ICLabel: An automated electroencephalographic independent component classifier, dataset, and website

The electroencephalogram (EEG) provides a non-invasive, minimally restrictive, and relatively low cost measure of mesoscale brain dynamics with high temporal resolution. Although signals recorded in parallel by multiple, near-adjacent EEG scalp electrode channels are highly-correlated and combine signals from many different sources, biological and non-biological, independent component analysis (ICA) has been shown to isolate the various source generator processes underlying those recordings. Independent components (IC) found by ICA decomposition can be manually inspected, selected, and interpreted, but doing so requires both time and practice as ICs have no particular order or intrinsic interpretations and therefore require further study of their properties. Alternatively, sufficiently-accurate automated IC classifiers can be used to classify ICs into broad source categories, speeding the analysis of EEG studies with many subjects and enabling the use of ICA decomposition in near-real-time applications. While many such classifiers have been proposed recently, this work presents the ICLabel project comprised of (1) an IC dataset containing spatiotemporal measures for over 200,000 ICs from more than 6,000 EEG recordings, (2) a website for collecting crowdsourced IC labels and educating EEG researchers and practitioners about IC interpretation, and (3) the automated ICLabel classifier. The classifier improves upon existing methods in two ways: by improving the accuracy of the computed label estimates and by enhancing its computational efficiency. The ICLabel classifier outperforms or performs comparably to the previous best publicly available method for all measured IC categories while computing those labels ten times faster than that classifier as shown in a rigorous comparison against all other publicly available EEG IC classifiers.Comment: Intended for NeuroImage. Updated from version one with minor editorial and figure change

Imaging and Demography of the Host Galaxies of High-Redshift Type Ia Supernovae

We present the results of a study of the host galaxies of high redshift Type Ia supernovae (SNe Ia). We provide a catalog of 18 hosts of SNe Ia observed with the Hubble Space Telescope (HST) by the High-z Supernova Search Team (HZT), including images, scale-lengths, measurements of integrated (Hubble equivalent) BVRIZ photometry in bands where the galaxies are brighter than m ~ 25 mag, and galactocentric distances of the supernovae. We compare the residuals of SN Ia distance measurements from cosmological fits to measurable properties of the supernova host galaxies that might be expected to correlate with variable properties of the progenitor population, such as host galaxy color and position of the supernova. We find mostly null results; the current data are generally consistent with no correlations of the distance residuals with host galaxy properties in the redshift range 0.42 < z < 1.06. Although a subsample of SN hosts shows a formally significant (3-sigma) correlation between apparent V-R host color and distance residuals, the correlation is not consistent with the null results from other host colors probed by our largest samples. There is also evidence for the same correlations between SN Ia properties and host type at low redshift and high redshift. These similarities support the current practice of extrapolating properties of the nearby population to high redshifts pending more robust detections of any correlations between distance residuals from cosmological fits and host properties.Comment: 35 pages, 12 figures, 4 tables, accepted for publication in A

Deep Learning and Music Adversaries

OA Monitor ExerciseOA Monitor ExerciseAn {\em adversary} is essentially an algorithm intent on making a classification system perform in some particular way given an input, e.g., increase the probability of a false negative. Recent work builds adversaries for deep learning systems applied to image object recognition, which exploits the parameters of the system to find the minimal perturbation of the input image such that the network misclassifies it with high confidence. We adapt this approach to construct and deploy an adversary of deep learning systems applied to music content analysis. In our case, however, the input to the systems is magnitude spectral frames, which requires special care in order to produce valid input audio signals from network-derived perturbations. For two different train-test partitionings of two benchmark datasets, and two different deep architectures, we find that this adversary is very effective in defeating the resulting systems. We find the convolutional networks are more robust, however, compared with systems based on a majority vote over individually classified audio frames. Furthermore, we integrate the adversary into the training of new deep systems, but do not find that this improves their resilience against the same adversary

LANDSAT-D investigations in snow hydrology

Work undertaken during the contract and its results are described. Many of the results from this investigation are available in journal or conference proceedings literature - published, accepted for publication, or submitted for publication. For these the reference and the abstract are given. Those results that have not yet been submitted separately for publication are described in detail. Accomplishments during the contract period are summarized as follows: (1) analysis of the snow reflectance characteristics of the LANDSAT Thematic Mapper, including spectral suitability, dynamic range, and spectral resolution; (2) development of a variety of atmospheric models for use with LANDSAT Thematic Mapper data. These include a simple but fast two-stream approximation for inhomogeneous atmospheres over irregular surfaces, and a doubling model for calculation of the angular distribution of spectral radiance at any level in an plane-parallel atmosphere; (3) incorporation of digital elevation data into the atmospheric models and into the analysis of the satellite data; and (4) textural analysis of the spatial distribution of snow cover

