Articles — University

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)

A template for muletter, a document class for the typesetting of letters at the Masaryk Univer­sity (Brno, Czech Repub­lic).

General Game Playing (GGP) is the playing of a wide variety games you may have never seen before, by being told nothing but the rules of the game at run time. This sets it apart from traditional specific game players, like the famous chess player Deep Blue. Deep Blue can beat the world chess champion at chess however, it has absolutely no idea how to play checkers. It is designed for one particular game and cannot adapt to rule changes, and certainly cannot play entirely different games. The goal of this project is to create a program that will play a wide variety of 2d games given descriptions of their rules without the creator of the program having ever known of the games. This report will cover the design and implementation of this project, as well as the background research performed and reflections on the outcome of the project.

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This is a project done as a part of academic curriculum

The aim of this thesis is to investigate the ability of cardiovascular biomarkers calculated from peripheral pulse waveforms to estimate central properties of the cardiovascular system (e.g. aortic stiffness) using nonlinear one-dimensional (1-D) modelling of pulse wave propagation in the arterial network. To test these biomarkers, I have produced novel 1-D models of pulse wave propagation under normal and pathological conditions. In the first part of my thesis, I extended the modelling capabilities of the existing 1-D/0-D code to represent arterial blood flow under diabetes, hypertension, and combined diabetes and hypertension. Cardiac and vascular parameters of the 1-D model were tailored to best match data available in the literature to produce generalised hypertensive, diabetic, and combined diabetic and hypertensive population models. Using these models, I have shown that the pulse waveform at the finger is strongly affected by the aortic flow wave and the muscular artery stiffness and diameter. Furthermore the peak to peak time measured from the pulse waveform at the finger can identify hypertensive from diabetic patients. In the second part, I developed a new methodology for optimising the number of arterial segments in 1-D modelling required to simulate precisely the blood pressure and flow waveforms at an arbitrary arterial location. This is achieved by systematically lumping peripheral 1-D model branches into 0-D models that preserve the net resistance and total compliance of the original model. The methodology is important to simplify the computational domain while maintaining the precision of the numerical predictions — an important step to translate 1-D modelling to the clinic. This thesis provides novel computational tools of blood flow modelling and waveform analysis for the design, development and testing of pulse wave biomarkers. These tools may help bridge the gap between clinical and computational approaches.

Template for students in physics at Reykjavik University.

In most corruption scandals, the use of front companies for money laundering is almost ubiquitous. This work proposes to apply image classification to detect such organizations, through the use of Convolutional Neural Networks (CNN), namely the AlexNet architecture. The images are obtained by address search in Google Street View API, and the resulting classification will be further used along with other features to detect front com- panies in order to help the auditors from the Ministry of Transparency and Office of the Comptroller General (CGU, in Portuguese). To this moment, we applied classification to almost 15 thousand suppliers scenes with active contracts with the Brazilian Government until September 2016, obtained through data matching between the Government Purchases database and the Brazilian Federal Revenue Office database (more recent scenes should be added as this work progresses). Preliminary results with a pre-trained AlexNet CNN show the need for developing new scene classes more suited to the Brazilian context. In order to do this, we propose to apply clustering algorithms in features extracted from the last fully-connected layer of this net. The classes obtained will be used to fine-tune the AlexNet CNN for future classification, through the use of training from scratch or fine tuning techniques.

Proposta Projeto Integrador