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Paxton trounces Cornyn in the Texas Senate Republican primary runoff; Trump waffles between a losing "peace deal" and a return to war in Iran; and congressional candidate Alex Bores makes the case for AI regulation. Please enable javascript to get your Slate Plus feeds. If you can't access your feeds, please contact customer support. Check your phone for a link to finish setting up your feed. Please enter a valid phone number.

Efficient benchmarking techniques aim to lower the computational cost of evaluating LLMs by predicting full benchmark scores using only a subset of a benchmark's questions. By reframing this problem as an instance of multiple regression with feature selection, we find that existing efficient benchmarking methods can be greatly improved by simply using kernel ridge regression at the prediction stage. Additionally, using an information-theoretic feature-selection algorithm called minimum redundancy maximum relevance (mRMR), we can further improve upon these methods by selecting question subsets that will be maximally useful for prediction. Except in very data-poor settings, these approaches consistently achieve smaller prediction errors (in both MAE and RMSE), and greater ranking correlation between predicted and true scores (in both Spearman $ρ$ and Kendall $τ$) across a range of benchmarks using both binary and continuous metrics. Furthermore, mRMR subsampling is much faster than competitor methods (which often involve fitting probabilistic models or running clustering algorithms), and is more likely to select the same questions under different random seeds or training data splits. Tutorial code can be found at https://github.com/sambowyer/mrmr_eval .

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.

Police probe Andrew Mountbatten-Windsor over'sex offences': Stunning update on investigation of former prince as officers appeal for potential'victim survivors' to come forward Trump celebrates Stephen Colbert's final show with brutal'no talent' swipe as bitter host takes one last jab at CBS on way out door Trump warns of possible military action in Cuba and says'I'd be happy to do it' as Marco Rubio declares the nation a'US national security threat' Dangerous truth about melatonin side effects... the astonishing dose you SHOULD be taking... and a new natural grocery store alternative hailed by doctors CIA Nostradamus warned Trump about Iran... now he's calling the President's doctors. Dirty secret Hollywood's Cool Girls don't want you to know. Mom-of-two abandons home in Pennsylvania to live on board CRUISE SHIP year-round - and her kids have'zero concept' their life isn't normal This quiet announcement from Prince William was missed by most... but this is why royal insiders tell me it spells disaster for Harry and Meghan's future: RICHARD EDEN White man charged after he was filmed screaming at black female neighbor and using the phrase'You people' Shock moment'slurring' Britney Spears is arrested for DUI after failing sobriety test Astonishing secret list of elite Hollywood liberals conspiring to elect Spencer Pratt revealed to KENNEDY by her LA moles. Suspected Somali fraudster filmed leaping off Minnesota balcony and driving away in luxury Genesis sedan after feds announced they were charging him with alleged $3.3m scam Inside Pizza Hut restaurant that's still EXACTLY like it was in the 90s... complete with checkered tablecloths, arcade and famous buffet Stephen Colbert's final Late Show episode leaves fans unimpressed as Ryan Reynolds leads series of surprise celebrity cameos How Meryl Streep's husband really feels about her secret relationship with Martin Short: Their years of agony... his hard red line... and why she won't divorce him Look away now, Carrie Bradshaw! Fears for Ariana Grande: Insiders lift the lid on Ethan Slater's costly sacrifice... her private nightmares... and what's really keeping them apart Inside the UK's first AI-powered fertility clinic using state-of-the-art technologies to help women get pregnant - as one couple says'artificial intelligence allowed us to hold our baby in our arms' If you were asked to think about artificial intelligence ( AI), visions of killer robots, dodgy chatbots, or deepfakes might spring to mind.

Time-to-event data is widespread across the life sciences and engineering, but it is typically encountered together with censoring, which complicates the application of standard machine learning methods. Deep Cox models have emerged as a popular method for analyzing time-to-event data because they gracefully handle censoring and can be used with unstructured data such as clinical text reports, genomic sequences, and pathology images. However, their predicted survival probabilities are often poorly calibrated, thus limiting their practical utility. In this paper, we propose a novel post hoc calibration method for Deep Cox models that uses isotonic regression to refine predicted survival probabilities without affecting discriminative power. We establish favorable theoretical guarantees, including a double-robustness property and asymptotic calibration. Experiments on synthetic and real-world clinical data demonstrate the empirical effectiveness of our method.

