Meghan unveils new As Ever line with Lilibet... amid claims Netflix has been left with huge $10m surplus of her unsold products after'split' with streamer Outrageous full story of scandalous affair that's the talk of Manhattan's exclusive private schools: Family insiders reveal humiliating sex secrets... shock'confession' letter... and the furious relative who exposed it all Sinister truth about explosive resignation of Trump's top counter-terror chief Joe Kent... and his shock claim Israel is manipulating the president: MARK HALPERIN Ugly new Nicole Kidman and Keith Urban divorce fight ERUPTS: Her friends share humiliating details of'midlife crisis'... and reveal brutal REAL reason daughter Sunday Rose'snubbed' him Kim Kardashian takes a VERY dramatic tumble in towering $80 'stripper heels' and accidentally grabs an'old lady' as she falls on her way out of Vanity Fair Oscars party USA baseball stars slammed over'disgraceful' national anthem gesture before WBC final vs Venezuela Israel says Iran's intelligence chief has been killed in overnight airstrike in latest attack on regime: Live updates Canada's ultimate revenge on Trump over tariffs gathers pace I ran America's only Supermax jail: What history's most notorious terrorists and serial killers told me as they waited to die Fox News anchor issues blistering takedown of liberal media's delusional take on Iran: 'A stalemate? How I lost 8st in my 50s and now finally have the figure of my dreams. I've been large my whole life, but I now feel happier than I ever did in my 20s. Presidential hopeful JB Pritzker's bold defiant bet against black caucus pays off Everything JFK Jr told friends about his love affair with'sexual dynamo' Madonna... her unprintable pillow talk... and his perverse incest request that she couldn't go through with Mamdani forces New York beloved preschool to hike annual fee to $36,000... and parents are fuming Heath Ledger's lookalike daughter Matilda steps out days after 17 year anniversary of late actor's Oscar win Supreme Court's top judge issues chilling warning as Trump targets his own appointees Robot goes BERSERK at a restaurant in California as desperate staff try to drag it away from customers - as one viewer asks'why isn't there a big red power off button?' This is the shocking moment a dancing robot goes berserk at a restaurant, sending food flying while staff try to drag it away.

In a world of plastic water filter pitchers, I tested two of the new generation of stainless-steel filter systems. I will admit that the popularity of those giant, stainless steel, gravity-fed water filters remained a mystery to me for some years--even as multi-gallon water filter systems from brands like British Berkefeld and Berkey seemed to proliferate equally among lovers of doomsday prepping and holistic wellness retreats. I have been testing much different breeds of water filters for more than a year now, including reverse osmosis filters and water pitchers. But often, the big water filter tanks have seemed as much like status symbols as functional items. If you see a big gravity-fed filter, you know the person in question is serious about wellness, survival, or both. What changed my mind about these big stainless steel filters was microplastics . Most water filter pitchers are made of BPA-free plastic. But as new research shows that bottled-water drinkers ingest tens of thousands of excess microplastic particles, wellness lovers have begun to look askance at water filters that are themselves made of plastic.

Three years on from the release of ChatGPT, two broad camps have formed: those people who refuse to use it, and those who use it every day. Three years on from the release of ChatGPT, two broad camps have formed: those people who refuse to use it, and those who use it every day. We asked experts about the most responsible ways to use AI tools - here's what they said Three years on from the release of ChatGPT, two broad camps have formed: those people who refuse to use it, and those who use it every day. A 2025 survey by the Pew Research Center found that one-third of US adults say they have been using ChatGPT. This includes 58% of US adults under 30 - roughly double the share two years ago.

We propose a novel method, \textbf{TwinAct}, to tackle the challenge of decoupling actions and actors in order to customize the text-guided diffusion models (TGDMs) for few-shot action image generation. TwinAct addresses the limitations of existing methods that struggle to decouple actions from other semantics (e.g., the actor's appearance) due to the lack of an effective inductive bias with few exemplar images. Our approach introduces a common action space, which is a textual embedding space focused solely on actions, enabling precise customization without actor-related details. Specifically, TwinAct involves three key steps: 1) Building common action space based on a set of representative action phrases; 2) Imitating the customized action within the action space; and 3) Generating highly adaptable customized action images in diverse contexts with action similarity loss. To comprehensively evaluate TwinAct, we construct a novel benchmark, which provides sample images with various forms of actions. Extensive experiments demonstrate TwinAct's superiority in generating accurate, context-independent customized actions while maintaining the identity consistency of different subjects, including animals, humans, and even customized actors.

