Exploring planetary bodies with lower gravity, such as the moon and Mars, allows legged robots to utilize jumping as an efficient form of locomotion thus giving them a valuable advantage over traditional rovers for exploration. Motivated by this fact, this paper presents the design, simulation, and learning-based "in-flight" attitude control of Olympus, a jumping legged robot tailored to the gravity of Mars. First, the design requirements are outlined followed by detailing how simulation enabled optimizing the robot's design - from its legs to the overall configuration - towards high vertical jumping, forward jumping distance, and in-flight attitude reorientation. Subsequently, the reinforcement learning policy used to track desired in-flight attitude maneuvers is presented. Successfully crossing the sim2real gap, extensive experimental studies of attitude reorientation tests are demonstrated.

Modern neural language models that are widely used in various NLP tasks risk memorizing sensitive information from their training data. Understanding this memorization is important in real world applications and also from a learningtheoretical perspective. An open question in previous studies of language model memorization is how to filter out "common" memorization. In fact, most memorization criteria strongly correlate with the number of occurrences in the training set, capturing memorized familiar phrases, public knowledge, templated texts, or other repeated data. We formulate a notion of counterfactual memorization which characterizes how a model's predictions change if a particular document is omitted during training. We identify and study counterfactually-memorized training examples in standard text datasets. We estimate the influence of each memorized training example on the validation set and on generated texts, showing how this can provide direct evidence of the source of memorization at test time.

We did not find significant difference between the two datasets.

Efficient use of cultivated areas is a necessary factor for sustainable development of agriculture and ensuring food security. Along with the rapid development of satellite technologies in developed countries, new methods are being searched for accurate and operational identification of cultivated areas. In this context, identification of cropland boundaries based on spectral analysis of data obtained from satellite images is considered one of the most optimal and accurate methods in modern agriculture. This article proposes a new approach to determine the suitability and green index of cultivated areas using satellite data obtained through the "Google Earth Engine" (GEE) platform. In this approach, two powerful algorithms, "SNIC (Simple Non-Iterative Clustering) Super Pixels" and "Canny Edge Detection Method", are combined. The SNIC algorithm combines pixels in a satellite image into larger regions (super pixels) with similar characteristics, thereby providing better image analysis. The Canny Edge Detection Method detects sharp changes (edges) in the image to determine the precise boundaries of agricultural fields. This study, carried out using high-resolution multispectral data from the Sentinel-2 satellite and the Google Earth Engine JavaScript API, has shown that the proposed method is effective in accurately and reliably classifying randomly selected agricultural fields. The combined use of these two tools allows for more accurate determination of the boundaries of agricultural fields by minimizing the effects of outliers in satellite images. As a result, more accurate and reliable maps can be created for agricultural monitoring and resource management over large areas based on the obtained data. By expanding the application capabilities of cloud-based platforms and artificial intelligence methods in the agricultural field.

Russian President Vladimir Putin has apologised to his Azerbaijani counterpart Ilham Aliyev for what he called a "tragic incident" following the deadly crash of an Azerbaijan Airlines plane this week in Kazakhstan. The plane was flying on Wednesday from Azerbaijan's capital of Baku to Grozny, the regional capital of the Russian republic of Chechnya, when it turned towards Kazakhstan and crashed while attempting to land. In a statement on Saturday, the Kremlin said Russian air defence systems were firing near Grozny due to a Ukrainian drone strike, but stopped short of saying one of these hit the plane. "Vladimir Putin apologised for the tragic incident that occurred in Russian airspace and once again expressed his deep and sincere condolences to the families of the victims and wished a speedy recovery to the injured," the Kremlin said. "At that time, Grozny, Mozdok and Vladikavkaz were being attacked by Ukrainian unmanned aerial vehicles, and Russian air defence systems repelled these attacks."

The Kremlin released a statement on Saturday noting Putin had spoken to Azerbaijan's president Ilham Aliyev by phone. "(President) Vladimir Putin apologised for the tragic incident that occurred in Russian airspace and once again expressed his deep and sincere condolences to the families of the victims and wished a speedy recovery to the injured," the statement said. Prior to Saturday, the Kremlin had not yet commented on the crash. But Russian aviation authorities had said the situation in the region was "very complicated" due to Ukrainian drone strikes on Chechnya. Aviation experts and others in Azerbaijan believe the plane's GPS systems were affected by electronic jamming and it was then damaged by shrapnel from Russian air defence missile blasts.

Azerbaijan is observing a day of mourning for the victims of an air crash that killed 38 people. At least 29 people survived the deadly crash on Christmas day. Azerbaijan observed a nationwide moment of silence on Thursday, with national flags lowered, traffic coming to a halt at noon, and signals sounding from ships and trains across the country. Earlier, Azerbaijani President Ilham Aliyev declared Thursday a day of mourning and cancelled a planned visit to Russia for an informal summit of the Commonwealth of Independent States (CIS), a grouping of former Soviet nations. Aliyev's office said the president "ordered the prompt initiation of urgent measures to investigate the causes of the disaster".

An Embraer passenger jet crashed near the city of Aktau in Kazakhstan on Wednesday, killing 38 people, after diverting from an area of Russia that Moscow has recently defended against Ukrainian drone attacks. Twenty-nine survivors received hospital treatment. Azerbaijan Airlines flight J2-8243 had flown hundreds of miles off its scheduled route from Azerbaijan to Russia to crash on the opposite shore of the Caspian Sea, after what Russia's aviation watchdog said was an emergency that may have been caused by a bird strike. But an aviation expert suggested that cause seemed unlikely.