Predicting the 3D conformation of small molecules within protein binding sites is a key challenge in drug design. When a crystallized reference ligand (template) is available, it provides geometric priors that can guide 3D pose prediction. We present a two-stage method for ligand conformation generation guided by such templates. In the first stage, we introduce a molecular alignment approach based on flow-matching to generate 3D coordinates for the ligand, using the template structure as a reference. In the second stage, a differentiable pose optimization procedure refines this conformation based on shape and pharmacophore similarities, internal energy, and, optionally, the protein binding pocket. We introduce a new benchmark of ligand pairs co-crystallized with the same target to evaluate our approach and show that it outperforms standard docking tools and open-access alignment methods, especially in cases involving low similarity to the template or high ligand flexibility.

Reinforcement learning (RL) in continuous state-action spaces remains challenging in scientific computing due to poor sample efficiency and lack of pathwise physical consistency. We introduce Differential Reinforcement Learning (Differential RL), a novel framework that reformulates RL from a continuous-time control perspective via a differential dual formulation. This induces a Hamiltonian structure that embeds physics priors and ensures consistent trajectories without requiring explicit constraints. To implement Differential RL, we develop Differential Policy Optimization (dfPO), a pointwise, stage-wise algorithm that refines local movement operators along the trajectory for improved sample efficiency and dynamic alignment. We establish pointwise convergence guarantees, a property not available in standard RL, and derive a competitive theoretical regret bound of O(K5/6). Empirically, dfPO outperforms standard RL baselines on representative scientific computing tasks, including surface modeling, grid control, and molecular dynamics, under low-data and physics-constrained conditions.

Embattled Gavin Newsom's stunning confession to Justin Trudeau caught on camera at World Cup when he thought no one was watching Secret list of celebrities attending billionaire Peter Thiel's invite-only society where elites learn about sex, cults and the next world war Malia and Sasha Obama steal the show during rare family outing for grand opening of dad Barack's library Haunting final video of beloved Bay Area coffee shop owner, 52, who vanished without a trace: Investigator reveals'unnerving' new clues found inside her home Watch horrifying drone video that follows woman's plunge to death after bungee team threw her from bridge without rope Tragic final moments of Hollywood legend's daughter and her husband revealed before being mysteriously found dead in their running SUV Ivanka Trump's youngest son, 8, spotted in middle of Knicks victory parade Scientists create first-ever'map' of female pleasure center that's confused men for centuries All my friends are suddenly getting divorced. Mid-life wives share taboo sex confessions about why they really leave... including common position that made one hate her husband: JANA HOCKING Taylor Swift's bottomless thirst for attention, her greed and sheer tackiness are now truly unbearable... this latest stunt has shown her true colors: MAUREEN CALLAHAN Mystery surrounds JD Vance's dash to Switzerland as world holds breath for Iranians to confirm peace deal Male Israeli hostage sexually assaulted by Hamas captor describes multiple attacks he suffered - blindfolded and stripped naked at knifepoint... and'brutal' 20-minute ordeal Boy, three, is thrown into crocodile enclosure at zoo: Man, 30, 'not known to him' arrested on suspicion of attempted murder Infection found in wildlife evolved to spread between humans, experts fear... after two clusters are identified Florida man hailed as a hero for jumping off of his bike to wrangle a dangerous 8-foot python... only to then be slapped with a $180 FINE Sensational REAL reason Jelly Roll is divorcing Bunnie XO: Insiders reveal'preacher's wife' bombshell that's the talk of Nashville... truth about legendary rocker cuckolding rumor... and G-string mishap Who will win the World Cup? Mathematician's 11 models predict four possible champions (but NOT England!) READ MORE: Supercomputer predicts England's World Cup journey England's World Cup journey begins tonight, but a mathematician warns that fans shouldn't get their hopes up. Dr Ari Joury, a particle physicist and founder of AI firm Wangari, created 11 different models to predict who will win this year's tournament. These digital tipsters crowned four different champions between them, but not a single one picked England. Seven models backed Spain, two singled out Argentina as the likeliest winner, while France and the Netherlands were each the favourite of one prediction system.

Snapchat's parent company has announced it is releasing new smart glasses, a decade after its original pair lost the company tens of millions of dollars . The new augmented reality (AR) glasses, called Specs, will allow users to see digital elements overlaid onto the world. They will cost £1,995 in the UK and $2,195 in the US when shipping begins this autumn. That makes them cheaper than Apple's Vision Pro mixed-reality headset and its $3,499 starting price, but far more than Meta's smart glasses, which start at $224. Evan Spiegel, co-founder and chief executive of Snap Inc, said the glasses marked the beginning of a new era in computing.

