Finansielle kontrakter i norske såkornfond : en empirisk analyse

Vi har i vår oppgave sett på kontrakter i såkornfond og sammenlignet disse med kontrakter i Venturekapitalfond. Vi har sett på dette i lys av prinsipal-agent teori, Miller og Modiligianis teori om kapitalstruktur og hold-up teori. Resultatene vi har funnet har vi vurdert opp mot resultater Kaplan og Strømberg (2003) fant for VC markedet i USA, og resultater Bienz og Walz (2005) fant for markedet i Tyskland. Vi har i oppgaven sett at det er mange av de samme problemene såkorn og VC står overfor, men grunnet at såkornfondenes porteføljebedrifter er svært unge og det er stor risiko knyttet til investeringer i disse selskapene, så løser de enkelte av disse problemene på forskjellig måte. Spesielt måten fondene finansierer selskapene på er svært forskjellig fra såkorn til VC. Måten såkorn velger å fokusere på det mulige oppsidepotensialet i en investering med opsjoner i stede for å sikre nedsiden skiller også såkorn fra VC

Maximum Entropy COICOP Classification using Entity Forest

This thesis proposes a generative approach to COICOP classification using entity resolution and maximum entropy classification as a formal framework. The current limitations in COICOP classification are related to the corpus of item descriptions and lack of data. I propose a new perspective on the classification task at hand, as I argue that the underlying problem in classification is the data itself. Therefore, corpus and feature engineering are crucial when improving classification. The proposed approach aims to engineer the corpus to construct an entity forest from the item descriptions, where terms in the description are mapped to the roots and branches of trees in the entity forest. The results of the proposed approach are illustrated by a proof-of-concept with data from Statistics Norway. This thesis provides insight into the problems with previous approaches to COICOP classification and shows how we potentially can achieve true resolution and more accurate classification

Fight or flight - subjektiv livskvalitet og syn på samfunnsutvikling under ekstrem luftforurensing i Beijing

Masteroppgave i økologisk økonomi (MBA) - Nord universitet, 201

Probabilistic non-linear registration with spatially adaptive regularisation

This paper introduces a novel method for inferring spatially varying regularisation in non-linear registration. This is achieved through full Bayesian inference on a probabilistic registration model, where the prior on the transformation parameters is parameterised as a weighted mixture of spatially localised components. Such an approach has the advantage of allowing the registration to be more flexibly driven by the data than a traditional globally defined regularisation penalty, such as bending energy. The proposed method adaptively determines the influence of the prior in a local region. The strength of the prior may be reduced in areas where the data better support deformations, or can enforce a stronger constraint in less informative areas. Consequently, the use of such a spatially adaptive prior may reduce unwanted impacts of regularisation on the inferred transformation. This is especially important for applications where the deformation field itself is of interest, such as tensor based morphometry. The proposed approach is demonstrated using synthetic images, and with application to tensor based morphometry analysis of subjects with Alzheimer’s disease and healthy controls. The results indicate that using the proposed spatially adaptive prior leads to sparser deformations, which provide better localisation of regional volume change. Additionally, the proposed regularisation model leads to more data driven and localised maps of registration uncertainty. This paper also demonstrates for the first time the use of Bayesian model comparison for selecting different types of regularisation

High-quality dense 3D point clouds with active stereo and a miniaturizable interferometric pattern projector

We have built and characterized a compact, simple and flexible 3D camera based on interferometric fringe projection and stereo reconstruction. The camera uses multi-frame active stereo as basis for 3D reconstruction, providing full-field 3D images with 3D measurement standard deviation of 0.09 mm, 12.5 Hz 3D image capture rate and 3D image resolution of 500 × 500 pixels. Interferometric projection enables a compact, low-power projector that consumes < 1 W of electrical power. The key component in the projector, a movable micromirror, has undergone initial vibration, thermal vacuum cycling (TVAC) and radiation testing, with no observed component degradation. The system's low power, small size and component longevity makes it well suitable for space applications.publishedVersio

Real-time super-resolved 3D in turbid water using a fast range-gated CMOS camera

We present a range-gated camera system designed for real-time (10 Hz) 3D estimation underwater. The system uses a fast-shutter CMOS sensor (1280×1024 1280×1024 ) customized to facilitate gating with 1.67 ns (18.8 cm in water) delay steps relative to the triggering of a solid-state actively Q -switched 532 nm laser. A depth estimation algorithm has been carefully designed to handle the effects of light scattering in water, i.e., forward and backward scattering. The raw range-gated signal is carefully filtered to reduce noise while preserving the signal even in the presence of unwanted backscatter. The resulting signal is proportional to the number of photons that are reflected during a small time unit (range), and objects will show up as peaks in the filtered signal. We present a peak-finding algorithm that is robust to unwanted forward scatter peaks and at the same time can pick out distant peaks that are barely higher than peaks caused by sensor and intensity noise. Super-resolution is achieved by fitting a parabola around the peak, which we show can provide depth precision below 1 cm at high signal levels. We show depth estimation results when scanning a range of 8 m (typically 1–9 m) at 10 Hz. The results are dependent on the water quality. We are capable of estimating depth at distances of over 4.5 attenuation lengths when imaging high albedo targets at low attenuation lengths, and we achieve a depth resolution () (σ)ranging from 0.8 to 9 cm, depending on signal level.publishedVersio

Autonomous subsea intervention (SEAVENTION)

This paper presents the main results and latest developments in a 4-year project called autonomous subsea intervention (SEAVENTION). In the project we have developed new methods for autonomous inspection, maintenance and repair (IMR) in subsea oil and gas operations with Unmanned Underwater Vehicles (UUVs). The results are also relevant for offshore wind, aquaculture and other industries. We discuss the trends and status for UUV-based IMR in the oil and gas industry and provide an overview of the state of the art in intervention with UUVs. We also present a 3-level taxonomy for UUV autonomy: mission-level, task-level and vehicle-level. To achieve robust 6D underwater pose estimation of objects for UUV intervention, we have developed marker-less approaches with input from 2D and 3D cameras, as well as marker-based approaches with associated uncertainty. We have carried out experiments with varying turbidity to evaluate full 6D pose estimates in challenging conditions. We have also devised a sensor autocalibration method for UUV localization. For intervention, we have developed methods for autonomous underwater grasping and a novel vision-based distance estimator. For high-level task planning, we have evaluated two frameworks for automated planning and acting (AI planning). We have implemented AI planning for subsea inspection scenarios which have been analyzed and formulated in collaboration with the industry partners. One of the frameworks, called T-REX demonstrates a reactive behavior to the dynamic and potentially uncertain nature of subsea operations. We have also presented an architecture for comparing and choosing between mission plans when new mission goals are introduced.publishedVersio

Image registration via stochastic gradient markov chain monte carlo

We develop a fully Bayesian framework for non-rigid registration of three-dimensional medical images, with a focus on uncertainty quantification. Probabilistic registration of large images along with calibrated uncertainty estimates is difficult for both computational and modelling reasons. To address the computational issues, we explore connections between the Markov chain Monte Carlo by backprop and the variational inference by backprop frameworks in order to efficiently draw thousands of samples from the posterior distribution. Regarding the modelling issues, we carefully design a Bayesian model for registration to overcome the existing barriers when using a dense, high-dimensional, and diffeomorphic parameterisation of the transformation. This results in improved calibration of uncertainty estimates