Like a mirage speeding across the dusty desert outside Pueblo, Colorado, the first hydrogen-fuel-cell passenger train in the United States is getting warmed up on its test track. It will soon be shipped to Southern California, where it is slated to carry riders on San Bernardino County's Arrow commuter rail service before the end of the year. The best way to decarbonize railroads is the subject of growing debate among regulators, industry, and activists. The debate is partly technological, revolving around whether hydrogen fuel cells, batteries, or overhead electric wires offer the best performance for different railroad situations. In the insular world of railroading, this hydrogen-powered train is a Rorschach test.

Question answering over heterogeneous data requires reasoning over diverse sources of data, which is challenging due to the large scale of information and organic coupling of heterogeneous data. Various approaches have been proposed to address these challenges. One approach involves training specialized retrievers to select relevant information, thereby reducing the input length. Another approach is to transform diverse modalities of data into a single modality, simplifying the task difficulty and enabling more straightforward processing. In this paper, we propose HProPro, a novel program-based prompting framework for the hybrid question answering task. HProPro follows the code generation and execution paradigm. In addition, HProPro integrates various functions to tackle the hybrid reasoning scenario. Specifically, HProPro contains function declaration and function implementation to perform hybrid information-seeking over data from various sources and modalities, which enables reasoning over such data without training specialized retrievers or performing modal transformations. Experimental results on two typical hybrid question answering benchmarks HybridQA and MultiModalQA demonstrate the effectiveness of HProPro: it surpasses all baseline systems and achieves the best performances in the few-shot settings on both datasets.

There's a hilarious illustration from Paris in late 1839, mere months after an early type of photograph called a daguerreotype was announced to the world, that warned what this tiny picture portended. In Théodore Maurisset's imagination, the daguerreotype would bring about a collective hysteria, La Daguerréotypomanie, in which crazed masses arrive from the ends of the earth and overrun a small photo studio. Some in the crowd want pictures of themselves, but, mon Dieu, others demand cameras to take their own pictures--Maurisset shows them loading the machines like contraband onto steamships bound for foreign ports--and still others throng simply to ogle at this newfangled thing and all the lunatic proceedings surrounding it. The clamor is so feverish that it brings about a mass hallucination, in which nearly everything else in the landscape around the studio, including railroad cars, a clock tower, a basket for a hot air balloon, indeed anything remotely boxy in shape, morphs into cameras. As they march to the studio, the crowds pass by half a dozen gallows, where in response to the daguerreotype's appearance artists have hung themselves.

Jason Allen, a video game designer in Pueblo, Colorado, spent roughly 80 hours working on his entry to the Colorado State Fair's digital arts competition. Judges awarded him first place, which came with a $300 prize. But when Allen posted about his win on social media late last month, his artwork went viral--for all the wrong reasons. Allen's victory took a turn when he revealed online that he'd created his prize-winning art using Midjourney, an artificial intelligence program that can turn text descriptions into images. He says he also made that clear to state fair officials when he dropped off his submission, called Théâtre D'opéra Spatial.

The winning artwork was created using the AI tool Midjourney – which turns lines of text into astonishingly realistic graphics. The award came with a $300 cash prize. AI tools to generate images have been around for years with companies such as Google and OpenAI being notable investors in these text-to-image systems. "I'm not going to apologise for it … I won and I didn't break any rules," Allen, who is from Pueblo, Colorado, told The New York Times newspaper in an interview published on Friday. However, many have taken to social media to express their anger and despair over the award, arguing it took away from the hard work invested by humans to physically create noteworthy art.

Jason M. Allen was almost too nervous to enter his first art competition. Now, his award-winning image is sparking controversy about whether art can be generated by a computer, and what, exactly, it means to be an artist. In August, Allen, a game designer who lives in Pueblo West, Colorado, won first place in the emerging artist division's "digital arts/digitally-manipulated photography" category at the Colorado State Fair Fine Arts Competition. His winning image, titled "Théâtre D'opéra Spatial" (French for "Space Opera Theater"), was made with Midjourney -- an artificial intelligence system that can produce detailed images when fed written prompts. A $300 prize accompanied his win.

This year, the Colorado State Fair's annual art competition gave out prizes in all the usual categories: painting, quilting, sculpture. He created it with Midjourney, an artificial intelligence program that turns lines of text into hyper-realistic graphics. Mr. Allen's work, "Théâtre D'opéra Spatial," took home the blue ribbon in the fair's contest for emerging digital artists -- making it one of the first A.I.-generated pieces to win such a prize, and setting off a fierce backlash from artists who accused him of, essentially, cheating. Reached by phone on Wednesday, Mr. Allen defended his work. He said that he had made clear that his work -- which was submitted under the name "Jason M. Allen via Midjourney" -- was created using A.I., and that he hadn't deceived anyone about its origins.

Distributionally robust optimization (DRO) problems are increasingly seen as a viable method to train machine learning models for improved model generalization. These min-max formulations, however, are more difficult to solve. We therefore provide a new stochastic gradient descent algorithm to efficiently solve this DRO formulation. Our approach applies gradient descent to the outer minimization formulation and estimates the gradient of the inner maximization based on a sample average approximation. The latter uses a subset of the data in each iteration, progressively increasing the subset size to ensure convergence. Theoretical results include establishing the optimal manner for growing the support size to balance a fundamental tradeoff between stochastic error and computational effort. Empirical results demonstrate the significant benefits of our approach over previous work, and also illustrate how learning with DRO can improve generalization.

Devin Butts walked the tiled halls of the Pueblo Mall early one Friday morning in April, amazed at what he saw. The mall, the main shopping center for the city of Pueblo in southern Colorado, was larger than anything the 25-year-old was used to while living on the streets of quiet prairie towns in north central Texas. He wandered through T-shirt stores and schlocky gift shops, past American flag-adorned beer bongs and marijuana-emblazoned "Rocky Mountain High" shirts, not noticing how employees warily eyed his baggy jeans and the tattoos peeking out from the sleeves and collar of his Bob Marley T-shirt. Or maybe he'd learned from experience to ignore the looks. Butts inquired at shop after shop. Often he received an apologetic shake of the head. Sometimes he was told to fill out an application online, no easy feat for someone who didn't own a computer.