Cognitive diagnosis is a fundamental and critical task in learning assessment, which aims to infer students' proficiency on knowledge concepts from their response logs. Current works assume each knowledge concept will certainly be tested and covered by multiple exercises. However, whether online or offline courses, it's hardly feasible to completely cover all knowledge concepts in several exercises. Restricted tests lead to undiscovered knowledge deficits, especially untested knowledge concepts(UKCs). In this paper, we propose a novel \underline{Dis}entangling Heterogeneous \underline{K}nowledge \underline{C}ognitive \underline{D}iagnosis framework on untested knowledge(DisKCD). Specifically, we leverage course grades, exercise questions, and resources to learn the potential representations of students, exercises, and knowledge concepts. In particular, knowledge concepts are disentangled into tested and untested based on the limiting actual exercises. We construct a heterogeneous relation graph network via students, exercises, tested knowledge concepts(TKCs), and UKCs. Then, through a hierarchical heterogeneous message-passing mechanism, the fine-grained relations are incorporated into the embeddings of the entities. Finally, the embeddings will be applied to multiple existing cognitive diagnosis models to infer students' proficiency on UKCs. Experimental results on real-world datasets show that the proposed model can effectively improve the performance of the task of diagnosing students' proficiency on UKCs. Our anonymous code is available at https://anonymous.4open.science/r/DisKCD.

Predicting ship trajectories is an essential task for maritime stakeholders, encompassing economic, security, and logistical considerations. Accurate trajectory prediction plays a pivotal role in optimising shipping routes, ensuring maritime safety, and managing resources efficiently. However, this endeavour has posed several challenges due to the vast amount of trajectory data generated in real-time and the intricate interplay of spatial and temporal factors. Traditionally, Long Short-Term Memory (LSTM) [1] and Gated Recurrent Units (GRU) [2] networks have been employed to model sequential and temporal data, and many researchers have tried to adapt these recurrent neural network (RNN) architectures to the spatio-temporal domain. While these RNN-based approaches have demonstrated success in various applications [3, 4, 5, 6], they typically neglect the crucial spatial component inherent in ship trajectories, such as the geographical coordinates and the intricate relationships between waypoints in a trajectory.

Accurately reconstructing the global ocean deoxygenation over a century is crucial for assessing and protecting marine ecosystem. Existing expert-dominated numerical simulations fail to catch up with the dynamic variation caused by global warming and human activities. Besides, due to the high-cost data collection, the historical observations are severely sparse, leading to big challenge for precise reconstruction. In this work, we propose OxyGenerator, the first deep learning based model, to reconstruct the global ocean deoxygenation from 1920 to 2023. Specifically, to address the heterogeneity across large temporal and spatial scales, we propose zoning-varying graph message-passing to capture the complex oceanographic correlations between missing values and sparse observations. Additionally, to further calibrate the uncertainty, we incorporate inductive bias from dissolved oxygen (DO) variations and chemical effects. Compared with in-situ DO observations, OxyGenerator significantly outperforms CMIP6 numerical simulations, reducing MAPE by 38.77%, demonstrating a promising potential to understand the "breathless ocean" in data-driven manner.

In this study, explainable machine learning techniques are applied to predict the toxicity of mussels in the Gulf of Trieste (Adriatic Sea) caused by harmful algal blooms. By analysing a newly created 28-year dataset containing records of toxic phytoplankton in mussel farming areas and toxin concentrations in mussels (Mytilus galloprovincialis), we train and evaluate the performance of ML models to accurately predict diarrhetic shellfish poisoning (DSP) events. The random forest model provided the best prediction of positive toxicity results based on the F1 score. Explainability methods such as permutation importance and SHAP identified key species (Dinophysis fortii and D. caudata) and environmental factors (salinity, river discharge and precipitation) as the best predictors of DSP outbreaks. These findings are important for improving early warning systems and supporting sustainable aquaculture practices.

