CMB Constraints on Principal Components of the Inflaton Potential

We place functional constraints on the shape of the inflaton potential from the cosmic microwave background through a variant of the generalized slow roll approximation that allows large amplitude, rapidly changing deviations from scale-free conditions. Employing a principal component decomposition of the source function G'~3(V'/V)^2 - 2V''/V and keeping only those measured to better than 10% results in 5 nearly independent Gaussian constraints that maybe used to test any single-field inflationary model where such deviations are expected. The first component implies < 3% variations at the 100 Mpc scale. One component shows a 95% CL preference for deviations around the 300 Mpc scale at the ~10% level but the global significance is reduced considering the 5 components examined. This deviation also requires a change in the cold dark matter density which in a flat LCDM model is disfavored by current supernova and Hubble constant data and can be tested with future polarization or high multipole temperature data. Its impact resembles a local running of the tilt from multipoles 30-800 but is only marginally consistent with a constant running beyond this range. For this analysis, we have implemented a ~40x faster WMAP7 likelihood method which we have made publicly available.Comment: 12 pages, 14 figures, submitted to Phys.Rev.D. Optimized WMAP7 likelihood code and principal component functions of the GSR source function available at http://background.uchicago.edu/wmap_fast

Face Cognition: A Set of Distinct Mental Abilities

Perceiving, learning, and recognizing faces swiftly and accurately is of paramount importance to humans as a social species. Though established functional models of face cognition&#x3c;sup&#x3e;1,2&#x3c;/sup&#x3e; suggest the existence of multiple abilities in face cognition, the number of such abilities and the relationships among them and to other cognitive abilities can only be determined by studying individual differences. Here we investigated individual differences in a broad variety of indicators of face cognition and identified for the first time three component abilities: face perception, face memory, and the speed of face cognition. These component abilities were replicated in an independent study and were found to be robustly separable from established cognitive abilities, specifically immediate and delayed memory, mental speed, general cognitive ability, and object cognition. The analysis of individual differences goes beyond functional and neurological models of face cognition by demonstrating the difference between face perception and face learning, and by making evident the distinction between speed and accuracy of face cognition. Our indicators also provide a means to develop tests and training programs for face cognition that are broader and more precise than those currently available).&#x3c;sup&#x3e;3,4&#x3c;/sup&#x3e

Double logarithms, ln2(1/x)ln^2(1/x), and the NLO DGLAP evolution for the non-singlet component of the nucleon spin structure function, g1g_1

Theoretical predictions show that at low values of Bjorken $x$ the spin structure function, $g_1$ is influenced by large logarithmic corrections, $ln^2(1/x)$, which may be predominant in this region. These corrections are also partially contained in the NLO part of the standard DGLAP evolution. Here we calculate the non-singlet component of the nucleon structure function, $g_1^{NS}=g_1^p-g_1^n$, and its first moment, using a unified evolution equation. This equation incorporates the terms describing the NLO DGLAP evolution and the terms contributing to the $ln^2(1/x)$ resummation. In order to avoid double counting in the overlapping regions of the phase-space, a unique way of including the NLO terms into the unified evolution equation is proposed. The scheme-independent results obtained from this unified evolution are compared to the NLO fit to experimental data, GRSV'2000. Analysis of the first moments of $g_1^{NS}$ shows that the unified evolution including the $ln^2(1/x)$ resummation goes beyond the NLO DGLAP analysis. Corrections generated by double logarithms at low $x$ influence the $Q^2$-dependence of the first moments strongly.Comment: 13 pages, latex, 2 figures; Appendix adde

Network connectivity and structural correlates of survival in progressive supranuclear palsy and corticobasal syndrome

There is a pressing need to understand the factors that predict prognosis in progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS), with high heterogeneity over the poor average survival. We test the hypothesis that the magnitude and distribution of connectivity changes in PSP and CBS predict the rate of progression and survival time, using datasets from the Cambridge Centre for Parkinson-plus and the UK National PSP Research Network (PROSPECT-MR). Resting-state functional MRI images were available from 146 participants with PSP, 82 participants with CBS, and 90 healthy controls. Large-scale networks were identified through independent component analyses, with correlations taken between component time series. Independent component analysis was also used to select between-network connectivity components to compare with baseline clinical severity, longitudinal rate of change in severity, and survival. Transdiagnostic survival predictors were identified using partial least squares regression for Cox models, with connectivity compared to patients' demographics, structural imaging, and clinical scores using five-fold cross-validation. In PSP and CBS, between-network connectivity components were identified that differed from controls, were associated with disease severity, and were related to survival and rate of change in clinical severity. A transdiagnostic component predicted survival beyond demographic and motion metrics but with lower accuracy than an optimal model that included the clinical and structural imaging measures. Cortical atrophy enhanced the connectivity changes that were most predictive of survival. Between-network connectivity is associated with variability in prognosis in PSP and CBS but does not improve predictive accuracy beyond clinical and structural imaging metrics

Statistical model of natural stimuli predicts edge-like pooling of spatial frequency channels in V2

BACKGROUND: It has been shown that the classical receptive fields of simple and complex cells in the primary visual cortex emerge from the statistical properties of natural images by forcing the cell responses to be maximally sparse or independent. We investigate how to learn features beyond the primary visual cortex from the statistical properties of modelled complex-cell outputs. In previous work, we showed that a new model, non-negative sparse coding, led to the emergence of features which code for contours of a given spatial frequency band. RESULTS: We applied ordinary independent component analysis to modelled outputs of complex cells that span different frequency bands. The analysis led to the emergence of features which pool spatially coherent across-frequency activity in the modelled primary visual cortex. Thus, the statistically optimal way of processing complex-cell outputs abandons separate frequency channels, while preserving and even enhancing orientation tuning and spatial localization. As a technical aside, we found that the non-negativity constraint is not necessary: ordinary independent component analysis produces essentially the same results as our previous work. CONCLUSION: We propose that the pooling that emerges allows the features to code for realistic low-level image features related to step edges. Further, the results prove the viability of statistical modelling of natural images as a framework that produces quantitative predictions of visual processing