Two minor-aged girls were thrown from a Ferris Wheel at Louisiana during the Harvest Festival. Witnesses recalled how they heard horrifying screams.

وقعت حادثة مأساوية في مهرجان الحصاد في لويزيانا عندما تم إلقاء فتاتين صغيرتين من عجلة فيريس، مما ترك الشهود في حالة صدمة وهم يتذكرون الصرخات المرعبة. يسلط هذا الحدث الضوء على أهمية تدابير السلامة في حدائق الملاهي ويثير القلق بشأن رفاهية الأطفال خلال مثل هذه الاحتفالات.

Un trágico incidente ocurrió en el Harvest Festival de Luisiana cuando dos niñas fueron arrojadas de una noria, dejando a los testigos en estado de shock al recordar los gritos horripilantes. Este evento resalta la importancia de las medidas de seguridad en los parques de atracciones y plantea preocupaciones sobre el bienestar de los niños durante tales festividades.

Un incident tragique s'est produit lors du Harvest Festival en Louisiane, lorsque deux jeunes filles ont été projetées d'une grande roue, laissant les témoins sous le choc en se remémorant les cris horrifiants. Cet événement souligne l'importance des mesures de sécurité dans les parcs d'attractions et soulève des inquiétudes quant au bien-être des enfants lors de telles festivités.

A tragic incident occurred at the Louisiana Harvest Festival when two young girls were thrown from a Ferris wheel, leaving witnesses in shock as they recalled the horrifying screams. This event highlights the importance of safety measures at amusement parks and raises concerns about the well-being of children during such festivities.

Louisiana Harvest Festival Tragedy: Witnesses Recall Screams as Two Girls Thrown From Ferris Wheel

arXiv:2511.03107v1 Announce Type: new 
Abstract: The uprising of deep learning methodology and practice in recent years has brought about a severe consequence of increasing carbon footprint due to the insatiable demand for computational resources and power. The field of text analytics also experienced a massive transformation in this trend of monopolizing methodology. In this paper, the original TF-IDF algorithm has been modified, and Clement Term Frequency-Inverse Document Frequency (CTF-IDF) has been proposed for data preprocessing. This paper primarily discusses the effectiveness of classical machine learning techniques in text analytics with CTF-IDF and a faster IRLBA algorithm for dimensionality reduction. The introduction of both of these techniques in the conventional text analytics pipeline ensures a more efficient, faster, and less computationally intensive application when compared with deep learning methodology regarding carbon footprint, with minor compromise in accuracy. The experimental results also exhibit a manifold of reduction in time complexity and improvement of model accuracy for the classical machine learning methods discussed further in this paper.

تقدم دراسة جديدة نموذج تصنيف مبتكر للنصوص السيبرانية يعدل خوارزمية TF-IDF التقليدية لمعالجة البصمة الكربونية المتزايدة المرتبطة بالتعلم العميق. هذه الخطوة مهمة لأنها تعزز تحليل النصوص وتروج لممارسات أكثر استدامة في استخدام الموارد الحاسوبية، مما يجعلها مساهمة في الوقت المناسب في هذا المجال.

Un nuevo estudio presenta un modelo de clasificación innovador para texto cibernético que modifica el algoritmo TF-IDF tradicional para abordar la creciente huella de carbono asociada con el aprendizaje profundo. Este avance es significativo ya que no solo mejora el análisis de texto, sino que también promueve prácticas más sostenibles en el uso de recursos computacionales, convirtiéndolo en una contribución oportuna al campo.

Une nouvelle étude présente un modèle de classification innovant pour le texte cybernétique qui modifie l'algorithme TF-IDF traditionnel afin de répondre à l'empreinte carbone croissante associée à l'apprentissage profond. Cette avancée est significative car elle améliore l'analyse de texte tout en promouvant des pratiques plus durables dans l'utilisation des ressources informatiques, ce qui en fait une contribution opportune au domaine.

A new study introduces an innovative classification model for cyber text that modifies the traditional TF-IDF algorithm to address the growing carbon footprint associated with deep learning. This advancement is significant as it not only enhances text analytics but also promotes more sustainable practices in computational resource usage, making it a timely contribution to the field.

