Relational visual cluster validity

The assessment of cluster validity plays a very important role in cluster analysis. Most commonly used cluster validity methods are based on statistical hypothesis testing or finding the best clustering scheme by computing a number of different cluster validity indices. A number of visual methods of cluster validity have been produced to display directly the validity of clusters by mapping data into two- or three-dimensional space. However, these methods may lose too much information to correctly estimate the results of clustering algorithms. Although the visual cluster validity (VCV) method of Hathaway and Bezdek can successfully solve this problem, it can only be applied for object data, i.e. feature measurements. There are very few validity methods that can be used to analyze the validity of data where only a similarity or dissimilarity relation exists – relational data. To tackle this problem, this paper presents a relational visual cluster validity (RVCV) method to assess the validity of clustering relational data. This is done by combining the results of the non-Euclidean relational fuzzy c-means (NERFCM) algorithm with a modification of the VCV method to produce a visual representation of cluster validity. RVCV can cluster complete and incomplete relational data and adds to the visual cluster validity theory. Numeric examples using synthetic and real data are presente

The rate of period change in DAV stars

Grids of DAV star models are evolved by \texttt{WDEC}, taking the element diffusion effect into account. The grid parameters are hydrogen mass log($M_{H}/M_{*}$), helium mass log($M_{He}/M_{*}$), stellar mass $M_{\rm *}$, and effective temperature $T_{\rm eff}$ for DAV stars. The core compositions are from white dwarf models evolved by \texttt{MESA}. Therefore, those DAV star models evolved by \texttt{WDEC} have historically viable core compositions. Based on those DAV star models, we studied the rate of period change ($\dot{P}(k)$) for different values of H, He, $M_{\rm *}$, and $T_{\rm eff}$. The results are consistent with previous work. Two DAV stars G117-B15A and R548 have been observed around forty years. The rates of period change of two large-amplitude modes were obtained through O-C method. We did asteroseismological study on the two DAV stars and then obtained a best-fitting model for each star. Based on the two best-fitting models, the mode identifications ($l$, $k$) of the observed modes for G117-B15A and R548 are consistent with previous work. Both the observed modes and the observed $\dot{P}$s can be fitted by calculated ones. The results indicate that our method of evolving DAV star models is feasible.Comment: 20pages, 12 figures, 6 tables, accepted by RAA on 3/18, 201

A sparse multinomial probit model for classification

A recent development in penalized probit modelling using a hierarchical Bayesian approach has led to a sparse binomial (two-class) probit classifier that can be trained via an EM algorithm. A key advantage of the formulation is that no tuning of hyperparameters relating to the penalty is needed thus simplifying the model selection process. The resulting model demonstrates excellent classification performance and a high degree of sparsity when used as a kernel machine. It is, however, restricted to the binary classification problem and can only be used in the multinomial situation via a one-against-all or one-against-many strategy. To overcome this, we apply the idea to the multinomial probit model. This leads to a direct multi-classification approach and is shown to give a sparse solution with accuracy and sparsity comparable with the current state-of-the-art. Comparative numerical benchmark examples are used to demonstrate the method

Volume growth, eigenvalue and compactness for self-shrinkers

In this paper, we show an optimal volume growth for self-shrinkers, and estimate a lower bound of the first eigenvalue of $\mathcal{L}$ operator on self-shrinkers, inspired by the first eigenvalue conjecture on minimal hypersurfaces in the unit sphere by Yau \cite{SY}. By the eigenvalue estimates, we can prove a compactness theorem on a class of compact self-shrinkers in \ir{3} obtained by Colding-Minicozzi under weaker conditions.Comment: 17 page

Molecular Dynamics Study of Bamboo-like Carbon Nanotube Nucleation

MD simulations based on an empirical potential energy surface were used to study the nucleation of bamboo-like carbon nanotubes (BCNTs). The simulations reveal that inner walls of the bamboo structure start to nucleate at the junction between the outer nanotube wall and the catalyst particle. In agreement with experimental results, the simulations show that BCNTs nucleate at higher dissolved carbon concentrations (i.e., feedstock pressures) than those where non-bamboolike carbon nanotubes are nucleated