We present an approach to denoising spatial transcriptomics images that is particularly effective for uncovering cell identities in the regime of ultra-low sequencing depths, and also allows for interpolation of gene expression. The method -- Spatial Transcriptomics via Adaptive Regularization and Kernels (STARK) -- augments kernel ridge regression with an incrementally adaptive graph Laplacian regularizer. In each iteration, we (1) perform kernel ridge regression with a fixed graph to update the image, and (2) update the graph based on the new image. The kernel ridge regression step involves reducing the infinite dimensional problem on a space of images to finite dimensions via a modified representer theorem. Starting with a purely spatial graph, and updating it as we improve our image makes the graph more robust to noise in low sequencing depth regimes. We show that the aforementioned approach optimizes a block-convex objective through an alternating minimization scheme wherein the sub-problems have closed form expressions that are easily computed. This perspective allows us to prove convergence of the iterates to a stationary point of this non-convex objective. Statistically, such stationary points converge to the ground truth with rate $\mathcal{O}(R^{-1/2})$ where $R$ is the number of reads. In numerical experiments on real spatial transcriptomics data, the denoising performance of STARK, evaluated in terms of label transfer accuracy, shows consistent improvement over the competing methods tested.

A supermassive black hole's star snack lit up the cosmos like never before. This artist's concept depicts a supermassive black hole in the process of shredding a massive star--at least 30 times the mass of our Sun--to pieces. Scientists propose this is what happened around the distant black hole referred to as J2245+3743, which in 2018, brightened dramatically to create the brightest black hole flare ever recorded, shining with the light of 10 trillion suns. Breakthroughs, discoveries, and DIY tips sent every weekday. It's hard to wrap your head around the fundamentals of a black hole .

Thousands of entrepreneurs are trying to rebuild the economy around AI. I set out to see how they're actually doing it. Earth, it's said, is home to more than 10,000 AI startups. The figure is a guess, of course--startups come, startups go. But last year, more than 2,000 of them got their first round of funding. As investors shovel their billions into AI, it's worth asking: What are all these creatures of the boom doing? I decided to approach as many recent AI founders as I could. The goal was not to try to pick winners but to see what it's like, on the ground, to build AI products--how AI tools have changed the nature of their work; how terrifying it is to compete in a crowded field.

Sign up to receive it in your inbox. Aside from a temporary love, or a new friend, you could easily stumble upon fabulous stage shows that were presented with such seriousness, often, that you wondered if--while watching the amazing Duelling Bankheads, for instance, or so many people who got up so brilliantly as Stevie Nicks on the Night of 1000 Stevies--you were high on the entertainment, or on dancing with your chosen community, or just amazed by what New York had to offer by way of creativity. Looking back, I can see that, for me at least, it was the combination of all three elements together that gave such hope about Manhattan's ability to foster noncommercial glamour, and to support young performers who were trying things out and seeing what stuck. Richard Move as Martha Graham. The shows I loved the most were at Jackie 60, spearheaded by the irreplaceable Chi Chi Valenti and Johnny Dynell, the resident d.j.

Large Language Models (LLMs) have become increasingly incorporated into everyday life for many internet users, taking on significant roles as advice givers in the domains of medicine, personal relationships, and even legal matters. The importance of these roles raise questions about how and what responses LLMs make in difficult political and moral domains, especially questions about possible biases. To quantify the nature of potential biases in LLMs, various works have applied Moral Foundations Theory (MFT), a framework that categorizes human moral reasoning into five dimensions: Harm, Fairness, Ingroup Loyalty, Authority, and Purity. Previous research has used the MFT to measure differences in human participants along political, national, and cultural lines. While there has been some analysis of the responses of LLM with respect to political stance in role-playing scenarios, no work so far has directly assessed the moral leanings in the LLM responses, nor have they connected LLM outputs with robust human data. In this paper we analyze the distinctions between LLM MFT responses and existing human research directly, investigating whether commonly available LLM responses demonstrate ideological leanings: either through their inherent responses, straightforward representations of political ideologies, or when responding from the perspectives of constructed human personas. We assess whether LLMs inherently generate responses that align more closely with one political ideology over another, and additionally examine how accurately LLMs can represent ideological perspectives through both explicit prompting and demographic-based role-playing. By systematically analyzing LLM behavior across these conditions and experiments, our study provides insight into the extent of political and demographic dependency in AI-generated responses.