Neural diffusion on graphs is a novel class of graph neural networks that has attracted increasing attention recently. The capability of graph neural partial differential equations (PDEs) in addressing common hurdles of graph neural networks (GNNs), such as the problems of over-smoothing and bottlenecks, has been investigated but not their robustness to adversarial attacks. In this work, we explore the robustness properties of graph neural PDEs. We empirically demonstrate that graph neural PDEs are intrinsically more robust against topology perturbation as compared to other GNNs. We provide insights into this phenomenon by exploiting the stability of the heat semigroup under graph topology perturbations. We discuss various graph diffusion operators and relate them to existing graph neural PDEs. Furthermore, we propose a general graph neural PDE framework based on which a new class of robust GNNs can be defined. We verify that the new model achieves comparable state-of-the-art performance on several benchmark datasets.

Capitalizing on the complementary advantages of generative and discriminative models has always been a compelling vision in machine learning, backed by a growing body of research. This work discloses the hidden semantic structure within score-based generative models, unveiling their potential as effective discriminative priors. Inspired by our theoretical findings, we propose DUSA to exploit the structured semantic priors underlying diffusion score to facilitate the test-time adaptation of image classifiers or dense predictors.

Recently, the diffusion-based generative paradigm has achieved impressive general image generation capabilities with text prompts due to its accurate distribution modeling and stable training process. However, generating diverse remote sensing (RS) images that are tremendously different from general images in terms of scale and perspective remains a formidable challenge due to the lack of a comprehensive remote sensing image generation dataset with various modalities, ground sample distances (GSD), and scenes. In this paper, we propose a Multi-modal, Multi-GSD, Multi-scene Remote Sensing (MMM-RS) dataset and benchmark for text-to-image generation in diverse remote sensing scenarios. Specifically, we first collect nine publicly available RS datasets and conduct standardization for all samples. To bridge RS images to textual semantic information, we utilize a large-scale pretrained vision-language model to automatically output text prompts and perform hand-crafted rectification, resulting in information-rich text-image pairs (including multi-modal images). In particular, we design some methods to obtain the images with different GSD and various environments (e.g., low-light, foggy) in a single sample. With extensive manual screening and refining annotations, we ultimately obtain a MMM-RS dataset that comprises approximately 2.1 million text-image pairs. Extensive experimental results verify that our proposed MMM-RS dataset allows off-the-shelf diffusion models to generate diverse RS images across various modalities, scenes, weather conditions, and GSD. The dataset is available at https://github.com/ljl5261/MMM-RS.

The sparsely activated mixture of experts (MoE) model presents an effective alternative to densely activated (dense) models, combining improved accuracy with computational efficiency. However, training MoE models from scratch requires extensive data and computational resources, a challenge that limits their widespread adoption. To address this, we introduce MoE Jetpack, a framework designed to fine-tune the abundant and easily accessible dense checkpoints into MoE models.

Drug-induced toxicity is one of the leading reasons new drugs fail clinical trials. Machine learning models that predict drug toxicity from molecular structure could help researchers prioritize less toxic drug candidates. However, current toxicity datasets are typically small and limited to a single organ system (e.g., cardio, renal, or liver). Creating these datasets often involved time-intensive expert curation by parsing drug labelling documents that can exceed 100 pages per drug. Here, we introduce UniTox, a unified dataset of 2,418 FDA-approved drugs with drug-induced toxicity summaries and ratings created by using GPT-4o to process FDA drug labels.

We consider the fundamental problem of sampling the optimal transport coupling between given source and target distributions. In certain cases, the optimal transport plan takes the form of a one-to-one mapping from the source support to the target support, but learning or even approximating such a map is computationally challenging for large and high-dimensional datasets due to the high cost of linear programming routines and an intrinsic curse of dimensionality. We study instead the Sinkhorn problem, a regularized form of optimal transport whose solutions are couplings between the source and the target distribution. We introduce a novel framework for learning the Sinkhorn coupling between two distributions in the form of a score-based generative model.