An Efficient Classification Model for Cyber Text

arXiv:2511.03060v1 Announce Type: new 
Abstract: We present a geometric framework for understanding Transformer-based language models, drawing an explicit analogy to General Relativity. Queries and keys induce an effective metric on representation space, and attention acts as a discrete connection that implements parallel transport of value vectors across tokens. Stacked layers provide discrete time-slices through which token representations evolve on this curved manifold, while backpropagation plays the role of a least-action principle that shapes loss-minimizing trajectories in parameter space. If this analogy is correct, token embeddings should not traverse straight paths in feature space; instead, their layer-wise steps should bend and reorient as interactions mediated by embedding space curvature. To test this prediction, we design experiments that expose both the presence and the consequences of curvature: (i) we visualize a curvature landscape for a full paragraph, revealing how local turning angles vary across tokens and layers; (ii) we show through simulations that excess counts of sharp/flat angles and longer length-to-chord ratios are not explainable by dimensionality or chance; and (iii) inspired by Einstein's eclipse experiment, we probe deflection under controlled context edits, demonstrating measurable, meaning-consistent bends in embedding trajectories that confirm attention-induced curvature.

تم تقديم إطار هندسي جديد لفهم نماذج اللغة المعتمدة على المحولات، مشبهاً عملياتها بمفاهيم من النسبية العامة. يكشف هذا النهج المبتكر كيف تخلق الاستفسارات والمفاتيح مقياساً فعالاً في فضاء التمثيل، حيث تعمل الانتباه كصلة تسهل حركة متجهات القيمة عبر الرموز. لا يعزز هذا الإطار فهمنا لهذه النماذج فحسب، بل يفتح أيضاً آفاقاً جديدة للبحث في الذكاء الاصطناعي وتعلم الآلة، مما يجعله تطوراً مهماً في هذا المجال.

Se ha introducido un nuevo marco geométrico para comprender mejor los modelos de lenguaje basados en Transformers, comparando sus operaciones con conceptos de la Relatividad General. Este enfoque innovador revela cómo las consultas y las claves crean una métrica efectiva en el espacio de representación, con la atención funcionando como una conexión que facilita el movimiento de vectores de valor a través de los tokens. Este marco no solo mejora nuestra comprensión de estos modelos, sino que también abre nuevas avenidas para la investigación en IA y aprendizaje automático, lo que lo convierte en un desarrollo significativo en el campo.

Un nouveau cadre géométrique a été introduit pour mieux comprendre les modèles de langage basés sur les Transformers, les comparant à des concepts de la relativité générale. Cette approche innovante révèle comment les requêtes et les clés créent une métrique efficace dans l'espace de représentation, l'attention agissant comme une connexion qui facilite le mouvement des vecteurs de valeur à travers les tokens. Ce cadre améliore non seulement notre compréhension de ces modèles, mais ouvre également de nouvelles voies de recherche en IA et en apprentissage automatique, ce qui en fait un développement significatif dans le domaine.

A new geometric framework has been introduced to better understand Transformer-based language models, likening their operations to concepts in General Relativity. This innovative approach reveals how queries and keys create an effective metric in representation space, with attention functioning as a connection that facilitates the movement of value vectors across tokens. This framework not only enhances our comprehension of these models but also opens up new avenues for research in AI and machine learning, making it a significant development in the field.

The Curved Spacetime of Transformer Architectures

arXiv:2511.03012v1 Announce Type: new 
Abstract: Metamaterials are engineered materials composed of specially designed unit cells that exhibit extraordinary properties beyond those of natural materials. Complex engineering tasks often require heterogeneous unit cells to accommodate spatially varying property requirements. However, designing heterogeneous metamaterials poses significant challenges due to the enormous design space and strict compatibility requirements between neighboring cells. Traditional concurrent multiscale design methods require solving an expensive optimization problem for each unit cell and often suffer from discontinuities at cell boundaries. On the other hand, data-driven approaches that assemble structures from a fixed library of microstructures are limited by the dataset and require additional post-processing to ensure seamless connections. In this work, we propose a neural network-based metamaterial design framework that learns a continuous two-scale representation of the structure, thereby jointly addressing these challenges. Central to our framework is a multiscale neural representation in which the neural network takes both global (macroscale) and local (microscale) coordinates as inputs, outputting an implicit field that represents multiscale structures with compatible unit cell geometries across the domain, without the need for a predefined dataset. We use a compatibility loss term during training to enforce connectivity between adjacent unit cells. Once trained, the network can produce metamaterial designs at arbitrarily high resolution, hence enabling infinite upsampling for fabrication or simulation. We demonstrate the effectiveness of the proposed approach on mechanical metamaterial design, negative Poisson's ratio, and mechanical cloaking problems with potential applications in robotics, bioengineering, and aerospace.

