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Dynamic hidden-variable network models

Physical Review E 2021年第5期103卷 052307-052307页

作者： Harrison Hartle Fragkiskos Papadopoulos Dmitri Krioukov Network Science Institute Northeastern University Boston 02115 Massachusetts USA Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology 3036 Limassol Cyprus Northeastern University Departments of Physics Mathematics and Electrical & Computer Engineering Boston 02115 Massachusetts USA

Models of complex networks often incorporate node-intrinsic properties abstracted as hidden variables. The probability of connections in the network is then a function of these variables. Real-world networks evolve over time and many exhibit dynamics of node characteristics as well as of linking structure. Here we introduce and study natural temporal extensions of static hidden-variable network models with stochastic dynamics of hidden variables and links. The dynamics is controlled by two parameters: one that tunes the rate of change of hidden variables and another that tunes the rate at which node pairs reevaluate their connections given the current values of hidden variables. Snapshots of networks in the dynamic models are equivalent to networks generated by the static models only if the link reevaluation rate is sufficiently larger than the rate of hidden-variable dynamics or if an additional mechanism is added whereby links actively respond to changes in hidden variables. Otherwise, links are out of equilibrium with respect to hidden variables and network snapshots exhibit structural deviations from the static models. We examine the level of structural persistence in the considered models and quantify deviations from staticlike behavior. We explore temporal versions of popular static models with community structure, latent geometry, and degree heterogeneity. While we do not attempt to directly model real networks, we comment on interesting qualitative resemblances to real systems. In particular, we speculate that links in some real networks are out of equilibrium with respect to hidden variables, partially explaining the presence of long-ranged links in geometrically embedded systems and intergroup connectivity in modular systems. We also discuss possible extensions, generalizations, and applications of the introduced class of dynamic network models.

关键词： Complex systems Evolving networks Network structure Block models Evolving network models Fitness models

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Adaptive DropBlock Enhanced Generative Adversarial Networks for Hyperspectral Image Classification

arXiv

引用

arXiv 2022年

作者： Wang, Junjie Gao, Feng Dong, Junyu Du, Qian The Qingdao Key Laboratory of Mixed Reality and Virtual Ocean School of Information Science and Engineering Ocean University of China Qingdao266100 China The Department of Electrical and Computer Engineering Mississippi State University Mississippi State MS39762 United States

In recent years, hyperspectral image (HSI) classification based on generative adversarial networks (GAN) has achieved great progress. GAN-based classification methods can mitigate the limited training sample dilemma to some extent. However, several studies have pointed out that existing GAN-based HSI classification methods are heavily affected by the imbalanced training data problem. The discriminator in GAN always contradicts itself and tries to associate fake labels to the minority-class samples, and thus impair the classification performance. Another critical issue is the mode collapse in GAN-based methods. The generator is only capable of producing samples within a narrow scope of the data space, which severely hinders the advancement of GAN-based HSI classification methods. In this paper, we proposed an Adaptive DropBlock-enhanced Generative Adversarial Networks (ADGAN) for HSI classification. First, to solve the imbalanced training data problem, we adjust the discriminator to be a single classifier, and it will not contradict itself. Second, an adaptive DropBlock (AdapDrop) is proposed as a regularization method employed in the generator and discriminator to alleviate the mode collapse issue. The AdapDrop generated drop masks with adaptive shapes instead of a fixed size region, and it alleviates the limitations of DropBlock in dealing with ground objects with various shapes. Experimental results on three HSI datasets demonstrated that the proposed ADGAN achieved superior performance over state-of-the-art GAN-based methods. Our codes are available at https://***/summitgao/HC_ADGAN. © 2022, CC BY.

关键词： Generative adversarial networks

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Predicting Word Learning in Children from the Performance of computer Vision Systems

arXiv

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arXiv 2022年

作者： Rane, Sunayana Nencheva, Mira L. Wang, Zeyu Lew-Williams, Casey Russakovsky, Olga Griffiths, Thomas L. Department of Computer Science Princeton University United States Department of Psychology Princeton University United States Department of Electrical and Computer Engineering Princeton University United States Departments of Psychology and Computer Science Princeton University United States

For human children as well as machine learning systems, a key challenge in learning a word is linking the word to the visual phenomena it describes. We explore this aspect of word learning by using the performance of computer vision systems as a proxy for the difficulty of learning a word from visual cues. We show that the age at which children acquire different categories of words is correlated with the performance of visual classification and captioning systems, over and above the expected effects of word frequency. The performance of the computer vision systems is correlated with human judgments of the concreteness of words, which are in turn a predictor of children’s word learning, suggesting that these models are capturing the relationship between words and visual phenomena. © 2022, CC BY-NC-ND.

