A drone attack on a mosque in central Sudan has killed two children and injured 13 more, according to a Sudanese doctor's association, amid a rise in similar attacks across the region. The Sudan Doctors Network said the attack was carried out at dawn on Wednesday by the Rapid Support Forces (RSF), a paramilitary group engaged in a three-year civil war with the Sudanese Armed Forces. "Targeting children inside mosques is a fully constituted crime that cannot be justified under any pretext and represents a dangerous escalation in the pattern of repeated violations against civilians," the doctors said. The Sudan Doctors Network said the RSF has previously targeted other religious buildings for attack, including a church in Khartoum and another mosque in el-Fasher, reflecting a "systematic pattern that shows clear disregard for the sanctity of life and religious sites". "The network calls on the international community, the United Nations, and human rights and humanitarian organizations to take urgent action to pressure for the end to the targeting of civilians, ensure their protection, open safe corridors for the delivery of medical and humanitarian aid, and work to document these violations and hold those responsible accountable," it said.

UN'gravely alarmed' by deteriorating situation in Sudan's el-Fasher The United Nations secretary-general has called for an immediate ceasefire in Sudan's Darfur region after a deadly drone attack on Friday killed more than 70 worshippers in el-Fasher, expressing "grave" alarm about the "rapidly deteriorating situation". "The fighting must stop now," Antonio Guterres said in a statement issued by his spokesperson on Saturday, urging the warring parties to engage in dialogue and provide humanitarian corridors, with the brutal civil war wracking the nation in its third year. El-Fasher, the capital of the North Darfur region, remains the government-backed Sudanese Armed Forces (SAF) and its allies' last major stronghold across Darfur. It has been under siege for more than a year by the paramilitary Rapid Support Forces (RSF), which launched a renewed offensive to capture the city in recent weeks. Humanitarian organisations have raised alarm about growing hunger in the city as hundreds of thousands of people remain trapped without access to food, medicine and other essentials.

Large reasoning models (LRMs) have demonstrated strong performance in complex, multi-step reasoning tasks. Existing methods enhance LRMs by sequentially integrating external knowledge retrieval; models iteratively generate queries, retrieve external information, and progressively reason over this information. However, purely sequential querying increases inference latency and context length, diminishing coherence and potentially reducing accuracy. To address these limitations, we introduce HDS-QA (Hybrid Deep Search QA), a synthetic dataset automatically generated from Natural Questions, explicitly designed to train LRMs to distinguish parallelizable from sequential queries. HDS-QA comprises hybrid-hop questions that combine parallelizable independent subqueries (executable simultaneously) and sequentially dependent subqueries (requiring step-by-step resolution), along with synthetic reasoning-querying-retrieval paths involving parallel queries. We fine-tune an LRM using HDS-QA, naming the model HybridDeepSearcher, which outperforms state-of-the-art baselines across multiple benchmarks, notably achieving +15.9 and +11.5 F1 on FanOutQA and a subset of BrowseComp, respectively, both requiring comprehensive and exhaustive search. Experimental results highlight two key advantages: HybridDeepSearcher reaches comparable accuracy with fewer search turns, significantly reducing inference latency, and it effectively scales as more turns are permitted. These results demonstrate the efficiency, scalability, and effectiveness of explicitly training LRMs to leverage hybrid parallel and sequential querying.

Three far-right extremists who amassed hundreds of weapons and planned to carry out attacks on targets including a mosque have been convicted of terrorism offences. Brogan Stewart, 25, from West Yorkshire, Christopher Ringrose, 34, from Staffordshire, and Marco Pitzettu, 25, from Derbyshire, were part of an online group who "idolised the Nazi regime". Sheffield Crown Court was told how Stewart had detailed torturing a Muslim leader using an "information extraction kit". All three were found guilty of terrorism offences at the same court on Wednesday and are due to be sentenced on 17 July.Counter Terrorism Policing North EastThe trio had amassed a cache of weapons as part of their planning During the nine-week trial, the court heard more than 200 weapons including machetes, hunting knives, swords and crossbows were found at their homes. Ringrose had also begun to build a 3D-printed semi-automatic firearm, which counter-terror police said would have been a "lethal weapon".

Muslims around the world have begun celebrating Eid al-Fitr, one of the biggest celebrations in the Islamic calendar. Eid al-Fitr - which means "festival of the breaking of the fast" - is celebrated at the end of Ramadan, a month of fasting for many adults, as well as spiritual reflection and prayer.ReutersHere in Moscow, worshippers are seen preparing for prayer.ReutersHundreds took part in prayers at Tononoka grounds, in Mombasa, KenyaGetty ImagesPrayers were also observed at a stadium in Port Sudan in the east of the countryGetty ImagesLittle children joined adults at the Moskee Essalam in Rotterdam, NetherlandsGetty ImagesGifts are handed out to Muslim children in Lviv, Ukraine, as Russia's war on the country continuesReuters Palestinians in Jabaliya in the northern Gaza Strip pray amidst the rubble of a mosque destroyed in the current war between Israel and HamasGetty ImagesFamilies gather at al-Aqsa mosque in Jerusalem - the third holiest site in IslamReutersA boy yawns during prayers at a stadium in QatarEPAMuslims greet each-other at Martim Moniz Square in Lisbon, PortugalGetty ImagesWomen worshippers gather in Burgess Park, London, for an outdoor prayerEPAThere were also worshippers gathered outside Plebiscito Square in Naples, ItalyReutersSome women took pictures after attending prayers at the Hagia Sophia Grand Mosque in Istanbul, TurkeyGetty ImagesAfghan refugees pray at a mosque on the outskirts of Peshawar, PakistanMiddle EastEuropeEid al-FitrReligionIslamRelated'I was afraid for my life': At the scene of the attack on Palestinian Oscar winner 5 days agoMiddle EastMore8 hrs ago'In Bradford, families spend thousands on new clothes for Eid' Muslims spend large amounts in Bradford's supermarkets, clothes shops and other services before Eid.8 hrs agoEngland1 day ago The tourist has received an award from the city's mayor after restraining a man during a stabbing.1 day agoEurope1 day ago Another 21 people are injured, as a restaurant and several buildings are set ablaze in the city, local officials say.1 day agoWorld1 day ago Town's successful Ramadan lights project expanded A Scunthorpe community group says it has seen an "amazing" response to its lights display.1 day agoLincolnshire1 day ago Bishop says school that changed Easter events'valued' The BBC is not responsible for the content of external sites.

