MaxEnt and dynamical information

The MaxEnt solutions are shown to display a variety of behaviors (beyond the traditional and customary exponential one) if adequate dynamical information is inserted into the concomitant entropic-variational principle. In particular, we show both theoretically and numerically that power laws and power laws with exponential cut-offs emerge as equilibrium densities in proportional and other dynamics

A Bayesian Variable Selection Approach Yields Improved Detection of Brain Activation From Complex-Valued fMRI

Voxel functional magnetic resonance imaging (fMRI) time courses are complex-valued signals giving rise to magnitude and phase data. Nevertheless, most studies use only the magnitude signals and thus discard half of the data that could potentially contain important information. Methods that make use of complex-valued fMRI (CV-fMRI) data have been shown to lead to superior power in detecting active voxels when compared to magnitude-only methods, particularly for small signal-to-noise ratios (SNRs). We present a new Bayesian variable selection approach for detecting brain activation at the voxel level from CV-fMRI data. We develop models with complex-valued spike-and-slab priors on the activation parameters that are able to combine the magnitude and phase information. We present a complex-valued EM variable selection algorithm that leads to fast detection at the voxel level in CV-fMRI slices and also consider full posterior inference via Markov chain Monte Carlo (MCMC). Model performance is illustrated through extensive simulation studies, including the analysis of physically based simulated CV-fMRI slices. Finally, we use the complex-valued Bayesian approach to detect active voxels in human CV-fMRI from a healthy individual who performed unilateral finger tapping in a designed experiment. The proposed approach leads to improved detection of activation in the expected motor-related brain regions and produces fewer false positive results than other methods for CV-fMRI. Supplementary materials for this article are available online

Firm Investment and Monetary Policy Transmission in the Euro Area.

We present a comparable set of results on the monetary transmission channels on firm investment for the four largest euro-area countries (Germany, France, Italy and Spain). With particularly rich micro datasets for each country containing over 215,000 observations from 1985 to 1999, we ex-plore what can be learned about the interest channel and the broad credit channel. For each of those countries, we estimate neo-classical investment relationships, explaining investment by its user cost, sales and cash flow. We find investment to be sensitive to user cost changes in all those four countries. This implies an operative interest channel in these euro-area countries. We also find in-vestment in all countries to be quite sensitive to cash flow movements. However, only in Italy do smaller firms react more to cash flow movements than large firms, implying that a broad credit channel might not be equally pervasive in all countries.Investment, Monetary transmission channels, User cost of capital.

On the exposure to mobile phone radiation in trains

This report presents theoretical estimates of the Power Density levels which may be reached inside trains. Two possible sources of high levels of radiation are discussed. The first one arises since the walls of the wagons are metallic and therefore bounce back almost all radiation impinging on them. The second is due to the simultaneous emission of a seemingly large number of nearby telephones. The theoretical study presented here shows that Power Densities stay at values below reference levels always.Comment: 9 pages, 1 figur

Topological Data Analysis for Directed Dependence Networks of Multivariate Time Series Data

Topological data analysis (TDA) approaches are becoming increasingly popular for studying the dependence patterns in multivariate time series data. In particular, various dependence patterns in brain networks may be linked to specific tasks and cognitive processes, which can be altered by various neurological impairments such as epileptic seizures. Existing TDA approaches rely on the notion of distance between data points that is symmetric by definition for building graph filtrations. For brain dependence networks, this is a major limitation that constrains practitioners to using only symmetric dependence measures, such as correlations or coherence. However, it is known that the brain dependence network may be very complex and can contain a directed flow of information from one brain region to another. Such dependence networks are usually captured by more advanced measures of dependence such as partial directed coherence, which is a Granger causality based dependence measure. These dependence measures will result in a non-symmetric distance function, especially during epileptic seizures. In this paper we propose to solve this limitation by decomposing the weighted connectivity network into its symmetric and anti-symmetric components using matrix decomposition and comparing the anti-symmetric component prior to and post seizure. Our analysis of epileptic seizure EEG data shows promising results

