Abstract:
O presente trabalho discute alguma ideias de aquisi o da linguagem que percorrem o programa gerativista, desde os primórdios da década de 50, até os anos mais recentes com o Programa Minimalista. Leva em considera o, principalmente, as discuss es em torno do conceito de Gramática Universal, uma constante entre as variadas adapta es da teoria. This work discusses some ideas of language acquisition borne in the beginning of the Generative enterprise and still quite alive today such as the concept of Universal Grammar (UG) a constant among the various phases of the Theory.

Abstract:
O presente artigo trata da discuss o em Filosofia da Linguagem sobre a validade ou n o de se considerar nome próprio como portador de significado.

Abstract:
Variable selection for a multiple regression model (Noisy Linear Perceptron) is studied with a mean field approximation. In our Bayesian framework, variable selection is formulated as estimation of discrete parameters that indicate a subset of the explanatory variables. Then, a mean field approximation is introduced for the calculation of the posterior averages over the discrete parameters. An application to a real world example, Boston housing data, is shown.

Abstract:
In this paper, we introduce a dynamical Monte Carlo algorithm for spin models in which the number of the spins fluctuates from zero to a given number by addition and deletion of spins with a probabilistic rule. Such simulations are realized with a variable-system-size ensemble, a mixture of canonical ensembles each of which corresponds to a system with different size. The weight of each component of the mixture is controlled by a penalty term and systematically tuned in a preliminary run in a way similar to the multicanonical algorithm. In a measurement run, the system grows and shrinks without violating the detailed balance condition and we can obtain the correct canonical averages if physical quantities is measured only when its size is equal to the prescribed maximum size. The mixing of Markov chain is facilitated by the fast relaxation at small system sizes. The algorithm is implemented for the SK model of spin glass and shows better performance than that of a conventional heat bath algorithm.

Abstract:
``Nishimori line'' is a line or hypersurface in the parameter space of systems with quenched disorder, where simple expressions of the averages of physical quantities over the quenched random variables are obtained. It has been playing an important role in the theoretical studies of the random frustrated systems since its discovery around 1980. In this paper, a novel interpretation of the Nishimori line from the viewpoint of statistical information processing is presented. Our main aim is the reconstruction of the whole theory of the Nishimori line from the viewpoint of Bayesian statistics, or, almost equivalently, from the viewpoint of the theory of error-correcting codes. As a byproduct of our interpretation, counterparts of the Nishimori line in models without gauge invariance are given. We also discussed the issues on the ``finite temperature decoding'' of error-correcting codes in connection with our theme and clarify the role of gauge invariance in this topic.

Abstract:
We give a cross-disciplinary survey on ``population'' Monte Carlo algorithms. In these algorithms, a set of ``walkers'' or ``particles'' is used as a representation of a high-dimensional vector. The computation is carried out by a random walk and split/deletion of these objects. The algorithms are developed in various fields in physics and statistical sciences and called by lots of different terms -- ``quantum Monte Carlo'', ``transfer-matrix Monte Carlo'', ``Monte Carlo filter (particle filter)'',``sequential Monte Carlo'' and ``PERM'' etc. Here we discuss them in a coherent framework. We also touch on related algorithms -- genetic algorithms and annealed importance sampling.

Abstract:
``Extended Ensemble Monte Carlo''is a generic term that indicates a set of algorithms which are now popular in a variety of fields in physics and statistical information processing. Exchange Monte Carlo (Metropolis-Coupled Chain, Parallel Tempering), Simulated Tempering (Expanded Ensemble Monte Carlo), and Multicanonical Monte Carlo (Adaptive Umbrella Sampling) are typical members of this family. Here we give a cross-disciplinary survey of these algorithms with special emphasis on the great flexibility of the underlying idea. In Sec.2, we discuss the background of Extended Ensemble Monte Carlo. In Sec.3, 4 and 5, three types of the algorithms, i.e., Exchange Monte Carlo, Simulated Tempering, Multicanonical Monte Carlo are introduced. In Sec.6, we give an introduction to Replica Monte Carlo algorithm by Swendsen and Wang. Strategies for the construction of special-purpose extended ensembles are discussed in Sec.7. We stress that an extension is not necessary restricted to the space of energy or temperature. Even unphysical (unrealizable) configurations can be included in the ensemble, if the resultant fast mixing of the Markov chain offsets the increasing cost of the sampling procedure. Multivariate (multi-component) extensions are also useful in many examples. In Sec.8, we give a survey on extended ensembles with a state space whose dimensionality is dynamically varying. In the appendix, we discuss advantages and disadvantages of three types of extended ensemble algorithms.

Abstract:
This paper proposes new languages for basic skills in the Creative Society, where people create their own goods, tools, concepts, knowledge, and mechanisms with their own hands: the skills of learning, presentation, and collaboration. These languages are written as a pattern language, which is a way of describing the tacit practical knowledge. In this paper, a new type of pattern languages are proposed as "Pattern Languages 3.0" and three examples are introduced: Learning Patterns, Collaboration Patterns, and Presentation Patterns. By analyzing the functions with the social systems theory and the creative systems theory, pattern languages are considered as communication media and discovery media.

Abstract:
In this paper, we show our discovery that state-transition networks in several chaotic dynamical systems are "scale-free networks," with a technique to understand a dynamical system as a whole, which we call the analysis for "Discretized-State Transition" (DST) networks; This scale-free nature is found universally in the logistic map, the sine map, the cubic map, the general symmetric map, the sine-circle map, the Gaussian map, and the delayed logistic map. Our findings prove that there is a hidden order in chaos, which has not detected yet. Furthermore, we anticipate that our study opens up a new way to a "network analysis approach to dynamical systems" for understanding complex phenomena.