Oleksandr Pivnenko, the commander of Ukraine's National Guard, said Russia was preparing "unpleasant surprises" and could try to advance on the northeastern city of Kharkiv, the second-biggest in the country, in the coming months. Pivnenko said Kyiv's forces were prepared to thwart any assault. Russia's Defence Minister Sergei Shoigu said Moscow would "increase the intensity of attacks on logistics centres and storage bases for Western weapons" in Ukraine, as he claimed advances on the front line in Pervomaiske, Bohdanivka and Novomykhailivka this month. At least nine people were injured after a Russian drone attack on the Black Sea port of Odesa, which damaged more than a dozen residential apartments. Four children, including two babies, were among the injured and were taken to hospital.

Autonomous Underwater Vehicles (AUVs) commonly utilize an inertial navigation system (INS) and a Doppler velocity log (DVL) for underwater navigation. To that end, their measurements are integrated through a nonlinear filter such as the extended Kalman filter (EKF). The DVL velocity vector estimate depends on retrieving reflections from the seabed, ensuring that at least three out of its four transmitted acoustic beams return successfully. When fewer than three beams are obtained, the DVL cannot provide a velocity update to bind the navigation solution drift. To cope with this challenge, in this paper, we propose a hybrid neural coupled (HNC) approach for seamless AUV navigation in situations of limited DVL measurements. First, we drive an approach to regress two or three missing DVL beams. Then, those beams, together with the measured beams, are incorporated into the EKF. We examined INS/DVL fusion both in loosely and tightly coupled approaches. Our method was trained and evaluated on recorded data from AUV experiments conducted in the Mediterranean Sea on two different occasions. The results illustrate that our proposed method outperforms the baseline loosely and tightly coupled model-based approaches by an average of 96.15%. It also demonstrates superior performance compared to a model-based beam estimator by an average of 12.41% in terms of velocity accuracy for scenarios involving two or three missing beams. Therefore, we demonstrate that our approach offers seamless AUV navigation in situations of limited beam measurements.

The robot uses its chest, hips and arms to handle objects -- just like we do. Theo works weekdays, weekends and nights and never complains about a sore spine despite performing hour upon hour of what, for a regular farm hand, would be backbreaking labor checking Dutch tulip fields for sick flowers. The boxy robot -- named after a retired employee at the WAM Pennings farm near the Dutch North Sea coast -- is a new high-tech weapon in the battle to root out disease from the bulb fields as they erupt into a riot of springtime color. On a windy spring morning, the robot trundled Tuesday along rows of yellow and red "goudstuk" tulips, checking each plant and, when necessary, killing diseased bulbs to prevent the spread of the tulip-breaking virus. The dead bulbs are removed from healthy ones in a sorting warehouse after they have been harvested.

It is, however, naval drones that have made Russia's Black Sea fleet particularly vulnerable. Under relentless attacks, Moscow was forced to withdraw the core of its fleet from Crimea and move them further east, to Novorossiysk. And even there, Russian ships remain within reach of Ukrainian drones.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Russian forces threatened to shoot down a French surveillance aircraft patrolling in international airspace over the Black Sea, a signal of increasingly aggressive behavior from Moscow as its invasion of Ukraine struggles to make headway, French defense officials said Thursday. "A Russian air traffic control system threatened to shoot down French aircraft in the Black Sea when we were in a free international zone where we patrol," the French defense minister, Sébastien Lecornu, said on RTL radio. A French military spokesman, Col. Pierre Gaudillière, said Lecornu was referring to an incident in mid-November that involved one of France's four giant Airborne Warning and Control System, or AWACS, surveillance aircraft that was flying over international waters in the Black Sea.

Ukraine said it critically damaged the Caesar Kunikov, a Russian landing warship, off occupied Crimea, in a drone attack, the latest blow to the Russian navy's Black Sea Fleet. Ukraine said the ship, one of Russia's newest vessels, had a crew of 87 and had taken part in wars in Georgia and Syria as well as Ukraine. There was no official comment from Russia on the attack. Newly-appointed Ukrainian armed forces chief Oleksandr Syrskyii visited troops fighting around the key flashpoint of Avdiivka on the eastern front line, and described the situation as "extremely complex and stressful". Syrskyii, who was accompanied by Defence Minister Rustem Umerov, said Russian forces had "a numerical advantage in personnel".