Dynamical systems modeling of the child–mother dyad:Causality between child-directed language complexity and language development

We model the causal links between child language (CL) andchild-directed language (CDL). We take pairs of sequences oflinguistic measurements from a longitudinal study. Each child-mother pair of sequences is considered as an instance of thetrajectory of a high-dimensional dynamical system. We thenuse Multispatial Convergent Cross Mapping to ascertain thedirections of causality between the pairs of sequences, that is,whether the complexity of CL drives that of CDL, the com-plexity of CDL drives that of CL, both, or neither. We find thatchildren are responsive to the amount of speech and the diver-sity of words produced by their mothers, but not vice-versa.However, the syntactic diversities of the children’s utterancesdrive the syntactic diversity of the mothers’ utterances. This isevidence for fine-grained fine-tuning of CDL in response onlyto the syntax of CL

Consistent estimation of panel data sample selection models

Consistent estimation of panel data sample selection model

Quinones Facilitate the Self-Assembly of the Phosphorylated Tubulin Binding Region of Tau into Fibrillar Polymers.

The fragment of tau containing the first and third tubulin-binding motifs, involved in self- assembly of tau, was phosphorylated by protein kinase A (PKA). In the presence of hydroxynonenal (HNE) or in the presence of quinones such as juglone, 2,3-dimethoxy-5-methyl-1,4-benzoquinone (coenzyme Q0 or DMM), or menadione, the polymerization of this phosphorylated tau fragment is catalyzed, whereas polymerization of the unmodified fragment takes place in a lesser extent. The quinones coenzyme Q0 and menadione are found in every cell, including neural cells, and may interact with tau protein to facilitate its assembly into filamentous structures. These tau filaments, assembled in the presence of quinones, have a fibrillar morphology very similar to that of paired helical filaments present in the brains of patients with Alzheimer’s disease.pre-print862 K

Modelling of chloride penetration in concrete under wet/dry cycle

This present study concerns modelling of chloride penetration in partially saturated concrete. To mimic the intermittent exposure of sea water to concrete, varying environmental conditions for relative humidity and chloride concentration were considered. As for the moisture distribution in concrete, statistical permeability model based on pore size distribution was used to represent influence of material properties on moisture transport. Then, a combined chloride diffusion and convection was modelled in variation of moisture level in concrete. As a result, smaller relative wet duration induces higher rate of chloride penetration due to enhanced moisture permeability from the surface, and also higher concentration gradient near the surface of concrete due to repeated wet/dry cycle. This implies that only diffusion analysis on chloride induced corrosion in concrete structure may underestimate the serviceability in given material performance

Propuesta de un modelo con redes neuronales y metodología Box & Jenkins para el pronóstico del precio de bolsa de la energía en Colombia

El presente trabajo se ha desarrollado haciendo uso de información del sector de la energía eléctrica generada por hidroeléctricas, donde se tomó principalmente de Xm información inteligente. Dentro de los datos obtenidos se busca realiza el pronóstico del precio de la bolsa de la energía. Se propone realizar un modelo de pronostico por medio de la metodología Box & Jenkins y redes neuronales con perceptor multicapa (MLP), donde el resultado es comparar cual es el pronóstico que mejor se ajusta se ajusta a la serie de datosThis work has been developed using information from the hydroelectric power sector, where intelligent information was mainly taken from Xm. Within the data obtained, the forecast of the price of the energy exchange is sought. It is proposed to make a prognostic model through the Box & Jenkins methodology and neural networks with multilayer perceptor (MLP), where the result is to compare which prognosis that best fits fits the data serie

Deep Sequence Learning with Auxiliary Information for Traffic Prediction

Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved. In this paper, we intend to improve traffic prediction by appropriate integration of three kinds of implicit but essential factors encoded in auxiliary information. We do this within an encoder-decoder sequence learning framework that integrates the following data: 1) offline geographical and social attributes. For example, the geographical structure of roads or public social events such as national celebrations; 2) road intersection information. In general, traffic congestion occurs at major junctions; 3) online crowd queries. For example, when many online queries issued for the same destination due to a public performance, the traffic around the destination will potentially become heavier at this location after a while. Qualitative and quantitative experiments on a real-world dataset from Baidu have demonstrated the effectiveness of our framework.Comment: KDD 2018. The first two authors share equal contribution

Azimuthal variations of oxygen abundance profiles in star-forming regions of disc galaxies in EAGLE simulations

The exploration of the spatial distribution of chemical abundances in star-forming regions of galactic discs can help us to understand the complex interplay of physical processes that regulate the star formation activity and the chemical enrichment across a galaxy. We study the azimuthal variations of the normalized oxygen abundance profiles in the highest numerical resolution run of the Evolution and Assembly of GaLaxies and their Environments (EAGLE) Project at z = 0. We use young stellar populations to trace the abundances of star-forming regions. Oxygen profiles are estimated along different line of sights from a centrally located observer. The mean azimuthal variation in the EAGLE discs are ∼0.12 ± 0.03 dex R−1 eff for slopes and ∼0.12 ± 0.03 dex for the zero-points, in agreement with previous works. Metallicity gradients measured along random directions correlate with those determined by averaging over the whole discs, although with a large dispersion. We find a slight trend for higher azimuthal variations in the disc components of low star-forming and bulge-dominated galaxies. We also investigate the metallicity profiles of stellar populations with higher and lower levels of enrichment than the average metallicity profiles, and we find that high star-forming regions with high metallicity tend to have slightly shallower metallicity slopes compared with the overall metallicity gradient. The simulated azimuthal variations in the EAGLE discs are in agreement with observations, although the large variety of metallicity gradients would encourage further exploration of the metal mixing in numerical simulations.Indexación: Scopu

Role of van der Waals forces in the diffraction of noble gases from metal surfaces

Theoretical Chemistr