode/tools/BT007316.

关键词： Transcription start site Machine learning Deep learning Meta-learning RNA sequencing

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Boosting the Performance of Image Restration Models Through Training with Deep-Feature Auxiliary Guidance

SSRN

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SSRN 2024年

作者： Jang, Cheolhun Ji, Deahyun Cho, Nam Ik Interdisciplinary Program in Artificial Intelligence INMC Seoul National University Seoul08826 Korea Republic of Samsung Advanced Institute of Technology Suwon-si Gyenggi-do16678 Korea Republic of Department of Electrical and Computer Engineering INMC Seoul National University Seoul08826 Korea Republic of

Many neural network architectures have been suggested for image restoration models to enhance the accuracy of restored images while keeping the computational time reasonable. However, most previous studies have primarily focused on introducing new network architectures, with relatively less emphasis on efficient training techniques. This paper introduces a novel training strategy that improves the accuracy of image restoration models without increasing inference times by preserving their structures. To achieve this, we add auxiliary guidance at a feature map of each multi-scale level during the training process. All modules added to the model for the application of auxiliary guidance are only used during training time and can be removed at the inference phase, thereby eliminating the need for additional computation. Our proposed training method, named deep-feature auxiliary guidance (DFAG), can be easily applied to existing encoder-decoder architectures. By applying DFAG to train existing image restoration models, we have obtained enhanced performance in real-image denoising, Gaussian denoising, motion deblurring, JPEG artifact reduction, and super-resolution for all tested models. The source code and pre-train models will be released. © 2024, The Authors. All rights reserved.

关键词： Image denoising

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NEW INSIGHTS FOR THE STABILITY-PLASTICITY DILEMMA IN ONLINE CONTINUAL LEARNING

arXiv

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arXiv 2023年

作者： Jung, Dahuin Lee, Dongjin Hong, Sunwon Jang, Hyemi Bae, Ho Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of DeepBrain AI Inc. Department of Cyber Security Ewha Womans University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of

The aim of continual learning is to learn new tasks continuously (i.e., plasticity) without forgetting previously learned knowledge from old tasks (i.e., stability). In the scenario of online continual learning, wherein data comes strictly in a streaming manner, the plasticity of online continual learning is more vulnerable than offline continual learning because the training signal that can be obtained from a single data point is limited. To overcome the stability-plasticity dilemma in online continual learning, we propose an online continual learning framework named multi-scale feature adaptation network (MuFAN) that utilizes a richer context encoding extracted from different levels of a pre-trained network. Additionally, we introduce a novel structure-wise distillation loss and replace the commonly used batch normalization layer with a newly proposed stability-plasticity normalization module to train MuFAN that simultaneously maintains high plasticity and stability. MuFAN outperforms other state-of-the-art continual learning methods on the SVHN, CIFAR100, miniImageNet, and CORe50 datasets. Extensive experiments and ablation studies validate the significance and scalability of each proposed component: 1) multi-scale feature maps from a pre-trained encoder, 2) the structure-wise distillation loss, and 3) the stability-plasticity normalization module in MuFAN. Code is publicly available at https://***/whitesnowdrop/MuFAN. Copyright © 2023, The Authors. All rights reserved.

关键词： Stability

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High-order mesh generation based on optimal affine combinations of nodal positions 12th

High-order mesh generation based on optimal affine combinati...

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12th International Conference on Spectral and High-Order Methods, ICOSAHOM 2018

作者： Stees, Mike Shontz, Suzanne M. Department of Electrical Engineering and Computer Science Information and Telecommunication Technology Center University of Kansas LawrenceKS United States Department of Electrical Engineering and Computer Science Bioengineering Program Information and Telecommunication Technology Center University of Kansas LawrenceKS United States

ISBN: (纸本)9783030396466

In this paper, we propose a new optimization-based method for high-order mesh generation. Our proposed method is based on affine combinations of nodal positions. For each interior node in the high-order straight-sided mesh, a set of weights that relates the node to its neighbors is calculated. After the high-order boundary nodes are curved to conform with the true boundary, the interior nodes are moved based on the generated weights and the new boundary positions. We present results from applying our method to several aerospace engineering geometries in 2D and 3D which demonstrate the capabilities of our method. © 2020, The Author(s).

