You're viewing the US edition You can switch to the UK, AU or IE homepage at any time using this menu. Ryan Reynolds' desperate attempt to save Blake Lively exposed in furious texts demanding Sony do his bidding... as studio bosses privately called his wife's behavior'epic level stupid' Read the uncensored'Cornell 7' Snapchat messages: Inside fraternity brothers' group chat... and Jane Doe's texts that came later Cornell Jane Doe texted student she later accused of gang-rape to try and identify campus gossips, telling him'I love you king' as they claim everyone was'jealous' of them Disturbing texts Massachusetts cop sent underage cadet who he groomed for years before'passing her around' with twin brother...now one of them is accused of staging her murder as a suicide Cornell Jane Doe's lawyer trashes new texts that suggest alleged gang-rape was consensual and says she was suffering from'delayed recognition' when she sent them I've been with my husband for ten years - but I will never give him my phone password. I know how abuse can start... and exactly how to protect my privacy in six simple steps Frail Julie Andrews won't act again and walks with a cane: As she turns 91 far from view, industry insider reveals what's happening with reclusive actress Megabank labeled'shameful' and'un-American' in explosive federal probe over'race-based' mortgages for black homebuyers in major Trump administration DEI crackdown Chilling new video and call recordings expose Gypsy-Rose Blanchard's relationship with baby daddy Ken Urker before his death on 34th birthday NYPD releases bodycam footage after ICE agents shoot migrant, 28, in NYC after he'used car as battering ram' with five-year-old son in vehicle Trump's simmering anger over secret plot to take down Barron and Melania... as he raids $400m war chest to combat'unprecedented' probe of first family if election fears come true Show-off Jackson Hole mother, 46, loved to brag about her $2.5m Hawaii home and luxury vacations while toting $2,600 Goyard bag... but now her greed has landed her in prison Meghan, Harry and the children WON'T stay in UK for Christmas with King Charles - heading back to Montecito instead of Sandringham, claims friend DC Insider poll: Trump recently likened loyal aide Natalie Harp to a Price is Right model. Which TV show would you like to see them go on together? Taylor Swift and Travis Kelce's war with new neighbors over $5m Ohio mansion turns putrid: Locals fear raw sewage explosion, impaled deer and finally say the quiet part out loud at furious showdown meeting NASA's Curiosity rover has captured breathtaking images of a Martian dawn, revealing mysterious cliffs emerging from the darkness.

Spring Break travelers facing TSA hell fume as it's revealed why only certain airports crippled by shutdown Chappell Roan apologises to Jude Law's daughter as she insists she did not ask security guard to approach her and says'I do not hate fans of my music or children' Democratic enclave tears down tent city in its latest'whack-a-mole' move as homeless crisis laid bare Infertile influencer Clavicular's dark fetish is far more alarming than anyone feared, claim high school enemies as they leak unrecognizable photos and humiliating secrets I was a producer on The Bachelor. I've seen what happens when the cameras stop rolling: JANA HOCKING reveals the humiliating crisis talks, sex secrets and forbidden relationships Under fire again: Embattled sheriff in Nancy Guthrie case was accused of'assaulting deputy'...as decades of complaints emerge My daughters begged me not to send them back to their mother... Inside Enya's off-grid life in a £2.5M remote castle with 12 cats and no partner or children after turning her back on fame and admitting she's'dark and difficult' to be around'He just didn't protect him': Insiders reveal REAL reason Justin Bieber and Usher's secret feud hit'boiling point' at Oscars MORE bad news for Austin's housing market as Texan city leads in plummeting prices Hawaii's worst flooding in 20 years caused over $1BILLION in damage as crews desperately search for woman swept away in deluge Princess Beatrice puts on united front with husband Edo during lunch out amid fears her'marriage is in trouble' in wake of Epstein scandal Friends reveal fears Princess Beatrice's'marriage is in trouble' in wake of Epstein scandal. I was the only one JFK Jr and Carolyn Bessette trusted when they burdened me with an extraordinarily intimate secret. Iran war live: Trump threatens to'obliterate' Tehran's power plants if Strait of Hormuz does not'fully open' in next 48 hours Trump's White House ballroom architect'has totally baffled colleagues' by taking on controversial project Oscars PANIC as ratings hemorrhage: Insiders reveal'existential crisis' inside the Academy... and why Hollywood's biggest night was a'big fat dud' My husband's filthy habit is so revolting I don't even want to kiss him: DEAR JANE READ MORE: Britain's best supermarket hot cross buns revealed There's nothing quite like a toasted hot cross bun slathered in butter. Now, experts have suggested an unusual way to make them taste even better - by slicing them into thirds.

