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During neurosurgical operations, surgeons can decide to acquire intraoperative data to better proceed with the removal of a tumor. A valid option is given by ultrasound (US) imaging, which can be easily obtained at subsequent surgical stages, giving therefore multiple u...

Many methods have been proposed to automatically extend knowledge bases, but the vast majority of these methods focus on finding plausible missing facts, and knowledge graph triples in particular. In this paper, we instead focus on automatically extending ontologies tha...

The presence of a robot in our everyday life can generate both positive and negative effects on us. While performing a difficult task, the presence of a robot can generate a negative effect on the performance and it can also increase the stress and anxiety levels. In or...

A metasurface is a surface that consists of artificial material, called metamaterial, with configurable electromagnetic properties. This paper presents work in progress on the design and formal verification of a programmable metasurface, the Hypersurface, as part of the...

Formal systems modelling offers a rigorous system-level analysis resulting in a precise and reliable specification. However, some issues remain: Modellers need to understand the requirements in order to formulate the models, formal verification may focus on safety prope...

In this paper, we present a deep learning architecture which addresses the problem of 3D semantic segmentation of unstructured point clouds (Fig. 1). Compared to previous work, we introduce grouping techniques which define point neighborhoods in the initial world space ...

As chip multi-processors (CMPs) are becoming more and more complex, software solutions such as parallel programming models are attracting a lot of attention. Task-based parallel programming models offer an appealing approach to utilize complex CMPs. However, the increas...

International audience; Recently, numerous physical attacks have been demonstrated against lattice-based schemes, often exploiting their unique properties such as the reliance on Gaussian distributions, rejection sampling and FFT-based polynomial multiplication. As the ...

Fundamentally, many problems in Machine Learning are understood as some form of function approximation; given a dataset \(\mathcal {D}\), learn a function \(\mathop {} f_{\textit{\textbf{X}} \rightarrow \textit{\textbf{Y}}}\). However, this overlooks the ubiquitous prob...

Multiscale simulations are an essential computational method in a range of research disciplines, and provide unprecedented levels of scientific insight at a tractable cost in terms of effort and compute resources. To provide this, we need such simulations to produce res...