High-definition 3D city maps underpin smart transportation, digital twins, and autonomous driving, where object level change detection across bi temporal LiDAR enables HD map maintenance, construction monitoring, and reliable localization. Classical DSM differencing and image based methods are sensitive to small vertical bias, ground slope, and viewpoint mismatch and yield cellwise outputs without object identity. Point based neural models and voxel encodings demand large memory, assume near perfect pre alignment, degrade thin structures, and seldom enforce class consistent association, which leaves split or merge cases unresolved and ignores uncertainty. We propose an object centric, uncertainty aware pipeline for city scale LiDAR that aligns epochs with multi resolution NDT followed by point to plane ICP, normalizes height, and derives a per location level of detection from registration covariance and surface roughness to calibrate decisions and suppress spurious changes. Geometry only proxies seed cross epoch associations that are refined by semantic and instance segmentation and a class constrained bipartite assignment with augmented dummies to handle splits and merges while preserving per class counts. Tiled processing bounds memory without eroding narrow ground changes, and instance level decisions combine 3D overlap, normal direction displacement, and height and volume differences with a histogram distance, all gated by the local level of detection to remain stable under partial overlap and sampling variation. On 15 representative Subiaco blocks the method attains 95.2% accuracy, 90.4% mF1, and 82.6% mIoU, exceeding Triplet KPConv by 0.2 percentage points in accuracy, 0.2 in mF1, and 0.8 in mIoU, with the largest gain on Decreased where IoU reaches 74.8% and improves by 7.6 points.

Cutaneous malignancies demand early detection for favorable outcomes, yet current diagnostics suffer from inter-observer variability and access disparities. While AI shows promise, existing dermatological systems are limited by homogeneous architectures, dataset biases across skin tones, and fragmented approaches that treat natural language processing as separate post-hoc explanations rather than integral to clinical decision-making. We introduce a unified framework that fundamentally reimagines AI integration for dermatological diagnostics through two synergistic innovations. First, a purposefully heterogeneous ensemble of architecturally diverse convolutional neural networks provides complementary diagnostic perspectives, with an intrinsic uncertainty mechanism flagging discordant cases for specialist review -- mimicking clinical best practices. Second, we embed large language model capabilities directly into the diagnostic workflow, transforming classification outputs into clinically meaningful assessments that simultaneously fulfill medical documentation requirements and deliver patient-centered education. This seamless integration generates structured reports featuring precise lesion characterization, accessible diagnostic reasoning, and actionable monitoring guidance -- empowering patients to recognize early warning signs between visits. By addressing both diagnostic reliability and communication barriers within a single cohesive system, our approach bridges the critical translational gap that has prevented previous AI implementations from achieving clinical impact. The framework represents a significant advancement toward deployable dermatological AI that enhances diagnostic precision while actively supporting the continuum of care from initial detection through patient education, ultimately improving early intervention rates for skin lesions.

'It's safe now': See students pulled from rubble of collapsed Indonesian school Dramatic rescue footage shows the boys in Indonesia pulled to safety after their school building collapsed on Monday. The three students, Yusuf, Haikal and Dani were all trapped under the rubble for several hours. It is thought around 38 people are still stuck and unaccounted for. Six students have died so far. Watch: Moments as 6.9 magnitude earthquake hit Philippines At least 69 people are killed after it struck on Tuesday night with officials declaring a state of calamity.

Four students have died after a school building collapsed in Indonesia on Monday, 99 others were taken to hospital but it is thought 38 people are still trapped. The BBC reports from a nearby centre where relatives face an anxious wait for any updates. Rescuers say they have been able to communicate with seven students and give them oxygen. Watch: Moments as 6.9 magnitude earthquake hit Philippines At least 69 people are killed after it struck on Tuesday night with officials declaring a state of calamity. Social media footage showed the massive crater in Thailand's capital leaving cars teetering on the edge.

A large fire broke out in two buildings in the Tondo district of Philippines capital, Manila on Saturday night, affecting around 700 families, according to local media reports. Footage of the scale of the fire was shared by the Manila Public Information Office, which said that the fire had been brought under control. Three people are said to have been injured. The cause of the fire remains under investigation. See Kathmandu's destroyed and barricaded streets after violence From'nepo kids' to PM resignation: How the Nepal protests unfolded The BBC's Charlotte Scarr explains how the use of two slogans sparked a wave of protests in Kathmandu.

