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Homogeneity Pursuit in Single Index Models based Panel Data Analysis

JOURNAL OF BUSINESS & ECONOMIC STATISTICS 2021年第2期39卷 386-401页

作者： Lian, Heng Qiao, Xinghao Zhang, Wenyang City Univ Hong Kong Dept Math Kowloon Hong Kong Peoples R China London Sch Econ Dept Stat London England Univ York Dept Math York YO10 5DD N Yorkshire England

Panel data analysis is an important topic in statistics and econometrics. Traditionally, in panel data analysis, all individuals are assumed to share the same unknown parameters, e.g. the same coefficients of covariates when the linear models are used, and the differences between the individuals are accounted for by cluster effects. This kind of modelling only makes sense if our main interest is on the global trend, this is because it would not be able to tell us anything about the individual attributes which are sometimes very important. In this paper, we propose a modelling based on the single index models embedded with homogeneity for panel data analysis, which builds the individual attributes in the model and is parsimonious at the same time. We develop a data driven approach to identify the structure of homogeneity, and estimate the unknown parameters and functions based on the identified structure. Asymptotic properties of the resulting estimators are established. Intensive simulation studies conducted in this paper also show the resulting estimators work very well when sample size is finite. Finally, the proposed modelling is applied to a public financial dataset and a UK climate dataset, the results reveal some interesting findings.

关键词： binary segmentation B-Spline homogeneity pursuit single index models

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BiSon-e: A Lightweight and High-Performance Accelerator for Narrow Integer Linear Algebra Computing on the Edge 27

BiSon-e: A Lightweight and High-Performance Accelerator for ...

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27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

作者： Reggiani, Enrico Ramirez Lazo, Cristobal Figueras Bague, Roger Cristal, Adrian Olivieri, Mauro Sabri Unsal, Osman Univ Politecn Cataluna Barcelona Supercomp Ctr Barcelona Spain Barcelona Supercomp Ctr Barcelona Spain Sapienza Univ Rome Rome Italy

ISBN: (纸本)9781450392051

Linear algebra computational kernels based on byte and sub-byte integer data formats are at the base of many classes of applications, ranging from Deep Learning to Pattern Matching. Porting the computation of these applications from cloud to edge and mobile devices would enable significant improvements in terms of security, safety, and energy efficiency. However, despite their low memory and energy demands, their intrinsically high computational intensity makes the execution of these workloads challenging on highly resource-constrained devices. In this paper, we present BiSon-e, a novel RISC-V based architecture that accelerates linear algebra kernels based on narrow integer computations on edge processors by performing Single Instruction Multiple Data (SIMD) operations on off-the-shelf scalar Functional Units (FUs). Our novel architecture is built upon the binary segmentation technique, which allows to significantly reduce the memory footprint and the arithmetic intensity of linear algebra kernels requiring narrow data sizes. We integrate BiSon-e into a complete System-on-Chip (SoC) based on RISC-V, synthesized and Place&Routed in 65nm and 22nm technologies, introducing a negligible 0.07% area overhead with respect to the baseline architecture. Our experimental evaluation shows that, when computing the Convolution and Fully-Connected layers of the AlexNet and VGG-16 Convolutional Neural Networks (CNNs) with 8-, 4-, and 2-bit, our solution gains up to 5.6x, 13.9x and 24x in execution time compared to the scalar implementation of a single RISC-V core, and improves the energy efficiency of string matching tasks by 5x when compared to a RISC-V-based Vector Processing Unit (VPU).

