Islamabad, Pakistan – On Wednesday evening, as Pakistan grappled with the aftermath of a wave of missile strikes from India that hit at least six cities, killing 31 people, the country's military spokesperson took to a microphone with a chilling warning. "When Pakistan strikes India, it will come at a time and place of its own choosing," Lieutenant General Ahmed Sharif Chaudhry said in a media briefing. "The whole world will come to know, and its reverberation will be heard everywhere." Two days later, India and Pakistan have moved even closer to the brink of war. On Thursday, May 8, Pakistan accused India of flooding its airspace with kamikaze drones that were brought down over major cities, including Lahore and Karachi.

Pakistan's military says it brought down 25 Indian drones over cities including Karachi and Lahore. India says Pakistan had targeted India and Indian-administered Kashmir with drones and missiles that were shot down. The exchanges are fueling fears of a new phase in the ongoing tensions between the nuclear-armed neighbours.

Fox News senior foreign affairs correspondent Greg Palkot has the latest on the crisis on'Special Report.' India launched multiple Israeli-made Harop drones targeting Pakistan overnight and into Thursday, wounding at least four soldiers, Pakistan army officials said. Pakistani forces downed 25 of the drones, Pakistan army spokesperson Lt. Gen. Ahmad Sharif told The Associated Press. Debris from a downed drone that fell into the Sindh province killed one civilian and injured another. A drone damaged a military site near the city of Lahore, injuring four soldiers, and another went down in Rawalpindi, which is near the capital, Sharif said.

Video shows drone debris in Lahore after Pakistan's army said it shot down 12 drones during an overnight Indian attack against several sites, killing one civilian and wounding four soldiers while targeting military positions.

England's latest fitness concern is over opener Ben Duckett, who injured his left groin in the third ODI. He will have a scan in the coming days before the Champions Trophy opener against Australia on 22 February in Lahore. "He's had quite a lot of cricket over the last little while," said McCullum. "We will make that call, work out if he's going to be at risk, if he's in or out." England have already lost all-rounder Jacob Bethell to a hamstring injury - he has been replaced by batter Tom Banton - while wicketkeeper Jamie Smith has not played since the third T20 on 28 January because of a calf injury.

Monitoring air pollution is crucial for protecting human health from exposure to harmful substances. Traditional methods of air quality monitoring, such as ground-based sensors and satellite-based remote sensing, face limitations due to high deployment costs, sparse sensor coverage, and environmental interferences. To address these challenges, this paper proposes a framework for high-resolution spatiotemporal Air Quality Index (AQI) mapping using sparse sensor data, satellite imagery, and various spatiotemporal factors. By leveraging Graph Neural Networks (GNNs), we estimate AQI values at unmonitored locations based on both spatial and temporal dependencies. The framework incorporates a wide range of environmental features, including meteorological data, road networks, points of interest (PoIs), population density, and urban green spaces, which enhance prediction accuracy. We illustrate the use of our approach through a case study in Lahore, Pakistan, where multi-resolution data is used to generate the air quality index map at a fine spatiotemporal scale.

Despite the recent advancement in Retrieval-Augmented Generation (RAG) systems, most retrieval methodologies are often developed for factual retrieval, which assumes query and positive documents are semantically similar. In this paper, we instead propose and study a more challenging type of retrieval task, called hidden rationale retrieval, in which query and document are not similar but can be inferred by reasoning chains, logic relationships, or empirical experiences. To address such problems, an instruction-tuned Large language model (LLM) with a cross-encoder architecture could be a reasonable choice. To further strengthen pioneering LLM-based retrievers, we design a special instruction that transforms the retrieval task into a generative task by prompting LLM to answer a binary-choice question. The model can be fine-tuned with direct preference optimization (DPO). The framework is also optimized for computational efficiency with no performance degradation. We name this retrieval framework by RaHoRe and verify its zero-shot and fine-tuned performance superiority on Emotional Support Conversation (ESC), compared with previous retrieval works. Our study suggests the potential to employ LLM as a foundation for a wider scope of retrieval tasks. Our codes, models, and datasets are available on https://github.com/flyfree5/LaHoRe.

In multi-label emotion classification, particularly for low-resource languages like Arabic, the challenges of class imbalance and label correlation hinder model performance, especially in accurately predicting minority emotions. To address these issues, this study proposes a novel approach that combines stacked embeddings, meta-learning, and a hybrid loss function to enhance multi-label emotion classification for the Arabic language. The study extracts contextual embeddings from three fine-tuned language models-ArabicBERT, MarBERT, and AraBERT-which are then stacked to form enriched embeddings. A meta-learner is trained on these stacked embeddings, and the resulting concatenated representations are provided as input to a Bi-LSTM model, followed by a fully connected neural network for multi-label classification. To further improve performance, a hybrid loss function is introduced, incorporating class weighting, label correlation matrix, and contrastive learning, effectively addressing class imbalances and improving the handling of label correlations. Extensive experiments validate the proposed model's performance across key metrics such as Precision, Recall, F1-Score, Jaccard Accuracy, and Hamming Loss. The class-wise performance analysis demonstrates the hybrid loss function's ability to significantly reduce disparities between majority and minority classes, resulting in a more balanced emotion classification. An ablation study highlights the contribution of each component, showing the superiority of the model compared to baseline approaches and other loss functions. This study not only advances multi-label emotion classification for Arabic but also presents a generalizable framework that can be adapted to other languages and domains, providing a significant step forward in addressing the challenges of low-resource emotion classification tasks.

Zeerak Ahmed has spent years in the U.S., working for some of the world's biggest tech companies. But one thing he has grown frustrated with is how "computing treats non-Latin languages as second class citizens." One such language is his mother tongue, Urdu, the national language and lingua franca of Pakistan, which is also widely spoken in India. Ahmed, who is from Lahore, has had many conversations with his friends and family about the difficulties of trying to use existing Urdu keyboards or read Urdu type. And he has witnessed many young people instead resorting to English or so-called Roman Urdu, using the Latin script to produce a phonetic transliteration, in the absence of a better solution.

Logistic hubs play a pivotal role in the last-mile delivery distance; even a slight increment in distance negatively impacts the business of the e-commerce industry while also increasing its carbon footprint. The growth of this industry, particularly after Covid-19, has further intensified the need for optimized allocation of resources in an urban environment. In this study, we use a hybrid approach to optimize the placement of logistic hubs. The approach sequentially employs different techniques. Initially, delivery points are clustered using K-Means in relation to their spatial locations. The clustering method utilizes road network distances as opposed to Euclidean distances. Non-road network-based approaches have been avoided since they lead to erroneous and misleading results. Finally, hubs are located using the P-Median method. The P-Median method also incorporates the number of deliveries and population as weights. Real-world delivery data from Muller and Phipps (M&P) is used to demonstrate the effectiveness of the approach. Serving deliveries from the optimal hub locations results in the saving of 815 (10%) meters per delivery.