Agile practices have become norm, also in large scale organizations. Applying agile methods includes introducing continuous practices, including continuous architecture. For web scale applications microservices is a rising star. This thesis investigates if microservices could be an answer also for embedded systems to tackle the synchronizing problem of many parallel teams.

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In order for the innovation process to be successful, not only do innovations need to be developed and reached the market, but, once they are available for users, they have to spread on a large scale. In the innovation literature, a complete explanation is lacking of why some innovations reach a phase of large-scale diffusion faster than others, including both actor- and system-level components. For instance, what drives and hinders adopters to decide to adopt the innovation on the actor and system levels, and how adopters who participate in the largescale diffusion handle the adoption process and the implementation of the innovation, are questions still unanswered. As a consequence, it remains unclear how the large-scale diffusion process can be facilitated and speeded up. This thesis addresses these issues by studying the case of renewable electricity (RE) innovations. After decades of technology development and improvements, RE innovations are now mature enough to be bought off-the-shelf by individuals and organizations. Yet, the pace of their large-scale diffusion is still too slow for countries to reach their RE generation targets and to limit global warming. Through qualitative and quantitative methods including 59 semi-structured interviews with adopters, project developers and experts in Sweden, France and Germany as well as a survey sent to the whole population of RE adopters in Sweden, an adopter perspective is taken in order to explore the adoption dynamics shaping large-scale diffusion of innovation. More specifically, the thesis identifies the drivers and challenges of adoption during large-scale diffusion and their impact on adoption decisions and strategies. The outcome of this work is presented in a compiling synthesis and six appended papers. Findings show that adopters are heterogeneous with regard to their characteristics, as well as to the drivers, challenges and strategies that affect their adoption processes. Depending on their perceptions, some adopters are more influenced by drivers and challenges than others and, as a consequence, adopters base their adoption decisions on different motives and follow different strategies to implement the innovation. Moreover, the results suggest that the dynamics that occur during the large-scale diffusion process does not only come from the actor level and the level of the system where the largescale diffusion takes place, but also from parallel systems, which are related to adopters and their contexts, including both the social networks and the industries they primarily belong. This makes adopters the central drivers of the innovation diffusion process and this distinguishes the dynamics of large-scale diffusion from the dynamics of innovation development and early diffusion, in which the innovation is the central component. Based on the findings about the adoption dynamics shaping large-scale diffusion, the thesis raises the need to consider large-scale diffusion as part of a new system, different from the innovation system and that acknowledges the specificities of this process. A tentative model accounting for the central role of adopters and for the interactions between adopters, the diffusion system and parallel systems is introduced. Finally, the implications of these findings for policy makers and managers are put forward. In particular, there is a need for policies acknowledging adopters’ heterogeneity as well as the new challenges of large-scale diffusion. Strategies developed by adopters can be a source of inspiration for policy-makers, who can for instance promote the use of intermediaries, of adopters’ task environment and networks, as well as the formation of coalitions among adopters.

Spatially varying depth and the characteristics of observing conditions, such as seeing, airmass, or sky background, are major sources of systematic uncertainties in modern galaxy survey analyses, particularly in deep multi-epoch surveys. We present a framework to extract and project these sources of systematics onto the sky, and apply it to the Dark Energy Survey (DES) to map the observing conditions of the Science Verification (SV) data. The resulting distributions and maps of sources of systematics are used in several analyses of DES-SV to perform detailed null tests with the data, and also to incorporate systematics in survey simulations. We illustrate the complementary nature of these two approaches by comparing the SV data with BCC-UFig, a synthetic sky catalog generated by forward-modeling of the DES-SV images. We analyze the BCC-UFig simulation to construct galaxy samples mimicking those used in SV galaxy clustering studies. We show that the spatially varying survey depth imprinted in the observed galaxy densities and the redshift distributions of the SV data are successfully reproduced by the simulation and are well-captured by the maps of observing conditions. The combined use of the maps, the SV data, and the BCC-UFig simulation allows us to quantify the impact of spatial systematics on N(z), the redshift distributions inferred using photometric redshifts. We conclude that spatial systematics in the SV data are mainly due to seeing fluctuations and are under control in current clustering and weak-lensing analyses. However, they will need to be carefully characterized in upcoming phases of DES in order to avoid biasing the inferred cosmological results. The framework presented here is relevant to all multi-epoch surveys and will be essential for exploiting future surveys such as the Large Synoptic Survey Telescope, which will require detailed null tests and realistic end-to-end image simulations to correctly interpret the deep, high-cadence observations of the sky.

