There was no confirmation from Russia's navy that the Caesar Kunikov had been sunk in the Black Sea, merely that six Ukrainian drones had been destroyed. Video appearing to show the aftermath of the Ukrainian attack was uploaded only recently, BBC Verify confirmed.

Russia's Black Sea Fleet suffered significant losses over the five months following the collapse of the U.N.-brokered grain deal as Ukraine staked a strong claim over major routes through the Black Sea. Russia's Black Sea fleet has suffered severe setbacks as Ukrainian forces continue to cripple a major piece of Moscow's war effort. Last week, Ukrainian media touted a major victory over the Russian fleet with the publication of a video that allegedly showed the destruction of a nearly 70 million missile ship, the Ivanovets. Multiple drones hit the vessel and sank it, with the crew's fate unknown. "As a result of a number of direct hits to the hull, the Russian ship received damage that was incompatible with further movement – the Ivanovets tilted to the stern and sank," said the Military Informant Telegram channel.

Ukraine has said it used sea drones to attack and destroy a Russian warship in the Black Sea near the Russian-annexed Crimean peninsula. The military intelligence agency, known by its Ukrainian acronym GUR, published a video on Thursday that it said depicted a naval drone attack on the missile-armed corvette Ivanovets the night before. The grainy footage, running about 2 and a half minutes and accompanied by a dramatic soundtrack, showed a number of explosions, and the ship eventually listing to one side. It ended with the vessel sinking stern-first into the sea. "As a result of a number of direct hits to the hull, the Russian ship suffered damage incompatible with further movement," the intelligence agency said in a statement accompanying the video, apparently made up of live feeds from the drones.

Oleksandr Prokudin, the governor of the southern Ukrainian region of Kherson, said two French volunteer aid workers were killed after a Russian drone attack on the town of Beryslav. Four people were injured, three of them foreigners. One person was killed and two injured in Russian shelling and rocket attacks on villages in the eastern Donetsk region, the Ukrainian presidential office said. Ukraine said four people were injured in a Russian missile attack on a medical facility in the eastern Kharkiv region, near the front line town of Kupiansk. Ukraine's military intelligence agency GUR, said it attacked and sank the Russian corvette Ivanovets in the Black Sea using undersea drones.

Autonomous underwater vehicles are specialized platforms engineered for deep underwater operations. Critical to their functionality is autonomous navigation, typically relying on an inertial navigation system and a Doppler velocity log. In real-world scenarios, incomplete Doppler velocity log measurements occur, resulting in positioning errors and mission aborts. To cope with such situations, a model and learning approaches were derived. This paper presents a comparative analysis of two cutting-edge deep learning methodologies, namely LiBeamsNet and MissBeamNet, alongside a model-based average estimator. These approaches are evaluated for their efficacy in regressing missing Doppler velocity log beams when two beams are unavailable. In our study, we used data recorded by a DVL mounted on an autonomous underwater vehicle operated in the Mediterranean Sea. We found that both deep learning architectures outperformed model-based approaches by over 16% in velocity prediction accuracy.

Ukraine's air force said Russia launched 14 attack drones and five missiles on the southern Black Sea regions with air defence systems destroying 11 of the drones. The Ministry of Internal Affairs of Ukraine said six people were injured in the historic city of Odesa and residential buildings and a warehouse were damaged. Ukrainian security sources said they orchestrated a drone attack on an oil refinery in the southern Russian town of Tuapse, about 240 kilometres (150 miles) southeast of the Russian-annexed Crimean peninsula. The attack caused a major fire, but there were no reports of casualties. Nepal's Foreign Minister Narayan Prakash Saud told the Associated Press news agency that Nepal had asked Russia to send back hundreds of Nepali nationals who had been recruited to fight against Ukraine and repatriate the bodies of those who had died in the conflict.

