Super Bowl champion Rod Woodson says the KKK tried to burn his family's home down when he was 11 years old Trevor Bauer says'playoff teams' wanted to sign him but team owners, MLB have blocked every move MLW owner talks Tessa Blanchard, Masha Slamovich signings: 'I believe in forgiveness' Dez Bryant's attorney alleges the ex-NFL star targeted in wild parking altercation: 'It was set up' JACKSON THOMPSON: Colorado Gov-elect Phil Weiser signals possible changes to girls' sports ballot measure Minnesota Vikings' soft benching of Kyler Murray leads to Carson Wentz taking over as new starting QB FIFA could see legal action if Women's World Cup players don't get equal pay: report Adult film star had no idea she was MLB player's side chick until he got engaged, Lindsey Vonn's happy & more LIV Golf is officially over after announcing plans to'wind-down' operations after failed rescue deal LeBron James sends cease and desist to'Bron AI' sports betting app over unauthorized likeness Putin accuses hockey organizations of'gross violation' as Canada bars Russia from World Juniors Democrats need to earn back Americans' trust: Former Pennsylvania congressman Melania Trump says'we have chosen humanity over war' amid partial Russia-Ukraine humanitarian ceasefire Former WH chief of staff says Vance-Rubio 2028 would be Trump's'dream ticket' DSA mayoral wins spark debate over Democratic Party's direction Colorado Democrat calls for'fast' end to Iran war amid Abdul El-Sayed's call for investigation UAW member: You don't get a choice if the government runs this program Son says father's medically assisted death in Canada happened with shocking speed Tom Brady lists his top 10 teams for Week 8 which include the Chicago Bears, Denver Broncos, Detroit Lions and more! Neither I nor ChatGPT wants to talk about last week . So, if I say we're not talking about it, we're not talking about it, OK? This page may contain affiliate links to legal sports betting partners. If you sign up or place a wager, FOX News may be compensated.

Our gifting experts are obsessively searching for holiday gifts that are unique, thoughtful and suitable for any budget. PowerToys is one of my favorite free Windows utilities. With more than 30 tools, it offers something for everyone from beginners to power users. Lance Whitney is a technology journalist with an IT background. He's written for TechRepublic, PCMag, Macworld, and Time, among others, He also teaches classes in AI, cybersecurity, and social media.

Microsoft has opened up preorders for the Surface RTX Spark Dev Box and shared that the device will start shipping to customers in November. The company announced the desktop PC alongside the Surface Laptop Ultra in June, and like that computer we now know it'll come at a hefty premium: 6,000. The Surface RTX Spark Dev Box is specifically designed for local AI workflows and the developers and engineers who run them, Microsoft says. The PC features an austere, anodized aluminum grille on the top, with a port selection that includes two USB-C, one USB-A, Ethernet, DisplayPort 2.1 and HDMI 2.1b on the back. Inside, the Dev Box uses NVIDIA's RTX Spark N1X chip and ships with 128GB of unified memory and 2TB of storage.

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The studio released an official catch-up list ahead of December's release. We've got links to watch every one of the movies if you want to get up to speed. Marvel and Disney+ have issued a stacked watch list for fans ahead of the cinema release of on December 18. The Marvel Cinematic Universe can be complex terrain to navigate. Its 38 movies and 25 series to date span varying superhero plot lines, crossovers, and multiverse action, with the latest release likely to spare no less lore and real fan knowledge.

Conditional inference trees (CIT) and conditional inference forests (CIF) reduce split-selection bias by testing features before choosing split thresholds, but repeated permutation tests and threshold searches can make these methods computationally expensive. We study CIT and CIF as top-$k$ feature-ranking methods for downstream prediction using real-data benchmarks, runtime ablations, and synthetic feature-recovery experiments. At a fixed node, if the features and permutation budget do not depend on the node responses, Bonferroni-corrected $+1$ Monte Carlo permutation $p$-values control nodewise rejection under the complete permutation null. CIF ranks 4th among 17 classification methods on 22 datasets and 3rd among 18 regression methods on 8 datasets. With Bonferroni correction held fixed, the CIF runtime ablations indicate that adaptive stopping and the number of thresholds searched have the largest measured effect on runtime: turning off adaptive stopping and using exact threshold search increase fitting time by 4.0--8.4$\times$ and 1.9--10.8$\times$, respectively, while downstream score changes are at most 0.011. Sparse high-$p$ simulations indicate that forest feature sampling can leave informative features out of many split decisions. Overall, the results support CIF as a top-$k$ feature-ranking method in the evaluated downstream prediction benchmarks.

