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Journal of Geophysical Research: Machine Learning and Computation 2025年第2期2卷

作者： Enrico Camporeale Raffaele Marino John Rundle Doris Folini Yangkang Chen Donald D. Lucas Xiaofeng Li Thomas E. Berger Geoffrey C. Fox Yixin Wen Chaopeng Shen Renata Wentzcovitch School of Physical and Chemical Sciences Queen Mary University of London London UK Space Weather Technology Research and Education Center University of Colorado Boulder CO USA Laboratoire de Mécanique des Fluides et d’Acoustique CNRS - École Centrale de Lyon Écully France Department of Physics and Astronomy Department of Earth and Planetary Sciences University of California Davis CA USA Department of Environmental System Science Institute for Atmospheric and Climate Science ETH Zurich Zurich Switzerland Bureau of Economic Geology Jackson School of Geosciences The University of Texas at Austin Austin TX USA Atmospheric Earth and Energy Division Lawrence Livermore National Laboratory Livermore CA USA Institute of Oceanology Chinese Academy of Sciences Qingdao P. R. China Biocomplexity Institute and Computer Science Department University of Virginia Charlottesville VA USA Department of Geography University of Florida Gainesville FL USA Civil and Environmental Engineering The Pennsylvania State University University Park PA USA Department of Applied Physics and Applied Mathematics Department of Earth and Environmental Sciences Columbia University New York NY USA

As editors of Journal of Geophysical Research: Machine Learning and Computation , we are delighted to celebrate the successes of our first 18 months. The journal's editors reflect on the first year and a half since its founding They express concern for the restriction or removal of publicly funded data sources Our effort focuses on ensuring that software shared in FAIR repositories is not only available, but also functional and citable

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Bubble Passage: Underwater Superaerophobicity/Superaerophilicity and Unidirectional Bubble Passage Based on the Femtosecond Laser‐Structured Stainless Steel Mesh (Adv. Mater. Interfaces 14/2020)

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Advanced Materials Interfaces 2020年第14期7卷

作者： Jinglan Huo Qing Yang Jiale Yong Peixun Fan Yong feng Lu Xun Hou Feng Chen State Key Laboratory for Manufacturing System Engineering and Shaanxi Key Laboratory of Photonics Technology for Information School of Electronics & Information Engineering Xi'an Jiaotong University Xi'an 710049 P. R. China School of Mechanical Engineering Xi'an Jiaotong University Xi'an 710049 P. R. China Department of Electrical and Computer Engineering University of Nebraska‐Lincoln Lincoln NE 68588 USA

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Novel Index Modulation Aided Non-Orthogonal Multiple Access for Visible Light Communication

Novel Index Modulation Aided Non-Orthogonal Multiple Access ...

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IEEE Global Communications Conference

作者： Tengjiao Wang Fang Yang Jian Song Department of Electronic Engineering Tsinghua University Beijing National Research Center for Information Science and Technology (BNRist) Beijing P. R. China Key Laboratory of Digital TV System of Guangdong Province and Shenzhen City Research Institute of Tsinghua University in Shenzhen Shenzhen P. R. China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

In this paper, a novel index modulation aided non-orthogonal multiple access (NOMA) scheme is proposed for visi-ble light communication (VLC). Unlike the conventional NOMA schemes, the proposed scheme conveys information for different users through both the index-domain and constellation-domain symbols with the aid of index modulation, which is able to cancel the intra-cluster interference completely and avoid the use of successive interference cancellation to decrease computational complexity. The achievable multi-user sum rate of the proposed scheme is analyzed and a closed-form expression of the lower bound is derived. Then, an approximation is further obtained to realize accurate prediction of the achievable multiuser sum rate. Simulation results validate the accuracy of the approximation of the achievable sum rate and the superior performance of the proposed scheme.