Former CIA station chief in Moscow Dan Hoffman breaks down two ways the United States can respond to Russia's attacks on Ukraine on'Fox Report.' In his 26th week back in the Oval Office, President Donald Trump is expected to make a "major announcement" related to Russia, hold a meeting with the NATO chief, and join a summit in Pennsylvania as America's race to lead the world on artificial intelligence continues. Trump spent the anniversary at his home in Bedminster, N.J., before traveling with first lady Melania Trump to the FIFA Club World Cup final on Sunday at MetLife Stadium in the Garden State. Trump returned to the White House on Sunday evening and is expected to have another whirlwind workweek. Trump will meet with NATO Secretary General Mark Rutte this week following the U.S. president saying last week that the U.S. is selling weapons to its NATO allies for them to be passed along to Ukraine as it continues battling Russia.

Human language and logic abilities are computationally quantified within the well-studied grammar-automata hierarchy. We identify three hierarchical tiers and two corresponding transitions and show their correspondence to specific abilities in transformer-based language models (LMs). These emergent abilities have often been described in terms of scaling; we show that it is the transition between tiers, rather than scaled size itself, that determines a system's capabilities. Specifically, humans effortlessly process language yet require critical training to perform arithmetic or logical reasoning tasks; and LMs possess language abilities absent from predecessor systems, yet still struggle with logical processing. We submit a novel benchmark of computational power, provide empirical evaluations of humans and fifteen LMs, and, most significantly, provide a theoretically grounded framework to promote careful thinking about these crucial topics. The resulting principled analyses provide explanatory accounts of the abilities and shortfalls of LMs, and suggest actionable insights into the expansion of their logic abilities.

This study explores computational approaches for measuring moral foundations (MFs) in non-English corpora. Since most resources are developed primarily for English, cross-linguistic applications of moral foundation theory remain limited. Using Chinese as a case study, this paper evaluates the effectiveness of applying English resources to machine translated text, local language lexicons, multilingual language models, and large language models (LLMs) in measuring MFs in non-English texts. The results indicate that machine translation and local lexicon approaches are insufficient for complex moral assessments, frequently resulting in a substantial loss of cultural information. In contrast, multilingual models and LLMs demonstrate reliable cross-language performance with transfer learning, with LLMs excelling in terms of data efficiency. Importantly, this study also underscores the need for human-in-the-loop validation of automated MF assessment, as the most advanced models may overlook cultural nuances in cross-language measurements. The findings highlight the potential of LLMs for cross-language MF measurements and other complex multilingual deductive coding tasks.

EXCLUSIVE: Sen. Lindsey Graham is demanding answers on reporting that British International Criminal Court (ICC) prosecutor Karim Khan was accused of sexual misconduct at the same time he was pursuing criminal charges against Israeli officials. "Public reports indicate that allegations of harassment surfaced in early May--just a few days before Prosecutor Khan applied for arrest warrants against the Prime Minister and Minister of Defense of Israel for alleged violations of law during the defensive Israeli-Hamas War," Graham wrote in a letter obtained by Fox News Digital. "The timing of the allegations is troubling, and only compounds the other strong legal, jurisdictional, and prudential objections I have expressed regarding the Prosecutor's decision to seek arrest warrants." On May 20, Khan requested arrest warrants for Israeli President Benjamin Netanyahu and Defence Minister Yoav Gallant, as well as Hamas leaders Yahya Sinwar, Ismail Haniyeh and Mohammed Deif. All three Hamas leaders have been killed in the past year.

There's a story about Sam Altman that has been repeated often enough to become Silicon Valley lore. In 2012, Paul Graham, a co-founder of the famed start-up accelerator Y Combinator and one of Altman's biggest mentors, sat Altman down and asked if he wanted to take over the organization. The decision was a peculiar one: Altman was only in his late 20s, and at least on paper, his qualifications were middling. He had dropped out of Stanford to found a company that ultimately hadn't panned out. After seven years, he'd sold it for roughly the same amount that his investors had put in.