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Social media provides a powerful way for people to share opinions and sentiments about a specific topic, allowing others to benefit from these thoughts and feelings. This procedure generates a huge amount of unstructured data, such as texts, images, and references that are constantly increasing through daily comments to related discussions. However, the vast amount of unstructured data presents risks to the information-extraction process, and so decision making becomes highly challenging. This is because data overload may cause the loss of useful data due to its inappropriate presentation and its accumulation. To this extent, this thesis contributed to the field of analyzing and detecting feelings in images and texts. And that by extracting the feelings and opinions hidden in a huge collection of image data and texts on social networks After that, these feelings are classified into positive, negative, or neutral, according to the features of the classified data. The process of extracting these feelings greatly helps in decision-making processes on various topics as will be explained in the first chapter of the thesis. A system has been built that can classify the feelings inherent in the images and texts on social media sites, such as people’s opinions about products and companies, personal posts, and general messages. This thesis begins by introducing a new method of reducing the dimension of text data based on data-mining approaches and then examines the sentiment based on neural and deep neural network classification algorithms. Subsequently, in contrast to sentiment analysis research in text datasets, we examine sentiment expression and polarity classification within and across image datasets by building deep neural networks based on the attention mechanism.
Social networks are ubiquitous structures that we generate and enrich every-day while connecting with people through social media platforms, emails, and any other type of interaction. While these structures are intangible to us, they carry important information. For instance, the political leaning of our friends can be a proxy to identify our own political preferences. Similarly, the credit score of our friends can be decisive in the approval or rejection of our own loans. This explanatory power is being leveraged in public policy, business decision-making and scientific research because it helps machine learning techniques to make accurate predictions. However, these generalizations often benefit the majority of people who shape the general structure of the network, and put in disadvantage under-represented groups by limiting their resources and opportunities. Therefore it is crucial to first understand how social networks form to then verify to what extent their mechanisms of edge formation contribute to reinforce social inequalities in machine learning algorithms.
To this end, in the first part of this thesis, I propose HopRank and Janus two methods to characterize the mechanisms of edge formation in real-world undirected social networks. HopRank is a model of information foraging on networks. Its key component is a biased random walker based on transition probabilities between k-hop neighborhoods. Janus is a Bayesian framework that allows to identify and rank plausible hypotheses of edge formation in cases where nodes possess additional information. In the second part of this thesis, I investigate the implications of these mechanisms - that explain edge formation in social networks - on machine learning. Specifically, I study the influence of homophily, preferential attachment, edge density, fraction of inorities, and the directionality of links on both performance and bias of collective classification, and on the visibility of minorities in top-k ranks. My findings demonstrate a strong correlation between network structure and machine learning outcomes. This suggests that systematic discrimination against certain people can be: (i) anticipated by the type of network, and (ii) mitigated by connecting strategically in the network.
This thesis explores a 3D object detection and pose estimation approach based on the point pair features method presented by Drost et. al. [Dro+10]. While pose estimation methods have shown good improvements, they still remain a crucial problem on the computer vision field. In this work, we implemented a program that takes point cloud scenes as input and returns the detected object with their estimated pose. The program fully covers an object detection pipeline by processing 3D models during an offline phase, extracting their point pair features and creating a global descriptor out of them. During an online phase, the same features are extracted from a point cloud scene and are matched to the model features. After the voting scheme, potential poses of the object are retrieved. The poses end being clustered together and post-processed to finally deliver a result. The program was tested using simulated and real data. We evaluate these tests and present the final results, by discussing the achieved accuracy of the detections and the estimated poses.
Background: For over a century, scientists have studied host-pathogen interactions between the crayfish plague disease agent Aphanomyces astaci and freshwater crayfish. It has been hypothesised that North American crayfish hosts are disease-resistant due to the long-lasting coevolution with the pathogen. Similarly, the increasing number of latent infections reported in the historically sensitive European crayfish hosts seems to indicate that similar coevolutionary processes are occurring between European crayfish and A. astaci. Our current understanding of these host-pathogen interactions is largely focused on the innate immunity processes in the crayfish haemolymph and cuticle, but the molecular basis of the observed disease-resistance and susceptibility remain unclear. To understand how coevolution is shaping the host’s molecular response to the pathogen, susceptible native European noble crayfish and invasive disease-resistant marbled crayfish were challenged with two A. astaci strains of different origin: a haplogroup A strain (introduced to Europe at least 50 years ago, low virulence) and a haplogroup B strain (signal crayfish in lake Tahoe, USA, high virulence). Here, we compare the gene expression profiles of the hepatopancreas, an integrated organ of crayfish immunity and metabolism.
