To obtain accurate adsorption energies of molecules on surfaces is a challenging task as the methods with sufficient accuracy are too computationally demanding to be applied to the systems of interest. Embedding theories provide a natural remedy: focus the computation on a small region and incorporate the effects of the environment. In this thesis, embedding schemes and the response of many-electron systems to an adsorbed impurity are investigated. To this end, two approaches are used: tight-binding and ab initio. In the tight-binding method, the Green's function formalism is studied and explicit expressions for Green's functions of various one- and two-dimensional models are obtained. Using this formalism, we study qualitatively the local density of states and adsorption energies. In the second part of this thesis, state-of-the-art ab initio methods are employed to study convergence of the subtractive embedding scheme for adsorption energies of small closed-shell systems on two-dimensional graphene and hexagonal boron nitride. The efficiency and applicability of the scheme are assessed for neon and hydrogen fluoride as adsorbates. We found that the studied embedding method works better for neon compared to hydrogen fluoride, which may be explained by the use of a two-body dispersion correction.

The evolution of a software system originates from its changes, whether it comes from changed user needs or adaption to its current environment. These changes are as encouraged as they are inevitable, although every change to a software system comes with a risk of introducing an error or a bug. This thesis aimed to investigate the possibilities of using the description of bug reports as a decision basis for detecting the provenance of a bug by using machine learning. K-means and agglomerative clustering have been applied to free text documents by using Natural Language Processing to initially divide the investigated software system into sub parts. Topic labelling is further on performed on the found clusters to find suitable names and get an overall understanding for the clusters.Finally, it was investigated if it was possible to find which cluster that were more likely to cause a bug from certain clusters and should be tested more thoroughly. By evaluating a subset of known causes, it was found that possible direct connections could be found in 50% of the cases, while this number increased to 58% if the cause were attached to clusters.

We study the problem of embedding high-dimensional visual data into low-dimensional vector representations. This is an important component in many computer vision applications involving nearest neighbor retrieval, as embedding techniques not only perform dimensionality reduction, but can also capture task-specific semantic similarities. In this thesis, we use deep neural networks to learn vector embeddings, and develop a gradient-based optimization framework that is capable of optimizing ranking-based retrieval performance metrics, such as the widely used Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG). Our framework is applied in three applications. First, we study Supervised Hashing, which is concerned with learning compact binary vector embeddings for fast retrieval, and propose two novel solutions. The first solution optimizes Mutual Information as a surrogate ranking objective, while the other directly optimizes AP and NDCG, based on the discovery of their closed-form expressions for discrete Hamming distances. These optimization problems are NP-hard, therefore we derive their continuous relaxations to enable gradient-based optimization with neural networks. Our solutions establish the state-of-the-art on several image retrieval benchmarks. Next, we learn deep neural networks to extract Local Feature Descriptors from image patches. Local features are used universally in low-level computer vision tasks that involve sparse feature matching, such as image registration and 3D reconstruction, and their matching is a nearest neighbor retrieval problem. We leverage our AP optimization technique to learn both binary and real-valued descriptors for local image patches. Compared to competing approaches, our solution eliminates complex heuristics, and performs more accurately in the tasks of patch verification, patch retrieval, and image matching. Lastly, we tackle Deep Metric Learning, the general problem of learning real-valued vector embeddings using deep neural networks. We propose a learning to rank solution through optimizing a novel quantization-based approximation of AP. For downstream tasks such as retrieval and clustering, we demonstrate promising results on standard benchmarks, especially in the few-shot learning scenario, where the number of labeled examples per class is limited.

The standard approach to pricing financial derivatives is to determine the discounted, risk-neutral expected payoff under a model. This model-based approach leaves us prone to model risk, as no model can fully capture the complex behaviour of asset prices in the real world. Alternatively, we could use the prices of some liquidly traded options to deduce no-arbitrage conditions on the contingent claim in question. Since the reference prices are taken from the market, we are not required to postulate a model and thus the conditions found have to hold under any model. In this thesis we are interested in the pricing of American put options using the latter approach. To this end, we will assume that European options on the same underlying and with the same maturity are liquidly traded in the market. We can then use the market information incorporated into these prices to derive a set of no-arbitrage conditions that are valid under any model. Furthermore, we will show that in a market trading only finitely many American and co-terminal European options it is always possible to decide whether the prices are consistent with a model or there has to exist arbitrage in the market.

