Along with the dataset, two tasks are proposed to evaluate machines'

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG . Timeline: NBC host Savannah Guthrie's mother disappears as sheriff says'everybody's still a suspect' Arizona family sues hospital, says staff'Ubered' sick son to sidewalk where he died Medical examiner determines Texas A&M student's manner of death as family attorney disputes finding: 'Flawed' Dramatic bodycam video captures deputy pulling woman from fiery car wreck: 'I got to her just in time' NJ tech boss convicted of quadruple murder in 2018 killing of brother's family Genealogy company exec slams Pima sheriff's'devastating' move to ship Nancy Guthrie evidence to Florida lab Walmart sales records become critical evidence as FBI investigates Nancy Guthrie's disappearance Feds double Nancy Guthrie reward as former FBI agents suggest they're seeking an insider tip Savannah Guthrie's mother abducted from upscale neighborhood as Tucson crime'spins out of control' SWAT was prepared for possibly'very dangerous' situation in Guthrie case, expert says A man is detained near Nancy Guthrie's house Second Pima County SWAT vehicle seen leaving scene of law enforcement operation in Tucson, Ariz.

Sign up to receive it in your inbox. The event has a storied history among mystery buffs; some of its first scripts were written by the celebrated author Donald E. Westlake, along with his wife Abby, and they often collaborated with notable writer friends, including Stephen King, Edward Gorey, and Isaac Asimov, on everything from performing to graphic design. A half century ago, few, if any, hotels offered "immersive theatre" as an amenity, and the Mystery Weekend became a hot ticket for city dwellers--the first weekend, in 1977, drew more than two hundred participants. Soon, mystery-solving events were de rigueur at many rural hotels, whose owners found that staging crime scenes was a surefire way to lure cosmopolitans to the country during the off-season. In 1992, the reporter Alessandra Stanley noted that the swelling glut of mystery parties came in three categories: serious, "in which participants form teams and spend two to three days"; semi-serious, which "take place in large hotels, over meals, and are meant to be more entertaining than challenging"; and those on cruise ships, which are fully unserious.

Environment Animals Wildlife Don't pick up frozen iguanas When the temperatures drop, so do Florida's iguanas. Breakthroughs, discoveries, and DIY tips sent six days a week. In Florida, giant invasive pythons, the state's signature alligators, and bears that sometimes roam around theme parks are typically among the most upfront wildlife in the news. But when the temperatures drop, one reptile stands ready to take the limelight and also drop-- iguanas . When air temperatures get cold enough, the reptiles will get stunned (or freeze) and fall from trees.

To incorporate 3D information completely and efficiently, we propose a novel message passing scheme that operates within 1-hop neighborhood.

Abstract--Graph clustering plays a pivotal role in unsupervised learning methods like spectral clustering, yet traditional methods for graph clustering often perpetuate bias through unfair graph constructions that may underrepresent some groups. The current research introduces novel approaches for constructing fair k-nearest neighbor (kNN) and fair ϵ-neighborhood graphs that proactively enforce demographic parity during graph formation. By incorporating fairness constraints at the earliest stage of neighborhood selection steps, our approaches incorporate proportional representation of sensitive features into the local graph structure while maintaining geometric consistency. Our work addresses a critical gap in pre-processing for fair spectral clustering, demonstrating that topological fairness in graph construction is essential for achieving equitable clustering outcomes. Widely used graph construction methods like kNN and ϵ-neighborhood graphs propagate edge based disparate impact on sensitive groups, leading to biased clustering results. Providing representation of each sensitive group in the neighborhood of every node leads to fairer spectral clustering results because the topological features of the graph naturally reflect equitable group ratios. This research fills an essential shortcoming in fair unsupervised learning, by illustrating how topological fairness in graph construction inherently facilitates fairer spectral clustering results without the need for changes to the clustering algorithm itself. Thorough experiments on three synthetic datasets, seven real-world tabular datasets, and three real-world image datasets prove that our fair graph construction methods surpass the current baselines in graph clustering tasks. Machine learning algorithms are widely used for decision-making in a variety of fields, including criminal justice [1], healthcare [2], [3], and finance [4]. The reason for this is that these algorithms have been shown to be very accurate and effective at analyzing big datasets. The increasing prevalence of these algorithms has raised questions regarding their fairness and potential to reinforce societal biases [5], [6]. These biases can result in unfair treatment of certain groups of people thereby create significant societal implications. Recently, concerns have been raised about the fairness of clusters produced by popular clustering algorithms.

As large language models become increasingly capable, there is growing concern that they may develop reasoning processes that are encoded or hidden from human oversight. To investigate whether current interpretability techniques can penetrate such encoded reasoning, we construct a controlled testbed by fine-tuning a reasoning model (DeepSeek-R1-Distill-Llama-70B) to perform chain-of-thought reasoning in ROT-13 encryption while maintaining intelligible English outputs. We evaluate mechanistic interpretability methods--in particular, logit lens analysis--on their ability to decode the model's hidden reasoning process using only internal activations. We show that logit lens can effectively translate encoded reasoning, with accuracy peaking in intermediate-to-late layers. Finally, we develop a fully unsupervised decoding pipeline that combines logit lens with automated paraphrasing, achieving substantial accuracy in reconstructing complete reasoning transcripts from internal model representations. These findings suggest that current mechanistic interpretability techniques may be more robust to simple forms of encoded reasoning than previously understood. Our work provides an initial framework for evaluating interpretability methods against models that reason in non-human-readable formats, contributing to the broader challenge of maintaining oversight over increasingly capable AI systems.

