Damning new video shows Alex Pretti running at ICE agents and screaming in their faces before smashing feds' tail light Costco faces lawsuit over beloved $4.99 rotisserie chicken amid claims customers were misled What bread's really doing to your body: ROSAMUND DEAN gave it up for a month and tested her health before and after. From blood pressure, to cholesterol and her weight, the results are truly shocking... Man who Beckhams have gone to war with: Why David and Victoria fear'smiling crocodile' Nelson Peltz is behind Brooklyn's outburst... as ALISON BOSHOFF reveals 14-year legal row that proves his'bully billionaire' reputation Kim Kardashian says Harry and Meghan's team asked for removal of party photos when they'realised it was Remembrance Day' 'I can kill you at any time:' Surgeon's fatal threats to ex-wife before she was found dead with her new partner at home, new documents show Jealous killer's cold last words as he's executed for brutal murders of his ex and her new boyfriend Ilhan Omar accuses Trump of being'obsessed' with her as police identify potential liquid sprayed at her during town hall attack Weekend's monster storm to bring freezing temperatures to MIAMI as cold snap ices over millions of Americans Megyn Kelly blasts Alex Pretti for'stalking, harassing and terrorizing' ICE after video shows him kicking SUV's tail light and spitting at agents Telling detail stitched into Melania's Dior dress that hints at her true ambitions, as the First Lady rings the New York Stock Exchange bell: JANE TIPPETT The Kristi Noem call that left Tom Homan seething. The freeze-out no one saw... and why insiders say his Minneapolis mission is do or die Origins of Egypt's Great Pyramid upended as new clues point to lost civilization from 20,000 years ago Margot Robbie stuns in Elizabeth Taylor's iconic necklace as she is joined by Jacob Elordi at Wuthering Heights premiere in LA Hilarious live gaffe on David Muir's World News Tonight that'triggered behind the scenes meltdown' Alex Pretti spits at ICE agents and smashes federal SUV's tail light in shocking footage taken 11 days before he was shot dead Extraordinary transformation of beloved child star who has'self-canceled' and ditched Hollywood to live off grid in POVERTY as'Catholic extremist' NASA gives a glimpse inside Orion's cramped quarters where four astronauts will live for 10 days as they whizz around the moon - 'the smell would be intolerable!' With the first launch window for Artemis II now just days away, NASA has shared a glimpse inside the cramped quarters of the Orion spacecraft. Four astronauts - Reid Wiseman, Victor Glover, Christina Koch, and Jeremy Hansen - will spend 10 days living inside the capsule as they whizz around the moon.

Mayor blames ICE for'creating chaos' in Minneapolis and tells protesters to go home after illegal migrant is shot Iran confirms protest hero will NOT be executed as Trump reveals Tehran told him'the killing has stopped' after he threatened to take military action'Heads will roll!' US Marshals under fire for sending'Fugitive Task Force' to raid Timothy Busfield's mountain home 60 minutes after he turned himself in on child sex charges My Heated Rivalry fantasy became a reality... and my secret hookup will soon be leading the country Banks seize 367,000 homes as housing pain spreads across US... and it is about to get much worse Shocking truth about Minneapolis woman dragged from car by ICE while screaming that she was on her way to a doctor's appointment Inside Tiger Woods' 50th birthday bash as he cozies up to girlfriend Vanessa Trump to watch Bon Jovi Somali'fraudsters' force out YouTuber who exposed massive scandal amid fraud investigation in Minnesota Shameless Gwyneth ...

If you want to put people back on the moon, don't gut the agency in charge of getting them there. The senator wanted a promise. For the last six years--or maybe the last decade or quarter century, depending on how you count it--the United States and China had been locked in a space race, a contest to see which nation could put its people on the moon . Senator Ted Cruz wanted President Donald Trump's nominee to run NASA, Jared Isaacman, to pledge that the US would not lose. Cruz brought a little surprise to Isaacman's confirmation hearing last April. It was a poster of the moon. On one side stood three astronauts and a giant Chinese flag. On the other were two more figures in space suits, with the tiniest Stars and Stripes planted in the lunar soil . Cruz apologized for the imbalance. "My team used ChatGPT," explained the senator, who chairs the committee that oversees NASA. Then Cruz, with a bit more seriousness, asked Isaacman, "Do we have your commitment that you will not allow the scenario on the right of this poster to happen? That China will not beat us to the moon?" Isaacman, a billionaire entrepreneur who had paid for his own missions to space, replied, "Senator, I only see the left-hand portion of that poster."

