Road accidents significantly threaten public safety and require in-depth analysis for effective prevention and mitigation strategies. This paper focuses on predicting accidents through the examination of a comprehensive traffic dataset covering 49 states in the United States. The dataset integrates information from diverse sources, including transportation departments, law enforcement, and traffic sensors. This paper specifically emphasizes predicting the number of accidents, utilizing advanced machine learning models such as regression analysis and time series analysis. The inclusion of various factors, ranging from environmental conditions to human behavior and infrastructure, ensures a holistic understanding of the dynamics influencing road safety. Temporal and spatial analysis further allows for the identification of trends, seasonal variations, and high-risk areas. The implications of this research extend to proactive decision-making for policymakers and transportation authorities. By providing accurate predictions and quantifiable insights into expected accident rates under different conditions, the paper aims to empower authorities to allocate resources efficiently and implement targeted interventions. The goal is to contribute to the development of informed policies and interventions that enhance road safety, creating a safer environment for all road users. Keywords: Machine Learning, Random Forest, Accident Prediction, AutoML, LSTM.

The Trump administration is considering a deal that could send hundreds of thousands of U.S.-designed artificial intelligence chips to G42, an Emirati A.I. firm that the U.S. government has scrutinized in the past for its ties to China, three people familiar with the discussions said. The negotiations, which are ongoing, highlight a major shift in U.S. tech policy ahead of President Trump's visit to the Persian Gulf states this week. The talks have also created tension inside the Trump administration between tech- and business-minded leaders who want to close a deal before Mr. Trump's trip and national security officials who worry that the technology could be misused by the Emiratis. The Trump administration has embraced cutting direct deals for A.I. chips with officials from the Middle East, as it looks to strengthen U.S. ties in the region, said the people, who spoke on the condition of anonymity because the negotiations are ongoing. The approach marks a break from the Biden administration, which had rejected similar A.I. chip sales over fears that they could give autocratic governments with strong ties to China an edge over the United States in developing the most cutting-edge A.I. models in coming years.

United States President Donald Trump will undertake a three-day tour of the Gulf for his first state visit since retaking office in January. The trip begins in Saudi Arabia, followed by Qatar and the United Arab Emirates. It marks Trump's second foreign visit as president after he attended Pope Francis's funeral in Rome in April. Trump will fly out of the US on Monday and start his trip in the Saudi capital, Riyadh, on Tuesday. He is expected to attend a Gulf summit in the city on Wednesday, visit Qatar later that day and conclude his visit in the UAE on Thursday.

As President Trump tours the Middle East this week, governments that are flush with oil wealth will be focused on a different treasure, found in America's Silicon Valley. Artificial intelligence chips, which are made by U.S. companies like Nvidia and AMD, are highly coveted by governments across the Middle East. Leaders of Saudi Arabia, Qatar and the United Arab Emirates want to pour billions of dollars into the construction of data centers to put their countries at the forefront of a new technology heralded for its power to disrupt businesses and create trillions of dollars in economic value. The Gulf States have plenty of energy and cash to build data centers, which house the supercomputers that run A.I. systems. But they need U.S. government approval to buy the American-designed chips to power them.

--Downstream probing has been the dominant method for evaluating model representations, an important process given the increasing prominence of self-supervised learning and foundation models. However, downstream probing primarily assesses the availability of task-relevant information in the model's latent space, overlooking attributes such as equivariance, invariance, and disentanglement, which contribute to the interpretability, adaptability, and utility of representations in real-world applications. While some attempts have been made to measure these qualities in representations, no unified evaluation framework with modular, generalizable, and interpretable metrics exists. In this paper, we argue for the importance of representation evaluation beyond downstream probing. We introduce a standardized protocol to quantify informativeness, equivariance, invariance, and disentanglement of factors of variation in model representations. We use it to evaluate representations from a variety of models in the image and speech domains using different architectures and pretraining approaches on identified controllable factors of variation. We find that representations from models with similar downstream performance can behave substantially differently with regard to these attributes. This hints that the respective mechanisms underlying their downstream performance are functionally different, prompting new research directions to understand and improve representations. Representation learning has become popular across many fields due to its effectiveness, computational efficiency, and the relative simplicity of using representations from pretrained models as features for various downstream tasks. Many architectures, training paradigms, and modalities have been used to learn representations that are effective in a variety of tasks, such as retrieval, classification, and generation.

