Artificial intelligence (AI) is being ubiquitously adopted to automate processes in science and industry. However, due to its often intricate and opaque nature, AI has been shown to possess inherent vulnerabilities which can be maliciously exploited with adversarial AI, potentially putting AI users and developers at both cyber and physical risk. In addition, there is insufficient comprehension of the real-world effects of adversarial AI and an inadequacy of AI security examinations; therefore, the growing threat landscape is unknown for many AI solutions. To mitigate this issue, we propose one of the first red team frameworks for evaluating the AI security of maritime autonomous systems. The framework provides operators with a proactive (secure by design) and reactive (post-deployment evaluation) response to securing AI technology today and in the future. This framework is a multi-part checklist, which can be tailored to different systems and requirements. We demonstrate this framework to be highly effective for a red team to use to uncover numerous vulnerabilities within a real-world maritime autonomous systems AI, ranging from poisoning to adversarial patch attacks. The lessons learned from systematic AI red teaming can help prevent MAS-related catastrophic events in a world with increasing uptake and reliance on mission-critical AI.

Open-World Recognition (OWR) is an emerging field that makes a machine learning model competent in rejecting the unknowns, managing them, and incrementally adding novel samples to the base knowledge. However, this broad objective is not practical for an agent that works on a specific task. Not all rejected samples will be used for learning continually in the future. Some novel images in the open environment may not belong to the domain of interest. Hence, identifying the unknown in the domain of interest is essential for a machine learning model to learn merely the important samples. In this study, we propose an evaluation protocol for estimating a model's capability in separating unknown in-domain (ID) and unknown out-of-domain (OOD). We evaluated using three approaches with an unknown domain and demonstrated the possibility of identifying the domain of interest using the pre-trained parameters through traditional transfer learning, Automated Machine Learning (AutoML), and Nearest Class Mean (NCM) classifier with First Integer Neighbor Clustering Hierarchy (FINCH). We experimented with five different domains: garbage, food, dogs, plants, and birds. The results show that all approaches can be used as an initial baseline yielding a good accuracy. In addition, a Balanced Accuracy (BACCU) score from a pre-trained model indicates a tendency to excel in one or more domains of interest. We observed that MobileNetV3 yielded the highest BACCU score for the garbage domain and surpassed complex models such as the transformer network. Meanwhile, our results also suggest that a strong representation in the pre-trained model is important for identifying unknown classes in the same domain. This study could open the bridge toward open-world recognition in domain-specific tasks where the relevancy of the unknown classes is vital.

The modeling and forecasting of sea ice conditions in the Arctic region are important tasks for ship routing, offshore oil production, and environmental monitoring. We propose the adaptive surrogate modeling approach named LANE-SI (Lightweight Automated Neural Ensembling for Sea Ice) that uses ensemble of relatively simple deep learning models with different loss functions for forecasting of spatial distribution for sea ice concentration in the specified water area. Experimental studies confirm the quality of a long-term forecast based on a deep learning model fitted to the specific water area is comparable to resource-intensive physical modeling, and for some periods of the year, it is superior. We achieved a 20% improvement against the state-of-the-art physics-based forecast system SEAS5 for the Kara Sea.

Satellite imagery solutions are widely used to study and monitor different regions of the Earth. However, a single satellite image can cover only a limited area. In cases where a larger area of interest is studied, several images must be stitched together to create a single larger image, called a mosaic, that can cover the area. Today, with the increasing number of satellite images available for commercial use, selecting the images to build the mosaic is challenging, especially when the user wants to optimize one or more parameters, such as the total cost and the cloud coverage percentage in the mosaic. More precisely, for this problem the input is an area of interest, several satellite images intersecting the area, a list of requirements relative to the image and the mosaic, such as cloud coverage percentage, image resolution, and a list of objectives to optimize. We contribute to the constraint and mixed integer lineal programming formulation of this new problem, which we call the \textit{satellite image mosaic selection problem}, which is a multi-objective extension of the polygon cover problem. We propose a dataset of realistic and challenging instances, where the images were captured by the satellite constellations SPOT, Pl\'eiades and Pl\'eiades Neo. We evaluate and compare the two proposed models and show their efficiency for large instances, up to 200 images.

