Some remarks on the class of continuous (Semi-) strictcly quasiconvex functions

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Generic optimality conditions for semi-algebraic convex programs

We consider linear optimization over a nonempty convex semi-algebraic feasible region F. Semidefinite programming is an example. If F is compact, then for almost every linear objective there is a unique optimal solution, lying on a unique \active" manifold, around which F is \partly smooth", and the second-order sufficient conditions hold. Perturbing the objective results in smooth variation of the optimal solution. The active manifold consists, locally, of these perturbed optimal solutions; it is independent of the representation of F, and is eventually identified by a variety of iterative algorithms such as proximal and projected gradient schemes. These results extend to unbounded sets F.

An algorithm for semi-infinite polynomial optimization

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Discrete time Non-homogeneous Semi-Markov Processes applied to Models for Disability Insurance

In this paper, we present a stochastic model for disability insurance contracts. The model is based on a discrete time non-homogeneous semi-Markov process (DTNHSMP) to which the backward recurrence time process is introduced. This permits a more exhaustive study of disability evolution and a more efficient approach to the duration problem. The use of semi-Markov reward processes facilitates the possibility of deriving equations of the prospective and retrospective mathematical reserves. The model is applied to a sample of contracts drawn at random from a mutual insurance company.

Semi-online neural-Q_leaming for real-time robot learning

Reinforcement learning (RL) is a very suitable technique for robot learning, as it can learn in unknown environments and in real-time computation. The main difficulties in adapting classic RL algorithms to robotic systems are the generalization problem and the correct observation of the Markovian state. This paper attempts to solve the generalization problem by proposing the semi-online neural-Q_learning algorithm (SONQL). The algorithm uses the classic Q_learning technique with two modifications. First, a neural network (NN) approximates the Q_function allowing the use of continuous states and actions. Second, a database of the most representative learning samples accelerates and stabilizes the convergence. The term semi-online is referred to the fact that the algorithm uses the current but also past learning samples. However, the algorithm is able to learn in real-time while the robot is interacting with the environment. The paper shows simulated results with the "mountain-car" benchmark and, also, real results with an underwater robot in a target following behavior

On AUV control architecture

This paper surveys control architectures proposed in the literature and describes a control architecture that is being developed for a semi-autonomous underwater vehicle for intervention missions (SAUVIM) at the University of Hawaii. Conceived as hybrid, this architecture has been organized in three layers: planning, control and execution. The mission is planned with a sequence of subgoals. Each subgoal has a related task supervisor responsible for arranging a set of pre-programmed task modules in order to achieve the subgoal. Task modules are the key concept of the architecture. They are the main building blocks and can be dynamically re-arranged by the task supervisor. In our architecture, deliberation takes place at the planning layer while reaction is dealt through the parallel execution of the task modules. Hence, the system presents both a hierarchical and an heterarchical decomposition, being able to show a predictable response while keeping rapid reactivity to the dynamic environment

Generació semi-automàtica de façanes procedurals

L’objectiu d’aquest PFC és desenvolupar una eina d’edició de façanes procedural apartir d’una imatge d’una façana real. L’aplicació generarà les regles procedurals de lafaçana a partir de dades adquirides del model que es vol representar, com unafotografia. L’usuari de l’aplicació generarà de forma semi-automàtica i interactiva lesregles de subdivisió i repetició, especificant també la inserció de elementsarquitectònics (portes, finestres), que podran ser instanciats a partir d’una llibreria. Uncop generades, les regles s’escriuran en el format del sistema BuildingEngine perintegrar-se completament dins el procés de modelatge urbà.Aquest projecte es desenvoluparà en Matlab

Fábrica de "Metalheads" : arquetipo de la representación del aficionado al Heavy Metal en España

El presente estudio expone los resultados del tema desarrollado en la línea investigativa de culturas urbanas contemporáneas y procesos comunicacionales. Trabajo que describe (construye) el arquetipo del aficionado al heavy metal en España.

Subsampling the mean of heavy-tailed dependent observations

We establish the validity of subsampling confidence intervals for themean of a dependent series with heavy-tailed marginal distributions.Using point process theory, we study both linear and nonlinear GARCH-liketime series models. We propose a data-dependent method for the optimalblock size selection and investigate its performance by means of asimulation study.

Parallel scheduling of multiclass M/M/m queues: Approximate and heavy-traffic optimization of achievable performance

We address the problem of scheduling a multiclass $M/M/m$ queue with Bernoulli feedback on $m$ parallel servers to minimize time-average linear holding costs. We analyze the performance of a heuristic priority-index rule, which extends Klimov's optimal solution to the single-server case: servers select preemptively customers with larger Klimov indices. We present closed-form suboptimality bounds (approximate optimality) for Klimov's rule, which imply that its suboptimality gap is uniformly bounded above with respect to (i) external arrival rates, as long as they stay within system capacity;and (ii) the number of servers. It follows that its relativesuboptimality gap vanishes in a heavy-traffic limit, as external arrival rates approach system capacity (heavy-traffic optimality). We obtain simpler expressions for the special no-feedback case, where the heuristic reduces to the classical $c \mu$ rule. Our analysis is based on comparing the expected cost of Klimov's ruleto the value of a strong linear programming (LP) relaxation of the system's region of achievable performance of mean queue lengths. In order to obtain this relaxation, we derive and exploit a new set ofwork decomposition laws for the parallel-server system. We further report on the results of a computational study on the quality of the $c \mu$ rule for parallel scheduling.

Study of the variability in chemical composition of bark layers of Quercus Suber L. from different production areas

Cork is the bark of the cork oak tree (Quercus suber L), a renewable and biodegradable raw bioresource concentrated mainly in the Mediterranean region. Development of its potential uses as a biosorbent will require the investigation of its chemical composition; such information can be of help to understand its interactions with organic pollutants. The present study investigates the summative chemical composition of three bark layers (back, cork, and belly) of five Spanish cork samples and one cork sample from Portugal. Suberin was the main component in all the samples (21.1 to 53.1%), followed by lignin (14.8 to 31%), holocellulose (2.3 to 33.6%), extractives (7.3 to 20.4%), and ash (0.4 to 3.3%). The Kruskal-Wallis test was used to determine whether the variations in chemical composition with respect to the production area and bark layers were significant. The results indicate that, with respect to the bark layer, significant differences were found only for suberin and holocellulose contents: they were higher in the belly and cork than in the back. Based on the results presented, cork is a material with a lot of potential because of its heterogeneity in chemical composition