Dynamic robustness evaluation for automated model selection in operation

Context: The increasing use of artificial neural network (ANN) classifiers in systems, especially safety-critical systems (SCSs), requires ensuring their robustness against out-of-distribution (OOD) shifts in operation, which are changes in the underlying data distribution from the data training the classifier. However, measuring the robustness of classifiers in operation with only unlabeled data is challenging. Additionally, machine learning engineers may need to compare different models or versions of the same model and switch to an optimal version based on their robustness. Objective: This paper explores the problem of dynamic robustness evaluation for automated model selection. We aim to find efficient and effective metrics for evaluating and comparing the robustness of multiple ANN classifiers using unlabeled operational data. Methods: To quantitatively measure the differences between the model outputs and assess robustness under OOD shifts using unlabeled data, we choose distance-based metrics. An empirical comparison of five such metrics, suitable for higher-dimensional data like images, is performed. The selected metrics include Wasserstein distance (WD), maximum mean discrepancy (MMD), Hellinger distance (HL), Kolmogorov–Smirnov statistic (KS), and Kullback–Leibler divergence (KL), known for their efficacy in quantifying distribution differences. We evaluate these metrics on 20 state-of-the-art models (ten CIFAR10-based models, five CIFAR100-based models, and five ImageNet-based models) from a widely used robustness benchmark (RobustBench) using data perturbed with various types and magnitudes of corruptions to mimic real-world OOD shifts. Results: Our findings reveal that the WD metric outperforms others when ranking multiple ANN models for CIFAR10- and CIFAR100-based models, while the KS metric demonstrates superior performance for ImageNet-based models. MMD can be used as a reliable second option for both datasets. Conclusion: This study highlights the effectiveness of distance-based metrics in ranking models’ robustness for automated model selection. It also emphasizes the significance of advancing research in dynamic robustness evaluation.publishedVersio

Optimization of Nativo Wood Fibre Insulation Board Through LCA Analysis

A previous LCA study on the insulation products has led to some uncertainty on the resulting Environmental Product Declaration (EPD) certification obtained by Hunton Fiber AS in Norway. There are certain themes included in the existing LCA which contain larger uncertainties than others. The bulk of uncertainties are related to allocation of impacts stemming from the phase of extraction and production of raw materials (A1), impacts from the transportation of raw materials (A2) and impacts of different scenarios of waste processing (C3). Goal of this study is to address these uncertainties by investigating (i) different scenarios for allocation of impacts from raw materials (wood chips), (ii) the impact of different modes of transportation of raw materials and (iii) different waste treatment scenarios. A comparative analysis of different LCA scenarios was assessed focused on the product’s impact on climate change and results are taken by the company as useful suggestions for future decisions.publishedVersio

3D Mechanical Wave Imaging for Myocardial Stiffness Assessment: Automation and measurements

The propagation velocity of mechanical waves (MW) in the heart is directly related to the properties of the myocardium. Direct measurement of changes in the tissue properties provides a more accurate assessment of cardiac function and health than current measures, such as ejection fraction and strain imaging, and often require complex evaluation procedures involving multiple parameters. The study of complex, high-velocity propagation patterns of natural MW in the heart can be achieved using high-frame-rate (HFR) imaging in three dimensions, made possible by recent advancements in ultrasound technology. In this work, we utilized 3D HFR imaging to quantify the MW propagation velocity based on the time-of-flight of the MW propagation in 3D. First, we developed a robust methodology for automatically measuring 3D MW velocity, enabling both high-throughput and in-depth analysis. This further facilitated the development of a clinical tool in the future. Secondly, we assessed the validity of this pipeline through a method comparison (1D vs. 2D vs. 3D) and evaluated the impact of various parameters. Finally, the optimized pipeline was tested clinically on patients with aortic stenosis and acute myocardial infarction and healthy volunteers. 3D wave propagation exhibited complex patterns that varied significantly depending on the source of the MW. We showed that these complexities can lead to measurement inaccuracies in 1D/2D velocity estimation methods due to misalignment with the wave direction, whereas 3D estimation is robust against such errors. Moreover, without 3D visualization, measurement errors in 1D/2D may go undetected or be mistaken for pathology. Our results indicate that, while the developed pipeline remained stable under minor variations, the processing parameters affected the results. Finally, 3D MW velocities were consistent with common indices, such as the wall motion score index and ejection fraction, in patients with acute myocardial infarction. The 3D velocity map may also provide the potential to detect infarction areas, improving treatment planning—an aspect that requires further investigation in future studies

