Do Taliban's drone attacks expose a chink in Pakistan's armour? On the evening of March 13, drones struck three locations across Pakistan. Two children were wounded in Quetta. Civilians were also injured in Kohat and in Rawalpindi, the garrison city that houses the headquarters of Pakistan's armed forces and neighbours the capital, Islamabad. Pakistan's military said the drones were intercepted before reaching their targets.

'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.

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.

Advanced automated AI techniques allow us to classify protein sequences and discern their biological families and functions. Conventional approaches for classifying these protein families often focus on extracting N-Gram features from the sequences while overlooking crucial motif information and the interplay between motifs and neighboring amino acids. Recently, convolutional neural networks have been applied to amino acid and motif data, even with a limited dataset of well-characterized proteins, resulting in improved performance. This study presents a model for classifying protein families using the fusion of 1D-CNN, BiLSTM, and an attention mechanism, which combines spatial feature extraction, long-term dependencies, and context-aware representations. The proposed model (ProFamNet) achieved superior model efficiency with 450,953 parameters and a compact size of 1.72 MB, outperforming the state-of-the-art model with 4,578,911 parameters and a size of 17.47 MB. Further, we achieved a higher F1 score (98.30% vs. 97.67%) with more instances (271,160 vs. 55,077) in fewer training epochs (25 vs. 30).

We present LEGAL-UQA, the first Urdu legal question-answering dataset derived from Pakistan's constitution. This parallel English-Urdu dataset includes 619 question-answer pairs, each with corresponding legal article contexts, addressing the need for domain-specific NLP resources in low-resource languages. We describe the dataset creation process, including OCR extraction, manual refinement, and GPT-4-assisted translation and generation of QA pairs. Our experiments evaluate the latest generalist language and embedding models on LEGAL-UQA, with Claude-3.5-Sonnet achieving 99.19% human-evaluated accuracy. We fine-tune mt5-large-UQA-1.0, highlighting the challenges of adapting multilingual models to specialized domains. Additionally, we assess retrieval performance, finding OpenAI's text-embedding-3-large outperforms Mistral's mistral-embed. LEGAL-UQA bridges the gap between global NLP advancements and localized applications, particularly in constitutional law, and lays the foundation for improved legal information access in Pakistan.