Correlations in the (Sub)millimeter Background from ACT × BLAST

We present measurements of the auto- and cross-frequency correlation power spectra of the cosmic (sub)millimeter background at 250, 350, and 500 μm (1200, 860, and 600 GHz) from observations made with the Balloon-borne Large Aperture Submillimeter Telescope (BLAST); and at 1380 and 2030 μm (218 and 148 GHz) from observations made with the Atacama Cosmology Telescope (ACT). The overlapping observations cover 8.6 deg^2 in an area relatively free of Galactic dust near the south ecliptic pole. The ACT bands are sensitive to radiation from the cosmic microwave background, to the Sunyaev-Zel'dovich effect from galaxy clusters, and to emission by radio and dusty star-forming galaxies (DSFGs), while the dominant contribution to the BLAST bands is from DSFGs. We confirm and extend the BLAST analysis of clustering with an independent pipeline and also detect correlations between the ACT and BLAST maps at over 25σ significance, which we interpret as a detection of the DSFGs in the ACT maps. In addition to a Poisson component in the cross-frequency power spectra, we detect a clustered signal at 4σ, and using a model for the DSFG evolution and number counts, we successfully fit all of our spectra with a linear clustering model and a bias that depends only on redshift and not on scale. Finally, the data are compared to, and generally agree with, phenomenological models for the DSFG population. This study demonstrates the constraining power of the cross-frequency correlation technique to constrain models for the DSFGs. Similar analyses with more data will impose tight constraints on future models

Exploring Two Novel Features for EEG-based Brain-Computer Interfaces: Multifractal Cumulants and Predictive Complexity

In this paper, we introduce two new features for the design of electroencephalography (EEG) based Brain-Computer Interfaces (BCI): one feature based on multifractal cumulants, and one feature based on the predictive complexity of the EEG time series. The multifractal cumulants feature measures the signal regularity, while the predictive complexity measures the difficulty to predict the future of the signal based on its past, hence a degree of how complex it is. We have conducted an evaluation of the performance of these two novel features on EEG data corresponding to motor-imagery. We also compared them to the most successful features used in the BCI field, namely the Band-Power features. We evaluated these three kinds of features and their combinations on EEG signals from 13 subjects. Results obtained show that our novel features can lead to BCI designs with improved classification performance, notably when using and combining the three kinds of feature (band-power, multifractal cumulants, predictive complexity) together.Comment: Updated with more subjects. Separated out the band-power comparisons in a companion article after reviewer feedback. Source code and companion article are available at http://nicolas.brodu.numerimoire.net/en/recherche/publication

Angular Power Spectra of the Millimeter Wavelength Background Light from Dusty Star-forming Galaxies with the South Pole Telescope

We use data from the first 100 square-degree field observed by the South Pole Telescope (SPT) in 2008 to measure the angular power spectrum of temperature anisotropies contributed by the background of dusty star-forming galaxies (DSFGs) at millimeter wavelengths. From the auto and cross-correlation of 150 and 220 GHz SPT maps, we significantly detect both Poisson distributed and, for the first time at millimeter wavelengths, clustered components of power from a background of DSFGs. The spectral indices between 150 and 220 GHz of the Poisson and clustered components are found to be 3.86 +- 0.23 and 3.8 +- 1.3 respectively, implying a steep scaling of the dust emissivity index beta ~ 2. The Poisson and clustered power detected in SPT, BLAST (at 600, 860, and 1200 GHz), and Spitzer (1900 GHz) data can be understood in the context of a simple model in which all galaxies have the same graybody spectrum with dust emissivity index of beta = 2 and dust temperature T_d = 34 K. In this model, half of the 150 GHz background light comes from redshifts greater than 3.2. We also use the SPT data to place an upper limit on the amplitude of the kinetic Sunyaev-Zel'dovich power spectrum at l = 3000 of 13 uK^2 at 95% confidence.Comment: 18 pages, 9 figure

Amplitudes of thermal and kinetic Sunyaev-Zel'dovich signals from small-scale CMB anisotropies

