A generalization of leftmost derivations

A generalization of leftmost derivation called depth-first derivation is defined. The main result, that the depth-first derivations of an arbitrary phrase-structure grammar generate a context-free language, is proved using a new technique in which families of equivalent depth-first derivations of one grammar are represented by single productions in a new grammar. This result is related to several others, including an analogous result for leftmost derivations, through the theorem of B. Baker [1] that every terminal-bounded grammar generates a context-free language.     

Third-order generalization: A new approach to categorizing higher-order generalization

Generalization, in its most basic form, is an artificial neural network's (ANN's) ability to automatically classify data that were not seen during training. This paper presents a framework in which generalization in ANNs is quantified and different types of generalization are viewed as orders. The ordering of generalization is a means of categorizing different behaviours. These orders enable generalization to be evaluated in a detailed and systematic way. The approach used is based on existing definitions which are augmented in this paper. The generalization framework is a hierarchy of categories which directly aligns an ANN's ability to perform table look-up, interpolation, extrapolation, and hyper-extrapolation tasks.

The framework is empirically validated. Validation is undertaken with three different types of regression task: (1) a one-to-one (o–o) task, f(x):x_i→y_j; (2) the second, in its f(x):{x_i,x_i+1, …}→y_j formulation, maps a many-to-one (m–o) task; and (3) the third f(x):x_i→{y_j,y_j+1, …} a one-to-many (o–m) task. The first and second are assigned to feedforward nets, while the third, due to its complexity, is assigned to a recurrent neural net.

Throughout the empirical work, higher-order generalization is validated with reference to the ability of a net to perform symmetrically related or isomorphic functions generated using symmetric transformations (STs) of a net's weights. The transformed weights of a base net (BN) are inherited by a derived net (DN). The inheritance is viewed as the reuse of information. The overall framework is also considered in the light of alignment to neural models; for example, which order (or level) of generalization can be performed by which specific type of neuron model.

The complete framework may not be applicable to all neural models; in fact, some orders may be special cases which apply only to specific neuron models. This is, indeed, shown to be the case. Lower-order generalization is viewed as a general case and is applicable to all neuron models, whereas higher-order generalization is a particular or special case. This paper focuses on initial results; some of the aims have been demonstrated and amplified through the experimental work.     

A generalization of dynamic constraints

This paper presents a constraint method for physically based computer graphics models, based on the constraint stabilization method of Baumgarte and on the dynamic constraints of Barzel and Barr. These new constraints are called generalized dynamic constraints (GDCs). GDCs extend dynamic constraints to obey the principle of virtual work and to fulfill time-varying and inequality constraints. The constraint forces of GDCs are computed by a sparse linear system and are proportional to the Lagrange multipliers of the constraints. GDCs are used to assemble deformable computer graphics models and to simulate collisions between the models.     

A generalization of Rosenbrock's theorem

This note presents the simple generalization of Rosenbroek's theorem, concerning the limits of linear state variable feedback in modifying the dynamics of constant linear multivariable systems.     

A non-associative generalization of effect algebras

Effect algebras play an important role in the logic of quantum mechanics. The aim of this paper is to drop the associativity of addition. However, some important properties of effect algebras are preserved, e.g. every so-called skew effect algebra is still a bounded poset with an antitone involution. Moreover, skew effect algebras are fully characterized as certain bounded posets with sectionally switching involutions.     

A generalization of the Jensen inequality

A new approach for estimating functions and functionals based on the concept of function superconvexity and a generalization of the Jensen inequality is proposed. A new concept is introduced and the associated analytical results are substantiated. Some nontrivial estimates of functions are derived. Translated from Kibemetika i Sistemnyi Analiz, No. 2, pp. 89–99, March–April, 2000.