You're viewing the US edition You can switch to the UK or AU homepage at any time using this menu. Luigi Mangione tells judge he'll plead guilty to stalking UnitedHealth CEO Brian Thompson who he's also accused of murdering Female Antifa goon's lies about what she was doing at Portland ICE protest backfire and end in her VERY public disgrace'Heinous' slurs about Lindsay Clancy's ex-husband Patrick explode... now his family speak out as murder trial grips America Oprah's frantically trying to distract from a looming Hollywood bombshell... the timeline is genuinely disturbing: MAUREEN CALLAHAN Iran's chilling midterm gamble against Trump revealed as insiders warn regime is'playing with fire' '80s sitcom child star who ditched Hollywood for a quiet career looks unrecognizable at 55 as he steps out with wife in LA... can you guess who? Perez Hilton's horrifying first request from hospital bed after knife livestream: This is'the worst thing possible' say devastated insiders... who reveal star's dark childhood secret Lindsay Clancy's final texts to husband on day she killed her kids emerge as trial is shown video of family museum trip weeks before tragedy'Perfectly fit' woman thought she had acid reflux... two hours later, she was dead. Wild-eyed boy, 17, killed mom and brother, 14, at $1m home after ChatGPT'murder my family' search - then spent $2,000 on CEREAL BARS, prosecutors say Shocking real reason Luigi Mangione is set to plead GUILTY... as defense attorney reveals all: 'There's a lot happening behind the scenes' Trump administration steps into surrogate's battle over baby with a heart defect biological parents asked her to abort Harrowing new details emerge about The Challenge star Tyler Duckworth's shock death aged 44 after he was found'unresponsive' in bathroom Trump official's X-rated sex toy video with foreign agent she lives with in debauched'frat house'... read their wild texts Matthew McConaughey reveals which blockbuster makes him the most residuals and it's not his Oscar winner Athletes who compete in the Olympics have usually spent most of their lives preparing for the event. But nearly four in 10 British men believe they could qualify for the next Games if they started training now, according to a poll.

We investigate whether Large Language Models (LLMs) exhibit altruistic tendencies, and critically, whether their implicit associations and self-reports predict actual altruistic behavior. Using a multi-method approach inspired by human social psychology, we tested 24 frontier LLMs across three paradigms: (1) an Implicit Association Test (IAT) measuring implicit altruism bias, (2) a forced binary choice task measuring behavioral altruism, and (3) a self-assessment scale measuring explicit altruism beliefs. Our key findings are: (1) All models show strong implicit pro-altruism bias (mean IAT = 0.87, p < .0001), confirming models "know" altruism is good. (2) Models behave more altruistically than chance (65.6% vs. 50%, p < .0001), but with substantial variation (48-85%). (3) Implicit associations do not predict behavior (r = .22, p = .29). (4) Most critically, models systematically overestimate their own altruism, claiming 77.5% altruism while acting at 65.6% (p < .0001, Cohen's d = 1.08). This "virtue signaling gap" affects 75% of models tested. Based on these findings, we recommend the Calibration Gap (the discrepancy between self-reported and behavioral values) as a standardized alignment metric. Well-calibrated models are more predictable and behaviorally consistent; only 12.5% of models achieve the ideal combination of high prosocial behavior and accurate self-knowledge.

Given their remarkable performance, being able to train GNNs efficiently is an important task.

We consider the problem of estimating the spectral norm of a matrix using only matrix-vector products. We propose a new Counterbalance estimator that provides upper bounds on the norm and derive probabilistic guarantees on its underestimation. Compared to standard approaches such as the power method, the proposed estimator produces significantly tighter upper bounds in both synthetic and real-world settings. Our method is especially effective for matrices with fast-decaying spectra, such as those arising in deep learning and inverse problems.

Weaknesses: After reading the rebuttals and reviewer discussion, I realise that I was wrong about EP overestimating the variance and the strength of the paper's empirical results, so I have decided to downgrade my score. I still believe this paper should be accepted, but I'm less confident of the matter. Here are the things I changed my mind about, to more critical: - Does EP really overestimate the posterior variance? EP should overestimate the *support* of distributions, because the forward-KL covers all modes with a (unimodal) Gaussian. But this does not necessarily imply that the variance is overestimated, and locally the variance is matched exactly.

The announcement comes a day after Microsoft announced it was also taking a number of steps to protect elections, including offering tools to watermark AI-generated content and deploying a "Campaign Success Team" to advise political campaigns on AI, cybersecurity, and other related issues. Next year will be the most significant year for elections so far this century, with the U.S., India, the U.K., Mexico, Indonesia, and Taiwan all headed to the polls. Although many are concerned about the impact deepfakes and misinformation could have on elections, many experts stress the evidence for their impacts on elections so far is limited at best. Experts welcome the measures taken by tech companies to defend election integrity but say more fundamental changes to political systems will be required to tackle misinformation. Tech companies have come under scrutiny after the role they played in previous elections.

With the ever-increasing availability of data, there has been an explosion of interest in applying modern machine learning methods to fields such as modeling and control. However, despite the flexibility and surprising accuracy of such black-box models, it remains difficult to trust them. Recent efforts to combine the two approaches aim to develop flexible models that nonetheless generalize well; a paradigm we call Hybrid Analysis and modeling (HAM). In this work we investigate the Corrective Source Term Approach (CoSTA), which uses a data-driven model to correct a misspecified physics-based model. This enables us to develop models that make accurate predictions even when the underlying physics of the problem is not well understood. We apply CoSTA to model the Hall-H\'eroult process in an aluminum electrolysis cell. We demonstrate that the method improves both accuracy and predictive stability, yielding an overall more trustworthy model.

In mathematical optimization and decision theory, a loss function or cost function (sometimes also called an error function) is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event. An optimization problem seeks to minimize a loss function. An objective function is either a loss function or its opposite (in specific domains, variously called a reward function, a profit function, a utility function, a fitness function, etc.), in which case it is to be maximized. The loss function could include terms from several levels of the hierarchy. The kind of loss function you are going to use depends on the kind of problem you are working i.e Regression or Classification.