A Noise-Resilient Semi-Supervised Graph Autoencoder for Overlapping Semantic Community Detection Abdelfateh Bekkair, Slimane Bellaouar and Slimane Oulad-Naoui Laboratoire des Mathématiques et Sciences Appliquées (LMSA), Université de Ghardaia, Ghardaia, Algeria Faculty of Sciences and Technology, Université de Ghardaia, Ghardaia, AlgeriaA R T I C L E I N F OKeywords: Overlapping community detection Graph attention autoencoder Semi-supervised learning Attributed networks Attribute noise analysis A B S T R A C T Community detection in networks with overlapping structures remains a significant challenge, particularly in noisy real-world environments where integrating topology, node attributes, and prior information is critical. To address this, we propose a semi-supervised graph autoencoder that combines graph multi-head attention and modularity maximization to robustly detect overlapping communities. The model learns semantic representations by fusing structural, attribute, and prior knowledge while explicitly addressing noise in node features. Key innovations include a noise-resistant architecture and a semantic semi-supervised design optimized for community quality through modularity constraints. Experiments demonstrate superior performance the model outperforms state-of-the-art methods in overlapping community detection (improvements in NMI and F1-score) and exhibits exceptional robustness to attribute noise, maintaining stable performance under 60% feature corruption.

Modular, Jacobi, and mock-modular forms serve as generating functions for BPS black hole degeneracies. By training feed-forward neural networks on Fourier coefficients of automorphic forms derived from the Dedekind eta function, Eisenstein series, and Jacobi theta functions, we demonstrate that machine learning techniques can accurately predict modular weights from truncated expansions. Our results reveal strong performance for negative weight modular and quasi-modular forms, particularly those arising in exact black hole counting formulae, with lower accuracy for positive weights and more complicated combinations of Jacobi theta functions. This study establishes a proof of concept for using machine learning to identify how data is organized in terms of modular symmetries in gravitational systems and suggests a pathway toward automated detection and verification of symmetries in quantum gravity.

Fox News contributor Brett Velicovich joins'Fox & Friends First' to discuss Secretary's Hegseth's sweeping Army transformation, how Russia has responded to the U.S. minerals deal with Ukraine and the military bolstering drone technology. This is how the Army will island hop in the Pacific to fend off China. And by the way, Chinese President Xi Jinping has nothing like it. With a stunning announcement, the Army did more than ax 40 generals and open the door to AI. The Army bet its future on this radical aircraft, whose engines swivel to take off and land like a helicopter, or fly high and fast like an airplane.

The emergence of multimodal content, particularly text and images on social media, has positioned Multimodal Named Entity Recognition (MNER) as an increasingly important area of research within Natural Language Processing. Despite progress in high-resource languages such as English, MNER remains underexplored for low-resource languages like Urdu. The primary challenges include the scarcity of annotated multimodal datasets and the lack of standardized baselines. To address these challenges, we introduce the U-MNER framework and release the Twitter2015-Urdu dataset, a pioneering resource for Urdu MNER. Adapted from the widely used Twitter2015 dataset, it is annotated with Urdu-specific grammar rules. We establish benchmark baselines by evaluating both text-based and multimodal models on this dataset, providing comparative analyses to support future research on Urdu MNER. The U-MNER framework integrates textual and visual context using Urdu-BERT for text embeddings and ResNet for visual feature extraction, with a Cross-Modal Fusion Module to align and fuse information. Our model achieves state-of-the-art performance on the Twitter2015-Urdu dataset, laying the groundwork for further MNER research in low-resource languages.

Small and medium sized businesses often struggle with data driven decision making do to a lack of advanced analytics tools, especially in African countries where they make up a majority of the workforce. Though many tools exist they are not designed to fit into the ways of working of SMB workers who are mobile first, have limited time to learn new workflows, and for whom social and business are tightly coupled. To address this, the Dukawalla prototype was created. This intelligent assistant bridges the gap between raw business data, and actionable insights by leveraging voice interaction and the power of generative AI. Dukawalla provides an intuitive way for business owners to interact with their data, aiding in informed decision making. This paper examines Dukawalla's deployment across SMBs in Nairobi, focusing on their experiences using this voice based assistant to streamline data collection and provide business insights