关键词： binary segmentation Edge Computing Convolutional Neural Network String Matching RISC-V Hardware Accelerator Narrow Integer Arithmetic Low-power design Number Representation

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Test Time Training for Industrial Anomaly segmentation

Test Time Training for Industrial Anomaly Segmentation

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Costanzino, Alex Ramirez, Pierluigi Zama Del Moro, Mirko Aiezzo, Agostino Lisanti, Giuseppe Salti, Samuele Di Stefano, Luigi Univ Bologna CVLAB Dept Comp Sci & Engn DISI Bologna Italy

ISBN: (纸本)9798350365474

Anomaly Detection and segmentation (AD&S) is crucial for industrial quality control. While existing methods excel in generating anomaly scores for each pixel, practical applications require producing a binary segmentation to identify anomalies. Due to the absence of labeled anomalies in many real scenarios, standard practices binarize these maps based on some statistics derived from a validation set containing only nominal samples, resulting in poor segmentation performance. This paper addresses this problem by proposing a test time training strategy to improve the segmentation performance. Indeed, at test time, we can extract rich features directly from anomalous samples to train a classifier that can discriminate defects effectively. Our general approach can work downstream to any AD&S method that provides an anomaly score map as output, even in multimodal settings. We demonstrate the effectiveness of our approach over baselines through extensive experimentation and evaluation on MVTec AD and MVTec 3D-AD.

关键词： anomaly anomaly detection anomaly localization anomaly scores anomaly segmentation binary segmentation mvtec mvtec3dad mvtecad pseudolabels test time adaptation test time training

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Approximation and segment count reduction in intensity modulated radiation therapy

Approximation and segment count reduction in intensity modul...

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International Conference on Industrial and Information Systems

作者： Gunawardena, Athula D. Meyer, Robert R. Univ Wisconsin Dept Math & Comp Sci Whitewater WI 53190 USA Univ Wisconsin Dept Comp Sci Madison WI 53706 USA

ISBN: (纸本)9781424403219

For a given m x n nonnegative real matrix A, a binary segmentation with 1-norm relative error e is a set of pairs (alpha, S) = {(alpha(1), S-1), (alpha(2), S-2),..., (alpha(k), S-k)}, where each alpha(i) is a positive number and S-i is an m x n binary matrix, and e = vertical bar A - Sigma(k)(i=1) alpha S-i(i)vertical bar(1)/vertical bar A vertical bar(1), where vertical bar A vertical bar(1), is the 1-norm of a vector which consists of exactly all the entries of the matrix A. Given A and positive scalars gamma, delta, we consider the optimization problem of finding a binary segmentation alpha S that minimizes z = Sigma(k)(i=1) alpha(i) + gamma k + delta e subject to certain constraints on S-i. This problem provides a major challenge in preparing a clinically acceptable treatment plan for Intensity Modulated Radiation Therapy (IMRT) and is known to be NP-hard. In this paper, we present an effective heuristic algorithm for the above problem with an additional constraint k <= K, where K is a given positive integer. Reducing k subject to achieving an acceptable value of e is very desirable in both IMRT and Intensity Modulated Arc Therapy (IMAT). Our algorithm can be used as a valuable tool to find a clinically deliverable solution with a tradeoff between these two competing metrics.

关键词： computational complexity optimisation radiation therapy 1-norm relative error NP-hard problems binary matrix binary segmentation clinically acceptable treatment planning heuristic algorithm intensity modulated arc therapy intensity modulated radiation th Nondeterministic polynomial-time hard Heuristic algorithms Radiotherapy complexity classes intensity modulated radiation therapy approximation Positive Number

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Investigation of bone age assessment with convolutional neural network by using DoG filtering and a tarous wavelet as preprocessing techniques 6

Investigation of bone age assessment with convolutional neur...

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6th International Conference on Control Engineering and Information Technology (CEIT)

作者： Asad, Mahdi Nezhad Canturk, Ismail Genc, Fatih Z. Ozyilmaz, Lale Yildiz Tech Univ Dept Elect & Commun Engn Istanbul Turkey

