Is the fall of Pokrovsk inevitable? Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? Russia launched "massive" attacks on Ukraine's capital Kyiv, killing at least six people in the Desnianskyi district, the city's Mayor Vitali Klitschko wrote on Telegram. At least 35 people were also injured.

In a highly globalized world, it is important for multi-modal large language models (MLLMs) to recognize and respond correctly to mixed-cultural inputs. For example, a model should correctly identify kimchi (Korean food) in an image both when an Asian woman is eating it, as well as an African man is eating it. However, current MLLMs show an over-reliance on the visual features of the person, leading to misclassification of the entities. To examine the robustness of MLLMs to different ethnicity, we introduce MixCuBe, a cross-cultural bias benchmark, and study elements from five countries and four ethnicities. Our findings reveal that MLLMs achieve both higher accuracy and lower sensitivity to such perturbation for high-resource cultures, but not for low-resource cultures. GPT-4o, the best-performing model overall, shows up to 58% difference in accuracy between the original and perturbed cultural settings in low-resource cultures. Our dataset is publicly available at: https://huggingface.co/datasets/kyawyethu/MixCuBe.

President Vladimir Putin on Saturday apologized to Azerbaijan's leader for what the Kremlin called a "tragic incident" over Russia in which an Azerbaijan Airlines plane crashed after Russian air defences were fired against Ukrainian drones. The extremely rare publicized apology from Putin was the closest Moscow had come to accepting some blame for Wednesday's disaster, although the Kremlin statement did not say Russia had shot down the plane, only noting that a criminal case had been opened. Flight J2-8243, en route from Baku to the Chechen capital Grozny, crash-landed on Wednesday near Aktau in Kazakhstan after diverting from southern Russia, where Ukrainian drones were reported to be attacking several cities. At least 38 people were killed.

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".

If you've ever got an advert on social media for something you were just talking about, it might be more than an uncanny coincidence. Thanks to virtual assistants like Siri and Alexa, your smartphone isconstantly listening to everything you say. Worryingly, as long as you have consented to the terms and conditions, there is nothing illegal about using that data to bombard you with hyper-specific adverts. Luckily, experts at NordVPN have devised a simple test to work out if your phone is really eavesdropping on your conversations. By deliberately discussing random topics within earshot of your phone, you can see how long it takes for these subjects to appear in your social media feeds.

We study an emerging and intriguing problem of multimodal temporal event forecasting with large language models. Compared to using text or graph modalities, the investigation of utilizing images for temporal event forecasting has not been fully explored, especially in the era of large language models (LLMs). To bridge this gap, we are particularly interested in two key questions of: 1) why images will help in temporal event forecasting, and 2) how to integrate images into the LLM-based forecasting framework. To answer these research questions, we propose to identify two essential functions that images play in the scenario of temporal event forecasting, i.e., highlighting and complementary. Then, we develop a novel framework, named MM-Forecast. It employs an Image Function Identification module to recognize these functions as verbal descriptions using multimodal large language models (MLLMs), and subsequently incorporates these function descriptions into LLM-based forecasting models. To evaluate our approach, we construct a new multimodal dataset, MidEast-TE-mm, by extending an existing event dataset MidEast-TE-mini with images. Empirical studies demonstrate that our MM-Forecast can correctly identify the image functions, and further more, incorporating these verbal function descriptions significantly improves the forecasting performance. The dataset, code, and prompts are available at https://github.com/LuminosityX/MM-Forecast.

In recent years, interplanetary exploration has gained significant momentum, leading to a focus on the development of launch vehicles. However, the critical technology of edl mechanisms has not received the same level of attention and remains less mature and capable. To address this gap, we took advantage of the REXUS program to develop a pioneering edl mechanism. We propose an alternative to conventional, parachute based landing vehicles by utilizing autorotation. Our approach enables future additions such as steerability, controllability, and the possibility of a soft landing. To validate the technique and our specific implementation, we conducted a sounding rocket experiment on REXUS29. The systems design is outlined with relevant design decisions and constraints, covering software, mechanics, electronics and control systems. Furthermore, an emphasis will also be the organization and setup of the team entirely made up and executed by students. The flight results on REXUS itself are presented, including the most important outcomes and possible reasons for mission failure. We have not archived an autorotation based landing, but provide a reliable way of building and operating such vehicles. Ultimately, future works and possibilities for improvements are outlined. The research presented in this paper highlights the need for continued exploration and development of edl mechanisms for future interplanetary missions. By discussing our results, we hope to inspire further research in this area and contribute to the advancement of space exploration technology.

The upcoming landscape of Earth Observation missions will defined by networked heterogeneous nanosatellite constellations required to meet strict mission requirements, such as revisit times and spatial resolution. However, scheduling satellite communications in these satellite networks through efficiently creating a global satellite Contact Plan (CP) is a complex task, with current solutions requiring ground-based coordination or being limited by onboard computational resources. The paper proposes a novel approach to overcome these challenges by modeling the constellations and CP as dynamic networks and employing graph-based techniques. The proposed method utilizes a state-of-the-art dynamic graph neural network to evaluate the performance of a given CP and update it using a heuristic algorithm based on simulated annealing. The trained neural network can predict the network delay with a mean absolute error of 3.6 minutes. Simulation results show that the proposed method can successfully design a contact plan for large satellite networks, improving the delay by 29.1%, similar to a traditional approach, while performing the objective evaluations 20x faster.