Total RNA was extracted from chicken oviduct epithelial cells. Avian Beta-defensin (AvBD)-2, -6, and -12 cDNAs were amplified by reverse transcription-PCR and cloned into pRSET A style='msoareast-language:ZH-CN'>, a protein expression vector. The class=SpellE>hexa-histidine-tagged class=SpellE>AvBD peptides were expressed in Escherichia coli (E. coli) BL21(DE3) class=SpellE>plysS and affinity-purified. The antimicrobial activities of the recombinant AvBDs against E. coli style='msoareast-language:ZH-CN;mso-bidiont-style:italic'>, Salmonella class=SpellE>enterica style='mso-bookmark:OLE_LINK9'> serovar Typhimurium (S. class=SpellE>typhimurium), and Staphylococcus aureus style='msoareast-language:ZH-CN'> (S. aureus) were determined. style='msoareast-language:ZH-CN;mso-bidiont-style:italic'> At 8, 16 and 32 µg/ml, all three rAvBDs killed and inhibited the growth style='msoareast-language:ZH-CN'> of E. coli style='msoareast-language:ZH-CN;mso-bidiont-style:italic'>, S. typhimurium, and S. aureus. The killing of rAvBD-2, -6, and -12 against stationary phase E. coli and S. class=SpellE>aureus was pH dependent in the range investigated. style='msoareast-language:ZH-CN'> In addition, the killing-curves showed that rAvBDs exerted their antimicrobial function within 30 minutes of treatment, suggesting the fast killing mechanisms of rAvBDs.

This thesis implements an automated system that counts pedestrians with 85% accuracy. Two approaches have been considered and evaluated in terms of count accuracy, cost and ease of deployment. The first approach employs the Autoscope Solo Terra, a traffic camera which is widely used to monitor vehicular traffic. The Solo Terra supports an image processing-based detector that counts the number of objects crossing user-defined areas in the captured image. The count is updated based on the amount of movement across the selected regions. Therefore, a second approach has been considered that uses a histogram of oriented gradients (HoG), an advanced vision based algorithm proposed by Dalal et al. which distinguishes a pedestrian from a non-pedestrian based on an omega shape formed by the head and shoulders of a human being. The implemented detection software processes video frames that are streamed from a low-cost digital camera. The frames are divided into sub-regions which are scanned for an omega shape whenever movement is detected in those regions. It has been found that the HoG-based approach degrades in performance due to occlusion under dense pedestrian traffic conditions whereas the Solo Terra approach appears to be more robust. Undercounts and overcounts were encountered using the Solo Terra approach. To combat the disadvantages of both the approaches, they were integrated to form a single system where count is incremented predominantly using the Solo Terra. The HoG-based approach corrects the obtained count under certain conditions. A preliminary prototype of the integrated system has been verified.

Ring artifacts are a common problem with the use of photon-counting detectors and commercial deployment rests on being able to compensate for them. Deep learning has been proposed as a candidate for tackling the inefficiency or high cost of traditional techniques. In that spirit, we propose a new approach to ring artifact reduction, namely one that employs Residual Networks in sinogram domain. We train them on data simulated via a realistic photon-counting CT model based on numerical phantoms of real scans acquired by the KiTS19 Challenge dataset. By exploring various architectures we find that shallow ResNets achieve a significant artifact reduction by staying more true to the ground truth in terms of not introducing new artifacts. All networks introduce a smoothing effect which is attributed to the use of MSE as a loss function. An alternative training scheme using patches instead of whole sinograms is tested and it shows a slightly improved model stability. Lastly, we demonstrate via a performance metric study that common metrics are not suitable for quantifying the performance in this problem, save for a potential new approach in the virtual mono-energetic domain. / Ringartefakter är ett vanligt problem vid användning av fotonräknande detektorer och kommersiell introduktion kräver att man kan kompensera för dem. Djupinlärning har föreslagits som en kandidat för att hantera ineffektiviteten eller de höga kostnaderna för traditionella tekniker. I den andan föreslår vi ett nytt tillvägagångssätt för att reducera ringartefakter, nämligen en som använder sig av residualnätverk i sinogramdomänen. Vi tränar dem på data simulerad via en realistisk fotonräkning CT modell baserad på numeriska fantomer av verkliga skanningar från datamängen KiTS19 Challenge. Genom att utforska olika arkitekturer finner vi att grunda ResNet uppnår en betydande minskning av artefakter genom bevara en större likhet med den sanna bilden när det gäller att inte introducera nya artefakter. Alla nätverk introducerar en utsmetningseffekt som tillskrivs användningen av MSE som en förlustfunktion. Ett alternativt träningsschema med utsnitt istället för hela sinogram testas och det visar en något förbättrad modellstabilitet. Slutligen visar vi genom en prestandamåttstudie att vanliga prestandamått inte är lämpliga för att kvantifiera prestandan i detta problem med undantag för ett potentiellt nytt tillvägagångssätt i den virtuella monoenergetiska domänen.