Understanding and continuously refining multimodal molecular knowledge is crucial for advancing biomedicine, chemistry, and materials science. Molecule language models (MoLMs) have become powerful tools in these domains, integrating structural representations (e.g., SMILES strings, molecular graphs) with rich contextual descriptions (e.g., physicochemical properties). However, MoLMs can encode and propagate inaccuracies due to outdated web-mined training corpora or malicious manipulation, jeopardizing downstream discovery pipelines. While knowledge editing has been explored for general-domain AI, its application to MoLMs remains uncharted, presenting unique challenges due to the multifaceted and interdependent nature of molecular knowledge. In this paper, we take the first step toward MoLM editing for two critical tasks: molecule-to-caption generation and caption-to-molecule generation. To address molecule-specific challenges, we propose MolEdit, a powerful framework that enables targeted modifications while preserving unrelated molecular knowledge. MolEdit combines a Multi-Expert Knowledge Adapter that routes edits to specialized experts for different molecular facets with an Expertise-Aware Editing Switcher that activates the adapters only when input closely matches the stored edits across all expertise, minimizing interference with unrelated knowledge. To systematically evaluate editing performance, we introduce MEBench, a comprehensive benchmark assessing multiple dimensions, including Reliability (accuracy of the editing), Locality (preservation of irrelevant knowledge), and Generality (robustness to reformed queries). Across extensive experiments on two popular MoLM backbones, MolEdit delivers up to 18.8% higher Reliability and 12.0% better Locality than baselines while maintaining efficiency. The code is available at: https://github.com/LzyFischer/MolEdit.

Sports broadcaster's wife suffers unimaginable tragedy just before he goes on air Bethany MaGee's family issue heartbreaking statement about her injuries after devout Christian, 26, was set ablaze'by 72-time arrestee' on Chicago train Couple left red-faced after buying $25K'dirt alley' at auction thinking it was bargain San Francisco home LIZ JONES: Sorry, but it's now time for Kate to stop making excuses Troubled 350lb son of Hollywood icon is forced to humiliating new low... as his movie star brother luxuriates in $7m Montecito mansion Ina Garten, 77, vulnerably addresses her decision not to have children: 'I can't imagine my life any other way' Doctors appalled by North West's new body modification warn parents to stop children from chasing the dangerous fad Alex appeared to have the dream Manhattan mom life. But she was hiding a dark secret... and it almost killed her Shocking extent America has turned on ICE is revealed as Joe Rogan breaks from conservatives still cheering Trump's army of masked men Sir Richard Branson's wife Joan dies: 'Heartbroken' Virgin tycoon pays tribute to his'best friend' after she passed away Trump gives Thanksgiving turkeys scathing nicknames and calls Pritzker a'fat slob' in fiery White House holiday speech How to tell if a man is using'therapy speak' to manipulate you: If he says any of these 15 toxic phrases, run for the hills... I'll tell you what he REALLY means: JANA HOCKING I know why Usha Vance ditched her wedding ring. Most women would do the same if they'd suffered her humiliation: KENNEDY As many as 172 black bears are at risk of death in Florida after a judge approved the first hunt in a decade. Leon County Circuit Judge Angela Dempsey rejected a request from Bear Warriors United, a Central Florida-based nonprofit, to halt this year's hunt, saying the group had failed to show a'substantial likelihood of success on the merits' in its lawsuit. The hunt is scheduled for December 6 through 28 on lands outside the wildlife management area system.

Accurate typhoon track forecasting is crucial for early system warning and disaster response. While Transformer-based models have demonstrated strong performance in modeling the temporal dynamics of dense trajectories of humans and vehicles in smart cities, they usually lack access to broader contextual knowledge that enhances the forecasting reliability of sparse meteorological trajectories, such as typhoon tracks. To address this challenge, we propose TyphoFormer, a novel framework that incorporates natural language descriptions as auxiliary prompts to improve typhoon trajectory forecasting. For each time step, we use Large Language Model (LLM) to generate concise textual descriptions based on the numerical attributes recorded in the North Atlantic hurricane database. The language descriptions capture high-level meteorological semantics and are embedded as auxiliary special tokens prepended to the numerical time series input. By integrating both textual and sequential information within a unified Transformer encoder, TyphoFormer enables the model to leverage contextual cues that are otherwise inaccessible through numerical features alone. Extensive experiments are conducted on HURDAT2 benchmark, results show that TyphoFormer consistently outperforms other state-of-the-art baseline methods, particularly under challenging scenarios involving nonlinear path shifts and limited historical observations.