Methods and Approaches for Characterizing Learning Related Changes Observed in functional MRI Data — A Review

Brain imaging data have so far revealed a wealth of information about neuronal circuits involved in higher mental functions like memory, attention, emotion, language etc. Our efforts are toward understanding the learning related effects in brain activity during the acquisition of visuo-motor sequential skills. The aim of this paper is to survey various methods and approaches of analysis that allow the characterization of learning related changes in fMRI data. Traditional imaging analysis using the Statistical Parametric Map (SPM) approach averages out temporal changes and presents overall differences between different stages of learning. We outline other potential approaches for revealing learning effects such as statistical time series analysis, modelling of haemodynamic response function and independent component analysis. We present example case studies from our visuo-motor sequence learning experiments to describe application of SPM and statistical time series analyses. Our review highlights that the problem of characterizing learning induced changes in fMRI data remains an interesting and challenging open research problem

RCW36: characterizing the outcome of massive star formation

Massive stars play a dominant role in the process of clustered star formation, with their feedback into the molecular cloud through ionizing radiation, stellar winds and outflows. The formation process of massive stars is poorly constrained because of their scarcity, the short formation timescale and obscuration. By obtaining a census of the newly formed stellar population, the star formation history of the young cluster and the role of the massive stars within it can be unraveled. We aim to reconstruct the formation history of the young stellar population of the massive star-forming region RCW 36. We study several dozens of individual objects, both photometrically and spectroscopically, look for signs of multiple generations of young stars and investigate the role of the massive stars in this process. We obtain a census of the physical parameters and evolutionary status of the young stellar population. Using a combination of near-infrared photometry and spectroscopy we estimate ages and masses of individual objects. We identify the population of embedded young stellar objects (YSO) by their infrared colors and emission line spectra. RCW 36 harbors a stellar population of massive and intermediate-mass stars located around the center of the cluster. Class 0/I and II sources are found throughout the cluster. The central population has a median age of 1.1 +/- 0.6 Myr. Of the stars which could be classified, the most massive ones are situated in the center of the cluster. The central cluster is surrounded by filamentary cloud structures; within these, some embedded and accreting YSOs are found. Our age determination is consistent with the filamentary structures having been shaped by the ionizing radiation and stellar winds of the central massive stars. The formation of a new generation of stars is ongoing, as demonstrated by the presence of embedded protostellar clumps, and two exposed jets.Comment: 18 pages, 10 figures, accepted for publication in Astronomy & Astrophysic

Statistical physics for cosmic structures

The recent observations of galaxy and dark matter clumpy distributions have provided new elements to the understating of the problem of cosmological structure formation. The strong clumpiness characterizing galaxy structures seems to be present in the overall mass distribution and its relation to the highly isotropic Cosmic Microwave Background Radiation represents a fundamental problem. The extension of structures, the formation of power-law correlations characterizing the strongly clustered regime and the relation between dark and visible matter are the key problems both from an observational and a theoretical point of view. We discuss recent progresses in the studies of structure formation by using concepts and methods of statistical physics.Comment: 8 pages, 4 figures, European Physical Journal B - STATPHYS 23 topical issue in the press (2007

Nonparametric Bayes Modeling of Populations of Networks

Replicated network data are increasingly available in many research fields. In connectomic applications, inter-connections among brain regions are collected for each patient under study, motivating statistical models which can flexibly characterize the probabilistic generative mechanism underlying these network-valued data. Available models for a single network are not designed specifically for inference on the entire probability mass function of a network-valued random variable and therefore lack flexibility in characterizing the distribution of relevant topological structures. We propose a flexible Bayesian nonparametric approach for modeling the population distribution of network-valued data. The joint distribution of the edges is defined via a mixture model which reduces dimensionality and efficiently incorporates network information within each mixture component by leveraging latent space representations. The formulation leads to an efficient Gibbs sampler and provides simple and coherent strategies for inference and goodness-of-fit assessments. We provide theoretical results on the flexibility of our model and illustrate improved performance --- compared to state-of-the-art models --- in simulations and application to human brain networks