See Kathmandu's destroyed and barricaded streets after violence There's a real sense of tension in Kathmandu, the BBC's Samira Hussain says, after protests against corruption spiralled into arson and violence. Nepal's army deployed patrols on the streets, as the Himalayan nation reeled from its worst unrest in decades. The prime minister quit and politicians' homes were vandalised, and government buildings and parliament were torched. The streets of Nepal's capital have a heavy military presence, with barricades erected outside parliament and the supreme court. The military parade was attended by world leaders including Vladimir Putin and Kim Jong Un and showcased China's new weapons.

From'nepo kids' to PM resignation: How the Nepal protests unfolded Nepal has been shaken by deadly protests that have led to the resignation of the country's Prime Minister KP Sharma Oli. The BBC's Charlotte Scarr is on the streets of Kathmandu, where she saw torched government buildings and military presence. The Himalayan nation has been experiencing its worst unrest in decades, after a campaign highlighting the lavish lifestyles of politicians' children and allegations of corruption took off on social media. Thirty people have been killed in the protests and more than 1,000 injured since the unrest began. The military parade was attended by world leaders including Vladimir Putin and Kim Jong Un and showcased China's new weapons.

Spatial grounding, the process of associating natural language expressions with corresponding image regions, has rapidly advanced due to the introduction of transformer-based models, significantly enhancing multimodal representation and cross-modal alignment. Despite this progress, the field lacks a comprehensive synthesis of current methodologies, dataset usage, evaluation metrics, and industrial applicability. This paper presents a systematic literature review of transformer-based spatial grounding approaches from 2018 to 2025. Our analysis identifies dominant model architectures, prevalent datasets, and widely adopted evaluation metrics, alongside highlighting key methodological trends and best practices. This study provides essential insights and structured guidance for researchers and practitioners, facilitating the development of robust, reliable, and industry-ready transformer-based spatial grounding models.

Vision Large Language Models (VLMs) combine visual understanding with natural language processing, enabling tasks like image captioning, visual question answering, and video analysis. While VLMs show impressive capabilities across domains such as autonomous vehicles, smart surveillance, and healthcare, their deployment on resource-constrained edge devices remains challenging due to processing power, memory, and energy limitations. This survey explores recent advancements in optimizing VLMs for edge environments, focusing on model compression techniques, including pruning, quantization, knowledge distillation, and specialized hardware solutions that enhance efficiency. We provide a detailed discussion of efficient training and fine-tuning methods, edge deployment challenges, and privacy considerations. Additionally, we discuss the diverse applications of lightweight VLMs across healthcare, environmental monitoring, and autonomous systems, illustrating their growing impact. By highlighting key design strategies, current challenges, and offering recommendations for future directions, this survey aims to inspire further research into the practical deployment of VLMs, ultimately making advanced AI accessible in resource-limited settings.

Echocardiography is the most widely used imaging to monitor cardiac functions, serving as the first line in early detection of myocardial ischemia and infarction. However, echocardiography often suffers from several artifacts including sensor noise, lack of contrast, severe saturation, and missing myocardial segments which severely limit its usage in clinical diagnosis. In recent years, several machine learning methods have been proposed to improve echocardiography views. Yet, these methods usually address only a specific problem (e.g. denoising) and thus cannot provide a robust and reliable restoration in general. On the other hand, cardiac MRI provides a clean view of the heart without suffering such severe issues. However, due to its significantly higher cost, it is often only afforded by a few major hospitals, hence hindering its use and accessibility. In this pilot study, we propose a novel approach to transform echocardiography into the cardiac MRI view. For this purpose, Echo2MRI dataset, consisting of echocardiography and real cardiac MRI image pairs, is composed and will be shared publicly. A dedicated Cycle-consistent Generative Adversarial Network (Cycle-GAN) is trained to learn the transformation from echocardiography frames to cardiac MRI views. An extensive set of qualitative evaluations shows that the proposed transformer can synthesize high-quality artifact-free synthetic cardiac MRI views from a given sequence of echocardiography frames. Medical evaluations performed by a group of cardiologists further demonstrate that synthetic MRI views are indistinguishable from their original counterparts and are preferred over their initial sequence of echocardiography frames for diagnosis in 78.9% of the cases.