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The field of maritime surveillance is one of great strategical importance from the point of view of both civil and military applications. The growing availability of spaceborne imagery makes it a great tool for ship detection, especially when paired with information from the automatic identification system (AIS). However, small vessels can be challenging targets for spaceborne sensors without relatively high resolution. Moreover, when faced with non-cooperative targets, hull detection alone is insufficient for obtaining critical information like target speed and heading. The wakes generated by the movement of ships can be used to solve both of these issues. Several interesting solutions have been developed over the years, based on both traditional and learning-based methodologies. This review aims to provide the first thorough overview of ship wake detection solutions, highlighting the key ideas behind traditional applications, then covering more innovative applications based on deep learning (DL), to serve as a solid starting point for present and future researchers interested in the field. [ABSTRACT FROM AUTHOR]

When speaking, people often produce gestures that are closely timed with the speech with which they constitute a semantically coherent unit. Analyzing the temporal patterns between the two modalities may reveal insights about how speakers plan them. Using elicited narratives, we tested English/French monolinguals and bilinguals to check whether bilinguals, known to experience a higher degree of competition in lexical access, show a different pattern of gesture-speech alignment compared to that of monolinguals. Results revealed no difference in the temporal patterns between gestures and co-semantic speech for the two language groups. Synchronous gestures were significantly more frequent than asynchronous ones; asynchronous gestures both preceded and followed the correlated speech, yet preceding gestures tended to occur more often. A qualitative analysis conducted for asynchronous gestures revealed that they may serve a rhetoric function. We argue that the variability in gesture-speech timing results from speakers' strategic use of gesture.

Observation frequency is analyzed over four areas of the Mediterranean basis where poaching, illegal fishing, and illegal trafficking of goods and people are active. To this end, a geometrical observation and dynamical model is utilized which accounts for multiple satellites and multiple orbital planes and is applied to SIASGE and Sentinel-1 missions. Statistics show that a few hours are needed in the mean to reobserve the same area. [ABSTRACT FROM AUTHOR]

We consider pure exchange economies with a finite number of private goods and the choice of non-Samuelsonian public goods. For this type of economies, [5] proposed the notion of cost share equilibrium with individual payments for public goods varying according to individual benefits. This situation arises naturally when a level of provision is interpreted as a whole configuration of public policies or when cost share functions are interpreted as voluntary contributions rather than predetermined tax systems (see [31]). We establish the equivalence of cost share equilibria with cooperative and non-cooperative game-theoretic solutions: 1. we characterize cost share equilibria as those allocations which cannot be improved upon by society; 2. we characterize cost share equilibria as the Nash equilibria of a game with two players. The cooperative solutions analyzed in the paper are defined via a contribution scheme which captures the fraction of the total cost of collective goods that each coalition of agents is expected to cover. [ABSTRACT FROM AUTHOR]

The wakes generated by moving vessels represent relevant patterns in remotely sensed images. They are a marker of ship presence and can be processed to infer route, speed, size, and type of ships. Automatic wake detection can be exploited by law enforcement agencies and local authorities for ensuring a wide range of applications, including maritime traffic surveillance, border control, and protection of marine protected areas. The topic is thus attracting increasing interest from the remote sensing community. This letter contributes in this context presenting a novel approach based on the detection of the keypoints of wake components by convolutional neural networks (CNNs) in electro-optical satellite imagery. The selected approach to deep learning (DL) relies on a transfer-learning procedure fine-tuning the ImageNet weights. This is performed through an ad hoc developed dataset realized from Sentinel-2 multispectral images and automatic identification system (AIS) data in northern Europe. The experimental results confirm the robustness of the proposed method, which is tested against different spectral bands from visible to near-infrared and also by a domain shifting on lower resolution Landsat-9 images. Fractional errors in the positioning of the wake vertex are lower than 10% and the achieved heading accuracy is below 10°. The proposed method is faster than the traditional approaches based on Radon transform (RT), and due to its lightweight nature, our model can be executed efficiently on edge-AI devices, enabling real-time processing onboard. [ABSTRACT FROM AUTHOR]