Randomization is a fundamental tool used in many theoretical and practical areas of computer science. We study here the role of randomization in the area of submodular function maximization. In this area most algorithms are randomized, and in almost all cases the approximation ratios obtained by current randomized algorithms are superior to the best results obtained by known deterministic algor...

In an industrial context, the activity of sensors is recorded at a high frequency. A challenge to automatically detect abnormal measurement behavior. Considering sensor measures as functional data, problem can be formulated detection outliers in multivariate data set. Due heterogeneity this set, proposed contaminated mixture model both clusters into homogeneous groups and detects outliers. The ...

-Maximum likelihood thresholding methods are presented on the basis of population mixture models. It turns out that the standard thresholding proposed by Otsu, which is based on a discriminant criterion and also minimizes the mean square errors between the original image and the resultant binary image, is equivalent to the maximization of the likelihood of the conditional distribution in the po...

We develop a Markov-Switching Autoregressive Conditional Intensity (MS-ACI) model with time-varying transitional probability, and show that it can be reliably estimated via the Stochastic Approximation Expectation–Maximization algorithm. Applying our to high-frequency transaction data, we detect two distinct regimes in intraday volatility process: dominant regime is observable throughout tradin...

This paper proposes a general probabilistic framework for shape-based modeling and classification of waveform data. A segmental hidden Markov model (HMM) is used to characterize waveform shape and shape variation is captured by adding random effects to the segmental model. The resulting probabilistic framework provides a basis for learning of waveform models from data as well as parsing and rec...

Abstract. In this paper, we propose a family of surrogate maximization (SM) algorithms for multi-class logistic regression models (also called conditional exponential models). An SM algorithm aims at turning an otherwise intractable maximization problem into a tractable one by iterating two steps. The S-step computes a tractable surrogate function to substitute the original objective function, ...

Abstract Telematicsdevices installed in insured vehicles provide actuaries with new risk factors, such as the time of day, average speeds, and other driving habits. This paper extends multivariate mixed model describing joint dynamics telematics data claim frequencies proposed by Denuit et al. (2019a) allowing for signals various formats, not necessarily integer-valued, replacing estimation pro...

Background: In this study, Quantitative 32P bremsstrahlung planar and SPECT imaging and consequent dose assessment were carried out as a comprehensive phantom study to define an appropriate method for accurate Dosimetry in clinical practice. Materials and Methods: CT, planar and SPECT bremsstrahlung images of Jaszczak phantom containing a known activity of 32P were acquired. In addition, Phanto...

The parameterized complexity of problems is often studied with respect to the size of their optimal solutions. However, for a maximization problem, the size of the optimal solution can be very large, rendering algorithms parameterized by it inefficient. Therefore, we suggest to study the parameterized complexity of maximization problems with respect to the size of the optimal solutions to their...