In several applications, when anomalies are detected, human experts have to investigate or verify them one by one. As they investigate, they unwittingly produce a label - true positive (TP) or false positive (FP). In this thesis, we propose two methods (PAD and Clustering-based OMD/OJRank) that exploit this label feedback to minimize the FP rate and detect more relevant anomalies, while minimizing the expert effort required to investigate them. These two methods iteratively suggest the top-1 anomalous instance to a human expert and receive feedback. Before suggesting the next anomaly, the methods re-ranks instances so that the top anomalous instances are similar to the TP instances and dissimilar to the FP instances. This is achieved by learning to score anomalies differently in various regions of the feature space (OMD-Clustering) and by learning to score anomalies based on the distance to the real anomalies (PAD). An experimental evaluation on several real-world datasets is conducted. The results show that OMD-Clustering achieves statistically significant improvement in both detection precision and expert effort compared to state-of-the-art interactive anomaly detection methods. PAD reduces expert effort but there was no improvement in detection precision compared to state-of-the-art methods. We submitted a paper based on the work presented in this thesis, to the ECML/PKDD Workshop on "IoT Stream for Data Driven Predictive Maintenance".

Dans ce projet de thèse, nous étudions le rôle joué par l'anomalie dû à la connexion composée dans les théories de supergravités dans l'espace-temps à huit dimensions. Ce genre d'anomalie est en effet engendrée par la structure quotient d'espace des champs moduli de la supergravité là où le nombre des super-charges posent des contraintes rigoureux. Notre accomplissement principal est de proposer des termes supplémentaires pour annuler cette anomalie dans la théorie de supergravité en huit dimensions avec seize supercharges. Ces termes, en outre, peuvent être considérés comme une manifestation des corrections provenant de la théorie de super-cordes et nous montrons par des calculs explicites qu'une amplitude sur une boucle dans la théorie de cordes correspondante reproduit ces termes. Motivés par cette démonstration de la cohérence entre la supergravité et la théorie de cordes, nous proposons un seuil mathématique pour la compactification de ces théories dans huit dimensions vers six dimensions sur une sphère en présence des branes de co-dimension 2. Ceci est une simulation de compactification sur une surface K3 à l'aide des branes. Nous montrons que la présence d'anomalie composé ne peut être justifiée que par des branes de co-dimensions deux. Nous discutons la dualité entre la théorie Heterotic et la théorie-F sous la lumière de 7-branes et puis la compactification des supergravités de dix dimensions sur K3 en présence des 5-branes. Tous cela nous ouvrent nouvelles voies pour étudier des aspects non-perturbatives de la théorie de cordes. Nous concluons avec un calcul sur deux boucles dans la théorie de cordes Heterotic de dix dimensions qui n'ont pas été beaucoup exploré dans la littérature. / We examine the structure of composite anomaly in maximal and half-maximal supergravity theories especially in eight space-time dimensions. The number of super-charges dictates the structure of the coset space of the moduli fields of the theory which in turn engenders the composite anomaly in such theories. Our main achievement lies in proposing counter-terms for such anomalies. These terms are of stringy nature and we show by explicit 1-loop amplitude calculations in corresponding string theories that those counter-terms are consistently provided by string amplitude. In the light of non-perturbative higher dimensional theories like F-theory, the anomaly cancelling counter-terms are seen to be related to co-dimension two branes e.g. 7-branes. We then use these results of 8-dimensional theories to provide for supergravity theories in six-dimensions by compactifying on a sphere in the presence of 5-branes. This is in fact a simulation of K3 compactification and our knowledge of composite connection provide us with threshold conditions to achieve such compactifications. All these analysis provide for greater insight into the non-perturbative regime of string theory. We then conclude with a calculation of 2-loop Heterotic string amplitude which has been very less explored in the literature.

abstract: This thesis reviews the initial cases of fetal surgery to correct myelomeningocele, a severe form of spina bifida, and discusses the human and social dimensions of the procedure. Myelomeningocele is a fetal anomaly that forms from improper closure of the spinal cord and the tissues that surround it. Physicians perform fetal surgery on a developing fetus, while it is in the womb, to mitigate its impacts. Fetal surgery to correct this condition was first performed experimentally in the mid-1990and as of 2020, it is commonly performed. The initial cases illuminated important human and social dimensions of the technique, including physical risks, psychological dimensions, physician bias, and religious convictions, which affect decision-making concerning this fetal surgery. Enduring questions remain in 2020. The driving question for this thesis is: given those human and social dimensions that surround fetal surgery to correct myelomeningocele, whether and when is the surgery justified? This thesis shows that more research is needed to answer or clarify this question. / Dissertation/Thesis / Masters Thesis Biology 2020

