Understanding Accretion Outbursts in Massive Protostars through Maser Imaging

The bright maser emission produced by several molecular species at centimeter to long millimeter wavelengths provides an essential tool for understanding the process of massive star formation. Unimpeded by the high dust optical depths that affect shorter wavelength observations, the high brightness temperature of these emission lines offers a way to resolve accretion and outflow motions down to scales below $\sim$1 au in deeply embedded Galactic star-forming regions at kiloparsec distances. The recent identification of extraordinary accretion outbursts in two high-mass protostars, both of which were heralded by maser flares, has rapidly impacted the traditional view of massive protostellar evolution, leading to new hydrodynamic simulations that can produce such episodic outbursts. In order to understand how these massive protostars evolve in response to such events, larger, more sensitive ground-based centimeter wavelength interferometers are needed that can simultaneously image multiple maser species in the molecular gas along with faint continuum from the central ionized gas. Fiducial observations of a large sample of massive protostars will be essential in order to pinpoint the progenitors of future accretion outbursts, and to quantify the outburst-induced changes in their protostellar photospheres and outflow and accretion structures. Knowledge gained from these studies will have broader impact on the general topic of accretion onto massive objects.Comment: Science white paper submitted to the Astro2020 Decadal Survey. arXiv admin note: substantial text overlap with arXiv:1806.0698

Automatic Discovery of Political Meme Genres with Diverse Appearances

Forms of human communication are not static -- we expect some evolution in the way information is conveyed over time because of advances in technology. One example of this phenomenon is the image-based meme, which has emerged as a dominant form of political messaging in the past decade. While originally used to spread jokes on social media, memes are now having an outsized impact on public perception of world events. A significant challenge in automatic meme analysis has been the development of a strategy to match memes from within a single genre when the appearances of the images vary. Such variation is especially common in memes exhibiting mimicry. For example, when voters perform a common hand gesture to signal their support for a candidate. In this paper we introduce a scalable automated visual recognition pipeline for discovering political meme genres of diverse appearance. This pipeline can ingest meme images from a social network, apply computer vision-based techniques to extract local features and index new images into a database, and then organize the memes into related genres. To validate this approach, we perform a large case study on the 2019 Indonesian Presidential Election using a new dataset of over two million images collected from Twitter and Instagram. Results show that this approach can discover new meme genres with visually diverse images that share common stylistic elements, paving the way forward for further work in semantic analysis and content attribution.Comment: 13 pages, 14 figure

Spotting the difference: Context retrieval and analysis for improved forgery detection and localization

As image tampering becomes ever more sophisticated and commonplace, the need for image forensics algorithms that can accurately and quickly detect forgeries grows. In this paper, we revisit the ideas of image querying and retrieval to provide clues to better localize forgeries. We propose a method to perform large-scale image forensics on the order of one million images using the help of an image search algorithm and database to gather contextual clues as to where tampering may have taken place. In this vein, we introduce five new strongly invariant image comparison methods and test their effectiveness under heavy noise, rotation, and color space changes. Lastly, we show the effectiveness of these methods compared to passive image forensics using Nimble [1], a new, state-of-the-art dataset from the National Institute of Standards and Technology (NIST)