What is Lebanon's Beaufort Castle? Why is Israel attacking Nabatieh? Israeli air strikes have continued to target towns in southern Lebanon despite an agreement between the United States and Iran set to be formally signed on Friday to end the war on all fronts. Israeli drones carried out three attacks in Tyre that resulted in injuries while a drone also targeted the Bint Jbeil district in Nabatieh, Lebanon's state-run National News Agency said on Wednesday. Earlier on Wednesday, Al Jazeera correspondents on the ground reported that Israeli forces carried out an air strike on the outskirts of Kfar Tebnit, also in the Nabatieh district.

Geometric models like DUSt3R have shown great advances in understanding the geometry of a scene from pairs of photos. However, they fail when the inputs are from vastly different viewpoints (e.g., aerial vs. ground) or modalities (e.g., photos vs. abstract drawings) compared to what was observed during training. This paper addresses a challenging version of this problem: predicting correspondences between ground-level photos and floor plans. Current datasets for joint photo-floor plan reasoning are limited, either lacking in varying modalities (VIGOR) or lacking in correspondences (WAFFLE). To address these limitations, we introduce a new dataset, C3, created by first reconstructing a number of scenes in 3D from Internet photo collections via structure-from-motion, then manually registering the reconstructions to floor plans gathered from the Internet, from which we can derive correspondences between images and floor plans.

Drug recommendation systems aim to identify optimal drug combinations for patient care, balancing therapeutic efficacy and safety. Advances in large-scale longitudinal EHRs have enabled learning-based approaches that leverage patient histories such as diagnoses, procedures, and previously prescribed drugs, to model complex patient-drug relationships. Yet, many existing solutions overlook standard clinical practices that favor certain drugs for specific conditions and fail to fully integrate the influence of molecular substructures on drug efficacy and safety. In response, we propose SubRec, a unified framework that integrates representation learning across both patient and drug spaces. Specifically, SubRec introduces a conditional information bottleneck to extract core drug substructures most relevant to patient conditions, thereby enhancing interpretability and clinical alignment. Meanwhile, an adaptive vector quantization mechanism is designed to generate patient-drug interaction patterns into a condition-aware codebook which reuses clinically meaningful patterns, reduces training overhead, and provides a controllable latent space for recommendation. Crucially, the synergy between condition-specific substructure learning and discrete patient prototypes allows SubRec to make accurate and personalized drug recommendations. Experimental results on the real-world MIMICIII and IV demonstrate our model's advantages. The source code is available at https://DrugRecommendation/.

In this paper, we introduce BMMR, a large-scale bilingual, multimodal, multidisciplinary reasoning dataset for the community to develop and evaluate large multimodal models (LMMs). BMMR comprises 110k college-level questions spanning 300 UNESCO-defined subjects, spanning diverse formats--multiplechoice, fill-in-the-blank, and open-ended QA--and sourced from both print and digital media such as books, exams, and quizzes. All data are curated and filtered via a human-in-the-loop and scalable framework, and each instance is paired with a high-quality reasoning path. The dataset is organized into two parts: BMMR-Eval that comprises 20,458high-quality instances to comprehensively assess LMMs' knowledge and reasoning across multiple disciplines in both Chinese and English; and BMMR-Train that contains 88,991 instances to support further research and development, extending the current focus on mathematical reasoning to diverse disciplines and domains. In addition, we propose the process-based multi-discipline verifier (i.e., BMMR-Verifier) for accurate and fine-grained evaluation of reasoning paths. Extensive experiments on 24 models reveal that (i) even SOTA models (e.g., o3and Gemini-2.5-Pro)

Classifier-free guidance (CFG) is a core technique powering state-of-the-art image generation systems, yet its underlying mechanisms remain poorly understood. In this work, we begin by analyzing CFG in a simplified linear diffusion model, where we show its behavior closely resembles that observed in the nonlinear case. Our analysis reveals that linear CFG improves generation quality via three distinct components: (i) a mean-shift term that approximately steers samples in the direction of class means, (ii) a positive Contrastive Principal Components (CPC) term that amplifies class-specific features, and (iii) a negative CPC term that suppresses generic features prevalent in unconditional data. We then verify these insights in real-world, nonlinear diffusion models: over a broad range of noise levels, linear CFG resembles the behavior of its nonlinear counterpart. Although the two eventually diverge at low noise levels, we discuss how the insights from the linear analysis still shed light on the CFG's mechanism in the nonlinear regime.

Driving into the sunset can actually form new neural pathways. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Seeing a landscape or place that takes your breath away is actually good for your brain. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

President Trump won on reindustrialization, but local opposition to data centers, mines, and factories risks handing strategic advantages to China.

PCWorld explains how Windows 11 users can leverage AI chatbots like ChatGPT, Claude, or Gemini to analyze their PC's health using MSINFO32 system reports. This method allows comprehensive system analysis without granting AI direct file access, maintaining privacy while getting actionable insights about hardware and software issues.

Upgrades include 360-degree audio and deeper integration with Gemini. Last fall Google teased that it was working on an all-new Google Home speaker due out sometime in mid 2026. And while it took a tiny bit longer than expected, today the company began taking pre-orders for its latest speaker ahead of its official on-sale date of June 25. While the new Google Home serves a similar role to the existing Nest Audio, including the ability to function as linked stereo speakers when paired with a second unit, there are a number of other design changes and upgrades. Instead of relying on directional sound, the Google Home was created to deliver clear 360-degree audio in an even more compact chassis.

Arriving six years after Google's last smart speaker, the new HomePod-style device was redesigned to play host to Gemini's chatbot. The last time Google released a smart speaker, the world was in the throes of a pandemic . Yes, it's been six years since the company trotted out a dedicated speaker. However, this newest Google Home Speaker brings a big change with it: The device has been redesigned to showcase the new Gemini assistant instead of the Google Assistant that powered all previous speakers and smart displays. Google announced the speaker last fall alongside new Nest smart home cameras and video doorbells, promising a spring 2026 launch.