تسلط دراسة حديثة حول المواد الميتامادية غير المتجانسة الضوء على الاستخدام المبتكر للتمثيل الضمني العصبي متعدد المقاييس لمواجهة التحديات المعقدة في تصميمها. يمكن أن تظهر هذه المواد الهندسية خصائص فريدة تتجاوز المواد الطبيعية، مما يجعلها ضرورية للتطبيقات الهندسية المتقدمة. هذه الأبحاث مهمة لأنها تفتح آفاقًا جديدة لإنشاء مواد مصممة لتلبية احتياجات محددة، مما قد يحدث ثورة في مختلف الصناعات.

Un estudio reciente sobre metamateriales heterogéneos destaca el uso innovador de la representación implícita neuronal multiescala para abordar los complejos desafíos en su diseño. Estos materiales ingenierizados pueden exhibir propiedades únicas que superan a las de los materiales naturales, lo que los hace cruciales para aplicaciones de ingeniería avanzadas. Esta investigación es significativa ya que abre nuevas avenidas para crear materiales adaptados a necesidades específicas, potencialmente revolucionando diversas industrias.

Une étude récente sur les métamatériaux hétérogènes met en avant l'utilisation innovante de la représentation implicite neuronale multiscale pour relever les défis complexes de leur conception. Ces matériaux conçus peuvent présenter des propriétés uniques qui dépassent celles des matériaux naturels, ce qui les rend essentiels pour des applications d'ingénierie avancées. Cette recherche est significative car elle ouvre de nouvelles voies pour créer des matériaux adaptés à des besoins spécifiques, révolutionnant potentiellement diverses industries.

A recent study on heterogeneous metamaterials highlights the innovative use of multiscale neural implicit representation to tackle the complex challenges in their design. These engineered materials can exhibit unique properties that surpass natural materials, making them crucial for advanced engineering applications. This research is significant as it opens new avenues for creating materials tailored to specific needs, potentially revolutionizing various industries.

Heterogeneous Metamaterials Design via Multiscale Neural Implicit Representation

arXiv:2511.03363v1 Announce Type: new 
Abstract: In this study, a modular, data-free pipeline for multi-label intention recognition is proposed for agentic AI applications in transportation. Unlike traditional intent recognition systems that depend on large, annotated corpora and often struggle with fine-grained, multi-label discrimination, our approach eliminates the need for costly data collection while enhancing the accuracy of multi-label intention understanding. Specifically, the overall pipeline, named DMTC, consists of three steps: 1) using prompt engineering to guide large language models (LLMs) to generate diverse synthetic queries in different transport scenarios; 2) encoding each textual query with a Sentence-T5 model to obtain compact semantic embeddings; 3) training a lightweight classifier using a novel online focal-contrastive (OFC) loss that emphasizes hard samples and maximizes inter-class separability. The applicability of the proposed pipeline is demonstrated in an agentic AI application in the maritime transportation context. Extensive experiments show that DMTC achieves a Hamming loss of 5.35% and an AUC of 95.92%, outperforming state-of-the-art multi-label classifiers and recent end-to-end SOTA LLM-based baselines. Further analysis reveals that Sentence-T5 embeddings improve subset accuracy by at least 3.29% over alternative encoders, and integrating the OFC loss yields an additional 0.98% gain compared to standard contrastive objectives. In conclusion, our system seamlessly routes user queries to task-specific modules (e.g., ETA information, traffic risk evaluation, and other typical scenarios in the transportation domain), laying the groundwork for fully autonomous, intention-aware agents without costly manual labelling.

تقدم دراسة جديدة خط أنابيب معياري خالٍ من البيانات للتعرف على النوايا متعددة العلامات في تطبيقات الذكاء الاصطناعي للنقل. تتناول هذه الطريقة المبتكرة قيود الأنظمة التقليدية التي تعتمد على جمع بيانات واسعة، مما يجعلها أكثر كفاءة وفعالية من حيث التكلفة. من خلال تحسين دقة فهم النوايا المتعددة في وقت واحد، يمكن أن يُحدث هذا التطور تحولًا كبيرًا في كيفية تفاعل الذكاء الاصطناعي في مجالات النقل، مما يمهد الطريق لأنظمة أكثر ذكاءً واستجابة.

