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Building large datasets annotated with semantic information, such as FrameNet, is an expensive process. Consequently, such resources are unavailable for many languages and specific domains. This problem can be alleviated by using unsupervised approaches to induce the frames evoked by a collection of documents. That is the objective of the second task of SemEval 2019, which comprises three subtasks: clustering of verbs that evoke the same frame and clustering of arguments into both frame-specific slots and semantic roles. We approach all the subtasks by applying a graph clustering algorithm on contextualized embedding representations of the verbs and arguments. Using such representations is appropriate in the context of this task, since they provide cues for word-sense disambiguation. Thus, they can be used to identify different frames evoked by the same words. Using this approach we were able to outperform all of the baselines reported for the task on the test set in terms of Purity F 1 , as well as in terms of BCubed F 1 in most cases.

The SEAGULL project aims at the development of intelligent systems to support maritime situation awareness based on unmanned aerial vehicles. It proposes to create an intelligent maritime surveillance system by equipping unmanned aerial vehicles (UAVs) with different types of optical sensors. Optical sensors such as cameras (visible, infrared, multi and hyper spectral) can contribute significantly to the generation of situational awareness of maritime events such as (i) detection and georeferencing of oil spills or hazardous and noxious substances; (ii) tracking systems (e.g. vessels, shipwrecked, lifeboat, debris, etc.); (iii) recognizing behavioral patterns (e.g. vessels rendezvous, high-speed vessels, atypical patterns of navigation, etc.); and (iv) monitoring parameters and indicators of good environmental status. On-board transponders will be used for collision detection and avoidance mechanism (sense and avoid). This paper describes the core of the research and development work done during the first 2 years of the project with particular emphasis on the following topics: system architecture, automatic detection of sea vessels by vision sensors and custom designed computer vision algorithms; and a sense and avoid system developed in the theoretical framework of zero-sum differential games.

Resources such as FrameNet, which provide sets of semantic frame definitions and annotated textual data that maps into the evoked frames, are important for several NLP tasks. However, they are expensive to build and, consequently, are unavailable for many languages and domains. Thus, approaches able to induce semantic frames in an unsupervised manner are highly valuable. In this paper we approach that task from a network perspective as a community detection problem that targets the identification of groups of verb instances that evoke the same semantic frame and verb arguments that play the same semantic role. To do so, we apply a graph-clustering algorithm to a graph with contextualized representations of verb instances or arguments as nodes connected by edges if the distance between them is below a threshold that defines the granularity of the induced frames. By applying this approach to the benchmark dataset defined in the context of SemEval 2019, we outperformed all of the previous approaches to the task, achieving the current state-of-the-art performance.

Osteoarthritis (OA) is a painful and disabling musculoskeletal disorder, with a large impact on the global population, resulting in several limitations on daily activities. In OA, inflammation is frequent and mainly controlled through inflammatory cytokines released by immune cells. These outbalanced inflammatory cytokines cause cartilage extracellular matrix (ECM) degradation and possible growth of neuronal fibers into subchondral bone triggering pain. Even though pain is the major symptom of musculoskeletal diseases, there are still no effective treatments to counteract it and the mechanisms behind these pathologies are not fully understood. Thus, there is an urgent need to establish reliable models for assessing the molecular mechanisms and consequently new therapeutic targets. Models have been established to support this research field by providing reliable tools to replicate the joint tissue in vitro. Studies firstly started with simple 2D culture setups, followed by 3D culture focusing mainly on cell-cell interactions to mimic healthy and inflamed cartilage. Cellular approaches were improved by scaffold-based strategies to enhance cell-matrix interactions as well as contribute to developing mechanically more stable in vitro models. The progression of the cartilage tissue engineering would then profit from the integration of 3D bioprinting technologies as these provide 3D constructs with versatile structural arrangements of the 3D constructs. The upgrade of the available tools with dynamic conditions was then achieved using bioreactors and fluid systems. Finally, the organ-on-a-chip encloses all the state of the art on cartilage tissue engineering by incorporation of different microenvironments, cells and stimuli and pave the way to potentially simulate crucial biological, chemical, and mechanical features of arthritic joint. In this review, we describe the several available tools ranging from simple cartilage pellets to complex organ-on-a-chip platforms, including 3D tissue-engineered constructs and bioprinting tools. Moreover, we provide a fruitful discussion on the possible upgrades to enhance the in vitro systems making them more robust regarding the physiological and pathological modeling of the joint tissue/OA.

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Unmanned air systems are becoming ever more important in modern societies but raise a number of unresolved problems. There are legal issues with the operation of these vehicles in nonsegregated airspace, and a pressing requirement to solve these issues is the development and testing of reliable and safe mechanisms to avoid collision in flight. In this article, we describe a sense and avoid subsystem developed for a maritime patrol unmanned air system. The article starts with a description of the unmanned air system, that was developed specifically for maritime patrol operations, and proceeds with a discussion of possible ways to guarantee that the unmanned air system does not collide with other flying objects. In the system developed, the position of the unmanned air system is obtained by the global positioning system and that of other flying objects is reported via a data link with a ground control station. This assumes that the detection of those flying objects is done by a radar in the ground or by self-reporting via a traffic monitoring system (such as automatic identification system). The algorithm developed is based on game theory. The approach is to handle both the procedures, threat detection phase and collision avoidance maneuver, in a unified fashion, where the optimal command for each possible relative attitude of the obstacle is computed off-line, therefore requiring low processing power for real-time operation. This work was done under the research project named SEAGULL that aims to improve maritime situational awareness using fleets of unmanned air system, where collision avoidance becomes a major concern.