Computed tomography angiography (CTA) of the brain poses challenges on the imaging system; the contrast between blood vessels and surrounding soft tissue is very low, and to render small intricate vessel structures high spatial resolution is needed. Higher precision angiography would facilitate more accurate diagnosis of pathological conditions. The aim of this work was to analyze the factors which contribute to the image quality in cerebrovascular imaging contexts and make a comparison between state-of-the-art energy-integrating and photon counting CT systems. A geometrical model was devised to mimic the conditions of cerebral angiography. Different parameters and detectors were used to reconstruct images of a spherical head phantom. Compton noise was added to several image acquisitions after a Monte Carlo study was used to estimate the scatter to primary ratio (SPR) with a spherical phantom. The images were evaluated qualitatively and quantitatively. A real phantom was scanned with an experimental photon counting detector and compared with the simulated approach. The work resulted in qualitative reconstructed images, a decrease in SPR when introducing air gaps and improved resolution but worsened contrast as a result of smaller detector sizes. The SPR was shown to be higher in cone-beam geometry than fan-beam geometry. Electronic noise present with energy integrating detectors was shown to degrade image quality significantly in low dose imaging, reducing contrast when imaging vascular-like structures. Photon counting detectors without electronic noise could provide greater image quality and better diagnostic information.

Semiconductor detectors are attracting interest for use in photon-counting spectral computed tomography. In order to obtain a high spatial resolution, it is of interest to find the photon interaction position. In this work we investigate if machine learning can be used to obtain a sub-pixel spatial resolution in a photon-counting silicon strip detector with pixels of 10 µm. Simulated charge distributions from events in one, three, and seven positions in each of three pixels were investigated using the MATLAB® Classification Learner application to determine the correct interaction position. Different machine learning models were trained and tested in order to maximize performance. With pulses originating from one and seven positions within each pixel, the model was able to find the originating pixel with an accuracy of 100% and 88.9% respectively. Further, the correct position within a pixel was found with an accuracy of 54.0% and 29.4% using three and seven positions per pixel respectively. These results show the possibility of improving the spatial resolution with machine learning. / Halvledardetektorer är av stigande intresse inom forskning för användning i fotonräknande datortomografi med spektral upplösning. För att erhålla en hög spatiell upplösning är det av intresse att hitta fotonens ursprungliga interaktionsposition. I detta arbete undersöks om maskininlärning kan användas för att erhålla en spatiell upplösning på subpixelnivå i en fotonräknande kiselstrippdetektor med 10 µm pixlar. Laddningsfördelningen från simulerade interaktioner i en, tre, och sju positioner inom var och en av tre pixlar undersöktes med hjälp av applikationen Classification Learner i MATLAB® för att bestämma den korrekta interaktionspositionen. Olika maskininlärningsmodeller tränades och testades för att maximera prestandan. När pulser från en och sju positioner inom pixeln användes, kunde modellen hitta den korrekta pixeln med en noggrannhet på 100% respektive 88.9%. Vidare kunde den korrekta positionen inom en pixel bestämmas med en noggrannhet på 54.0% och 29.4% när tre respektive sju positioner inom varje pixel användes. Resultaten visar att det skulle vara möjligt att förbättra den spatiella upplösningen med hjälp av maskininlärning.

