Creator Playbook Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable Voices Look Up Trending Now Say More Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List Switch Off In My Bag All Series Every hint, nudge and outright answer you need to complete today's NYT Strands puzzle. Today's NYT Strands hints are easy in an abstract way. Strands, the ' elevated word-search game, requires the player to perform a twist on the classic word search. Words can be made from linked letters -- up, down, left, right, or diagonal, but words can also change direction, resulting in quirky shapes and patterns. Every single letter in the grid will be part of an answer.

Creator Playbook Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable Voices Look Up Trending Now Say More Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List Switch Off In My Bag All Series On the eve of the'Grand Theft Auto VI' extended look premiere on Netflix, Cyberleek has started posting true story spoilers. Timothy Beck Werth is the Tech Editor at Mashable, where he leads coverage and assignments for the Tech and Shopping verticals. Tim has over 15 years of experience as a journalist and editor, and he has particular experience covering and testing consumer technology, smart home gadgets, and men's grooming and style products. Previously, he was the Managing Editor and then Site Director of SPY.com, a men's product review and lifestyle website. As a writer for GQ, he covered everything from bull-riding competitions to the best Legos for adults, and he's also contributed to publications such as The Daily Beast, Gear Patrol, and The Awl.

Creator Playbook Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable Voices Look Up Trending Now Say More Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List Switch Off In My Bag All Series'GTA VI' Cyberleek update: Everything leaked Wednesday and earlier this week The leaker made good on a threat to release footage of the Lucia character. Alex Perry is a tech reporter at Mashable who primarily covers video games and consumer tech. Alex has spent most of the last decade reviewing games, smartphones, headphones, and laptops, and he doesn't plan on stopping anytime soon. He is also a Pisces, a cat lover, and a Kansas City sports fan. Leaked gameplay clips of keep appearing on the internet.

You can minimize the risk of films and shows being spoiled for you by muting comments, conversations, and keywords on various platforms. With multiple streaming services to choose from and the entire history of cinema and television to dig into, you'd be forgiven for not being quite up-to-date with the latest films and shows. That's where spoilers can hit you. Whether it's a twisty Netflix thriller or the season finale of a show on Apple TV, there will be times when you haven't gotten around to watching something and yet you don't want the plot spoiled for you. When you're in that scenario, going online is fraught with risk.

Kindle's in-book AI assistant can answer all your questions without spoilers But the catch is authors and publishers can't opt out of having this feature in their works. If you're several chapters into a novel and forgot who a character was, Amazon is hoping its new Kindle feature will jog your memory without ever having to put the e-reader down. This feature, called Ask this Book, was announced during Amazon's hardware event in September, but is finally available for US users on the Kindle iOS app. According to Amazon, the feature can currently be found on thousands of English best-selling Kindle titles and only reveals information up to your current reading position for spoiler-free responses. To use it, you can highlight a passage in any book you've bought or borrowed and ask it questions about plot, characters or other crucial details, and the AI assistant will offer immediate, contextual, spoiler-free information.

Graph Neural Networks (GNNs) address two key challenges in applying deep learning to graph-structured data: they handle varying size input graphs and ensure invariance under graph isomorphism. While GNNs have demonstrated broad applicability, understanding their expressive power remains an important question. In this paper, we propose GNN architectures that correspond precisely to prominent fragments of first-order logic (FO), including various modal logics as well as more expressive two-variable fragments. To establish these results, we apply methods from finite model theory of first-order and modal logics to the domain of graph representation learning. Our results provide a unifying framework for understanding the logical expressiveness of GNNs within FO.

It turns out Tesla's camera-vision-only approach to self-driving is no match for a Wile E. Coyote-style fake wall. Earlier this week, former NASA engineer and YouTuber Mark Rober posted a video where he tried to see if he could trick a Tesla Model Y using its Autopilot driver-assist function into driving through a Styrofoam wall disguised to look like part of the road in front of it. The Tesla hurls towards the wall at 40 mph and, rather than stopping, plows straight through it, leaving a giant hole. "It turns out my Tesla is less Road Runner, more Wile E. Coyote," Rober says as he inspects the damage on the front hood. The video, posted only a couple days ago, had racked up over 20 million views by Wednesday morning.

The ability of graph neural networks (GNNs) to count homomorphisms has recently been proposed as a practical and fine-grained measure of their expressive power. Although several existing works have investigated the homomorphism counting power of certain GNN families, a simple and unified framework for analyzing the problem is absent. In this paper, we first propose \emph{generalized folklore Weisfeiler-Leman (GFWL)} algorithms as a flexible design basis for expressive GNNs, and then provide a theoretical framework to algorithmically determine the homomorphism counting power of an arbitrary class of GNN within the GFWL design space. As the considered design space is large enough to accommodate almost all known powerful GNNs, our result greatly extends all existing works, and may find its application in the automation of GNN model design.

Clickbait posts are a widespread problem in the webspace. The generation of spoilers, i.e. short texts that neutralize clickbait by providing information that satisfies the curiosity induced by it, is one of the proposed solutions to the problem. Current state-of-the-art methods are based on passage retrieval or question answering approaches and are limited to generating spoilers only in the form of a phrase or a passage. In this work, we propose an ensemble of fine-tuned large language models for clickbait spoiler generation. Our approach is not limited to phrase or passage spoilers, but is also able to generate multipart spoilers that refer to several non-consecutive parts of text. Experimental evaluation demonstrates that the proposed ensemble model outperforms the baselines in terms of BLEU, METEOR and BERTScore metrics.

This study introduces 'clickbait spoiling', a novel technique designed to detect, categorize, and generate spoilers as succinct text responses, countering the curiosity induced by clickbait content. By leveraging a multi-task learning framework, our model's generalization capabilities are significantly enhanced, effectively addressing the pervasive issue of clickbait. The crux of our research lies in generating appropriate spoilers, be it a phrase, an extended passage, or multiple, depending on the spoiler type required. Our methodology integrates two crucial techniques: a refined spoiler categorization method and a modified version of the Question Answering (QA) mechanism, incorporated within a multi-task learning paradigm for optimized spoiler extraction from context. Notably, we have included fine-tuning methods for models capable of handling longer sequences to accommodate the generation of extended spoilers. This research highlights the potential of sophisticated text processing techniques in tackling the omnipresent issue of clickbait, promising an enhanced user experience in the digital realm.