Cardi B claims Donald Trump's attendance brought a'dark' energy to NBA Finals Game 3 Orioles' Leody Taveras suffers most embarrassing strikeout of the pitch clock era against his former team'World's Best Ex-Girlfriend' Morgan Riddle done dating athletes, Nikki Spoelstra's selfies for haters & malls Dodgers catcher Dalton Rushing executes a slide so illegal it would've made the 1980s proud The magic of Omaha: Why the College World Series is unlike anything else in sports that's worth the trip Kyle Busch's son suffers heartbreak in emotional return to racing after father's stunning death Why the under 4.5 through five innings is the play in Nationals-Giants with Foster Griffin facing Robbie Ray Dana White brings legendary stuntman Travis Pastrana's dirt bike backflip to White House USMNT legend Landon Donovan talks World Cup, American soccer's influence overseas during Raising Cane's shift Athletics wild first game in Las Vegas leads to 29 runs, 11 home runs in ominous sign for area's MLB future LIV Golf CEO refuses to guarantee circuit's remaining events will go on as scheduled with awkward sales pitch Steve Doocy explores Bentonville, Arkansas, the'Mountain Bike Capital of the World' Steve Doocy traces Walmart's origins in Arkansas Pompeo warns Iranian regime will'not go away' after US helicopter downed House approves resolution to limit Trump's war powers Trump's reveals new details on Iran drone attack downing US Apache helicopter OutKick Sports Trump tears into Stephen A Smith as feud grows: 'Arrogant fool, a low IQ individual' The ESPN host questioned Trump's policies after the president first mocked his aptitude for political office President Donald Trump responded to ESPN's Stephen A Smith's critique about showing up to the New York Knicks' NBA Finals game. President Donald Trump took another swipe at ESPN personality Stephen A. Smith as the two traded barbs over the president's attendance at the New York Knicks' NBA Finals game. Smith initially said Trump's attendance would be a detriment to NBA fans and the city. Trump was asked to respond to Smith's comments by Fox News Digital/OutKick on Monday night. The president said he wasn't sure that Smith had the aptitude or a high IQ to run for office.

Christopher Nolan's'The Odyssey' uncorks a Trojan Horse popcorn bucket that stores the goods in its crotch New trailer released for upcoming post-apocalyptic thriller'The Dog Stars' with Jacob Elordi'House of the Dragon' Season 3 premiere runtime and details revealed for hit HBO series You're not getting away with watering your grass with your'crank' out on Sheriff Grady Judd's watch Taylor Sheridan's hit CIA/military series'Lioness' gets official season release date on Paramount+ It wasn't on his shopping list, but a man managed to accidentally shoot himself in the groin at Walmart anyway Smokin' Charley Hull gets off to a fast start at U.S. Women's Open before fading, Stephen A weight loss & MEAT Waka Flocka Flame picks Trump over Kamala Harris, compares choice to Kobe Bryant vs. LeBron James Model Penny Lane's work on the SI Swimsuit runway last weekend still has the internet talking Velma from'Scooby-Doo' cranks up the heat, Nick Saban dragged on Capitol Hill & the great car dealership scam Steve Doocy explores Bentonville, Arkansas, the'Mountain Bike Capital of the World' Steve Doocy traces Walmart's origins in Arkansas Pompeo warns Iranian regime will'not go away' after US helicopter downed House approves resolution to limit Trump's war powers Trump's reveals new details on Iran drone attack downing US Apache helicopter The'Nicotine' singer's comments in Lexington drew massive support from fans praising her boldness Ella Langley discusses releasing a series of singles and building momentum toward her sophomore album during coverage tied to the 2026 iHeartRadio Music Awards held March 26, 2026, at the Dolby Theatre in Los Angeles. Country music sensation Ella Langley continues to prove she's one of the best people in entertainment. Langley has been on an unstoppable role lately, and she's built herself a massive following in the genre she's dominating. It's not just the music fans love. It's the fact that the Nicotine singer comes off as incredibly authentic, and is very open about who she is as a person.

