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Frechet-Statistics-Based Change Point Detection in Dynamic Social Networks

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第2期11卷 2863-2871页

作者： Luo, Rui Krishnamurthy, Vikram City Univ Hong Kong Dept Syst Engn Hong Kong Peoples R China Cornell Univ Sch Elect & Comp Engn Ithaca NY USA

This article proposes a method to detect change points in dynamic social networks using Frechet statistics. We address two main questions: 1) what metric can quantify the distances between graph Laplacians in a dynamic network and enable efficient computation, and 2) how can the Frechet statistics be extended to detect multiple change points while maintaining the significance level of the hypothesis test? Our solution defines a metric space for graph Laplacians using the log-Euclidean metric, enabling a closed-form formula for Frechet mean and variance. We present a framework for change point detection using Frechet statistics and extend it to multiple change points with binary segmentation. The proposed algorithm uses incremental computation for Frechet mean and variance to improve efficiency and is validated on simulated and four real-world datasets, namely, the UCI message dataset, the SFHH interaction dataset, the stack overflow Q & A dataset, and the Enron email dataset.

关键词： binary segmentation change point detection dynamic social network Frechet statistics metric space

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Exact change point detection with improved power in small-sample binomial sequences

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BIOMETRICAL JOURNAL 2021年第3期63卷 558-574页

作者： Ellenberger, David Lausen, Berthold Friede, Tim Univ Med Ctr Gottingen Dept Med Stat Humboldtallee 32 D-37073 Gottingen Germany Univ Essex Dept Math Sci Colchester Essex England

To detect a change in the probability of a sequence of independent binomial random variables, a variety of asymptotic and exact testing procedures have been proposed. Whenever the sample size or the event rate is small, asymptotic approximations of maximally selected test statistics have been shown to be inaccurate. Although exact methods control the type I error rate, they can be overly conservative due to the discreteness of the test statistics in these situations. We extend approaches by Worsley and Halpern to develop a test that is less discrete to increase the power. Building on ideas from binary segmentation, the proposed test utilizes unused information in the binomial sequences to add a new ordering to test statistics that are of equal value. The exact distributions are derived under side conditions that arise in hypothetical segmentation steps and do not depend on the type of test statistic used (e.g., log likelihood ratio, cumulative sum, or Fisher's exact test). Using the proposed exact segmentation procedure, we construct a change point test and prove that it controls the type-I-error rate at any given nominal level. Furthermore, we prove that the new test is uniformly at least as powerful as Worsley's exact test. In a Monte Carlo simulation study, the gain in power can be remarkable, especially in scenarios with small sample size. Giving a clinical database example about pin site infections and an example assessing publication bias in neuropsychiatric drug research, we demonstrate the wide-ranging applicability of the test.

关键词： binary segmentation change point disorder detection exact test Worsley&apos s test

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A Change Point Approach for Phase I Analysis in Multistage Processes

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TECHNOMETRICS 2008年第3期50卷 344-356页

作者： Zou, Changliang Tsung, Fugee Liu, Yukun Nankai Univ Sch Math Sci LPMC Tianjin 300071 Peoples R China Nankai Univ Sch Math Sci Dept Stat Tianjin 300071 Peoples R China Hong Kong Univ Sci & Technol Dept Ind Engn & Logist Management Kowloon Hong Kong Peoples R China

We study the phase I analysis of a multistage process where the input of the current process stage may be closely related to the output(s) of the earlier stage(s). We frame univariate observations from each of the stages in a multistage process as a single vector and recognize that the directions in which these vectors can shift are limited when attention is restricted to a single step shift in the mean of one stage. This allows us to focus detection power on a limited subspace with improved sensitivity. Taking advantage of this particular characteristic, we propose a change point approach that integrates the classical binary segmentation test with the directional information based on the state-space model for testing the stability of a batch of historical data. We give an accurate approximation for the significance level of the proposed test. Our simulation results show that the proposed approach consistently outperforms existing methods for multistage processes.

