Health-related quality of life, adiposity, and sedentary behavior in patients with early schizophrenia: Preliminary study

Objective: To examine adiposity and sedentary behavior in relation to health-related quality of life (QoL) in patients with early schizophrenia. Methods: A cross-sectional study was used to assess adiposity by dual-energy X-ray absorptiometry scans, habitual physical activity and idle sitting time by the Short Form International Physical Activity Questionnaire, and health-related QoL by the RAND Medical Outcomes Study SF-36. QoL scores were compared with age-adjusted Canadian normative population data. Results: There were 36 participants with early schizophrenia, average age 25.1 (±3.6). Twenty-nine (72.5%) were males. Mean illness duration was 30 (±18) months, and mean body mass index was 28.3 (±5). Females had higher body fat content than males (30.8±6.9 vs 24.7±10.6; t=-2.6, df=34; P=0.015). Total body fat (F=14; P=0.001), lean body mass (F=10.2; P=0.001), and sedentary behavior (F=5; P=0.013) significantly increased across body mass index categories. Total body fat was correlated with sedentary behavior (r=0.62; P=0.001), and total lean body mass was negatively correlated with sedentary behavior (r=0.39; P=0.03). Based on SF-36scores, participants had significantly lower physical functioning (P=0.0034), role physical (P=0.0003), general health (P,0.0001), vitality (P=0.03), and physical component scores (P=0.003) than Canadian population comparisons. Habitual sedentary behavior, more than activity or adiposity levels, was associated with health-related QoL in early schizophrenia. Conclusion: Health-related QoL is lower in early schizophrenia and is predominantly experienced in the physical domain. QoL in early schizophrenia relates to sedentary behavior more than to activity and adiposity levels. © 2012 Strassnig etal, publisher and licensee Dove Medical Press Ltd

A mathematical theory of semantic development in deep neural networks

An extensive body of empirical research has revealed remarkable regularities in the acquisition, organization, deployment, and neural representation of human semantic knowledge, thereby raising a fundamental conceptual question: what are the theoretical principles governing the ability of neural networks to acquire, organize, and deploy abstract knowledge by integrating across many individual experiences? We address this question by mathematically analyzing the nonlinear dynamics of learning in deep linear networks. We find exact solutions to this learning dynamics that yield a conceptual explanation for the prevalence of many disparate phenomena in semantic cognition, including the hierarchical differentiation of concepts through rapid developmental transitions, the ubiquity of semantic illusions between such transitions, the emergence of item typicality and category coherence as factors controlling the speed of semantic processing, changing patterns of inductive projection over development, and the conservation of semantic similarity in neural representations across species. Thus, surprisingly, our simple neural model qualitatively recapitulates many diverse regularities underlying semantic development, while providing analytic insight into how the statistical structure of an environment can interact with nonlinear deep learning dynamics to give rise to these regularities

Exact solutions to the nonlinear dynamics of learning in deep linear neural networks

Despite the widespread practical success of deep learning methods, our theoretical understanding of the dynamics of learning in deep neural networks remains quite sparse. We attempt to bridge the gap between the theory and practice of deep learning by systematically analyzing learning dynamics for the restricted case of deep linear neural networks. Despite the linearity of their input-output map, such networks have nonlinear gradient descent dynamics on weights that change with the addition of each new hidden layer. We show that deep linear networks exhibit nonlinear learning phenomena similar to those seen in simulations of nonlinear networks, including long plateaus followed by rapid transitions to lower error solutions, and faster convergence from greedy unsupervised pretraining initial conditions than from random initial conditions. We provide an analytical description of these phenomena by finding new exact solutions to the nonlinear dynamics of deep learning. Our theoretical analysis also reveals the surprising finding that as the depth of a network approaches infinity, learning speed can nevertheless remain finite: for a special class of initial conditions on the weights, very deep networks incur only a finite, depth independent, delay in learning speed relative to shallow networks. We show that, under certain conditions on the training data, unsupervised pretraining can find this special class of initial conditions, while scaled random Gaussian initializations cannot. We further exhibit a new class of random orthogonal initial conditions on weights that, like unsupervised pre-training, enjoys depth independent learning times. We further show that these initial conditions also lead to faithful propagation of gradients even in deep nonlinear networks, as long as they operate in a special regime known as the edge of chaos.Comment: Submission to ICLR2014. Revised based on reviewer feedbac