In recent years, the usage of 3D deep learning techniques has seen a surge,mainly driven by advancements in autonomous driving and medical applications.This thesis investigates the applicability of existing state-of-the-art 3Ddeep learning network architectures to dense voxel grids from single photoncounting 3D LiDAR. This work also examine the choice of loss function asa means of dealing with extreme data imbalance, in order to segment peopleand vehicles in outdoor forest scenes. Due to data similarities with volumetricmedical data, such as computer tomography scans, this thesis investigates ifa model for 3D deep learning used for medical applications, the commonlyused 3D U-Net, can be used for photon counting data. The results showthat segmentation of people and vehicles is possible in this type of data butthat performance depends on the segmentation task, light conditions, and theloss function. For people segmentation the final models are able to predictall targets, but with a significant amount of false positives, something that islikely caused by similar LiDAR responses between people and tree trunks.For vehicle detection, the results are more inconsistent and varies greatlybetween different loss functions as well as the position and orientation of thevehicles. Overall, we consider the 3D U-Net model a successful proof-ofconceptregarding the applicability of 3D deep learning techniques to this kindof data. / Under de senaste åren har användningen för djupinlärningstekniker för 3Dsett en kraftig ökning, främst driven av framsteg inom autonoma fordon ochmedicinska tillämpningar. Denna avhandling undersöker befintliga modernadjupinlärningsnätverk för 3D i täta voxelgriddar från fotonräknande 3D LiDARför att segmentera människor och fordon i skogsscener. Vidare undersöksvalet av målfunktion som ett sätt att hantera extrem dataobalans. På grundav datalikheter med volymetriska medicinska data, såsom datortomografi,kommer denna avhandling att undersöka om en modell för 3D-djupinlärningsom används för medicinska applikationer, nämligen 3D U-Net, kan användasför fotonräknande data. Resultaten visar att segmentering av människor ochfordon är möjligt men att prestanda varier avsevärt med segmenteringsuppgiften,ljusförhållanden, och målfunktioner. För segmentering av människorkan de slutgiltiga modellerna segmentera alla mål men med en betydandemängd falska utslag, något som sannolikt orsakas av liknande LiDAR-svarmellan människor och trädstammar. För segmentering av fordon är resultatenmer oberäkneliga och varierar kraftigt mellan olika målfunktioner såväl somfordonens position och orientering. Sammantaget anser vi att 3D U-Netmodellenvisar på en framgångsrik konceptvalidering när det gäller tillämpningav djupinlärningstekniker för 3D på denna typ av data.

This thesis contains 2 parts (Part A and B) to fulfill the requirements for the degree of Master of Engineering. Part A: McMaster (on-campus) project. Part B: McMaster (industrial) project. / Part A abstract: The demodulation of analogue F.M. signals using frequency counting techniques is examined and implemented through the use of modern high speed T.T.L. integrated circuit technology. The entire demodulation unit was derived from exclusively digital components particularly compatible to frequency counting methods. The device was tested with carrier frequencies up to 2MHz and signal frequencies over the entire audio range with varying degrees of modulation. The main limitations appear to lay not in the hardware but in the actual counting technique itself which required quite large frequency deviations to resolve the higher audio frequency signals employed. Part B abstract: Investigation of SnO(2):B:Sb semiconductor species over the temperature range -60°C to +175°C reveals that electrical resistivity in this region is determined by the complex superposition of stable thin film scattering phenomena. Transient effects due to lattice imperfections inherent in the fabrication process start to "anneal” out at temperatures greater than 50°c and can be characterized by an activation energy of the order of .013 eV. Uncompensated samples doped heavily with boron illustrate a trend toward ionized impurity scattering at lower temperatures but mainly the species exhibits a complicated interplay of acoustical and optical phonon scattering modulated by doping level in such a manner as to lower T.C.R. An empirical expression relating resistivity-temperature behaviour to doping is developed. / Thesis / Master of Engineering (ME)

