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Triangular meshes are commonly used to reconstruct the surfaces of 3-dimensional (3D) objects based on the point cloud data. With an increasing demand for high-quality approximation, the sizes of point cloud data and the generated triangular meshes ...

In this paper, we develop an online change-point detection procedure in the covariance structure of high-dimensional data. A new stopping rule is proposed to terminate the process as early as possible when a change in covariance structure occurs. The ...

During pandemics, effective interventions require monitoring the problem at different scales and understanding the various tradeoffs between efficacy, privacy, and economic burden. To address these challenges, we propose a framework where we perform ...

In fluid team sports such as soccer and basketball, analyzing team formation is one of the most intuitive ways to understand tactics from domain participants' point of view. However, existing approaches either assume that team formation is consistent ...

Non-stationary environments are challenging for reinforcement learning algorithms. If the state transition and/or reward functions change based on latent factors, the agent is effectively tasked with optimizing a behavior that maximizes performance over ...

Detecting when the underlying distribution changes for the observed time series is a fundamental problem arising in a broad spectrum of applications. In this paper, we study multiple change-point localization in the high-dimensional regression setting, ...

The change-point detection problem seeks to identify distributional changes at an unknown change-point k^* in a stream of data. This problem appears in many important practical settings involving personal data, including biosurveillance, fault detection, ...

Recent probabilistic model checking techniques can verify reliability and performance properties of software systems affected by parametric uncertainty. This involves modelling the system behaviour using interval Markov chains, i.e., Markov models with ...

Functional principal component analysis (FPCA) is the most commonly used technique to analyze infinite‐dimensional functional data in finite lower‐dimensional space for the ease of computational intensity. However, the power of a test detecting ...

The Mobile IoT domain has been significantly expanded with the proliferation of drones and unmanned robotic devices. In this new landscape, the communication between the resource-constrained device and the fixed infrastructure is similarly expanded to ...

The modern vehicle has transformed from a purely mechanical system to a system that embeds several electronic devices. These devices communicate through the in-vehicle network for enhanced safety and comfort but are vulnerable to cyber-physical risks ...

The objective of this work is to use a 1‐dimensional signal that reflects the dissimilarity between multidimensional probability densities for detection. With the modified Kullback‐Leibler divergence, faults can be directly detected without any ...

We study different approaches for modelling intervention effects in time series of counts, focusing on the so-called integer-valued GARCH models. A previous study treated a model where an intervention affects the non-observable underlying mean process ...

This paper deals with hardware acceleration of statistical methods for detection of anomalies on 100 Gb/s Ethernet. The approach is demonstrated by implementing a sequential Non-Parametric Cumulative Sum (NP-CUSUM) procedure. We use high-level synthesis ...

The complex computing systems employed by governments, corporations, and other institutions are frequently targeted by cyber-attacks designed for espionage and sabotage. The malicious software used in such attacks are typically custom-designed or ...

Applying advanced statistical techniques, we characterize the peculiarities of a locally observed peer population in a popular P2P overlay network. The latter is derived from a mesh-pull architecture. Using flow data collected at a single peer, we show ...

The objective of change-point detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical change-point detection algorithm that is based on non-parametric divergence estimation between two ...

In segmentation problems, inference on change-point position and model selection are two difficult issues due to the discrete nature of change-points. In a Bayesian context, we derive exact, explicit and tractable formulae for the posterior distribution ...

Change-point detection is the problem of discovering time points at which properties of time-series data change. This covers a broad range of real-world problems and has been actively discussed in the community of statistics and data mining. In this ...

A simple and efficient algorithm for robust end of-utterance detection of speech signal in noisy environments is proposed in the paper. To detect speech-block end-points, we use entropy sequence of the input speech signal, and hierarchically compare the ...