Digital image watermarking is used to protect the copyright of digital images. In this thesis, a novel blind logo image watermarking technique for RGB images is proposed. The proposed technique exploits the error correction capabilities of the Human Visual System (HVS). It embeds two different watermarks in the wavelet/multiwavelet domains. The two watermarks are embedded in different sub-bands, are orthogonal, and serve different purposes. One is a high capacity multi-bit watermark used to embed the logo, and the other is a 1-bit watermark which is used for the detection and reversal of geometrical attacks. The two watermarks are both embedded using a spread spectrum approach, based on a pseudo-random noise (PN) sequence and a unique secret key. Robustness against geometric attacks such as Rotation, Scaling, and Translation (RST) is achieved by embedding the 1-bit watermark in the Wavelet Transform Modulus Maxima (WTMM) coefficients of the wavelet transform. Unlike normal wavelet coefficients, WTMM coefficients are shift invariant, and this important property is used to facilitate the detection and reversal of RST attacks. The experimental results show that the proposed watermarking technique has better distortion parameter detection capabilities, and compares favourably against existing techniques in terms of robustness against geometrical attacks such as rotation, scaling, and translation.

No description available.

'The Company' had introduced 'Being Responsible' a program for the embedding of responsibility-sustainability. Corporate responsibility-sustainability here describes efforts to address entangled cares of responsibility and sustainability. The program showed potential to change the business model, which led to the research problem: 'How can responsibility-sustainability programs change business models?'In this thesis, business models are understood as three dynamically interlinked states: Logics, devices and actor networks. Business model change may happen through the embedding of responsibility-sustainability into any of these states, and through the dynamics between them. Main conceptual lenses are organizational institutionalism and actor-network theory, which are connected through a social constructionist philosophy. Qualitative methods used include an in-depth case study of The Company (104 interviews with 72 interviewees) and thematic analyses of business model descriptions (devices) of FTSE corporations (100 documents).Seven papers study distinct aspects of the research problem: Papers 1 and 2 provide a conceptual basis. Papers 3 and 4 study how the embedding of responsibility-sustainability into the FTSE100s' business model devices changed the logics they described. Papers 5-7 study embedding into The Company's business model actor network. I found how embedding of responsibility-sustainability into the three states of commercial business models happened through three processes: Blending of logics, combination of device elements and translation between actors. Such embedding of responsibility-sustainability led to misalignment and tensions between responsibility-sustainability and the dominant commercial logic. This misalignment in turn fueled the dynamics of change between logics, devices and actor networks. First, this thesis contributes to an emerging literature on the dynamics of business model logics, devices and actor networks. It makes explicit the distinction between these states and illustrates how their dynamics provide novel insight into business model change. Secondly, I showcase how actor-network theory may complement the activity systems study of business models as well as stakeholder thinking in responsibility-sustainability research. Insights into how to use devices to change business models and to embed responsibility-sustainability appear relevant for practitioners.

A virtualização de redes tem se tornado cada vez mais proeminente nos últimos anos. Tal técnica permite a criação de infraestruturas de rede que se adaptam a necessidades específicas de aplicações de rede distintas, além de dar suporte à instanciação de ambientes favoráveis para o desenvolvimento e avaliação de novas arquiteturas e protocolos. Apesar de esforços recentes (motivados principalmente pela busca de mecanismos para avaliar propostas relacionadas à Internet do Futuro) terem contribuído substancialmente para a materialização desse conceito, nenhum preocupou-se em conciliar alocação eficiente de recursos e satisfação de requisitos de segurança (e.g., confidencialidade). É importante ressaltar que, no contexto de redes virtuais, a proteção de infraestruturas de rede compartilhadas constitui condição fundamental para seu uso em larga escala. É de grande importância que o impacto negativo causado pelo aprovisionamento de segurança seja considerado no processo de mapeamento de redes virtuais, de forma a permitir o uso integral dos recursos físicos sem subestimar requisitos de capacidade. Portanto, nesta dissertação, são propostos um modelo ótimo e um al- goritmo heurístico para realizar o mapeamento de redes virtuais em substratos físicos que têm por objetivo otimizar a utilização de recursos físicos garantindo a satisfação de requisitos de segurança. Ambas as abordagens possuem uma modelagem precisa de custos adicionais associados a mecanismos de segurança usados para proteger re- des virtuais, e são capazes de atender requisições de redes virtuais de forma online. Além disso, são apresentados os resultados de um extensivo processo de avaliação realizado, incluindo uma comparação detalhada entre o modelo ótimo e o algoritmo heurístico. Os experimentos revelam que o modelo baseado em Programação Linear Inteira é capaz de alocar redes virtuais de forma ótima em substratos físicos com até cem roteadores, enquanto que o algoritmo heurístico é capaz de adaptar-se a infraestruturas maiores, provendo mapeamentos sub-ótimos em um curto espaço de tempo. / Network virtualization has become increasingly prominent in recent years. It enables the creation of network infrastructures that are specifically tailored to the needs of distinct network applications and supports the instantiation of favorable en- vironments for the development and evaluation of new architectures and protocols. Although recent efforts (motivated mainly by the search for mechanisms to eval- uate Future Internet proposals) have contributed substantially to materialize this concept, none of them has attempted to combine efficient resource mapping with ful- fillment of security requirements (e.g., confidentiality). It is important to note that, in the context of virtual networks, the protection of shared network infrastructures constitutes a fundamental condition to enable its use in large scale. Considering the negative impact of security provisions in the virtual network embedding process is of paramount importance in order to fully utilize physical re- sources without underestimating capacity requirements. Therefore, in this thesis we propose both an optimal model and a heuristic algorithm for embedding virtual networks on physical substrates that aim to optimize physical resource usage while meeting security requirements. Both approaches feature precise modeling of over- head costs of security mechanisms used to protect virtual networks, and are able to handle virtual network requests in an online manner. In addition, we present the results of an extensive evaluation we carried out, including a detailed comparison of both the optimal model and the heuristic algorithm. Our experiments show that the Integer Linear Programming (ILP) model is capable of optimally embedding virtual networks on physical infrastructures with up to a hundred routers, while the heuristic algorithm is capable of scaling to larger infrastructures, providing timely, sub-optimal mappings.

