Nominal number in Cushitic

Cushitic languages have a number of interesting properties in the category of number. None of these are valid for all Cushitic languages. Number is not obligatorily expressed in various Cushitic languages which have a general number form that is unspeci^ed for number. Nonetheless morphological number marking in the noun is often complex in two ways: there are many competing lexically determined morphological markers and many di^erent constellations of derived singular and derived plurals.Number and gender show complex interactions in Cushitic. Number formatives impose gender and hence di^erent gender values for di^erent number forms in the same lexeme, sometimes apparent gender polarity (singular and plural having opposite values for gender). A theoretically challenging property of some languages is that that there is a third gender, here labelled ‘plural’ because it takes the agreement morphology of 3PL pronouns.</p

Don’t Believe in Underspecified Semantics Neg Raising in Lexical Resource Semantics

Neg raising is a construction that has been widely studied from different theoretical perspectives, going back to the classic philosophers (cf. Horn (1989)). Yet even the most central properties have not received a satisfactory integration into a linguistic framework. In this paper I will try to approach the phenomenon from a new angle

Optimizing the cost function of histogram of oriented gradient-based INRIA dataset

Person detection in images requires both image processing and machine learning concepts. Image processing techniques are used in extracting feature descriptor sets. The extracted features are then used as inputs for training a machine learning algorithm to perform classification of objects are persons. One of the feature description algorithms used for image classification is the Histogram of Oriented Gradients (HOG). HOG is based on gradient vectors and the use of sliding windows in order to obtain the feature descriptor sets. For machine learning, support vector machine (SVM) is used for person classification. In this paper, the images used are based on the INRIA person dataset, which contains 3542 human images with varying range of pose and backgrounds. This paper presents the finding of the optimized cost function C for each type of linear-based SVM models, for person detection in the INRIA person data set, based on the HOG feature detector set