Contractile function of striated muscle cells depends crucially on the almost crystalline order of actin and myosin filaments in myofibrils, but the physical mechanisms that lead to myofibril assembly remains ill-defined. Passive diffusive sorting of actin filaments into sarcomeric order is kinetically impossible, suggesting a pivotal role of active processes in sarcomeric pattern formation. Us...

syrian rue or harmal ( peganum harmala l.), belonged to the family of peganaceae, grows in semi-arid climates such as the middle east and north africa. traditionally, this plant, especially the seeds, has been recognized for its several medicinal uses. in this stydy, genetic diversity between 21 harmal accessions, collected from different regions of iran were evaluated by inter simple sequence ...

We present a novel framework for the discovery and representation of general semantic relationships that hold between lexical items. We propose that each such relationship can be identified with a cluster of patterns that captures this relationship. We give a fully unsupervised algorithm for pattern cluster discovery, which searches, clusters and merges highfrequency words-based patterns around...

The starting point of this work is the definition of local pattern detection given in [10] as the unsupervised detection of local regions with anomalously high data density, which represent real underlying phenomena. We discuss some aspects of this definition and examine the differences between clustering and pattern detection (if any), before we investigate how to utilize clustering algorithms...

Typical content-based methods for music classification and retrieval mainly deal with global statistics or features of pre-divided songs. However, focusing on local, heterogeneous fragments and features is mandatory for flexible analysis. We propose a musical word scheme based on clustering as the base for local pattern extraction, robust classification and flexible music retrieval.

1 Choose k cluster centers, which are usually k randomly-chosen patterns or k randomly defined points inside the vector space. 2 Assign each pattern to the closest cluster center (using the cosine measure). 3 Recompute the cluster centers using the current cluster memberships. 4 If a convergence criterion is met (e.g. no reassignment of patterns to new cluster centers), stop the algorithm. Othe...

The study of feature selection methods has become an area of intensive research in pattern recognition. In this paper, a new feature selection approach, called cluster-based pattern discrimination (CPD), is introduced. Classes are independently partitioned into clusters to group together similar patterns: a different subspace is defined for each cluster by determining an optimal subset of featu...