关键词： Learning systems

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Simulation of the Flux-to-voltage Characteristics of DC SQUID with Small Screening Parameter and a Large Stewart-McCumber Parameter

Simulation of the Flux-to-voltage Characteristics of DC SQUI...

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2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2020

作者： Wu, Bao Wang, Hai Zhang, Zhidan Kong, Xiangyan Wang, Meiling Ningbo University Faculty of Electrical Engineering Anc Computer Science Ningbo China College of Engineering Science and Technology Shanghai Ocean University Shanghai China

ISBN: (纸本)9781728152158

There are three important parameters in the RCSJ Model of direct current superconducting quantum interference devices (DC SQUIDs), namely screening parameter β L, Stewart-McCumber parameter β C, and thermal noise parameter Γ. The three parameters will dominate the SQUID sensor intrinsic noise, but not the application system noise, because the electric noise of readout in room temperature always cannot be neglected. In order to suppress the readout electric noise, a large flux to voltage (VΦ) character of SQUID sensor is essential in direct readout scheme. In the real application system, SQUID sensor temperature fluctuation noise is negligible, so we simulated influence of the parameter β L and β C to SQUID's VΦ. In the simulation we used three combinations based on β L and β C under the condition of biased current i=1.65 μA and noise parameter Γ=0.05. We found that the weakly damped SQUID with small β L have a high flux response V Φ. At last some experiment have been done, and the results fit well to our simulation. © 2020 IEEE.

关键词： SQUIDs

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Nearest Neighbor-Based Contrastive Learning for Hyperspectral and LiDAR Data Classification

arXiv

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arXiv 2023年

作者： Wang, Meng Gao, Feng Dong, Junyu Li, Heng-Chao Du, Qian The School of Information Science and Engineering Ocean University of China Qingdao266100 China The Sichuan Provincial Key Laboratory of Information Coding and Transmission Southwest Jiaotong University Chengdu610031 China The Department of Electrical and Computer Engineering Mississippi State University StarkvilleMS39762 United States

The joint hyperspectral image (HSI) and LiDAR data classification aims to interpret ground objects at more detailed and precise level. Although deep learning methods have shown remarkable success in the multisource data classification task, self-supervised learning has rarely been explored. It is commonly nontrivial to build a robust self-supervised learning model for multisource data classification, due to the fact that the semantic similarities of neighborhood regions are not exploited in existing contrastive learning framework. Furthermore, the heterogeneous gap induced by the inconsistent distribution of multisource data impedes the classification performance. To overcome these disadvantages, we propose a Nearest Neighbor-based Contrastive Learning Network (NNCNet), which takes full advantage of large amounts of unlabeled data to learn discriminative feature representations. Specifically, we propose a nearest neighbor-based data augmentation scheme to use enhanced semantic relationships among nearby regions. The intermodal semantic alignments can be captured more accurately. In addition, we design a bilinear attention module to exploit the second-order and even high-order feature interactions between the HSI and LiDAR data. Extensive experiments on four public datasets demonstrate the superiority of our NNCNet over state-of-the-art methods. The source codes are available at https://***/summitgao/NNCNet. © 2023, CC BY.

