Learning shape correspondence with anisotropic convolutional neural networks

Establishing correspondence between shapes is a fundamental problem in geometry processing, arising in a wide variety of applications. The problem is especially difficult in the setting of non-isometric deformations, as well as in the presence of topological noise and missing parts, mainly due to the limited capability to model such deformations axiomatically. Several recent works showed that invariance to complex shape transformations can be learned from examples. In this paper, we introduce an intrinsic convolutional neural network architecture based on anisotropic diffusion kernels, which we term Anisotropic Convolutional Neural Network (ACNN). In our construction, we generalize convolutions to non-Euclidean domains by constructing a set of oriented anisotropic diffusion kernels, creating in this way a local intrinsic polar representation of the data (`patch'), which is then correlated with a filter. Several cascades of such filters, linear, and non-linear operators are stacked to form a deep neural network whose parameters are learned by minimizing a task-specific cost. We use ACNNs to effectively learn intrinsic dense correspondences between deformable shapes in very challenging settings, achieving state-of-the-art results on some of the most difficult recent correspondence benchmarks

Automated Classification of Periodic Variable Stars detected by the Wide-field Infrared Survey Explorer

We describe a methodology to classify periodic variable stars identified using photometric time-series measurements constructed from the Wide-field Infrared Survey Explorer (WISE) full-mission single-exposure Source Databases. This will assist in the future construction of a WISE Variable Source Database that assigns variables to specific science classes as constrained by the WISE observing cadence with statistically meaningful classification probabilities. We have analyzed the WISE light curves of 8273 variable stars identified in previous optical variability surveys (MACHO, GCVS, and ASAS) and show that Fourier decomposition techniques can be extended into the mid-IR to assist with their classification. Combined with other periodic light-curve features, this sample is then used to train a machine-learned classifier based on the random forest (RF) method. Consistent with previous classification studies of variable stars in general, the RF machine-learned classifier is superior to other methods in terms of accuracy, robustness against outliers, and relative immunity to features that carry little or redundant class information. For the three most common classes identified by WISE: Algols, RR Lyrae, and W Ursae Majoris type variables, we obtain classification efficiencies of 80.7%, 82.7%, and 84.5% respectively using cross-validation analyses, with 95% confidence intervals of approximately +/-2%. These accuracies are achieved at purity (or reliability) levels of 88.5%, 96.2%, and 87.8% respectively, similar to that achieved in previous automated classification studies of periodic variable stars.Comment: 48 pages, 17 figures, 1 table, accepted by A

NEOWISE observations of comet C/2013 A1 (Siding Spring) as it approaches Mars

The Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE) mission observed comet C/2013 A1 (Siding Spring) three times at 3.4 {\mu}m and 4.6 {\mu}m as the comet approached Mars in 2014. The comet is an extremely interesting target since its close approach to Mars in late 2014 will be observed by various spacecraft in-situ. The observations were taken in 2014 Jan., Jul. and Sep. when the comet was at heliocentric distances of 3.82 AU, 1.88 AU, and 1.48 AU. The level of activity increased significantly between the Jan. and Jul. visits but then decreased by the time of the observations in Sep., approximately 4 weeks prior to its close approach to Mars. In this work we calculate Af\r{ho} values, and CO/CO2 production rates.Comment: 9 pages, 3 figures, accepted by Astrophysical Journal Letter

The MAL Interactors Animator: Supporting model validation through animation

The IVY workbench is a model checking based tool for the analysis of interactive system designs. Experience shows that there is a need to complement the analytic power of model checking with support for model validation and analysis of verification results. Animation of the model provides this support by allowing iterative exploration of its behaviour. This paper introduces a new model animation plugin for the IVY workbench. The plugin (AniMAL) complements the modelling and verification capabilities of IVY by providing users with the possibility to interact directly with the model.The authors wish to thank Michael D. Harrison for comments on an earlier version of this paper. Jose C. Campos acknowledges support from project NanoSTIMA (reference NORTE-01-0145-FEDER-000016) financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF)

Prediction of hearing recovery in sudden deafness treated with intratympanic steroids