Demonstrating the effect of solvent aging on the volatile and aerosol-based emissions of the AMP/PZ-based solvent CESAR1 after 1,000 h and 30,000 h operation

acceptedVersio

New Public Management in the Norwegian Hospital Sector: Budgeting, efficiency, and economies of scope

In the 1990s, the Norwegian hospital sector struggled with long waiting lists and a lack of cost control. In response to this, several reforms labelled under the umbrella term “New Public Management” (NPM) were introduced in the Norwegian hospital sector in the late 1990s and early 2000s. In brief terms, NPM consists of introducing management practices inspired from the private sector into the public sector, with the goal of making the public sector more efficient. The reforms introduced radical changes in how the hospitals were organized, managed, and financed. The most extensive reform was the 2002 Hospital Ownership Reform. This reform moved the ownership of the hospitals from the counties to the state, while reorganizing the hospitals as health trusts. These health trusts were organized as self-governing entities with control of their own personnel and capital. This thesis covers a study period between 2011-2019, when the Norwegian hospital sector was relatively stable, in terms of both financing and organization. The thesis consists of three papers, as empirical studies, investigating three different aspects of the 2002 Hospital Ownership Reform one decade after its initial implementation. Paper 1 investigates how the health trusts adapted to a model whereby they are responsible for financing both the day-to-day operations of the health trust, as well as investments. Specifically, we look at both at the degree to which the health trusts have planned for budget surpluses, and the accuracy of this planning. We furthermore investigate whether there have been any associations between structural/organizational characteristics and the accuracy of budgeted surpluses. We find that the health trust for the most part budgets for a positive result of between 0-3 per cent of total operating costs. When comparing the budgeted results with the actual results, we find indications pointing towards the health trusts being too optimistic when planning future surpluses, but we also find examples of pessimism. Larger health trusts seem to have a greater accuracy in their budgeted results than smaller health trusts, while health trusts with more a more complex pool of patients have lower accuracy in their surplus budgeting. Paper 2 investigates one of the main objectives of the NPM reforms, namely efficiency. In the study, we first measure the efficiency of the whole hospital sector through a non-parametric method. Secondly, we investigate how NPM-related tools are related to the efficiency. We find that from 2011 to 2019, the average efficiency level of Norwegian health trusts increased somewhat. We find that a variable capturing the NPM component of incentivization is associated with the efficiency score, while a variable capturing the NPM component of competition is not associated with the efficiency of the health trusts. Paper 3 investigates the potential presence of economies of scope in the Norwegian hospital sector. Following the 2002 Hospital Ownership Reform, the Regional Health Authorities had the freedom to decide on the separation of functions within the health region. The individual health trusts were also given greater management autonomy. Economies of scope refer to situations where cost savings occur from the joint production of services in the same unit, rather than from separate production in specialized units. For the 2013-2019 period, the study investigates whether there were any differences in average efficiency between relatively specialized and differentiated hospitals, and whether the Norwegian hospital sector was characterized by economies or diseconomies of scope While the findings concerning the first question are somewhat ambiguous, the findings concerning the second question indicate that the sector is characterized by economies of scope

Pioneering Generative Models: Novel Techniques for Anomaly Detection, Image Modeling, and Video Prediction

Generative models are becoming increasingly important in the field of machine learning (ML) as they demonstrate impressive capabilities in synthesizing data and have a wide range of applications. These models are useful in data augmentation, anomaly detection, and prediction tasks. In this thesis, we propose three novel generative models, each designed for specific applications, including i) cyber security and anomaly detection, ii) image reconstruction and generation, and iii) future video prediction. Our contribution to the field includes expanding the scope of generative models and suggesting new possibilities for their use. In the first application, we investigate the domains of anomaly detection and cyber security. As technology advances and crucial infrastructures become more interconnected with the virtual world, the need for efficient cyber threat detection and risk mitigation becomes more significant. In this regard, the thesis proposes an innovative generative model called STEPGAN, which is specifically designed for anomaly detection. The proposed STEP-GAN has an impressive ability to identify imperceptible patterns that indicate potential threats and deviations from normal behavior, which is assumed to follow the complementary distribution of normal data. STEP-GAN is a robust tool for improving cyber threat prevention and protecting digital systems. The second application explains the reconstruction and generation of images. This challenge focuses on compressing images while also reconstructing high-quality images that capture the important features of the original data. To this end, we present a novel generative model based on a vector quantized variational autoencoder (VQ-VAE) called HR-VQVAE, which utilizes hierarchical residual learning. Unlike traditional VAE-based methods, HRVQVAE has a unique encoding structure that maps continuous latent representations to multiple layers of discrete representations through hierarchical codebooks. Experimental results prove that such an architecture not only improves image reconstruction but also improves the quality of image generation while operating faster compared to the baseline methods. The third application focuses on future video prediction, which requires predicting future video frames given a sequence of preceding observations. This task has vast potential in areas such as video surveillance, autonomous driving, and virtual reality. To address this challenge, we introduce S-HR-VQVAE, a novel sequential hierarchical residual learning vector quantized variational autoencoder. This innovative approach builds on the capabilities of the HR-VQVAE, resulting in highly accurate predictions of future video frames. HR-VQVAE is specifically designed to model the spatiotemporal dependencies between discrete representations across frames. Our experimental results demonstrate that S-HRVQVAE surpasses state-of-the-art methods while requiring significantly fewer parameters