A new modal-based damage location indicator

Vibration-based damage detection techniques use the change in modal data as an indicator to assess damages in the structure. Knowing the structural dynamic characteristics of the healthy and damaged structure, the estimation of the damage location and severity is possible by solving an inverse problem. This paper presents a mathematical expression relating damage location and depth to the frequency shifts of the bending vibration modes. This expression permits the extraction of a series of coefficients that characterize each damage location and are independent of the damage severity. The vector aggregating these coefficients for a given location constitutes a Damage Location Indicator (DLI) that unambiguously characterizes the position of a geometrical discontinuity in the beam. A set of vectors typifying all locations along the beam may be used as patters opposable to the damage signature found by measurements. The similarity between the signature and one of the patterns indicates the location of damage

Moments of convex distribution functions and completely alternating sequences

We solve the moment problem for convex distribution functions on $[0,1]$ in terms of completely alternating sequences. This complements a recent solution of this problem by Diaconis and Freedman, and relates this work to the L\'{e}vy-Khintchine formula for the Laplace transform of a subordinator, and to regenerative composition structures.Comment: Published in at http://dx.doi.org/10.1214/193940307000000374 the IMS Collections (http://www.imstat.org/publications/imscollections.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

A non-separable stochastic model for pulse-like ground motions

A phenomenological non-separable non-stationary stochastic model is proposed to represent near-fault pulse-like ground motions (PLGMs) by means of a parametrically defined evolutionary power spectrum (EPSD). Numerical data pertaining to ensembles of EPSD compatible realizations and considering statistical analysis of peak elastic and inelastic spectral ordinates demonstrate the applicability of the model to capture the salient effects of PLGMs to structural responses. To this aim, the model parameters are calibrated against a field recorded PLGM. Further numerical data considering stochastic processes compatible with the response spectrum of the European aseismic code (EC8) are furnished to demonstrate the potential of the proposed model for including near-fault effects to spectrum compatible representations of the seismic action. It is foreseen that this model can be a useful tool in accounting for the low-frequency content of PLGMs in both Monte Carlo simulation-based analyses and in statistical linearization based studies

Multiscale cosmology and structure-emerging Dark Energy: A plausibility analysis

Cosmological backreaction suggests a link between structure formation and the expansion history of the Universe. In order to quantitatively examine this connection, we dynamically investigate a volume partition of the Universe into over-- and underdense regions. This allows us to trace structure formation using the volume fraction of the overdense regions \lambda_{\CM} as its characterizing parameter. Employing results from cosmological perturbation theory and extrapolating the leading mode into the nonlinear regime, we construct a three--parameter model for the effective cosmic expansion history, involving \lambda_{\CM_{0}}, the matter density \Omega_{m}^{\CD_{0}}, and the Hubble rate H_{\CD_{0}} of today's Universe. Taking standard values for \Omega_{m}^{\CD_{0}} and H_{\CD_{0}} as well as a reasonable value for \lambda_{\CM_{0}}, that we derive from $N$--body simulations, we determine the corresponding amounts of backreaction and spatial curvature. We find that the obtained values that are sufficient to generate today's structure also lead to a $\Lambda$CDM--like behavior of the scale factor, parametrized by the same parameters \Omega_{m}^{\CD_{0}} and H_{\CD_{0}}, but without a cosmological constant. However, the temporal behavior of \lambda_{\CM} does not faithfully reproduce the structure formation history. Surprisingly, however, the model matches with structure formation with the assumption of a low matter content, \Omega_{m}^{\CD_{0}}\approx3\%, a result that hints to a different interpretation of part of the backreaction effect as kinematical Dark Matter. (truncated)Comment: 25 pages, 10 figures, includes calculation of luminosity distances, matches published version in Phys. Rev.