Pope Leo says AI must be'disarmed' in first major teaching Pope Leo has presented the first major teaching document of his papacy, warning that artificial intelligence needs to be disarmed. The word is strong, I know, but deliberately chosen because this moment needs words capable of attracting attention, the Pope said. Encyclicals are technically letters to Catholic bishops, but over recent decades the missives have become messages to the world from a Pope. While this letter was largely focused on AI, Pope Leo also included one of the strongest, most comprehensive apologies from the Vatican for the Catholic Church's role in slavery. It was impossible not to feel deep sorrow when contemplating the immense suffering and humiliation endured by so many, the Pope wrote, adding that he sincerely asked for pardon in the name of the Church.

Kyle Busch's widow revealed haunting plan to have his baby if he ever died - six months before NASCAR great's shock passing Wife goes scorched earth on cop husband with divorce filings so scandalous he has now lost his job, as family's perfect life is shattered These billion-dollar projects were sold as a green revolution for struggling communities. Megyn Kelly is torched by MAGA after she issued direct hit at Trump for'cheating on every wife he's had' I got addicted to the stimulant that Trump insiders are secretly using... it can obliterate your sexual performance and ruined my life Scientists discover a third eye hidden in the human body and the reason it's there Mother who abandoned her children blindfolded in Portuguese woods is sent to the country's toughest women's prison - as videos of her partner decrying'end of the world' emerge Harrowing map shows cancer explosion that'll make you put down your favorite drink... have you left it too late? Ozempic and Wegovy can lead to devastating muscle and bone loss... now experts reveal exactly how to fight it Fans go wild as Kyle Richards' forgotten role in ER resurfaces... and it's a long way from RHOBH I lost five pounds in six weeks when I discovered'Nature's Ozempic': All the benefits of the jabs with NONE of the side effects - and I just stir it into my morning coffee... by BEATRICE AIDIN The dangerously overdue Northeast hurricane we can't ignore: Catastrophic damage and biggest New England danger zone revealed by top forecaster China's answer to the Rolls-Royce: Self-parking, £130,000 18ft-long beast is packed with gadgets, a 40-inch screen and gold trim Tiger Woods breaks his silence after his'return to rehab' in Switzerland following brief reunion with Vanessa Trump America's best kept sex secret. This unassuming hotspot has women going wild for untamed lovers who know EXACTLY what they're doing: 'It's sex central. Watermelon is more than just a hot-weather treat... it may help fight one of the most common cancers and aid weight loss, according to research Devastating new details about Beartooth frontman's marriage as he comes out as'proudly' gay: Wife's heartbreak revealed by insiders and red flag that was overlooked Scientists discover a third eye hidden in the human body and the reason it's there MORE: Four species of aliens recovered from crashed UFOs according to CIA scientist... here's what they look like Scientists have found a third eye buried in the middle of the human head and say it still plays a key role after millions of years of evolution.

A Ukrainian flying ace is leveraging his aerobatics skills to protect his countrymen from nightly attacks. The most challenging part of an international aerobatics contest is the Free Unknown. Pilots arrive at a competition after having polished sequences of loops, stall turns, and barrel rolls. But for the Free Unknown section they learn which assortment of tricks they must perform only a day in advance. Contestants plan out how they will string together the stipulated moves in the most pleasing fashion, but they cannot rehearse the routine, except in their minds. It's a test of imagination and airmanship that often decides the competition. In 2019, the World Intermediate Aerobatics Championship, which was held on an airfield in the Czech town of Břeclav, contained three Free Unknowns. The winner of the first was a twenty-five-year-old Ukrainian pilot named Timur Fatkullin. At the controls of his red-and-silver Extra 330LX--a nimble German sports plane--he made the unusual move of starting his sequence upside down. He then executed a complicated routine as if he'd practiced it for months. The Ukrainian team, boosted by Fatkullin's performance, won gold. Trevor Dugan, who served as a navigator with the R.A.F. in Afghanistan and Iraq, was on the British team, which took bronze. Fatkullin, he said, was "absolutely phenomenal." Not long after that championship, Fatkullin stopped entering aerobatics competitions: first came the pandemic, then the war with Russia. He moves through life impatiently. Now thirty-two, he has five children. He is tall, with a tight beard, pale-green eyes, and a square jaw. Even in casual situations, he stands ramrod straight, as though about to give or receive an order. He often wears a shirt with three buttons undone, a beige leather flying jacket with the collar turned up, combat pants, and Nike high-tops. He plays the guitar, a little piano. He often carries a thick fold of high-value bills. He speaks several languages, including English (almost perfectly) and Spanish (conversationally). He once spent thirty days in jail after breaking the ribs of a man who'd threatened his wife. He can dance the tango. When Fatkullin was in his mid-twenties, he started doing stunts with a group of other extreme athletes: parachutists, motorcyclists, a free diver.

Premier League predictions - how accurate were BBC Sport pundits? Last summer, 33 BBC TV and radio pundits made their predictions for the Premier League season, picking their champions and their top four. Twenty-one of them thought Liverpool would win it, and none of them got more than two clubs right. Although six pundits correctly picked Arsenal as champions, and everyone had the Gunners and Manchester City in their top four, Matthew Upson was the only one to have the top two in the order they actually finished. Martin Keown, Thomas Hitzlsperger, Sue Smith, Leon Osman and Jermaine Beckford were the other pundits who also backed Mikel Arteta's side.

