The 2MASS Wide-Field T Dwarf Search. IV Unting out T dwarfs with Methane Imaging

We present first results from a major program of methane filter photometry for low-mass stars and brown dwarfs. The definition of a new methane filter photometric system is described. A recipe is provided for the differential calibration of methane imaging data using existing 2MASS photometry. We show that these filters are effective in discriminating T dwarfs from other types of stars, and demonstrate this with Anglo-Australian Telescope observations using the IRIS2 imager. Methane imaging data and proper motions are presented for ten T dwarfs identified as part of the 2MASS "Wide Field T Dwarf Search" -- seven of them initially identified as T dwarfs using methane imaging. We also present near-infrared moderate resolution spectra for five T dwarfs, newly discovered by this technique. Spectral types obtained from these spectra are compared to those derived from both our methane filter observations, and spectral types derived by other observers. Finally, we suggest a range of future programs to which these filters are clearly well suited: the winnowing of T dwarf and Y dwarf candidate objects coming from the next generation of near-infrared sky surveys; the robust detection of candidate planetary-mass brown dwarfs in clusters; the detection of T dwarf companions to known L and T dwarfs via deep methane imaging; and the search for rotationally-modulated time-variable surface features on cool brown dwarfs.Comment: 20 pages. To appear in The Astronomical Journal, Nov. 200

Automated Crowdturfing Attacks and Defenses in Online Review Systems

Malicious crowdsourcing forums are gaining traction as sources of spreading misinformation online, but are limited by the costs of hiring and managing human workers. In this paper, we identify a new class of attacks that leverage deep learning language models (Recurrent Neural Networks or RNNs) to automate the generation of fake online reviews for products and services. Not only are these attacks cheap and therefore more scalable, but they can control rate of content output to eliminate the signature burstiness that makes crowdsourced campaigns easy to detect. Using Yelp reviews as an example platform, we show how a two phased review generation and customization attack can produce reviews that are indistinguishable by state-of-the-art statistical detectors. We conduct a survey-based user study to show these reviews not only evade human detection, but also score high on "usefulness" metrics by users. Finally, we develop novel automated defenses against these attacks, by leveraging the lossy transformation introduced by the RNN training and generation cycle. We consider countermeasures against our mechanisms, show that they produce unattractive cost-benefit tradeoffs for attackers, and that they can be further curtailed by simple constraints imposed by online service providers

Heliophysics Event Knowledgebase for the Solar Dynamics Observatory and Beyond

The immense volume of data generated by the suite of instruments on SDO requires new tools for efficient identifying and accessing data that is most relevant to research investigations. We have developed the Heliophysics Events Knowledgebase (HEK) to fill this need. The HEK system combines automated data mining using feature-detection methods and high-performance visualization systems for data markup. In addition, web services and clients are provided for searching the resulting metadata, reviewing results, and efficiently accessing the data. We review these components and present examples of their use with SDO data.Comment: 17 pages, 4 figure

Program Similarity Detection with Checksims

In response to growing academic dishonesty in low- level computer science and electrical and computer engineering courses, we present extit{checksims}, a similarity detector designed to highlight suspicious assignments for instructor review. We report the design rationale for the software, and describe our detection of dozens of previously undetected cases of academic dishonesty in previous classes

Read Mapping on Genome Variation Graphs

Genome variation graphs are natural candidates to represent a pangenome collection. In such graphs, common subsequences are encoded as vertices and the genomic variations are captured by introducing additional labeled vertices and directed edges. Unlike a linear reference, a reference graph allows a rich representation of the genomic diversities and avoids reference bias. We address the fundamental problem of mapping reads to genome variation graphs. We give a novel mapping algorithm V-MAP for efficient identification of small subgraph of the genome graph for optimal gapped alignment of the read. V-MAP creates space efficient index using locality sensitive minimizer signatures computed using a novel graph winnowing and graph embedding onto metric space for fast and accurate mapping. Experiments involving graph constructed from the 1000 Genomes data and using both real and simulated reads show that V-MAP is fast, memory efficient and can map short reads, as well as PacBio/Nanopore long reads with high accuracy. V-MAP performance was significantly better than the state-of-the-art, especially for long reads