Large Margin Object Tracking with Circulant Feature Maps

Structured output support vector machine (SVM) based tracking algorithms have shown favorable performance recently. Nonetheless, the time-consuming candidate sampling and complex optimization limit their real-time applications. In this paper, we propose a novel large margin object tracking method which absorbs the strong discriminative ability from structured output SVM and speeds up by the correlation filter algorithm significantly. Secondly, a multimodal target detection technique is proposed to improve the target localization precision and prevent model drift introduced by similar objects or background noise. Thirdly, we exploit the feedback from high-confidence tracking results to avoid the model corruption problem. We implement two versions of the proposed tracker with the representations from both conventional hand-crafted and deep convolution neural networks (CNNs) based features to validate the strong compatibility of the algorithm. The experimental results demonstrate that the proposed tracker performs superiorly against several state-of-the-art algorithms on the challenging benchmark sequences while runs at speed in excess of 80 frames per second. The source code and experimental results will be made publicly available

Inference about Reliability Parameter with Underlying Gamma and Exponential Distribution

The statistical inference about the reliability parameter R involving independent gamma stress and exponential strength is considered. Assuming the shape parameter of gamma is a known arbitrary real number and the scale parameters of gamma and exponential are unknown, the UMVUE and MLE of R are obtained. A pivot is proposed. Some inference about R derived from this pivot is presented. It will be shown that the pivot can be used for testing hypothesis and constructing condence interval. A procedure of constructing the condence interval for R is derived. The performances of the UMVUE and MLE are compared numerically based on extensive Monte Carlo simulation. Simulation studies indicate that the performance of the two estimators is about the same. The MLE is preferred because of the simplicity of its computation

Stochastic Differential Equations: Simulation, Parameter Estimation and Applications

Stochastic differential equations (SDEs), including time-homogeneous Itoˆ diffusion processes, play an essential role in modelling phenomena in various fields, including physics, biology and finance. The parameters of the stochastic model are usually unknown in reality. Statistical inference on the unknown parameters of an Itoˆ diffusion process has continued to attract in- creasing attention in the last decades. Because in general, the maximum likelihood estimation is not directly applicable to the Itoˆ diffusion process, sue to the transition density usually not being available in closed form, an approximation to the transition density is developed. We aim to formulate a skew-normal approximation method motivated by the fact that the well- known Gaussian approximation method [Kessler, 1997] is inadequate in a skewed situation. The solution of an SDE, also known as the numerical method for solving the SDE, is crucial to model various phenomena. We built a simulation scheme of the two commonly used numerical methods for a general Itoˆ diffusion process across various grid widths in R. In addition to the numerical method simulation scheme, we extended the existing parameter estimation scheme [Lu et al., 2021] to the skew-normal method, and can be applied to a general Itoˆ diffusion process. In the practical implementation of our parameter estimation scheme, we applied the Gaussian approximation method and the skew-normal approximation method to estimate the parameters of two commonly used interest rate models, the Cox–Ingersoll–Ross model and the Vasicek model, for a 3-year Australian government bond yield data set. The accuracy is verified by simulating the sample paths of the estimated models using the numerical method simulation scheme for the general Itoˆ diffusion processes. The Vasicek model is demonstrated to exhibit a better performance as a model for the bond yield data under parametric bootstrap hypothesis testing

Statistical Analysis of Functional Connectivity in Brain Imaging: Measurement Reliability and Clinical Applications

Measurement reliability is crucial for the research of functional connectivity data in the context of pursuing more reproducible research. Unfortunately, the utility of traditional reliability measures, such as the intraclass correlation coefficient, is limited given the size and complexity of functional connectivity data. In recent work, novel reliability measures have been introduced in the context where a set of subjects are measured twice or more, including: fingerprinting, rank sums, and generalizations of the intraclass correlation coefficient. However, the relationships between, and the best practices among these measures remains largely unknown. In this thesis, we consider a novel reliability measure, discriminability. We show that it is deterministically linked with the correlation coefficient under univariate random effect models, and has desired property of optimal accuracy for inferential tasks using multivariate measurements. Additionally, we propose a universal framework of reliability test based on permutations of the statistics.The power of permutation tests derived from these measures are compared numerically under Gaussian and non-Gaussian settings, with and without simulated batch effects. Motivated by both theoretical and empirical results, we provide methodological recommendations for each benchmark setting to serve as a resource for future analyses. We investigate the Poisson and Gaussian approximations of the tests so that the computational cost is reduced. We demonstrate possible follow-up research using reliability tests via applications on the Human Connectome Project functional connectivity data. We believe these results will play an important role towards improving reproducibility not only for functional connectivity, but also in fields such as functional magnetic resonance imaging in general, genomics, pharmacology, and more. Lastly, we illustrate the potential of functional connectivity as a source of causal biomarkers with an example of analyzing the trial data for an aphasia treatment

FedEdge AI-TC: A Semi-supervised Traffic Classification Method based on Trusted Federated Deep Learning for Mobile Edge Computing

As a typical entity of MEC (Mobile Edge Computing), 5G CPE (Customer Premise Equipment)/HGU (Home Gateway Unit) has proven to be a promising alternative to traditional Smart Home Gateway. Network TC (Traffic Classification) is a vital service quality assurance and security management method for communication networks, which has become a crucial functional entity in 5G CPE/HGU. In recent years, many researchers have applied Machine Learning or Deep Learning (DL) to TC, namely AI-TC, to improve its performance. However, AI-TC faces challenges, including data dependency, resource-intensive traffic labeling, and user privacy concerns. The limited computing resources of 5G CPE further complicate efficient classification. Moreover, the "black box" nature of AI-TC models raises transparency and credibility issues. The paper proposes the FedEdge AI-TC framework, leveraging Federated Learning (FL) for reliable Network TC in 5G CPE. FL ensures privacy by employing local training, model parameter iteration, and centralized training. A semi-supervised TC algorithm based on Variational Auto-Encoder (VAE) and convolutional neural network (CNN) reduces data dependency while maintaining accuracy. To optimize model light-weight deployment, the paper introduces XAI-Pruning, an AI model compression method combined with DL model interpretability. Experimental evaluation demonstrates FedEdge AI-TC's superiority over benchmarks in terms of accuracy and efficient TC performance. The framework enhances user privacy and model credibility, offering a comprehensive solution for dependable and transparent Network TC in 5G CPE, thus enhancing service quality and security.Comment: 13 pages, 13 figure