Semantic and inferencing abilities in children with communication disorders

Background: Semantic and inferencing abilities have not been fully examined in children with communication difficulties. Aims: To investigate the inferential and semantic abilities of children with communication difficulties using newly designed tasks. Methods & Procedures: Children with different types of communication disorder were compared with each other and with three groups of typically developing children: those of the same chronological age and two groups of younger children. In total, 25 children aged 11 years with specific language impairment and 22 children, also 11 years of age, with primary pragmatic difficulties were recruited. Typically developing groups aged 11 (n = 35; age‐match), and those aged 9 (n = 40) and 7 (n = 37; language similar) also participated as comparisons. Outcomes & Results: For Semantic Choices, children with specific language impairment performed significantly more poorly than 9‐ and 11‐year‐olds, whilst the pragmatic difficulties group scored significantly lower than all the typically developing groups. Borderline differences between specific language impairment and pragmatic difficulties groups were found. For inferencing, children with communication impairments performed significantly below the 11‐year‐old peers, but not poorer than 9‐ and 7‐year‐olds, suggesting that this skill is in line with language ability. Six children in the pragmatic difficulties group who met diagnosis for autism performed more poorly than the other two clinical groups on both tasks, but not statistically significantly so. Conclusions: Both tasks were more difficult for those with communication impairments compared with peers. Semantic but not inferencing abilities showed a non‐significant trend for differences between the two clinical groups and children with pragmatic difficulties performed more poorly than all typically developing groups. The tasks may relate to each other in varying ways according to type of communication difficulty

Stability analysis of a model for the market dynamics of a smart grid

We consider the dynamics of a smart grid system characterized by widespread distributed generation and storage devices. We assume that agents are free to trade electric energy over the network and we focus on the emerging market dynamics. We consider three different models for the market dynamics for which we present a stability analysis. We see that stability depends on the specific form of the market dynamics and it may depend on the structure of the underlying network topology. We run numerical simulations that confirm our theoretical predictions. As an example, we test our model for the market dynamics over a real network topology, namely, the Tramway 11 Feeder from New Mexico's power network

Dynamic filtering of static dipoles in magnetoencephalography

We consider the problem of estimating neural activity from measurements of the magnetic fields recorded by magnetoencephalography. We exploit the temporal structure of the problem and model the neural current as a collection of evolving current dipoles, which appear and disappear, but whose locations are constant throughout their lifetime. This fully reflects the physiological interpretation of the model. In order to conduct inference under this proposed model, it was necessary to develop an algorithm based around state-of-the-art sequential Monte Carlo methods employing carefully designed importance distributions. Previous work employed a bootstrap filter and an artificial dynamic structure where dipoles performed a random walk in space, yielding nonphysical artefacts in the reconstructions; such artefacts are not observed when using the proposed model. The algorithm is validated with simulated data, in which it provided an average localisation error which is approximately half that of the bootstrap filter. An application to complex real data derived from a somatosensory experiment is presented. Assessment of model fit via marginal likelihood showed a clear preference for the proposed model and the associated reconstructions show better localisation

Weak Liouville-Arnold Theorems & Their Implications

This paper studies the existence of invariant smooth Lagrangian graphs for Tonelli Hamiltonian systems with symmetries. In particular, we consider Tonelli Hamiltonians with n independent but not necessarily involutive constants of motion and obtain two theorems reminiscent of the Liouville-Arnold theorem. Moreover, we also obtain results on the structure of the configuration spaces of such systems that are reminiscent of results on the configuration space of completely integrable Tonelli Hamiltonians.Comment: 24 pages, 1 figure; v2 corrects typo in online abstract; v3 includes new title (was: A Weak Liouville-Arnold Theorem), re-arrangement of introduction, re-numbering of main theorems; v4 updates the authors' email and physical addresses, clarifies notation in section 4. Final versio

Inverse Modeling for MEG/EEG data

We provide an overview of the state-of-the-art for mathematical methods that are used to reconstruct brain activity from neurophysiological data. After a brief introduction on the mathematics of the forward problem, we discuss standard and recently proposed regularization methods, as well as Monte Carlo techniques for Bayesian inference. We classify the inverse methods based on the underlying source model, and discuss advantages and disadvantages. Finally we describe an application to the pre-surgical evaluation of epileptic patients.Comment: 15 pages, 1 figur