The extensive catalogue of gamma-ray selected flat-spectrum radio quasars (FSRQs) produced by Fermi during a four-year survey has generated considerable interest in determining their gamma-ray luminosity function (GLF) and its evolution with cosmic time. In this paper, we introduce the novel idea of using this extensive database to test the differential volume expansion rate predicted by two specific models, the concordance Lambda cold darkmatter (Lambda CDM) and R-h = ct cosmologies. For this purpose, we use two well-studied formulations of the GLF, one based on pure luminosity evolution (PLE) and the other on a luminosity-dependent density evolution (LDDE). Using a Kolmogorov-Smirnov test on one-parameter cumulative distributions (in luminosity, redshift, photon index and source count), we confirm the results of earlier works showing that these data somewhat favour LDDE over PLE; we show that this is the case for both Lambda CDM and R-h = ct. Regardless of which GLF one chooses, however, we also show that model selection tools very strongly favour R-h = ct over Lambda CDM. We suggest that such population studies, though featuring a strong evolution in redshift, may none the less be used as a valuable independent check of other model comparisons based solely on geometric considerations.

We show that the projected number density profiles of Sloan Digital Sky Survey photometric galaxies around galaxy clusters display strong evidence for the splashback radius, a sharp halo edge corresponding to the location of the first orbital apocenter of satellite galaxies after their infall. We split the clusters into two subsamples with different mean projected radial distances of their members, < R-mem >, at fixed richness and redshift. The sample with smaller < R-mem > has a smaller ratio of the splashback radius to the traditional halo boundary R-200m than the subsample with larger < R-mem >, indicative of different mass accretion rates for these subsamples. The same subsamples were recently used by Miyatake et al. to show that their large-scale clustering differs despite their similar weak lensing masses, demonstrating strong evidence for halo assembly bias. We expand on this result by presenting a 6.6 sigma difference in the clustering amplitudes of these samples using cluster-photometric galaxy cross-correlations. This measurement is a clear indication that halo clustering depends on parameters other than halo mass. If < R-mem > is related to the mass assembly history of halos, the measurement is a manifestation of the halo assembly bias. However, our measured splashback radii are smaller, while the strength of the assembly bias signal is stronger, than the predictions of collisionless. cold dark matter simulations. We show that dynamical friction, cluster mis-centering, or projection effects are not likely to be the sole source of these discrepancies. However, further investigations regarding unknown catastrophic weak lensing or cluster identification systematics are warranted.

This thesis details the creation of ASKNet (Automated Semantic Knowledge Network), a system for creating large scale semantic networks from natural language texts. Using ASKNet as an example, we will show that by using existing natural language processing (NLP) tools, combined with a novel use of spreading activation theory, it is possible to efficiently create high quality semantic networks on a scale never before achievable. The ASKNet system takes naturally occurring English text (e.g., newspaper articles), and processes them using existing NLP tools. It then uses the output of those tools to create semantic network fragments representing the meaning of each sentence in the text. Those fragments are then combined by a spreading activation based algorithm that attempts to decide which portions of the networks refer to the same real-world entity. This allows ASKNet to combine the small fragments together into a single cohesive resource, which has more expressive power than the sum of its parts. Systems aiming to build semantic resources have typically either overlooked information integration completely, or else dismissed it as being AI-complete, and thus unachievable. In this thesis we will show that information integration is both an integral component of any semantic resource, and achievable through a combination of NLP technologies and novel applications of spreading activation theory. While extraction and integration of all knowledge within a text may be AI-complete, we will show that by processing large quantities of text efficiently, we can compensate for minor processing errors and missed relations with volume and creation speed. If relations are too difficult to extract, or we are unsure which nodes should integrate at any given stage, we can simply leave them to be picked up later when we have more information or come across a document which explains the concept more clearly. ASKNet is primarily designed as a proof of concept system. However, this thesis will show that it is capable of creating semantic networks larger than any existing similar resource in a matter of days, and furthermore that the networks it creates of are sufficient quality to be used for real world tasks. We will demonstrate that ASKNet can be used to judge semantic relatedness of words, achieving results comparable to the best state-of-the-art systems.

