What is in the 28-point US plan for Ukraine? Why is Europe opposing Trump's peace plan? Is the fall of Pokrovsk inevitable? 'A corruption scandal may well end the Ukraine war' Russia says talks to end Ukraine war'serious' but rules out concessions Russia says the United States-brokered talks to end the war with Ukraine are "serious", but its officials caution that an agreement is a long way off and Moscow would offer no major concessions to Kyiv. Kremlin spokesman Dmitry Peskov said in televised comments on Wednesday that the negotiations were ongoing and "the process is serious."

Can Ukraine restore its pre-war borders? Is Russia testing NATO with aerial incursions in Europe? Russia will give'decisive response' if provoked by the West, says Lavrov Russia's Foreign Minister Sergey Lavrov has warned NATO and the EU that "any aggression against my country will be met with a decisive response". Speaking at the United Nations General Assembly (UNGA) in New York on Saturday, Lavrov insisted that Moscow had no plans to attack the West, but that it was prepared to act if provoked. "Russia is testing their ability to defend themselves and trying to influence societies so people begin to ask: 'If we can't protect ourselves, why should we keep supporting Ukraine?'. This is intended to reduce assistance to Ukraine, especially ahead of winter," he wrote on X. Moscow continues to deny violating Polish airspace with drones and Estonian airspace with fighter jets this month.

Continual learning is a key feature of biological neural systems, but artificial neural networks often suffer from catastrophic forgetting. Instead of backpropagation, biologically plausible learning algorithms may enable stable continual learning. This study proposes columnar-organized spiking neural networks (SNNs) with local learning rules for continual learning and catastrophic forgetting. Using CoLaNET (Columnar Layered Network), we show that its microcolumns adapt most efficiently to new tasks when they lack shared structure with prior learning. We demonstrate how CoLaNET hyperparameters govern the trade-off between retaining old knowledge (stability) and acquiring new information (plasticity). We evaluate CoLaNET on two benchmarks: Permuted MNIST (ten sequential pixel-permuted tasks) and a two-task MNIST/EMNIST setup. Our model learns ten sequential tasks effectively, maintaining 92% accuracy on each. It shows low forgetting, with only 4% performance degradation on the first task after training on nine subsequent tasks.

This paper presents a lightweight software-based approach for running spiking neural networks (SNNs) without relying on specialized neuromorphic hardware or frameworks. Instead, we implement a specific SNN architecture (CoLaNET) in Rust and optimize it for common computing platforms. As a case study, we demonstrate our implementation, called Spiffy, on a Raspberry Pi using the MNIST dataset. Spiffy achieves 92% accuracy with low latency - just 0.9 ms per training step and 0.45 ms per inference step. The code is open-source.

Russia has launched 479 drones against Ukraine in the biggest overnight drone bombardment of the three-year war, according to the Ukrainian air force. The air force said early on Monday that it had downed 460 drones as well as 19 missiles launched overnight. Russia's continued to step up its drone and missile attacks on Ukraine, despite declaring, under pressure from United States President Donald Trump, that it is interested in pursuing peace talks. The record launch came just ahead of the start of a prisoner swap agreed at recent talks between the pair. Of the hundreds of projectiles fired at numerous targets, only 10 reached their destination, Kyiv officials said.

A Ukrainian drone hit an industrial facility overnight in Russia's Volga River region of Chuvashia, some 1,300km (800 miles) from the border with Ukraine, the regional governor said on Sunday. There were no casualties in the strike, which was one of the deepest yet into Russia by a Ukrainian drone.

In the last decade, neural networks (NNs) have driven significant progress across various fields, notably in deep reinforcement learning, highlighted by studies like [1, 2, 3]. This progress has the potential to make changes in many areas such as embedded devices, IoT and Robotics. Although modern Deep Learning models have demonstrated impressive gains in accuracy, their large sizes pose limits to their practical use in many real-world applications [4]. These applications may impose requirements in energy consumption, inference latency, inference throughput, memory footprint, real-time inference and hardware costs. Numerous studies have attempted to make neural networks more efficient.

In October last year, a Russian news site published a short video of Yevgeny Prigozhin, founder of the Wagner Group, the Russian mercenary army, sitting with four men on a rooftop terrace in the resort town of Gelendzhik, on Russia's Black Sea coast. Two are missing parts of a leg. A third lost an arm. They are identified as pardoned former convicts, returned from the front in Ukraine after joining Wagner from prison. "You were an offender, now you're a war hero," Prigozhin tells one man in the clip. It was the first video to depict the return of some of the thousands of convicts who joined Wagner in return for the promise of a pardon if they survived six months of the war. Reuters news agency used facial recognition software to examine this video and more than a dozen others and photographs of homecoming convict fighters, published between October 2022 and February 2023.