Exploratory data analysis requires (i) to define a set of patterns hypothesized to exist in the data, (ii) to specify a suitable quantification principle or cost function to rank these patterns and (iii) to validate the inferred patterns. For data clustering, the patterns are object partitionings into k groups; for PCA or truncated SVD, the patterns are orthogonal transformations with projectio...

This article presents a pattern-based language designed to select (a set of) subterms of a given term in a concise and robust way. Building on this language, we implement a single-step rewriting tactic in the Isabelle theorem prover, which removes the need for obscure "occurrence numbers" for subterm selection. The language was inspired by the language of patterns of Gonthier and Tassi, but pro...

The results of an experimental investigation of granular segregation in a thin rotating drum are presented. A new mechanism based on the presence of an uphill wave of particles has been found to govern the observed pattern of petals. Specifically we develop a simple model that captures the essential physics of the segregation and yields an algebraic expression that predicts the number of petals...

Hierarchies of patterns of features, of subassemblies, or of CSG sub-expressions are used in architectural and mechanical CAD to eliminate laborious repetitions from the design process. Yet, often the placement, shape, or even existence of a selection of the repeated occurrences in the pattern must be adjusted. The specification of a desired selection of occurrences in a hierarchy of patterns i...

the current study was conducted to investigate the effect of short-term divergent selection on the shape growth curve in different lines of japanese quail. the quail lines utilized in this study were two divergently selected for high (hw) and low (lw) 4-wk body weight during 7 generation and also a control line (c). the richard function parameters were used to describe growth curves of differen...

An alternative to the typical technique of selecting training examples independently from a fixed distribution is fonnulated and analyzed, in which the current example is presented repeatedly until the error for that item is reduced to some criterion value, ~; then, another item is randomly selected. The convergence time can be dramatically increased or decreased by this heuristic, depending on...

Training SVM requires large memory and long cpu time when the pattern set is large. To alleviate the computational burden in SVM training, we propose a fast preprocessing algorithm which selects only the patterns near the decision boundary. Preliminary simulation results were promising: Up to two orders of magnitude, training time reduction was achieved including the preprocessing, without any ...