Elements of algebra, being an abridgment of Day's Algebra, adapted to the capacities of the young, and the method of instruction, in schools and academies.

Mode of access: Internet.

An introduction to the elements of algebra, designed for the use of those who are acquainted only with the first principles of arithmetic.

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Algebra, with Arithmetic and mensuration, from the Sanscrit of Brahmegupta and Bháscara.

The 12th and 18th chapters, Gaṇitādhyāya and Kuṭṭakādhyāya, of Brahmagupta's Brahmasiddhānta ; and the first two parts, Līlāvatī and Bījagaṇita, of Bhāskara's Siddhāntaśirḿanṇi.

An introduction to the algebra of quantics,

Mode of access: Internet.

Algebra; an elementary text-book, for the higher classes of secondary schools and for colleges.

Mode of access: Internet.

Logic and Boolean algebra.

Includes bibliography.

A treatise on infinitesimal calculus, containing differential and integral calculus, calculus of variations, applications to algebra and geometry, and analytical mechanics.

Vol. 3 and 4 form the author's Treatise on analytical mechanics.

Central elements of the elliptic Zn monodromy matrix algebra at roots of unity

The central elements of the algebra of monodromy matrices associated with the Z(n) R-matrix are studied. When the crossing parameter w takes a special rational value w = n/N, where N and n are positive coprime integers, the center is substantially larger than that in the generic case for which the quantum determinant provides the center. In the trigonometric limit, the situation corresponds to the quantum group at roots of unity. This is a higher rank generalization of the recent results by Belavin and Jimbo. (c) 2004 Elsevier B.V. All rights reserved.

Learning task specific distributed paragraph representations using a 2-tier convolutional neural network

We introduce a type of 2-tier convolutional neural network model for learning distributed paragraph representations for a special task (e.g. paragraph or short document level sentiment analysis and text topic categorization). We decompose the paragraph semantics into 3 cascaded constitutes: word representation, sentence composition and document composition. Specifically, we learn distributed word representations by a continuous bag-of-words model from a large unstructured text corpus. Then, using these word representations as pre-trained vectors, distributed task specific sentence representations are learned from a sentence level corpus with task-specific labels by the first tier of our model. Using these sentence representations as distributed paragraph representation vectors, distributed paragraph representations are learned from a paragraph-level corpus by the second tier of our model. It is evaluated on DBpedia ontology classification dataset and Amazon review dataset. Empirical results show the effectiveness of our proposed learning model for generating distributed paragraph representations.