Polychlorinated biphenyls (PCBs) are persistent environmental chemicals. Mono-hydroxylated polychlorinated biphenyls (OH-PCBs) are PCB metabolites found commonly in human blood, environmental water and sediment samples. Detection of small amounts of PCBs and their OH-PCB metabolites in biological matrices from epidemiological and laboratory studies remains a challenge. The application of aptamers is studied as a means to identify and quantify PCBs and OH-PCBs. Aptamers are single stranded short oligonucleotides that arrange into unique shape of three-dimensional structures when binding to their target. Like antibodies they have high affinity and specificity for their specific target. The hypothesis is that aptamers can identify PCBs and PCB metabolites in environmental and biological samples. To test this hypothesis, three different OH-PCBs, 4’-OH-PCB3, 4-OH-PCB72 and 2-OH-PCB106 along with 4-OH-biphenyl as a control, were covalently attached to beads with carboxylic acid groups on their surface. Several methods were explored to characterize covalent binding of OH-PCBs to the beads: FTIR-spectroscopy, Dynamic Light Scattering (DLS) and Zeta-Potential (ZP) measurements. The beads were then used in in vitro assays to test binding of two different aptamers specific to OH-PCBs. In this study, these aptamers were tested for the ability to distinguish structurally different OH-PCB congeners and other environmental pollutants. In future studies, aptamers can be selected for a PCB metabolite of interest, 4’-OH-PCB3, via a modified form of Systemic Evolution of Ligands by Exponential Enrichment (SELEX). Single stranded DNA (ssDNA) aptamers generated will be applied as a biosensor for the detection and quantification of traces of 4’-OH-PCB3.

Most current algorithms typically lack in robustness to noise or do not handle T-shaped curve joining properly. There is a challenge to not only detect features in the noisy 3D-data obtained from the digital scanners. Moreover, most of the algorithms even when they are robust to noise, they lose the feature information near the T-shaped junctions as the triplet of lines ``confuses'' the algorithm so it treats it as a plane. Powered by TCPDF (www.tcpdf.org)

In many machine learning problems and application domains, the data are naturally organized by groups. For example, a video sequence is a group of images, an image is a group of patches, a document is a group of paragraphs/words, and a community is a group of people. We call them the collective data. In this thesis, we study how and what we can learn from collective data. Usually, machine learning focuses on individual objects, each of which is described by a feature vector and studied as a point in some metric space. When approaching collective data, researchers often reduce the groups into vectors to which traditional methods can be applied. We, on the other hand, will try to develop machine learning methods that respect the collective nature of data and learn from them directly. Several different approaches were taken to address this learning problem. When the groups consist of unordered discrete data points, it can naturally be characterized by its sufficient statistics – the histogram. For this case we develop efficient methods to address the outliers and temporal effects in the data based on matrix and tensor factorization methods. To learn from groups that contain multi-dimensional real-valued vectors, we develop both generative methods based on hierarchical probabilistic models and discriminative methods using group kernels based on new divergence estimators. With these tools, we can accomplish various tasks such as classification, regression, clustering, anomaly detection, and dimensionality reduction on collective data. We further consider the practical side of the divergence based algorithms. To reduce their time and space requirements, we evaluate and find methods that can effectively reduce the size of the groups with little impact on the accuracy. We also proposed the conditional divergence along with an efficient estimator in order to correct the sampling biases that might be present in the data. Finally, we develop methods to learn in cases where some divergences are missing, caused by either insufficient computational resources or extreme sampling biases. In addition to designing new learning methods, we will use them to help the scientific discovery process. In our collaboration with astronomers and physicists, we see that the new techniques can indeed help scientists make the best of data.