Could AI Data Centers Be Moved to Outer Space? Massive data centers for generative AI are bad for the Earth. Data centers are being built at a frantic pace all over the world, driven by the AI boom. These facilities consume staggering amounts of electricity. By 2028, AI servers alone may use as much energy as 22 percent of US households.

Why do elephants have such big ears? The multi-use appendages are kind of like their superpower. The African elephant has some of the world's biggest ears, measuring more than six feet long and more than four feet wide. Breakthroughs, discoveries, and DIY tips sent every weekday. While real life elephants can't fly, they certainly have enormous ears.

Step-by-step verifiers -- also known as process reward models (PRMs) -- are a key ingredient for test-time scaling. PRMs require step-level supervision, making them expensive to train. This work aims to build data-efficient PRMs as verbalized step-wise reward models that verify every step in the solution by generating a verification chain-of-thought (CoT). We propose ThinkPRM, a long CoT verifier fine-tuned on orders of magnitude fewer process labels than those required by discriminative PRMs. Our approach capitalizes on the inherent reasoning abilities of long CoT models, and outperforms LLM-as-a-Judge and discriminative verifiers -- using only 1% of the process labels in PRM800K -- across several challenging benchmarks. Specifically, ThinkPRM beats the baselines on ProcessBench, MATH-500, and AIME '24 under best-of-N selection and reward-guided search. In an out-of-domain evaluation on a subset of GPQA-Diamond and LiveCodeBench, our PRM surpasses discriminative verifiers trained on the full PRM800K by 8% and 4.5%, respectively. Lastly, under the same token budget, ThinkPRM scales up verification compute more effectively compared to LLM-as-a-Judge, outperforming it by 7.2% on a subset of ProcessBench. Our work highlights the value of generative, long CoT PRMs that can scale test-time compute for verification while requiring minimal supervision for training. Our code, data, and models are released at https://github.com/mukhal/thinkprm.

Robustness verification is a promising technique for rigorously proving Recurrent Neural Networks (RNNs) robustly. A key challenge is to over-approximate the nonlinear activation functions with linear constraints, which can transform the verification problem into an efficiently solvable linear programming problem. Existing methods over-approximate the nonlinear parts with linear bounding planes individually, which may cause significant over-estimation and lead to lower verification accuracy. In this paper, in order to tightly enclose the three-dimensional nonlinear surface generated by the Hadamard product, we propose a novel truncated rectangular prism formed by two linear relaxation planes and a refinement-driven method to minimize both its volume and surface area for tighter over-approximation. Based on this approximation, we implement a prototype DeepPrism for RNN robustness verification. The experimental results demonstrate that \emph{DeepPrism} has significant improvement compared with the state-of-the-art approaches in various tasks of image classification, speech recognition and sentiment analysis.

Shape measures have emerged as promising descriptors of white matter tractography, offering complementary insights into anatomical variability and associations with cognitive and clinical phenotypes. However, conventional methods for computing shape measures are computationally expensive and time-consuming for large-scale datasets due to reliance on voxel-based representations. We propose Tract2Shape, a novel multimodal deep learning framework that leverages geometric (point cloud) and scalar (tabular) features to predict ten white matter tractography shape measures. To enhance model efficiency, we utilize a dimensionality reduction algorithm for the model to predict five primary shape components. The model is trained and evaluated on two independently acquired datasets, the HCP-YA dataset, and the PPMI dataset. We evaluate the performance of Tract2Shape by training and testing it on the HCP-YA dataset and comparing the results with state-of-the-art models. To further assess its robustness and generalization ability, we also test Tract2Shape on the unseen PPMI dataset. Tract2Shape outperforms SOTA deep learning models across all ten shape measures, achieving the highest average Pearson's r and the lowest nMSE on the HCP-YA dataset. The ablation study shows that both multimodal input and PCA contribute to performance gains. On the unseen testing PPMI dataset, Tract2Shape maintains a high Pearson's r and low nMSE, demonstrating strong generalizability in cross-dataset evaluation. Tract2Shape enables fast, accurate, and generalizable prediction of white matter shape measures from tractography data, supporting scalable analysis across datasets. This framework lays a promising foundation for future large-scale white matter shape analysis.

