Understanding the gains from wage flexibility: The exchange rate connection

We study the gains from increased wage flexibility and their dependence on exchange rate policy, using a small open economy model with staggered price andwage setting. Two results stand out: (i) the impact of wage adjustments on employment is smaller the more the central bank seeks to stabilize the exchange rate,and (ii) an increase in wage flexibility often reduces welfare, and more likely so ineconomies under an exchange rate peg or an exchange rate-focused monetary policy.Our findings call into question the common view that wage flexibility is particularlydesirable in a currency union.

On the tractability of the piecewise-linear approximation for general discrete-choice network revenue management

The choice network revenue management (RM) model incorporates customer purchase behavioras customers purchasing products with certain probabilities that are a function of the offeredassortment of products, and is the appropriate model for airline and hotel network revenuemanagement, dynamic sales of bundles, and dynamic assortment optimization. The underlyingstochastic dynamic program is intractable and even its certainty-equivalence approximation, inthe form of a linear program called Choice Deterministic Linear Program (CDLP) is difficultto solve in most cases. The separation problem for CDLP is NP-complete for MNL with justtwo segments when their consideration sets overlap; the affine approximation of the dynamicprogram is NP-complete for even a single-segment MNL. This is in contrast to the independentclass(perfect-segmentation) case where even the piecewise-linear approximation has been shownto be tractable. In this paper we investigate the piecewise-linear approximation for network RMunder a general discrete-choice model of demand. We show that the gap between the CDLP andthe piecewise-linear bounds is within a factor of at most 2. We then show that the piecewiselinearapproximation is polynomially-time solvable for a fixed consideration set size, bringing itinto the realm of tractability for small consideration sets; small consideration sets are a reasonablemodeling tradeoff in many practical applications. Our solution relies on showing that forany discrete-choice model the separation problem for the linear program of the piecewise-linearapproximation can be solved exactly by a Lagrangian relaxation. We give modeling extensionsand show by numerical experiments the improvements from using piecewise-linear approximationfunctions.

SAT Modulo Linear Arithmetic for Solving Polynomial

Polynomial constraint solving plays a prominent role in several areas of hardware and software analysis and verification, e.g., termination proving, program invariant generation and hybrid system verification, to name a few. In this paper we propose a new method for solving non-linear constraints based on encoding the problem into an SMT problem considering only linear arithmetic. Unlike other existing methods, our method focuses on proving satisfiability of the constraints rather than on proving unsatisfiability, which is more relevant in several applications as we illustrate with several examples. Nevertheless, we also present new techniques based on the analysis of unsatisfiable cores that allow one to efficiently prove unsatisfiability too for a broad class of problems. The power of our approach is demonstrated by means of extensive experiments comparing our prototype with state-of-the-art tools on benchmarks taken both from the academic and the industrial world.

The VAD connection : connecting ventricular assist devices to the aorta in silico

Les systèmes d'assistance ventriculaire sont apparus durant la dernière décade comme une approche thérapeutique efficace du traitement de l'insuffisance cardiaque terminale, en particulier dans le contexte de manque de donneurs d'organes. Néanmoins, et ceci malgré les progrès techniques majeurs, les taux de complications restent élevés et sont en partie liés à la configuration géométrique, en particulier le site d'implantation de la cannule de sortie à l'aorte thoracique. Bien que l'anastomose à l'aorte descendante permette une chirurgie moins invasive, les bénéfices de cette technique sont toujours controversés, comparée à la méthode standard de l'aorte ascendante, en raison du risque thrombo-embolique possiblement augmenté et des modifications hémodynamiques induites au niveau de l'arc aortique. Dans ce travail, nous comparons in silico en terme de débit et pression les deux possibilités anastomotiques. Nous développons un réseau de modèles mathématiques unidimensionnels, et l'appliquons à diverses situations cliniques, pour différents stades d'insuffisance cardiaque et de vitesses de rotation de la machine. Les données initiales sont obtenues grâce à un modèle OD (c'est-à-dire qui dépend uniquement du temps mais pas de l'espace) du système cardiovasculaire comprenant une assistance circulatoire, validé avec des données cliniques. Les simulations réalisées montrent que les deux méthodes sont similaires, en terme de débit et courbes de pression, ceci pour tous les cas cliniques étudiés. Ces résultats numériques soutiennent la possibilité d'utiliser la technique d'anastomose à l'aorte thoracique descendante, permettant une chirurgie moins invasive. Sur un plan plus fondamental, le système cardiovasculaire peut être simulé par le biais de multiples modèles de niveau de complexité différents, au prix d'un coût computationnel toujours plus élevé. Nous évaluons les avantages de modèles géométriques à plusieurs échelles (uni- et tridimensionnelle) avec données provenant de patients, comparés à des modèles simplifiés. Les résultats montrent que ces modèles de dimensions hétérogènes apportent un bénéfice important en terme de ressources de calcul, tout en conservant une précision acceptable. En conclusion, ces résultats encourageant montrent la relevance des études numériques dans le domaine médical, tant sur le plan fondamental et la compréhension des mécanismes physiopathologiques, que sur le plan applicatif et le développement de nouvelles thérapeutiques.