Convex optimization problems are a class of mathematical problems which arise in numerous applications. Although interior-point methods can in principle solve these problems efficiently, they may become intractable for solving large-scale problems or be unsuitable for real-time embedded applications. Iterations of operator splitting methods are relatively simple and computationally inexpensive, which makes them suitable for these applications. However, some of their known limitations are slow asymptotic convergence, sensitivity to ill-conditioning, and inability to detect infeasible problems. The aim of this thesis is to better understand operator splitting methods and to develop reliable software tools for convex optimization. The main analytical tool in our investigation of these methods is their characterization as the fixed-point iteration of a nonexpansive operator. The fixed-point theory of nonexpansive operators has been studied for several decades. By exploiting the properties of such an operator, it is possible to show that the alternating direction method of multipliers (ADMM) can detect infeasible problems. Although ADMM iterates diverge when the problem at hand is unsolvable, the differences between subsequent iterates converge to a constant vector which is also a certificate of primal and/or dual infeasibility. Reliable termination criteria for detecting infeasibility are proposed based on this result. Similar ideas are used to derive necessary and sufficient conditions for linear (geometric) convergence of an operator splitting method and a bound on the achievable convergence rate. The new bound turns out to be tight for the class of averaged operators. Next, the OSQP solver is presented. OSQP is a novel general-purpose solver for quadratic programs (QPs) based on ADMM. The solver is very robust, is able to detect infeasible problems, and has been extensively tested on many problem instances from a wide variety of application areas. Finally, operator splitting methods can also be effective in nonconvex optimization. The developed algorithm significantly outperforms a common approach based on convex relaxation of the original nonconvex problem.

In today’s world the internet and social media are omnipresent and information is accessible to everyone. This shifted the advantage from those who have access to information to those who do so first. Identifying new events as they emerge is of substantial value to financial institutions who consider realtime information in their decision making processes, as well as for journalists that report about breaking news and governmental agencies that collect information and respond to emergencies. First Story Detection is the task of identifying those documents in a stream of documents that talk about new events first. This seemingly simple task is non-trivial as the computational effort increases with every processed document. Standard approaches to solve First Story Detection determine a document’s novelty by comparing it to previously seen documents. This results in the highest reported accuracy but even the currently fastest system only scales to 10% of the Twitter stream. In this thesis, we propose a new algorithm family, called memory-based methods, able to scale to the full Twitter stream on a single core. Our memory-based method computes a document’s novelty up to two orders of magnitude faster than state-of-the-art systems without sacrificing accuracy. This thesis additional provides original work on the impact of processing unbounded data streams on detection accuracy. Our experiments reveal for the first time that the novelty scores of state-of-the-art comparison based and memory-based methods decay over time. We show how to counteract the discovered novelty decay and increase detection accuracy. Additionally, we show that memory-based methods are applicable beyond First Story Detection by building the first real time rumour detection system on social media streams.

When processing materials, there are a number of factors which influence the final quality of products. The possibility of defects occurrence, both sub-surface and surface ones, is one of the factors. In the precision engineering production, the automotive, bearing or aerospace industry in particular, no level of defects is tolerable. The thesis deals with the causes and consequences of the occurrence of such defects, and the methods for their identification. For this purpose, a new contactless optical method for the fully automatic inspection of the surface defects has been developed by Mesing, spol. s r.o. In order to verify the performance of the aforementioned method, four specific products have been tested by the author using the method. Finally, there are examples of the successful implementation of the automatic machines, for a design of which the results of the model testing were utilized.

The diploma thesis deals with the issue of creative accounting and subsequent manipulation of financial statements. This is the current topic in our country and in the world. After several world scandals in the area of deliberate misrepresentation of accounting, an increasing emphasis is placed on the reporting ability of financial statements and, in particular, their true and fair value. Financial statements are often the basis for making economic decisions by external users of financial statements. Therefore, it is necessary to emphasize other possible methods leading to the detection of the misstatements in them. The diploma thesis is divided into literary and practical part. In the literary part the reader is acquainted with basic concepts, definition of creative accounting and accounting fraud. There is also a set of selected identification methods used both to prevent the creation and to detect accounting frauds. The practical part of the thesis applies four selected models to the financial statements of the selected entity. It is a small entity without an audit obligation where the main activity is the provision of IT services. Applied and further compared are the Beneish M-Score, Jones Nondiscretionary accruals, CFEBT and Piotroski F-Score models. An add-on is a bankrupt Altman model that evaluates the financial health of the entity, but it does not indicate anything about the possibility of disturbing the true and fair value. Risk periods are analyzed in detail using basic financial analysis techniques. The conclusion of the thesis provides an evaluation of the applicability and suitability of the used methods for the given type of business.

