Title: Filtre bubbles and facebook pages about climate crisis Author: Mgr. Kateřina Janovská Institute: Institute of Information Studies and Librarianship Supervisor: Mgr. Josef Šlerka, Ph.D. Abstract: The climate crisis is one of the biggest challenges society is facing today. And while its existence is subject to scientific consensus, it is a highly polarising issue among the public. The social network Facebook, which has become a platform for spreading misinformation and filter bubbles, has also played its part. This thesis analyses the potential risk of filter bubbles being created on Facebook pages that address the topic of climate change - with a focus on similar pages suggestions. It categorizes these pages according to their stance on the existence of anthropocentric climate change.

In den vergangenen Jahren wurde ein Anstieg der Anzahl infektiöser Gastroenteritiden beobachtet, welche durch Viren hervorgerufen wurden. In vielen Fällen ließen sich Infektionen auf den Verzehr kontaminierter Lebensmittel zurückführen. In diesem Zusammenhang wurde der Begriff der sogenannten „food borne viruses“ geprägt. Welche Bedeutung Rohwurstprodukten bei der Übertragung humanpathogener Viren zukommt, kann derzeit nur anhand weniger objektiver wissenschaftlicher Fakten eingeschätzt werden. Die vorliegende Arbeit beschäftigte sich mit Studien zu Tenazität und Inaktivierung von Viren in Rohwurstprodukten. Dabei wurde ECHO-Virus stellvertretend für die Spezies humaner Enteroviren untersucht. Enteroviren zeichnen sich durch eine hohe Tenazität aus. Als ein wichtiger Vertreter kann Polio-Virus angeführt werden, welches bekanntlich durch Lebensmittel übertragbar ist. Im Zuge der aktuellen Problematik mit aviären Influenzaviren wurden stellvertretend zwei niedrigpathogene Isolate aviärer Influenzaviren für die Versuchsreihen gewählt. Die gewonnenen Ergebnisse sollten dazu beitragen, die Rolle des Lebensmittels Rohwurst bei der Übertragung lebensmittelassoziierter Virusinfektionen besser abschätzen zu können (hinsichtlich einer Risikobewertung) und Aussagen hinsichtlich möglicher Maßnahmen zur Risikominimierung zu ermöglichen. vorgenommen werden.

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<p>We present an observational protocol to observe and interpret asymmetries in stellar explosions using scattered light echoes. Spectroscopy of multiple light echoes are used to observe single astronomical sources from multiple viewing angles, allowing for direct observations of explosion asymmetries, when they exist. We present asymmetry detections for two famous historical supernovae: the ~25-year-old SN 1987A and the ~330-year-old Cassiopeia A. In both supernovae we find asymmetries in the first few hundred days of the explosion that appear to be correlated with the geometry of Fe-rich material in the remnant states.</p> <p>Spectroscopy of SN 1987A light echoes reveals a variation in the Hα line profile as a function of echo azimuth, with maximum asymmetry at position angles 16◦ and 186◦, in agreement with the major-axis of the elongated remnant ejecta. We interpret our asymmetry detection as evidence for a two-sided distribution of high-velocity 56Ni in the first few hundred days of SN 1987A, with the most dominant asymmetry redshifted in the south. For Cassiopeia A, we find evidence for a ~4000 km/s velocity excess in the first hundred days of the explosion, roughly aligned with an Fe-rich outflow in the supernova remnant and approximately opposite in direction to the motion of the compact object.</p> <p>Core-collapse supernovae have not yet been successfully modelled despite decades of progress in input physics and computing capability. Despite the significance of thermonuclear Type Ia supernovae to cosmology, the progenitor systems and explosion details also remain unclear. Both observational and theoretical work suggest that non-spherical effects are not only common in supernovae, but may in fact aid in generating successful explosions. In addition to offering a new technique for observing supernova asymmetries, spectroscopy of scattered light echoes allows a direct causal connection to be made between stellar explosions and their observed remnant states.</p> / Doctor of Philosophy (PhD)

