Israeli forces reportedly used quadcopter drones to drop incendiary bombs on ambulances parked outside a clinic in Gaza City, causing a fire. The use of incendiary weapons is prohibited in civilian-populated areas under international humanitarian law.

Fox News senior White House correspondent Jacqui Heinrich reports on China hosting North Korea's Kim Jong Un during a military parade and President Trump warning Vladimir Putin of consequences if he holds no meeting with Volodymyr Zelenskyy. China displayed new weapons Wednesday at a military parade marking the 80th anniversary of World War II's end. Beijing sought to display its growing military power as Chinese President Xi Jinping was accompanied by North Korean dictator Kim Jong Un and Russian President Vladimir Putin. Highlights from China's arms exhibition included submarine drones, hypersonic missiles and laser weapons. Additionally, China showed off its fighter jets and bombers during the 90-minute display.

An F/A-18F Super Hornet and an F-35C Lightning II arrive at Luke Air Force Base, Arizona, Feb. 7, 2023, in preparation for their flyover of Super Bowl LVII at State Farm Stadium in Glendale on Feb. 12. EXCLUSIVE: Eight fighter jets will conduct a flyover when Polish President Karol Nawrocki arrives at the White House Wednesday morning, Fox News Digital has learned. President Donald Trump's meeting with Nawrocki, whom Trump backed in the Polish elections earlier in 2025, comes amid ongoing negotiations between Poland's neighboring Russia and Ukraine to end the conflict between the two countries. "President Trump is looking forward to welcoming President Nawrocki to the White House, who recently won a historic election in Poland," White House spokeswoman Anna Kelly said in a Tuesday statement to Fox News Digital. The "spectacular flyover will honor the memory of a brave Polish fighter pilot whose life was tragically taken too soon and capture the special relationship between our two countries." Four F-16 fighter jets are slated to perform a missing man formation Wednesday to honor a Polish Army F-16 pilot who died in an August crash during a rehearsal for an airshow in Radom, Poland.

China also showed off its GJ-11 stealth attack drone, dubbed the "loyal wingman", which can fly alongside a manned fighter jet and aid it in its attacks. Besides an array of conventional aerial drones, there were also "robotic wolves". Experts say these could be used for a variety of tasks from reconnaissance and sweeping for mines, to hunting down enemy soldiers. The drone display shows a clear direction that China wants to take with its military strategy, where it "not only wants to augment, but replace traditional structures". It has clearly taken lessons from the Ukraine war, where one can "just throw drones at the enemy" to wear down their defences, Dr Raska notes.

From massive, underwater torpedoes to state-of-the-art laser weapons that shoot down drones, China's latest military parade will now be broken down and analysed by Pentagon experts and defence officials around the world. The PLA has embarked on an extensive military modernisation programme that has seen it catching up - and in some areas - overtaking the United States. Hypersonic missiles that travel at more than five times the speed of sound is one area where China leads the world. Dr Sidharth Kaushal, a leading expert on missiles at the London think tank RUSI, highlights the YJ-17 - a hypersonic glide vehicle - and the YJ-19, a hypersonic cruise missile. China has also been investing heavily in artificial intelligence and autonomous weapons.

A Victorian lawyer has become the first in Australia to face professional sanctions for using artificial intelligence in a court case, being stripped of his ability to practise as a principal lawyer after AI generated false citations that he had failed to verify. Guardian Australia reported in October last year that in a 19 July 2024 hearing, the anonymous solicitor representing a husband in a dispute between a married couple provided the court with a list of prior cases that had been requested by Justice Amanda Humphreys in relation to an enforcement application in the case. When Humphreys returned to her chambers, she said in a ruling that neither herself nor her associates were able to identify the cases in the list. When the matter returned to court the lawyer confirmed that the list had been prepared using legal software that utilised AI. He acknowledged he did not verify the accuracy of the information before submitting it to the court.

