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The Tvären structure in southeastern Sweden has been listed as a confirmed marine-target impact structure for decades. However, to date, no measurements and/or indexed data of planar deformation features in quartz grains from the structure have been published or any other unequivocal evidence of impact. Here, we present an investigation aimed at searching for shocked quartz in the 224 m deep Tväre
In this thesis, we focus on the problem of estimating receiver and sender node positions given some form of distance measurements between them. This kind of localization problem has several applications, e.g., global and indoor positioning, sensor network calibration, molecular conformations, data visualization, graph embedding, and robot kinematics. More concretely, this thesis makes contribution
Objective Ultrasound is a promising tool to foster much-needed improvement of skin assessment in systemic sclerosis (SSc). Our aim was to develop evidence and expert opinion-based recommendations to promote the standardisation and harmonisation of technical execution and reporting of skin ultrasound studies in SSc. Methods A multidisciplinary task force of 16 members from five European countries a
Despite laws, policies and visions to create cities and societies for all, barriers still exclude persons with disabilities from using buildings and public places. Our study aimed to identify choices made during the urban development process that include or exclude users in the built environment; how and when these choices arise during the process; and what is needed to implement universal design
The risk assessment of pesticide residues in food is a key priority in the area of food safety. Most jurisdictions have implemented pre-marketing authorization processes, which are supported by prospective risk assessments. These prospective assessments estimate the expected residue levels in food combining results from residue trials, resembling the pesticide use patterns, with food con-sumption
Bayesian optimization (BO) has become an established framework and popular tool for hyperparameter optimization (HPO) of machine learning (ML) algorithms. While known for its sample-efficiency, vanilla BO can not utilize readily available prior beliefs the practitioner has on the potential location of the optimum. Thus, BO disregards a valuable source of information, reducing its appeal to ML prac
While Weighted Lasso sparse regression has appealing statistical guarantees that would entail a major real-world impact in finance, genomics, and brain imaging applications, it is typically scarcely adopted due to its complex high-dimensional space composed by thousands of hyperparameters. On the other hand, the latest progress with high-dimensional hyperparameter optimization (HD-HPO) methods for
In this thesis, we present and study a few approaches for constructing controllers for uncertain systems, using a combination of classical control theory and modern machine learning methods. The thesis can be divided into two subtopics. The first, which is the focus of the first two papers, is dual control. The second, which is the focus of the third and last paper, is multiple-input multiple-outp
Motivated by a need to characterize transient behaviors in large network systems in terms of relevant signal norms and worst-case input scenarios, we propose a novel approach based on existing theory for matrix pseudospectra. We extend pseudospectral theorems, pertaining to matrix exponentials, to an input-output setting, where matrix exponentials are pre- and post-multiplied by input and output m
Here, we report, for the first time, a microsporidian infection in laboratory-reared larvae of the damselfly Ischnura elegans. Infected larvae originated from field-collected adult females, which were caught in southern Poland in August 2020 (the second half of the flight season). Higher rearing temperatures and the presence of predator cues from the invasive alien signal crayfish (Pacifastacus le
In this study, we modeled the aerosol particle formation along air mass trajectories arriving at the remote Arctic research stations Gruvebadet (67 m a.s.l.) and Zeppelin (474 m a.s.l.), Ny-Ålesund, during May 2018. The aim of this study was to improve our understanding of processes governing secondary aerosol formation in remote Arctic marine environments. We run the Lagrangian chemistry transpor
The inherent flexibility of 5G networks come with a high degree of configuration and management complexity. This makes the performance outcome for UEs, more than ever, dependent on intricate configurations and interplay between algorithms at various network components. In this paper, we take initial steps towards a performance exposure system at the base station using a data-driven approach for pr
While the Transformer architecture has become ubiquitous in the machine learning field, its adaptation to 3D shape recognition is non-trivial. Due to its quadratic computational complexity, the self-attention operator quickly becomes inefficient as the set of input points grows larger. Furthermore, we find that the attention mechanism struggles to find useful connections between individual points
We introduce the Atmospherically Relevant Chemistry and Aerosol box model ARCA box (v.1.2.2). It is a zero-dimensional process model with a focus on atmospheric chemistry and submicron aerosol processes, including cluster formation. A novel feature in the model is its comprehensive graphical user interface, allowing for detailed configuration and documentation of the simulation settings, flexible
This abstract describes the decomposition of a matrix representing a scattering dyadic into characteristic modes. Scattering dyadic, as compared to conventionally used impedance matrices, are independent of numerical method used to compute them and the same characteristic mode formulation can be used for decomposition of composite and inhomogeneous materials. The utilization of scattering dyadic m
Dimethyl sulfide (DMS) influences climate via cloud condensation nuclei (CCN) formation resulting from its oxidation products (mainly methanesulfonic acid, MSA, and sulfuric acid, H2SO4). Despite their importance, accurate prediction of MSA and H2SO4 from DMS oxidation remains challenging. With comprehensive experiments carried out in the Cosmics Leaving Outdoor Droplets (CLOUD) chamber at CERN, w
Predictions of cloud droplet activation in the late summertime (September) central Arctic Ocean are made using κ-Kohler theory with novel observations of the aerosol chemical composition from a high-resolution time-of-flight chemical ionization mass spectrometer with a filter inlet for gases and aerosols (FIGAERO-CIMS) and an aerosol mass spectrometer (AMS), deployed during the Arctic Ocean 2018 e
Background: Although many people infected with SARS-CoV-2 (severe acute respiratory syndrome coronavirus-2) experience no or mild symptoms, some individuals can develop severe illness and may die, particularly older people and those with underlying medical problems. Providing evidence-based interventions to prevent SARS-CoV-2 infection has become more urgent with the spread of more infectious SARS