In recent years, researchers have increasingly used magnetic resonance imaging (MRI) to study temporal skeletal muscle changes using gradient echo (GRE) echo planar imaging (EPI). These studies, typically involving exercise or ischemic challenges, have differentiated healthy subjects from athletic or unhealthy populations, such as those with peripheral vascular disease. However, the analysis methodologies have been lacking. In this thesis, two sessions of post-exercise GRE EPI data were collected from six subjects' lower legs using a 3 Tesla MRI scanner and a custom built ergometer. Past studies used common medical imaging software for motion correction. This work shows that such tools degrade leg image data by introducing motion, increasing root mean squared error in rest data by 22%. A new approach decreased it by 12%. EPI distortion correction in muscle images was also achieved, with the correlation ratio of functional and structural images increasing by up to 8%. In addition, a brief but intense artifact in GRE EPI muscle images results from muscle tissue moving in and out of the imaged volume. This through-plane artifact was successfully modelled as a mono-exponential decay for regression analysis, increasing the utility of the residual signal. The regression parameters were also leveraged to produce muscle displacement maps, identifying 44% of voxels as displaced. The maps were validated in a motion phantom and in-vivo using ultrasound. Finally, independent component analysis (ICA) was applied to post-exercise GRE EPI images to detect features in a data-driven, multivariate way and improve on conventional ROI selection methods. ICA produced parametric maps that were spatially correlated to working muscles from every trial (most with |R| > 0.4). The components were also separated from the susceptibility, motion, and blood vessel signals, and temporally reliable within individuals. These methodological advances represent increased rigour in the analysis of muscle GRE EPI images. / Thesis / Doctor of Philosophy (PhD) / Adequate blood circulation to muscles is important for good health. Researchers have used magnetic resonance imaging (MRI) techniques to assess blood and oxygen supply to muscles. The work in this thesis improves upon the analysis methods in prior work, especially in the areas of motion correction of the images and selection of individual muscle regions for analysis. Previous techniques could sometimes make motion in muscle images worse. This work provides valuable motion and distortion correction for muscle imaging, ensuring that measurements truly reflect muscle physiology. It also describes a method to remove an unwanted signal from post-exercise muscle data, and create a map of the internal muscle motion that occurred. Finally, an advanced mathematical technique was used to extract signals of interest and important spatial features from muscle image data automatically. The technique produced reliable results within and among subjects.

Traditionellt har större fartyg bestyckade med ekolod använts för att utföra batymetriska mätningar av sjö- och havsbottnar. Att utföra mätningar i grunda vatten har varit problematiskt eftersom större fartyg inte kan nå dessa grunda vatten. För att tackla det problemet har mindre obemannade ytfarkoster (USV) utvecklats för att mäta grunda vatten. Dessa USVs hjälper även till vid områden nära stenar som inte har fått uppdaterade djupvärden. Den här undersökningens syfte är att utvärdera hur en Seafloor HydroLite TM enkelstrålsekolod monterad på en USV skiljer sig från insamlingsmetoderna GNSS och med måttband. Den syftar även till att utvärdera vilken interpolationsteknik