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Experimental And Quasi Experimental Designs For Research

We extend Q-learning to a noncooperative multiagent context, using the framework of general-sum stochastic games. A learning agent maintains Q-functions over joint actions, and performs updates based on assuming Nash equilibrium behavior over the current Q-values. This learning protocol provably converges given certain restrictions on the stage games (defined by Q-values) that arise during learning. Experiments with a pair of two-player grid games suggest that such restrictions on the game structure are not necessarily required. Stage games encountered during learning in both grid environments violate the conditions. However, learning consistently converges in the first grid game, which has a unique equilibrium Q-function, but sometimes fails to converge in the second, which has three different equilibrium Q-functions. In a comparison of offline learning performance in both games, we find agents are more likely to reach a joint optimal path with Nash Q-learning than with a single-agent Q-learning method. When at least one agent adopts Nash Q-learning, the performance of both agents is better than using single-agent Q-learning. We have also implemented an online version of Nash Q-learning that balances exploration with exploitation, yielding improved performance.

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Nash q-learning for general-sum stochastic games

Metaheuristics are the most exciting development in approximate optimization techniques of the last two decades. They have had widespread successes in attacking a variety of difficult combinatorial optimization problems that arise in many practical areas. This bibliography provides a classification of a comprehensive list of 1380 references on the theory and application of metaheuristics. Metaheuristics include but are not limited to constraint logic programming; greedy random adaptive search procedures; natural evolutionary computation; neural networks; non-monotonic search strategies; space-search methods; simulated annealing; tabu search; threshold algorithms and their hybrids. References are presented in alphabetical order under a number of subheadings.

Metaheuristics: A bibliography

Effects and mechanisms of wakefulness on local cortical networks.

50 Years of object recognition: Directions forward☆

Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence.

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AI methods in algorithmic composition: a comprehensive survey

Music is a potent mood regulator that can induce relaxation and reduce anxiety in different situations. While several studies demonstrate that certain types of music have a subjective anxiolytic effect, the reported results from physiological responses are less conclusive. Virtual reality allows us to study diverse scenarios of real life under strict experimental control while preserving high ecological validity. We aimed to study the modulating effect of music on the anxiety responses triggered by an immersive virtual reality scenario designed to induce fear of heights. Subjects experienced a virtual scenario depicting an exterior elevator platform ascending and descending the total height of its 350 meters tall supporting structure. Participants were allocated to either a group that experienced the elevator ride with background music or without, in a between-groups design. Furthermore, each group included participants with different degrees of fear of heights, ranging from low to high fear. Recordings of heart rate, galvanic skin response, body balance, and head movements were obtained during the experiments. Subjective anxiety was measured by means of three questionnaires. The scenario produced significant changes in subjective and physiological measures, confirming its efficacy as a stressor. A significant increase in state anxiety was found between pre and post-assessment in the silence group, but not in the music group, indicating that post-stress recovery was faster in the musical group. Results suggest that music can ameliorate the subjective anxiety produced by fear of heights.

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Influence of Music on Anxiety Induced by Fear of Heights in Virtual Reality.

Foveal vision features imagers with graded acuity coupled with context sensitive sensor gaze control, analogous to that prevalent throughout vertebrate vision. Foveal vision operates more efficiently than uniform acuity vision because resolution is treated as a dynamically allocatable resource, but requires a more refined visual attention mechanism. We demonstrate that reinforcement learning (RL) significantly improves the performance of foveal visual attention, and of the overall vision system, for the task of model based target recognition. A simulated foveal vision system is shown to classify targets with fewer fixations by learning strategies for the acquisition of visual information relevant to the task, and learning how to generalize these strategies in ambiguous and unexpected scenario conditions.

Residual Q-Learning Applied to Visual Attention

Background 
The neuronal cortical network generates slow (<1 Hz) spontaneous rhythmic activity that emerges from the recurrent connectivity. This activity occurs during slow wave sleep or anesthesia and also in cortical slices, consisting of alternating up (active, depolarized) and down (silent, hyperpolarized) states. The search for the underlying mechanisms and the possibility of analyzing network dynamics in vitro has been subject of numerous studies. This exposes the need for a detailed quantitative analysis of the membrane fluctuating behavior and computerized tools to automatically characterize the occurrence of up and down states.


Methodology/Principal Findings 
Intracellular recordings from different areas of the cerebral cortex were obtained from both in vitro and in vivo preparations during slow oscillations. A method that separates up and down states recorded intracellularly is defined and analyzed here. The method exploits the crossover of moving averages, such that transitions between up and down membrane regimes can be anticipated based on recent and past voltage dynamics. We demonstrate experimentally the utility and performance of this method both offline and online, the online use allowing to trigger stimulation or other events in the desired period of the rhythm. This technique is compared with a histogram-based approach that separates the states by establishing one or two discriminating membrane potential levels. The robustness of the method presented here is tested on data that departs from highly regular alternating up and down states.


Conclusions/Significance 
We define a simple method to detect cortical states that can be applied in real time for offline processing of large amounts of recorded data on conventional computers. Also, the online detection of up and down states will facilitate the study of cortical dynamics. An open-source MATLAB® toolbox, and Spike 2®-compatible version are made freely available.

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Robust off- and online separation of intracellularly recorded up and down cortical states.

Objectives To know the variations of serum transferrin receptor (sTfR) and its indices depending on the status of body iron and the presence of infection in children, to evaluate their usefulness for recognizing the nature of anemia in infection, and to know the role of erythropoietic activity in these conditions. Design and methods Three hundred and sixty-eight children between 1 and 10 years were included: 206 healthy children; 60 iron deficient anemic children (IDA); 102 with anemia and infectious disease, 58 of them meeting criteria for IDA. We measured hemoglobin, red cell indices, reticulocytes, transferrin saturation, serum ferritin, erythrocyte protoporphyrin, serum erythropoietin, and sTfR. Statistic method: ANOVA test, multiple linear regression, and ROC curve. Results sTfR, sTfR/ferritin ratio, and sTfR-logferritin index values were found to increase significantly in IDA children. These values were significantly lower in infectious anemia than iron deficiency states. Serum erythropoietin only was elevated significantly in iron deficiency states. In children without infection, mean corpuscular hemoglobin, erythrocyte protoporphirin, erythropoietin logarithm, and total-iron-binding-capacity logarithm predicted 81% of sTfR variability. sTfR and its indices showed a very high sensitivity and specificity for recognizing iron deficiency states. In children with IDA and infection sensitivity for sTfR/ferritin ratio was low (area under the curve: 0.71; 95% confidence interval: 0.64-0.88). For discriminating the nature of anemia in infection the cut-off point obtained for sTfR, sTfR/ferritin ratio, and sTfR-F index were 3, 70, and 1.8, respectively, and their sensitivity and specificity were also very high. Conclusions sTfR, sTfR/ferritin ratio, and sTfR-F index are useful parameters for recognizing iron deficiency and the nature of anemia in infection. In IDA+infection, sTfR/ferritin ratio should not be recommended in the diagnosis of iron deficiency. In iron deficiency, erythropoietic activity has a secondary role as predictor factor of sTfR levels.

Francisco J. Vico

Papers

AI methods in algorithmic composition: a comprehensive survey

Influence of Music on Anxiety Induced by Fear of Heights in Virtual Reality.

Residual Q-Learning Applied to Visual Attention

Robust off- and online separation of intracellularly recorded up and down cortical states.

Serum transferrin receptor in children: usefulness for determinating the nature of anemia in infection.