You're viewing the US edition You can switch to the UK, AU or IE homepage at any time using this menu. Dr. Oz storms into NYC senior care centers to uncover suspected'Chinese mafia' fraud ring: 'A clubhouse for crime' Prince Harry's interview calling for'reconciliation' with his family which aired during a State Visit is greeted by a chorus of royal eye-rolling Bryan Kohberger's only friend hints at killer's bombshell real motive for Idaho murders... as he reveals event before slaughter that left him'unable to stop shaking' The uncomfortable truth about Christina Applegate's MS misdiagnosis that Hollywood can't ignore: MAUREEN CALLAHAN Ilhan Omar vows not to be intimidated as she surfaces to ask for donations during'challenging time' Leaked Noah Presgrove filing makes explosive claims about alleged lover's plot to kill Oklahoma teen hours before his naked body was found on remote road next to his teeth Battle for the soul of Texas: Paxton vs Talarico gets more frantic by the day ...

Swimming can take a lot of muscle. But as MIT engineers have found, even a single layer of muscle cells can power through water if designed right. In a paper appearing today in the journal, the team presents a design for a thin, muscle-powered swimming robot. The "skeleton" of the aquabot is made from a film of gel that is about the length and width of a stick of gum. Each fin is covered with a layer of live muscle cells that is much thinner than a single strand of hair.

The controversial dinosaur species was officially described last year after 80 years of debate. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Almost one year ago, paleontologists finally settled a longstanding debate about, arguably Earth's most famous dinosaur . After reviewing decades of mounting evidence, researchers documented around 150 morphological differences across multiple supposed juvenile skeletons. Instead of young dinosaurs, these were actually adult examples of an entirely separate species now known as .

More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Sled dogs pull sleds for recreation and work. Sled dogs have been helping humans travel across the planet's coldest regions for centuries, hauling supplies, people, and equipment across the snow and ice. In the process, this important role that goes above and beyond simply being "man's best friend" could have a visible impact on their evolution. Sled dogs may have evolved shorter legs to help them pull loads more efficiently.

Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable @Home Look Up Trending Now Mashable Selects Say More Top creators, ranked Gift Ideas For Everyone On Your List Creator Playbook In My Bag AI at School Safety Net All Series These are spooky good deals. Bethany Allard is a Los Angeles-based shopping reporter at Mashable covering beauty tech, dating, sex and relationships, and headphones. That basically means she puts her hair through a lot, scrolls through a lot of dating apps, and rotates through a lot of different headphones. In addition to testing out and rounding up the best products, she also covers deals for Mashable, paying an especially obsessive amount of attention to Apple deals and prices. That knowledge comes in handy when she's covering shopping holidays like Prime Day and Black Friday, which she's now done for three years at Mashable.

We present OSKAR, the first multimodal foundation model based on bootstrapped latent feature prediction. Unlike generative or contrastive methods, it avoids memorizing unnecessary details (e.g., pixels), and does not require negative pairs, large memory banks, or hand-crafted augmentations. We propose a novel pretraining strategy: given masked tokens from multiple modalities, predict a subset of missing tokens per modality, supervised by momentum-updated uni-modal target encoders.

Score 0 4 (normal) is most common across cohorts, while score 3 (severe) is rare--especially in PD-GaM 5 and 3DGait, highlighting class imbalance challenges. BMCLab offers a balanced ON/OFF medication split, 7 while E-LC is skewed toward ON-medication. DNE includes healthy, Parkinsonian, and other disease 8 groups for broader contrastive training. Figure A.3 shows label distributions for FoG-related cohorts. This artifact likely stems from the unusual top-down perspective--different from the front15 facing or side views seen in WHAM's training data [1]. While motion encoder-based models may be 16 robust to such distortions, feature-based gait classifiers rely on precise kinematic measurements and 17 thus require carefully corrected input data. To correct this slope artifact, we perform a frame-wise 18 rigid alignment of the reconstructed SMPL skeleton using the Kabsch algorithm [2]. The goal is to 19 rotate each frame so that anatomical directions align with canonical coordinate axes (up, forward), 20 while preserving natural gait structure. This motion 28 vector is then projected onto the ground plane (xz-plane) and used as the walking axis. In frames where the sacrum displacement is less than 30 4mm--indicating near-stationary posture--we fall back on a proxy direction: the cross product of 31 the hip vector (left hip to right hip) and the vertical vector.

We introduce Auto-Connect, a novel approach for automatic rigging that explicitly preserves skeletal connectivity through a connectivity-preserving tokenization scheme. Unlike previous methods that predict bone positions represented as two joints or first predict points before determining connectivity, our method employs special tokens to define endpoints for each joint's children and for each hierarchical layer, effectively automating connectivity relationships. This approach significantly enhances topological accuracy by integrating connectivity information directly into the prediction framework. To further guarantee high-quality topology, we implement a topology-aware reward function that quantifies topological correctness, which is then utilized in a post-training phase through reward-guided Direct Preference Optimization. Additionally, we incorporate implicit geodesic features for latent top-k bone selection, which substantially improves skinning quality. By leveraging geodesic distance information within the model's latent space, our approach intelligently determines the most influential bones for each vertex, effectively mitigating common skinning artifacts. This combination of connectivity-preserving tokenization, reward-guided fine-tuning, and geodesicaware bone selection enables our model to consistently generate more anatomically plausible skeletal structures with superior deformation properties.

An artificial intelligence must have a model of its environment that is causal, supporting reasoning about interventions and counterfactuals, and also combinatorial, supporting generalization to unseen combinations of objects. In this work, we formally study when and how such a model can be learned. We develop relational structural causal models, extending structural causal models (Pearl 2009) to settings where objects and their relations vary. First, we show how answers to not only causal but also observational queries about unseen combinations of objects can not be identified without further assumptions. To enable such identification--including in the presence of unobserved confounding--we define relational causal graphs and derive symbolic identification criteria. Finally, we propose relational neural causal models, a provably correct approach that outperforms non-relational baselines on simulated traffic scenes with varying cars, signals, and pedestrians.

This work establishes a novel link between the problem of PAC-learning high-dimensional graphical models and the task of (efficient) counting and sampling of graph structures, using an online learning framework. The problem of efficiently counting and sampling graphical structures, such as spanning trees and acyclic orientations, has been a vibrant area of research in algorithms. We show that this rich algorithmic foundation can be leveraged to develop new algorithms for learning high-dimensional graphical models. We present the first efficient algorithm for (both realizable and agnostic) learning of Bayes nets with a chordal skeleton. In particular, we present an algorithm that, given integers $k,d > 0$, error parameter $\varepsilon > 0$, an undirected chordal graph $G$ on $n$ vertices, and sample access to a distribution $P^\ast$ on $[k]^n$; (1) returns a Bayes net $\widehat{P}$ with skeleton $G$ and indegree $d$, whose KL-divergence from $P^\ast$ is at most $\varepsilon$ more than the optimal KL-divergence between $P^\ast$ and any Bayes net with skeleton $G$ and indegree $d$, (2) uses $\widetilde{O}(n^3k^{d+1}/\varepsilon^2)$ samples from $P^\ast$ and runs in time $\mathrm{poly}(n,k,\varepsilon^{-1})$ for constant $d$. Prior results in this spirit were for only for trees ($d=1$, tree skeleton) via Chow-Liu, and in the realizable setting for polytrees (arbitrary $d$ but tree skeleton). Thus, our result significantly extends the state-of-the-art in learning Bayes net distributions. We also establish new results for learning tree and polytree distributions.