Acoustic channel management for increased autonomy in underwater multi-agent patrol forces

Selvgående undervannskjøretøy (“Autonomous Underwater Vehicles”, AUVer) er tilganger som har mange anvendelser. Disse kjøretøyene er viktige for å kunne overvåke utilgjengelige og strategisk viktige områder, særlig i tider med pågående militære konflikter. Denne avhandlingen undersøker distribuerte løsninger og kommunikasjonsprotokoll for å gjøre flåter av AUVer mer selvgående til sammen. Spesifikt presenterer avhandlingen alminnelige løsninger og fremgangsmåter for AUVer, som vil danne en patruljetropp for å overvåke undervannsmiljøet, men også for fikse noder som trenger å kommunisere. Løsningene som foreslås hviler på fire pilarer. Første pilaren er en konsensusprotokoll, med hensikt å la agenter bli enige om tilstanden til den akustiske kanalen. Demonstrasjoner i simulering viser at strikt enighet er oppnåelig, med en feilsannsynlighet på mindre enn 10 % og en tidsadgang avhengig av nettverkets topologi og protokollens tidsparametere. Feltforsøk bekrefter en tilsvarende prestanda i et virkelig miljø og i nettverk på opp til seks noder. Andre pilaren er en metode som bygger på distribuert optimalisering for å bestemme de optimale parameterene for modulasjon og koding. Metoden benytter robust asynkron Newton-Raphson-optimalisering til dette formålet. Simulering viser at metoden hever forventet kommunikasjonshastighet i ett nettverk om fem noder, trass at optimaliseringsproblemet har et ikke-konvekst tvangsvilkår. Tredje pilaren er en distribuert modelle-prediktiv reguleringsalgoritm med gjennomførbar akustisk kommunikasjon. Mer spesifikt tilpasses algoritmen det akustiske undervannsmiljøet ved kringkastingbasert kommunikasjon, asynkron oppdatering og mekanismer for å håndtere forsinkete eller tapte pakker. Disse tilpasningene fører til at antakelsene som forgjengeren hviler på mykes opp. Numerisk simulering viser at tilpasningene muliggjør store besparinger i kommunikasjonshastighet samtidig som reguleringsprestandaen forblir tilfredsstillende. Fjerde pilaren er en akustisk undervannskommunikasjonskanal med mange innganger, som går kun én vei. Denne kanalen bruker teknologien “distribuert akustisk sansing” og kan benyttes for å formidle korte meldinger på en diskré måte. Anvendelsen demonstreres praktisk, i sanntid og ved lave frekvenser, ved hjelp av feltforsøk. I tillegg estimeres følsomheten til kabelen uttrykt i relativ deformasjon per trykkenhet. Avhandlingen diskuterer så disse fire bidragene, fordelene og ulempene deres, og sammenligner dem med relatert forskningsarbeid. Videre foreslår avhandlingen nye retninger for fremtidig arbeid, siden nye spørsmål vekkes med de inkluderte bidragene.Abstract Autonomous underwater vehicles (AUVs) are assets with a wide range of applications. In times of global insecurity, with several ongoing military conflicts, such vehicles are instrumental to monitoring inaccessible but strategically important areas. This thesis investigates communication protocols and distributed solutions that enable and facilitate autonomy for fleets of AUVs. More specifically, the thesis proposes solutions and approaches in general applicable to AUVs wanting to form a patrol squad for underwater surveillance, but also potentially fixed nodes that need to communicate. The proposed solutions stand on four pillars. First is a consensus protocol that lets agents agree on the conditions of the acoustic channel. This protocol is demonstrated to converge to consensus under a consensus error rate of less than 10% in simulation, requiring a variable amount of time depending on network topology and timing parameters. The algorithm has also been demonstrated experimentally to attain similar performance in field-deployed networks of up to six nodes. Second is a distributed-optimisation-based approach to optimally determine the modulation and coding parameters using robust asynchronous Newton-Raphson optimisation. The algorithm is shown by simulation to improve the expected data rate in a five-node network, even though the optimization problem at its core has non-convex constraints. Third is a distributed model-predictive control algorithm with acoustically feasible communication requirements. Specifically, the algorithm is adapted to operate asynchronously, on a broadcast basis, and with mechanisms for handling delayed and lost packets, hence relaxing the assumptions of the predecessor. Numerical simulations show that the adaptations admit large savings in data rate while at the same time maintaining adequate tracking performance. Fourth is a one-way multiple-input underwater acoustic communications channel that can be used to covertly relay short messages to shore, based on the principles of distributed acoustic sensing. This channel is demonstrated with field trials to admit low-frequency communication from sea to shore in real time. In addition, the sensitivity of the cable is estimated in terms of strain units per pressure unit. This thesis discusses thus these four contributions, their advantages and disadvantages, and sets them in relation to relevant work. The thesis lists also possible directions for future work, as new questions arise with the aforementioned contributions.In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of NTNU’s products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink

