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information Technology (OCIT), OITS International Conference on

作者： Prasanthi Lingala Bethalam Venkata Siva Sai Greeshma Patta Supraja Sanjay Kumar Matta Durga Prakash Department of Electronics and Communication Engineering SRM University- AP Guntur India Department of Computer Science and Engineering Visual Information Processing Lab SRM University- AP Guntur India

ISBN: (数字)9798331510404

ISBN: (纸本)9798331510411

In the huge growth of semiconductor industry, it is noticed that the device simulation is a very sluggish process. It is very promising to use Machine Learning (ML) techniques in device modeling as their combination will create great results in semiconductor industry and reduce the computational time. Organic Thin Film Transistor (OTFT) is a promising alternative to amorphous silicon devices due to its flexibility, low cost, and can be manufactured at reduced temperatures. In traditional TCAD simulation, at once only a single simulation of OTFT for fixed length, width and dielectric thickness can be done, for change in any of the input parameter again simulation has to be done. To avoid this ML is used to predict drain current for simultaneous changes in input parameters. This introduces a machine learning based structure to model OTFT integrated with ML algorithm named Random Forest Regressor (RFR). ML based device model for p-type OTFT takes length, width and thickness of dielectric layer as input parameters and drain current as output parameter. Experimental results has shown that our ML-based model can predict drain current accurately. $\mathbf{R}^{2}$ -value is found be around 0.997253. ML based performance optimization is a promising alternative to traditional technology computer aided design (TCAD) tools. The highest $\mathbf{I}_{\text{ON}}/\mathbf{I}_{\text{OFF}}$ ratio, very high ON current (I ON ), very low OFF current (I OFF ) is achieved for OTFT. $\mathbf{I}_{\text{ON}}/\mathbf{I}_{\text{OFF}}$ ratio is obtained to be 10 11 . The trained RFR models can accelerate the optimization in terms of performance and serves as promising alternative.

关键词： Machine learning algorithms Computational modeling Semiconductor devices Electronics industry Prediction algorithms Organic thin film transistors Dielectrics Optimization Tuning Random forests

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Device-Simulation-Based Machine Learning Technique and performance optimization of NSFET 22

Device-Simulation-Based Machine Learning Technique and perfo...

引用

22nd OITS International Conference on information Technology, OCIT 2024

作者： Gowthami, Ummadisetti Sandhya, Bhuvanagiri Venkata Naga Supraja, Patta Kumar, Sanjay Prakash, Matta Durga Department of Electronics and Communication Engineering SRM University- AP Guntur India Visual Information Processing Lab Department of Computer Science and Engineering SRM University- AP Guntur India

ISBN: (纸本)9798331510404

With the rapid growth of the semiconductor industry, it is clear that device simulation has been considered as slow process. As a result of semiconductor device downscaling, obtaining the inevitable device simulation data is significantly more expensive because it requires complex analysis of multiple factors. Using Machine Learning (ML) techniques to device modeling is promising, as their combination will lead to great outcomes in the semiconductor industry. Nanosheet Field Effect Transistor (NSFET) is a promising device for high-performance integrated circuits due to their superior electrical control and reduced short-channel effects. This paper presents a ML based Nanosheet Field Effect Transistor modeling. In traditional Technology computer-Aided Design (TCAD) simulation, at once only a single simulation of NSFET for fixed length, width and thickness can be done, for change in any of the input parameter again simulation has to be done. To overcome this, simultaneous changes in input parameters are predicted using machine learning. The length, width, and thickness of the dielectric layer are input parameters and the drain current is the output parameter for the ML-based device model for NSFET. Experimental results have shown that our ML-based model can predict drain current accurately. R2-value is found be around 0.99832. The highest ION/IOFF ratio, very high ON current (ION), very low OFF current (IOFF) is achieved for NSFET. The primary goal of this work is to explore the possibility of ML model that can replace the device simulation to reduce the computational cost and drive energy-efficient devices. © 2024 IEEE.

关键词： Nanosheets

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Device-Simulation-Based Machine Learning Technique and Performance Optimization of NSFET

Device-Simulation-Based Machine Learning Technique and Perfo...