Results: We characterised several novel innate immune-related gene groups in both crayfish spe cies. Across allchallenge groups, we detected 412 differentially expressed genes (DEGs) in the noble crayfish, and 257 DEGs in the marbled crayfish. In the noble crayfish, a clear immune response was detected to the haplogroup B strain, but not to the haplogroup A strain. In contrast, in the marbled crayfish we detected an immune response to the haplogroup A strain, but not to the haplogroup B strain. Conclusions: We highlight the hepatopancreas as an important hub for the synthesis of immune molecules in the response to A. astaci. A clear distinction between the innate immune response in the marbled crayfish and the noble crayfish is the capability of the marbled crayfish to mobilise a higher variety of innate immune response effectors.
Objectives: Crayfish plague disease, caused by the oomycete pathogen Aphanomyces astaci represents one of the greatest risks for the biodiversity of the freshwater crayfish. This data article covers the de novo transcriptome assembly and annotation data of the noble crayfish and the marbled crayfish challenged with Ap. astaci. Following the controlled infection experiment (Francesconi et al. in Front Ecol Evol, 2021, https://doi.org/10.3389/fevo.2021.647037), we conducted a differential gene expression analysis described in (Boštjančić et al. in BMC Genom, 2022, https://doi.org/10.1186/s12864-022-08571-z) Data description: In total, 25 noble crayfish and 30 marbled crayfish were selected. Hepatopancreas tissue was isolated, followed by RNA sequencing using the Illumina NovaSeq 6000 platform. Raw data was checked for quality with FastQC, adapter and quality trimming were conducted using Trimmomatic followed by de novo assembly with Trinity. Assembly quality was assessed with BUSCO, at 93.30% and 93.98% completeness for the noble crayfish and the marbled crayfish, respectively. Transcripts were annotated using the Dammit! pipeline and assigned to KEGG pathways. Respective transcriptome and raw datasets may be reused as the reference transcriptome assemblies for future expression studies.
The ongoing loss of species is a global threat to biodiversity, affecting ecosystems worldwide. This also concerns arthropods such as insects and spiders, which are especially endangered in agricultural ecosystems. Here, one of the main causing factors is management intensification. In areas with a high proportion of traditionally managed grassland, extensive hay meadows that are cut only once per year can still hold high levels of biodiversity, but are threatened by conversion into highly productive silage grassland. The Westerwald mountain range, western Germany, is such a region. In this thesis, I compare the local diversity of bees, beetles, hoverflies, leafhoppers, and spiders of five grassland management regimes along a gradient of land-use intensity. These comprise naturally occurring grassland fallows, three types of traditionally managed hay meadows, and intensively used silage grassland. By using three different sampling methods, I recorded ground-dwelling, flower-visiting, and vegetation-dwelling species. The results show that in most cases species richness and diversity are highest on fallows, whereas variation among different managed grassland types is very low. Also, for most sampled taxa, fallows harbour the most distinct species assemblages, while that of other management regimes are largely overlapping. Management has the largest effect on species composition, whereas environmental parameters are of minor importance. Long-term grassland fallows seem to be highly valuable for arthropod conservation, even in a landscape with a low overall land-use intensity, providing structural heterogeneity. In conclusion, such fallows should be subsidized agri-environmental schemes, to preserve insect and spider diversity.
In this thesis the possibilities for real-time visualization of OpenVDB
files are investigated. The basics of OpenVDB, its possibilities, as well
as NanoVDB and its GPU port, were studied. A system was developed
using PNanoVDB, the graphics API port of OpenVDB. Techniques were
explored to improve and accelerate a single ray approach of ray tracing.
To prove real-time capability, two single scattering approaches were
also implemented. One of these was selected, further investigated and
optimized to achieve interactive real-time rendering.
