A former OceanGate Expeditions customer who took a trip to see the Titanic wreckage two years ago described the dive as a "kamikaze operation." An international search and rescue operation is ongoing for five crew members on OceanGate's Titan sub, which went missing Sunday on a planned deep sea tourist expedition. Arthur Loibl, a retired German businessman and adventurer who went on the same trip in 2021, shared his experience with OceanGate in an interview with The Associated Press. "You have to be a little bit crazy to do this sort of thing," Loibl said. He explained that the idea of touring the Titanic wreckage came to him on a trip to the South Pole in 2016.

While ROVs vary in design and capability, they can generally travel much deeper than manned vessels, Englot said. "Those kind of vehicles usually have robotic arms that are capable of carrying a payload, grasping an object, grabbing and turning a knob or a valve or something like that," he added. As of Thursday morning, several with the ability to reach the ocean floor had been deployed in the Atlantic as the Titan's estimated initial supply of 96 hours of oxygen dwindled – including the Victor 6000, which descended from the French L'Atalante research vessel to the ocean floor. File image of an asset of the rescue efforts – the Victor 6000 – an unmanned French robot which can dive up to 6,000 metres. It has arms that can be remotely controlled to cut cables or otherwise help release a stuck vessel. However, it does not have the capability of lifting the submersible on its own.

Dik Barton, the first British man to dive to the Titanic wreck, speculates what could have happened to the Titan submersible missing in the North Atlantic. The U.S. Coast Guard announced Thursday that the Canadian vessel Horizon Arctic deployed a remotely operated vehicle (ROV) "that has reached the sea floor and began its search" for the missing OceanGate Titan submarine. It is the first time during the search that a vessel is combing the floor of the Atlantic Ocean for the missing vessel and its five passengers. Previous search efforts have involved the use of aircraft and sonar. "The French vessel L'Atalante is preparing their ROV to enter the water," the Coast Guard also said.

Fox News correspondent Molly Line has more on the search to rescue the five individuals on the Titanic voyage on'Special Report.' BOSTON – Three new vessels arrived "on-scene" in the Atlantic Ocean Wednesday morning to join search and rescue efforts for the missing OceanGate Titan sub as the estimated oxygen supply on board continues to dwindle. The U.S. Coast Guard said the new vessels bring additional tools to scan the ocean floor as they race against the clock to save the five people onboard: OceanGate CEO Stockton Rush, British businessman Hamish Harding, father-and-son Shahzada and Suleman Dawood, who are members of one of Pakistan's wealthiest families, and Paul-Henry Nargeolet, a former French navy officer and leading Titanic expert. "The John Cabot has side-scanning sonar capabilities and is conducting search patterns alongside the Skandi Vinland and the Atlantic Merlin," the Coast Guard said. The John Cabot is a Canadian coast guard vessel, the Atlantic Merlin is a Canadian remotely operated vehicle (ROV), and the Skandi Vinland is a commercial ROV, authorities said.

Russia has accused Ukraine of planning to attack annexed Crimea with long-range United States and British missiles and warned it would retaliate if that happened. Russian Defence Minister Sergey Shoigu told a meeting of military officials on Tuesday that Moscow possesses information that Ukraine plans to strike Crimea with US-supplied HIMARS long-range rocket systems and British-supplied Storm Shadow cruise missiles. "The use of these missiles outside the zone of our special military operation would mean that the United States and Britain would be fully dragged into the conflict and would entail immediate strikes on decision-making centres in Ukraine," Shoigu said. Russia annexed Ukraine's Crimean Peninsula in 2014 and considers it to be outside the scope of its invasion – which is focused in eastern and southern Ukraine, where Ukraine is fighting to retake territory. Kyiv, which says it is battling for its survival in a war of colonial conquest, said it wants to reclaim all of its territory, including Crimea, the home of Russia's Black Sea naval base.

Spatial processes observed in various fields, such as climate and environmental science, often occur on a large scale and demonstrate spatial nonstationarity. Fitting a Gaussian process with a nonstationary Mat\'ern covariance is challenging. Previous studies in the literature have tackled this challenge by employing spatial partitioning techniques to estimate the parameters that vary spatially in the covariance function. The selection of partitions is an important consideration, but it is often subjective and lacks a data-driven approach. To address this issue, in this study, we utilize the power of Convolutional Neural Networks (ConvNets) to derive subregions from the nonstationary data. We employ a selection mechanism to identify subregions that exhibit similar behavior to stationary fields. In order to distinguish between stationary and nonstationary random fields, we conducted training on ConvNet using various simulated data. These simulations are generated from Gaussian processes with Mat\'ern covariance models under a wide range of parameter settings, ensuring adequate representation of both stationary and nonstationary spatial data. We assess the performance of the proposed method with synthetic and real datasets at a large scale. The results revealed enhanced accuracy in parameter estimations when relying on ConvNet-based partition compared to traditional user-defined approaches.