We use recently published redshift space distortion measurements of the cosmological growth rate, f sigma(8)(z), to examine whether the linear evolution of perturbations in the R-h = ct cosmology is consistent with the observed development of large-scale structure. We find that these observations favour R-h = ct over the version of Lambda cold dark matter (Lambda CDM) optimized with the joint analysis of Planck and linear growth rate data, particularly in the redshift range 0 < z < 1, where a significant curvature in the functional form of f sigma(8)(z) predicted by the standard model-but not by R-h = ct-is absent in the data. When Lambda CDM is optimized using solely the growth rate measurements; however, the two models fit the observations equally well though, in this case, the low-redshift measurements find a lower value for the fluctuation amplitude than is expected in Planck Lambda CDM. Our results strongly affirm the need for more precise measurements of f sigma(8)(z) at all redshifts, but especially at z < 1.

The aim of this dissertation is to traceand discuss the practices and challenges of physical planning within an Entrepreneurial City approach to urban policy. The research aim is addressed by focusing on three questions: 1) how have the practices of physicalplanning been influenced by the context of an Entrepreneurial City approach to urban policy 2) how has physical planning responded to this urban policy context, and 3) which potential dilemmas for physical planning practice derive from this new context? By an Entrepreneurial City approach tourban policy I understand an approach whereby there is an attention placed over strategies to promote local economic growth and attract investments, companies and specific types of people in to the city. Arguably urban policies focus less on welfare-related and redistribution strategies. There is an adoption of private sector discourses and tools to promote the city as a place to live, work and invest in. These discourses and tools pass through place-making strategies, marketing, engagement in speculative, risk-taking market-led projects, and seeking partners with whom to establish alliances that will serve to promote the city. The strong emphasis of Entrepreneurial City approaches on interventions over the built environment of a city or neighborhood implies a greater attention to what is happening to the practices of physical planning in municipalities that have adopted this approach. Existing studies tend to emphasize that it signifies a decrease in the scope of influence for public sector, and by extention for physical planning, in the governance and steering of these projects. The dissertation focuses on large-scale urban development projects – Brunnshög, in Lund, and Bo01, Norra Sorgenfri and Hyllie, in Malmö. The projects were chosen due to their likelihood toillustrate physical planning practices marked by an Entrepreneurial City approach. The main findings of this thesis refute the idea of a turn in urban policy towards entrepreneurial city approaches, and illustrate instead a process by which new practices and values coincide with previously established settings and practices. Physical planning is adopting the discourses of an urban policy approach where intercity competition for new industries (preferably in knowledge-intensive sectors) and residents(preferably the “creative classes”) guide urban development projects. The governance setting is marked by the need to establish working networks and partnerships that will create the capacity to act. Experimentation, piece-meal approaches and inter-project learning mark the adaptation strategies to an urban policy context that is still changing. Potential dilemmas lie in the fragmented character of the partnerships required to execute the projects, and in the assumption that these projects will result in the rehabilitation of the socio-economic trends of the city and promote local economic growth. Additionally the resulting built environments are prone to processes of gentrification and displacement, and spatial and socioeconomic polarization.

This dissertation introduces a novel model for solving the L2 support vector machine dubbed Direct L2 Support Vector Machine (DL2 SVM). DL2 SVM represents a new classification model that transforms the SVM's underlying quadratic programming problem into a system of linear equations with nonnegativity constraints. The devised system of linear equations has a symmetric positive definite matrix and a solution vector has to be nonnegative. Furthermore, this dissertation introduces a novel algorithm dubbed Non-Negative Iterative Single Data Algorithm (NN ISDA) which solves the underlying DL2 SVM's constrained system of equations. This solver shows significant speedup compared to several other state-of-the-art algorithms. The training time improvement is achieved at no cost, in other words, the accuracy is kept at the same level. All the experiments that support this claim were conducted on various datasets within the strict double cross-validation scheme. DL2 SVM solved with NN ISDA has faster training time on both medium and large datasets. In addition to a comprehensive DL2 SVM model we introduce and derive its three variants. Three different solvers for the DL2's system of linear equations with nonnegativity constraints were implemented, presented and compared in this dissertation.