Graph theory reveals dysconnected hubs in 22q11DS and altered nodal efficiency in patients with hallucinations.

Schizophrenia is postulated to be the prototypical dysconnection disorder, in which hallucinations are the core symptom. Due to high heterogeneity in methodology across studies and the clinical phenotype, it remains unclear whether the structural brain dysconnection is global or focal and if clinical symptoms result from this dysconnection. In the present work, we attempt to clarify this issue by studying a population considered as a homogeneous genetic sub-type of schizophrenia, namely the 22q11.2 deletion syndrome (22q11.2DS). Cerebral MRIs were acquired for 46 patients and 48 age and gender matched controls (aged 6-26, respectively mean age = 15.20 ± 4.53 and 15.28 ± 4.35 years old). Using the Connectome mapper pipeline (connectomics.org) that combines structural and diffusion MRI, we created a whole brain network for each individual. Graph theory was used to quantify the global and local properties of the brain network organization for each participant. A global degree loss of 6% was found in patients' networks along with an increased Characteristic Path Length. After identifying and comparing hubs, a significant loss of degree in patients' hubs was found in 58% of the hubs. Based on Allen's brain network model for hallucinations, we explored the association between local efficiency and symptom severity. Negative correlations were found in the Broca's area (p < 0.004), the Wernicke area (p < 0.023) and a positive correlation was found in the dorsolateral prefrontal cortex (DLPFC) (p < 0.014). In line with the dysconnection findings in schizophrenia, our results provide preliminary evidence for a targeted alteration in the brain network hubs' organization in individuals with a genetic risk for schizophrenia. The study of specific disorganization in language, speech and thought regulation networks sharing similar network properties may help to understand their role in the hallucination mechanism.

Planning under partial observability by classical replanning: theory and experiments

Planning with partial observability can be formulated as a non-deterministic search problem in belief space. The problem is harder than classical planning as keeping track of beliefs is harder than keeping track of states, and searching for action policies is harder than searching for action sequences. In this work, we develop a framework for partial observability that avoids these limitations and leads to a planner that scales up to larger problems. For this, the class of problems is restricted to those in which 1) the non-unary clauses representing the uncertainty about the initial situation are nvariant, and 2) variables that are hidden in the initial situation do not appear in the body of conditional effects, which are all assumed to be deterministic. We show that such problems can be translated in linear time into equivalent fully observable non-deterministic planning problems, and that an slight extension of this translation renders the problem solvable by means of classical planners. The whole approach is sound and complete provided that in addition, the state-space is connected. Experiments are also reported.

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.

Estudi sociològic del lleure al món rural de Catalunya i Guatemala. L'oci a la població de 50 a 70 anys de Malla i San Juan la Laguna, semblances i diferències

L’estudi que es presenta a continuació té l’objectiu de comprendre quina és la realitat ociosa de les persones de 50 a 70 anys als municipis de Malla (Catalunya, Espanya) i de San Juan la Laguna (Sololá, Guatemala) des de la perspectiva humanista en termes de concepció i pràctica, i veure quina és la influència i la força que prenen les característiques de la societat en la qual es desenvolupa. Per tal de dur-ho a terme, primerament s’ha realitzat un procés d’aproximació respecte el concepte de l’oci, i una recerca concreta vers l’oci humanista. A partir d’aquí, s’ha fet l’estudi amb una mostra formada per deu persones del municipi de Malla i deu membres de San Juan la Laguna que es troben entre els 50 i 70 anys, i amb unes condicions econòmiques i uns estils de vida diferents. Per tal de realitzar la recerca i l’anàlisi de l’oci humanista en els contexts de Malla i San Juan la Laguna s’ha emprat una metodologia qualitativa, i s’ha utilitzat l’instrument corresponent a l’entrevista. Aquesta ha estat elaborada prenent com a marc de referència la metodologia de la Grounded Theory (Glaser y Strauss, 1967). El projecte també compte amb una vessant d’etnografia. Els resultats que s’han obtingut demostren que hi ha una presència significativa de l’oci humanista en els contexts analitzats, però que en el cas de San Juan la Laguna aquest esdevé un element en construcció.

Machine learning for spatial environmental data. Theory, applications and software

The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.

Fast Approximation of Nonlinearities for improving inversion algorithms of PNL mixtures and Wiener systems

This paper proposes a very fast method for blindly approximating a nonlinear mapping which transforms a sum of random variables. The estimation is surprisingly good even when the basic assumption is not satisfied.We use the method for providing a good initialization for inverting post-nonlinear mixtures and Wiener systems. Experiments show that the algorithm speed is strongly improved and the asymptotic performance is preserved with a very low extra computational cost.