Diffusion-based video editing have reached impressive quality and can transform either the global style, local structure, and attributes of given video inputs, following textual edit prompts. However, such solutions typically incur heavy memory and computational costs to generate temporally-coherent frames, either in the form of diffusion inversion and/or cross-frame attention. In this paper, we conduct an analysis of such inefficiencies, and suggest simple yet effective modifications that allow significant speed-ups whilst maintaining quality. Moreover, we introduce Object-Centric Diffusion, coined as OCD, to further reduce latency by allocating computations more towards foreground edited regions that are arguably more important for perceptual quality. We achieve this by two novel proposals: i) Object-Centric Sampling, decoupling the diffusion steps spent on salient regions or background, allocating most of the model capacity to the former, and ii) Object-Centric 3D Token Merging, which reduces cost of cross-frame attention by fusing redundant tokens in unimportant background regions. Both techniques are readily applicable to a given video editing model \textit{without} retraining, and can drastically reduce its memory and computational cost. We evaluate our proposals on inversion-based and control-signal-based editing pipelines, and show a latency reduction up to 10x for a comparable synthesis quality.

Sea wave monitoring is key in many applications in oceanography such as the validation of weather and wave models. Conventional in situ solutions are based on moored buoys whose measurements are often recognized as a standard. However, being exposed to a harsh environment, they are not reliable, need frequent maintenance, and the datasets feature many gaps. To overcome the previous limitations, we propose a system including a buoy, a micro-seismic measuring station, and a machine learning algorithm. The working principle is based on measuring the micro-seismic signals generated by the sea waves. Thus, the machine learning algorithm will be trained to reconstruct the missing buoy data from the micro-seismic data. As the micro-seismic station can be installed indoor, it assures high reliability while the machine learning algorithm provides accurate reconstruction of the missing buoy data. In this work, we present the methods to process the data, develop and train the machine learning algorithm, and assess the reconstruction accuracy. As a case of study, we used experimental data collected in 2014 from the Northern Tyrrhenian Sea demonstrating that the data reconstruction can be done both for significant wave height and wave period. The proposed approach was inspired from Data Science, whose methods were the foundation for the new solutions presented in this work. For example, estimating the period of the sea waves, often not discussed in previous works, was relatively simple with machine learning. In conclusion, the experimental results demonstrated that the new system can overcome the reliability issues of the buoy keeping the same accuracy.

Sperm whales (Physeter macrocephalus) navigate underwater with a series of impulsive, click-like sounds known as echolocation clicks. These clicks are characterized by a multipulse structure (MPS) that serves as a distinctive pattern. In this work, we use the stability of the MPS as a detection metric for recognizing and classifying the presence of clicks in noisy environments. To distinguish between noise transients and to handle simultaneous emissions from multiple sperm whales, our approach clusters a time series of MPS measures while removing potential clicks that do not fulfil the limits of inter-click interval, duration and spectrum. As a result, our approach can handle high noise transients and low signal-to-noise ratio. The performance of our detection approach is examined using three datasets: seven months of recordings from the Mediterranean Sea containing manually verified ambient noise; several days of manually labelled data collected from the Dominica Island containing approximately 40,000 clicks from multiple sperm whales; and a dataset from the Bahamas containing 1,203 labelled clicks from a single sperm whale. Comparing with the results of two benchmark detectors, a better trade-off between precision and recall is observed as well as a significant reduction in false detection rates, especially in noisy environments. To ensure reproducibility, we provide our database of labelled clicks along with our implementation code.

Change-point detection (CPD) is crucial for identifying abrupt shifts in data, which influence decision-making and efficient resource allocation across various domains. To address the challenges posed by the costly and time-intensive data acquisition in CPD, we introduce the Derivative-Aware Change Detection (DACD) method. It leverages the derivative process of a Gaussian process (GP) for Active Learning (AL), aiming to pinpoint change-point locations effectively. DACD balances the exploitation and exploration of derivative processes through multiple data acquisition functions (AFs). By utilizing GP derivative mean and variance as criteria, DACD sequentially selects the next sampling data point, thus enhancing algorithmic efficiency and ensuring reliable and accurate results. We investigate the effectiveness of DACD method in diverse scenarios and show it outperforms other active learning change-point detection approaches.