Insight into the genetic basis of cardiorespiratory fitness and resting heart rate, and their relation to cardiovascular disease

Cardiovascular disease (CVD) is the leading cause of death worldwide. Cardiorespiratory fitness (CRF) and resting heart rate (RHR) are two independent risk factors for CVD. Both CRF, RHR, and CVD are heritable and have a strong genetic component. Hence, understanding more of the genetic architecture of CRF and RHR could improve the understanding of how CRF and RHR relate to CVD. The aim of this thesis was to explore the genetic contribution to CRF by identifying genetic variants associated with CRF. Further, we aimed to examine how RHR affects the risk of different CVDs, by 1) explore whether RHR has a causal effect on risk of atrial fibrillation (AF), and 2) examine the genetic contribution to RHR, develop a polygenic risk score (PRS) for RHR, and investigate its association with different CVD outcomes. We have used two large independent cohorts throughout this thesis; The Trøndelag Health Study and United Kingdom Biobank. We suggest 38 novel genetic variants associated with CRF. Further biological analyses pointed to several interesting genes and pathways associated with cardiac function, cardiac remodeling, contractility, vascular tone, blood pressure, calcium signaling, and endurance capacity, in addition to different CVDs including dilated cardiomyopathy (DCM). For RHR, we found an inverse causal association between RHR and AF in the MR framework. This inverse causal association was also observed when allowing for a non-linear relationship. This indicates that low RHR increases risk of AF. We hypothesize that this association is mostly driven by a strong inverse association for low RHR values. The estimated causal effect can be a result of a true causal effect or pleiotropic pathways on cardiac remodeling, atrial myopathy, autonomic tone, and/or heart structure. Further, the genetic study of RHR confirmed previous reported loci associated with RHR and identified one novel genetic variant in women. The sex-specific analyses pointed to several genetic loci, genes, and pathways that were only associated with RHR in one of the sexes. The RHR PRS was associated with increased risk of hypertension, early-onset hypertension, DCM, and a decreased risk of AF. There were differences in the association between the RHR PRS and disease risk in women and men. To conclude, we highlighted novel genetic variants associated with CRF and present possible biological mechanisms linking CRF to CVD. For RHR, our study indicated that low RHR causally increases risk of AF. We have explored the genetic contribution to RHR and how it relates to CVD, with a special focus on sex differences. Further exploration of these results could elucidate biological pathways, identifying possible targets for prevention and improve current risk prediction for CVD

Improved decision support for bridge safety assessment and maintenance by probabilistic methods

When assessing the safety of existing bridges, uncertainties, information, and consequences are different compared to the design stage. Higher-level verification formats, such as probabilistic and risk-based methods, may help overcome these challenges but are rarely used in practice. With this thesis, the implementation of these methods into practice is accelerated by i) demonstration of information integration by probabilistic methods on realistic assessment situations, ii) application of Bayesian decision-making to bridge maintenance to find optimal intervention strategies and iii) improvement of the design value method by tailored alpha values. This PhD project thereby improves the basis for safely keeping existing bridges in service to a greater extent, instead of building new ones