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arXiv 2022年

作者： Wang, Muhan Zhang, Zhe Wang, Yue Gao, Silin Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Electrical and Computer Engineering Department George Mason University FairfaxVA22030 United States

Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable interest for its ability in achieving three-dimensional reconstruction along the elevation direction from multiple observations. In recent years, compressed sensing (CS) technique has been introduced into TomoSAR considering for its super-resolution ability with limited samples. Whereas, the CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity and complex parameter fine-tuning. Among the different CS algorithms, iterative soft-thresholding algorithm (ISTA) is widely used as a robust reconstruction approach, however, the parameters in the ISTA algorithm are manually chosen, which usually requires a time-consuming fine-tuning process to achieve the best performance. Aiming at efficient TomoSAR imaging, a novel sparse unfolding network named analytic learned ISTA (ALISTA) is proposed towards the TomoSAR imaging problem in this paper, and the key parameters of ISTA are learned from training data via deep learning to avoid complex parameter fine-tuning and significantly relieves the training burden. In addition, experiments verify that it is feasible to use traditional CS algorithms as training labels, which provides a tangible supervised training method to achieve better 3D reconstruction performance even in the absence of labeled data in real applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Compressed sensing

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A rigorous and efficient approach to finding and quantifying symmetries in complex networks

arXiv

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arXiv 2021年

作者： Long, Yong-Shang Zhai, Zheng-Meng Tang, Ming Liu, Ying Lai, Ying-Cheng State Key Laboratory of Precision Spectroscopy School of Physics and Electronic Science East China Normal University Shanghai200241 China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China School of Computer Science Southwest Petroleum University Chengdu610500 China School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States

Symmetries are fundamental to dynamical processes in complex networks such as cluster synchronization, which have attracted a great deal of current research. Finding symmetric nodes in large complex networks, however, has relied on automorphism groups in algebraic group theory, which are solvable in quasipolynomial time. We articulate a conceptually appealing and computationally extremely efficient approach to finding and characterizing all symmetric nodes by introducing a structural position vector (SPV) for each and every node in the network. We prove mathematically that nodes with the identical SPV are symmetrical to each other. Utilizing six representative complex networks from the real world, we demonstrate that all symmetric nodes can be found in linear time, and the SPVs can not only characterize the similarity of nodes but also quantify the nodal influences in spreading dynamics on the network. Our SPV-based framework, in additional to being rigorously justified, provides a physically intuitive way to uncover, understand and exploit symmetric structures in complex networks. Copyright © 2021, The Authors. All rights reserved.

关键词： Complex networks

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Plant Stem Segmentation Using Fast Ground Truth Generation

Plant Stem Segmentation Using Fast Ground Truth Generation

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IEEE Southwest Symposium on Image Analysis and Interpretation

作者： Changye Yang Sriram Baireddy Yuhao Chen Enyu Cai Denise Caldwell Valérian Méline Anjali S. Iyer-Pascuzzi Edward J. Delp Video and Image Processing Laboratory (VIPER) School of Electrical and Computer Engineering Iyer-Pascuzzi Lab Purdue University West Lafayette Indiana USA

ISBN: (数字)9781728157450

ISBN: (纸本)9781728157467

Accurately phenotyping plant wilting is important for understanding responses to environmental stress. Analysis of the shape of plants can potentially be used to accurately quantify the degree of wilting. Plant shape analysis can be enhanced by locating the stem, which serves as a consistent reference point during wilting. In this paper, we show that deep learning methods can accurately segment tomato plant stems. We also propose a control-point-based ground truth method that drastically reduces the resources needed to create a training dataset for a deep learning approach. Experimental results show the viability of both our proposed ground truth approach and deep learning based stem segmentation.

关键词： Image segmentation Splines (mathematics) Machine learning Diseases Training Task analysis Shape

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Robust Target Localization in Distributed MIMO Radars Based on Iterative Reweight Least Squares

Robust Target Localization in Distributed MIMO Radars Based ...