This paper investigates the influence of incorporating spatiotemporal wind data on the performance of wind forecasting neural networks. While previous studies have shown that including spatial data enhances the accuracy of such models, limited research has explored the impact of different spatial and temporal scales of input wind data on the learnability of neural network models. In this study, convolutional neural networks (CNNs) are employed and trained using various scales of spatiotemporal wind data. The research demonstrates that using spatiotemporally correlated data from the surrounding area and past time steps for training a CNN favorably affects the predictive performance of the model. The study proposes correlation analyses, including autocorrelation and Pearson correlation analyses, to unveil the influence of spatiotemporal wind characteristics on the predictive performance of different CNN models. The spatiotemporal correlations and performances of CNN models are investigated in three regions: Korea, the USA, and the UK. The findings reveal that regions with smaller deviations of autocorrelation coefficients (ACC) are more favorable for CNNs to learn the regional and seasonal wind characteristics. Specifically, the regions of Korea, the USA, and the UK exhibit maximum standard deviations of ACCs of 0.100, 0.043, and 0.023, respectively. The CNNs wind prediction performances follow the reverse order of the regions: UK, USA, and Korea. This highlights the significant impact of regional and seasonal wind conditions on the performance of the prediction models.

Pragmatic reasoning plays a pivotal role in deciphering implicit meanings that frequently arise in real-life conversations and is essential for the development of communicative social agents. In this paper, we introduce a novel challenge, DiPlomat, aiming at benchmarking machines' capabilities on pragmatic reasoning and situated conversational understanding. Compared with previous works that treat different figurative expressions (e.g. metaphor, sarcasm) as individual tasks, DiPlomat provides a cohesive framework towards general pragmatic understanding. Our dataset is created through the utilization of Amazon Mechanical Turk ( AMT ), resulting in a total of 4, 177 multi-turn dialogues. In conjunction with the dataset, we propose two tasks, Pragmatic Identification and Reasoning (PIR) and Conversational Question Answering (CQA). Experimental results with state-of-the-art (SOTA) neural architectures reveal several significant findings: 1) large language models ( LLMs) exhibit poor performance in tackling this subjective domain; 2) comprehensive comprehension of context emerges as a critical factor for establishing benign human-machine interactions; 3) current models defect in the application of pragmatic reasoning. As a result, we call on more attention to improve the ability of context understanding, reasoning, and implied meaning modeling.

In recent years, there has been an increase in using deep learning for climate and weather modeling. Though results have been impressive, explainability and interpretability of deep learning models are still a challenge. A third wave of Artificial Intelligence (AI), which includes logic and reasoning, has been described as a way to address these issues. Neuro-symbolic AI is a key component of this integration of logic and reasoning with deep learning. In this work we propose a neuro-symbolic approach called Neuro-Symbolic Question-Answer Program Translator, or NS-QAPT, to address explainability and interpretability for deep learning climate simulation, applied to climate tipping point discovery. The NS-QAPT method includes a bidirectional encoder-decoder architecture that translates between domain-specific questions and executable programs used to direct the climate simulation, acting as a bridge between climate scientists and deep learning models. We show early compelling results of this translation method and introduce a domain-specific language and associated executable programs for a commonly known tipping point, the collapse of the Atlantic Meridional Overturning Circulation (AMOC).

Sea surface temperature (SST) is an essential climate variable that can be measured via ground truth, remote sensing, or hybrid model methodologies. Here, we celebrate SST surveillance progress via the application of a few relevant technological advances from the late 20th and early 21st century. We further develop our existing water cycle observation framework, Flux to Flow (F2F), to fuse AMSR-E and MODIS into a higher resolution product with the goal of capturing gradients and filling cloud gaps that are otherwise unavailable. Our neural network architecture is constrained to a deep convolutional residual regressive neural network. We utilize three snapshots of twelve monthly SST measurements in 2010 as measured by the passive microwave radiometer AMSR-E, the visible and infrared monitoring MODIS instrument, and the in situ Argo dataset ISAS. The performance of the platform and success of this approach is evaluated using the root mean squared error (RMSE) metric. We determine that the 1:1 configuration of input and output data and a large observation region is too challenging for the single compute node and dcrrnn structure as is. When constrained to a single 100 x 100 pixel region and a small training dataset, the algorithm improves from the baseline experiment covering a much larger geography. For next discrete steps, we envision the consideration of a large input range with a very small output range. Furthermore, we see the need to integrate land and sea variables before performing computer vision tasks like those within. Finally, we see parallelization as necessary to overcome the compute obstacles we encountered.