While the arcminute-scale Cosmic Microwave Background (CMB) anisotropies are due to secondary effects, point sources dominate the total anisotropy power spectrum. At high frequencies the point sources are primarily in the form of dusty, star-forming galaxies. Both Herschel and Planck have recently measured the anisotropy power spectrum of cosmic infrared background (CIB) generated by dusty, star-forming galaxies from degree to sub-arcminute angular scales, including the non-linear clustering of these galaxies at multipoles of 3000 to 6000 relevant to CMB secondary anisotropy studies. We scale the CIB angular power spectra to CMB frequencies and interpret the combined WMAP-7 year and arcminute-scale Atacama Cosmology Telescope (ACT) and South Pole Telescope (SPT) CMB power spectra measurements to constrain the Sunyaev-Zel'dovich (SZ) effects. Allowing the CIB clustering amplitude to vary, we constrain the amplitudes of thermal and kinetic SZ power spectra at 150 GHz.Comment: 8 pages, 3 figures, 2 table

Multiscale adaptive smoothing models for the hemodynamic response function in fMRI

In the event-related functional magnetic resonance imaging (fMRI) data analysis, there is an extensive interest in accurately and robustly estimating the hemodynamic response function (HRF) and its associated statistics (e.g., the magnitude and duration of the activation). Most methods to date are developed in the time domain and they have utilized almost exclusively the temporal information of fMRI data without accounting for the spatial information. The aim of this paper is to develop a multiscale adaptive smoothing model (MASM) in the frequency domain by integrating the spatial and frequency information to adaptively and accurately estimate HRFs pertaining to each stimulus sequence across all voxels in a three-dimensional (3D) volume. We use two sets of simulation studies and a real data set to examine the finite sample performance of MASM in estimating HRFs. Our real and simulated data analyses confirm that MASM outperforms several other state-of-the-art methods, such as the smooth finite impulse response (sFIR) model.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS609 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Audio Source Separation Using Sparse Representations

This is the author's final version of the article, first published as A. Nesbit, M. G. Jafari, E. Vincent and M. D. Plumbley. Audio Source Separation Using Sparse Representations. In W. Wang (Ed), Machine Audition: Principles, Algorithms and Systems. Chapter 10, pp. 246-264. IGI Global, 2011. ISBN 978-1-61520-919-4. DOI: 10.4018/978-1-61520-919-4.ch010file: NesbitJafariVincentP11-audio.pdf:n\NesbitJafariVincentP11-audio.pdf:PDF owner: markp timestamp: 2011.02.04file: NesbitJafariVincentP11-audio.pdf:n\NesbitJafariVincentP11-audio.pdf:PDF owner: markp timestamp: 2011.02.04The authors address the problem of audio source separation, namely, the recovery of audio signals from recordings of mixtures of those signals. The sparse component analysis framework is a powerful method for achieving this. Sparse orthogonal transforms, in which only few transform coefficients differ significantly from zero, are developed; once the signal has been transformed, energy is apportioned from each transform coefficient to each estimated source, and, finally, the signal is reconstructed using the inverse transform. The overriding aim of this chapter is to demonstrate how this framework, as exemplified here by two different decomposition methods which adapt to the signal to represent it sparsely, can be used to solve different problems in different mixing scenarios. To address the instantaneous (neither delays nor echoes) and underdetermined (more sources than mixtures) mixing model, a lapped orthogonal transform is adapted to the signal by selecting a basis from a library of predetermined bases. This method is highly related to the windowing methods used in the MPEG audio coding framework. In considering the anechoic (delays but no echoes) and determined (equal number of sources and mixtures) mixing case, a greedy adaptive transform is used based on orthogonal basis functions that are learned from the observed data, instead of being selected from a predetermined library of bases. This is found to encode the signal characteristics, by introducing a feedback system between the bases and the observed data. Experiments on mixtures of speech and music signals demonstrate that these methods give good signal approximations and separation performance, and indicate promising directions for future research