Umaa Rebbapragada

Class Noise Mitigation Through Instance Weighting

We describe a novel framework for class noise mitigation that assigns a vector of class membership probabilities to each training instance, and uses the confidence on the current label as a weight during training. The probability vector should be calculated such that clean instances have a high confidence on its current label, while mislabeled instances have a low confidence on its current label and a high confidence on its correct label. Past research focuses on techniques that either discard or correct instances. This paper proposes that discarding and correcting are special cases of instance weighting, and thus, part of this framework. We propose a method that uses clustering to calculate a probability distribution over the class labels for each instance. We demonstrate that our method improves classifier accuracy over the original training set. We also demonstrate that instance weighting can outperform discarding.

A strong ultraviolet pulse from a newborn type Ia supernova

Type Ia supernovae are destructive explosions of carbon-oxygen white dwarfs. Although they are used empirically to measure cosmological distances, the nature of their progenitors remains mysterious. One of the leading progenitor models, called the single degenerate channel, hypothesizes that a white dwarf accretes matter from a companion star and the resulting increase in its central pressure and temperature ignites thermonuclear explosion. Here we report observations with the Swift Space Telescope of strong but declining ultraviolet emission from a type Ia supernova within four days of its explosion. This emission is consistent with theoretical expectations of collision between material ejected by the supernova and a companion star, and therefore provides evidence that some type Ia supernovae arise from the single degenerate channel.

Finding anomalous periodic time series

Catalogs of periodic variable stars contain large numbers of periodic light-curves (photometric time series data from the astrophysics domain). Separating anomalous objects from well-known classes is an important step towards the discovery of new classes of astronomical objects. Most anomaly detection methods for time series data assume either a single continuous time series or a set of time series whose periods are aligned. Light-curve data precludes the use of these methods as the periods of any given pair of light-curves may be out of sync. One may use an existing anomaly detection method if, prior to similarity calculation, one performs the costly act of aligning two light-curves, an operation that scales poorly to massive data sets. This paper presents PCAD, an unsupervised anomaly detection method for large sets of unsynchronized periodic time-series data, that outputs a ranked list of both global and local anomalies. It calculates its anomaly score for each light-curve in relation to a set of centroids produced by a modified k-means clustering algorithm. Our method is able to scale to large data sets through the use of sampling. We validate our method on both light-curve data and other time series data sets. We demonstrate its effectiveness at finding known anomalies, and discuss the effect of sample size and number of centroids on our results. We compare our method to naive solutions and existing time series anomaly detection methods for unphased data, and show that PCAD’s reported anomalies are comparable to or better than all other methods. Finally, astrophysicists on our team have verified that PCAD finds true anomalies that might be indicative of novel astrophysical phenomena.

Disk Aware Discord Discovery: Finding Unusual Time Series in Terabyte Sized Datasets

The problem of finding unusual time series has recently attracted much attention, and several promising methods are now in the literature. However, virtually all proposed methods assume that the data reside in main memory. For many real-world problems this is not be the case. For example, in astronomy, multi-terabyte time series datasets are the norm. Most current algorithms faced with data which cannot fit in main memory resort to multiple scans of the disk/tape and are thus intractable. In this work we show how one particular definition of unusual time series, the time series discord, can be discovered with a disk aware algorithm. The proposed algorithm is exact and requires only two linear scans of the disk with a tiny buffer of main memory. Furthermore, it is very simple to implement. We use the algorithm to provide further evidence of the effectiveness of the discord definition in areas as diverse as astronomy, Web query mining, video surveillance, etc., and show the efficiency of our method on datasets which are many orders of magnitude larger than anything else attempted in the literature.

Detection of Broad Hα Emission Lines in the Late-time Spectra of a Hydrogen-poor Superluminous Supernova

iPTF13ehe is a hydrogen-poor superluminous supernova (SLSN) at z=0.3434, with a slow-evolving light curve and spectral features similar to SN2007bi. It rises within (83-148)days (rest-frame) to reach a peak bolometric luminosity of 1.3x

VAST: An ASKAP survey for variables and slow transients

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Disk aware discord discovery: finding unusual time series in terabyte sized datasets

erg/s, then decays very slowly at 0.015mag. per day. The measured ejecta velocity is 13000km/s. The inferred explosion characteristics, such as the ejecta mass (67-220

Type II supernova energetics and comparison of light curves to shock-cooling models

M_\odot

Confined dense circumstellar material surrounding a regular type II supernova

), the total radiative and kinetic energy (

iPTF search for an optical counterpart to gravitational-wave transient GW150914

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