Texas Tech cannot play the victim in Brendan Sorsby saga, it could've dealt with PR mess months ago Cardi B claims Donald Trump's attendance brought a'dark' energy to NBA Finals Game 3 Trump tears into Stephen A Smith as feud grows: 'Arrogant fool, a low IQ individual' 'World's Best Ex-Girlfriend' Morgan Riddle done dating athletes, Nikki Spoelstra's selfies for haters & malls Dodgers catcher Dalton Rushing executes a slide so illegal it would've made the 1980s proud The magic of Omaha: Why the College World Series is unlike anything else in sports that's worth the trip Kyle Busch's son suffers heartbreak in emotional return to racing after father's stunning death Why the under 4.5 through five innings is the play in Nationals-Giants with Foster Griffin facing Robbie Ray Dana White brings legendary stuntman Travis Pastrana's dirt bike backflip to White House USMNT legend Landon Donovan talks World Cup, American soccer's influence overseas during Raising Cane's shift Athletics wild first game in Las Vegas leads to 29 runs, 11 home runs in ominous sign for area's MLB future LIV Golf CEO refuses to guarantee circuit's remaining events will go on as scheduled with awkward sales pitch Steve Doocy explores Bentonville, Arkansas, the'Mountain Bike Capital of the World' Steve Doocy traces Walmart's origins in Arkansas Pompeo warns Iranian regime will'not go away' after US helicopter downed House approves resolution to limit Trump's war powers Trump's reveals new details on Iran drone attack downing US Apache helicopter OutKick Sports Orioles' Leody Taveras suffers most embarrassing strikeout of the pitch clock era against his former team Not all strikeouts are created equal, and nothing may exemplify that more than the punch-out Baltimore Orioles center fielder Leody Taveras endured against the Seattle Mariners. Taveras, who played 28 games for Seattle a season ago, was Baltimore's leadoff man in the bottom of the ninth with his club trailing 4-2 at home. In a spot where he could have jump-started a comeback, he instead struck out in what very well could be the most embarrassing fashion possible of the modern era. Sitting on a 2-2 count, Taveras took a pitch well outside and began walking to first base, thinking he had just been walked. He even began taking off his elbow guard before realizing that he had the pitch count wrong.

The integration of multi-omics data presents a major challenge in precision medicine, requiring advanced computational methods for accurate disease classification and biological interpretation. This study introduces the Multi-Omics Graph Kolmogorov-Arnold Network (MOGKAN), a deep learning model that integrates messenger RNA, micro RNA sequences, and DNA methylation data with Protein-Protein Interaction (PPI) networks for accurate and interpretable cancer classification across 31 cancer types. MOGKAN employs a hybrid approach combining differential expression with DESeq2, Linear Models for Microarray (LIMMA), and Least Absolute Shrinkage and Selection Operator (LASSO) regression to reduce multi-omics data dimensionality while preserving relevant biological features. The model architecture is based on the Kolmogorov-Arnold theorem principle, using trainable univariate functions to enhance interpretability and feature analysis. MOGKAN achieves classification accuracy of 96.28 percent and demonstrates low experimental variability with a standard deviation that is reduced by 1.58 to 7.30 percents compared to Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs). The biomarkers identified by MOGKAN have been validated as cancer-related markers through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The proposed model presents an ability to uncover molecular oncogenesis mechanisms by detecting phosphoinositide-binding substances and regulating sphingolipid cellular processes. By integrating multi-omics data with graph-based deep learning, our proposed approach demonstrates superior predictive performance and interpretability that has the potential to enhance the translation of complex multi-omics data into clinically actionable cancer diagnostics.

Autonomous surgical robots have demonstrated significant potential to standardize surgical outcomes, driving innovations that enhance safety and consistency regardless of individual surgeon experience. Deep anterior lamellar keratoplasty (DALK), a partial thickness corneal transplant surgery aimed at replacing the anterior part of cornea above Descemet membrane (DM), would greatly benefit from an autonomous surgical approach as it highly relies on surgeon skill with high perforation rates. In this study, we proposed a novel autonomous surgical robotic system (AUTO-DALK) based on a customized neural network capable of precise needle control and consistent big bubble demarcation on cadaver and live rabbit models. We demonstrate the feasibility of an AI-based image-guided vertical drilling approach for big bubble generation, in contrast to the conventional horizontal needle approach. Our system integrates an optical coherence tomography (OCT) fiber optic distal sensor into the eye-mountable micro robotic system, which automatically segments OCT M-mode depth signals to identify corneal layers using a custom deep learning algorithm. It enables the robot to autonomously guide the needle to targeted tissue layers via a depth-controlled feedback loop. We compared autonomous needle insertion performance and resulting pneumo-dissection using AUTO-DALK against 1) freehand insertion, 2) OCT sensor guided manual insertion, and 3) teleoperated robotic insertion, reporting significant improvements in insertion depth, pneumo-dissection depth, task completion time, and big bubble formation. Ex vivo and in vivo results indicate that the AI-driven, AUTO-DALK system, is a promising solution to standardize pneumo-dissection outcomes for partial thickness keratoplasty.

