ESPN reporter claims she'made it out alive' after vintage Bill Belichick sideline interview Rams coach Sean McVay's '60 Minutes' interview reveals why you shouldn't find your identity in your job Chase Daniel nearly made Tim Tebow walk off the set of'SEC Nation' with'derogatory' comment about Florida Pat McAfee might have made an illegal immigration joke on'College GameDay,' liberals lose their minds Miami QB Darian Mensah continues to set records, but could soft schedule cost him a Heisman? Entire California high school football team suspended after mascot's obscene gesture Caitlin Clark'would sign up' for potential WNBA game overseas after returning from World Cup'He's Dust': Ole Miss students rip Lane Kiffin as LSU coach prepares for hatred in Oxford Kyler Murray out with concussion so door opens for Carson Wentz as Vikings' new starter - perhaps for a while Lane Kiffin's visit to Oxford isn't the first time a coach made an unwelcome return to his former school Assistant AG warns'vulnerabilities are severe' amid alleged election fraud crackdown Saudi Arabia's capital of Riyadh on edge after reported explosions Assistant AG warns'vulnerabilities are severe' amid alleged election fraud crackdown Saudi Arabia's capital of Riyadh on edge after reported explosions'Believe in yourself': Miracle Mop creator marks 30 years of invention The direction the Democratic Party wants to take our nation is'frightening': Rep Jim Jordan Trump's Greenland deal would expand US military rights Threat of AI is a'global problem,' Rep Brian Fitzpatrick says I'm sad about this because I think college football was more interesting when Colorado had Shedeur Sanders and Travis Hunter and Deion was fresh, but they crashed last year. Seems like there's a lot of things. This seems to be headed in a bad direction. And as importantly, I guess it's not interesting anymore.

Contrastive Clustering loss and update the prototypical vectors.Algorithm 1: Prototypical Constrative Clustering Loss Compute Prototypical Constrative Clustering Loss based on Eqn. Compute Prototypical Constrative Clustering Loss based on Eqn. Two segmentation network architectures have been used in our experiments, i.e., (1) DeepLab-V3 The learning rate is set individually for each step and dataset. Similarly, to illustrate the effectiveness and robustness of our method in the non-incremental setting. We also perform an additional ablation study on the ADE20K (100-50) benchmark to investigate the impact of the delta.

Continual semantic segmentation aims to learn new classes while maintaining the information from the previous classes.

Arthroscopic procedures can greatly benefit from navigation systems that enhance spatial awareness, depth perception, and field of view. However, existing optical tracking solutions impose strict workspace constraints and disrupt surgical workflow. Vision-based alternatives, though less invasive, often rely solely on the monocular arthroscope camera, making them prone to drift, scale ambiguity, and sensitivity to rapid motion or occlusion. We propose DualVision ArthroNav, a multi-camera arthroscopy navigation system that integrates an external camera rigidly mounted on the arthroscope. The external camera provides stable visual odometry and absolute localization, while the monocular arthroscope video enables dense scene reconstruction. By combining these complementary views, our system resolves the scale ambiguity and long-term drift inherent in monocular SLAM and ensures robust relocalization. Experiments demonstrate that our system effectively compensates for calibration errors, achieving an average absolute trajectory error of 1.09 mm. The reconstructed scenes reach an average target registration error of 2.16 mm, with high visual fidelity (SSIM = 0.69, PSNR = 22.19). These results indicate that our system provides a practical and cost-efficient solution for arthroscopic navigation, bridging the gap between optical tracking and purely vision-based systems, and paving the way toward clinically deployable, fully vision-based arthroscopic guidance.

Vision-Language Models (VLMs) have shown strong performance in tasks like visual question answering and multimodal text generation, but their effectiveness in scientific domains such as materials science remains limited. While some machine learning methods have addressed specific challenges in this field, there is still a lack of foundation models designed for broad tasks like polymer property prediction using multimodal data. In this work, we present a multimodal polymer dataset to fine-tune VLMs through instruction-tuning pairs and assess the impact of multimodality on prediction performance. Our fine-tuned models, using LoRA, outperform unimodal and baseline approaches, demonstrating the benefits of multimodal learning. Additionally, this approach reduces the need to train separate models for different properties, lowering deployment and maintenance costs.

Accurate, real-time estimation of barge quantity on inland waterways remains a critical challenge due to the non-self-propelled nature of barges and the limitations of existing monitoring systems. This study introduces a novel method to use Automatic Identification System (AIS) vessel tracking data to predict the number of barges in tow using Machine Learning (ML). To train and test the model, barge instances were manually annotated from satellite scenes across the Lower Mississippi River. Labeled images were matched to AIS vessel tracks using a spatiotemporal matching procedure. A comprehensive set of 30 AIS-derived features capturing vessel geometry, dynamic movement, and trajectory patterns were created and evaluated using Recursive Feature Elimination (RFE) to identify the most predictive variables. Six regression models, including ensemble, kernel-based, and generalized linear approaches, were trained and evaluated. The Poisson Regressor model yielded the best performance, achieving a Mean Absolute Error (MAE) of 1.92 barges using 12 of the 30 features. The feature importance analysis revealed that metrics capturing vessel maneuverability such as course entropy, speed variability and trip length were most predictive of barge count. The proposed approach provides a scalable, readily implementable method for enhancing Maritime Domain Awareness (MDA), with strong potential applications in lock scheduling, port management, and freight planning. Future work will expand the proof of concept presented here to explore model transferability to other inland rivers with differing operational and environmental conditions.

In this paper, we take an inspiration from human perception and explore a compositional approach for egocentric video representation.

--Automated poultry processing lines still rely on humans to lift slippery, easily bruised carcasses onto a shackle conveyor . Deformability, anatomical variance, and strict hygiene rules make conventional suction and scripted motions unreliable. An independently actuated dual-jaw pneumatic gripper clamps both chicken legs, while a conditional diffusion-policy controller, trained from only 50 multi-view teleoperation demonstrations (RGB + proprioception), plans 5-DoF end-effector motion, which includes jaw commands in one shot. On individually presented raw broiler carcasses, our system achieves a 40.6% grasp-and-lift success rate and completes the pick-to-shackle cycle in 38 s, whereas state-of-the-art implicit behaviour cloning (IBC) and LSTM-GMM baselines fail entirely. All CAD, code, and datasets will be open-source. ChicGrasp shows that imitation learning can bridge the gap between rigid hardware and variable bio-products, offering a reproducible benchmark and a public dataset for researchers in agricultural engineering and robot learning. OBOTS and intelligent agents are increasingly deployed in unstructured, dynamic environments where manual programming struggles to capture the intricacies of real-world tasks [1].