It is important to give artists immediate feedback on their adjustments, as
well as the possibility to change all parameters to ensure a user friendly
creation process.
In addition to the optical rendering, corresponding benchmarks were
collected to compare different improvement approaches and to prove
their relevance. Attention was paid to the rendering times and memory
consumption on the GPU to ensure optimal use. A special focus, when
rendering OpenVDB files, was put on the integrability and extensibility of
the program to allow easy integration into an existing real-time renderer
like U-Render.
To render the surface of a material capable of withstanding mechanical and electrochemical loads, and to perform well in service, the deposition of a thin film or coating is a solution. In this project, such a thin film deposition is carried out. The coating material chosen is titanium nitride (TiN) which is a ceramic material known to possess a high hardness (>10 GPa) as well as good corrosion resistance. The method of deposition selected is high power impulse magnetron sputtering (HiPIMS) that results in coatings with high quality and enhanced properties. Sputtering is a physical process that represents the removal or dislodgment of surface atoms by energetic particle bombardment. The term magnetron indicates that a magnetic field is utilized to increase the efficiency of the sputtering process. In HiPIMS, a high power is applied in pulses of low duty cycles to a cathode that is sputtered and that consists of the coating material. As result of the high power, the ionization of the sputtered material takes place giving the possibility to control these species with electric and magnetic field allowing thereby the improvement and tuning of coating properties. However, the drawback of HiPIMS is a low deposition rate.
In this project, it is demonstrated first that it is possible to deposit TiN coating using HiPIMS with an optimized deposition rate, by varying the magnetic field strength. It was found that low magnetic field strength (here 22mT) results in a deposition rate similar to that of conventional magnetron sputtering in which the average power is applied continuously, called also direct current magnetron sputtering (dcMS). The high deposition rate at low magnetic field strength was attributed to a reduction in the back attraction probability of the sputtered species. The magnetic field strength did not show noticeable influence on the mechanical properties. The proposed explanation was that the considered peak current density interval 1.22-1.72 A∙cm-2 does not exhibit dramatic changes in the plasma dynamics.
In a second part, using the optimized deposition rate, the optimized chemical composition of TiN was determined. It was shown that the chemical composition of TiN does not significantly influence the corrosion performance but impacts considerably the mechanical properties. It was also shown that the corrosion resistance of the coatings deposited using HiPIMS was higher than that of the coatings deposited using dcMS.
The third study was the effect of annealing post deposition on the properties of TiN coating deposited using HiPIMS. The hardness of the coatings showed a maximum at 400°C reaching 24.8 GPa. Above 400°C however, a lowering of the hardness was measured and was due to the oxidation of TiN which led to the formation of TiN-TiO2 composites with lower mechanical properties.
The coating microscopic properties such as crystal orientation, residual stresses, average grain size were determined from X-ray diffraction data and the roughness was measured using atomic force microscopy. These properties were found to vary with the magnetic field strength, the chemical composition as well as the annealing temperature.
This thesis was motivated by the need to advance the knowledge on the variability and dynamics of energy fluxes in lakes and reservoirs, as well as about the physical processes that regulate the fluxes at both the air and water side of the air-water interface.
In the first part, I re-examine how mechanical energy, resulting from its major source – the vertical wind energy flux - distributes into the various types of water motions, including turbulent flows and surface and internal waves. Although only a small fraction of the wind energy flux from the atmosphere is transferred to the water, it is crucial for physical, biogeochemical and ecological processes in lentic ecosystems. Based on extensive air- and water-side measurements collected at two small water bodies (< 10 km2), we estimated the energy fluxes and energy content in surface and in internal waves. Overall, the estimated energy fluxes and energy content agree well with results reported for larger water bodies, suggesting that the energetics driving the water motions in enclosed basins is similar, independently of the basin size. Our findings highlight the importance of the surface waves that receive the largest fraction of the wind energy flux, which strongly nonlinearly increases for wind speeds exceeding 3 m s-1. We found that the existing parameterization of the wave height as a function of wind speed and fetch length did not reproduce the measured wave amplitude in lakes. On average, the highest energy content was observed in basin-scale internal waves, together with high-frequency internal waves exhibiting seasonal variability and varies among the aquatic systems. During our analysis, we discovered the diurnal variability of the energy dissipation rates in the studied lake, suggesting biogenic turbulence generation, which appears to be a widespread phenomenon in lakes and reservoirs.
