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In the manufacturing process of Carbon Fiber Reinforced Polymer (CFRP) mirrors (replicated from a mandrel) the orientation of the unidirectional carbon fiber layers (layup) has a direct influence on different aspects of the final product, like its general (large scale) shape and local deformations. In particular, optical methods used to evaluate the surfaces quality, can reveal the presence of print-through, a very common issue in CFPR manufacture. In practical terms, the surfaces irregularities induced, among other artifacts, by print-through, produce unwanted scattering effects, which are usually mitigated applying extra layers of different materials to the surface. Since one of the main goals of CFPR mirrors is to decrease the final weight of the whole mirror system, adding more material goes in the opposite direction of that. For this reason a different layup method is being developed with the goal of decreasing print-through and improving sphericity while maintaining mechanical qualities and without the addition of extra material in the process.
Convolutional neural networks have demonstrated high accuracy on various tasks in recent years. However, they are extremely vulnerable to adversarial examples. For example, imperceptible perturbations added to clean images can cause convolutional neu
Planet Formation research is blooming in an era where we are moving from speaking about protoplanetary disks to planet forming disks (Andrews et al., 2018). However, this transition is still motivated by indirect (but convincing) hints. Up to date, t
The surface quality of replicated CFRP mirrors is ideally expected to be as good as the mandrel from which they are manufactured. In practice, a number of factors produce surface imperfections in the final mirrors at different scales. To understand w
Current large-scale language models can be politically biased as a result of the data they are trained on, potentially causing serious problems when they are deployed in real-world settings. In this paper, we describe metrics for measuring political
Automatic detection of toxic language plays an essential role in protecting social media users, especially minority groups, from verbal abuse. However, biases toward some attributes, including gender, race, and dialect, exist in most training dataset