Average Symmetry and Complexity of Binary Sequences

The concept of complexity as average symmetry is here formalised by introducing a general expression dependent on the relevant symmetry and a related discrete set of transformations. This complexity has hybrid features of both statistical complexities and of those related to algorithmic complexity. Like the former, random objects are not the most complex while they still are more complex than the more symmetric ones (as in the latter). By applying this definition to the particular case of rotations of binary sequences, we are able to find a precise expression for it. In particular, we then analyse the behaviour of this measure in different well-known automatic sequences, where we find interesting new properties. A generalisation of the measure to statistical ensembles is also presented and applied to the case of i.i.d. random sequences and to the equilibrium configurations of the one-dimensional Ising model. In both cases, we find that the complexity is continuous and differentiable as a function of the relevant parameters and agrees with the intuitive requirements we were looking for.Comment: 9 pages, 5 figure

Online Learning in Discrete Hidden Markov Models

We present and analyse three online algorithms for learning in discrete Hidden Markov Models (HMMs) and compare them with the Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalisation error we draw learning curves in simplified situations. The performance for learning drifting concepts of one of the presented algorithms is analysed and compared with the Baldi-Chauvin algorithm in the same situations. A brief discussion about learning and symmetry breaking based on our results is also presented.Comment: 8 pages, 6 figure

Measuring complexity through average symmetry

This work introduces a complexity measure which addresses some conflicting issues between existing ones by using a new principle - measuring the average amount of symmetry broken by an object. It attributes low (although different) complexity to either deterministic or random homogeneous densities and higher complexity to the intermediate cases. This new measure is easily computable, breaks the coarse graining paradigm and can be straightforwardly generalized, including to continuous cases and general networks. By applying this measure to a series of objects, it is shown that it can be consistently used for both small scale structures with exact symmetry breaking and large scale patterns, for which, differently from similar measures, it consistently discriminates between repetitive patterns, random configurations and self-similar structure

Bayesian online algorithms for learning in discrete Hidden Markov Models

We propose and analyze two different Bayesian online algorithms for learning in discrete Hidden Markov Models and compare their performance with the already known Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalization we draw learning curves in simplified situations for these algorithms and compare their performances

Office user work performance indicator in warm temperate summer period

El objetivo del trabajo que aquí se presenta fue desarrollar una herramienta metodológica que evaluara el rendimiento laboral de los espacios de oficina durante el período de verano. La herramienta propuesta se tradujo en un indicador de rendimiento laboral óptimo denominado IRLO, que combina variables ambientales de influencia térmica, calidad del aire, visual y acústica. Para su desarrollo, se practicaron mediciones integradas y, paralelamente, encuestas a los usuarios-trabajadores de un edificio de oficinas de la Ciudad de San Juan-Argentina. Los resultados develan los rangos de preferencia de cada variable, reconociendo que en las oficinas de tipología abierta acontece una mayor capacidad adaptativa ambiental que en las de tipología cerrada. Se concluye que el indicador destaca por sentar una base para identificar rendimientos laborales conforme a variables ambientales que deben, en adelante, ser consideradas en fase de diseño.The purpose of this work was to develop a methodological tool to evaluate office space work performance during the summer period. The proposed tool is an optimal work performance indicator called IRLO, which combines environmental variables on thermal, air quality, visual and acoustic influence. Integrated measurements were run for its development alongside surveys to users-workers of an office building in the city of San Juan - Argentina. The results reveal the preference ranges of each variable, recognizing that in open plan offices, there is a greater environmental adaptive capacity than in closed plan offices. It is concluded, that the indicator stands out by providing a basis to identify work performance considering environmental variables that should, in the future, be considered in the design phase