关键词： binary segmentation Directional information Generalized likelihood ratio test Statistical process control

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Fuzzy Reasoning to More Accurately Determine Void Areas on Optical Micrographs of Composite Structures

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APPLIED COMPOSITE MATERIALS 2013年第6期20卷 1125-1134页

作者： Dominguez, Jesus A. Tate, Lanetra C. Wright, M. Clara Caraccio, Anne QinetiQ North Amer Inc Kennedy Space Ctr FL 32899 USA NASA Kennedy Space Ctr FL 32899 USA

Accomplishing the best-performing composite matrix (resin) requires that not only the processing method but also the cure cycle generate low-void-content structures. If voids are present, the performance of the composite matrix will be significantly reduced. This is usually noticed by significant reductions in matrix-dominated properties, such as compression and shear strength. Voids in composite materials are areas that are absent of the composite components: matrix and fibers. The characteristics of the voids and their accurate estimation are critical to determine for high performance composite structures. One widely used method of performing void analysis on a composite structure sample is acquiring optical micrographs or Scanning Electron Microscope (SEM) images of lateral sides of the sample and retrieving the void areas within the micrographs/images using an image analysis technique. segmentation for the retrieval and subsequent computation of void areas within the micrographs/images is challenging as the gray-scaled values of the void areas are close to the gray-scaled values of the matrix leading to the need ofmanually performing the segmentation based on the histogram of the micrographs/images to retrieve the void areas. The use of an algorithm developed by NASA and based on Fuzzy Reasoning (FR) proved to overcome the difficulty of suitably differentiate void and matrix image areas with similar gray-scaled values leading not only to a more accurate estimation of void areas on composite matrix micrographs but also to a faster void analysis process as the algorithm is fully autonomous.

关键词： Composite void analysis Optical micrographs analysis Void content composites Void content resin Void content matrix Fuzzy reasoning Fuzzy logic Image segmentation Image binarization binary segmentation

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Optimal Nonparametric Multivariate Change Point Detection and Localization

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IEEE TRANSACTIONS ON INFORMATION THEORY 2022年第3期68卷 1922-1944页

作者： Padilla, Oscar Hernan Madrid Yu, Yi Wang, Daren Rinaldo, Alessandro Univ Calif Los Angeles Dept Stat Los Angeles CA 90095 USA Univ Warwick Dept Stat Coventry CV4 7AL W Midlands England Univ Notre Dame Dept ACMS Notre Dame IN 46556 USA Carnegie Mellon Univ Dept Stat & Data Sci Baker Hall Carnegie Mellon Univ Pittsburgh PA 15213 USA

We study the multivariate nonparametric change point detection problem, where the data are a sequence of independent p-dimensional random vectors whose distributions are piecewise-constant with Lipschitz densities changing at unknown times, called change points. We quantify the size of the distributional change at any change point with the supremum norm of the difference between the corresponding densities. We are concerned with the localization task of estimating the positions of the change points. In our analysis, we allow for the model parameters to vary with the total number of time points, including the minimal spacing between consecutive change points and the magnitude of the smallest distributional change. We provide information-theoretic lower bounds on both the localization rate and the minimal signal-to-noise ratio required to guarantee consistent localization. We formulate a novel algorithm based on kernel density estimation that nearly achieves the minimax lower bound, save possibly for logarithm factors. We have provided extensive numerical evidence to support our theoretical findings.

关键词： Location awareness Task analysis Kernel Estimation Data models Testing Lips Multivariate nonparametric kernel density estimation CUSUM binary segmentation

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A Multi-Kink quantile regression model with common structure for panel data analysis

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JOURNAL OF ECONOMETRICS 2024年第1期239卷

作者： Sun, Yan Wan, Chuang Zhang, Wenyang Zhong, Wei Shanghai Univ Finance & Econ Sch Econ Shanghai Peoples R China Nankai Univ Sch Stat & Data Sci Tianjin Peoples R China Univ York Dept Math York England Xiamen Univ Sch Econ Dept Stat & Data Sci MOE Key Lab EconometrWISE Xiamen Peoples R China

