Armed with some Python and a white-hot sense of injustice, one medical student spent six months trying to figure out whether an algorithm trashed his job application. It was mid-October, peak leaf-peeping season in Hanover, New Hampshire, and Chad Markey was on a rare break between clinical rotations during his last year of medical school. He should have been inhaling Green Mountain air and gossiping with his Dartmouth classmates about life after graduation. In a few months, they'd all be going their separate ways to start residency training at hospitals around the country. Instead, Markey was alone in his apartment, deep down a rabbit hole, preparing to go to war. He'd wake each morning, eat breakfast, open his laptop at the kitchen table or settle into the tan armchair with the good back support, and start coding . Some days, he wouldn't notice the sun had gone down until one of his roommates came home and asked why the lights weren't on. For days, Markey had been scrolling through a Discord group about medical residency, a font of crowdsourced knowledge where students report back to their peers on every stage of the application and selection process. He'd watched as other students, lots of them, posted about the interview invitations they'd received.

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Here, we propose a hybrid model, the Hyper-HMM, that simultaneously aligns both temporal and spatial features across brains.

Is the Dictionary Done For? The print edition of Merriam-Webster was once a touchstone of authority and stability. Then the internet brought about a revolution. Wars over words are inevitably culture wars, and debates over the dictionary have raged for as long as it has existed. Once, every middle-class home had a piano and a dictionary. The purpose of the piano was to be able to listen to music before phonographs were available and affordable. Later on, it was to torture young persons by insisting that they learn to do something few people do well. The purpose of the dictionary was to settle intra-family disputes over the spelling of words like "camaraderie" and "sesquipedalian," or over the correct pronunciation of "puttee." This was the state of the world not that long ago. In the late nineteen-eighties, Merriam-Webster's Collegiate Dictionary was on the best-seller list for a hundred and fifty-five consecutive weeks. Fifty-seven million copies were sold, a number believed to be second only, in this country, to sales of the Bible. There was good money in the word business.

This paper develops the first theoretical analysis on SVGD.

We study the design of deep architectures for lossy image compression. We present two architectural recipes in the context of multi-stage progressive encoders and empirically demonstrate their importance on compression performance.

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