关键词： Mesh generation

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Comprehensive Study on Cherenkov Radiation for Ultrafast PET Detectors, Considering Refractive Index and Wavelength

Comprehensive Study on Cherenkov Radiation for Ultrafast PET...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： H.S. Shim H. Kang J. Kim J.S. Lee Y. Jeong Interdisciplinary Program of Bioengineering Seoul National University Seoul Republic of Korea Integrated Major in Innovative Medical Science Seoul National University Seoul Republic of Korea Department of Electrical and Computer Engineering Seoul National University Seoul Republic of Korea

This study aims to investigate the characteristics of Cherenkov radiation with respect to wavelength and refractive index. Refractive index is a function of wavelength, and when it changes, not only does the gain of Cherenkov radiation increase, but also the photon loss in the medium increases. Therefore, we examine how photons of different wavelengths behave under changing refractive index conditions. We adjust the refractive index to maintain a constant emission angle of Cherenkov radiation based on the deceleration rate of electrons moving in the medium. When electrons generating Cherenkov radiation move a total of 200 μm, both long-wavelength and short-wavelength photons are emitted at the initial stage; however, only short-wavelength photons are emitted at the late stage. Although the shorter-wavelength photons arrive first among the photons that reach the detector, the longer-wavelength photons are detected more, forming a significant peak, which is due to the fact that the former experience more absorption-based photon loss in the medium than the latter.

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Deep Subwavelength Slotted Photonic Crystals Fabricated in a Monolithic Silicon Photonics Technology

Deep Subwavelength Slotted Photonic Crystals Fabricated in a...

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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 Kevin Dezfulian Michal Rakowski Rod Augur Takako Hirokawa 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 deep subwavelength-engineered slotted photonic crystal nanobeams with minimum features < 70 nm fabricated using a 90 nm monolithic silicon photonics technology at GlobalFoundries. This work enables potent... 详细信息

ISBN: (纸本)9781957171258

关键词： Deep ultraviolet Integrated photonics Photonic crystals Photonics applications Scanning electron microscopy Silicon photonics

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Reducing information bottleneck for weakly supervised semantic segmentation 21

Reducing information bottleneck for weakly supervised semant...

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Jungbeom Lee Jooyoung Choi Jisoo Mok Sungroh Yoon Department of Electrical and Computer Engineering Seoul National University Department of Electrical and Computer Engineering Seoul National University and Interdisciplinary Program in Artificial Intelligence Seoul National University

ISBN: (纸本)9781713845393

Weakly supervised semantic segmentation produces pixel-level localization from class labels; however, a classifier trained on such labels is likely to focus on a small discriminative region of the target object. We interpret this phenomenon using the information bottleneck principle: the final layer of a deep neural network, activated by the sigmoid or softmax activation functions, causes an information bottleneck, and as a result, only a subset of the task-relevant information is passed on to the output. We first support this argument through a simulated toy experiment and then propose a method to reduce the information bottleneck by removing the last activation function. In addition, we introduce a new pooling method that further encourages the transmission of information from non-discriminative regions to the classification. Our experimental evaluations demonstrate that this simple modification significantly improves the quality of localization maps on both the PASCAL VOC 2012 and MS COCO 2014 datasets, exhibiting a new state-of-the-art performance for weakly supervised semantic segmentation.

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A DIRECT METHOD OF GENERATING QUADRATIC CURVILINEAR TETRAHEDRAL MESHES USING AN ADVANCING FRONT APPROACH 29

A DIRECT METHOD OF GENERATING QUADRATIC CURVILINEAR TETRAHED...

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29th International Meshing Roundtable, IMR 2021

作者： Mohammadi, Fariba Shontz, Suzanne M. Department of Mechanical Engineering Information and Telecommunication Technology Center The 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)9781733489010

Computational modeling and simulation of real-world problems, e.g., various applications in the automotive, aerospace, and biomedical industries, often involve geometric objects which are bounded by curved surfaces. The geometric modeling of such objects can be performed via high-order meshes. Such a mesh, when paired with a high-order partial differential equation (PDE) solver, can realize more accurate solution results with a decreased number of mesh elements (in comparison to a low-order mesh). There are several types of high-order mesh generation approaches, such as direct methods, a posteriori methods, and isogeometric analysis (IGA)-based spline modeling approaches. In this paper, we propose a direct, high-order, curvilinear tetrahedral mesh generation method using an advancing front technique. After generating the mesh, we apply mesh optimization to improve the quality and to take advantage of the degrees of freedom available in the initially straight-sided quadratic elements. Our method aims to generate high-quality tetrahedral mesh elements from various types of boundary representations including the cases where no computer-aided design files are available. Such a method is essential, for example, for generating meshes for various biomedical models where the boundary representation is obtained from medical images instead of CAD files. We present several numerical examples of second-order tetrahedral meshes generated using our method based on input triangular surface meshes. © 2021 held by the authors of the individual papers.

关键词： Mesh generation

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