Context-Free Session Types for Applied Pi-Calculus

We present a binary session type system using context-free session types to a version of the applied pi-calculus of Abadi et. al. where only base terms, constants and channels can be sent. Session types resemble process terms from BPA and we use a version of bisimulation equivalence to characterize type equivalence. We present a quotiented type system defined on type equivalence classes for which type equivalence is built into the type system. Both type systems satisfy general soundness properties; this is established by an appeal to a generic session type system for psi-calculi.Comment: In Proceedings EXPRESS/SOS 2018, arXiv:1808.0807

Valuation of XXL ASA

This master thesis presents a comprehensive valuation analysis of XXL ASA, a prominent Norwegian retail company operating in the Nordic sports industry. A fundamental valuation method, in combination with a supplementary comparative valuation has been utilized to estimate the equity value of the company as of December 31, 2021. The valuation has been performed from an investor perspective, using only publicly available information. The purpose of this study is to provide a trading strategy: buy, hold, or sell, based on the estimated equity value and the corresponding stock price. Therefore, the research question addressed in this study is as follows: “What is the fair value of XXL ASA’s equity as of December 31, 2021?” To address this research question, a strategic analysis of internal and external factors has been conducted to map XXL’s strengths and weaknesses, as well as to identify opportunities and threats. Furthermore, a financial statement and credit risk analysis of historical data has been performed, which together with the strategic analysis forms the basis for future expectations for the industry and the projected financials for XXL. The analysis indicated that XXL in the short term is expected to face challenges due to macroeconomics conditions, high inventory levels, and increased competition. However, in the long term, the company is expected to reverse this trend, regaining profitability and growth. Based on the assumptions about the future and the projected financials, the value per share using a fundamental approach for XXL is estimated to be NOK 5.98. To test the uncertainty in the valuation estimate, a sensitivity analysis has been conducted to assess the robustness and reasonableness. In addition, the comparative valuation yielded a value per share of NOK 51.61 based on the average of multiples. For comparison, the market price of XXL on the Oslo Stock Exchange at the valuation date was NOK 14.03. In determining the final value estimate of XXL’s equity and the corresponding stock price, the comparative valuation has been excluded due to an excessively high valuation and its weaknesses. Therefore, only the fundamental valuation is taken into consideration in estimating the value per share to be NOK 5.98. This leads to the following conclusion: The XXL stock is overpriced, and we recommend selling the stock as of December 31, 2021

Valuation of XXL ASA

This master thesis presents a comprehensive valuation analysis of XXL ASA, a prominent Norwegian retail company operating in the Nordic sports industry. A fundamental valuation method, in combination with a supplementary comparative valuation has been utilized to estimate the equity value of the company as of December 31, 2021. The valuation has been performed from an investor perspective, using only publicly available information. The purpose of this study is to provide a trading strategy: buy, hold, or sell, based on the estimated equity value and the corresponding stock price. Therefore, the research question addressed in this study is as follows: “What is the fair value of XXL ASA’s equity as of December 31, 2021?” To address this research question, a strategic analysis of internal and external factors has been conducted to map XXL’s strengths and weaknesses, as well as to identify opportunities and threats. Furthermore, a financial statement and credit risk analysis of historical data has been performed, which together with the strategic analysis forms the basis for future expectations for the industry and the projected financials for XXL. The analysis indicated that XXL in the short term is expected to face challenges due to macroeconomics conditions, high inventory levels, and increased competition. However, in the long term, the company is expected to reverse this trend, regaining profitability and growth. Based on the assumptions about the future and the projected financials, the value per share using a fundamental approach for XXL is estimated to be NOK 5.98. To test the uncertainty in the valuation estimate, a sensitivity analysis has been conducted to assess the robustness and reasonableness. In addition, the comparative valuation yielded a value per share of NOK 51.61 based on the average of multiples. For comparison, the market price of XXL on the Oslo Stock Exchange at the valuation date was NOK 14.03. In determining the final value estimate of XXL’s equity and the corresponding stock price, the comparative valuation has been excluded due to an excessively high valuation and its weaknesses. Therefore, only the fundamental valuation is taken into consideration in estimating the value per share to be NOK 5.98. This leads to the following conclusion: The XXL stock is overpriced, and we recommend selling the stock as of December 31, 2021