关键词： Image classification

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Objective diagnosis of ADHD through movement analysis by using a smart chair with piezoelectric material

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Pediatrics and Neonatology 2023年第1期64卷 46-52页

作者： Chang, Tung-Ming Wu, Rong-Ching Yang, Rei-Cheng Chiang, Ching-Tai Chiu, Yi-Hung Ouyang, Chen-Sen Wang, Yun-Ming Wu, Ming-Hsu Lin, Guang-Chung Lin, Lung-Chang Department of Pediatric Neurology Changhua Christian Children's Hospital Changhua Taiwan Department of Electrical Engineering I-Shou University Taiwan Departments of Pediatrics Kaohsiung Medical University Hospital Kaohsiung Medical University Taiwan Department of Computer and Communication National Pingtung University Taiwan Department of Information Engineering I-Shou University Taiwan Department of Biological Science and Technology Institute of Molecular Medicine and Bioengineering Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B) National Yang Ming Chiao Tung University Taiwan Department of Pediatrics School of Medicine College of Medicine Kaohsiung Medical University Taiwan Department of Biological Science and Technology College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan

Background: Attention-deficit/hyperactivity disorder (ADHD) is the most common neuropsychiatric disorder in schoolchildren. ADHD diagnoses are generally made based on criteria from the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. The diagnosis is made clinically based on observation and information provided by parents and teachers, which is highly subjective and can lead to disparate results. Considering that hyperactivity is one of the main symptoms of ADHD, the inaccuracy of ADHD diagnosis based on subjective criteria necessitates the identification of a method to objectively diagnose ADHD. Methods: In this study, a medical chair containing a piezoelectric material was applied to objectively analyze movements of patients with ADHD, which were compared with those of patients without ADHD. This study enrolled 62 patients—31 patients with ADHD and 31 patients without ADHD. During the clinical evaluation, participants’ movements were recorded by the piezoelectric material for analysis. The variance, zero-crossing rate, and high energy rate of movements were subsequently analyzed. Results: The results revealed that the variance, zero-crossing rate, and high energy rate were significantly higher in patients with ADHD than in those without ADHD. Classification performance was excellent in both groups, with the area under the curve as high as 98.00%. Conclusion: Our findings suggest that the use of a smart chair equipped with piezoelectric material is an objective and potentially useful method for supporting the diagnosis of ADHD. © 2022 Taiwan Pediatric Association

关键词： Attention-deficit/hyperactivity disorder piezoelectric material smart chair

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Algebraic and Statistical Properties of the Ordinary Least Squares Interpolator

arXiv

引用

arXiv 2023年

作者： Shen, Dennis Song, Dogyoon Ding, Peng Sekhon, Jasjeet S. Department of Data Sciences & Operations University of Southern California United States Department of Electrical Engineering and Computer Science University of Michigan United States Department of Statistics University of California Berkeley United States Departments of Statistics & Data Science and Political Science Yale University United States

Deep learning research has uncovered the phenomenon of benign overfitting for overparameterized statistical models, which has drawn significant theoretical interest in recent years. Given its simplicity and practicality, the ordinary least squares (OLS) interpolator has become essential to gain foundational insights into this phenomenon. While properties of OLS are well established in classical, underparameterized settings, its behavior in high-dimensional, overparameterized regimes is less explored (unlike for ridge or lasso regression) though significant progress has been made of late. We contribute to this growing literature by providing fundamental algebraic and statistical results for the minimum 2-norm OLS interpolator. In particular, we provide algebraic equivalents of (i) the leave-k-out residual formula, (ii) Cochran’s formula, and (iii) the Frisch-Waugh-Lovell theorem in the overparameterized regime. These results aid in understanding the OLS interpolator’s ability to generalize and have substantive implications for causal inference. Under the Gauss-Markov model, we present statistical results such as an extension of the Gauss-Markov theorem and an analysis of variance estimation under homoskedastic errors for the overparameterized regime. To substantiate our theoretical contributions, we conduct simulations that further explore the stochastic properties of the OLS interpolator. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Domain Generalization for Medical Image Analysis: A Review

arXiv

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arXiv 2023年

作者： Yoon, Jee Seok Oh, Kwanseok Shin, Yooseung Mazurowski, Maciej A. Suk, Heung-Il Department of Brain and Cognitive Engineering Korea University Seoul02841 Korea Republic of Department of Artificial Intelligence Korea University Seoul02841 Korea Republic of Departments of Radiology Biostatistics and Bioinformatics Electrical and Computer Engineering and Computer Science Duke University DurhamNC27705 United States Department of Artificial Intelligence The Department of Brain and Cognitive Engineering Korea University Seoul02841 Korea Republic of

