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This paper addresses the task of finding acronym-definition pairs in text. Most of the previous work on the topic is about systems that involve manually generated rules or regular expressions. In this paper, we present a supervised learning approach to the acronym identification task. Our approach reduces the search space of the supervised learning system by putting some weak constraints on the kinds of acronym-definition pairs that can be identified. We obtain results comparable to hand-crafted systems that use stronger constraints. We describe our method for reducing the search space, the features used by our supervised learning system, and our experiments with various learning schemes.

Cognitive functions such as a perception, thinking and acting are based on the working of the brain, one of the most complex systems we know. The traditional scientific methodology, however, has proved to be not sufficient to understand the relation between brain and cognition. The aim of this paper is to review an alternative methodology – nonlinear dynamical analysis – and to demonstrate its benefit for cognitive neuroscience in cases when the usual reductionist method fails.

According to recent studies in developmental psychology and neuroscience, symbolic language is essentially intersubjective. Empathetically relating to others renders possible the acquisition of linguistic constructs. Intersubjectivity develops in early ontogenetic life when interactions between mother and infant mutually shape their relatedness. Empirical findings suggest that the shared attention and intention involved in those interactions is sustained as it becomes internalized and embodied. Symbolic language is derivative and emerges from shared intentionality. In this paper, we present a formalization of shared intentionality based upon a quantum approach. From a phenomenological viewpoint, we investigate the nonseparable, dynamic and sustainable nature of social cognition and evaluate the appropriateness of quantum interaction for modelling intersubjectivity.

Volume visualizations can have drastically different appearances when viewed using a variety of transfer functions. A problem then occurs in trying to organize many different views on one screen. We conducted a user study of four layout techniques for these multiple views. We timed participants as they separated different aspects of volume data for both time-invariant and time-variant data using one of four different layout schemes. The layout technique had no impact on performance when used with time-invariant data. With time-variant data, however, the multiple view layouts all resulted in better times than did a single view interface. Surprisingly, different layout techniques for multiple views resulted in no noticeable difference in user performance. In this paper, we describe our study and present the results, which could be used in the design of future volume visualization software to improve the productivity of the scientists who use it.

Volume visualizations can have drastically different appearances when viewed using a variety of transfer functions. A problem then occurs in trying to organize many different views on one screen. We conducted a user study of four layout techniques for these multiple views. We timed participants as they separated different aspects of volume data for both time-invariant and time-variant data using one of four different layout schemes. The layout technique had no impact on performance when used with time-invariant data. With time-variant data, however, the multiple view layouts all resulted in better times than did a single view interface. Surprisingly, different layout techniques for multiple views resulted in no noticeable difference in user performance. In this paper, we describe our study and present the results, which could be used in the design of future volume visualization software to improve the productivity of the scientists who use it.

Rough Set Systems, can be made into several logic-algebraic structures (for instance, semi-simple Nelson algebras, Heyting algebras, double Stone algebras, three-valued £ukasiewicz algebras and Chain Based Lattices). In the present paper, Rough Set Systems are analysed from the point of view of co-Heyting algebras. This new chapter in the algebraic analysis of Rough Sets does not follow from aesthetic or completeness issues, but it is a pretty immediate consequence of interpreting the basic features of co-Heyting algebras (originally introduced by C. Rauszer and investigated by W. Lawvere in the context of Continuum Physics), through the lenses of incomplete information analysis. Indeed Lawvere pointed out the role that the co-intuitionistic negation ''non'' (dual to the intuitionistic negation ''not'') plays in grasping the geometrical notion of ''boundary'' as well as the physical concepts of ''sub-body'' and ''essential core of a body'' and we aim at providing an outline of how and to what extent they are mirrored by the basic features of incomplete information analysis.

The goal in this paper is to show how the classification of phonetic features to phonemes can be acquired. This classificational process is modeled by a supervised feature selection method, based on adaptive distance measures. Exception handling is incorporated into a learned distance function by pointwise additions of Boolean functions for individual pattern combinations. An important result is the differentiation of rules and exceptions during learning.

The goal in this paper is to show how the classification of phonetic features to phonemes can be acquired. This classificational process is modeled by a supervised feature selection method, based on adaptive distance measures. Exception handling is incorporated into a learned distance function by pointwise additions of Boolean functions for individual pattern combinations. An important result is the differentiation of rules and exceptions during learning.

Metabolic-repair models, or (M,R)-systems were introduced in Relational Biology by Robert Rosen. Subsequently, Rosen represented such (M,R)-systems (or simply MRs) in terms of categories of sets, deliberately selected without any structure other than the discrete topology of sets. Theoreticians of life’s origins postulated that Life on Earth has begun with the simplest possible organism, called the primordial. Mathematicians interested in biology attempted to answer this important question of the minimal living organism by defining the functional relations that would have made life possible in such a minimal system- a grandad and grandma of all living organisms on Earth.

A categorical and Łukasiewicz-Topos framework for Algebraic Logic models of nonlinear dynamics in complex functional systems such as Neural Networks, Cell Genome and Interactome Networks is introduced. Łukasiewicz Algebraic Logic models of both neural and genetic networks and signaling pathways in cells are formulated in terms of nonlinear dynamic systems with n-state components that allow for the generalization of previous logical models of both genetic activities and neural networks. An algebraic formulation of variable next-state/transfer functions is extended to a Łukasiewicz Topos with an N-valued Łukasiewicz Algebraic Logic subobject classifier description that represents non-random and nonlinear network activities as well as their transformations in developmental processes and carcinogenesis.

We describe further progress towards the development of a MAV (micro aerial vehicle) designed as an enabling tool to investigate aerial flocking. Our research focuses on the use of low cost off the shelf vehicles and sensors to enable fast prototyping and to reduce development costs. Details on the design of the embedded electronics and the modification of the chosen toy helicopter are presented, and the technique used for state estimation is described. The fusion of inertial data through an unscented Kalman filter is used to estimate the helicopter’s state, and this forms the main input to the control system. Since no detailed dynamic model of the helicopter in use is available, a method is proposed for automated system identification, and for subsequent controller design based on artificial evolution. Preliminary results obtained with a dynamic simulator of a helicopter are reported, along with some encouraging results for tackling the problem of flocking.

Evolutionary car racing (ECR) is extended to the case of two cars racing on the same track. A sensor representation is devised, and various methods of evolving car controllers for competitive racing are explored. ECR can be combined with co-evolution in a wide variety of ways, and one aspect which is explored here is the relative-absolute fitness continuum. Systematical behavioural differences are found along this continuum; further, a tendency to specialization and the reactive nature of the controller architecture are found to limit evolutionary progress.

Operation of autonomic communication networks with complicated user-oriented functions should be described as unreduced many-body interaction process. The latter gives rise to complex-dynamic behaviour including fractally structured hierarchy of chaotically changing realisations. We recall the main results of the universal science of complexity (http://cogprints.org/4471/) based on the unreduced interaction problem solution and its application to various real systems, from nanobiosystems (http://cogprints.org/4527/) and quantum devices to intelligent networks (http://cogprints.org/4114/) and emerging consciousness (http://cogprints.org/3857/). We concentrate then on applications to autonomic communication leading to fundamentally substantiated, exact science of intelligent communication and software. It aims at unification of the whole diversity of complex information system behaviour, similar to the conventional, "Newtonian" science order for sequential, regular models of system dynamics. Basic principles and first applications of the unified science of complex-dynamic communication networks and software are outlined to demonstrate its advantages and emerging practical perspectives.