Revealed: The message Norwegian PM sent Trump that sparked president's outburst saying Nobel Peace Prize snub justified Greenland land-grab The Kristi Noem photo that reveals why anti-ICE mob stormed Minnesota church as terrified child worshipper sobbed in father's arms We were $460,000 in debt. We weren't high earners and paid it off using simple but life-changing tricks... anyone can do it Idyllic city was hit by a surge in cancers and miscarriages when Trump's'beautiful baby' arrived. Joseph Gordon-Levitt was the hottest actor in Hollywood... then vanished: Unearthing family tragedy that sparked disappearance and has left'lasting' scars Mayor of gorgeous Oregon city that's home to Nike HQ explains simple reasons why it is thriving while neighboring Portland circles the drain Spanish train disaster victims flew through windows and were found hundreds of yards away, with more than 39 feared dead - as mystery over what caused'truly strange' crash grows Dark side of America's favorite vacation hotspot... where women are subjected to the most horrific sex attacks imaginable Dietitian reveals the game-changing supplements that work like Ozempic... and will super-charge your weight loss without side-effects Pierce Brosnan fans defend star's wife Keely Shaye Smith, 62, after cruel troll posts a photo of her when she met the 72-year-old Bond star alongside a recent snap as a'reminder to avoid marriage' My husband was acting odd for months. But nothing prepared me for what was hidden under the couch... undeniable proof of my worst fear John Barrowman breaks down in tears while cradling his dog's body after the beloved pet'waited until I got home' before dying peacefully in his arms Country singer Karley Scott Collins responds to rumors she's living with Keith Urban after Nicole Kidman split Scientists discover Greenland's'Achilles heel' that could force Trump to rethink his Arctic playbook MORE: I'm a Risk board game champion... Here's how Trump's tactics are a winning strategy for global domination Scientists have identified a hidden geological weakness beneath Greenland's ice sheet that could accelerate its collapse, and complicate US ambitions in the Arctic. A new study discovered a hidden layer of sediment, composed of soft dirt and sand, which has driven more of the Danish territory's glaciers to melt, break apart, and fall into the ocean.

Recently, Foursquare released a global dataset with more than 100 million points of interest (POIs), each representing a real-world business on its platform. However, many entries lack complete metadata such as addresses or categories, and some correspond to non-existent or fictional locations. In contrast, OpenStreetMap (OSM) offers a rich, user-contributed POI dataset with detailed and frequently updated metadata, though it does not formally verify whether a POI represents an actual business. In this data paper, we present a methodology that integrates the strengths of both datasets: Foursquare as a comprehensive baseline of commercial POIs and OSM as a source of enriched metadata. The combined dataset totals approximately 1 TB. While this full version is not publicly released, we provide filtered releases with adjustable thresholds that reduce storage needs and make the data practical to download and use across domains. We also provide step-by-step instructions to reproduce the full 631 GB build. Record linkage is achieved by computing name similarity scores and spatial distances between Foursquare and OSM POIs. These measures identify and retain high-confidence matches that correspond to real businesses in Foursquare, have representations in OSM, and show strong name similarity. Finally, we use this filtered dataset to construct a graph-based representation of POIs enriched with attributes from both sources, enabling advanced spatial analyses and a range of downstream applications.

While Speech Large Language Models (Speech-LLMs) show strong performance in many applications, their robustness is critically under-tested, especially to speech disfluency. Existing evaluations often rely on idealized inputs, overlooking common disfluencies, particularly those associated with conditions like Parkinson's disease. This work investigates whether current Speech-LLMs can maintain performance when interacting with users who have speech impairments. To facilitate this inquiry, we introduce VocalBench-DF, a framework for the systematic evaluation of disfluency across a multi-dimensional taxonomy. Our evaluation of 22 mainstream Speech-LLMs reveals substantial performance degradation, indicating that their real-world readiness is limited. Further analysis identifies phoneme-level processing and long-context modeling as primary bottlenecks responsible for these failures. Strengthening recognition and reasoning capability from components and pipelines can substantially improve robustness. These findings highlight the urgent need for new methods to improve disfluency handling and build truly inclusive Speech-LLMs

There is growing concern that male reproduction is affected by environmental chemicals. One way to determine the adverse effect of environmental pollutants is to use wild animals as monitors and evaluate testicular toxicity using histopathology. Automated methods are necessary tools in the quantitative assessment of histopathology to overcome the subjectivity of manual evaluation and accelerate the process. We propose an automated method to process histology images of testicular tissue. Segmenting the epithelial layer of the seminiferous tubule is a prerequisite for developing automated methods to detect abnormalities in tissue. We suggest an encoder-decoder fully connected convolutional neural network (F-CNN) model to segment the epithelial layer of the seminiferous tubules in histological images. Using ResNet-34 modules in the encoder adds a shortcut mechanism to avoid the gradient vanishing and accelerate the network convergence. The squeeze & excitation (SE) attention block is integrated into the encoding module improving the segmentation and localization of epithelium. We applied the proposed method for the 2-class problem where the epithelial layer of the tubule is the target class. The f-score and IoU of the proposed method are 0.85 and 0.92. Although the proposed method is trained on a limited training set, it performs well on an independent dataset and outperforms other state-of-the-art methods. The pretrained ResNet-34 in the encoder and attention block suggested in the decoder result in better segmentation and generalization. The proposed method can be applied to testicular tissue images from any mammalian species and can be used as the first part of a fully automated testicular tissue processing pipeline. The dataset and codes are publicly available on GitHub.