Symbolic Analysis for Boundary Problems: From Rewriting to Parametrized Groebner Bases

We review our algebraic framework for linear boundary problems (concentrating on ordinary differential equations). Its starting point is an appropriate algebraization of the domain of functions, which we have named integro-differential algebras. The algebraic treatment of boundary problems brings up two new algebraic structures whose symbolic representation and computational realization is based on canonical forms in certain commutative and noncommutative polynomial domains. The first of these, the ring of integro-differential operators, is used for both stating and solving linear boundary problems. The other structure, called integro-differential polynomials, is the key tool for describing extensions of integrodifferential algebras. We use the canonical simplifier for integro-differential polynomials for generating an automated proof establishing a canonical simplifier for integro-differential operators. Our approach is fully implemented in the THEOREMA system; some code fragments and sample computations are included

A New Thesis concerning Synchronised Parallel Computing - Simplified Parallel ASM Thesis

A behavioural theory consists of machine-independent postulates characterizing a particular class of algorithms or systems, an abstract machine model that provably satisfies these postulates, and a rigorous proof that any algorithm or system stipulated by the postulates is captured by the abstract machine model. The class of interest in this article is that of synchronous parallel algorithms. For this class a behavioural theory has already been developed by Blass and Gurevich, which unfortunately, though mathematically correct, fails to be convincing, as it is not intuitively clear that the postulates really capture the essence of (synchronous) parallel algorithms. In this article we present a much simpler (and presumably more convincing) set of four postulates for (synchronous) parallel algorithms, which are rather close to those used in Gurevich's celebrated sequential ASM thesis, i.e. the behavioural theory of sequential algorithms. The key difference is made by an extension of the bounded exploration postulate using multiset comprehension terms instead of ground terms formulated over the signature of the states. In addition, all implicit assumptions are made explicit, which amounts to considering states of a parallel algorithm to be represented by meta-finite first-order structures. The article first provides the necessary evidence that the axiomatization presented in this article characterizes indeed the whole class of deterministic, synchronous, parallel algorithms, then formally proves that parallel algorithms are captured by Abstract State Machines (ASMs). The proof requires some recourse to methods from finite model theory, by means of which it can be shown that if a critical tuple defines an update in some update set, then also every other tuple that is logically indistinguishable defines an update in that update set

Quadratic invariants for discrete clusters of weakly interacting waves

We consider discrete clusters of quasi-resonant triads arising from a Hamiltonian three-wave equation. A cluster consists of N modes forming a total of M connected triads. We investigate the problem of constructing a functionally independent set of quadratic constants of motion. We show that this problem is equivalent to an underlying basic linear problem, consisting of finding the null space of a rectangular M × N matrix with entries 1, −1 and 0. In particular, we prove that the number of independent quadratic invariants is equal to J ≡ N − M* ≥ N − M, where M* is the number of linearly independent rows in Thus, the problem of finding all independent quadratic invariants is reduced to a linear algebra problem in the Hamiltonian case. We establish that the properties of the quadratic invariants (e.g., locality) are related to the topological properties of the clusters (e.g., types of linkage). To do so, we formulate an algorithm for decomposing large clusters into smaller ones and show how various invariants are related to certain parts of a cluster, including the basic structures leading to M* < M. We illustrate our findings by presenting examples from the Charney–Hasegawa–Mima wave model, and by showing a classification of small (up to three-triad) clusters

Niveles de algebrización de la actividad matemática escolar : implicaciones para la formación de maestros

El desarrollo del razonamiento algebraico elemental desde los primeros niveles educativos es un objetivo propuesto en diversas investigaciones y orientaciones curriculares. En consecuencia, es importante que el profesor de educación primaria conozca las características del razonamiento algebraico y sea capaz de seleccionar y elaborar tareas matemáticas adecuadas que permitan la progresiva introducción del razonamiento algebraico en la escuela primaria. En este trabajo, presentamos un modelo en el que se diferencian tres niveles de razonamiento algebraico elemental que puede utilizarse para reconocer características algebraicas en la resolución de tareas matemáticas. Presentamos el modelo junto con ejemplos de actividades matemáticas, clasificadas según los distintos niveles de algebrización. Estas actividades pueden ser usadas en la formación de profesores a fin de capacitarlos para el desarrollo del sentido algebraico en sus alumnos.Developing elementary algebraic thinking since the earliest levels of education is a goal proposed in different research works and curricular guidelines. Consequently, primary school teachers should know the characteristics of algebraic reasoning and be able to select and develop appropriate mathematical tasks that serve to gradually introduce algebraic reasoning in primary school. In this paper we present a model that distinguish three levels of elementary algebraic thinking and is useful in analyzing the algebraic features in solving mathematical tasks. We describe this model with examples of mathematical activities, classified according to the different levels of algebraization. These activities can be used in the education of teachers to prepare them to develop their students' algebraic sense

Text Anomaly Detection with ARAE-AnoGAN

Generative adversarial networks (GANs) are now one of the key techniques for detecting anomalies in images, yielding remarkable results. Applying similar methods to discrete structures, such as text sequences, is still largely an unknown. In this work, we introduce a new GAN-based text anomaly detection method, called ARAE-AnoGAN, that trains an adversarially regularized autoencoder (ARAE) to reconstruct normal sentences and detects anomalies via a combined anomaly score based on the building blocks of ARAE. Finally, we present experimental results demonstrating the effectiveness of ARAE-AnoGAN and other deep learning methods in text anomaly detection