arXiv:2511.07990v1 Announce Type: new 
Abstract: Weeds significantly reduce crop yields worldwide and pose major challenges to sustainable agriculture. Traditional weed management methods, primarily relying on chemical herbicides, risk environmental contamination and lead to the emergence of herbicide-resistant species. Precision weeding, leveraging computer vision and machine learning methods, offers a promising eco-friendly alternative but is often limited by reliance on high-power computational platforms. This work presents an optimized, low-power edge AI system for weeds detection based on the YOLOv8n object detector deployed on the STM32U575ZI microcontroller. Several compression techniques are applied to the detection model, including structured pruning, integer quantization and input image resolution scaling in order to meet strict hardware constraints. The model is trained and evaluated on the CropAndWeed dataset with 74 plant species, achieving a balanced trade-off between detection accuracy and efficiency. Our system supports real-time, in-situ weeds detection with a minimal energy consumption of 51.8mJ per inference, enabling scalable deployment in power-constrained agricultural environments.

تم تطوير نظام جديد للذكاء الاصطناعي منخفض الطاقة للكشف عن الأعشاب الضارة باستخدام كاشف الكائنات YOLOv8n على المتحكم الدقيق STM32U575ZI. يعالج هذا النظام التحديات التي تواجه إدارة الأعشاب الضارة التقليدية، والتي تعتمد غالبًا على مبيدات الأعشاب الضارة. من خلال تطبيق تقنيات ضغط مختلفة، يحقق النموذج كشفًا فعالًا في الوقت الحقيقي مع استهلاك طاقة منخفض، مما يجعله مناسبًا للزراعة المستدامة.

Se ha desarrollado un nuevo sistema de IA de borde de bajo consumo para la detección de malas hierbas utilizando el detector de objetos YOLOv8n en el microcontrolador STM32U575ZI. Este sistema aborda los desafíos de la gestión tradicional de malas hierbas, que a menudo depende de herbicidas dañinos. Al aplicar diversas técnicas de compresión, el modelo logra una detección en tiempo real eficiente con un consumo de energía mínimo, lo que lo hace adecuado para la agricultura sostenible.

Un nouveau système d'IA en périphérie à faible consommation d'énergie pour la détection des mauvaises herbes a été développé en utilisant le détecteur d'objets YOLOv8n sur le microcontrôleur STM32U575ZI. Ce système répond aux défis de la gestion traditionnelle des mauvaises herbes, qui repose souvent sur des herbicides nocifs. En appliquant diverses techniques de compression, le modèle atteint une détection en temps réel efficace avec une consommation d'énergie minimale, ce qui le rend adapté à l'agriculture durable.

A new low-power edge AI system for weed detection has been developed using the YOLOv8n object detector on the STM32U575ZI microcontroller. This system addresses the challenges of traditional weed management, which often relies on harmful herbicides. By employing various compression techniques, the model achieves efficient real-time detection with minimal energy consumption, making it suitable for sustainable agriculture.

Hardware-Aware YOLO Compression for Low-Power Edge AI on STM32U5 for Weeds Detection in Digital Agriculture

Although Black Friday is still two weeks away, you can find great Nintendo Switch and Switch 2 deals now. I've collected the best from Walmart, Best Buy, and more.

مع اقتراب يوم الجمعة السوداء بعد أسبوعين، تتوفر بالفعل عروض مبكرة على أجهزة نينتندو سويتش وسويتش 2. تقدم متاجر كبيرة مثل وول مارت وبيست باي أكثر من 20 عرضًا، مما يوفر للمستهلكين فرصة لتوفير المال على منتجات الألعاب الشهيرة قبل موسم التسوق للعطلات.

A medida que se acerca el Black Friday en dos semanas, ya están disponibles ofertas anticipadas en las consolas Nintendo Switch y Switch 2. Grandes minoristas como Walmart y Best Buy están ofreciendo más de 20 ventas, brindando a los consumidores la oportunidad de ahorrar en productos de videojuegos populares antes de la locura de compras navideñas.

À l'approche de Black Friday dans deux semaines, des offres anticipées sur les consoles Nintendo Switch et Switch 2 sont déjà disponibles. Des détaillants majeurs comme Walmart et Best Buy proposent plus de 20 ventes, offrant aux consommateurs l'occasion d'économiser sur des produits de jeu populaires avant la ruée des achats de vacances.

As Black Friday approaches in two weeks, early deals on Nintendo Switch and Switch 2 consoles are already available. Major retailers like Walmart and Best Buy are offering over 20 sales, providing consumers with an opportunity to save on popular gaming products ahead of the holiday shopping rush.