ISBN: (纸本)9781538676417

Bone age assessment is one of the most important problems to assess Pediatric growth. Various methods have been investigated to develop bone age assessment. The study of bone age assessment helps to diagnose disorders in growth progress. It is very common to monitor growth progress with left hand x-ray images. There are many method such as Tanner-Whitehouse ITWI, Greulich and Pyle (G&P) methods. In this paper, we applied convolutional neural network resnet50 by preprocessing the images using different filters based on edge detection to compare effects on CNN accuracy. The main purpose of the paper is to compare the effect of DoG filtering and a trous wavelet as preprocessing techniques on bone-age assessment. It has been proved that suggested methods improved the accuracy of CNN (resnet50) in filtered images compared to the result of the non-filtered ones. The ages between 0 and 7 are analyzed to assess bone for female, male, and female male together. It is observed that there is over% 14 improvement for Female for Male %11 and for male and female %10 percent between filtered images and non-filtered images. The proposed method reached% 87.78 female and% 80 for male within 7.9 month.

关键词： bone age assessment- x-ray radiology left hand convolutional neural network a wavelet filter Differential Gaussian filter binary segmentation resnet50

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A pairwise strategic network formation model with group heterogeneity: With an application to international travel

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NETWORK SCIENCE 2022年第2期10卷 170-189页

作者： Hoshino, Tadao Waseda Univ Sch Polit Sci & Econ Shinjuku Ku 1-6-1 Nishi Waseda Tokyo 1698050 Japan

This study considers a network formation model in which each dyad of agents strategically determines the link status. Our model allows the agents to have unobserved group heterogeneity in the propensity of link formation. For the model estimation, we propose a three-step maximum likelihood method, in which the latent group structure is estimated using the binary segmentation algorithm in the second step. As an empirical illustration, we focus on the network data of international visa-free travels. The results indicate the presence of significant strategic complementarily and a certain level of degree heterogeneity in the network formation behavior.

关键词： binary game binary segmentation degree heterogeneity latent group structure network formation

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SEARCHing for Solutions: Applying a Novel Person-Centered Analysis to the Problem of Dropping Out of Preventive Parent Education

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PREVENTION SCIENCE 2015年第4期16卷 621-632页

作者： Minney, Jessica A. Lochman, John E. Guadagno, Rosanna E. Univ Alabama Tuscaloosa AL 35487 USA Univ Texas Dallas Richardson TX 75083 USA Univ Alabama Dept Psychol Tuscaloosa AL 35487 USA

Behavioral parent training is an effective intervention for many child behavior problems;however, low parent attendance and premature termination of intervention have been chronic barriers to successful implementation. Socioeconomic factors, demographic characteristics, social support, stressful life events, and parental depression have all been identified in prior research as risk factors for premature termination. The present study tested whether these risk factors were valid predictors in a targeted prevention sample using a novel methodology, a binary segmentation procedure (SEARCH), to identify meaningful subgroups within the sample. The SEARCH procedure, a person-centered approach to analysis, resulted in five mutually exclusive groups. These groups were classified based on social support and stressful life events, and group membership significantly predicted attendance at parent training. Other frequently studied predictors, such as income, were not significant predictors within this sample. The groups which were characterized by higher social support and fewer life events typically attended more sessions;however, the relationship between these risk factors was not linear and would not have been detected by many other methods of analysis. These findings both contribute to the overall literature on parent training preventions, and suggest that binary segmentation procedures, such as SEARCH, may have widespread utility in prevention research because such procedures allow for the detection of non-linear interactions between risk factors.

关键词： Cluster-type analyses binary segmentation Parent training preventions Person-centered analysis

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segmentation and analysis of breast cancer pathological images by an adaptive-sized hybrid neural network

Segmentation and analysis of breast cancer pathological imag...

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Conference on Image Processing - Medical Imaging 1996

作者： Hasegawa, A Cullen, KJ Mun, SK GEORGETOWN UNIV MED CTRDEPT RADIOLWASHINGTONDC 20007

ISBN: (纸本)0819420859

The number of nuclei on a pathology image assists pathologists in consistent diagnosis of breast cancer. Currently, most pathologists make a diagnosis based on a rough estimation of the number of nuclei on pathology images. Because of the rough estimation, the diagnosis is not objective. To assist pathologists to make a consistent, objective and fast diagnosis, it is necessary to develop a computer system to automatically recognize and count several kinds of nuclei. We have developed an algorithm for the automatic segmentation and counting of nuclei in breast cancer pathology images. In the development of the algorithm, we proposed two novel methods: an adaptive-sized hybrid neural network for the automatic segmentation of nuclei, insulin-like growth factor-II messenger RNAs and other structures, and the combined use of both the focused gradient filter and the watersheds algorithm for segmentation of overlapped nuclei.