The present study aims to obtain a probability model allowing the prediction of the auditory recovery in patients affected by sudden sensorineural hearing loss treated exclusively with intratympanic steroids. A monocentric retrospective chart review of three-hundred eighty-one patients has been performed. A Probit model was used to investigate the correlation between the success of the treatment (marked or total recovery according to Furuashi's criteria), and the delay between the onset of disease and the beginning of therapy. The age of the patients and the audiometric curve shapes were included in the analysis. Results show that delay is negatively correlated with the variable success. Considering the entire sample, each day of delay decreases by 3% the probability of success. The prediction model shows that for every day that passes from the onset of the disease the probability of success declines in absence of the medical treatment, hence we conclude that early treatment is strongly recommended

An Optical and Infrared Time-Domain Study of the Supergiant Fast X-ray Transient Candidate IC 10 X-2

We present an optical and infrared (IR) study of IC 10 X-2, a high-mass X-ray binary in the galaxy IC 10. Previous optical and X-ray studies suggest X-2 is a Supergiant Fast X-ray Transient: a large-amplitude (factor of $\sim$ 100), short-duration (hours to weeks) X-ray outburst on 2010 May 21. We analyze R- and g-band light curves of X-2 from the intermediate Palomar Transient Factory taken between 2013 July 15 and 2017 Feb 14 show high-amplitude ($\gtrsim$ 1 mag), short-duration ($\lesssim8$ d) flares and dips ($\gtrsim$ 0.5 mag). Near-IR spectroscopy of X-2 from Palomar/TripleSpec show He I, Paschen-$\gamma$, and Paschen-$\beta$ emission lines with similar shapes and amplitudes as those of luminous blue variables (LBVs) and LBV candidates (LBVc). Mid-IR colors and magnitudes from Spitzer/IRAC photometry of X-2 resemble those of known LBV/LBVcs. We suggest that the stellar companion in X-2 is an LBV/LBVc and discuss possible origins of the optical flares. Dips in the optical light curve are indicative of eclipses from optically thick clumps formed in the winds of the stellar counterpart. Given the constraints on the flare duration ($0.02 - 0.8$ d) and the time between flares ($15.1\pm7.8$ d), we estimate the clump volume filling factor in the stellar winds, $f_V$, to be $0.01 < f_V < 0.71$, which overlaps with values measured from massive star winds. In X-2, we interpret the origin of the optical flares as the accretion of clumps formed in the winds of an LBV/LBVc onto the compact object.Comment: 15 pages, 4 figures. Submitted to ApJ on Sep 26 201

Breaking the Habit: The Peculiar 2016 Eruption of the Unique Recurrent Nova M31N 2008-12a

Since its discovery in 2008, the Andromeda galaxy nova M31N 2008-12a has been observed in eruption every single year. This unprecedented frequency indicates an extreme object, with a massive white dwarf and a high accretion rate, which is the most promising candidate for the single-degenerate progenitor of a Type Ia supernova known to date. The previous three eruptions of M31N 2008-12a have displayed remarkably homogeneous multiwavelength properties: (i) from a faint peak, the optical light curve declined rapidly by two magnitudes in less than two days, (ii) early spectra showed initial high velocities that slowed down significantly within days and displayed clear He/N lines throughout, and (iii) the supersoft X-ray source (SSS) phase of the nova began extremely early, six days after eruption, and only lasted for about two weeks. In contrast, the peculiar 2016 eruption was clearly different. Here we report (i) the considerable delay in the 2016 eruption date, (ii) the significantly shorter SSS phase, and (iii) the brighter optical peak magnitude (with a hitherto unobserved cusp shape). Early theoretical models suggest that these three different effects can be consistently understood as caused by a lower quiescence mass accretion rate. The corresponding higher ignition mass caused a brighter peak in the free–free emission model. The less massive accretion disk experienced greater disruption, consequently delaying the re-establishment of effective accretion. Without the early refueling, the SSS phase was shortened. Observing the next few eruptions will determine whether the properties of the 2016 outburst make it a genuine outlier in the evolution of M31N 2008-12a