Un nuevo estudio presenta un pipeline modular y sin datos para el reconocimiento de intenciones multi-etiqueta en aplicaciones de IA en transporte. Este enfoque innovador aborda las limitaciones de los sistemas tradicionales que dependen de una extensa recopilación de datos, haciéndolo más eficiente y rentable. Al mejorar la precisión en la comprensión de múltiples intenciones simultáneamente, este desarrollo podría mejorar significativamente la forma en que la IA interactúa en entornos de transporte, allanando el camino para sistemas más inteligentes y receptivos.

Une nouvelle étude présente un pipeline modulaire et sans données pour la reconnaissance d'intentions multi-label dans les applications d'IA de transport. Cette approche innovante répond aux limites des systèmes traditionnels qui dépendent d'une collecte de données extensive, la rendant plus efficace et économique. En améliorant la précision de la compréhension de plusieurs intentions simultanément, ce développement pourrait considérablement améliorer la manière dont l'IA interagit dans les contextes de transport, ouvrant la voie à des systèmes plus intelligents et réactifs.

A new study introduces a modular, data-free pipeline for multi-label intention recognition in transportation AI applications. This innovative approach addresses the limitations of traditional systems that rely on extensive data collection, making it more efficient and cost-effective. By enhancing the accuracy of understanding multiple intentions simultaneously, this development could significantly improve how AI interacts in transportation settings, paving the way for smarter and more responsive systems.

A Modular, Data-Free Pipeline for Multi-Label Intention Recognition in Transportation Agentic AI Applications

arXiv:2511.02957v1 Announce Type: new 
Abstract: Pavement infrastructure monitoring is challenged by complex spatial dependencies, changing environmental conditions, and non-linear deterioration across road networks. Traditional Pavement Management Systems (PMS) remain largely reactive, lacking real-time intelligence for failure prevention and optimal maintenance planning. To address this, we propose a unified Digital Twin (DT) and Graph Neural Network (GNN) framework for scalable, data-driven pavement health monitoring and predictive maintenance. Pavement segments and spatial relations are modeled as graph nodes and edges, while real-time UAV, sensor, and LiDAR data stream into the DT. The inductive GNN learns deterioration patterns from graph-structured inputs to forecast distress and enable proactive interventions. Trained on a real-world-inspired dataset with segment attributes and dynamic connectivity, our model achieves an R2 of 0.3798, outperforming baseline regressors and effectively capturing non-linear degradation. We also develop an interactive dashboard and reinforcement learning module for simulation, visualization, and adaptive maintenance planning. This DT-GNN integration enhances forecasting precision and establishes a closed feedback loop for continuous improvement, positioning the approach as a foundation for proactive, intelligent, and sustainable pavement management, with future extensions toward real-world deployment, multi-agent coordination, and smart-city integration.

إطار جديد يجمع بين تقنية التوأم الرقمي والشبكات العصبية الرسومية على وشك إحداث ثورة في مراقبة صحة الرصف والصيانة. تتناول هذه الطريقة المبتكرة التحديات التي تواجه الأنظمة التقليدية، التي غالبًا ما تتفاعل مع المشكلات بدلاً من منعها. من خلال الاستفادة من البيانات في الوقت الحقيقي، يعد هذا الأسلوب بتحسين إدارة البنية التحتية، مما يضمن شبكات طرق أكثر أمانًا وكفاءة. هذه الخطوة مهمة لأنها قد تقلل التكاليف وتحسن من عمر أنظمة الرصف، مما يعود بالنفع على المجتمعات والاقتصادات.

Un nuevo marco que combina la tecnología Digital Twin y las Redes Neuronales Gráficas está a punto de revolucionar la monitorización de la salud del pavimento y el mantenimiento. Este enfoque innovador aborda los desafíos de los sistemas tradicionales, que a menudo reaccionan a los problemas en lugar de prevenirlos. Al aprovechar datos en tiempo real, este método promete mejorar la gestión de infraestructuras, garantizando redes viales más seguras y eficientes. Este avance es significativo, ya que podría reducir costos y mejorar la longevidad de los sistemas de pavimento, beneficiando a las comunidades y economías.