关键词： neural networks focused gradient filter watersheds algorithm binary segmentation breast cancer pathology image

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Use of color transformation for extraction of handwriting

Use of color transformation for extraction of handwriting

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Conference on Investigative and Trial Image Processing

作者： BIJHOLD, J NETHERLANDS FORENS LAB 2288 GD RIJSWIJKNETHERLANDS

ISBN: (纸本)0819419265

In the last two years we have been testing FISH in the Netherlands police practice. FISH is a system for automatic recognition of handwriting originally developed by the BKA in Germany. Some of the most important classification algorithms that are used compute a feature vector from a digital binary image that shows handwriting without background patterns. This image is currently obtained in three steps: scanning the document (200 dpi, 64 bits), binary segmentation using a interactively selected threshold value, and interactive removal of background patterns. However, the resulting image sometimes shows black structures or white spaces that are not visible in the original document. In this study we investigated the use of color transformation for obtaining better binary images from digital 3 by 8 bit RGB color images. In each distinctive area of the image, the ink lines, background, and background patterns, a number of pixels are selected interactively and their RGB-values are sorted. Analysis of their distributions showed that separation of ink line and background using R, G and B threshold values can often be improved by performing, prior to the segmentation, a 3D rotation of the RGB-values of all pixels. Methods and results are presented.

关键词： FORENSIC IMAGE PROCESSING WRITER RECOGNITION binary segmentation COLOR TRANSFORMATION SKELETONS OF HANDWRITING CLASSIFICATION OF HANDWRITING COLOR DISTRIBUTIONS BACKGROUND REMOVAL PRINCIPAL COMPONENTS ANALYSIS

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High-dimensional change point detection for mean and location parameters

High-dimensional change point detection for mean and locatio...

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作者： Yu, Mengjia University of Illinois – Urbana-Champaign

学位级别：博士

Change point inference refers to detection of structural breaks of a sequence observation, which may have one or more distributional shifts subject to models such as mean or covariance changes. In this dissertation, we consider the offline multiple change point problem that the sample size is fixed in advance or after observation. In particular, we concentrate on high-dimensional setup where the dimension $p$ can be much larger than the sample size $n$ and traditional distribution assumptions can easily fail. The goal is to employ non-parametric approaches to identify change points without involving intermediate estimation to cross-sectional dependence. In the first part, we consider cumulative sum (CUSUM) statistics that are widely used in the change point inference and identification. We study two problems for high-dimensional mean vectors based on the $\ell^{\infty}$-norm of the CUSUM statistics. For the problem of testing for the existence of a change point in an independent sample generated from the mean-shift model, we introduce a Gaussian multiplier bootstrap to calibrate critical values of the CUSUM test statistics in high dimensions. The proposed bootstrap CUSUM test is fully data-dependent and it has strong theoretical guarantees under arbitrary dependence structures and mild moment conditions. Specifically, we show that with a boundary removal parameter the bootstrap CUSUM test enjoys the uniform validity in size under the null and it achieves the minimax separation rate under the sparse alternatives when $p \gg n$. Once a change point is detected, we estimate the change point location by maximizing the $\ell^{\infty}$-norm of the generalized CUSUM statistics at two different weighting scales. The first estimator is based on the covariance stationary CUSUM statistics, and we prove its consistency in estimating the location at the nearly parametric rate $n^{-1/2}$ for sub-exponential observations. The second estimator is based on non-stationary CUSUM stat

关键词： High-dimensional data Change point analysis CUSUM Gaussian approximation Bootstrap binary segmentation U-statistics

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