13 Darlington, C. D., Jour. Genetics, 24, 65-96 (1931). 14 Painter, T. S., Science, 73, 647-648 (1931). 16 Muller, H. J., and Painter, T. S., Z. I. A. V., 62, 316-365 (1932). 16 Dobzhansky, Th., Biol. Zbl., 52, 493-509 (1932). 17 Sivertzev-Dobzhansky, N. P., and Dobzhansky, Th., Genetics, 18, 173-192 (1933). 18 Metz, C. W., Zeit. Zellforsch., 4, 1-28 (1926). 19 Darlington, C. D., Genetics (in p...

An online Ramsey game (G,H) is a game between Builder and Painter, alternating in turns. During each turn, Builder draws an edge, and Painter colors it blue or red. Builder’s goal is to force Painter to create a monochromatic copy of G, while Painter’s goal is to prevent this. The only limitation for Builder is that after each of his moves, the resulting graph has to belong to the class of grap...

We study on-line version of size-Ramsey numbers of graphs defined via a game played between Builder and Painter: in one round Builder joins two vertices by an edge and Painter paints it red or blue. The goal of Builder is to force Painter to create a monochromatic copy of a fixed graph H in as few rounds as possible. The minimum number of rounds (assuming both players play perfectly) is the on-...

Correct interpretation of the text frequently requires knowledge of semantic categories of nouns, especially in languages with free word order. For example, in Spanish the phrases pintó un cuadro un pintor (lit. painted a picture a painter) and pintó un pintor un cuadro (lit. painted a painter a picture) mean the same: ‘a painter painted a picture’; with the only way to tell the subject from th...

When graph Ramsey theory is viewed as a game, “Painter” 2-colors the edges of a graph presented by “Builder”. Builder wins if every coloring has a monochromatic copy of a fixed graph G. In the on-line version, iteratively, Builder presents one edge and Painter must color it. Builder must keep the presented graph in a class H. Builder wins the game (G,H) if a monochromatic copy of G can be force...

The goal of this paper is to offer a framework for image classification “by type”. For example, one may want to classify an image of a certain office as man-made – as opposed to outdoor – scene, even if no image of a similar office exists in the training set. This is accomplished by using local features, and using the naive Bayes classifier. The application presented here is classification of p...