Parametric quantum circuits play a crucial role in the performance of many variational algorithms. To successfully implement such algorithms, one must design efficient that sufficiently approximate solution space while maintaining low parameter count and circuit depth. In this paper, develop method to analyze dimensional expressivity parametric circuits. Our technique allows for identifying sup...

Bu çalışmada; dış rotorlu ve yüzeye monte mıknatıslara sahip bir Vernier makinanın tüm geometrik parametrelerinin performans karakteristiklerine olan etkisi detaylı şekilde incelenmiştir. çalışmanın amacı, düşük hız yüksek moment uygulamalarında kullanılan Kalıcı Mıknatıslı makinalar (KMVM) için mıknatıs, diş açıklığı kalınlık genişlikleri, hava aralığı genişliği, rotor çaplarının iç çapına ora...

A Bayesian semiparametric proportional hazards model is presented to describe the failure behavior of machine tools. The semiparametric setup is introduced using a mixture of Dirichlet processes prior. A Bayesian analysis is performed on real machine tool failure data using the semiparametric setup, and development of optimal replacement strategies are discussed. The results of the semiparametr...

Studies of evolutionary correlations commonly use phylogenetic regression (i.e., independent contrasts and phylogenetic generalized least squares) to assess trait covariation in a phylogenetic context. However, while this approach is appropriate for evaluating trends in one or a few traits, it is incapable of assessing patterns in highly multivariate data, as the large number of variables relat...

In this paper we propose a novel clustering algorithm based on maximizing the mutual information between data points and clusters. Unlike previous methods, we neither assume the data are given in terms of distributions nor impose any parametric model on the within-cluster distribution. Instead, we utilize a non-parametric estimation of the average cluster entropies and search for a clustering t...

Parametric, model-based algorithms learn generative models from the data, with each model corresponding to one particular cluster. Accordingly, the model-based partitional algorithm will select the most suitable model for any data object (Clustering step), and will recompute parametric models using data specifically from the corresponding clusters (Maximization step). This Clustering-Maximizati...

In this paper, we provide a large bandwidth analysis for a class of local likelihood methods. This work complements the small bandwidth analysis of Park, Kim and Jones (2002). Our treatment is more general than the large bandwidth analysis of Eguchi and Copas (1998). We provide a higher order asymptotic analysis for the risk of the local likelihood density estimator, from which a direct compari...