引用

information Technology (OCIT), OITS International Conference on

作者： Ummadisetti Gowthami Bhuvanagiri Venkata Naga Sandhya Patta Supraja Sanjay Kumar Matta Durga Prakash Department of Electronics and Communication Engineering SRM University-AP Guntur India Department of Computer Science and Engineering Visual Information Processing Lab SRM University Guntur AP India

ISBN: (数字)9798331510404

ISBN: (纸本)9798331510411

With the rapid growth of the semiconductor industry, it is clear that device simulation has been considered as slow process. As a result of semiconductor device downscaling, obtaining the inevitable device simulation data is significantly more expensive because it requires complex analysis of multiple factors. Using Machine Learning (ML) techniques to device modeling is promising, as their combination will lead to great outcomes in the semiconductor industry. Nanosheet Field Effect Transistor (NSFET) is a promising device for high-performance integrated circuits due to their superior electrical control and reduced short-channel effects. This paper presents a ML based Nanosheet Field Effect Transistor modeling. In traditional Technology computer-Aided Design (TCAD) simulation, at once only a single simulation of NSFET for fixed length, width and thickness can be done, for change in any of the input parameter again simulation has to be done. To overcome this, simultaneous changes in input parameters are predicted using machine learning. The length, width, and thickness of the dielectric layer are input parameters and the drain current is the output parameter for the ML-based device model for NSFET. Experimental results have shown that our ML-based model can predict drain current accurately. $\mathrm{R}^{2}$ -value is found be around 0.99832. The highest $\mathrm{I}_{\text{ON}}/\mathrm{I}_{\text{OFF}}$ ratio, very high ON current (ION), very low OFF current (IOFF) is achieved for NSFET. The primary goal of this work is to explore the possibility of ML model that can replace the device simulation to reduce the computational cost and drive energy-efficient devices.

关键词： Machine learning algorithms Design automation Computational modeling Semiconductor devices Field effect transistors Electronics industry Prediction algorithms Nanoscale devices Integrated circuit modeling Tuning

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Neural Underwater Scene Representation

Neural Underwater Scene Representation

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yunkai Tang Chengxuan Zhu Renjie Wan Chao Xu Boxin Shi National Engineering Research Center of Visual Technology School of Computer Science Peking University National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Key Lab of General AI School of Intelligence Science and Technology Peking University Department of Computer Science Hong Kong Baptist University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Among the numerous efforts towards digitally recovering the physical world, Neural Radiance Fields (NeRFs) have proved effective in most cases. However, underwater scene introduces unique challenges due to the absorbing water medium, the local change in lighting and the dynamic contents in the scene. We aim at developing a neural under-water scene representation for these challenges, modeling the complex process of attenuation, unstable in-scattering and moving objects during light transport. The proposed method can reconstruct the scenes from both established datasets and in-the-wild videos with outstanding fidelity.

关键词： Solid modeling visualization Computational modeling Urban areas Lighting Virtual reality Neural radiance field

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Colorizing Monochromatic Radiance Fields

arXiv

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arXiv 2024年

作者： Cheng, Yean Wan, Renjie Weng, Shuchen Zhu, Chengxuan Chang, Yakun Shi, Boxin National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China National Engineering Research Center of Visual Technology School of Computer Science Peking University China AI Innovation Center School of Computer Science Peking University China Department of Computer Science Hong Kong Baptist University Hong Kong National Key Lab of General AI School of Intelligence Science and Technology Peking University China

Though Neural Radiance Fields (NeRF) can produce colorful 3D representations of the world by using a set of 2D images, such ability becomes non-existent when only monochromatic images are provided. Since color is necessary in representing the world, reproducing color from monochromatic radiance fields becomes crucial. To achieve this goal, instead of manipulating the monochromatic radiance fields directly, we consider it as a representation-prediction task in the lab color space. By first constructing the luminance and density representation using monochromatic images, our prediction stage can recreate color representation on the basis of an image colorization module. We then reproduce a colorful implicit model through the representation of luminance, density, and color. Extensive experiments have been conducted to validate the effectiveness of our approaches. Our project page: https://***/color-nerf. Copyright © 2024, The Authors. All rights reserved.