Russian drone attacks and shelling killed three people and injured five others in Ukraine's Dnipropetrovsk region, Governor Serhiy Lysak wrote on Telegram. Two people were killed in Russian attacks on the Polohivskyi district, as Russian forces launched 578 attacks on 18 settlements in Ukraine's Zaporizhia region, Governor Ivan Fedorov said. Separate Russian attacks also killed one person in Kherson, one person in the Kyiv region and one person in Donetsk, local officials reported, according to the Kyiv Independent news outlet. A Ukrainian drone injured three people in the village of Proletarsky, in Russia's Belgorod region, Governor Vyacheslav Gladkov said. Russian forces seized the Ukrainian settlement of Fedorivka in the Donetsk region, Russian state news agency TASS reported, citing the Russian Ministry of Defence.

As large language models (LLMs) gain popularity in conducting prediction tasks in-context, understanding the sources of uncertainty in in-context learning becomes essential to ensuring reliability. The recent hypothesis of in-context learning performing predictive Bayesian inference opens the avenue for Bayesian uncertainty estimation, particularly for decomposing uncertainty into epistemic uncertainty due to lack of in-context data and aleatoric uncertainty inherent in the in-context prediction task. However, the decomposition idea remains under-explored due to the intractability of the latent parameter posterior from the underlying Bayesian model. In this work, we introduce a variational uncertainty decomposition framework for in-context learning without explicitly sampling from the latent parameter posterior, by optimising auxiliary queries as probes to obtain an upper bound to the aleatoric uncertainty of an LLM's in-context learning procedure, which also induces a lower bound to the epistemic uncertainty. Through experiments on synthetic and real-world tasks, we show quantitatively and qualitatively that the decomposed uncertainties obtained from our method exhibit desirable properties of epistemic and aleatoric uncertainty.

Bayesian inference is useful to obtain a predictive distribution with a small generalization error. However, since posterior distributions are rarely evaluated analytically, we employ the variational Bayesian inference or sampling method to approximate posterior distributions. When we obtain samples from a posterior distribution, Hamiltonian Monte Carlo (HMC) has been widely used for the continuous variable part and Markov chain Monte Carlo (MCMC) for the discrete variable part. Another sampling method, the bouncy particle sampler (BPS), has been proposed, which combines uniform linear motion and stochastic reflection to perform sampling. BPS was reported to have the advantage of being easier to set simulation parameters than HMC. To accelerate the convergence to a posterior distribution, we introduced parallel tempering (PT) to BPS, and then proposed an algorithm when the inverse temperature exchange rate is set to infinity. We performed numerical simulations and demonstrated its effectiveness for multimodal distribution.

Operator learning has emerged as a promising tool for accelerating the solution of partial differential equations (PDEs). The Deep Operator Networks (DeepONets) represent a pioneering framework in this area: the "vanilla" DeepONet is valued for its simplicity and efficiency, while the modified DeepONet achieves higher accuracy at the cost of increased training time. In this work, we propose a series of Transformer-inspired DeepONet variants that introduce bidirectional cross-conditioning between the branch and trunk networks in DeepONet. Query-point information is injected into the branch network and input-function information into the trunk network, enabling dynamic dependencies while preserving the simplicity and efficiency of the "vanilla" DeepONet in a non-intrusive manner. Experiments on four PDE benchmarks -- advection, diffusion-reaction, Burgers', and Korteweg-de Vries equations -- show that for each case, there exists a variant that matches or surpasses the accuracy of the modified DeepONet while offering improved training efficiency. Moreover, the best-performing variant for each equation aligns naturally with the equation's underlying characteristics, suggesting that the effectiveness of cross-conditioning depends on the characteristics of the equation and its underlying physics. To ensure robustness, we validate the effectiveness of our variants through a range of rigorous statistical analyses, among them the Wilcoxon Two One-Sided Test, Glass's Delta, and Spearman's rank correlation.