Maximum likelihood Linear Programming Data Fusion for Speaker Recognition

Biometric system performance can be improved by means of data fusion. Several kinds of information can be fused in order to obtain a more accurate classification (identification or verification) of an input sample. In this paper we present a method for computing the weights in a weighted sum fusion for score combinations, by means of a likelihood model. The maximum likelihood estimation is set as a linear programming problem. The scores are derived from a GMM classifier working on a different feature extractor. Our experimental results assesed the robustness of the system in front a changes on time (different sessions) and robustness in front a change of microphone. The improvements obtained were significantly better (error bars of two standard deviations) than a uniform weighted sum or a uniform weighted product or the best single classifier. The proposed method scales computationaly with the number of scores to be fussioned as the simplex method for linear programming.

A non-linear VAD for noisy environments

This paper deals with non-linear transformations for improving the performance of an entropy-based voice activity detector (VAD). The idea to use a non-linear transformation has already been applied in the field of speech linear prediction, or linear predictive coding (LPC), based on source separation techniques, where a score function is added to classical equations in order to take into account the true distribution of the signal. We explore the possibility of estimating the entropy of frames after calculating its score function, instead of using original frames. We observe that if the signal is clean, the estimated entropy is essentially the same; if the signal is noisy, however, the frames transformed using the score function may give entropy that is different in voiced frames as compared to nonvoiced ones. Experimental evidence is given to show that this fact enables voice activity detection under high noise, where the simple entropy method fails.

Non-Linear and Non-Conventional Speech Processing: Alternative Techniques

This special issue aims to cover some problems related to non-linear and nonconventional speech processing. The origin of this volume is in the ISCA Tutorial and Research Workshop on Non-Linear Speech Processing, NOLISP’09, held at the Universitat de Vic (Catalonia, Spain) on June 25–27, 2009. The series of NOLISP workshops started in 2003 has become a biannual event whose aim is to discuss alternative techniques for speech processing that, in a sense, do not fit into mainstream approaches. A selected choice of papers based on the presentations delivered at NOLISP’09 has given rise to this issue of Cognitive Computation.

A Canonical Genetic Algorithm for Blind Inversion of Linear Channels

It is well known the relationship between source separation and blind deconvolution: If a filtered version of an unknown i.i.d. signal is observed, temporal independence between samples can be used to retrieve the original signal, in the same manner as spatial independence is used for source separation. In this paper we propose the use of a Genetic Algorithm (GA) to blindly invert linear channels. The use of GA is justified in the case of small number of samples, where other gradient-like methods fails because of poor estimation of statistics.

The Connection, September 5, 2013, Vol. 1

The Connection is to educate parents, family members, community leaders and teachers about the most current trend in drug abuse and emerging threats we face in Iowa.

The Connection, October 22, 2013, Vol. 3

The Connection is to educate parents, family members, community leaders and teachers about the most current trend in drug abuse and emerging threats we face in Iowa.

The Connection, December 11, 2013, Vol. 4, no.1

The Connection is to educate parents, family members, community leaders and teachers about the most current trend in drug abuse and emerging threats we face in Iowa.

The Connection, January 21, 2014, Vol. 5, no.2

The Connection is to educate parents, family members, community leaders and teachers about the most current trend in drug abuse and emerging threats we face in Iowa.

The Connection, February 11, 2014, Vol. 6

The Connection is to educate parents, family members, community leaders and teachers about the most current trend in drug abuse and emerging threats we face in Iowa.

The Connection, April 23, 2014, Vol. 7

The Connection is to educate parents, family members, community leaders and teachers about the most current trend in drug abuse and emerging threats we face in Iowa.

The Connection, March 11, 2015, Vol. 8

The Connection is to educate parents, family members, community leaders and teachers about the most current trend in drug abuse and emerging threats we face in Iowa.