We introduce a novel multilingual hierarchical corpus annotated for entity framing and role portrayal in news articles. The dataset uses a unique taxonomy inspired by storytelling elements, comprising 22 fine-grained roles, or archetypes, nested within three main categories: protagonist, antagonist, and innocent. Each archetype is carefully defined, capturing nuanced portrayals of entities such as guardian, martyr, and underdog for protagonists; tyrant, deceiver, and bigot for antagonists; and victim, scapegoat, and exploited for innocents. The dataset includes 1,378 recent news articles in five languages (Bulgarian, English, Hindi, European Portuguese, and Russian) focusing on two critical domains of global significance: the Ukraine-Russia War and Climate Change. Over 5,800 entity mentions have been annotated with role labels. This dataset serves as a valuable resource for research into role portrayal and has broader implications for news analysis. We describe the characteristics of the dataset and the annotation process, and we report evaluation results on fine-tuned state-of-the-art multilingual transformers and hierarchical zero-shot learning using LLMs at the level of a document, a paragraph, and a sentence.

In this work, the ability of rare VHE gamma ray selection with neural network methods is investigated in the case when cosmic radiation flux strongly prevails (ratio up to {10^4} over the gamma radiation flux from a point source). This ratio is valid for the Crab Nebula in the TeV energy range, since the Crab is a well-studied source for calibration and test of various methods and installations in gamma astronomy. The part of TAIGA experiment which includes three Imaging Atmospheric Cherenkov Telescopes observes this gamma-source too. Cherenkov telescopes obtain images of Extensive Air Showers. Hillas parameters can be used to analyse images in standard processing method, or images can be processed with convolutional neural networks. In this work we would like to describe the main steps and results obtained in the gamma/hadron separation task from the Crab Nebula with neural network methods. The results obtained are compared with standard processing method applied in the TAIGA collaboration and using Hillas parameter cuts. It is demonstrated that a signal was received at the level of higher than 5.5{\sigma} in 21 hours of Crab Nebula observations after processing the experimental data with the neural network method.

Imaging Atmospheric Cherenkov Telescopes (IACTs) of gamma ray observatory TAIGA detect the Extesnive Air Showers (EASs) originating from the cosmic or gamma rays interactions with the atmosphere. Thereby, telescopes obtain images of the EASs. The ability to segregate gamma rays images from the hadronic cosmic ray background is one of the main features of this type of detectors. However, in actual IACT observations simultaneous observation of the background and the source of gamma ray is needed. This observation mode (called wobbling) modifies images of events, which affects the quality of selection by neural networks. Thus, in this work, the results of the application of neural networks (NN) for image classification task on Monte Carlo (MC) images of TAIGA-IACTs are presented. The wobbling mode is considered together with the image adaptation for adequate analysis by NNs. Simultaneously, we explore several neural network structures that classify events both directly from images or through Hillas parameters extracted from images. In addition, by employing NNs, MC simulation data are used to evaluate the quality of the segregation of rare gamma events with the account of all necessary image modifications.

A postal drone in Russia crashed into a wall and smashed into pieces during its maiden flight. The unmanned aerial vehicle took off to deliver a small package to a village near Ulan-Ude, a city in Siberia, but hit a three-storey building shortly after lifting off from a mini launch pad in front of a crowd of spectators. The drone had been touted as a new way to deliver post in the rural Buryatia region, located more than 2,700 miles from the Russian capital Moscow. Video footage of the crash showed the vehicle taking off before veering into the apartment building and showering onlookers with debris. No one was harmed in the incident.

A Russian-made drone on its way to making its first parcel delivery has crashed into a wall just moments after taking off. The smash shocked local residents and regional officials who had gathered in the Siberian city of Ulan-Ude on Monday to watch the drone's maiden flight. The drone was sent to deliver a small package to a neighbouring village in the sparsely populated Buryatia region, more than 4,400km east of Moscow. Video footage showed the drone lifting off from a miniature launch pad bearing Russian Post's blue and white logo. A small crowd of spectators, present for the ceremony intended to showcase a new way to deliver mail in the region, were heard uttering expletives after the crash.

Want to know why mail drones aren't ready for prime time? The Siberian town of Ulan-Ude was expecting to beam with pride as organizer Rudron/Expeditor 3M tested a postal drone in the area for the first time, but they left red-faced after the inaugural flight went spectacularly wrong. The hexacopter courier went haywire moments after takeoff, smacking into the side of a building at high speed -- as you can see in the video below, it went from technological triumph to an embarrassing pile of scrap metal in a matter of seconds. It was a costly crash, too, as the drone reportedly cost about $20,000. It's not certain what went wrong, although regional leader Alexei Tsydenov speculated to Reuters that the 100-plus WiFi signals in the area might have played a part.

Russia's test launch of mail delivery service by drone ends in spectacular failure in the Siberian city Ulan-Ude.