The deputy commander of Russia's 14th Army Corps, Major-General Vladimir Zavadsky, has been killed in Ukraine, a top regional official confirmed. Zavadsky died "at a combat post in the special operation zone", Alexander Gusev, the governor of Russia's Voronezh region, said on Monday without providing any further details. The "special military operation" is the term Russia uses to describe the war in Ukraine, which it launched in February 2022. Gusev paid tribute to Zavadsky, calling him "a courageous officer, a real general and a worthy man". Zavadsky's death marked the seventh major-general confirmed dead by Russia, making him the 12th senior officer reported deceased since the onset of the war, investigative news outlet iStories reported. Meanwhile, on Tuesday, Ukrainian authorities reported that their military successfully downed 10 out of 17 attack drones launched by Russia overnight.

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.

Light Detection and Ranging (LiDAR) are fast emerging sensors in the field of Earth Observation. It is a remote sensing technology that utilizes laser beams to measure distances and create detailed three-dimensional representations of objects and environments. The potential of Full Waveform LiDAR is much greater than just height estimation and 3D reconstruction only. Overall shape of signal provides important information about properties of water body. However, the shape of FWL is unexplored as most LiDAR software work on point cloud by utilizing the maximum value within the waveform. Existing techniques in the field of LiDAR data analysis include depth estimation through inverse modeling and regression of logarithmic intensity and depth for approximating the attenuation coefficient. However, these methods suffer from limitations in accuracy. Depth estimation through inverse modeling provides only approximate values and does not account for variations in surface properties, while the regression approach for the attenuation coefficient is only able to generalize a value through several data points which lacks precision and may lead to significant errors in estimation. Additionally, there is currently no established modeling method available for predicting bottom reflectance. This research proposed a novel solution based on neural networks for parameter estimation in LIDAR data analysis. By leveraging the power of neural networks, the proposed solution successfully learned the inversion model, was able to do prediction of parameters such as depth, attenuation coefficient, and bottom reflectance. Performance of model was validated by testing it on real LiDAR data. In future, more data availability would enable more accuracy and reliability of such models.

There has been huge recent interest in the potential of making operational weather forecasts using machine learning techniques. As they become a part of the weather forecasting toolbox, there is a pressing need to understand how well current machine learning models can simulate high-impactweather events. We compare forecasts of Storm Ciar\'an, a European windstorm that caused sixteen deaths and extensive damage in Northern Europe, made by machine learning and numericalweather prediction models. The four machine learning models considered (FourCastNet, Pangu-Weather, GraphCast and FourCastNet-v2) produce forecasts that accurately capture the synoptic-scale structure of the cyclone including the position of the cloud head, shape of the warm sector and location of warm conveyor belt jet, and the large-scale dynamical drivers important for the rapid storm development such as the position of the storm relative to the upper-level jet exit. However, their ability to resolve the more detailed structures important for issuing weather warnings is more mixed. All of the machine learning models underestimate the peak amplitude of winds associated with the storm, only some machine learning models resolve the warm core seclusion and none of the machine learning models capture the sharp bent-back warm frontal gradient. Our study shows there is a great deal about the performance and properties of machine learning weather forecasts that can be derived from case studies of high-impact weather events such as Storm Ciar\'an.

The Manifold Hypothesis is a widely accepted tenet of Machine Learning which asserts that nominally high-dimensional data are in fact concentrated near a low-dimensional manifold, embedded in high-dimensional space. This phenomenon is observed empirically in many real world situations, has led to development of a wide range of statistical methods in the last few decades, and has been suggested as a key factor in the success of modern AI technologies. We show that rich and sometimes intricate manifold structure in data can emerge from a generic and remarkably simple statistical model -- the Latent Metric Model -- via elementary concepts such as latent variables, correlation and stationarity. This establishes a general statistical explanation for why the Manifold Hypothesis seems to hold in so many situations. Informed by the Latent Metric Model we derive procedures to discover and interpret the geometry of high-dimensional data, and explore hypotheses about the data generating mechanism. These procedures operate under minimal assumptions and make use of well known, scaleable graph-analytic algorithms.

NSC Communications Coordinator John Kirby responds to progressive pushback against U.S. aid to Israel on'FOX News Sunday.' Three commercial vessels were attacked in the Red Sea on Sunday, prompting a U.S. warship to shoot down multiple unmanned aerial vehicles (UAV) headed toward them. The development could signify a serious escalation in a series of maritime attacks in the Middle East linked to the Israel-Hamas war. "Today, there were four attacks against three separate commercial vessels operating in international waters in the southern Red Sea," a statement by U.S. Central Command (CENTCOM) explained. "These three vessels are connected to 14 separate nations." The USS Carney was in the southern Red Sea, just north of the Bab al-Mandab Strait, when it shot down three Houthi drones heading in its direction, a U.S. official told Fox News, adding that the action was taken in self-defense. The drones were launched from Houthi-controlled areas of Yemen, the official claimed.