Recent advances in diffusion models have shown remarkable potential for antibody design, yet existing approaches apply uniform generation strategies that cannot adapt to each antigen's unique requirements. Inspired by B cell affinity maturation--where antibodies evolve through multi-objective optimization balancing affinity, stability, and self-avoidance--we propose the first biologically-motivated framework that leverages physics-based domain knowledge within an online meta-learning system. Our method employs multiple specialized experts (van der Waals, molecular recognition, energy balance, and interface geometry) whose parameters evolve during generation based on iterative feedback, mimicking natural antibody refinement cycles. Instead of fixed protocols, this adaptive guidance discovers personalized optimization strategies for each target. Our experiments demonstrate that this approach: (1) discovers optimal SE(3)-equivariant guidance strategies for different antigen classes without pre-training, preserving molecular symmetries throughout optimization; (2) significantly enhances hotspot coverage and interface quality through target-specific adaptation, achieving balanced multi-objective optimization characteristic of therapeutic antibodies; (3) establishes a paradigm for iterative refinement where each antibody-antigen system learns its unique optimization profile through online evaluation; (4) generalizes effectively across diverse design challenges, from small epitopes to large protein interfaces, enabling precision-focused campaigns for individual targets.

The microphysical properties of ice crystals are important because they significantly alter the radiative properties and spatiotemporal distributions of clouds, which in turn strongly affect Earth's climate. However, it is challenging to measure key properties of ice crystals, such as mass or morphological features. Here, we present a framework for predicting three-dimensional (3D) microphysical properties of ice crystals from in situ two-dimensional (2D) imagery. First, we computationally generate synthetic ice crystals using 3D modeling software along with geometric parameters estimated from the 2021 Ice Cryo-Encapsulation Balloon (ICEBall) field campaign. Then, we use synthetic crystals to train machine learning (ML) models to predict effective density ($ρ_{e}$), effective surface area ($A_e$), and number of bullets ($N_b$) from synthetic rosette imagery. When tested on unseen synthetic images, we find that our ML models can predict microphysical properties with high accuracy. For $ρ_{e}$ and $A_e$, respectively, our best-performing single view models achieved $R^2$ values of 0.99 and 0.98. For $N_b$, our best single view model achieved a balanced accuracy and F1 score of 0.91. We also quantify the marginal prediction improvements from incorporating a second view. A stereo view ResNet-18 model reduced RMSE by 40% for both $ρ_e$ and $A_e$, relative to a single view ResNet-18 model. For $N_b$, we find that a stereo view ResNet-18 model improved the F1 score by 8%. This work provides a novel ML-driven framework for estimating ice microphysical properties from in situ imagery, which will allow for downstream constraints on microphysical parameterizations, such as the mass-size relationship.

Peptide compounds demonstrate considerable potential as therapeutic agents due to their high target affinity and low toxicity, yet their drug development is constrained by their low membrane permeability. Molecular weight and peptide length have significant effects on the logD of peptides, which in turn influences their ability to cross biological membranes. However, accurate prediction of peptide logD remains challenging due to the complex interplay between sequence, structure, and ionization states. This study introduces LengthLogD, a predictive framework that establishes specialized models through molecular length stratification while innovatively integrating multi-scale molecular representations. We constructed feature spaces across three hierarchical levels: atomic (10 molecular descriptors), structural (1024-bit Morgan fingerprints), and topological (3 graph-based features including Wiener index), optimized through stratified ensemble learning. An adaptive weight allocation mechanism specifically developed for long peptides significantly enhances model generalizability. Experimental results demonstrate superior performance across all categories: short peptides (R^2=0.855), medium peptides (R^2=0.816), and long peptides (R^2=0.882), with a 34.7% reduction in prediction error for long peptides compared to conventional single-model approaches. Ablation studies confirm: 1) The length-stratified strategy contributes 41.2% to performance improvement; 2) Topological features account for 28.5% of predictive importance. Compared to state-of-the-art models, our method maintains short peptide prediction accuracy while achieving a 25.7% increase in the coefficient of determination (R^2) for long peptides. This research provides a precise logD prediction tool for peptide drug development, particularly demonstrating unique value in optimizing long peptide lead compounds.