Multilingual Name Entity Recognition and Intent Classification Employing Deep Learning Architectures

Named Entity Recognition and Intent Classification are among the most important subfields of the field of Natural Language Processing. Recent research has lead to the development of faster, more sophisticated and efficient models to tackle the problems posed by those two tasks. In this work we explore the effectiveness of two separate families of Deep Learning networks for those tasks: Bidirectional Long Short-Term networks and Transformer-based networks. The models were trained and tested on the ATIS benchmark dataset for both English and Greek languages. The purpose of this paper is to present a comparative study of the two groups of networks for both languages and showcase the results of our experiments. The models, being the current state-of-the-art, yielded impressive results and achieved high performance.Comment: 24 pages, 5 figures, 11 tables, dataset availabl

Ultrafine Fe-Fe2Ti eutectics produced by laser metal deposition: processing and characterization

Additive manufacturing (AM) presents advantages over the traditional production methods such as the ability to produce near net-shape components with reduced material usage and a weight-optimized component design. Laser-based AM process methods, i.e. Laser Metal Deposition (LMD) and Selective Laser Melting (SLM), provide rapid and directional solidification in the melt pool and hence the possibility to produce ultrafine microstructures. These conditions hold great promise for the production of novel eutectic materials with ultrafine microstructural length scales. Eutectics can benefit immediately and dramatically from rapid solidification, since it has been shown [1, 2] that the reduction of the eutectic spacing leads to increasing the materials strength along with increasing ductility and fracture toughness. In this presentation, we describe our research on Fe-Fe2Ti eutectics produced by Laser Metal Deposition (LMD). We will discuss the effect of process parameters such as Laser scanning velocity and Laser power on the microstructure on distinct length scales, encompassing features such as eutectic grain size, interlayer boundary morphology and eutectic spacing. We will show that the lamellar eutectic consisting of hexagonal Laves-phase Fe2Ti and the BCC a(Fe) can be processed to above 99.8 % density, while reaching lamellar spacing in the range of 50 to 200 nm. Furthermore, mechanical properties will be discussed based on microhardness measurements and in-situ micromechanical testing inside an SEM

Strategies and performance of the CMS silicon tracker alignment during LHC Run 2

The strategies for and the performance of the CMS silicon tracking system alignment during the 2015–2018 data-taking period of the LHC are described. The alignment procedures during and after data taking are explained. Alignment scenarios are also derived for use in the simulation of the detector response. Systematic effects, related to intrinsic symmetries of the alignment task or to external constraints, are discussed and illustrated for different scenarios