Stimulated by the analysis of a data set on financial portfolio returns, we propose a multi-kink quantile regression (MKQR) model with latent homogeneous structure for panel data analysis. The proposed model accounts for both homogeneity and heterogeneity among individuals and parameters in panel data analysis. From statistical modeling point of view, it well balances the risk of misspecification and the model parsimony. From practical point of view, it is able to reveal not only the impacts of covariates in the global sense, but also individual attributes. An estimation procedure is presented to estimate both the unknown parameters and the latent homogeneous structure in the proposed model. Computational issues with the implementation of the estimation procedure are also discussed. Asymptotic theory of the estimators is established. It shows the necessity of taking into account both homogeneity and heterogeneity in panel data analysis. Monte Carlo simulation studies are conducted to demonstrate the finite sample performance of the proposed estimation and the risk of ignoring the homogeneity or heterogeneity among individuals. Finally, we apply the proposed model and the estimation procedure to the data set which stimulates this work and reveal some interesting findings. Crown Copyright (c) 2022 Published by Elsevier B.V. All rights reserved.

关键词： binary segmentation Common structure Homogeneity pursuit Multi -kink quantile regression Panel data analysis

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Extracting Building Footprints from High-resolution Aerial Imagery Using Refined Cross AttentionNet

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IETE TECHNICAL REVIEW 2022年第3期39卷 494-505页

作者： Das, Prativa Chand, Satish Jawaharlal Nehru Univ Sch Comp & Syst Sci Delhi 110067 India

Automatic building extraction has significant socio-economic applications such as population estimation, urban planning, rapid disaster response, monitoring illegal land acquisition, and change detection. The traditional methods fail to deal with the challenges entirely associated with building extraction, i.e. different shape, size, texture of buildings, missing and incomplete buildings due to the occlusion, and high intra-class variation. The existing CNN-based approaches are incapable of recovering the boundary information, especially when the building structures are small and complex. To alleviate the issues faced by current methods, we propose a lightweight attention mechanism-based model - refined cross attention neural network (RCA-Net) for precisely extracting the coarse-to-fine building features. Unlike recent attention mechanism-based approaches, the RCA-Net utilizes spatial and channel attention to capture the long-range multi-scale context. Then, we introduce an efficient attention module, the Global Attention Fuse (GAF) module, that fuses the local and global cross-channel relationships to capture the essential features without enhancing the computational complexity. A loss function, unified loss, is also presented that combines BCE loss and dice loss to alleviate the imbalanced class distribution problem. Experimental results show that our proposed method outperforms the latest method DSNet by 2.06% and 1.47% in IoU and 2.11% and 1.27% in F1-score on the publicly available datasets: Massachusetts building dataset and Inria Aerial Image Labeling dataset, respectively.

关键词： Aerial imagery attention mechanism binary segmentation building footprints extraction deep learning FCN feature fusion

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Two-stage filtering of compressed depth images with Markov Random Field

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2017年 54卷 11-22页

作者： Zhao, Lijun Bai, Huihui Wang, Anhong Zhao, Yao Zeng, Bing Beijing Jiaotong Univ Inst Informat Sci 3 Shangyuancun Haidian Dist Peoples R China Taiyuan Univ Sci & Technol Inst Digital Media & Commun 66 Waliu Rd Taiyuan Peoples R China Univ Elect Sci & Technol China Inst Image Proc Chengdu 611731 Sichuan Peoples R China

Virtual view synthesis and image comprehension have become easier with the aid of depth information. However, when a depth image is compressed, severe distortions along boundaries may occur, thus leading to performance degradation. To solve this problem, we propose in this paper a two-stage filtering that consists of binary segmentation-based depth filtering and the reconstruction using a Markov Random Field (MRF) model. The MRF model adopted in our work consists of a data term and a smoothness term so as to preserve the boundary and maintain the smoothness simultaneously. We notice that directly applying the MRF model to a distorted depth image is usually unable to produce a satisfactory performance. Then, we propose that binary segmentation based depth filtering is used to remove artifacts over discontinuous regions in the distorted depth image. Experimental results show that, through our processing, the compressed depth image can render better quality for the synthesized images than many existing depth filtering methods.

