From Cognition to Consciousness:\ud a discussion about learning, reality representation and decision making.

The scientific understanding of cognition and consciousness is currently hampered by the lack of rigorous and universally accepted definitions that permit comparative studies. This paper proposes new functional and un- ambiguous definitions for cognition and consciousness in order to provide clearly defined boundaries within which general theories of cognition and consciousness may be developed. The proposed definitions are built upon the construction and manipulation of reality representation, decision making and learning and are scoped in terms of an underlying logical structure. It is argued that the presentation of reality also necessitates the concept of ab- sence and the capacity to perform transitive inference. Explicit predictions relating to these new definitions, along with possible ways to test them, are also described and discussed

Dissimilarity Clustering by Hierarchical Multi-Level Refinement

We introduce in this paper a new way of optimizing the natural extension of the quantization error using in k-means clustering to dissimilarity data. The proposed method is based on hierarchical clustering analysis combined with multi-level heuristic refinement. The method is computationally efficient and achieves better quantization errors than theComment: 20-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012), Bruges : Belgium (2012

Better Optimism By Bayes: Adaptive Planning with Rich Models

The computational costs of inference and planning have confined Bayesian model-based reinforcement learning to one of two dismal fates: powerful Bayes-adaptive planning but only for simplistic models, or powerful, Bayesian non-parametric models but using simple, myopic planning strategies such as Thompson sampling. We ask whether it is feasible and truly beneficial to combine rich probabilistic models with a closer approximation to fully Bayesian planning. First, we use a collection of counterexamples to show formal problems with the over-optimism inherent in Thompson sampling. Then we leverage state-of-the-art techniques in efficient Bayes-adaptive planning and non-parametric Bayesian methods to perform qualitatively better than both existing conventional algorithms and Thompson sampling on two contextual bandit-like problems.Comment: 11 pages, 11 figure

Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search

Bayesian model-based reinforcement learning is a formally elegant approach to learning optimal behaviour under model uncertainty, trading off exploration and exploitation in an ideal way. Unfortunately, finding the resulting Bayes-optimal policies is notoriously taxing, since the search space becomes enormous. In this paper we introduce a tractable, sample-based method for approximate Bayes-optimal planning which exploits Monte-Carlo tree search. Our approach outperformed prior Bayesian model-based RL algorithms by a significant margin on several well-known benchmark problems -- because it avoids expensive applications of Bayes rule within the search tree by lazily sampling models from the current beliefs. We illustrate the advantages of our approach by showing it working in an infinite state space domain which is qualitatively out of reach of almost all previous work in Bayesian exploration.Comment: 14 pages, 7 figures, includes supplementary material. Advances in Neural Information Processing Systems (NIPS) 201

Warm molecular gas, dust and ionized gas in the 500 central pc of the Galaxy

We present infrared and millimeter observations of molecular gas, dust and ionized gas towards a sample of clouds distributed along the 500 central pc of the Galaxy. The clouds were selected to investigate the physical state, in particular the high gas temperatures, of the Galactic center region (GCr) clouds located far from far-infrared of thermal radio continuum sources. We have found that there is ionized gas associated with the molecular gas. The ionizing radiation is hard (~35000 K) but diluted due to the inhomogeneity of the medium. We estimate that ~30 % of the warm molecular gas observed in the GCr clouds is heated by ultra-violet radiation in photo-dissociation regions.Comment: 5 pages, to be published in: Astron. Nachr., Vol. 324, No. S1 (2003), Special Supplement "The central 300 parsecs of the Milky Way", Eds. A. Cotera, H. Falcke, T. R. Geballe, S. Markof

Deep Reinforcement Learning with Double Q-learning

The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known whether, in practice, such overestimations are common, whether they harm performance, and whether they can generally be prevented. In this paper, we answer all these questions affirmatively. In particular, we first show that the recent DQN algorithm, which combines Q-learning with a deep neural network, suffers from substantial overestimations in some games in the Atari 2600 domain. We then show that the idea behind the Double Q-learning algorithm, which was introduced in a tabular setting, can be generalized to work with large-scale function approximation. We propose a specific adaptation to the DQN algorithm and show that the resulting algorithm not only reduces the observed overestimations, as hypothesized, but that this also leads to much better performance on several games.Comment: AAAI 201

Archaeology and dental forensic: what’s the relationship?

Archaeology is a science combining numerous skills in a multidisciplinary approach. In the presence of human remains, its objectives are the recovery, identification, and analysis on an anthropological purpose to reconstruct the context of the individual's past lives. One of these approaches is in the forensic medicine sciences whose main purpose is the identification of human bodies through bones and teeth in deteriorated corpses. Archaeology and forensic medicine are therefore two intertwined sciences. The goal of this bibliographical review is to summarize the first steps to reconstruct the biological profile of a person using teeth as an object of study. Beginning by explaining the preservation and the type of the tooth sample, then by assessing the two most important biological factors being the sex and the age. This study does not cover all the existing tooth forensic aspects, but the most used one and the promising methods for archaeological context.A arqueologia é uma ciência que combina uma abordagem multidisciplinar. Na presença de restos cadavéricos, os seus principais objetivos são a recuperação, identificação e análise com o propósito antropológico de reconstruir o contexto das vidas passadas do indivíduo. Uma dessas abordagens relaciona-se com a medicina forense, cujo objetivo principal é a identificação humana através da ossada e dos dentes em cadáveres deteriorados. A arqueologia e a medicina forense são, portanto, duas ciências interligadas. O objetivo desta revisão bibliográfica é resumir os primeiros passos para a reconstrução do perfil biológico de uma pessoa, utilizando as peças dentárias como objeto de estudo, começando por explicar a preservação e o tipo da amostra dentária e avaliando dois fatores biológicos, tais como o sexo e a idade. Este estudo não abrange todos os aspetos forenses dentários existentes, mas os métodos mais utilizados e os métodos mais promissores para o contexto arqueológico