Determinants of long‐term bank relationships : an empirical study of the Norwegian bank market 

This paper investigates the determinants of long-term bank relationships using a new data set for the time period between 1998 and 2008, and comprised of 9,476 firms in Norway. We document that firms are more likely to end a relationship as the relationship matures. This result casts doubt on theories suggesting that firms become locked in by their banks. Further, looking at firm specific variables we find that old, highly profitable firms with high liquidity and high creditor concentration maintain longer relationships. We extend our study to bank- and relationship specific effects and report that relationships with savings banks are longer, as are relationships where the firm holds both deposits and loans in the bank. Our study is robust to left-censoring, alternate specification for the distribution of relationship duration, and control variables such as the bank market concentration in Norway and primary banks. Overall the main contribution of our study to the existing literature is that the unique data set enables us to study new explanatory variables on a relatively large sample

Performance principles for trusted computing with intel SGX

Accepted manuscript version of the following article Gjerdrum, A.T., Pettersen, R., Johansen, H.D. & Johansen, D. (2018). Performance principles for trusted computing with intel SGX. Communications in Computer and Information Science, 864. © Springer International Publishing AG, part of Springer Nature 2018. Published version available at https://doi.org/10.1007/978-3-319-94959-8_1.Cloud providers offering Software-as-a-Service (SaaS) are increasingly being trusted by customers to store sensitive data. Companies often monetize such personal data through curation and analysis, providing customers with personalized application experiences and targeted advertisements. Personal data is often accompanied by strict privacy and security policies, requiring data processing to be governed by non-trivial enforcement mechanisms. Moreover, to offset the cost of hosting the potentially large amounts of data privately, SaaS companies even employ Infrastructure-as-a-Service (IaaS) cloud providers not under the direct supervision of the administrative entity responsible for the data. Intel Software Guard Extensions (SGX) is a recent trusted computing technology that can mitigate some of these privacy and security concerns through the remote attestation of computations, establishing trust on hardware residing outside the administrative domain. This paper investigates and demonstrates the added cost of using SGX, and further argues that great care must be taken when designing system software in order to avoid the performance penalty incurred by trusted computing. We describe these costs and present eight specific principles that application authors should follow to increase the performance of their trusted computing systems

Reducing Objective Function Mismatch in Deep Clustering with the Unsupervised Companion Objective

Preservation of local similarity structure is a key challenge in deep clustering. Many recent deep clustering methods therefore use autoencoders to help guide the model's neural network towards an embedding which is more reflective of the input space geometry. However, recent work has shown that autoencoder-based deep clustering models can suffer from objective function mismatch (OFM). In order to improve the preservation of local similarity structure, while simultaneously having a low OFM, we develop a new auxiliary objective function for deep clustering. Our Unsupervised Companion Objective (UCO) encourages a consistent clustering structure at intermediate layers in the network -- helping the network learn an embedding which is more reflective of the similarity structure in the input space. Since a clustering-based auxiliary objective has the same goal as the main clustering objective, it is less prone to introduce objective function mismatch between itself and the main objective. Our experiments show that attaching the UCO to a deep clustering model improves the performance of the model, and exhibits a lower OFM, compared to an analogous autoencoder-based model

Assessing stream bank condition using airborne LiDAR and high spatial resolution image data in temperate semirural areas in Victoria, Australia

Stream bank condition is an important physical form indicator for streams related to the environmental condition of riparian corridors. This research developed and applied an approach for mapping bank condition from airborne light detection and ranging (LiDAR) and high-spatial resolution optical image data in a temperate forest/woodland/urban environment. Field observations of bank condition were related to LiDAR and optical image-derived variables, including bank slope, plant projective cover, bank-full width, valley confinement, bank height, bank top crenulation, and ground vegetation cover. Image-based variables, showing correlation with the field measurements of stream bank condition, were used as input to a cumulative logistic regression model to estimate and map bank condition. The highest correlation was achieved between field-assessed bank condition and image-derived average bank slope (R2 1/4 0.60, n 1/4 41), ground vegetation cover (R2 1/4 0.43, n 1/4 41), bank width/height ratio (R2 1/4 0.41, n 1/4 41), and valley confinement (producer's accuracy 1/4 100%, n 1/4 9). Crossvalidation showed an average misclassification error of 0.95 from an ordinal scale from 0 to 4 using the developed model. This approach was developed to support the remotely sensed mapping of stream bank condition for 26,000 km of streams in Victoria, Australia, from 2010 to 2012

Discussions on "Riemann manifold Langevin and Hamiltonian Monte Carlo methods"

This is a collection of discussions of `Riemann manifold Langevin and Hamiltonian Monte Carlo methods" by Girolami and Calderhead, to appear in the Journal of the Royal Statistical Society, Series B.Comment: 6 pages, one figur

The Relationships Between Self-Concept, Self-Efficacy, and Military Skills and Abilities

Abstract. This study investigated the relationship between academic self-concept, academic self-efficacy and the self-reported acquirement of certain specific military skills and abilities. Our sample consisted of 141 military cadets from the Norwegian Military Academy (Army), the Royal Norwegian Naval Academy, and the Royal Norwegian Air Force Academy. Supporting our hypotheses, it was found that perceived academic self-concept related positively to self-efficacy, after controlling for initial levels of self-efficacy, and that self-efficacy relates positively to self-reported Individual Coping Capacity (ICC), Cooperation in Difficult Situations (CDS), and Motivation to Achievement (MA), this after controlling for the initial levels of these Military Skills and Abilities (MSA). We discuss the implications of these findings.publishedVersio

Application of satellite precipitation data to analyse and model arbovirus activity in the tropics

Background: Murray Valley encephalitis virus (MVEV) is a mosquito-borne Flavivirus (Flaviviridae: Flavivirus) which isclosely related to Japanese encephalitis virus, West Nile virus and St. Louis encephalitis virus. MVEV is enzootic innorthern Australia and Papua New Guinea and epizootic in other parts of Australia. Activity of MVEV in WesternAustralia (WA) is monitored by detection of seroconversions in flocks of sentinel chickens at selected sample sitesthroughout WA.Rainfall is a major environmental factor influencing MVEV activity. Utilising data on rainfall and seroconversions,statistical relationships between MVEV occurrence and rainfall can be determined. These relationships can be usedto predict MVEV activity which, in turn, provides the general public with important information about diseasetransmission risk. Since ground measurements of rainfall are sparse and irregularly distributed, especially in northWA where rainfall is spatially and temporally highly variable, alternative data sources such as remote sensing (RS)data represent an attractive alternative to ground measurements. However, a number of competing alternatives areavailable and careful evaluation is essential to determine the most appropriate product for a given problem.Results: The Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42product was chosen from a range of RS rainfall products to develop rainfall-based predictor variables and buildlogistic regression models for the prediction of MVEV activity in the Kimberley and Pilbara regions of WA. Twomodels employing monthly time-lagged rainfall variables showed the strongest discriminatory ability of 0.74 and0.80 as measured by the Receiver Operating Characteristics area under the curve (ROC AUC).Conclusions: TMPA data provide a state-of-the-art data source for the development of rainfall-based predictivemodels for Flavivirus activity in tropical WA. Compared to ground measurements these data have the advantage ofbeing collected spatially regularly, irrespective of remoteness. We found that increases in monthly rainfall andmonthly number of days above average rainfall increased the risk of MVEV activity in the Pilbara at a time-lag oftwo months. Increases in monthly rainfall and monthly number of days above average rainfall increased the risk ofMVEV activity in the Kimberley at a lag of three months.I