Drone games put Ukraine's best military pilots to the test TRUSKAVETS, Ukraine - In the sky over western Ukraine, a bullet-shaped P1-SUN interceptor drone dived toward its target as dozens of soldiers looked on. A cheer went up as it cut through a tow line from another drone to a balloon, which drifted away. Ukraine's most skilled military drone pilots squared off this week not against Russia, but against each other in a competition to win bragging rights and state-of-the-art hardware for their units. Drone technology has transformed the war in Ukraine. Young men using video game consoles to operate strike drones packed with explosives -- sometimes from command centers far behind the front line -- are deeply feared by enemy soldiers.

Machine unlearning is a fundamental mechanism that enforces the right to be forgotten. Existing unlearning studies that rely on label manipulation or task-gradient reversal often deliver limited unlearning effectiveness. Moreover, they can undermine the original learning objective and typically do not guarantee equivalence to standard unlearning by retraining. In this paper, we propose \textbf{ManiF-SMC} (\textbf{Mani}fold \textbf{F}orgetting with \textbf{S}elf \textbf{M}ode \textbf{C}onnectivity), motivated by the observation that a model retrained on the remaining data tends to classify erased samples by their semantic similarity to the retained data. We begin with systematically recasting the approximate unlearning as pushing each erased sample away from its original learned manifold representation centroid toward its nearest semantic neighbors in the retained data. This reformulation aligns unlearning with retraining behavior and operates purely in representation space, reducing reliance on labels and task-specific gradients. To tackle the manifold representation-based unlearning problem, ManiF-SMC encapsulates the unlearning and representation preservation goals in a margin-based triplet loss. Because finding a suitable margin for unlearning is challenging, we propose a self-mode-connectivity module that rapidly reconstructs the local manifold to guide the adaptive margins generation for each unlearning case. Extensive experiments on four representative datasets show that ManiF-SMC achieves unlearning effectiveness comparable to state-of-the-art approximate methods while operating solely within the model's representation space.

Score matching is an alternative to maximum likelihood estimation when the normalizing constant is unknown or too costly to evaluate. However, vanilla score matching has shown to be inefficient relative to maximum likelihood estimation for multimodal distributions with well-separated modes, which are commonly encountered in practical applications. We compare a novel diffusion-based denoising score matching estimator (DDSME) to the vanilla score matching estimator (SME) in this scenario. In particular, we prove statistical guarantees for both estimators, showing that the error bound for the vanilla SME worsens when the separation between the modes increases, which can be avoided in case of the DDSME with suitable hyperparameter tuning. This provides a novel theoretical explanation for the superior behavior of diffusion-based score matching over the vanilla version.

Individual fairness, the notion that "similar individuals should be treated similarly," provides a strong and flexible fairness guarantee for algorithmic decision makers. However, a barrier to implementing individual fairness in practice is the difficulty of learning the similarity metric over individuals. In this work, we present an algorithm for learning a Mahalanobis similarity metric from triplet queries of the form "is individual $i$ more similar to individual $j$ or $k$?" We work in the standard Bradley-Terry model for pairwise comparisons. Our algorithm consists of a spectral initialization step followed by gradient descent. We provide extensive theoretical guarantees on our algorithm, showing that it converges quickly to the ground truth metric despite the non-convexity of the loss in our model. Because our focus is on fairness, we also show that individual fairness with respect to an estimated metric is sufficient to achieve similar fairness with respect to the true metric. We also discuss potential applications of our work to AI model tuning. Finally, we present experimental results that demonstrate the convergence of our algorithm and the fairness performance of downstream fair predictors trained on our estimated metric.

Directed acyclic graphs (DAGs) constitute a central modeling tool to enable principled reasoning about cause-effect interactions in complex systems. However, since the causal structure underlying a group of variables is often unknown and interventions may be infeasible or ethically challenging to implement, there is a need to address the task of inferring DAGs from observational data. However, most classical structure identification approaches face two key obstacles: the combinatorial challenge of enforcing acyclicity, which severely limits scalability, and identifiability challenges arising from latent confounding or heterogeneous noise. This tutorial offers an overview of recent signal processing and optimization advances that address these issues by recasting DAG structure learning as a continuous, score-based estimation problem over adjacency matrices. We begin with a didactic introduction to structural equation models and the formulation of causal graph recovery, followed by a historical survey of score-based methods ranging from early combinatorial search schemes and greedy heuristics to modern continuous frameworks that leverage smooth characterizations of acyclicity. Building on this foundation, we describe concomitant DAG estimation methods that jointly infer sparse causal structure and exogenous noise levels, improving robustness under heteroscedasticity and distribution shifts by rendering the estimator noise adaptive. All in all, the tutorial introduces readers to challenges and opportunities for signal processing research at the crossroads of causal inference, high-dimensional statistics, and scalable graph learning, while outlining emerging directions including online, nonlinear, and neural causal discovery.

Bradley-Terry-Luce (BTL) model estimation is a well-established strategy to rank a collection of items given a dataset of pairwise comparisons. Although the theoretical performance of BTL estimation methods, such as spectral and maximum likelihood estimation, is well studied in the regime of uniformly sampled graphs, generalizing such results to a wider class of random graphs has proved challenging. In this work, we investigate the entry-wise error of spectral algorithms against a semi-random adversary that can arbitrarily boost the sampling probabilities of certain edges. We find that the performance of the unweighted spectral method is heavily dependent on the spectral properties of the generated graph. Furthermore, we show that asymptotic performance approaching that of uniformly sampled graphs can be recovered by appropriately reweighting the observed edges to counteract the adversary and restore the spectral gap. Finally, we provide numerical simulations that support our theoretical findings.