In the second part of the thesis, I addressed current knowledge gaps related to the bulk transfer coefficients (also known as the drag coefficient, the Stanton number and the Dalton number), which are of a particular importance for the bulk estimation of the surface turbulent fluxes of momentum, sensible and latent heat in the atmospheric boundary layer. Their inaccurate representation may lead to significant errors in flux estimates, affecting, for example, the weather and climate predictions or estimations of the near-surface current velocities in lake hydrodynamic models. Although the bulk transfer coefficients have been extensively studied over the past several decades (mainly in marine and large-lake environments), there has been no systematic analysis of measurements obtained at lakes of different size. A significant increase of the transfer coefficients at low wind speeds (< 3 m s-1) has been observed in several studies, but, to date, it has remained unexplained. We evaluated
the bulk transfer coefficients using flux measurements from 31 lakes and reservoirs. The estimates were generally within the range reported in previous studies for large lakes and oceans. All transfer coefficients increased substantially at low wind speeds, which was found to be associated with the presence of gusts and capillary waves (except the Dalton number). We found that the Stanton number is systematically higher than the Dalton number. This challenges the assumption made in the Bowen-ratio method, which is widely used for estimating evaporation rates from micrometeorological measurements. We found that the variability of the transfer coefficients among the lakes could be associated with lake surface area. In flux parameterizations at lake surfaces, it is recommended to consider variations in the drag coefficient and the Stanton number due to wind gustiness and capillary wave roughness while the Dalton number could be considered as constant at all wind speeds.
In the third part of the thesis, I address the key drivers of the near-surface turbulence that control the gas exchange in a large regulated river. As all inland waters, rivers are an important natural source of greenhouse gases. The effects of the widespread alteration and regulation of river flow for human demands on gas exchange is largely unknown. In particular, the near-surface turbulence in regulated rivers has been rarely measured and its drivers have not been identified. While in lakes and reservoirs, near-surface turbulence is mainly related to atmospheric forcing, in shallow rivers and streams it is generated by bottom friction of the gravity-forced flow. The studied large regulated river represents a transition between these two cases. Atmospheric forcing and gravity were the dominant drivers of the near-surface turbulence for a similar fraction of the measurement period. Based on validated scalings, we derived a simple model for estimating the relative contributions of wind and bottom friction to near-surface turbulence in lotic ecosystems with different flow depths. Large diel variability in the near-surface energy dissipation rates due to flow regulation leads to the same variability in gas exchange. This suggests that estimates of gas fluxes from rivers are biased by measurements performed predominantly during daytime.
In addition, the novelty in all the analyses described above is the use of the turbulent surface fluxes measured directly by the eddy-covariance technique – at the moment of writing, the most advanced method. Overall, this thesis is of a potential interest for a broad range of scientific disciplines, including limnology, micrometeorology and open channel hydraulics.
For software engineers, conceptually understanding the tools they are using in the context of their projects is a daily challenge and a prerequisite for complex tasks. Textual explanations and code examples serve as knowledge resources for understanding software languages and software technologies. This thesis describes research on integrating and interconnecting
existing knowledge resources, which can then be used to assist with understanding and comparing software languages and software technologies on a conceptual level. We consider the following broad research questions that we later refine: What knowledge resources can be systematically reused for recovering structured knowledge and how? What vocabulary already exists in literature that is used to express conceptual knowledge? How can we reuse the
online encyclopedia Wikipedia? How can we detect and report on instances of technology usage? How can we assure reproducibility as the central quality factor of any construction process for knowledge artifacts? As qualitative research, we describe methodologies to recover knowledge resources by i.) systematically studying literature, ii.) mining Wikipedia, iii.) mining available textual explanations and code examples of technology usage. The theoretical findings are backed by case studies. As research contributions, we have recovered i.) a reference semantics of vocabulary for describing software technology usage with an emphasis on software languages, ii.) an annotated corpus of Wikipedia articles on software languages, iii.) insights into technology usage on GitHub with regard to a catalog of pattern and iv.) megamodels of technology usage that are interconnected with existing textual explanations and code examples.