Abstract: This contribution summarizes the considerations that are of major importance in inorganic mineral formation before we look at specific biological minerals. Some factors which have to be taken into account (other than those that are well-known from inorganic (abiological) precipitations) are: the nature of the biological organic matrix; the restricted volume, outside or inside the cytoplasm, which can cause differences in impurity content (Mg); crystal morphology; and isotopic fractionation. Cases such as those of corals, foraminifera and coccoliths are taken as examples. Factors affecting mineralization Equilibria The formation of a mineral from a solution is under a number of different controlling factors (Crick 1986; Fraústo da Silva & Williams 1991). The simplest case we can consider is an equilibrium between a particular form of a mineral, say calcite, and the activities, not concentrations, of

In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular training set

We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing

The Internet has been a startling and dramatic success. However, multimedia applications, with their novel traffic characteristics and service requirements, pose an interesting challenge to the technical foundations of the Internet. In this paper we address some of the fundamental architectural

-product of reward sensitivity, rather than the core feature of the trait. Extraversion has a rich history in the field of personality psy-chology. Ttie roots of this tradition extend as far back as the work Editor's Note. David Watson served as the action editor for this

This paper is a comparative study of feature selection methods in statistical learning of text categorization. The focus is on aggressive dimensionality reduction. Five methods were evaluated, including term selection based on document frequency (DF), information gain (IG), mutual information (MI

This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting “spatial

Abstract—Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion based on mutual information. Because of the difficulty in directly implementing the maximal dependency condition, we

Stable local feature detection and representation is a fundamental component of many image registration and object recognition algorithms. Mikolajczyk and Schmid [14] recently evaluated a variety of approaches and identified the SIFT [11] algorithm as being the most resistant to common image

of such fundamental notions as objects, classes, and inheritance. Much progress was made by investigating the notion of subtyping within procedural languages and their theoretical models (lambda calculi). These studies clarified the role of subtyping in object-oriented languages, but still relied on complex encodings