Papaya: Global Typestate Analysis of Aliased Objects

Typestates are state machines used in object-oriented programming to specify and verify correct order of method calls on an object. To avoid inconsistent object states, typestates enforce linear typing, which eliminates - or at best limits - aliasing. However, aliasing is an important feature in programming, and the state-of-the-art on typestates is too restrictive if we want typestates to be adopted in real-world software systems. In this paper, we present a type system for an object-oriented language with typestate annotations, which allows for unrestricted aliasing, and as opposed to previous approaches it does not require linearity constraints. The typestate analysis is global and tracks objects throughout the entire program graph, which ensures that well-typed programs conform and complete the declared protocols. We implement our framework in the Scala programming language and illustrate our approach using a running example that shows the interplay between typestates and aliases

Projection of primary and revision hip arthroplasty surgery in Denmark from 2020 to 2050

BACKGROUND AND PURPOSE: The incidence of primary and revision total hip arthroplasty (THA) has increased over the last decades. Previous forecasts from different healthcare systems have predicted a continuous increase. We present a forecast of both primary and revision surgery from 2020 to 2050 based on 25 years data from the healthcare system in Denmark. PATIENTS AND METHODS: We retrieved data from the Danish Hip Arthroplasty Register on 198,835 primary and 29,456 revision surgeries. Historical censuses and population forecasts were retrieved from Statistics Denmark. Logistic and Gompertz regression analysis was used to forecast incidence rates (IR) and total numbers in the next 30 years. RESULTS: Our forecast predicts an increase in IR of 3–9% and an increase in total numbers of primary THA of between 12% and 19% in 2050. For revision THA the IRs have reached a plateau but total numbers are predicted to increase by 19% in 2050. CONCLUSION: Our forecast shows that both primary and revision THA will increase in total numbers in the next decades, but the IR for primary THA is near its plateau and for revision THA the plateau has already been reached. The forecast may aid in healthcare resource planning for the decades to come

Machine-learning vs. logistic regression for preoperative prediction of medical morbidity after fast-track hip and knee arthroplasty-a comparative study

BACKGROUND: Machine-learning models may improve prediction of length of stay (LOS) and morbidity after surgery. However, few studies include fast-track programs, and most rely on administrative coding with limited follow-up and information on perioperative care. This study investigates potential benefits of a machine-learning model for prediction of postoperative morbidity in fast-track total hip (THA) and knee arthroplasty (TKA).METHODS: Cohort study in consecutive unselected primary THA/TKA between 2014-2017 from seven Danish centers with established fast-track protocols. Preoperative comorbidity and prescribed medication were recorded prospectively and information on length of stay and readmissions was obtained through the Danish National Patient Registry and medical records. We used a machine-learning model (Boosted Decision Trees) based on boosted decision trees with 33 preoperative variables for predicting "medical" morbidity leading to LOS &gt; 4 days or 90-days readmissions and compared to a logistical regression model based on the same variables. We also evaluated two parsimonious models, using the ten most important variables in the full machine-learning and logistic regression models. Data collected between 2014-2016 (n:18,013) was used for model training and data from 2017 (n:3913) was used for testing. Model performances were analyzed using precision, area under receiver operating (AUROC) and precision recall curves (AUPRC), as well as the Mathews Correlation Coefficient. Variable importance was analyzed using Shapley Additive Explanations values.RESULTS: Using a threshold of 20% "risk-patients" (n:782), precision, AUROC and AUPRC were 13.6%, 76.3% and 15.5% vs. 12.4%, 74.7% and 15.6% for the machine-learning and logistic regression model, respectively. The parsimonious machine-learning model performed better than the full logistic regression model. Of the top ten variables, eight were shared between the machine-learning and logistic regression models, but with a considerable age-related variation in importance of specific types of medication.CONCLUSION: A machine-learning model using preoperative characteristics and prescriptions slightly improved identification of patients in high-risk of "medical" complications after fast-track THA and TKA compared to a logistic regression model. Such algorithms could help find a manageable population of patients who may benefit most from intensified perioperative care.</p