Validity of the acoustic approximation in full-waveform seismic crosshole tomography

Acoustic waveform inversions are an increasingly popular tool for extracting subsurface information from seismic data. They are computationally much more efficient than elastic inversions. Naturally, an inherent disadvantage is that any elastic effects present in the recorded data are ignored in acoustic inversions. We investigate the extent to which elastic effects influence seismic crosshole data. Our numerical modeling studies reveal that in the presence of high contrast interfaces, at which P-to-S conversions occur, elastic effects can dominate the seismic sections, even for experiments involving pressure sources and pressure receivers. Comparisons of waveform inversion results using a purely acoustic algorithm on synthetic data that is either acoustic or elastic, show that subsurface models comprising small low-to-medium contrast (?30%) structures can be successfully resolved in the acoustic approximation. However, in the presence of extended high-contrast anomalous bodies, P-to-S-conversions may substantially degrade the quality of the tomographic images. In particular, extended low-velocity zones are difficult to image. Likewise, relatively small low-velocity features are unresolved, even when advanced a priori information is included. One option for mitigating elastic effects is data windowing, which suppresses later arriving seismic arrivals, such as shear waves. Our tests of this approach found it to be inappropriate because elastic effects are also included in earlier arriving wavetrains. Furthermore, data windowing removes later arriving P-wave phases that may provide critical constraints on the tomograms. Finally, we investigated the extent to which acoustic inversions of elastic data are useful for time-lapse analyses of high contrast engineered structures, for which accurate reconstruction of the subsurface structure is not as critical as imaging differential changes between sequential experiments. Based on a realistic scenario for monitoring a radioactive waste repository, we demonstrated that acoustic inversions of elastic data yield substantial distortions of the tomograms and also unreliable information on trends in the velocity changes.

A Fast Gradient Approximation for Nonlinear Blind Signal Processing

When dealing with nonlinear blind processing algorithms (deconvolution or post-nonlinear source separation), complex mathematical estimations must be done giving as a result very slow algorithms. This is the case, for example, in speech processing, spike signals deconvolution or microarray data analysis. In this paper, we propose a simple method to reduce computational time for the inversion of Wiener systems or the separation of post-nonlinear mixtures, by using a linear approximation in a minimum mutual information algorithm. Simulation results demonstrate that linear spline interpolation is fast and accurate, obtaining very good results (similar to those obtained without approximation) while computational time is dramatically decreased. On the other hand, cubic spline interpolation also obtains similar good results, but due to its intrinsic complexity, the global algorithm is much more slow and hence not useful for our purpose.

Theory of Wetting-Induced Fluid Entrainment by Advancing Contact Lines on Dry Surfaces

We report on the onset of fluid entrainment when a contact line is forced to advance over a dry solid of arbitrary wettability. We show that entrainment occurs at a critical advancing speed beyond which the balance between capillary, viscous, and contact-line forces sustaining the shape of the interface is no longer satisfied. Wetting couples to the hydrodynamics by setting both the morphology of the interface at small scales and the viscous friction of the front. We find that the critical deformation that the interface can sustain is controlled by the friction at the contact line and the viscosity contrast between the displacing and displaced fluids, leading to a rich variety of wetting-entrainment regimes. We discuss the potential use of our theory to measure contact-line forces using atomic force microscopy and to study entrainment under microfluidic conditions exploiting colloid-polymer fluids of ultralow surface tension.

On the approximation of the subgrid scale for systems of equations

Postprint (published version)

Constraints on effective field theory parameters for the Lambda N -> NN transition

The relation between the low-energy constants appearing in the effective field theory description of the Lambda N -> NN transition potential and the parameters of the one-meson-exchange model previously developed is obtained. We extract the relative importance of the different exchange mechanisms included in the meson picture by means of a comparison to the corresponding operational structures appearing in the effective approach. The ability of this procedure to obtain the weak baryon-baryon-meson couplings for a possible scalar exchange is also discussed.

Constraints on effective field theory parameters for the Lambda N -> NN transition

The relation between the low-energy constants appearing in the effective field theory description of the Lambda N -> NN transition potential and the parameters of the one-meson-exchange model previously developed is obtained. We extract the relative importance of the different exchange mechanisms included in the meson picture by means of a comparison to the corresponding operational structures appearing in the effective approach. The ability of this procedure to obtain the weak baryon-baryon-meson couplings for a possible scalar exchange is also discussed.