В диссертации рассматривается проблема учета труб на производственных предприятиях. Целью данного исследования является предоставление автоматизированного решения проблемы, которое потребует меньше времени для подсчета труб и будет более эффективным, чем подсчет вручную. Разработан алгоритм, основанный на технологии компьютерного зрения. Для выполнения задачи компьютерного зрения была использована библиотека OpenCV, языком программирования был выбран Python. После разработки алгоритма, основанного на технологии компьютерного зрения, стал возможен автоматический подсчет труб. Дальнейшее исследование может быть проведено для удовлетворения всех необходимых потребностей предприятия. / The dissertation addresses the problem of pipe counting in the manufacturing enterprises. The aim of this study is to provide an automated solution to the problem that will take less time to count pipes and will be more efficient than manual counting. An algorithm based on computer vision technology is developed. The library for undertaking the computer vision task was Open Source Computer Vision (OpenCV) and it was performed in Python. After the development of an algorithm based on computer vision, automatic pipe counting became possible. Further research might be conducted to meet all the required needs of the enterprise.

The lattice point problem in dynamical systems investigates the distribution of certain objects with some length property in the space that the dynamics is defined. This problem in different contexts can be interpreted differently. In the context of symbolic dynamical systems, we are trying to investigate the growth of N(T), the number of finite words subject to a specific ergodic length T, as T tends to infinity. This problem has been investigated by Pollicott and Urbański to a great extent. We try to investigate it further, by relaxing a condition in the context of deterministic dynamical systems. Moreover, we investigate this problem in the context of random dynamical systems. The method for us is considering the Fourier-Stieltjes transform of N(T) and expressing it via a Poincaré series for which the spectral gap property of the transfer operator, enables us to apply some appropriate Tauberian theorems to understand asymptotic growth of N(T). For counting in the random dynamics, we use some results from probability theory.

