Linguistic and cognitive motivations for the typological primacy model of third language (L3) transfer: timing of acquisition and proficiency considered

This article elucidates the Typological Primacy Model (TPM; Rothman, 2010, 2011, 2013) for the initial stages of adult third language (L3) morphosyntactic transfer, addressing questions that stem from the model and its application. The TPM maintains that structural proximity between the L3 and the L1 and/or the L2 determines L3 transfer. In addition to demonstrating empirical support for the TPM, this article articulates a proposal for how the mind unconsciously determines typological (structural) proximity based on linguistic cues from the L3 input stream used by the parser early on to determine holistic transfer of one previous (the L1 or the L2) system. This articulated version of the TPM is motivated by argumentation appealing to cognitive and linguistic factors. Finally, in line with the general tenets of the TPM, I ponder if and why L3 transfer might obtain differently depending on the type of bilingual (e.g. early vs. late) and proficiency level of bilingualism involved in the L3 process.

Study of charge transport in blends of natural rubber and poly(o-methoxyaniline) based on a resistor network statistical model

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The use of artificial neural networks in analysing the nutritional ecology of Chrysomya megacephala (F.) (Diptera: Calliphoridae), compared with a statistical model

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Statistical Model to DNA Melting

Um modelo estatístico para o DNA é estudado a fim de se obter informações sobre o comportamento de variáveis termodinâmicas. Atenção especial é dada à desnaturação térmica desta macromolécula.

Quark sea structure functions of the nucleon in a statistical model

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

RECENT HEAVY-ION EXPERIMENTS AND STATISTICAL-MODEL OF HADRONS

The combined CERN and Brookhaven heavy ion (H.I.) data supports a scenario of hadron gas which is in chemical and thermal equilibrium at a temperature T of about 140 MeV. Using the Brown-Stachel-Welke model (which gives 150 MeV) we show that in this scenario, the hot nucleons have mass 3 pi T and the pi and rho mesons have masses close to pi T and 2 pi T, respectively. A simple model with pions and quarks supports the co-existence of two phases in these heavy ion experiments, suggesting a second order phase transition. The masses of the pion, rho and the nucleon are intriguingly close to the lattice screening masses.

Statistical model applied to NetFlow for network intrusion detection

The computers and network services became presence guaranteed in several places. These characteristics resulted in the growth of illicit events and therefore the computers and networks security has become an essential point in any computing environment. Many methodologies were created to identify these events; however, with increasing of users and services on the Internet, many difficulties are found in trying to monitor a large network environment. This paper proposes a methodology for events detection in large-scale networks. The proposal approaches the anomaly detection using the NetFlow protocol, statistical methods and monitoring the environment in a best time for the application. © 2010 Springer-Verlag Berlin Heidelberg.

A statistical model of aggregate fragmentation

Processo FAPESP: 11/08171-3

Statistical properties for a dissipative model of relativistic particles in a wave packet: A parameter space investigation

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Evaluation of the Computer-based Battery for Oral Language (BILOv3) through the Rasch model for a Brazilian Sample

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Problem of the statistical model in deep inelastic scattering phenomenology

Recent deep inelastic data leads to an up-down quark asymmetry of the nucleon sea. Explanations of the flavour asymmetry and the di-lepton production in proton-nucleus collisions call for a temperature T ≈ 100 MeV in a statistical model. This T may be conjectured as being due to the Fulling-Davies-Unruh effect. But it is not possible to fit the structure function itself.

On the statistical and transport properties of a non-dissipative Fermi-Ulam model

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The use of artificial neural networks in analysing the nutritional ecology of Chrysomya megacephala (F.) (Diptera: Calliphoridae), compared with a statistical model

Artificial neural networks (ANNs) have been widely applied to the resolution of complex biological problems. An important feature of neural models is that their implementation is not precluded by the theoretical distribution shape of the data used. Frequently, the performance of ANNs over linear or non-linear regression-based statistical methods is deemed to be significantly superior if suitable sample sizes are provided, especially in multidimensional and non-linear processes. The current work was aimed at utilising three well-known neural network methods in order to evaluate whether these models would be able to provide more accurate outcomes in relation to a conventional regression method in pupal weight predictions of Chrysomya megacephala, a species of blowfly (Diptera: Calliphoridae), using larval density (i.e. the initial number of larvae), amount of available food and pupal size as input data. It was possible to notice that the neural networks yielded more accurate performances in comparison with the statistical model (multiple regression). Assessing the three types of networks utilised (Multi-layer Perceptron, Radial Basis Function and Generalised Regression Neural Network), no considerable differences between these models were detected. The superiority of these neural models over a classical statistical method represents an important fact, because more accurate models may clarify several intricate aspects concerning the nutritional ecology of blowflies.

Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE)

Abstract Background An important challenge for transcript counting methods such as Serial Analysis of Gene Expression (SAGE), "Digital Northern" or Massively Parallel Signature Sequencing (MPSS), is to carry out statistical analyses that account for the within-class variability, i.e., variability due to the intrinsic biological differences among sampled individuals of the same class, and not only variability due to technical sampling error. Results We introduce a Bayesian model that accounts for the within-class variability by means of mixture distribution. We show that the previously available approaches of aggregation in pools ("pseudo-libraries") and the Beta-Binomial model, are particular cases of the mixture model. We illustrate our method with a brain tumor vs. normal comparison using SAGE data from public databases. We show examples of tags regarded as differentially expressed with high significance if the within-class variability is ignored, but clearly not so significant if one accounts for it. Conclusion Using available information about biological replicates, one can transform a list of candidate transcripts showing differential expression to a more reliable one. Our method is freely available, under GPL/GNU copyleft, through a user friendly web-based on-line tool or as R language scripts at supplemental web-site.