关键词： Indexes Modulation NOMA Mutual information OFDM Light emitting diodes Interference

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Image recognition and classification by deep belief-convolutional neural networks

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Qinghua Daxue Xuebao/Journal of Tsinghua University 2018年第9期58卷 781-787页

作者： Liu, Qiong Li, Zongxian Sun, Fuchun Tian, Yonghong Zeng, Wei School of Automation Beijing Information Science and Technology University Beijing100192 China National Engineering Laboratory for Video Technology School of Electronics Engineering and Computer Science Peking University Beijing100871 China State Key Laboratory of Intelligence Technology and System Department of Computer Science Tsinghua University Beijing100084 China

Convolutional neural network (CNN) would easily converge to the local minimum if the network was randomly initialized in image classification tasks. A deep belief network pre-training method was developed by merging unsupervised and supervised methods. Feature sets were extracted from the image patches of zero component analysis (ZCA) whitening and deep belief pre-training to initialize weights of CNNs. Then, convolution features were extracted from the training samples by applying convolution and pooling operations and classified to a specific category through a fully connected network. Finally, the loss value was computed for global optimization. Extensive experimental evaluations on some public datasets show that this method is simple but very effective with the error rate decrease of 0.1% on MNIST and the accuracy increase of 0.56% on Caltech101, which indicates that this method is superior to similar methods. © 2018, Tsinghua University Press. All right reserved.

关键词： Image recognition

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Preface

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CEUR Workshop Proceedings 2022年 3091卷

作者： Kovalev, Igor Semenkin, Eugene Schwenker, Friedhelm Shaidurov, Vladimir Ganchev, Todor Škraba, Andrej Stanimirović, Predrag Kolehmainen, Mikko Kronberger, Gabriel Affenzeller, Michael Brester, Christina Ryzhikov, Ivan Kazakovtsev, Lev Sopov, Evgenii Semenkina, Maria Akhmedova, Shakhnaz Stanovov, Vladimir Krasnoyarsk Regional Science and Technology City Hall Department of Computer Science of Siberian Federal University Krasnoyarsk Russia Siberian Institute of Applied System Analysis named after A.N. Antamoshkin The Department ofsystem analysis and operations research of Reshetnev University Krasnoyarsk Russia Institute of Neural Information Processing Ulm University Ulm Germany The Institute of Computational Modeling SB RAS Krasnoyarsk Russia Technical University of Varna Varna Bulgaria University of Maribor UM Faculty of Organizational Sciences Cybernetics and Decision Support Systems Laboratory Slovenia Department of Computer Science Niš Serbia Department of Environmental and Biological Sciences Environmental Informatics University of Eastern Finland Kuopio Finland University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria University of Eastern Finland UEF · Department of Environmental Science Kuopio Finland The Department of Systems Analysis Reshetnev University of Science and Technology Krasnoyarsk Russia Siberian Institute of Applied System Analysis Krasnoyarsk Russia University of Applied Sciences Upper Austria and Softwarepark Hagenberg Austria Reshetnev Siberian States University of Science and Technology Siberian Institute of Applied System Analysis Krasnoyarsk Russia SibSAU · Institute of computer science and telecommunications Krasnoyarsk Russia

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HARNESSING digital PATHOLOGY AND CAUSAL LEARNING TO IMPROVE EOSINOPHILIC ESOPHAGITIS DIETARY TREATMENT ASSIGNMENT

arXiv

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arXiv 2023年

作者： Aknin, Eliel Larey, Ariel Caldwell, Julie M. Collins, Margaret H. Rothenberg, Marc E. Savir, Yonatan Abonia, Juan P. Aceves, Seema S. Arva, Nicoleta C. Chehade, Mirna Dellon, Evan S. Gonsalves, Nirmala Gupta, Sandeep K. Leung, John Peterson, Kathryn A. Shoda, Tetsuo Spergel, Jonathan M. Department of Physiology Biophysics and System Biology Faculty of Medicine Technion Israel Institute of Technology Haifa Israel Faculty of Industrial Engineering Technion Israel Institute of Technology Haifa Israel Faculty of Computer Science Technion Israel Institute of Technology Haifa Israel Division of Allergy and Immunology Cincinnati Children's Hospital Medical Center CincinnatiOH United States Dept. of Pediatrics University of Cincinnati College of Medicine CincinnatiOH United States Division of Pathology and Laboratory Medicine Cincinnati Children's Hospital Medical Center CincinnatiOH United States Division of Allergy and Immunology University of California at San Diego Rady Children's Hospital San DiegoCA United States Dept. of Pathology and Laboratory Ann and Robert H Lurie Children's Hospital Northwestern University Feinberg School of Medicine ChicagoIL United States Mount Sinai Center for Eosinophilic Disorders Icahn School of Medicine at Mount Sinai New YorkNY United States Division of Gastroenterology and Hepatology University of North Carolina School of Medicine Chapel HillNC United States Division of Gastroenterology and Hepatology Northwestern University Feinberg School of Medicine ChicagoIL United States Division of Pediatric Gastroenterology Hepatology and Nutrition Children's of Alabama University of Alabama at Birmingham BirminghamAL United States Boston Specialists BostonMA United States Division of Gastroenterology University of Utah Salt Lake CityUT United States Division of Allergy and Immunology Children's Hospital of Philadelphia Dept. of Pediatrics Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States

