A note on conditional risk measures of Orlicz spaces and Orlicz-type modules

We consider conditional and dynamic risk measures of Orlicz spaces and study their robust representation. For this purpose, given a probability space $(\Omega,\mathcal{E},\mathbb{P})$, a sub-$\sigma$-algebra $\mathcal{F}$ of $\mathcal{E}$, and a Young function $\varphi$, we study the relation between the classical Orlicz space $L^\varphi(\mathcal{E})$ and the modular Orlicz-type module $L^\varphi_\mathcal{F}(\mathcal{E})$; based on conditional set theory, we describe the conditional order continuous dual of a Orlicz-type module; and by using scalarization and modular extensions of conditional risk measures together with elements of conditional set theory, we finally characterize the robust representation of conditional risk measures of Orlicz spaces

On the processing of agreement morphology

The paper deals with the role played by morphology in core syntax within a generative minimalist framework: more specifically it deals with the theory of valuation of agreement or phi-features (that is, person and number features) and the valuation of tense or t-features (that is, features like [+/-present]), and there is a second part where I focus on the computation of both kinds of features according to the neuroimaging literature. The core idea that I aim to defend is that agreement features and tense features are each valued by a distinct head, namely 'v' and T. I argue that the number of steps in the 'real' processing or computation of agreement features and of tense feaures must be the same, irrespective of whether the morphological realization is 'zero' (as is frequently the case in a language like English) or not (as is typically the case in a Romance language like Spanish).Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Learning backward induction: a neural network agent approach

This paper addresses the question of whether neural networks (NNs), a realistic cognitive model of human information processing, can learn to backward induce in a two-stage game with a unique subgame-perfect Nash equilibrium. The NNs were found to predict the Nash equilibrium approximately 70% of the time in new games. Similarly to humans, the neural network agents are also found to suffer from subgame and truncation inconsistency, supporting the contention that they are appropriate models of general learning in humans. The agents were found to behave in a bounded rational manner as a result of the endogenous emergence of decision heuristics. In particular a very simple heuristic socialmax, that chooses the cell with the highest social payoff explains their behavior approximately 60% of the time, whereas the ownmax heuristic that simply chooses the cell with the maximum payoff for that agent fares worse explaining behavior roughly 38%, albeit still significantly better than chance. These two heuristics were found to be ecologically valid for the backward induction problem as they predicted the Nash equilibrium in 67% and 50% of the games respectively. Compared to various standard classification algorithms, the NNs were found to be only slightly more accurate than standard discriminant analyses. However, the latter do not model the dynamic learning process and have an ad hoc postulated functional form. In contrast, a NN agent’s behavior evolves with experience and is capable of taking on any functional form according to the universal approximation theorem.

Automation of motor dexterity assessment

Motor dexterity assessment is regularly performed in rehabilitation wards to establish patient status and automatization for such routinary task is sought. A system for automatizing the assessment of motor dexterity based on the Fugl-Meyer scale and with loose restrictions on sensing technologies is presented. The system consists of two main elements: 1) A data representation that abstracts the low level information obtained from a variety of sensors, into a highly separable low dimensionality encoding employing t-distributed Stochastic Neighbourhood Embedding, and, 2) central to this communication, a multi-label classifier that boosts classification rates by exploiting the fact that the classes corresponding to the individual exercises are naturally organized as a network. Depending on the targeted therapeutic movement class labels i.e. exercises scores, are highly correlated-patients who perform well in one, tends to perform well in related exercises-; and critically no node can be used as proxy of others - an exercise does not encode the information of other exercises. Over data from a cohort of 20 patients, the novel classifier outperforms classical Naive Bayes, random forest and variants of support vector machines (ANOVA: p <; 0.001). The novel multi-label classification strategy fulfills an automatic system for motor dexterity assessment, with implications for lessening therapist's workloads, reducing healthcare costs and providing support for home-based virtual rehabilitation and telerehabilitation alternatives

Granada, between the Zirids and the Nasrids

Despite the many studies done since the end of the 19th century on the urban evolution of the city of Granada, many unresolved questions still remain and researchers have differing opinions concerning important issues.1 Numerous, often urgent archaeological excavations have been carried out since the early 1980s that have provided a great deal of new data. However, because of the wide disparity in methodology and quality, compounded by limited press runs and difficult access to original reports, these investigations have cleared up fewer doubts than might be expected after such considerable public and private financial effort.Peer reviewe