Distorted wurtzite unit cells: Determination of lattice parameters of non-polar a-plane AlGaN and estimation of solid phase Al content

Unlike c-plane nitrides, ``non-polar" nitrides grown in e.g. the a-plane or m-plane orientation encounter anisotropic in-plane strain due to the anisotropy in the lattice and thermal mismatch with the substrate or buffer layer. Such anisotropic strain results in a distortion of the wurtzite unit cell and creates difficulty in accurate determination of lattice parameters and solid phase group-III content (x_solid) in ternary alloys. In this paper we show that the lattice distortion is orthorhombic, and outline a relatively simple procedure for measurement of lattice parameters of non-polar group III-nitrides epilayers from high resolution x-ray diffraction measurements. We derive an approximate expression for x_solid taking into account the anisotropic strain. We illustrate this using data for a-plane AlGaN, where we measure the lattice parameters and estimate the solid phase Al content, and also show that this method is applicable for m-plane structures as well

Weak turbulence theory of the non-linear evolution of the ion ring distribution

The nonlinear evolution of an ion ring instability in a low-beta magnetospheric plasma is considered. The evolution of the two-dimensional ring distribution is essentially quasilinear. Ignoring nonlinear processes the time-scale for the quasilinear evolution is the same as for the linear instability 1/t_ql gamma_l. However, when nonlinear processes become important, a new time scale becomes relevant to the wave saturation mechanism. Induced nonlinear scattering of the lower-hybrid waves by plasma electrons is the dominant nonlinearity relevant for plasmas in the inner magnetosphere and typically occurs on the timescale 1/t_ql w(M/m)W/nT, where W is the wave energy density, nT is the thermal energy density of the background plasma, and M/m is the ion to electron mass ratio, which has the consequence that the wave amplitude saturates at a low level, and the timescale for quasilinear relaxation is extended by orders of magnitude

Weak Turbulence in the Magnetosphere: Formation of Whistler Wave Cavity by Nonlinear Scattering

We consider the weak turbulence of whistler waves in the in low-\beta\ inner magnetosphere of the Earth. Whistler waves with frequencies, originating in the ionosphere, propagate radially outward and can trigger nonlinear induced scattering by thermal electrons provided the wave energy density is large enough. Nonlinear scattering can substantially change the direction of the wave vector of whistler waves and hence the direction of energy flux with only a small change in the frequency. A portion of whistler waves return to the ionosphere with a smaller perpendicular wave vector resulting in diminished linear damping and enhanced ability to pitch-angle scatter trapped electrons. In addition, a portion of the scattered wave packets can be reflected near the ionosphere back into the magnetosphere. Through multiple nonlinear scatterings and ionospheric reflections a long-lived wave cavity containing turbulent whistler waves can be formed with the appropriate properties to efficiently pitch-angle scatter trapped electrons. The primary consequence on the Earth's radiation belts is to reduce the lifetime of the trapped electron population.Comment: 13 pages, 9 figures, 4 table

High pressure studies on properties of FeGa3: role of on-site coulomb correlation

High pressure X-ray diffraction measurements have been carried out on the intermetallic semiconductor FeGa$_3$ and the equation of state for FeGa$_3$ has been determined. First principles based DFT calculations within the GGA approximation indicate that although the unit cell volume matches well with the experimentally obtained value at ambient pressure, it is significantly underestimated at high pressures and the difference between them increases as pressure increases. GGA + U calculations with increasing values of U$_{Fe(3d)}$ (on-site Coulomb repulsion between the Fe 3d electrons) at high pressures, correct this discrepancy. Further, the GGA+U calculations also show that along with U$_{Fe(3d)}$, the Fe 3d band width also increases with pressure and around a pressure of 4 GPa, a small density of states appear at the Fermi level. High pressure resistance measurements carried out on FeGa$_3$ also clearly show a signature of an electronic transition. Beyond the pressure of 19.7 GPa, the diffraction peaks reduce in intensity and are not observable beyond $\sim$ 26 GPa, leading to an amorphous state