Earth observation foundation models have shown strong generalization across multiple Earth observation tasks, but their robustness under real-world perturbations remains underexplored. To bridge this gap, we introduce REOBench, the first comprehensive benchmark for evaluating the robustness of Earth observation foundation models across six tasks and twelve types of image corruptions, including both appearance-based and geometric perturbations. To ensure realistic and fine-grained evaluation, our benchmark focuses on high-resolution optical remote sensing images, which are widely used in critical applications such as urban planning and disaster response. We conduct a systematic evaluation of a broad range of models trained using masked image modeling, contrastive learning, and vision-language pre-training paradigms. Our results reveal that existing Earth observation foundation models experience significant performance degradation when exposed to input corruptions. The severity of degradation varies across tasks, model architectures, backbone sizes, and types of corruption, with performance drop varying from less than 1% to over 25%. Vision-language models show enhanced robustness, particularly in multimodal tasks. REOBench underscores the vulnerability of current Earth observation foundation models to real-world corruptions and provides actionable insights for developing more robust and reliable models. Code and data are publicly available at https://github.com/lx709/REOBench.

Craig interpolation plays a central role in formal verification tasks such as model checking, invariant generation, and abstraction refinement. In the domain of linear integer arithmetic (LIA), interpolants are crucial for deriving inductive invariants that characterize unreachable or safe program states, enabling scalable and precise reasoning about software and hardware correctness. Despite progress in interpolation algorithms, generating concise and interpretable interpolants remains a key challenge. We propose a lightweight learning-based approach to generating simple interpolants for LIA. Our model learns to lazily sample input problems directly and is complementary to existing logical methods. We show that when Z3 is guided by our learned model, the complexity of the interpolants it produces can be reduced by up to 47.3%. For older solvers, the reduction rate can reach up to 69.1%.

Self-supervised learning (SSL) has achieved major advances in natural images and video understanding, but challenges remain in domains like echocardiography (heart ultrasound) due to subtle anatomical structures, complex temporal dynamics, and the current lack of domain-specific pre-trained models. Existing SSL approaches such as contrastive, masked modeling, and clustering-based methods struggle with high intersample similarity, sensitivity to low PSNR inputs common in ultrasound, or aggressive augmentations that distort clinically relevant features.

While large language models (LLMs) have emerged as promising tools for CO--either by directly generating solutions or synthesizing solver-specific codes--existing approaches often neglect critical structural priors inherent to CO problems, leading to suboptimality and iterative inefficiency. Inspired by human experts' success in leveraging CO structures for algorithm design, we propose STRCMP, a novel structure-aware LLM-based algorithm discovery framework that systematically integrates structure priors to enhance solution quality and solving efficiency. Our framework combines a graph neural network (GNN) for extracting structural embeddings from CO instances with an LLM conditioned on these embeddings to identify high-performing algorithms in the form of solver-specific codes. This composite architecture ensures syntactic correctness, preserves problem topology, and aligns with natural language objectives, while an evolutionary refinement process iteratively optimizes generated algorithm. Extensive evaluations across Mixed Integer Linear Programming and Boolean Satisfiability problems, using nine benchmark datasets, demonstrate that our proposed STRCMPoutperforms five strong neural and LLM-based methods by a large margin, in terms of both solution optimality and computational efficiency.

Determining the 3D pose of a patient from a limited set of 2DX-ray images is imaging a critical (e.g., task CT and in interv MRI) entional provides settings.

For Iran's Athletes, There Is No Separating Sports From Politics From defections and protests to moments of national pride, the 2026 World Cup arrives amid decades of tension between identity and the state. Iran's national soccer team has made its 2026 World Cup debut amid a tumultuous backdrop: an abrupt and tentative ceasefire after months of war, an extraordinary set-up in Mexico after the US prevented the team from residing in-country between matches, and political uncertainty that has now expanded to the international stage. But for many Iranians, professional sports have always sat at an intersection between athleticism, identity, and politics. From sporting defections and political activism to moments of immense national sporting pride, the trajectory of Iranian sports underscores what's at stake this World Cup. The Iranian team, on Tuesday morning, drew 2-2 in their debut against New Zealand and will next face Belgium and Egypt, traveling to and from Mexico in between.