A uvbyCaHbeta Analysis of the Old Open Cluster, NGC 6819

NGC 6819 is a richly populated, older open cluster situated within the Kepler field. A CCD survey of the cluster on the uvbyCaHbeta system, coupled with proper-motion membership, has been used to isolate 382 highly probable, single-star unevolved main-sequence members over a 20-arcminute field centered on the cluster. From 278 F dwarfs with high precision photometry in all indices, a mean reddening of E(b-y) = 0.117 +/- 0.005 or E(B-V) = 0.160 +/- 0.007 is derived, where the standard errors of the mean include both internal errors and the photometric zero-point uncertainty. With the reddening fixed, the metallicity derived from the same 278 stars is [Fe/H] = -0.116 +/- 0.101 from m_1 and -0.055 +/- 0.033 from hk, for a weighted average of [Fe/H] = -0.06 +/- 0.04, where the quoted standard errors of the mean values include the internal errors from the photometric scatter plus the uncertainty in the photometric zero points. If metallicity is derived using individual reddening values for each star to account for potential reddening variation across the face of the cluster, the analogous result is unchanged. The cluster members at the turnoff of the color-magnitude diagram are used to test and confirm the recently discovered variation in reddening across the face of the cluster, with a probable range in the variation of Delta[E(B-V)] = 0.045 +/-0.015. With the slightly higher reddening and lower [Fe/H] compared to commonly adopted values, isochrone fitting leads to an age of 2.3 +/- 0.2 Gyr for an apparent modulus of (m-M) = 12.40 +/-0.12.Comment: WIYN Open Cluster Study LXI; accepted to Astronomical Journal. 11 figures, 2 table

EVOLUTION OF IR-SELECTED GALAXIES IN Z~0.4 CLUSTERS

Wide-field optical and near--IR ($JHK$) imaging is presented for two rich galaxy clusters: Abell~370 at $z=0.374$ and Abell~851 (Cl0939+47) at $z=0.407$. Galaxy catalogs selected from the near--IR images are 90\% complete to approximately 1.5 mag below $K^\ast$ resulting in samples with $\sim$100 probable member galaxies per cluster in the central $\sim$2 Mpc. Comparison with $HST$ WFPC images yields subsamples of $\sim$70 galaxies in each cluster with morphological types. Analysis of the complete samples and the $HST$ subsamples shows that the $z\sim 0.4$ E/S0s are bluer than those in the Bower et al.\ (1992) Coma sample in the optical$-K$ color by $0.13$~mag for Abell~370 and by $0.18$~mag for Abell~851. If real, the bluing of the E/S0 populations at moderate redshift is consistent with that calculated from the Bruzual and Charlot (1993) models of passive elliptical galaxy evolution. In both clusters the intrinsic scatter of the known E/S0s about their optical$-K$ color--mag relation is small ($\sim 0.06$ mag) and not significantly different from that of Coma E/S0s as given by Bower et al.\ (1992), indicating that the galaxies within each cluster formed at the same time at an early epoch.Comment: uuencoded gzipped tar file containing latex files of manuscript (42 pages) plus tables (9 pages); figures available by anonymous ftp at ftp://ipac.caltech.edu//pub/pickup/sed ; accepted for publication in the Ap

Grouping Normal Type Ia Supernovae by UV to Optical Color Differences

Observations of many SNe Ia with the UVOT instrument on the Swift satellite has revealed that there exists order to the differences in the UV-OPT colors of normal SNe. We examine UV-OPT color curves for 25 SNe Ia, dividing them into 4 groups, finding that ~1/3 of these SNe Ia have bluer UV-OPT colors than the larger group, with these "NUV-blue" SNe Ia 0.4 mag bluer than the "NUV-red" SNe Ia in u-v. Another group of events feature colors similar to NUV-red SNe Ia in the u-v to uvw1-v colors, but similar to the NUV-blue SNe Ia in the uvm2-v color. We name these events "MUV-blue". The last group initially has colors similar to NUV-red SNe Ia, but with color curves that feature more modest changes than the larger NUV-red group. These "irregular" events are comprised of all the NUV-red events with the broadest optical peaks, which leads us to consider this minor group a subset of the NUV-red group. When so separated and the accounting is made for the rapid time evolution of the UV-OPT colors, we find that the scatter in two NUV-OPT colors, u-v & uvw1-v, is at the level of the scatter in b-v. This finding is promising for extending the cosmological utilization of SNe Ia into the NUV. We generate spectrophotometry of SNe Ia that have been observed with HST and argue that there is a fundamental spectral difference in the 2900-3500A wavelength range, a range suggested to be dominated by absorption from iron-peak elements. The NUV-blue SNe Ia feature less NUV absorption than the NUV-red SNe Ia. We show that all the NUV-blue SNe Ia in this sample have also featured evidence of unburned carbon in optical spectra, whereas only one NUV-red SN Ia features that absorption line. Every NUV-blue event also exhibits a low gradient of the SiII 6355A absorption feature, but many NUV-red events also exhibit a low gradient, perhaps suggestive that NUV-blue events are a subset of the larger LVG group.Comment: Accepted to the Astrophysical Journal Updated version: Sept 16, 201