A drone attack by the RSF and its allied al-Hilou group on a preschool in Kalogi in Sudan has killed more than 100 people, dozens of whom were children. It sparked international condemnation amid worsening violence as the RSF fights Sudan's Armed Forces in South Kordofan state. Francesca Albanese tells Al Jazeera US sanctions have made her'non-person' Ukraine official warns of a'new law of power' after Russian aggression At Doha Forum, Qatar PM warns Gaza ceasefire is at'critical moment'

Sudanese refugee children watch the sunset in the Tine transit camp amid the conflict between the paramilitary Rapid Support Forces (RSF) and the Sudanese Army, in eastern Chad on Nov. 23. Port Sudan, Sudan - A recent paramilitary drone attack on the army-held town of Kalogi in Sudan's South Kordofan state hit a kindergarten and a hospital, killing dozens of civilians including children, a local official said Sunday. The attack, which took place on Thursday, involved three strikes, first a kindergarten, then a hospital and a third time as people tried to rescue the children, Essam al-Din al-Sayed, head of the Kalogi administrative unit, said using a Starlink satellite internet connection. He blamed the assault on the Rapid Support Forces and their ally, the Sudan People's Liberation Movement-North faction (SPLM-N) led by Abdelaziz al-Hilu, which controls much of South Kordofan and parts of Blue Nile state. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Activists sailed to Gaza to deliver aid, but were met with drone attacks and imprisonment. "All of this preparation, all of this work--it's actually come together and we're sailing east, finally," said Dane Hunter. Get your news from a source that's not owned and controlled by oligarchs. Earlier this fall, hundreds of activists from all over the world crowded onto several dozen boats and set sail for Gaza. They thought that by sharing their journey through social media, they could capture the world's attention.

What is in the 28-point US plan for Ukraine? 'Ukraine is running out of men, money and time' Can the US get all sides to end the war? Why is Europe opposing Trump's peace plan? IAEA flags damage to Chornobyl nuclear plant's protective shield in Ukraine A drone strike has damaged a protective shield at the Chornobyl nuclear plant in Ukraine, rendering it unable to contain the radioactive material from the 1986 explosion of the plant, the United Nations nuclear watchdog said. The International Atomic Energy Agency (IAEA) said on Friday that the shield can no longer perform its main safety function, following an inspection of the steel structure last week.

What is in the 28-point US plan for Ukraine? 'Ukraine is running out of men, money and time' Can the US get all sides to end the war? Why is Europe opposing Trump's peace plan? Here's where things stand on Saturday, December 6: A Russian drone attack killed two men, aged 52 and 67, in the Ukrainian city of Izyum as they were unloading firewood from a truck, according to local officials. Russian forces also killed a 12-year-old boy in an attack on the Vasylkivska community in Ukraine's Dnipropetrovsk region, and wounded more than a dozen Ukrainians in attacks on the Kherson, Donetsk and Sumy regions, local officials said.

Drones are becoming indispensable in many application domains. In data-driven missions, besides sensing, the drone must process the collected data at runtime to decide whether additional action must be taken on the spot, before moving to the next point of interest. If processing does not reveal an event or situation that requires such an action, the drone has waited in vain instead of moving to the next point. If, however, the drone starts moving to the next point and it turns out that a follow-up action is needed at the previous point, it must spend time to fly-back. To take this decision, we propose different machine-learning methods based on branch prediction and reinforcement learning. We evaluate these methods for a wide range of scenarios where the probability of event occurrence changes with time. Our results show that the proposed methods consistently outperform the regression-based method proposed in the literature and can significantly improve the worst-case mission time by up to 4.1x. Also, the achieved median mission time is very close, merely up to 2.7% higher, to that of a method with perfect knowledge of the current underlying event probability at each point of interest.

Visual geo-localization for drones faces critical degradation under weather perturbations, \eg, rain and fog, where existing methods struggle with two inherent limitations: 1) Heavy reliance on limited weather categories that constrain generalization, and 2) Suboptimal disentanglement of entangled scene-weather features through pseudo weather categories. We present WeatherPrompt, a multi-modality learning paradigm that establishes weather-invariant representations through fusing the image embedding with the text context. Our framework introduces two key contributions: First, a Training-free Weather Reasoning mechanism that employs off-the-shelf large multi-modality models to synthesize multi-weather textual descriptions through human-like reasoning. It improves the scalability to unseen or complex weather, and could reflect different weather strength. Second, to better disentangle the scene and weather feature, we propose a multi-modality framework with the dynamic gating mechanism driven by the text embedding to adaptively reweight and fuse visual features across modalities. The framework is further optimized by the cross-modal objectives, including image-text contrastive learning and image-text matching, which maps the same scene with different weather conditions closer in the respresentation space. Extensive experiments validate that, under diverse weather conditions, our method achieves competitive recall rates compared to state-of-the-art drone geo-localization methods. Notably, it improves Recall@1 by +13.37\% under night conditions and by 18.69\% under fog and snow conditions.

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Unfortunately, there are some usability bumps. The Antigravity A1 is what happens when Insta360's 360-degree cameras are given wings and flying feels like a video game. Spinning out as its own brand, Antigravity's debut drone is a big swing: a three-piece set with a drone that captures 8K 360-degree video, FPV goggles and a motion controller. Challenging the dominance of DJI's (many!) consumer drones is a big ask. Antigravity's approach is to play to its strengths in 360-degree video and smartphone-first editing.

The world's first 360-degree drone is fun all around, if you don't mind the steep price or wearing goggles to control it. As someone who has been reviewing camera drones for over a decade, it's rare for me to encounter one that feels genuinely new. While DJI's continual stream of steadily improving, ever-reliable drones almost always impresses, what Antigravity has done with its first-ever product, the A1, essentially invents an entirely novel subcategory: the 360 drone. Using the same shoot-first, frame-later technology as the Insta360 X5 (Antigravity is technically a distinct company from Insta360, but the brands have close ties), the A1 has twin cameras to capture everything around it, allowing the user to reframe the footage later using mobile or desktop apps. Each of the cameras uses a 1/1.28-inch sensor and an ultrawide lens to capture a hemispherical view.