AI assistants are becoming an integral part of society, used for asking advice or help in personal and confidential issues. In this paper, we unveil a novel side-channel that can be used to read encrypted responses from AI Assistants over the web: the token-length side-channel. We found that many vendors, including OpenAI and Microsoft, have this side-channel. However, inferring the content of a response from a token-length sequence alone proves challenging. This is because tokens are akin to words, and responses can be several sentences long leading to millions of grammatically correct sentences. In this paper, we show how this can be overcome by (1) utilizing the power of a large language model (LLM) to translate these sequences, (2) providing the LLM with inter-sentence context to narrow the search space and (3) performing a known-plaintext attack by fine-tuning the model on the target model's writing style. Using these methods, we were able to accurately reconstruct 29\% of an AI assistant's responses and successfully infer the topic from 55\% of them. To demonstrate the threat, we performed the attack on OpenAI's ChatGPT-4 and Microsoft's Copilot on both browser and API traffic.

We study the classical problem of deriving minimax rates for density estimation over convex density classes. Building on the pioneering work of Le Cam (1973), Birge (1983, 1986), Wong and Shen (1995), Yang and Barron (1999), we determine the exact (up to constants) minimax rate over any convex density class. This work thus extends these known results by demonstrating that the local metric entropy of the density class always captures the minimax optimal rates under such settings. Our bounds provide a unifying perspective across both parametric and nonparametric convex density classes, under weaker assumptions on the richness of the density class than previously considered. Our proposed `multistage sieve' MLE applies to any such convex density class. We further demonstrate that this estimator is also adaptive to the true underlying density of interest. We apply our risk bounds to rederive known minimax rates including bounded total variation, and Holder density classes. We further illustrate the utility of the result by deriving upper bounds for less studied classes, e.g., convex mixture of densities.

The delivery drivers of the future may not leave a package at your door. Instead, they'll be sitting several miles or even time zones away in a control room overseeing a fleet of delivery robots or drones. A look at how innovation and technology are transforming the way we live, work and play. Companies are plowing billions of dollars into autonomous technologies they hope will improve efficiency and solve worker shortages. But executives in these industries say true autonomy is many years away–and may never come.

BENTONVILLE, Ark. and SAN DIEGO, Ca.--Sam's Club has completed a roll out of inventory scanning towers that have been added to its existing fleet of floor scrubbing robots. The club store had started adding the inventory towers to its robots in January, and now all locations have the technology added. "Our initial goal at Sam's Club was to convert time historically spent on scrubbers to more member-focused activities. Our autonomous scrubbers have exceeded this goal. In addition to increasing the consistency and frequency of floor cleaning, intelligent scrubbers have empowered associates with critical insights," said Todd Garner, vice president, in-club product management.

One characteristic that makes humans superior to modern artificially intelligent models is the ability to interpret images beyond what is visually apparent. Consider the following two natural language search queries - (i) "a queue of customers patiently waiting to buy ice cream" and (ii) "a queue of tourists going to see a famous Mughal architecture in India." Interpreting these queries requires one to reason with (i) Commonsense such as interpreting people as customers or tourists, actions as waiting to buy or going to see; and (ii) Fact or world knowledge associated with named visual entities, for example, whether the store in the image sells ice cream or whether the landmark in the image is a Mughal architecture located in India. Such reasoning goes beyond just visual recognition. To enable both commonsense and factual reasoning in the image search, we present a unified framework, namely Knowledge Retrieval-Augmented Multimodal Transformer (KRAMT), that treats the named visual entities in an image as a gateway to encyclopedic knowledge and leverages them along with natural language query to ground relevant knowledge. Further, KRAMT seamlessly integrates visual content and grounded knowledge to learn alignment between images and search queries. This unified framework is then used to perform image search requiring commonsense and factual reasoning. The retrieval performance of KRAMT is evaluated and compared with related approaches on a new dataset we introduce - namely COFAR. We make our code and dataset available at https://vl2g.github.io/projects/cofar