Mosques are worship places of Allah and must be preserved clean, immaculate, provide all the comforts of the worshippers in them. The prophet's mosque in Medina/ Saudi Arabia is one of the most important mosques for Muslims. It occupies second place after the sacred mosque in Mecca/ Saudi Arabia, which is in constant overcrowding by all Muslims to visit the prophet Mohammad's tomb. This paper aims to propose a smart dome model to preserve the fresh air and allow the sunlight to enter the mosque using artificial intelligence techniques. The proposed model controls domes movements based on the weather conditions and the overcrowding rates in the mosque. The data have been collected from two different resources, the first one from the database of Saudi Arabia weather's history, and the other from Shanghai Technology Database. Congested Scene Recognition Network (CSRNet) and Fuzzy techniques have applied using Python programming language to control the domes to be opened and closed for a specific time to renew the air inside the mosque. Also, this model consists of several parts that are connected for controlling the mechanism of opening/closing domes according to weather data and the situation of crowding in the mosque. Finally, the main goal of this paper has been achieved, and the proposed model has worked efficiently and specifies the exact duration time to keep the domes open automatically for a few minutes for each hour head.

We present LayerSkip, an end-to-end solution to speed-up inference of large language models (LLMs). First, during training we apply layer dropout, with low dropout rates for earlier layers and higher dropout rates for later layers, and an early exit loss where all transformer layers share the same exit. Second, during inference, we show that this training recipe increases the accuracy of early exit at earlier layers, without adding any auxiliary layers or modules to the model. Third, we present a novel self-speculative decoding solution where we exit at early layers and verify and correct with remaining layers of the model. Our proposed self-speculative decoding approach has less memory footprint than other speculative decoding approaches and benefits from shared compute and activations of the draft and verification stages. We run experiments on different Llama model sizes on different types of training: pretraining from scratch, continual pretraining, finetuning on specific data domain, and finetuning on specific task. We implement our inference solution and show speedups of up to 2.16x on summarization for CNN/DM documents, 1.82x on coding, and 2.0x on TOPv2 semantic parsing task.

Millions of mosques around the world are suffering some problems such as ventilation and difficulty getting rid of bacteria, especially in rush hours where congestion in mosques leads to air pollution and spread of bacteria, in addition to unpleasant odors and to a state of discomfort during the pray times, where in most mosques there are no enough windows to ventilate the mosque well. This paper aims to solve these problems by building a model of smart mosques domes using weather features and outside temperatures. Machine learning algorithms such as k Nearest Neighbors and Decision Tree were applied to predict the state of the domes open or close. The experiments of this paper were applied on Prophet mosque in Saudi Arabia, which basically contains twenty seven manually moving domes. Both machine learning algorithms were tested and evaluated using different evaluation methods. After comparing the results for both algorithms, DT algorithm was achieved higher accuracy 98% comparing with 95% accuracy for kNN algorithm. Finally, the results of this study were promising and will be helpful for all mosques to use our proposed model for controlling domes automatically.

In this July 30, 2019, photo, Paul Hildreth, emergency operations coordinator for the Fulton County School District, works in the emergency operations center at the Fulton County School District Administration Center in Atlanta. Artificial Intelligence is transforming surveillance cameras from passive sentries into active observers that can immediately spot a gunman, alert retailers when someone is shoplifting and help police quickly find suspects. Schools, such as the Fulton County School District, are among the most enthusiastic adopters of the technology. Paul Hildreth peered at a display of dozens of images from security cameras surveying his Atlanta school district and settled on one showing a woman in a bright yellow shirt walking a hallway. A mouse click instructed the artificial intelligence-equipped system to find other images of the woman, and it immediately stitched them into a video narrative of where she was currently, where she had been and where she was going. There was no threat, but Hildreth's demonstration showed what's possible with AI-powered cameras.

Paul Hildreth peered at a display of dozens of images from security cameras surveying his Atlanta school district and settled on one showing a woman in a bright yellow shirt walking a hallway. A mouse click instructed the artificial intelligence-equipped system to find other images of the woman, and it immediately stitched them into a video narrative of where she was currently, where she had been and where she was going. There was no threat, but Hildreth's demonstration showed what's possible with AI-powered cameras. If a gunman were in one of his schools, the cameras could quickly identify the shooter's location and movements, allowing police to end the threat as soon as possible, said Hildreth, emergency operations coordinator for the Fulton County School District. AI is transforming surveillance cameras from passive sentries into active observers that can identify people, suspicious behavior and guns, amassing large amounts of data that help them learn over time to recognize mannerisms, gait and dress.