Phase transitions are fundamental phenomena in (quantum) many-body systems. They are associated with changes in the macroscopic physical properties of the system in response to the alteration in the conditions controlled by one or more parameters, like temperature or coupling constants. Quantum phase transitions are particularly intriguing as they reveal new insights into the fundamental nature of matter and the laws of physics. The study of phase transitions in such systems is crucial in aiding our understanding of how materials behave in extreme conditions, which are difficult to replicate in laboratory, and also the behavior of exotic states of matter with unique and potentially useful properties like superconductors and superfluids. Moreover, this understanding has other practical applications and can lead to the development of new materials with specific properties or more efficient technologies, such as quantum computers. Hence, detecting the transition point from one phase of matter to another and constructing the corresponding phase diagram is of great importance for examining many-body systems and predicting their response to external perturbations. Traditionally, phase transitions have been identified either through analytical methods like mean field theory or numerical simulations. The pinpointing of the critical value normally involves the measure of specific quantities such as local observables, correlation functions, energy gaps, etc. reflecting the changes in the physics through the transition. However, the latter approach requires prior knowledge of the system to calculate the order parameter of the transition, which is uniquely associated to its universality class. Recently, another method has gained more and more attention in the physics community. By using raw and very general representative data of the system, one can resort to machine learning techniques to distinguish among patterns within the data belonging to different phases. The relevance of these techniques is rooted in the ability of a properly trained machine to efficiently process complex data for the sake of pursuing classification tasks, pattern recognition, generating brand new data and even developing decision processes. The aim of this thesis is to explore phase transitions from this new and promising data-centric perspective. On the one hand, our work is focused on the developement of new machine learning architectures using state-of-the-art and interpretable models. On the other hand, we are interested in the study of the various possible data which can be fed to the artificial intelligence model for the mapping of a quantum many-body system phase diagram. Our analysis is supported by numerical examples obtained via matrix-product-states (MPS) simulations for several one-dimensional zero-temperature systems on a lattice such as the XXZ model, the Extended Bose-Hubbard model (EBH) and the two-species Bose Hubbard model (BH2S). In Part I, we provide a general introduction to the background concepts for the understanding of the physics and the numerical methods used for the simulations and the analysis with deep learning. In Part II, we first present the models of the quantum many-body systems that we study. Then, we discuss the machine learning protocol to identify phase transitions, namely anomaly detection technique, that involves the training of a model on a dataset of normal behavior and use it to recognize deviations from this behavior on test data. The latter can be applied for our purpose by training in a known phase so that, at test-time, all the other phases of the system are marked as anomalies. Our method is based on Generative Adversarial Networks (GANs) and improves the networks adopted by the previous works in the literature for the anomaly detection scheme taking advantage of the adversarial training procedure. Specifically, we train the GAN on a dataset composed of bipartite entanglement spectra (ES) obtained from Tensor Network simulations for the three aforementioned quantum systems. We focus our study on the detection of the elusive Berezinskii-Kosterlitz-Thouless (BKT) transition that have been object of intense theoretical and experimental studies since its first prediction for the classical two-dimensional XY model. The absence of an explicit symmetry breaking and its gappless-to-gapped nature which characterize such a transition make the latter very subtle to be detected, hence providing a challenging testing ground for the machine-driven method. We train the GAN architecture on the ES data in the gapless side of BKT transition and we show that the GAN is able to automatically distinguish between data from the same phase and beyond the BKT. The protocol that we develop is not supposed to become a substitute to the traditional methods for the phase transitions detection but allows to obtain a qualitative map of a phase diagram with almost no prior knowledge about the nature and the arrangement of the phases -- in this sense we refer to it as agnostic -- in an automatic fashion. Furthermore, it is very general and it can be applied in principle to all kind of representative data of the system coming both from experiments and numerics, as long as they have different patterns (even hidden to the eye) in different phases. Since the kind of data is crucially linked with the success of the detection, together with the ES we investigate another candidate: the probability density function (PDF) of a globally U(1) conserved charge in an extensive sub-portion of the system. The full PDF is one of the possible reductions of the ES which is known to exhibit relations and degeneracies reflecting very peculiar aspects of the physics and the symmetries of the system. Its patterns are often used to tell different kinds of phases apart and embed information about non-local quantum correlations. However, the PDF is measurable, e.g. in quantum gas microscopes experiments, and it is quite general so that it can be considered not only in the cases of the study but also in other systems with different symmetries and dimensionalities. Both the ES and the PDF can be extracted from the simulation of the ground state by dividing the one-dimensional chain into two complementary subportions. For the EBH we calculate the PDF of the bosonic occupation number in a wide range of values of the couplings and we are able to reproduce the very rich phase diagram containing several phases (superfluid, Mott insulator, charge density wave, phase separation of supersolid and superfluid and the topological Haldane insulator) just with an educated gaussian fit of the PDF. Even without resorting to machine learning, this analysis is instrumental to show the importance of the experimentally accessible PDF for the task. Moreover, we highlight some of its properties according to the gapless and gapped nature of the ground state which require a further investigation and extension beyond zero-temperature regimes and one-dimensional systems. The last chapter of the results contains the description of another architecture, namely the Concrete Autoencoder (CAE) which can be used for detecting phase transitions with the anomaly detection scheme while being able to automatically learn what the most relevant components of the input data are. We show that the CAE can recognize the important eigenvalues out of the entire ES for the EBH model in order to characterize the gapless phase. Therefore the latter architecture can be used to provide not only a more compact version of the input data (dimensionality reduction) -- which can improve the training -- but also some meaningful insights in the spirit of machine learning interpretability. In conclusion, in this thesis we describe two advances in the solution to the problem of phase recognition in quantum many-body systems. On one side, we improve the literature standard anomaly detection protocol for an automatic and agnostic identification of the phases by employing the GAN network. Moreover, we implement and test an explainable model which can make the interpretation of the results easier. On the other side we put the focus on the PDF as a new candidate quantity for the scope of discerning phases of matter. We show that it contains a lot of information about the many-body state being very general and experimentally accessible.