Medical image analysis (MedIA) has become an essential tool in medicine and healthcare, aiding in disease diagnosis, prognosis, and treatment planning, and recent successes in deep learning (DL) have made significant contributions to its advances. However, deploying DL models for MedIA in real-world situations remains challenging due to their failure to generalize across the distributional gap between training and testing samples — a problem known as domain shift. Researchers have dedicated their efforts to developing various DL methods to adapt and perform robustly on unknown and out-of-distribution data distributions. This paper comprehensively reviews domain generalization studies specifically tailored for MedIA. We provide a holistic view of how domain generalization techniques interact within the broader MedIA system, going beyond methodologies to consider the operational implications on the entire MedIA workflow. Specifically, we categorize domain generalization methods into data-level, feature-level, model-level, and analysis-level methods. We show how those methods can be used in various stages of the MedIA workflow with DL equipped from data acquisition to model prediction and analysis. Furthermore, we critically analyze the strengths and weaknesses of various methods, unveiling future research opportunities. Copyright © 2023, The Authors. All rights reserved.

关键词： Reviews

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Achieving BSREM image quality at the speed of OSEM reconstruction using the cGAN framework

Achieving BSREM image quality at the speed of OSEM reconstru...

引用

IEEE Symposium on Nuclear science (NSS/MIC)

作者： S. Nazari Farsani J. Fisher J. R. Kim M. Jafaritadi C. S. Levin Department of Radiology Stanford University Stanford California United States of America Department of Electrical Engineering Stanford University Stanford California United States of America Department of Computer Science Stanford University Stanford California United States of America Departments of Radiology Physics Electrical Engineering and Bioengineering Stanford University Stanford California United States of America

Positron emission tomography (PET) is an imaging method for cancer, heart disease, and neurological disorders. PET images are reconstructed from emitted positron data using complex mathematical algorithms, among which ordered subset algorithms, including ordered subset expectation maximization (OSEM) and block sequential regularized expectation maximization (BSREM), are commonly employed. OSEM provides faster image reconstruction but with higher noise, while BSREM offers improved image quality, and reduced noise, albeit at the cost of increased computational complexity. To improve the accuracy and quality of OSEM-reconstructed brain PET images while maintaining time efficiency, we utilized a generative adversarial network (GAN) to enhance OSEM images to a level of quality and accuracy comparable to that of images generated by the BSREM algorithm. We collected 18 FDG PET scans from Alzheimer’s disease patients, which were preprocessed into a list-mode format and reconstructed using OSEM with 2 iterations and 2 subsets, and BSREM with 25 iterations. We trained a cGAN model, consisting of a U-net generator and a discriminator that received OSEM-reconstructed PET images as input, BSREM images as the target, and generated higher quality and accuracy PET images as output. Results were evaluated using PSNR, SSIM, and NRMSE. Using the cGAN model, we improved the SNR and contrast of the OSEM-reconstructed PET images with a 17% enhancement in the PSNR and a 60% decrease in the NRMSE, although the SSIM was not significantly improved. Our findings show that the GAN model can convert low-SNR, low-contrast OSEM images to high-quality, high-accuracy images similar to those generated by the BSREM algorithm. This approach offers potential advantages in achieving the image quality and accuracy of BSREM with the shorter reconstruction time of OSEM.

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Polynomial Preconditioners for Regularized Linear Inverse Problems

arXiv

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arXiv 2022年

作者： Iyer, Siddharth S. Ong, Frank Cao, Xiaozhi Liao, Congyu Daniel, Luca Tamir, Jonathan I. Setsompop, Kawin Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Department of Electrical Engineering Stanford University StanfordCA United States Department of Radiology Stanford University StanfordCA United States Departments of Electrical and Computer Engineering Diagnostic Medicine Oden Institute University of Texas at Austin AustinTX United States

This work aims to accelerate the convergence of proximal gradient methods used to solve regularized linear inverse problems. This is achieved by designing a polynomial-based preconditioner that targets the eigenvalue spectrum of the normal operator derived from the linear operator. The preconditioner does not assume any explicit structure on the linear function and thus can be deployed in diverse applications of interest. The efficacy of the preconditioner is validated on three different Magnetic Resonance Imaging applications, where it is seen to achieve faster iterative convergence while achieving similar reconstruction quality. Copyright © 2022, The Authors. All rights reserved.

关键词： Gradient methods

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