– A built–in Test Pattern Generation (TPG) mechanism that is very popular for pseudorandom or pseudo–exhaustive TPG is a shift register whose initial portion is configured as a Linear Feedback Shift Register (LFSR), or some other kind of a Linear Finite State Machine (LFSM). A fundamental problem that exists in this mechanism is the presence of linear dependencies among the bit sequences produc...

Abstract Universal dependencies (UD) is a framework for morphosyntactic annotation of human language, which to date has been used create treebanks more than 100 languages. In this article, we outline the linguistic theory UD framework, draws on long tradition typologically oriented grammatical theories. Grammatical relations between words are centrally explain how predicate–argument structures ...

The validity of many efficiency measurement methods rely upon the assumption that variables such as input quantities and output mixes are independent of (or uncorrelated with) technical efficiency, however few studies have attempted to test these assumptions. In a recent paper, Wilson (2003) investigates a number of independence tests and finds that they have poor size properties and low power ...

Let μ be a probability distribution on a vector space V . When m vectors u1, . . . , um are drawn from μ, how likely are they to be linearly dependent? How is the dimension of their linear span distributed? Such questions have been addressed in a number of papers (e.g. [1],[2],[3],[6],[7]). Our work is motivated by problems in coding theory, and we address these problems in the following contex...

Linearly organized structures in language are supposed to be easy, while hierarchical information is difficult to process. Traditional accounts attribute the difficulty of processing hierarchical sentences (the dog the man walks, barks) to the long positional distances between dependencies (Gibson, 1998). Alternately, linear structures (the man walks the dog that barks) are easier to process. I...

Linear causal analysis is central to a wide range of important application spanning finance, the physical sciences, and engineering. Much of the existing literature in linear causal analysis operates in the time domain. Unfortunately, the direct application of time domain linear causal analysis to many real-world time series presents three critical challenges: irregular temporal sampling, long ...

Traditional linear methods for forecasting multivariate time series are not able to satisfactorily model the non-linear dependencies that may exist in non-Gaussian series. We build on the theory of learning vector-valued functions in the reproducing kernel Hilbert space and develop a method for learning prediction functions that accommodate such non-linearities. The method not only learns the p...

The article is a study of probabilistic dependencies between attribute-defined partitions of a universe in hierarchies of probabilistic decision tables. The dependencies are expressed through two measures: the probabilistic generalization of the Pawlak’s measure of the dependency between attributes and the expected certainty gain measure introduced by the author. The expected certainty gain mea...

The large volume of web content needs to be annotated by ontologies (called Semantic Annotation), and our empirical study shows that strong dependencies exist across different types of information (it means that identification of one kind of information can be used for identifying the other kind of information). Conditional Random Fields (CRFs) are the state-of-the-art approaches for modeling t...

Given d complex numbers z1, ..., zd, it is classical that linear dependencies λ1 z1+ ···+ λd zd=0 with λ1, ..., λd∈Z can be guessed using the LLL-algorithm. Similarly, given d formal power series f1, ..., fd ∈ C[[z]], algorithms for computing Padé-Hermite forms provide a way to guess relations P1 f1 + ···+ Pd fd = 0 with P1, ..., Pd ∈C[z]. Assuming that f1, ..., fd have a radius of convergence ...