Un nouveau cadre combinant la technologie Digital Twin et les réseaux de neurones graphiques est sur le point de révolutionner la surveillance de la santé des chaussées et l'entretien. Cette approche innovante répond aux défis des systèmes traditionnels, qui réagissent souvent aux problèmes plutôt que de les prévenir. En s'appuyant sur des données en temps réel, cette méthode promet d'améliorer la gestion des infrastructures, garantissant des réseaux routiers plus sûrs et plus efficaces. Cette avancée est significative car elle pourrait réduire les coûts et améliorer la longévité des systèmes de chaussée, bénéficiant ainsi aux communautés et aux économies.

A new framework combining Digital Twin technology and Graph Neural Networks is set to revolutionize pavement health monitoring and maintenance. This innovative approach addresses the challenges of traditional systems, which often react to issues rather than prevent them. By leveraging real-time data, this method promises to enhance infrastructure management, ensuring safer and more efficient road networks. This advancement is significant as it could lead to reduced costs and improved longevity of pavement systems, ultimately benefiting communities and economies.

Digital Twin-Driven Pavement Health Monitoring and Maintenance Optimization Using Graph Neural Networks

arXiv:2511.03047v1 Announce Type: new 
Abstract: Large language models (LLMs) have seen increasing popularity in enterprise applications where AI agents and humans engage in objective-driven interactions. However, these systems are difficult to evaluate: data may be complex and unlabeled; human annotation is often impractical at scale; custom metrics can monitor for specific errors, but not previously-undetected ones; and LLM judges can produce unreliable results. We introduce the first set of unsupervised metrics for objective-driven interactions, leveraging statistical properties of unlabeled interaction data and using fine-tuned LLMs to adapt to distributional shifts. We develop metrics for labeling user goals, measuring goal completion, and quantifying LLM uncertainty without grounding evaluations in human-generated ideal responses. Our approach is validated on open-domain and task-specific interaction data.

تسلط دراسة جديدة الضوء على التحديات المتعلقة بتقييم نماذج اللغة الكبيرة (LLMs) في بيئات الأعمال، حيث تتفاعل وكلاء الذكاء الاصطناعي مع البشر لتحقيق أهداف محددة. تقدم الأبحاث طرقًا مبتكرة لتقييم هذه التفاعلات، مع معالجة مشكلات مثل البيانات المعقدة وصعوبة التقييم البشري على نطاق واسع. هذا مهم لأنه مع تزايد دمج الذكاء الاصطناعي في العمليات التجارية، تصبح طرق التقييم الموثوقة ضرورية لضمان الفعالية والثقة في هذه التقنيات.

Un nuevo estudio destaca los desafíos de evaluar los grandes modelos de lenguaje (LLMs) en entornos empresariales, donde los agentes de IA interactúan con humanos para objetivos específicos. La investigación introduce métodos innovadores para evaluar estas interacciones, abordando problemas como datos complejos y la impracticabilidad de la anotación humana a gran escala. Esto es significativo porque, a medida que la IA se integra más en los procesos comerciales, los métodos de evaluación confiables son cruciales para garantizar la efectividad y la confianza en estas tecnologías.

Une nouvelle étude met en lumière les défis de l'évaluation des grands modèles de langage (LLMs) dans les environnements d'entreprise, où les agents d'IA interagissent avec des humains pour des objectifs spécifiques. La recherche introduit des méthodes innovantes pour évaluer ces interactions, abordant des problèmes tels que les données complexes et l'impraticabilité de l'annotation humaine à grande échelle. Cela est significatif car, à mesure que l'IA devient plus intégrée dans les processus commerciaux, des méthodes d'évaluation fiables sont cruciales pour garantir l'efficacité et la confiance dans ces technologies.

A new study highlights the challenges of evaluating large language models (LLMs) in enterprise settings, where AI agents interact with humans for specific objectives. The research introduces innovative methods to assess these interactions, addressing issues like complex data and the impracticality of human annotation at scale. This is significant because as AI becomes more integrated into business processes, reliable evaluation methods are crucial for ensuring effectiveness and trust in these technologies.