Eosinophilic esophagitis (EoE) is a chronic, food antigen-driven, allergic inflammatory condition of the esophagus associated with elevated esophageal eosinophils. EoE is a top cause of chronic dysphagia after GERD. Diagnosis of EoE relies on counting eosinophils in histological slides, a manual and time-consuming task that limits the ability to extract complex patient-dependent features. The treatment of EoE includes medication and food elimination. A personalized food elimination plan is crucial for engagement and efficiency, but previous attempts failed to produce significant results. In this work, on the one hand, we utilize AI for inferring histological features from the entire biopsy slide, features that cannot be extracted manually. On the other hand, we develop causal learning models that can process this wealth of data. We applied our approach to the 'Six-Food vs. One-Food Eosinophilic Esophagitis Diet Study', where 112 symptomatic adults aged 18-60 years with active EoE were assigned to either a six-food elimination diet (6FED) or a one-food elimination diet (1FED) for six weeks. Our results show that the average treatment effect (ATE) of the 6FED treatment compared with the 1FED treatment is not significant, that is, neither diet was superior to the other. We examined several causal models and show that the best treatment strategy was obtained using T-learner with two XGBoost modules. While 1FED only and 6FED only provide improvement for 35%-38% of the patients, which is not significantly different from a random treatment assignment, our causal model yields a significantly better improvement rate of 58.4%. This study illustrates the significance of AI in enhancing treatment planning by analyzing molecular features' distribution in histological slides through causal learning. Our approach can be harnessed for other conditions that rely on histology for diagnosis and treatment. Copyright © 2023, The Authors. All rights reserved.

关键词： Diagnosis

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Modeling detailed cloud scene from multi-source images 26

Modeling detailed cloud scene from multi-source images

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26th Pacific Conference on computer Graphics and Applications, Pacific Graphics 2018

作者： Cen, Yunchi Liang, Xiaohui Chen, Junping Yang, Bailin Li, Frederick W.B. State Key Laboratory of Virtual Reality Technology and System Beihang University Beijing100191 China School of Computer Science and Information Engineering Zhejiang Gongshang University Hangzhou310018 China Department of Computer Science University of Durham DurhamDH1 3LE United Kingdom

ISBN: (纸本)9783038680734

Realistic cloud is essential for enhancing the quality of computer graphics applications, such as flight simulation. Data-driven method is an effective way in cloud modeling, but existing methods typically only utilize one data source as input. For example, natural images are usually used to model small-scale cloud with details, and satellite images and WRF data are used to model large scale cloud without details. To construct large-scale cloud scene with details, we propose a novel method to extract relevant cloud information from both satellite and natural images. Experiments show our method can produce more detailed cloud scene comparing with existing methods. © 2018 The Author(s).

关键词： Flight simulators

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Naive gabor networks for hyperspectral image classification

arXiv

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arXiv 2019年

作者： Liu, Chenying Li, Jun He, Lin Plaza, Antonio Li, Shutao Li, Bo Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation School of Geography and Planning Sun Yat-sen University Guangzhou510275 China School of Automation Science and Engineering South China University of Technology Guangzhou510640 China College of Electrical and Information Engineering Hunan University Changsha410082 China Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politécnica University of Extremadura CáceresE-10071 Spain Beijing Key Laboratory of Digital Media School of Computer Science and Engineering State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China