关键词： Color

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Balanced Contrastive Learning for Long-Tailed visual Recognition

arXiv

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arXiv 2022年

作者： Zhu, Jianggang Wang, Zheng Chen, Jingjing Chen, Yi-Ping Phoebe Jiang, Yu-Gang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Department of Computer Science and Information Technology La Trobe University Australia

Real-world data typically follow a long-tailed distribution, where a few majority categories occupy most of the data while most minority categories contain a limited number of samples. Classification models minimizing cross-entropy struggle to represent and classify the tail classes. Although the problem of learning unbiased classifiers has been well studied, methods for representing imbalanced data are under-explored. In this paper, we focus on representation learning for imbalanced data. Recently, supervised contrastive learning has shown promising performance on balanced data recently. However, through our theoretical analysis, we find that for long-tailed data, it fails to form a regular simplex which is an ideal geometric configuration for representation learning. To correct the optimization behavior of SCL and further improve the performance of long-tailed visual recognition, we propose a novel loss for balanced contrastive learning (BCL). Compared with SCL, we have two improvements in BCL: class-averaging, which balances the gradient contribution of negative classes;class-complement, which allows all classes to appear in every mini-batch. The proposed balanced contrastive learning (BCL) method satisfies the condition of forming a regular simplex and assists the optimization of cross-entropy. Equipped with BCL, the proposed two-branch framework can obtain a stronger feature representation and achieve competitive performance on long-tailed benchmark datasets such as CIFAR-10-LT, CIFAR-100-LT, ImageNet-LT, and iNaturalist2018. Our code is available at this URL. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

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Development and Validation of an Automatic Ultrawide-Field Fundus Imaging Enhancement System for Facilitating Clinical Diagnosis:A Cross-Sectional Multicenter Study

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Engineering 2024年第10期41卷 179-188页

作者： Qiaoling Wei Zhuoyao Gu Weimin Tan Hongyu Kong Hao Fu Qin Jiang Wenjuan Zhuang Shaochi Zhang Lixia Feng Yong Liu Suyan Li Bing Qin Peirong Lu Jiangyue Zhao Zhigang Li Songtao Yuan Hong Yan Shujie Zhang Xiangjia Zhu Jiaxu Hong Chen Zhao Bo Yan Department of Ophthalmology Eye&ENT Hospital of Fudan UniversityShanghai 200231China School of Computer Science Shanghai Key Laboratory of Intelligent Information ProcessingFudan UniversityShanghai 200433China The Affiliated Eye Hospital of Nanjing Medical University Nanjing 210029China Ningxia Eye Hospital People’s Hospital of Ningxia Hui Autonomous RegionThird Clinical Medical College of Ningxia Medical UniversityYinchuan 750002China Department of Ophthalmology The First Affiliated Hospital of Anhui Medical UniversityHefei 230022China Southwest Hospital/Southwest Eye Hospital Third Military Medical University(Army Medical University)Chongqing 400038China Key Lab of Visual Damage and Regeneration Restoration of Chongqing Chongqing 400038China Department of Ophthalmology The Affiliated Xuzhou Municipal Hospital of Xuzhou Medical UniversityXuzhou 221000China Department of Ophthalmology Suqian First Hospital of Jiangsu Province HospitalSuqian 223800China Department of Ophthalmology The First Affiliated Hospital of Soochow UniversitySuzhou 215006China Department of Ophthalmology The Fourth Affiliated Hospital of China Medical UniversityEye Hospital of China Medical UniversityShenyang 110000China Department of Ophthalmology The First Affiliated Hospital of Zhengzhou UniversityZhengzhou 450052China Department of Ophthalmology Jiangsu Province HospitalNanjing 210029China Shaanxi Eye Hospital Xi’an People’s Hospital(Xi’an Fourth Hospital)The Affiliated People’s Hospital of Northwest UniversityXi’an 710004China

In ophthalmology,the quality of fundus images is critical for accurate diagnosis,both in clinical practice and in artificial intelligence(AI)-assisted *** the broad view provided by ultrawide-field(UWF)imaging,pseudocolor images may conceal critical lesions necessary for precise *** address this,we introduce UWF-Net,a sophisticated image enhancement algorithm that takes disease characteristics into *** the Fudan University ultra-wide-field image(FDUWI)dataset,which includes 11294 Optos pseudocolor and 2415 Zeiss true-color UWF images,each of which is rigorously annotated,UWF-Net combines global style modeling with feature-level lesion *** consistency loss is also applied to maintain fundus feature integrity,significantly improving image *** and qualitative evaluations demonstrated that UWF-Net outperforms existing methods such as contrast limited adaptive histogram equalization(CLAHE)and structure and illumination constrained generative adversarial network(StillGAN),delivering superior retinal image quality,higher quality scores,and preserved feature details after *** disease classification tasks,images enhanced by UWF-Net showed notable improvements when processed with existing classification systems over those enhanced by StillGAN,demonstrating a 4.62%increase in sensitivity(SEN)and a 3.97%increase in accuracy(ACC).In a multicenter clinical setting,UWF-Net-enhanced images were preferred by ophthalmologic technicians and doctors,and yielded a significant reduction in diagnostic time((13.17±8.40)s for UWF-Net enhanced images vs(19.54±12.40)s for original images)and an increase in diagnostic accuracy(87.71%for UWF-Net enhanced images vs 80.40%for original images).Our research verifies that UWF-Net markedly improves the quality of UWF imaging,facilitating better clinical outcomes and more reliable AI-assisted disease *** clinical integration of UWF-Net holds great