Best early Black Friday Nintendo Switch deals 2025: 20+ sales out early

ZDNET sat down with Andrew Ng at AI Dev 25 in New York to talk about developer futures, responsible AI, and why AGI is overhyped.

You should still learn to code, says top Google AI exec - here's why

Black Friday is just over one week away, but you can get a jump on your holiday shopping list with great deals on gaming desktop PCs, monitors, SSDs, and more.

Best early Black Friday gaming PC deals 2025: My favorite sales out early

Although Black Friday is still two weeks away, you can find great PlayStation deals now from across the internet. I've collected some of the best.

على الرغم من أن يوم الجمعة السوداء لا يزال بعيدًا لمدة أسبوعين، إلا أن هناك بالفعل عروض رائعة على بلاي ستيشن متاحة عبر الإنترنت. يبرز المقال أكثر من 20 عرضًا يمكن العثور عليها عبر منصات مختلفة، مما يوفر فرصة مبكرة للمتسوقين للاستفادة من الخصومات قبل التاريخ الرسمي ليوم الجمعة السوداء.

Aunque el Black Friday aún está a dos semanas de distancia, ya hay excelentes ofertas de PlayStation disponibles en línea. El artículo destaca más de 20 ventas que se pueden encontrar en varias plataformas, brindando una oportunidad anticipada para que los compradores aprovechen los descuentos antes de la fecha oficial del Black Friday.

Bien que le Black Friday soit encore dans deux semaines, il existe déjà d'excellentes offres PlayStation disponibles en ligne. L'article met en avant plus de 20 ventes que l'on peut trouver sur diverses plateformes, offrant ainsi une occasion précoce aux acheteurs de profiter des réductions avant la date officielle du Black Friday.

Although Black Friday is still two weeks away, there are already great PlayStation deals available online. The article highlights over 20 sales that can be found across various platforms, providing an early opportunity for shoppers to take advantage of discounts ahead of the official Black Friday date.

Best early Black Friday PlayStation deals 2025: 20+ sales out now

Black Friday is just over a week away, and you can already find great deals on TVs and home theater equipment from Samsung, Sony, and more.

Best early Black Friday TV deals 2025: Save on Samsung, TCL, and more

This new AI coding environment looks like a real winner. Here's why.

Google's Antigravity puts coding productivity before AI hype - and the result is astonishing

arXiv:2511.07947v2 Announce Type: replace-cross 
Abstract: Machine learning models constitute valuable intellectual property, yet remain vulnerable to model extraction attacks (MEA), where adversaries replicate their functionality through black-box queries. Model watermarking counters MEAs by embedding forensic markers for ownership verification. Current black-box watermarks prioritize MEA survival through representation entanglement, yet inadequately explore resilience against sequential MEAs and removal attacks. Our study reveals that this risk is underestimated because existing removal methods are weakened by entanglement. To address this gap, we propose Watermark Removal attacK (WRK), which circumvents entanglement constraints by exploiting decision boundaries shaped by prevailing sample-level watermark artifacts. WRK effectively reduces watermark success rates by at least 88.79% across existing watermarking benchmarks.
  For robust protection, we propose Class-Feature Watermarks (CFW), which improve resilience by leveraging class-level artifacts. CFW constructs a synthetic class using out-of-domain samples, eliminating vulnerable decision boundaries between original domain samples and their artifact-modified counterparts (watermark samples). CFW concurrently optimizes both MEA transferability and post-MEA stability. Experiments across multiple domains show that CFW consistently outperforms prior methods in resilience, maintaining a watermark success rate of at least 70.15% in extracted models even under the combined MEA and WRK distortion, while preserving the utility of protected models.

Class-feature Watermark: A Resilient Black-box Watermark Against Model Extraction Attacks

arXiv:2511.11688v1 Announce Type: new 
Abstract: Diffusion probabilistic models have set a new standard for generative fidelity but are hindered by a slow iterative sampling process. A powerful training-free strategy to accelerate this process is Schedule Optimization, which aims to find an optimal distribution of timesteps for a fixed and small Number of Function Evaluations (NFE) to maximize sample quality. To this end, a successful schedule optimization method must adhere to four core principles: effectiveness, adaptivity, practical robustness, and computational efficiency. However, existing paradigms struggle to satisfy these principles simultaneously, motivating the need for a more advanced solution. To overcome these limitations, we propose the Hierarchical-Schedule-Optimizer (HSO), a novel and efficient bi-level optimization framework. HSO reframes the search for a globally optimal schedule into a more tractable problem by iteratively alternating between two synergistic levels: an upper-level global search for an optimal initialization strategy and a lower-level local optimization for schedule refinement. This process is guided by two key innovations: the Midpoint Error Proxy (MEP), a solver-agnostic and numerically stable objective for effective local optimization, and the Spacing-Penalized Fitness (SPF) function, which ensures practical robustness by penalizing pathologically close timesteps. Extensive experiments show that HSO sets a new state-of-the-art for training-free sampling in the extremely low-NFE regime. For instance, with an NFE of just 5, HSO achieves a remarkable FID of 11.94 on LAION-Aesthetics with Stable Diffusion v2.1. Crucially, this level of performance is attained not through costly retraining, but with a one-time optimization cost of less than 8 seconds, presenting a highly practical and efficient paradigm for diffusion model acceleration.

