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In the last few years the resolution of NLP tasks with architectures composed of neural models has taken vogue. There are many advantages to using these approaches especially because there is no need to do features engineering. In this paper, we make a survey of a Deep Learning architecture that propose a resolutive approach to some classical tasks of the NLP. The Deep Learning architecture is based on a cutting-edge model that exploits both word-level and character-level representations through the combination of bidirectional LSTM, CNN and CRF. This architecture has provided cutting-edge performance in several sequential labeling activities for the English language. The architecture that will be treated uses the same approach for the Italian language. The same guideline is extended to perform a multi-task learning involving PoS labeling and sentiment analysis. The results show that the system performs well and achieves good results in all activities. In some cases it exceeds the best systems previously developed for Italian.

This study is focused on lives of twelve women who prepared their doctorates in mathematics at the Faculty of Philosophy of the German University in Prague in the years 1882–1945, respectively at the Faculty of Science of the Czech University in Prague in the years 1882–1920 and 1921–1945 (known as Charles University in Prague in the latter period). In the first part, a short description of the historical background about women's studies at the universities in the Czech lands and a statistical overview of all PhD degrees in mathematics awarded at both universities in Prague is given for a better understanding of the situation with women's doctoral procedures. In the second part, a description of the successful doctoral procedures in mathematics of three women at the German University in Prague and of eight women at Charles University in Prague, as well as one unsuccessful doctoral procedure, are presented.

On découpe ce document complexe en plusieurs sous-fichiers séparés. Cela permettra notamment de réarranger les transparents facilement lors de l'élaboration du document.

Esta presentación algunas definiciones y resultados del análisis complejo; todas ellas presentadas con el fin de dar una prueba completa del principio de identidad y del principio del argumento. Referencias de la presentación: Basic Complex ANalysis, 3rd Ed. Jerrold E. Marsden, Michael J. Hoffman.

This paper implements Simultaneous Localization and Mapping (SLAM) technique to construct a map of a given environment. A Real Time Appearance Based Mapping (RTAB-Map) approach was taken for accomplishing this task. Initially, a 2d occupancy grid and 3d octomap was created from a provided simulated environment. Next, a personal simulated environment was created for mapping as well. In this appearance based method, a process called Loop Closure is used to determine whether a robot has seen a location before or not. In this paper, it is seen that RTAB-Map is optimized for large scale and long term SLAM by using multiple strategies to allow for loop closure to be done in real time and the results depict that it can be an excellent solution for SLAM to develop robots that can map an environment in both 2d and 3d.

The Antarctic Impulsive Transient Antenna (ANITA) is a NASA long-duration balloon experiment with the primary goal of detecting ultra-high-energy (\(> 10^{18}\)eV) neutrinos via the Askaryan Effect. In the fourth ANITA mission, the Tunable Universal Filter Frontend (TUFF) boards were deployed for mitigation of narrow-band, anthropogenic noise with tunable, switchable notch filters. They contributed to a factor of 2.8 higher total instrument livetime in ANITA-4 compared to ANITA-3. A search for a diffuse flux of ultra-high-energy neutrinos was conducted using the data collected during the ANITA-3 flight with a new approach where the Antarctic ice area is sectioned off into bins and a search is performed with different thresholds in different bins. The binned analysis methods were extended to the development of a search for neutrinos from Gamma Ray Bursts, implementing constraints in time, and for the first time, in direction. Lower analysis thresholds were achieved in a feasibility search even when extending the search to include longer afterglow periods. Authored with osudiss-2.cls (v0.9.1)

Rohan Kuntoji's Curriculum Vitae

Soumya Madhava's CV

Este proyecto se ha desarrollado con el objetivo principal de dotar a Shelly, el robot asistencial de RoboLab, de un sistema de reconocimiento y localizacio ́n de objetos en el espacio.