Learning Rates of Regularized Regression With Multiple Gaussian Kernels for Multi-Task Learning.

This paper considers a least square regularized regression algorithm for multi-task learning in a union of reproducing kernel Hilbert spaces (RKHSs) with Gaussian kernels. It is assumed that the optimal prediction function of the target task and those of related tasks are in an RKHS with the same but with unknown Gaussian kernel width. The samples for related tasks are used to select the Gaussian kernel width, and the sample for the target task is used to obtain the prediction function in the RKHS with this selected width.

A new sequence convergent to Euler-Mascheroni constant.

In this paper, we provide a new sequence converging to the Euler-Mascheroni constant. Finally, we establish some inequalities for the Euler-Mascheroni constant by the new sequence.

Identification and evolutionary analysis of long non-coding RNAs in zebra finch.

Background: Long non-coding RNAs (lncRNAs) are important in various biological processes, but very few studies on lncRNA have been conducted in birds. To identify IncRNAs expressed during feather development, we analyzed single-stranded RNA-seq (ssRNA-seq) data from the anterior and posterior dorsal regions during zebra finch (Taeniopygia guttata) embryonic development. Using published transcriptomic data, we further analyzed the evolutionary conservation of IncRNAs in birds and amniotes.

Detection of human papillomavirus in squamous cell carcinoma arising from dermoid cysts.

Objective: Primary squamous cell carcinoma (SCC) of the ovary in humans is rare. Most cases represent a malignant transformation of ovarian teratoma, Brenner tumor, or endometriosis. The etiology of this cancer remains largely unknown.

Transcriptomic analyses of regenerating adult feathers in chicken.

Background: Feathers have diverse forms with hierarchical branching patterns and are an excellent model for studying the development and evolution of morphological traits. The complex structure of feathers allows for various types of morphological changes to occur. The genetic basis of the structural differences between different parts of a feather and between different types of feather is a fundamental question in the study of feather diversity, yet there is only limited relevant information for gene expression during feather development.

Least square regularized regression in sum space.

This paper proposes a least square regularized regression algorithm in sum space of reproducing kernel Hilbert spaces (RKHSs) for nonflat function approximation, and obtains the solution of the algorithm by solving a system of linear equations. This algorithm can approximate the low- and high-frequency component of the target function with large and small scale kernels, respectively. The convergence and learning rate are analyzed.

Learning with convex loss and indefinite kernels.

We consider a kind of kernel-based regression with general convex loss functions in a regularization scheme. The kernels used in the scheme are not necessarily symmetric and thus are not positive semidefinite; l(1)-norm of the coefficients in the kernel ensembles is taken as the regularizer. Our setting in this letter is quite different from the classical regularized regression algorithms such as regularized networks and support vector machines regression.

Anatomical and transcriptional dynamics of maize embryonic leaves during seed germination.

Our anatomical analysis revealed that a dry maize seed contains four to five embryonic leaves at different developmental stages. Rudimentary kranz structure (KS) is apparent in the first leaf with a substantial density, but its density decreases toward younger leaves. Upon imbibition, leaf expansion occurs rapidly with new KSs initiated from the palisade-like ground meristem cells in the middle of the leaf.