Sanskrit in 30 Lectures: Intended Especially to Teach by Dharmendra Nath Shastri

By Dharmendra Nath Shastri

The paintings is split usually in parts-(i) the central paintings, i.e., the 30 lectures, and (ii) the 3 Appendices. along with, there's a exact bankruptcy entitled 'Introduction'.
It is key that earlier than beginning the examine of this paintings, one may still rigorously learn the advent which provides (i) in a few element the significance and importance of the learn of Sanskrit, (ii) a serious be aware on Pâriini's and different Sanskrit grammars written in eu or Indian languages, and (iii) the main points of the plan of the proposed process which makes a neo-scientific method of the examine of Sanskrit with emphasis at the learn of participles.
First six lectures :
As for the valuable paintings of the thirty lectures, the 1st six lectures provide basic information about Sanskrit and its grammar, facing (i) Sanskrit within the class of languages, (ii) Sanskrit alphabet, (iii) Nature of euphonic alterations, (iv) constitution of Sanskrit and common class of its phrases, (v) Voices and Participles, and (vi) Conjugation.

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Radolfz II K OIl . l,IlL (a) straight lines (b) balanced rotation Fig. 6. 2 Internet Multicast Backbone Internet connections represent another example of an organically grown, and growing, geographic network. To support their analysis, which is crucial for 31 maintenance and development, advanced systems such as [7,20] provide effective interactive visualizations of network topologies. The environment of [20] is publicly available, 1 and generates a geometric scene description that can be explored in an interactive browser such as Geomview [2].

In this paper we will present three overview techniques for combining exploratory and direct search methods into one system for image retrieval. We first briefly describe the underlying content-based image retrieval system, CIRCUS. 1 The CIRCUS IR System The retrieval model used by CIRCUS 2 is an adaptation to image retrieval of the Latent Semantic Indexing [3] method. The core idea is to extract a compact, useful representation of the relations between terms and documents. This is achieved through a lowerrank approximation of the term-document occurrence matrix.

Typing the phrase "paintings of nature" resulted in only one image. Not satisfied with the image, we typed in the words "painting and nature" (word order is not important), 5 images were found. Still unsatisfied with these few possibilities, we then typed in "painting," and obtained 720 images. But surprisingly, a large number of the initial results are images about painting, and not of paintings as shown in Fig. 2. Realizing that recently a category search method has been added to the site, we drilled down following Art and Architecture -+ Fine Art -+ Paintings and found 1748 images of paintings.

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