While diffusion has drawn considerable recent attention from the language modeling community, continuous diffusion has appeared less scalable than discrete approaches. To challenge this belief we revisit Plaid, a likelihood-based continuous diffusion language model (DLM), and construct RePlaid by aligning the architecture of Plaid with modern discrete DLMs. In this unified setting, we establish the first scaling law for continuous DLMs that rivals discrete DLMs: RePlaid exhibits a compute gap of only $20\times$ compared to autoregressive models, outperforms Duo while using fewer parameters, and outperforms MDLM in the over-trained regime. We benchmark RePlaid against recent continuous DLMs: on OpenWebText, RePlaid achieves a new state-of-the-art PPL bound of $22.1$ among continuous DLMs and superior generation quality. These results suggest that continuous diffusion, when trained via likelihood, is a highly competitive and scalable alternative to discrete DLMs. Moreover, we offer theoretical insights to understand the advantage of likelihood-based training. We show that optimizing the noise schedule to minimize the ELBO's variance naturally yields linear cross-entropy (information loss) over time. This evenly distributes denoising difficulty without any case-specific time reparameterization. In addition, we find that optimizing embeddings via likelihood creates structured geometries and drives the most significant likelihood gain.

Amazon is rolling out its ultra-fast delivery service, Amazon Now, in dozens of cities in the US, promising deliveries of groceries and household essentials in 30 minutes or less. Amazon says the service is also now widely available in Atlanta and Dallas-Fort Worth, and will rapidly expand into Austin, Houston, Minneapolis, Orlando, Phoenix, Denver, Oklahoma City and more throughout the rest of 2026. If Amazon Now is available in your area you'll see a 30-Minute Delivery option in the Amazon app or on the homepage when you're in a browser. Amazon Now offers will also be highlighted when you're browsing products. You can search by category, and as well as groceries and basic household items such as eggs, diary and laundry detergent, you can also order select electronics on the service, which Amazon says operates 24 hours a day in most places.

We develop graph-based methods for semi-supervised learning based on label propagation on a data similarity graph. When data is abundant or arrive in a stream, the problems of computation and data storage arise for any graph-based method. We propose a fast approximate online algorithm that solves for the harmonic solution on an approximate graph. We show, both empirically and theoretically, that good behavior can be achieved by collapsing nearby points into a set of local representative points that minimize distortion. Moreover, we regularize the harmonic solution to achieve better stability properties. We also present graph-based methods for detecting conditional anomalies and apply them to the identification of unusual clinical actions in hospitals. Our hypothesis is that patient-management actions that are unusual with respect to the past patients may be due to errors and that it is worthwhile to raise an alert if such a condition is encountered. Conditional anomaly detection extends standard unconditional anomaly framework but also faces new problems known as fringe and isolated points. We devise novel nonparametric graph-based methods to tackle these problems. Our methods rely on graph connectivity analysis and soft harmonic solution. Finally, we conduct an extensive human evaluation study of our conditional anomaly methods by 15 experts in critical care.

Roughly 500 labour groups across the United States have organised a widespread economic blackout calling for "no school, no work, no shopping" to mark May Day, also known as International Workers' Day. The events, organised as part of an initiative called May Day Strong, were inspired by economic boycotts following ramped-up immigration enforcement operations in Minneapolis, Minnesota, and the deaths of US citizens Renee Good and Alex Pretti in January. May Day Strong has a broad set of demands, including "tax the rich" and abolishing Immigration and Customs Enforcement (ICE) -- a call that comes as Republicans voted on Wednesday on a budgetary measure that would fund the agency under the Department of Homeland Security. It also calls for ending war and "expanding democracy", according to a statement from the group. While the tent is broad in nature, organisers stressed that it is a result of a wide set of challenges facing the US worker.