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IEEE International Conference on Signal and Image processing (ICSIP)

作者： Zehua Yu Jun Li Qinghua Guo Xinke Li Hailong Kang National Laboratory of Radar Signal Processing Xidian University Xi’an China School of Electrical Computer and Telecommunications Engineering University of Wollongong Wollongong Australia

ISBN: (数字)9781728168968

ISBN: (纸本)9781728168975

In this paper, a robust target localization method based on iterative reweight least squares (IRWLS) is presented to against outliers in distributed MIMO radars. Unlike conventional weight least squares model which derived from the maximum likelihood estimator (MLE), the proposed robust global cost function is established based on least absolute model. Since the least absolute is nontrivial to solve directly, we convert it into a reweight least squares problem. After that, we linearize the relation between target location and bistatic range (BR) without introducing any nuisance parameters, which is different from existing linearization methods. The solution of proposed method can be obtained efficiently via iteration with closed-form expression. Simulation results validate the robust performance of proposed method under low SNR condition and outliers.

关键词： Location awareness Maximum likelihood estimation Closed-form solutions MIMO radar Simulation Cost function Signal to noise ratio

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Biphasic Face Photo-Sketch Synthesis via Semantic-Driven Generative Adversarial Network with Graph Representation Learning

arXiv

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arXiv 2022年

作者： Qi, Xingqun Sun, Muyi Wang, Zijian Liu, Jiaming Li, Qi Zhao, Fang Zhang, Shanghang Shan, Caifeng Academy of Interdisciplinary Studies Hong Kong University of Science and Technology Hong Kong CRIPAC NLPR Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China School of Intelligence Science and Technology Nanjing University Nanjing210023 China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao266590 China

Biphasic face photo-sketch synthesis has significant practical value in wide-ranging fields such as digital entertainment and law enforcement. Previous approaches directly generate the photo-sketch in a global view, they always suffer from the low quality of sketches and complex photo variations, leading to unnatural and low-fidelity results. In this paper, we propose a novel Semantic-Driven Generative Adversarial Network to address the above issues, cooperating with Graph Representation Learning. Considering that human faces have distinct spatial structures, we first inject class-wise semantic layouts into the generator to provide style-based spatial information for synthesized face photos and sketches. Additionally, to enhance the authenticity of details in generated faces, we construct two types of representational graphs via semantic parsing maps upon input faces, dubbed the IntrA-class Semantic Graph (IASG) and the InteR-class Structure Graph (IRSG). Specifically, the IASG effectively models the intra-class semantic correlations of each facial semantic component, thus producing realistic facial details. To preserve the generated faces being more structure-coordinated, the IRSG models inter-class structural relations among every facial component by graph representation learning. To further enhance the perceptual quality of synthesized images, we present a biphasic interactive cycle training strategy by fully taking advantage of the multi-level feature consistency between the photo and sketch. Extensive experiments demonstrate that our method outperforms the state-of-the-art competitors on the CUFS and CUFSF datasets. © 2022, CC BY-SA.

关键词： Semantics

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Optimal inference of the start of COVID-19

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Physical Review Research 2021年第1期3卷 013155-013155页

作者： Zheng-Meng Zhai Yong-Shang Long Ming Tang Zonghua Liu Ying-Cheng Lai State Key Laboratory of Precision Spectroscopy and School of Physics and Electronic Science East China Normal University Shanghai 200241 China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai 200241 China School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA Department of Physics Arizona State University Tempe Arizona 85287 USA

According to the official report, the first case of COVID-19 and the first death in the United States occurred on January 20 and February 29, 2020, respectively. On April 21, California reported that the first death in the state occurred on February 6, implying that community spreading of COVID-19 might have started earlier than previously thought. Exactly what is time zero, i.e., when did COVID-19 emerge and begin to spread in the U.S. and other countries? We develop a comprehensive predictive modeling framework to address this question. Using available data of confirmed infections to obtain the optimal values of the key parameters, we validate the model and demonstrate its predictive power. We then carry out an inverse inference analysis to determine time zero for 10 representative states in the U.S., plus New York City, United Kingdom, Italy, and Spain. The main finding is that, in both the U.S. and Europe, COVID-19 started around the new year day.