Fast Gibbs sampling for high-dimensional Bayesian inversion

Solving ill-posed inverse problems by Bayesian inference has recently attracted considerable attention. Compared to deterministic approaches, the probabilistic representation of the solution by the posterior distribution can be exploited to explore and quantify its uncertainties. In applications where the inverse solution is subject to further analysis procedures, this can be a significant advantage. Alongside theoretical progress, various new computational techniques allow to sample very high dimensional posterior distributions: In [Lucka2012], a Markov chain Monte Carlo (MCMC) posterior sampler was developed for linear inverse problems with $\ell_1$-type priors. In this article, we extend this single component Gibbs-type sampler to a wide range of priors used in Bayesian inversion, such as general $\ell_p^q$ priors with additional hard constraints. Besides a fast computation of the conditional, single component densities in an explicit, parameterized form, a fast, robust and exact sampling from these one-dimensional densities is key to obtain an efficient algorithm. We demonstrate that a generalization of slice sampling can utilize their specific structure for this task and illustrate the performance of the resulting slice-within-Gibbs samplers by different computed examples. These new samplers allow us to perform sample-based Bayesian inference in high-dimensional scenarios with certain priors for the first time, including the inversion of computed tomography (CT) data with the popular isotropic total variation (TV) prior.Comment: submitted to "Inverse Problems

Motor skills in children with primary headache: A pilot case-control study

Background: Headache is the most common painful manifestation in the developmental age, often accompanied by severe disability such as scholastic absenteeism, low quality of academic performance and compromised emotional functioning. The aim of the study is to evaluate praxic abilities in a population of children without aural migraine. Materials and methods: The test population consists of 10 subjects without migraine without aura (MwA), (8 Males) (mean age 8.40, SD ± 1.17) and 11 healthy children (7 Males) (mean age 8.27; SD ± 1.10; p = 0.800). All subjects underwent evaluation of motor coordination skills through the Battery for Children Movement Assessment (M-ABC). Results: The two groups (10 MwA vs 11 Controls) were similar for age (8.40 ± 1.17 vs 8.27 ± 1.10; p = 0.800), sex (p = 0.730), and BMI (p = 0.204). The migraine subjects show an average worse performance than the Movement ABC; specifically, migraineurs show significantly higher total score values (31.00 ± 23.65 vs 4.72 ± 2.61; p = 0.001), manual dexterity (12.10 ± 11.20 vs 2.04 ± 2.65; p = 0.009) and balance (14.85 ± 10.08 vs. 1.04 ± 1.05; p <0.001). The mean percentile of migraine performance is significantly reduced compared to controls (9.00 ± 3.82 vs 51.00 ± 24.34, p <0.001) (Table 1). Conclusion: Migraine can alter many cognitive and executive functions such as motor skills in developmental age

Animals-assisted therapy: A brief review

In rehabilitative setting, the presence of animals can be considered as an important stimulus for verbal and social communication, and for mood regulation. Interaction with an animal is beneficial for children's development and numerous psychological tests have revealed that growing up with pets has a beneficial effect on children's self-esteem and self-confidence, can improve empathy, a sense of responsibility and cognitive development, as well as social status within the peer group

Nodal dynamics, not degree distributions, determine the structural controllability of complex networks

Structural controllability has been proposed as an analytical framework for making predictions regarding the control of complex networks across myriad disciplines in the physical and life sciences (Liu et al., Nature:473(7346):167-173, 2011). Although the integration of control theory and network analysis is important, we argue that the application of the structural controllability framework to most if not all real-world networks leads to the conclusion that a single control input, applied to the power dominating set (PDS), is all that is needed for structural controllability. This result is consistent with the well-known fact that controllability and its dual observability are generic properties of systems. We argue that more important than issues of structural controllability are the questions of whether a system is almost uncontrollable, whether it is almost unobservable, and whether it possesses almost pole-zero cancellations.Comment: 1 Figures, 6 page

Psychopathological and psychodynamic hypotheses for pediatric stuttering

Stuttering is a common language alteration in pediatric age consisting in repetitions and blocks, which entail a break in the rhythm and melody of the speech. According to the WHO it is a disorder of the rhythm of the word, the subject knows precisely what he would like to say, but at the same time he is not able to say it. It is a great inconvenience for those affected, also because the slowing down of speaking is not about thought or cognitive skills