Travel chaos warning as hazardous'radiation fog' alert is issued in three states Real reason Bill Hader and Ali Wong's two-year relationship ended: Insiders reveal open secret about him in Hollywood... his cruel nickname... and his month from hell after Reiner murders horror It's madness NOT to annex Greenland: SCOTT JENNINGS spells out, as only he can, why America must act... before its enemies strike Kendall Jenner finally breaks silence on the rumors she's secretly a lesbian Real reason ICE refused to let medics rush to aid of Renee Nicole Good after she was shot dead in her car... as shocking video spread like wildfire The foods that actually block the body from gaining weight... even in people who eat high-fat diets Shocking study linking covid jabs and cancer'censored' by mysterious cyberattack Peppers will help protect you from the'super flu'... but which color you eat matters I gave up a middle-class family life at 40 to become an escort. Years later I discovered a common condition that affects so many women was to blame. Painful cause of death revealed for adorable child, 4, found dead in the woods two miles from dad's home Insiders reveal how the Reiner family decided to ax'despicable' Nick's legal fund: 'He's on his own' No nonsense uncle humiliates rude women for singing and talking during Broadway performance of Mamma Mia! - then has them thrown out of theater The REAL Princess Catherine: On her birthday, an intimate portrait of her marriage, how she finally solved the Meghan problem, her brave cancer fight... and a thrilling new rumor about her in America'Best medical drama ever' rockets up the Netflix charts as'broken' fans left sobbing by'perfect' ending after binge-watching every episode Inside NASA's high-stakes plan to evacuate astronauts from the ISS after medical emergency NASA is preparing to conduct its first-ever medical evacuation from the International Space Station (ISS), activating a contingency plan to return a crew to Earth months ahead of schedule. The plan, developed decades ago for medical emergencies in space, has never before been implemented during an ISS mission, agency officials said Thursday. Under the program, the returning astronauts will seal themselves inside the capsule, undock from the ISS, perform a controlled departure and reenter Earth's atmosphere for a parachute-assisted splashdown in the Pacific Ocean off the California coast.

Capsule networks have recently gained a great deal of interest as a new architecture of neural networks that can be more robust to input perturbations than similar-sized CNNs. Capsule networks have two major distinctions from the conventional CNNs: (i) each layer consists of a set of capsules that specialize in disjoint regions of the feature space and (ii) the routing-by-agreement coordinates connections between adjacent capsule layers. Although the routing-by-agreement is capable of filtering out noisy predictions of capsules by dynamically adjusting their influences, its unsupervised clustering nature causes two weaknesses: (i) high computational complexity and (ii) cluster assumption that may not hold in presence of heavy input noise. In this work, we propose a novel and surprisingly simple routing strategy called self-routing where each capsule is routed independently by its subordinate routing network. Therefore, the agreement between capsules is not required anymore but both poses and activations of upper-level capsules are obtained in a way similar to Mixture-of-Experts. Our experiments on CIFAR-10, SVHN and SmallNORB show that the self-routing performs more robustly against white-box adversarial attacks and affine transformations, requiring less computation.

The recent advances in Deep Convolutional Neural Networks (DCNNs) have shown extremely good results for video human action classification, however, action detection is still a challenging problem. The current action detection approaches follow a complex pipeline which involves multiple tasks such as tube proposals, optical flow, and tube classification. In this work, we present a more elegant solution for action detection based on the recently developed capsule network. We propose a 3D capsule network for videos, called VideoCapsuleNet: a unified network for action detection which can jointly perform pixel-wise action segmentation along with action classification. The proposed network is a generalization of capsule network from 2D to 3D, which takes a sequence of video frames as input.

We propose a self-supervised capsule architecture for 3D point clouds. We compute capsule decompositions of objects through permutation-equivariant attention, and self-supervise the process by training with pairs of randomly rotated objects. Our key idea is to aggregate the attention masks into semantic keypoints, and use these to supervise a decomposition that satisfies the capsule invariance/equivariance properties. This not only enables the training of a semantically consistent decomposition, but also allows us to learn a canonicalization operation that enables object-centric reasoning. To train our neural network we require neither classification labels nor manually-aligned training datasets. Yet, by learning an object-centric representation in a self-supervised manner, our method outperforms the state-of-the-art on 3D point cloud reconstruction, canonicalization, and unsupervised classification.

I ntelligent drug recommendation based on Electronic Health Records (EHRs) is critical for improving the quality and efficiency of clinical decision - making . By leveraging large - scale patient data, drug recommendation systems can assist physicians in selecting the most appropriate medications according to a patient's medical history, diagnoses, laboratory results, and comorbidities. Recent advances in large language models (LLMs) have shown remarkable capabilities in complex reasoning and medical text understanding, making them promising tools for drug recommendation tasks. However, the application of LLMs for Chinese clinical medication recommendation remains l argely unexplored. In this work, we conduct a systematic investigation of LLM - based methodologies for Chinese discharge medication recommendation . W e evaluate several representative LLM families (GLM, Llama, Qwen) under a unified methodological framework including zero - shot prompting, in - context learning, chain - of - thought prompting, and supervised fine - tuning using LoRA. W e analyze model behavior acro ss reasoning styles, error patterns, domain adaptation mechanisms, and robustness . Experimental results show that while supervised fine - tuning improves model performance, there remains substantial room for improvement, with the best model achieving the F1 score of 0.5648 and Jaccard score of 0.4477 . Our findings highlight both the potential and limitations of LLMs for Chinese drug recommendation.