يتناول المقال بعنوان 'تحسين الجدول الهرمي لعملية أخذ العينات السريعة والموثوقة لنماذج الانتشار' عملية أخذ العينات التكرارية البطيئة للنماذج الاحتمالية للانتشار، المعروفة بدقتها في التوليد. لتحسين سرعة أخذ العينات دون المساس بالجودة، يقدم المؤلفون مُحسِّن الجدول الهرمي (HSO)، وهو إطار تحسين ذو مستويين. يهدف HSO إلى تحسين توزيع الخطوات مع الالتزام بمبادئ الفعالية، والتكيف، والموثوقية، والكفاءة الحسابية، متجاوزًا القيود المفروضة على الأساليب الحالية.

El artículo titulado 'Optimización Jerárquica del Horario para un Muestreo Rápido y Robusto de Modelos de Difusión' aborda el lento proceso de muestreo iterativo de los modelos probabilísticos de difusión, conocidos por su fidelidad generativa. Para mejorar la velocidad de muestreo sin comprometer la calidad, los autores presentan el Optimizador de Horario Jerárquico (HSO), un marco de optimización de dos niveles. HSO busca optimizar la distribución de los pasos mientras se adhiere a los principios de efectividad, adaptabilidad, robustez y eficiencia computacional, superando las limitaciones d…

L'article intitulé 'Optimisation Hiérarchique des Horaires pour un Échantillonnage Rapide et Robuste des Modèles de Diffusion' traite du processus d'échantillonnage itératif lent des modèles probabilistes de diffusion, connus pour leur fidélité générative. Pour améliorer la vitesse d'échantillonnage sans compromettre la qualité, les auteurs introduisent l'Optimiseur d'Horaire Hiérarchique (HSO), un cadre d'optimisation à deux niveaux. HSO vise à optimiser la distribution des étapes tout en respectant les principes d'efficacité, d'adaptabilité, de robustesse et d'efficacité computationnelle, su…

The paper titled 'Hierarchical Schedule Optimization for Fast and Robust Diffusion Model Sampling' addresses the slow iterative sampling process of diffusion probabilistic models, which are known for their generative fidelity. To enhance sampling speed without compromising quality, the authors introduce the Hierarchical-Schedule-Optimizer (HSO), a bi-level optimization framework. HSO aims to optimize the distribution of timesteps while adhering to principles of effectiveness, adaptivity, robustness, and computational efficiency, overcoming limitations of existing methods.

Hierarchical Schedule Optimization for Fast and Robust Diffusion Model Sampling

arXiv:2511.12098v1 Announce Type: new 
Abstract: Generating synthetic CT images from CBCT or MRI has a potential for efficient radiation dose planning and adaptive radiotherapy. However, existing CNN-based models lack global semantic understanding, while Transformers often overfit small medical datasets due to high model capacity and weak inductive bias. To address these limitations, we propose a DINOv3-Guided Cross Fusion (DGCF) framework that integrates a frozen self-supervised DINOv3 Transformer with a trainable CNN encoder-decoder. It hierarchically fuses global representation of Transformer and local features of CNN via a learnable cross fusion module, achieving balanced local appearance and contextual representation. Furthermore, we introduce a Multi-Level DINOv3 Perceptual (MLDP) loss that encourages semantic similarity between synthetic CT and the ground truth in DINOv3's feature space. Experiments on the SynthRAD2023 pelvic dataset demonstrate that DGCF achieved state-of-the-art performance in terms of MS-SSIM, PSNR and segmentation-based metrics on both MRI$\rightarrow$CT and CBCT$\rightarrow$CT translation tasks. To the best of our knowledge, this is the first work to employ DINOv3 representations for medical image translation, highlighting the potential of self-supervised Transformer guidance for semantic-aware CT synthesis. The code is available at https://github.com/HiLab-git/DGCF.

Hardware-Aware YOLO Compression for Low-Power Edge AI on STM32U5 for Weeds Detection in Digital Agriculture

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