An evolutionary psychologist is challenging the popular understanding of kids and technology. W hen the 82-year-old psychologist Peter Gray describes the way he grew up, he punctuates the anecdotes by saying that modern parents would be arrested for letting a child have such fun. When he was 4 years old, he would walk to a store in Minneapolis to buy cigarettes for his grandmother. When he was 11, he would sometimes stay home from school in Hill City, Minnesota, to operate a newspaper printing press owned by his mother and stepfather. His parents were not arrested, and that's because the childhood they permitted him to have was basically normal at the time, even if his family did have a newspaper printing press in the house. As a boy, Peter was obsessed with fishing and baseball; neighborhood friends taught him how to ride his bike and catch grasshoppers. Although Gray's career as a scientist would begin with laboratory studies of rat hormones, he eventually found his way to writing about his childhood, in a fashion.

One of Kalshi's most prominent traders tells WIRED he's swearing off Spotify-related markets until the issue is resolved. Top Kalshi trader Caleb Davies usually speaks to the press about how prediction markets help him rake in money. The Minneapolis-based IT worker estimates he's made $1.2 million overall across different prediction platforms, with $414,000 in winnings from Kalshi's culture markets alone. He especially enjoys wagering on music charts, because he carefully analyzes Spotify data to pick winners. "Every single morning, I'm going in, downloading the data, and updating my projections," he tells WIRED.

The president's Justice Department tried to "coerce" a group of top Democrats. A Republican-appointed judge was not amused. This story is adapted from Slate's new and revamped Slatest newsletter. You can sign up here to get it in your inbox every day. A federal judge just slapped down an attempt by the Trump administration to intimidate Minnesota Democrats.

Low-Rank Adaptation (LoRA) lowers the computational and memory overhead of fine-tuning large models by updating a low-dimensional subspace of the pretrained weight matrix. Albeit efficient, LoRA exhibits suboptimal convergence and noticeable performance degradation, due to inconsistent and imbalanced weight updates induced by its nonunique low-rank factorizations. To overcome these limitations, this article identifies the optimal low-rank factorization per step that minimizes an upper bound on the loss. The resultant refactored low-rank adaptation (RefLoRA) method promotes a flatter loss landscape, along with consistent and balanced weight updates, thus speeding up stable convergence. Extensive experiments evaluate RefLoRA on natural language understanding, and commonsense reasoning tasks with popular large language models including DeBERTaV3, LLaMA-7B, LLaMA2-7B and LLaMA3-8B. The numerical tests corroborate that RefLoRA converges faster, outperforms various benchmarks, and enjoys negligible computational overhead compared to state-of-the-art LoRA variants.

Polyak-Ruppert averaging is a widely used technique to achieve the optimal asymptotic variance of stochastic approximation (SA) algorithms, yet its high-probability performance guarantees remain underexplored in general settings. In this paper, we present a general framework for establishing non-asymptotic concentration bounds for the error of averaged SA iterates. Our approach assumes access to individual concentration bounds for the unaveraged iterates and yields a sharp bound on the averaged iterates. We also construct an example, showing the tightness of our result up to constant multiplicative factors. As direct applications, we derive tight concentration bounds for contractive SA algorithms and for algorithms such as temporal difference learning and Q-learning with averaging, obtaining new bounds in settings where traditional analysis is challenging.

Robust Subspace Recovery (RSR) aims to identify an underlying d-dimensional subspace from a dataset heavily corrupted by outliers. Complexity-theoretic results establish a threshold for the problem's computational hardness based on the dimensionscaled signal-to-noise ratio (DS-SNR): the problem is SSE-hard when the DS-SNR is strictly less than 1, and solvable via practical algorithms when it is greater than 1 under general position assumptions. However, the exact behavior of practical algorithms at the critical boundary DS-SNR = 1 has remained unknown. Specifically, we prove that TME converges exactly to the true subspace for DS-SNR 1 under a new stability condition, which is less restrictive than the general position assumptions used in prior literature. I. Introduction Robust Subspace Recovery (RSR) is a fundamental problem in robust statistics, machine learning, and computer vision. The primary goal of RSR is to identify an underlying low-dimensional linear subspace from a dataset that is heavily corrupted by outliers. The standard formulation of the noiseless RSR problem assumes a dataset X = {xi}Ni=1 RD consisting of n1 inliers lying exactly on a d-dimensional linear subspace L RD, and n0 outliers lying strictly off L . We refer to such a dataset as a noiseless inlier-outlier dataset, where the total number of points is N = n0 +n1. The central algorithmic question in noiseless RSR is under what conditions one can exactly and efficiently recover the underlying d-subspace L . A natural metric for characterizing the difficulty of this problem is the ratio of inliers to outliers, n1/n0, which can be viewed as a signal-to-noise ratio (SNR) [8], [11], [12]. This leads to the dimension-scaled SNR (DS-SNR), denoted by δS: δS:= n1/d n0/(D d) . Hardt and Moitra [5] established a fundamental lower bound, showing that when δS < 1, the noiseless RSR problem is Small Set Expansion (SSE)-hard, a property conjectured to be equivalent to NP-hardness [15]. In the special case of hyperplanes (d = D 1), they showed NP-hardness by invoking a result from [7]. The noiseless RSR problem is SSE-hard if δS < 1.

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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.

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.

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.