In South Africa, nearly 80 % of electricity is generated from coal fired power plants. Due to the complexity of the interconnected systems that make up a typical power plant, analysis of the root causes of load losses is not a straightforward process. This often leads to losses incorrectly being ascribed to the Induced Draught (ID) fan, where detection occurs, while the problem actually originates elsewhere in the plant. The focus of this study was to develop and demonstrate a modelling methodology to quantify the effects of major plant anomalies on the capacity of ID fans in coal fired power plants. The ensuing model calculates the operating point of the ID fan that is a result of anomalies experienced elsewhere in the plant. This model can be applied in conjunction with performance test data as part of a root cause analysis procedure. The model has three main sections that are integrated to determine the ID fan operating point. The first section is a water/steam cycle model that was pre-configured in VirtualPlantTM. The steam plant model was verified via energy balance calculations and validated against original heat balance diagrams. The second is a draught group model developed using FlownexSETM. This onedimensional network is a simplification of the flue gas side of the five main draught group components, from the furnace inlet to the chimney exit, characterising only the aggregate heat transfer and pressure loss in the system. The designated ID fan model is based on the original fan performance curves. The third section is a Boiler Mass and Energy Balance (BMEB) specifically created for this purpose to: (1) translate the VirtualPlant results for the steam cycle into applicable boundary conditions for the Flownex draught group model; and (2) to calculate the fluid properties applicable to the draught group based on the coal characteristics and combustion process. The integrated modelling methodology was applied to a 600 MW class coal fired power plant to investigate the impact of six major anomalies that are typically encountered. These are: changes in coal quality; increased boiler flue gas exit temperatures; air ingress into the boiler; air heater inleakage to the flue gas stream; feed water heaters out-of-service; and condenser backpressure degradation. It was inter alia found that a low calorific value (CV) coal of 14 MJ/kg compared to a typical 17 MJ/kg reduced the fan's capacity by 2.1 %. Also, having both HP FWH out of service decreased the fan's capacity by 16.2 %.

京都大学 / 新制・課程博士 / 博士(理学) / 甲第22993号 / 理博第4670号 / 新制||理||1670(附属図書館) / 京都大学大学院理学研究科物理学・宇宙物理学専攻 / (主査)准教授 吉岡 興一, 教授 田中 貴浩, 准教授 髙山 史宏 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

Nonlinear sigma-models in physics have been a source of interesting and important ideas in geometry, topology, and algebra. One such model is the curved beta gamma system. This purely bosonic model studies maps from a Riemann surface to a target complex manifold X. The solutions to the classical equations of motion are holomorphic maps. An extension of this model - the so-called heterotic model, incorporates fermionic fields valued in a holomorphic vector bundle E on the complex manifold. In this thesis, I study this extended model within the framework of effective field theory and BV quantization developed by Kevin Costello. Building on earlier work of Gorbounov-Gwilliam-Williams in the purely bosonic case, my approach uses tools of Gelfand-Kazhdan formal geometry and derived deformation theory to extract obstructions to quantization (anomalies) and identify these with characteristic classes of the target manifold. Specifically, I show that the obstruction to solving the Quantum Master Equation can be identified with the class ch_2 (TX)-ch_2(E), and the obstruction to the quantizing equivariantly with respect to holomorphic vector fields on the source Riemann surface can be identified with c_1 (TX) - c_1(E). By analyzing the theory where the source is an elliptic curve, an explicit geometric construction of the partition function is given.

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This paper deals with a problem of detection of network traffic anomaly and classification of network flows. Based on existing methods, paper describes proposal and implementaion of a tool, which can automatically classify network flows. The tool uses CUDA platform for network data processing and computation of network flow metrics using graphics processing unit. Processed flows are subsequently classified by proposed methods for network anomaly detection.

This thesis deals with the failure mode detection of aircraft engines. The main approach to the detection is searching for anomalies in the sensor data. In order to get a comprehensive idea of the system and the particular sensors, the description of the whole system, namely the aircraft engine HTF7000 as well as the description of the sensors, are dealt with at the beginning of the thesis. A proposal of the anomaly detection algorithm based on three different detection methods is discussed in the second chapter. The above-mentioned methods are SVM (Support Vector Machine), K-means a ARIMA (Autoregressive Integrated Moving Average). The implementation of the algorithm including graphical user interface proposal are elaborated on in the next part of the thesis. Finally, statistical analysis of the results,the comparison of efficiency particular models and the discussion of outputs of the proposed algorithm can be found at the end of the thesis.