Unsupervised Evaluation of Multi-Turn Objective-Driven Interactions

Rapper-turned-mogul 50 Cent has become one of the largest private property owners in Shreveport, Louisiana, and is investing in a $50 million entertainment district in an effort to boost the city's economy.

يحدث مغني الراب 50 سنت ضجة في شريفبورت، لويزيانا، من خلال استثماره 50 مليون دولار لإنشاء منطقة ترفيهية جديدة. لا تضع هذه المبادرة فقط 50 سنت كواحد من أكبر مالكي العقارات الخاصة في المنطقة، بل تهدف أيضًا إلى revitalizing الاقتصاد المحلي في واحدة من أكثر المدن احتياجًا في أمريكا. يمكن أن يؤدي استثماره إلى خلق وظائف وفرص جديدة، مما سيكون له تأثير كبير على المجتمع.

El rapero 50 Cent está causando revuelo en Shreveport, Luisiana, al invertir 50 millones de dólares para crear un nuevo distrito de entretenimiento. Esta iniciativa no solo lo posiciona como uno de los mayores propietarios privados de la zona, sino que también busca revitalizar la economía local en una de las ciudades más necesitadas de Estados Unidos. Su inversión podría generar empleos y nuevas oportunidades, teniendo un impacto significativo en la comunidad.

Le rappeur 50 Cent fait sensation à Shreveport, en Louisiane, en investissant 50 millions de dollars pour créer un nouveau district de divertissement. Cette initiative le positionne non seulement comme l'un des plus grands propriétaires privés de la région, mais vise également à revitaliser l'économie locale dans l'une des villes les plus nécessiteuses d'Amérique. Son investissement pourrait créer des emplois et de nouvelles opportunités, ayant un impact significatif sur la communauté.

Rapper 50 Cent is making waves in Shreveport, Louisiana, by investing $50 million to create a new entertainment district. This initiative not only positions him as one of the largest private property owners in the area but also aims to revitalize the local economy in one of America's most needy cities. His investment could bring jobs and new opportunities, making a significant impact on the community.

50 Cent Invests Millions in Shreveport, One of America's Most Needy Cities, to Build New Entertainment Empire

Australia expanded its world-first under-16 social media ban to include Reddit and Kick, effective December 10. Nine platforms—Facebook, Instagram, TikTok, X, YouTube, Snapchat, Threads, Reddit, and Kick—face AU$49.5M fines for non-compliance. WhatsApp and gaming platforms are exempt.

Australia has taken a bold step by expanding its strict social media law, now requiring nine platforms, including Reddit and Kick, to block users under 16 or face hefty fines. This law, effective December 10, aims to protect minors online but raises concerns about freedom of expression and the feasibility of enforcement. With fines reaching AU$49.5 million for non-compliance, the implications for social media companies are significant, potentially reshaping how they operate in Australia.

World's Strictest Social Media Law: 9 Platforms Must Block Everyone Under 16 or Pay $33M

NASA confirms 3I/ATLAS visible tonight — learn when and where to spot the rare interstellar comet lighting up Earth's skies before it vanishes again.

Tonight, skywatchers are in for a treat as NASA confirms that the rare interstellar comet 3I/ATLAS will be visible from Earth. This is a unique opportunity to witness a celestial phenomenon that won't last long, making it a must-see for astronomy enthusiasts and casual observers alike. Knowing when and where to look can enhance the experience, so grab your binoculars and enjoy this spectacular event while it lasts!

3I/ATLAS Comet Visible Tonight — Astronomer Reveals How to See the Interstellar Visitor from Earth

Colombian President Gustavo Petro again slammed U.S. counterpart Donald Trump over the sanctions against him and his family, saying they are "not only profoundly rude, but an insult to the Colombian nation, a humiliation."

Colombian President Gustavo Petro has criticized U.S. President Donald Trump for imposing sanctions on him and his family, calling the actions 'profoundly rude' and an 'insult to the Colombian nation.' This exchange highlights the strained relations between Colombia and the U.S., emphasizing the impact of international politics on national dignity.

Louisiana Harvest Festival Tragedy: Witnesses Recall Screams as Two Girls Thrown From Ferris Wheel

Louisiana Harvest Festival Tragedy: Witnesses Recall Screams as Two Girls Thrown From Ferris Wheel

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