som är mest lämpad för skapande av djupmodeller med enkelstrålsekolodsdata. Det kommer också studeras hur tvärsektioner påverkar djupmodellerna skapade med enkelstrålsekolod. De experimentella mätningarna med GNSS, måttband och enkelstrålsekolod utfördes i sjön Öjaren som ligger utanför Sandviken. I undersökningen inmättes totalt 91 punkter med GNSS och måttband samt 8 mätstråk och 9 tvärsektioner med enkelstrålsekolod monterad på en USV. Djupmodellerna skapades i Surfer 10 med interpolationsteknikerna kriging, natural neighbor och triangulation with linear interpolation. Alla beräkningar genomfördes i Microsoft Excel och data insamlat med måttband ansågs vara det sanna värdet vid jämförelsen mellan insamlingsmetoderna. Resultaten visade att djupmodellerna skapade med GNSS-data är snarlika till djupmodellerna skapade med måttbandsdata samt att djupmodellerna med GNSS-data visar på den minsta skillnaden mot djupmodellerna skapade med enkelstrålsekolodsdata. Resultatet från jämförelsen mellan interpolationsteknikerna visar på att användandet av de olika interpolationsteknikerna inte har en signifikant påverkan på djupmodellen. Våra slutsatser av undersökningen blev att användande av ett enkelstrålsekolod kan bidra till att skapa en mer detaljerad djupmodell än om enbart GNSS eller måttbandsdata används. Det är också en mer kostnadseffektiv metod eftersom mer data kan samlas in på kortare tid. Det kan dock uppstå felmätningar vid insamlade av data med enkelstrålsekolod som kan vara svåra att upptäcka. Tilläggande av tvärsektioner kan bidra till att skapa en ännu mer detaljerad djupmodell och kan användas som kontrollpunkter vid kontroll av enkelstrålsekolodsdata. / Traditionally, large vessels armed with echo sounders have been used to conduct bathymetric surveys of the seas and oceans. Conducting surveys of shallow water have been troublesome since larger vessels cannot reach and survey shallow waters. To tackle that problem smaller unmanned surface vessels (USV) have been developed to survey shallow waters. It also helps in the areas closest to rocks that do not have updated depth measurements. This study aims to assess how a Seafloor HydroLite TM single-beam echo sounder mounted on a USV differs from other surveying methods. It also aims to evaluate which interpolation methods is most suitable for creating depth models by utilizing single-beam echo sounder data. It will also be studied how cross section lines affect the created depth using the USV. The experimental surveys with GNSS, measuring tape and single-beam echo sounder were used in the lake Öjaren that is located outside of Sandviken. In this study a total of 91 points were collected with GNSS and measuring tape and 8 sounding lines and 9 cross sections lines were collected using echo sounder mounted on the USV. The depth models were created in Surfer 10 using different interpolation methods i.e. kriging, natural neighbor and triangulation with linear interpolation. All calculation were performed in Microsoft Excel and the measurements collected with measuring tape were assumed as a “true” value to evaluate the different surveying techniques. The results showed that the depth model obtained using GNSS data is close to the depth model created using measuring tape data and shows lowest difference in comparison to the USV technique. The results from the comparison between interpolation methods showed that the use of different interpolation methods not have a significant impact on the depth model. The study concludes that the use of a single-beam echo sounder can help to create a more detailed depth model than using GNSS or measuring tape. It is also a cost effective method that helps collect more data in a short time. Though, some errors can appear in the data collected using the single-beam echo sounder that can be hard to detect. The cross section lines can contribute to a more detailed depth model and can be used as control points.