Recently, many convolutional neural network (CNN) methods have been designed for hyperspectral image (HSI) classification since CNNs are able to produce good representations of data, which greatly benefits from a huge number of parameters. However, solving such a high-dimensional optimization problem often requires a large amount of training samples in order to avoid overfitting. Additionally, it is a typical non-convex problem affected by many local minima and flat regions. To address these problems, in this paper, we introduce naive Gabor Networks or Gabor-Nets which, for the first time in the literature, design and learn CNN kernels strictly in the form of Gabor filters, aiming to reduce the number of involved parameters and constrain the solution space, and hence improve the performances of CNNs. Specifically, we develop an innovative phase-induced Gabor kernel, which is trickily designed to perform the Gabor feature learning via a linear combination of local low-frequency and high-frequency components of data controlled by the kernel phase. With the phase-induced Gabor kernel, the proposed Gabor-Nets gains the ability to automatically adapt to the local harmonic characteristics of the HSI data and thus yields more representative harmonic features. Also, this kernel can fulfill the traditional complex-valued Gabor filtering in a real-valued manner, hence making Gabor-Nets easily perform in a usual CNN thread. We evaluated our newly developed Gabor-Nets on three well-known HSIs, suggesting that our proposed Gabor-Nets can significantly improve the performance of CNNs, particularly with a small training set. Copyright © 2019, The Authors. All rights reserved.

关键词： Spectroscopy

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A real-time unsupervised background extraction-based target detection method for hyperspectral imagery

A real-time unsupervised background extraction-based target ...

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作者： Li, Cong Gao, Lianru Wu, Yuanfeng Zhang, Bing Plaza, Javier Plaza, Antonio Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Beijing100094 China University of Chinese Academy of Sciences Beijing100049 China The College of Computer Science and Software Engineering Computer Vision Research Institute Shenzhen University Shenzhen518060 China Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politecnica de Cáceres University of Extremadura Cáceres Spain

Target detection is an important technique in hyperspectral image analysis. The high dimensionality of hyperspectral data provides the possibility of deeply mining the information hiding in spectra, and many targets that cannot be visualized by inspection can be detected. But this also brings some problems such as unknown background interferences at the same time. In this way, extracting and taking advantage of the background information in the region of interest becomes a task of great significance. In this paper, we present an unsupervised background extraction-based target detection method, which is called UBETD for short. The proposed UBETD takes advantage of the method of endmember extraction in hyperspectral unmixing, another important technique that can extract representative material signatures from the images. These endmembers represent most of the image information, so they can be reasonably seen as the combination of targets and background signatures. Since the background information is known, algorithm like target-constrained interference-minimized filter could then be introduced to detect the targets while inhibiting the interferences. To meet the rapidly rising demand of real-time processing capabilities, the proposed algorithm is further simplified in computation and implemented on a FPGA board. Experiments with synthetic and real hyperspectral images have been conducted comparing with constrained energy minimization, adaptive coherence/cosine estimator and adaptive matched filter to evaluate the detection and computational performance of our proposed method. The results indicate that UBETD and its hardware implementation RT-UBETD can achieve better performance and are particularly prominent in inhibiting interferences in the background. On the other hand, the hardware implementation of RT-UBETD can complete the target detection processing in far less time than the data acquisition time of hyperspectral sensor like HyMap, which confirms strict real-ti

关键词： Field programmable gate arrays (FPGA)

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Photonic rf and microwave arbitrary waveform generator based on a soliton crystal 49ghz kerr micro-comb

arXiv

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arXiv 2020年

作者： Tan, Mengxi Xu, Xingyuan Boes, Andreas Corcoran, Bill Wu, Jiayang Nguyen, Thach G. Chu, Sai T. Little, Brent E. Morandotti, Roberto Mitchell, Arnan Moss, David J. Optical Sciences Centre Swinburne University of Technology HawthornVIC3122 Australia School of Engineering RMIT University MelbourneVIC3001 Australia Department of Electrical and Computer System Engineering Monash University ClaytonVIC3168 Australia Department of Physics City University of Hong Kong Tat Chee Avenue Hong Kong State Key Laboratory of Transient Optics and Photonics Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Science Xi'an China INRS - Énergie Matériaux et Télécommunications 1650 Boulevard Lionel-Boulet VarennesQCJ3X 1S2 Canada Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu610054 China

We report a photonic-based radio frequency (RF) arbitrary waveform generator (AWG) using a soliton crystal micro-comb source with a free spectral range (FSR) of 48.9 GHz. The comb source provides over 80 wavelengths, or channels, that we use to successfully achieve arbitrary waveform shapes including square waveforms with a tunable duty ratio ranging from 10% to 90%, sawtooth waveforms with a tunable slope ratio of 0.2 to 1, and a symmetric concave quadratic chirp waveform with an instantaneous frequency of sub GHz. We achieve good agreement between theory and experiment, validating the effectiveness of this approach towards realizing high-performance, broad bandwidth, nearly user-defined RF waveform generation. Copyright © 2020, The Authors. All rights reserved.

关键词： Solitons

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