Another difficulty is entanglement of the labels.

The British skeleton team - among Team GB's best hopes for medals at the Winter Olympics - have been told their helmets do not meet safety standards only days out from the competition starting. The British team have appealed to the Court of Arbitration for sport (Cas) after the International Bobsleigh and Skeleton Federation (IBSF) said the helmets did not comply with the IBSF skeleton rules based on its shape. The British Bobsleigh and Skeleton Association (BBSA) said the helmet was designed with safety in mind. The team would currently not be able to wear the helmets in competition, but the Cas ruling will be heard on Thursday, with the result expected on Friday, before competition begins on 12 February. The British skeleton team enjoyed a successful 2024-25 season, with Matt Weston winning overall World Cup gold and Marcus Wyatt silver, winning all seven races between them.

Stay strong, fair-weather friends--you can keep biking to work even through the darkest, coldest days. Biking to work is a thing. A regular bike commute gives you the chance to squeeze in extra cardio, and that extra exercise can do remarkable things for your health. Startling research has discovered that cyclists have about a 41 percent lower risk of dying overall (assuming you stay safe, obviously!), a 46 percent lower risk of cardiovascular disease, a 45 percent lower risk of cancer incidence, compared with non-active commuters. Swapping car trips for bike rides cuts fuel and parking costs; results in fewer sick days and higher productivity; and is great for your carbon footprint, besides easing congestion and improving air quality. Then the idea of commuting by bike becomes a whole lot less appealing, even if it good for you. That's why we wrote this guide to the best bike gear for winter commuting. Instead, we just want you to stay warm, safe, and dry. Be sure to also check out our other outdoor buying guides, including, Best Bike Lights, Best Electric Bikes, Best Laptop Backpacks for Work, Best Rain Jackets and Best Base Layers .

Road safety is a critical global concern, with manual enforcement of helmet laws and vehicle safety standards (e.g., rear-view mirror presence) being resource-intensive and inconsistent. This paper presents an AI-powered system to automate traffic violation detection, significantly enhancing enforcement efficiency and road safety. The system leverages YOLOv8 for robust object detection and EasyOCR for license plate recognition. Trained on a custom dataset of annotated images (augmented for diversity), it identifies helmet non-compliance, the absence of rear-view mirrors on motorcycles, an innovative contribution to automated checks, and extracts vehicle registration numbers. A Streamlit-based interface facilitates real-time monitoring and violation logging. Advanced image preprocessing enhances license plate recognition, particularly under challenging conditions. Based on evaluation results, the model achieves an overall precision of 0.9147, a recall of 0.886, and a mean Average Precision (mAP@50) of 0.843. The mAP@50 95 of 0.503 further indicates strong detection capability under stricter IoU thresholds. This work demonstrates a practical and effective solution for automated traffic rule enforcement, with considerations for real-world deployment discussed.

How long does it take to identify the helmet of a hiker lost in a 183-hectare mountain area, analyzing 2,600 frames taken by a drone from approximately 50 meters away? If done with a human eye, weeks or months. If analyzed by an artificial intelligence system, one afternoon. The National Alpine and Speleological Rescue Corps, known by it's Italian initialism CNSAS, relied on AI to find the body of a person missing in Italy's Piedmont region on the north face of Monviso--the highest peak in the Cottian Alps--since September 2024. According to Saverio Isola, the CNSAS drone pilot who intervened along with his colleague Giorgio Viana, the operation--including searching for any sign of the missing hiker, the discovery and recovery of his body, and a stoppage due to bad weather--lasted less than three days.

The success of machine learning models in industrial applications is heavily dependent on the quality of the datasets used to train the models. However, large-scale datasets, specially those constructed from crowd-sourcing and web-scraping, often suffer from label noise, inconsistencies, and errors. This problem is particularly pronounced in manufacturing domains, where obtaining high-quality labels is costly and time-consuming. This paper introduces Vision-Language Sanitization and Refinement (VLSR), which is a vision-language-based framework for label sanitization and refinement in multi-label manufacturing image datasets. This method embeds both images and their associated textual labels into a shared semantic space leveraging the CLIP vision-language model. Then two key tasks are addressed in this process by computing the cosine similarity between embeddings. First, label sanitization is performed to identify irrelevant, misspelled, or semantically weak labels, and surface the most semantically aligned label for each image by comparing image-label pairs using cosine similarity between image and label embeddings. Second, the method applies density-based clustering on text embeddings, followed by iterative cluster merging, to group semantically similar labels into unified label groups. The Factorynet dataset, which includes noisy labels from both human annotations and web-scraped sources, is employed to evaluate the effectiveness of the proposed framework. Experimental results demonstrate that the VLSR framework successfully identifies problematic labels and improves label consistency. This method enables a significant reduction in label vocabulary through clustering, which ultimately enhances the dataset's quality for training robust machine learning models in industrial applications with minimal human intervention.

Humanity is on the verge of being replaced by a race of superhuman hybrids with powers only dreamt about in movies. Herbert Sim, a millionaire tech investor and futurist in London, has begun pouring his wealth into the study of transhumanism - the enhancement of humans through science and technology. At that point, Sim claims that the human race will essentially be obsolete as these real life'X-Men' make it impossible for regular people to match their abilities. The brainwaves are projected onto a computer which then reads and turns them into actions. Sim said it's one of the first steps in'upgrading' humanity, allowing this new race of mutants to live longer and defeat diseases.

High-quality long-context instruction data is essential for aligning long-context large language models (LLMs). Despite the public release of models like Qwen and Llama, their long-context instruction data remains proprietary. Human annotation is costly and challenging, while template-based synthesis methods limit scale, diversity, and quality. We introduce LongMagpie, a self-synthesis framework that automatically generates large-scale long-context instruction data. Our key insight is that aligned long-context LLMs, when presented with a document followed by special tokens preceding a user turn, auto-regressively generate contextually relevant queries. By harvesting these document-query pairs and the model's responses, LongMagpie produces high-quality instructions without human effort. Experiments on HELMET, RULER, and Longbench v2 demonstrate that LongMagpie achieves leading performance on long-context tasks while maintaining competitive performance on short-context tasks, establishing it as a simple and effective approach for open, diverse, and scalable long-context instruction data synthesis.

There have been many benchmarks for evaluating long-context language models (LCLMs), but developers often rely on synthetic tasks like needle-in-a-haystack (NIAH) or arbitrary subsets of tasks. It remains unclear whether they translate to the diverse downstream applications of LCLMs, and the inconsistency further complicates model comparison. We investigate the underlying reasons behind current practices and find that existing benchmarks often provide noisy signals due to low coverage of applications, insufficient lengths, unreliable metrics, and incompatibility with base models. In this work, we present HELMET (How to Evaluate Long-context Models Effectively and Thoroughly), a comprehensive benchmark encompassing seven diverse, application-centric categories. We also address many issues in previous benchmarks by adding controllable lengths up to 128k tokens, model-based evaluation for reliable metrics, and few-shot prompting for robustly evaluating base models. Consequently, we demonstrate that HELMET offers more reliable and consistent rankings of frontier LCLMs. Through a comprehensive study of 51 LCLMs, we find that (1) synthetic tasks like NIAH are not good predictors of downstream performance; (2) the diverse categories in HELMET exhibit distinct trends and low correlation with each other; and (3) while most LCLMs achieve perfect NIAH scores, open-source models significantly lag behind closed ones when the task requires full-context reasoning or following complex instructions -- the gap widens with increased lengths. Finally, we recommend using our RAG tasks for fast model development, as they are easy to run and more predictive of other downstream performance; ultimately, we advocate for a holistic evaluation across diverse tasks.