Relationship between sources and manifestations of stress among faculty members in Isabela State University

Stress is inevitable in any workplace. Stressed teachers in every school are prone to exhaustion and commit errors. In the Philippines, few studies have been discussed due to stress among faculty members, especially in tertiary education. In this study, the researchers shed light on sources, manifestations, and levels of stress, and discovered the relationship between sources and their manifestations among faculty members of the eight colleges of the Isabela State University-Main Campus. Data were randomly collected from 165 respondents, through the Teacher Stress Inventory developed by Fimian. Data revealed that the main sources and manifestations of stress by the respondents were Work-related and Professional Investment, and Fatigue Manifestations. The level of stress among the respondents was moderate. Likewise, the sources and manifestations of stress were found significantly correlated to each other. Results of the study press on the development of a proposed Stress Management Program supportive and essential in managing and coping stress of the faculty membe

Extending the generalized Chaplygin gas model by using geometrothermodynamics

We use the formalism of geometrothermodynamics (GTD) to derive fundamental thermodynamic equations that are used to construct general relativistic cosmological models. In particular, we show that the simplest possible fundamental equation, which corresponds in GTD to a system with no internal thermodynamic interaction, describes the different fluids of the standard model of cosmology. In addition, a particular fundamental equation with internal thermodynamic interaction is shown to generate a new cosmological model that correctly describes the dark sector of the Universe and contains as a special case the generalized Chaplygin gas model.Comment: 18 pages, 7 figures. Section added: Basics aspects of geometrothermodynamic

Variational Principle underlying Scale Invariant Social Systems

MaxEnt's variational principle, in conjunction with Shannon's logarithmic information measure, yields only exponential functional forms in straightforward fashion. In this communication we show how to overcome this limitation via the incorporation, into the variational process, of suitable dynamical information. As a consequence, we are able to formulate a somewhat generalized Shannonian Maximum Entropy approach which provides a unifying "thermodynamic-like" explanation for the scale-invariant phenomena observed in social contexts, as city-population distributions. We confirm the MaxEnt predictions by means of numerical experiments with random walkers, and compare them with some empirical data

Extensions of modified Chaplygin gas from Geometrothermodynamics

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Spin-orbit coupling in curved graphene, fullerenes, nanotubes, and nanotube caps

A continuum model for the effective spin orbit interaction in graphene is derived from a tight-binding model which includes the $\pi$ and $\sigma$ bands. We analyze the combined effects of the intra-atomic spin-orbit coupling, curvature, and applied electric field, using perturbation theory. We recover the effective spin-orbit Hamiltonian derived recently from group theoretical arguments by Kane and Mele. We find, for flat graphene, that the intrinsic spin-orbit coupling \Hi \propto \Delta^ 2 and the Rashba coupling due to a perpendicular electric field ${\cal E}$, $\Delta_{\cal E} \propto \Delta$, where $\Delta$ is the intra-atomic spin-orbit coupling constant for carbon. Moreover we show that local curvature of the graphene sheet induces an extra spin-orbit coupling term $\Delta_{\rm curv} \propto \Delta$. For the values of $\cal E$ and curvature profile reported in actual samples of graphene, we find that \Hi < \Delta_{\cal E} \lesssim \Delta_{\rm curv}. The effect of spin-orbit coupling on derived materials of graphene, like fullerenes, nanotubes, and nanotube caps, is also studied. For fullerenes, only \Hi is important. Both for nanotubes and nanotube caps $\Delta_{\rm curv}$ is in the order of a few Kelvins. We reproduce the known appearance of a gap and spin-splitting in the energy spectrum of nanotubes due to the spin-orbit coupling. For nanotube caps, spin-orbit coupling causes spin-splitting of the localized states at the cap, which could allow spin-dependent field-effect emission.Comment: Final version. Published in Physical Review