In the realm of autonomous vehicles, ensuring safety through advanced anomaly detection is crucial. This thesis integrates Reservoir Computing with temporal-aware data analysis to enhance driver behavior assessment and traffic flow prediction. Our approach combines Reservoir Computing with autoencoder-based feature extraction to analyze driving metrics from vehicle sensors, capturing complex temporal patterns efficiently. Additionally, we extend our analysis to forecast traffic flow dynamics within road networks using the same framework. We evaluate our model using the PEMS-BAY and METRA-LA datasets, encompassing diverse traffic scenarios, along with a GPS dataset of 10,000 taxis, providing real-world driving dynamics. Through a support vector machine (SVM) algorithm, we categorize drivers based on their performance, offering insights for tailored anomaly detection strategies. This research advances anomaly detection for autonomous vehicles, promoting safer driving experiences and the evolution of vehicle safety technologies. By integrating Reservoir Computing with temporal-aware data analysis, this thesis contributes to both driver behavior assessment and traffic flow prediction, addressing critical aspects of autonomous vehicle systems. / Master of Science / Our cities are constantly growing, and traffic congestion is a major challenge. This project explores how innovative technology can help us predict traffic patterns and develop smarter management strategies. Inspired by the rigorous safety systems being developed for self-driving cars, we'll delve into the world of machine learning. By combining advanced techniques for identifying unusual traffic patterns with tools that analyze data over time, we'll gain a deeper understanding of traffic flow and driver behavior. We'll utilize data collected by car sensors, such as speed and turning patterns, to not only predict traffic jams but also see how drivers react in different situations. However, our project has a broader scope than just traffic flow. We aim to leverage this framework to understand driver behavior in general, with a particular focus on its implications for self-driving vehicles. Through meticulous data analysis and sophisticated algorithms, we can categorize drivers based on their performance. This valuable information can be used to develop improved methods for detecting risky situations, ultimately leading to safer roads and smoother traffic flow for everyone. To ensure the effectiveness of our approach, we'll rigorously test it using real-world data from GPS data from taxi fleets and nationally recognized traffic datasets. By harnessing the power of machine learning and tools that can adapt to changing data patterns, this project has the potential to revolutionize traffic management in cities. This paves the way for a future with safer roads, less congestion, and a more positive experience for everyone who lives in and travels through our bustling urban centers.

Echo State Network (ESN) is a recently developed machine-learning paradigm whose processing capabilities rely on the dynamical behavior of recurrent neural networks (RNNs). Its performance metrics outperform traditional RNNs in nonlinear system identification and temporal information processing. In this thesis, we design and implement ESNs through Field-programmable gate array (FPGA) and explore their full capacity of digital signal processors (DSPs) to target low-cost and low-power applications. We propose a cost-optimized and scalable ESN architecture on FPGA, which exploits Xilinx DSP48E1 units to cut down the need of configurable logic blocks (CLBs). The proposed work includes a linear combination processor with negligible deployment of CLBs, as well as a high-accuracy non-linear function approximator, both with the help of only 9 DSP units in each neuron. The architecture is verified with the classical NARMA dataset, and a symbol detection task for an orthogonal frequency division multiplexing (OFDM) system on a wireless communication testbed. In the worst-case scenario, our proposed architecture delivers a matching bit error rate (BER) compares to its corresponding software ESN implementation. The performance difference between the hardware and software approach is less than 6.5%. The testbed system is built on a software-defined radio (SDR) platform, showing that our work is capable of processing the real-world data. / Master of Science / Machine learning is a study of computer algorithms that evolves itself by learning through experiences. Currently, machine learning thrives as it opens up promising opportunities of solving the problems that is difficult to deal with conventional methods. Echo state network (ESN), a recently developed machine-learning paradigm, has shown extraordinary effectiveness on a wide variety of applications, especially in nonlinear system identification and temporal information processing. Despite the fact, ESN is still computationally expensive on battery-driven and cost-sensitive devices. A fast and power-saving computer for ESN is desperately needed. In this thesis, we design and implement an ESN computational architecture through the field-programmablegate array (FPGA). FPGA allows designers to build highly flexible customized hardware with rapid development time. Our design further explores the full capacity of digital signal processors (DSP) on Xilinx FPGA to target low-cost and low-power applications. The proposed cost-optimized and scalable ESN architecture exploits Xilinx DSP48E1 units to cut down the need of configurable logic blocks (CLBs). The work includes a linear combination processor with negligible deployment of CLBs, and a high-accuracy non-linear function approximator, both with the help of only 9 DSP units in each neuron. The architecture is verified with the classical NARMA dataset, and a symbol detection task for an orthogonal frequency division multiplexing (OFDM) system in a wireless communication testbed. In the worst-case scenario, our proposed architecture delivers a matching bit error rate (BER) compares to its corresponding software ESN implementation. The performance difference between the hardware and software approach is less than 6.5%. The testbed system is built on a software-defined radio (SDR) platform, showing that our work is capable of processing the real-world data.