关键词： Medical physics Viral diseases Epidemic spreading Spreading models

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Deepgt: Deep Learning-Based Quantification of Nanosized Bioparticles in Bright-Field Micrographs of Gires-Tournois Biosensor

SSRN

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SSRN 2023年

作者： Kang, Jiwon Yoo, Young Jin Park, Jin-Hwi Ko, Joo Hwan Kim, Seungtaek Stanciu, Stefan G. Stenmark, Harald A. Lee, Jinah Mahmud, Abdullah Al Jeon, Hae-Gon Song, Young Min School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States AI Graduate School Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of Zoonotic Virus Laboratory Institut Pasteur Korea Seongnam13488 Korea Republic of Center for Microscopy-Microanalysis and Information Processing Politehnica University of Bucharest Bucharest060042 Romania Department of Molecular Cell Biology Institute for Cancer Research Oslo University Hospital Oslo0379 Norway Anti-Viral Research Center Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of

Rapid and decentralized quantification of viral load profiles in infected patients is vital for assessing clinical severity and tailoring appropriate therapeutic strategies. Although microscopic imaging offers potential for label-free and amplification-free quantitative diagnostics, the small size (~100 nm in diameter) and low refractive index (~1.5) of bioparticles present challenges in achieving accurate estimations, consequently increasing the limit of detection (LoD). In this study, we present a novel synergistic biosensing approach, DeepGT, combining Gires-Tournois (GT) sensing platforms with deep learning algorithms to enhance nanoscale bioparticle counting accuracy. The GT sensing platform serves as a photonic resonator, increasing bioparticle visibility in bright-field microscopy and maximizing chromatic contrast. By employing a back-end with a dilated convolutional neural network architecture, DeepGT effectively refines artifacts and color deviations, significantly improving particle estimation accuracy (MAE ~ 2.37 across 1,596 images) compared to rule-based algorithms (MAE ~ 13.47). Notably, the enhanced accuracy in detecting invisible particles (e.g., two- or three-particles) enables an LoD of 138 pg ml-1, facilitating a dynamic linear correlation at low viral concentration ranges within the clinical spectrum of infection, from asymptomatic to severe cases. Leveraging transfer learning, DeepGT, which relies on a chromatometry-based strategy instead of a spatial resolution approach, exhibits exceptional precision when analyzing particles of diverse dimensions smaller than the microscopy system’s minimum diffraction limit in visible light ( © 2023, The Authors. All rights reserved.

关键词： Biosensors

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Enhanced Machine Learning Approaches for Diagnosing Building Systems

Enhanced Machine Learning Approaches for Diagnosing Building...

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2019 International Conference on Internet of Things, Embedded Systems and Communications, IINTEC 2019

作者： Gharsellaoui, Sondes Mansouri, Majdi Refaat, Shady S. Abu-Rub, Haitham Messaoud, Hassani National Higher Engineering School of Tunis Electrical Engineering Department Monfleury Tunisia Electrical and Computer Engineering Program Texas AM University at Qatar Doha Qatar National Engineering School of Monastir Laboratory of Automatic Signal and Image Processing Monastir Tunisia

ISBN: (纸本)9781728151847

Fault Detection and Classification (FDC) in Heating, Ventilation, and Air Conditioning (HVAC) systems is an important approach to guarantee the human safety of these systems. Therefore, the implementation of a FDC framework is the focus in this paper. The developed approach aims at reducing the energy needs for buildings and improving indoor environment quality. It merges the benefits of multiscale representation, Principal Component Analysis (PCA), and Machine Learning (ML) classifiers in order to improve the efficiency of FDC in heating systems. Firstly, a multiscale decomposition is used to extract the dynamics of the systems at different scales. The multiscale representation gives several advantages for monitoring heating systems generally driven by events in different time and frequency responses. Secondly, the multiscaled data-sets are then introduced into the PCA model to extract more efficient characteristics. Thirdly, the ML algorithms are applied to the extracted and selected characteristics to deal with the problem of fault diagnosis. The FDC efficiency of the developed technique is evaluated using a simulated data extracted from heating systems. © 2019 IEEE.