关键词： Ultrawide-field imaging Fundus photography Image enhancement algorithm Artificial intelligence Multicenter study Artificial intelligence-assisted diagnostics Diagnostic accuracy

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Towards Transferable Adversarial Attacks on Vision Transformers

arXiv

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arXiv 2021年

作者： Wei, Zhipeng Chen, Jingjing Goldblum, Micah Wu, Zuxuan Goldstein, Tom Jiang, Yu-Gang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Department of Computer Science University of Maryland United States

Vision transformers (ViTs) have demonstrated impressive performance on a series of computer vision tasks, yet they still suffer from adversarial examples. In this paper, we posit that adversarial attacks on transformers should be specially tailored for their architecture, jointly considering both patches and self-attention, in order to achieve high transferability. More specifically, we introduce a dual attack framework, which contains a Pay No Attention (PNA) attack and a PatchOut attack, to improve the transferability of adversarial samples across different ViTs. We show that skipping the gradients of attention during backpropagation can generate adversarial examples with high transferability. In addition, adversarial perturbations generated by optimizing randomly sampled subsets of patches at each iteration achieve higher attack success rates than attacks using all patches. We evaluate the transferability of attacks on state-of-the-art ViTs, CNNs and robustly trained CNNs. The results of these experiments demonstrate that the proposed dual attack can greatly boost transferability between ViTs and from ViTs to CNNs. In addition, the proposed method can easily be combined with existing transfer methods to boost performance. Code is available at https://***/zhipeng-wei/PNA-PatchOut. © 2021, CC BY.

关键词： Iterative methods

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Slow and weak attractor computation embedded in fast and strong E-I balanced neural dynamics 23

Slow and weak attractor computation embedded in fast and str...

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Proceedings of the 37th International Conference on Neural information processing Systems

作者： Xiaohan Lin Liyuan Li Boxin Shi Tiejun Huang Yuanyuan Mi Si Wu Nat'l Key Lab for Multimedia Information Processing Nat'l Eng. Lab of Visual Technology School of Computer Science Peking University and Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies School of Psychological and Cognitive Sciences Beijing Key Laboratory of Behavior and Mental Health IDG/McGovern Institute for Brain Research Center of Quantitative Biology Peking University Department of Mathematics Peking University and Institute of Neuroinformatics University of Zurich Nat'l Key Lab for Multimedia Information Processing Nat'l Eng. Lab of Visual Technology School of Computer Science Peking University and Institute for Artificial Intelligence Peking University Department of Psychology Tsinghua University Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary Studies School of Psychological and Cognitive Sciences Beijing Key Laboratory of Behavior and Mental Health IDG/McGovern Institute for Brain Research Center of Quantitative Biology Peking University

Attractor networks require neuronal connections to be highly structured in order to maintain attractor states that represent information, while excitation and inhibition balanced networks (E-INNs) require neuronal connections to be random and sparse to generate irregular neuronal firing. Despite being regarded as canonical models of neural circuits, both types of networks are usually studied independently, and it remains unclear how they coexist in the brain, given their very different structural demands. In this study, we investigate the compatibility of continuous attractor neural networks (CANNs) and E-INNs. In line with recent experimental data, we find that a neural circuit can exhibit both the traits of CANNs and E-INNs when the neuronal synapses consist of two sets: one set is strong and fast for irregular firing, and the other set is weak and slow for attractor dynamics. In addition, both simulations and theoretical analysis reveal that the network exhibits enhanced performance compared to the case of using only one set of synapses, with accelerated convergence of attractor states and retained E-I balanced condition for localized input. We hope that this study provides insight into how structured neural computations are realized by irregular firings of neurons.

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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