Ractopamine hydrochloride (RAC) is a β-agonist additive that has been used in many countries as a repartitioning agent, redirecting nutrients in order to increase leanness and decrease lipid deposition in pigs. Countries from the European Union and Asia question their safety, while American countries and Australia allow their controlled use as an additive added to the feed of pigs in the finishing phase. In Brazil, the Ministry of Agriculture, Livestock and Food Supply together with the national production sector, developed a Program called \"SplitSystem\" to ensure a safe product without RAC in order to meet international sanitary requirements. However, co-products used in animal feed may contain RAC, such as meat and bone meal (MBM), one of the main feed ingredients used in many countries which can partially replace soybean meal to lower costs. As the level of RAC in this protein source has not been established an experiment was under taken to examine the impact on pig tissues of increasing amounts of meat and bone meal (MBM) in four dietary groups: 0, 7, 14 and 21% w/w of MBM-containing RAC (53.5 µg kg-1) in the diet. The purpose was to verify if ractopamine residues remain in pig tissues (muscle, liver, kidneys, and lungs) and how much is eliminated through urine. To address these concerns, gilts were fed RAC via MBM daily, from weaning until slaughter. RAC was determined in muscle, liver, kidneys, and lungs with a limit of detection (LOD) = 0.15, 0.5, 0.5 and 1.0 µg kg-1, respectively), and no RAC residues were quantified above the limit of quantification (LOQ) = 0.5, 2.5, 2.5 and 2.5 µg kg-1, respectively). In urine, RAC concentration remained below 1.35 µg L-1. These values are below the maximum residue limits (MRLs) established by legislation. Therefore, MBM (53.5 µg kg-1 of RAC) can be used up to 21% in pig diets, however when considering restrictive markets, it is recommended not to use MBM. / O cloridrato de ractopamina (RAC) é um aditivo β-agonista que tem sido usado em muitos países para redirecionar nutrientes a fim de aumentar a deposição de tecido muscular e diminuição de lipídios em suínos. Países da União Européia e Ásia questionam sua segurança, enquanto os países da América e a Austrália permitem seu uso controlado como um aditivo adicionado à ração na fase de terminação em suínos. No Brasil, o Ministério da Agricultura, em conjunto com o setor produtivo nacional, desenvolveu um programa chamado \"SplitSystem\" para garantir um produto seguro sem RAC, a fim de atender aos requisitos sanitários internacionais. No entanto, os co-produtos utilizados na ração animal podem conter RAC, como farinha de carne e ossos (FCO), um dos principais ingredientes utilizados em muitos países para substituir parcialmente o farelo de soja, a fim de reduzir os custos de produção. Como o nível de RAC nessa fonte de proteína não foi estabelecido, um experimento foi conduzido para examinar o impacto sobre os tecidos suínos que receberam níveis crescentes de FCO, divididos em quatro grupos: 0, 7, 14 e 21% de FCO contendo 53,5 µg kg-1 de RAC, na dieta dos animais. O objetivo foi verificar se resíduos de RAC permanecem nos tecidos suínos (lombo, fígado, rim e pulmão) e o quanto é eliminado através da urina. Para atender a essas preocupações, as leitoas foram alimentadas com RAC via FCO diariamente, desde o desmame até o abate. A RAC foi determinada em lombos, rins, fígados e pulmões com um limite de detecção (LOD) = 0,15; 0,5; 0,5 e 1,0 µg kg-1, respectivamente, e nenhum resíduo de RAC foi quantificado acima do limite de quantificação (LOQ) = 0,5; 2,5; 2,5 e 2,5 µg kg-1, respectivamente. Na urina, a concentração de RAC permaneceu abaixo de 1,35 µg L-1. Estes valores são inferiores aos limites máximos residuais (LMRs) estabelecidos pela legislação. Concluindo que a FCO (53.5 µg kg-1 de RAC) pode ser utilizada com até 21% em rações para suínos, entretanto, ao considerar mercados restritivos, recomenda-se não usar a FCO.

The master´s thesis describes the basic information on the reporting of water losses. Means and methods to reduce water losses. Devices used to monitoring water supply, or detect malfunctioning of water supply line. Describes and evaluates the functionality of UFR on the laboratory model.

The Doctoral thesis demonstrates the design of reliable industrial visual systems. The special emphasis is dedicated to the detection of defects on webs in industrial applications based on line-scan cameras. This system makes possible detection and classification of defects originating during the real production conditions. This work covers a theoretical study of a visual system for the defect detection on endless bands as well as of appropriate lighting and the scene arrangement. Further to that have been selected, adjusted and designed key components of hardware. Following the design and optimization of algorithms a system prototype had been installed on non-woven textiles production line. Eight visual systems implemented into real-life industrial conditions based on this prototype