关键词： Fault detection

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Deep Transfer Learning For Plant Center Localization

Deep Transfer Learning For Plant Center Localization

引用

IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Enyu Cai Sriram Baireddy Changye Yang Melba Crawford Edward J. Delp Video and Image Processing Laboratory (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA School of Civil Engineering Purdue University West Lafayette Indiana USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Plant phenotyping focuses on the measurement of plant characteristics throughout the growing season, typically with the goal of evaluating genotypes for plant breeding. Estimating plant location is important for identifying genotypes which have low emergence, which is also related to the environment and management practices such as fertilizer applications. The goal of this paper is to investigate methods that estimate plant locations for afield-based crop using RGB aerial images captured using Unmanned Aerial Vehicles (UAVs). Deep learning approaches provide promising capability for locating plants observed in RGB images, but they require large quantities of labeled data (ground truth) for training. Using a deep learning architecture fine-tuned on a single field or a single type of crop on fields in other geographic areas or with other crops may not have good results. The problem of generating ground truth for each new field is labor-intensive and tedious. In this paper, we propose a method for estimating plant centers by transferring an existing model to a new scenario using limited ground truth data. We describe the use of transfer learning using a model fine-tuned for a single field or a single type of plant on a varied set of similar crops and fields. We show that transfer learning provides promising results for detecting plant locations.

关键词： Training Machine learning Task analysis Agriculture Data models Training data Shape

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Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network

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PLoS Computational Biology 2021年第9期17卷 e1009345-e1009345页

作者： Zhao, Zhengqiao Woloszynek, Stephen Agbavor, Felix Mell, Joshua Chang Sokhansanj, Bahrad A. Rosen, Gail L. Ecological and Evolutionary Signal-Processing and Informatics Laboratory Department of Electrical and Computer Engineering College of Engineering Drexel University PhiladelphiaPA United States Beth Israel Deaconess Medical Center BostonMA United States Harvard Medical School BostonMA United States School of Biomedical Engineering Science and Health Systems Drexel University PhiladelphiaPA United States College of Medicine Drexel University PhiladelphiaPA United States

Recurrent neural networks with memory and attention mechanisms are widely used in natural language processing because they can capture short and long term sequential information for diverse tasks. We propose an integrated deep learning model for microbial DNA sequence data, which exploits convolutional neural networks, recurrent neural networks, and attention mechanisms to predict taxonomic classifications and sample-associated attributes, such as the relationship between the microbiome and host phenotype, on the read/ sequence level. In this paper, we develop this novel deep learning approach and evaluate its application to amplicon sequences. We apply our approach to short DNA reads and full sequences of 16S ribosomal RNA (rRNA) marker genes, which identify the heterogeneity of a microbial community sample. We demonstrate that our implementation of a novel attention- based deep network architecture, Read2Pheno, achieves read-level phenotypic prediction. Training Read2Pheno models will encode sequences (reads) into dense, meaningful representations: learned embedded vectors output from the intermediate layer of the network model, which can provide biological insight when visualized. The attention layer of Read2Pheno models can also automatically identify nucleotide regions in reads/sequences which are particularly informative for classification. As such, this novel approach can avoid pre/post-processing and manual interpretation required with conventional approaches to microbiome sequence classification. We further show, as proof-of-concept, that aggregating read-level information can robustly predict microbial community properties, host phenotype, and taxonomic classification, with performance at least comparable to conventional approaches. An implementation of the attention-based deep learning network is available at https://***/EESI/sequence_attention (a python package) and https://***/ EESI/seq2att (a command line tool). Copyright: © 2021 Zhao et

关键词： Classification (of information)

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