Most existing multitask learning approaches are limited by their reliance on task-specific loss functions tailored to the scale and type of each outcome. When outcomes differ across tasks, these losses are generally not directly comparable, which makes it difficult to formulate a unified objective and may limit information sharing across tasks. We propose a multitask transformation framework in which task-specific responses may differ through unknown monotone transformations. Motivated by high-dimensional biological applications in which the predictor dimension may diverge with the sample size while only a common subset of predictors is informative, we consider shared sparsity across tasks. Under this framework, we estimate the target functions and identify important predictors by optimizing a smoothed rank-based criterion with a group-Lasso penalty, implemented through a multitask deep neural network with a shared first layer. We establish the nonasymptotic excess-risk bounds, and variable-selection consistency for the proposed estimator. Simulation studies show that the proposed method achieves competitive prediction and variable-selection performance compared with competing approaches. Analyses of gene-expression studies with continuous, binary, and mixed outcomes further illustrate that the proposed method improves prediction and identifies biologically meaningful shared predictors.

People overthink; language models over-sample, and the extra effort can talk both into a worse answer. Reasoning systems answer a hard question by sampling it many times (test-time scaling), and the more they draw, the more often a correct answer turns up somewhere, so coverage, the fraction of problems with at least one correct try, climbs and appears to be progress. But a deployed system must return one answer, and choosing it, not knowing which try is right, is selection; selection is capped, and past a point extra samples only make the model surer of a confident mistake, even as every draw adds cost. The gap between climbing coverage and stalled selection, the identifiability gap, is the answer a model can produce but not pick. So the real question is not whether to sample but how far, and the answer is: not far. For picking an answer, the vote has already settled within a few dozen draws, the modal ceiling; for scoring a benchmark, sooner still, the correlation ceiling. Beyond that, extra draws cost compute and add nothing, and can even make the answer worse. This paper turns the cutoff into a single number, the effective number of samples, that any sampling run already reveals. The bottleneck is recognizing a right answer, not generating one.

A central challenge in statistical modeling is identifying the subset of features that belong in the true regression model. The classical best subset selection problem, recently made tractable via mixed-integer optimization (MIO), finds the globally optimal sparse solution. It does not, however, make use of any information beyond the observed data. In many applied settings, domain experts can meaningfully rank or score the relevance of candidate predictors, yet no existing framework integrates such probabilistic expert assessments directly into the best-subsets objective. This paper presents Expert-Implied Bayesian Best Subsets (EBBS), a method that incorporates domain-expert probability estimates of feature relevance into the MIO best-subsets problem through a maximum a posteriori (MAP) framework. Expert views from multiple respondents are aggregated into a single prior probability per feature using the Poisson binomial distribution for marginal probability estimates, the pairwise win rate for pairwise comparisons, or the normalized mean rank for ordinal rankings. This probability enters the objective function as a log-odds penalty term that smoothly encourages or discourages the selection of each feature consistent with the expert consensus. This paper provides analytic derivations of the MAP formulation and characterizes its theoretical properties. The proposed model reduces to Best Subsets when experts all have no views. Empirical results on synthetic and real datasets are forthcoming.

This paper introduces the R package spca, which provides a computational framework for least squares sparse principal component analysis (LS-SPCA). Unlike other SPCA methods, LS-SPCA generates uncorrelated sparse principal components (sPCs) that effectively maximize the explained variance while maintaining strong correlations with standard principal components (PCs). The framework also includes more computationally efficient variants that produce mildly correlated sPCs, which often have lower cardinality while explaining equal or greater variance than the LS-SPCA optimal sPCs. The spca package is built on an efficient C++ backend for matrix computations, with distinct engines for tall and fat matrices, and a flexible R frontend. The user interface offers several options for computing sPCs, such as deciding whether sparsification should stop when a threshold for cumulative variance explained or R2 with the PCs is reached, and choosing between simple forward selection, stepwise forward selection, or backward elimination for variable selection. In addition to the print(), summary(), and plot() methods, the package includes tools for comparing different "spca" solutions, grouping sparse loadings, and representing foreign SPCA solutions as "spca" objects. This article demonstrates with real datasets the use of the package in a typical LS-SPCA workflow and briefly contrasts LS-SPCA with conventional SPCA solutions . Then it compares different LS-SPCA solutions obtained from the dataset. Finally, the performance of spca on large tall and fat matrices is discussed, showing that spca offers a computationally efficient alternative for computing interpretable sPCs.