Rebalancing Unemployment Benefits in a Unionized Labour Market.

The basic trade union model is extended to allow for a more sophisticated unemployment benefit system consisting of two benefit levels, one for short-term and one for long-term unemployed, and a rule determining whether an unemployed is short- or long-term. The purpose of this extension is twofold; to get a more realistic analysis of the actual benefit systems in most countries, and to analyse alternative reforms to the traditional one of changing a uniform benefit level. Reforms that rebalance the benefit rates holding constant either expected utility of an unemployed, aggregate benefit expenditures, or aggregate utility of union members can reduce unemployment.

Mechanistic Modelling of Radial Polymer Flow in Porous Media

Polymer flooding is one of the most successful chemically enhanced oil recovery (EOR) methods, and has primarily been implemented to accelerate oil production by sweep improvement. However, research from the last couple of decades have identified several additional benefits associated with polymer flooding. Firstly, improved polymer properties have extended their use in reservoirs with high temperature and high salinity. Secondly, improved understanding of the viscoelastic flow behavior of flexible polymers have revealed that they may in some cases mobilize capillary trapped oil as well. Despite of the recent progress, extensive research remains to quantify the appropriate flow mechanisms and accurately describe polymer flow in porous media. Simulations and history match operations performed in this thesis are aimed at improving the modelling of radial polymer flow in porous media. This has been achieved by (1) evaluating the accuracy and robustness of two different history match methods which are used to estimate the in-situ rheology of non-Newtonian fluids in radial flow, (2) investigating potential rate and memory effects (at the Darcy scale) of viscoelastic polymer solutions in radial flow, and (3) quantifying polymer in-situ rheology and polymer injectivity. The accuracy and robustness of both history match methods which are used to estimate the in-situ rheology of non-Newtonian fluids in radial flow was clearly demonstrated in radial flow experiments where effective (or cumulative) error was below 5 % of the maximum preset transducer pressure range. Thereby, the observed shear-thinning behavior of partially hydrolyzed polyacrylamide (HPAM) at low flux in porous media could not be attributed to insufficiently accurate pressure transducers during in-house flow experiments, as suggested by some researchers. The estimation of polymer in-situ rheology showed invariance between excluding and including the polymer pressure data outside the near-wellbore region. Thus, it was proposed that the polymer in-situ rheology is mainly defined by the pressure data originating from the near-wellbore region during radial polymer flow. Results showed that not only could the polymer in-situ rheology be (quantitatively) estimated from measurements of stabilized pressure, but could also be (qualitatively) identified from the pressure build-up during radial flow experiments. Consequently, the anchoring data from the pressure build-up during radial polymer flow was proposed as an additional tool for history matching field injectivity tests. Rate and memory effects (at the Darcy scale) of several HPAM polymers were investigated in flow through Bentheimer sandstone discs. Results showed that no rate effects occurred for mechanically undegraded polymer (Flopaam 3330S). However, rate effects were observed for mechanically degraded polymers (Flopaam 3630S and Flopaam 5115SH) where the onset of shear-thickening increased with volumetric injection rate. While memory effects (at the Darcy scale) were absent for the mechanically undegraded and relatively low molecular weight polymer, Flopaam 3330S, the mechanically degraded and relatively higher molecular weight (18 MDa) polymer, Flopaam 3630S, exhibited memory effects in which apparent viscosity decreased with radial distance. As mechanical degradation is suggested to be confined to the near-wellbore region in radial polymer flow, the memory effect was proposed to originate from the elastic properties of the polymer. In accordance with recent literature, the in-situ rheology of HPAM was shown to depend on flow geometry. During single and two-phase polymer flow, the shear-thinning behavior of HPAM was much more pronounced, and the extent of shear-thickening significantly reduced in radial compared to linear flow. Furthermore, the onset of shear-thickening during single-phase flow occurred at significantly higher velocities in radial relative to linear flow. However, this behavior was not consistent during two-phase flow as the onset of shear-thickening during linear and radial polymer flow coincided. Moreover, comparative studies of polymer flow in radial versus linear flow geometries during single and two-phase flow revealed that the impact of oil was to reduce apparent in-situ viscosity of HPAM. The low-flux in-situ rheology behavior was addressed and showed Newtonian behavior in linear flow while significant shear-thinning was observed during radial flow. Thus, both flow geometry and presence of oil were suggested to be key factors for estimating polymer in-situ rheology

A local Bayesian optimizer for atomic structures

A local optimization method based on Bayesian Gaussian Processes is developed and applied to atomic structures. The method is applied to a variety of systems including molecules, clusters, bulk materials, and molecules at surfaces. The approach is seen to compare favorably to standard optimization algorithms like conjugate gradient or BFGS in all cases. The method relies on prediction of surrogate potential energy surfaces, which are fast to optimize, and which are gradually improved as the calculation proceeds. The method includes a few hyperparameters, the optimization of which may lead to further improvements of the computational speed.Comment: 10 pages, 5 figure

DET: Data Enhancement Technique for Aerial Images

Deep learning and computer vision are two thriving research areas within machine learning. In recent years, as the available computing power has grown, it has led to the possibility of combining the approaches, achieving state-of-the-art results. An area of research that has greatly benefited from this development is building detection. Although the algorithms produce satisfactory results, there are still many limitations. One significant problem is the quality and edge sharpness of the segmentation masks, which are not up to the standard required by the mapping industry. The predicted mask boundaries need to be sharper and more precise to have practical use in map production. This thesis introduces a novel Data Enhancement Technique (DET) to improve the boundary quality of segmentation masks. DET has two approaches, Seg-DET, which uses a segmentation network, and Edge-DET, which uses an edge-detection network. Both techniques highlight buildings, creating a better input foundation for a secondary segmentation model. Additionally, we introduce ABL(RMI), a new compounding loss consisting of Region Mutual Information Loss (RMI), Lovasz-Softmax Loss (Lovasz), and Active Boundary Loss (ABL). The combination of loss functions in ABL(RMI) is optimized to enhance and improve mask boundaries. This thesis empirically shows that DET can successfully improve segmentation boundaries, but the practical results suggest that further refinement is needed. Additionally, the results show improvements when using the new compounding loss ABL(RMI) compared to its predecessor, ABL(CE) which substitutes RMI with Cross-Entropy loss(CE)

Sosiale nettverk som kommunikasjonsplatform For offentlige tjenestetilbydere

Denne oppgaven beskriver en tenkt kommunikasjonsplatform mellom en offentlig tjenestetilbyder og en bruker i et sosialt nettverk. Oppgaven beskriver teknikker, peker på teori og fremtidige løsninge