A virtualização de redes é um mecanismo que permite a coexistência de múltiplas redes virtuais sobre um mesmo substrato físico. Um dos desafios de pesquisa abordados na literatura é o mapeamento eficiente de recursos virtuais em infraestruturas físicas. Embora o referido desafio tenha recebido considerável atenção, as abordagens que constituem o estado-da-arte apresentam alta taxa de rejeição, i.e., a proporção de solicitações de redes virtuais negadas em relação ao total de solicitações efetuadas ao substrato é elevada. Nesta dissertação, caracteriza-se, inicialmente, a relação entre a qualidade dos mapeamentos de redes virtuais e as estruturas topológicas dos substratos subjacentes. Avalia-se as soluções exatas de um modelo de mapeamento online sob diferentes classes de topologias de rede. A partir do entendimento dos fatores topológicos que influenciam diretamente o processo de mapeamento de redes virtuais, propõe-se uma estratégia para planejar a expansão de redes de provedores de infraestrutura de forma a reduzir consistentemente a taxa de rejeição de requisições de redes virtuais e melhor aproveitar os recursos ociosos da mesma. Os resultados obtidos evidenciam que grande parte das rejeições de redes virtuais ocorre em situações em que há grande disponibilidade de recursos, mas alguns poucos já saturados acabam inviabilizando, em função de características de conectividade do substrato, o atendimento de novas requisições. Ademais, os resultados obtidos utilizando a estratégia proposta evidenciam que o fortalecimento de partes-chave da infraestrutura levam a uma ocupação muito mais satisfatória. Uma expansão de 10% a 20% dos recursos da infraestrutura contribui para um aumento sustentado de até 30% no número de redes virtuais aceitas e de até 45% no aproveitamento dos recursos em comparação com a rede original. / Network virtualization is a mechanism that allows the coexistence of multiple virtual networks on top of a single physical substrate. One of the research challenges addressed recently in the literature is the efficient mapping of virtual resources on physical infrastructures. Although this challenge has received considerable attention, state-of-the-art approaches present, in general, a high rejection rate, i.e., the ratio between the number of denied virtual network requests and the total amount of requests is considerably high. In this thesis, we characterize the relationship between the quality of virtual network mappings and the topological structures of the underlying substrates. Exact solutions of an online embedding model are evaluated under different classes of network topologies. From the understanding of the topological factors that directly influence the virtual network embedding process, we propose an expansion strategy of physical infrastructure in order to suggest adjustments that lead to higher virtual network acceptance and, in consequence, to improved physical resource utilization. The obtained results demonstrate that most of rejections occur in situations in which a significant amount of resource is available, but a few saturated devices and links, depending on connectivity features of the physical substrate, hinder the acceptance of new requests. Moreover, the obtained results using the proposed strategy evidence that an expansion of 10% to 20% of the infrastructure resources leads to a sustained increase of up to 30% in the number of accepted virtual networks and of up to 45% in resource usage compared to the original network.

The two variable Fitzhugh Nagumo model behaves qualitatively like the four variable Hodgkin-Huxley space clamped system and is more mathematically tractable than the Hodgkin Huxley model, thus allowing the action potential and other properties of the Hodgkin Huxley system to be more readily be visualized. In this thesis, it is shown that the Carleman Embedding Technique can be applied to both the Fitzhugh Nagumo model and to Van der Pol's model of nonlinear oscillation, which are both finite nonlinear systems of differential equations. The Carleman technique can thus be used to obtain approximate solutions of the Fitzhugh Nagumo model and to study neural activity such as excitability.