关键词： MRF binary segmentation Filtering Compression Depth image Distortion

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IMU Airtime Detection in Snowboard Halfpipe: U-Net Deep Learning Approach Outperforms Traditional Threshold Algorithms

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SENSORS 2024年第21期24卷 6773页

作者： Gorges, Tom Davidson, Padraig Boeschen, Myriam Hotho, Andreas Merz, Christian Inst Appl Training Sci Dept Strength Power & Tech Sports Res Grp Snowboard D-04109 Leipzig Germany Univ Wurzburg Chair Data Sci Ctr Artificial Intelligence & Data Sci D-97074 Wurzburg Germany German Sport Univ Cologne Inst Biomech & Orthopaed D-50933 Cologne Germany

Airtime is crucial for high-rotation tricks in snowboard halfpipe performance, significantly impacting trick difficulty, the primary judging criterion. This study aims to enhance the detection of take-off and landing events using inertial measurement unit (IMU) data in conjunction with machine learning algorithms since manual video-based methods are too time-consuming. Eight elite German National Team snowboarders performed 626 halfpipe tricks, recorded by two IMUs at the lateral lower legs and a video camera. The IMU data, synchronized with video, were labeled manually and segmented for analysis. Utilizing a 1D U-Net convolutional neural network (CNN), we achieved superior performance in all of our experiments, establishing new benchmarks for this binary segmentation task. In our extensive experiments, we achieved an 80.34% lower mean Hausdorff distance for unseen runs compared with the threshold approach when placed solely on the left lower leg. Using both left and right IMUs further improved performance (83.37% lower mean Hausdorff). For data from an algorithm-unknown athlete (Zero-Shot segmentation), the U-Net outperformed the threshold algorithm by 67.58%, and fine-tuning on athlete-specific (Few-Shot segmentation) runs improved the lower mean Hausdorff to 78.68%. The fine-tuned model detected takeoffs with median deviations of 0.008 s (IQR 0.030 s), landing deviations of 0.005 s (IQR 0.020 s), and airtime deviations of 0.000 s (IQR 0.027 s). These advancements facilitate real-time feedback and detailed biomechanical analysis, enhancing performance and trick execution, particularly during critical events, such as take-off and landing, where precise time-domain localization is crucial for providing accurate feedback to coaches and athletes.

关键词： event detection freestyle sports binary segmentation airtime convolutional neural network elite athletes

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Q-Seg: Quantum Annealing-Based Unsupervised Image segmentation

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IEEE COMPUTER GRAPHICS AND APPLICATIONS 2024年第5期44卷 27-39页

作者： Venkatesh, Supreeth Mysore Macaluso, Antonio Nuske, Marlon Klusch, Matthias Dengel, Andreas German Res Ctr Artificial Intelligence D-66123 Saarbrucken Germany

We present Q-Seg, a novel unsupervised image segmentation method based on quantum annealing, tailored for existing quantum hardware. We formulate the pixelwise segmentation problem, which assimilates spectral and spatial information of the image, as a graph-cut optimization task. Our method efficiently leverages the interconnected qubit topology of the D-wave advantage device, offering superior scalability over existing quantum approaches and outperforming several tested state-of-the-art classical methods. Empirical evaluations on synthetic datasets have shown that Q-Seg has better runtime performance than the state-of-the-art classical optimizer Gurobi. The method has also been tested on earth observation image segmentation, a critical area with noisy and unreliable annotations. In the era of noisy intermediate-scale quantum, Q-Seg emerges as a reliable contender for real-world applications in comparison to advanced techniques like Segment Anything. Consequently, Q-Seg offers a promising solution using available quantum hardware, especially in situations constrained by limited labeled data and the need for efficient computational runtime.

关键词： Image segmentation Quantum Computing Annealing Quantum Annealing Image Edge Detection Optimization Measurement Image segmentation Unsupervised Image segmentation Computer Vision Unsupervised Methods segmentation Method Solution Space Edge Detection Earth Observation Quantum Computing Optimization Task segmentation Problem segmentation Quality Image segmentation Methods Flood Mapping Multi Object Tracking Special Case Training Data Intensity Values Similarity Measure Edge Weights Minimum Cut Noisy Labels Grid Graph Gradient Boosting Decision Tree Real Devices Synthetic Aperture Radar binary segmentation Specific Use Case segmentation Task

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