Filetype
Dynamic resource management is a difficult problem in modern microservice applications. Many proposed methods rely on the availability of an analytical performance model, often based on queueing theory. Such models can always be hand-crafted, but this takes time and requires expert knowledge. Various methods have been proposed that can automatically extract models from logs or tracing data. Howeve
New-particle formation from vapors through molecular cluster formation is a central process affecting atmospheric aerosol and cloud condensation nuclei numbers, and a significant source of uncertainty in assessments of aerosol radiative forcing. While advances in experimental and computational methods provide improved assessments of particle formation rates from different species, the standard app
Big Data analytics and Artificial Intelligence (AI) technologies have become the focus of recent research due to the large amount of data. Dimensionality reduction techniques are recognized as an important step in these analyses. The multidimensional nature of Quality of Experience (QoE) is based on a set of Influence Factors (IFs) whose dimensionality is preferable to be higher due to better QoE
Current FPGA programming tools require extensive hardware-specific manual code tuning to achieve performance, which is intractable for most software application teams. We present HPVM2FPGA, a novel end-to-end compiler and autotuning system that can automatically tune hardware-agnostic programs for FPGAs. HPVM2FPGA uses a hardware-agnostic abstraction of parallelism as an intermediate representatio
Recent advances have extended the scope of Bayesian optimization (BO) to expensive-to-evaluate black-box functions with dozens of dimensions, aspiring to unlock impactful applications, for example, in the life sciences, neural architecture search, and robotics. However, a closer examination reveals that the state-of-the-art methods for high-dimensional Bayesian optimization (HDBO) suffer from degr
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
Serious outcomes as stroke, arrythmia and death are relatively frequent in patients presenting with dizziness. Within 30 days almost one in five patients re-attended the ED due to dizziness and 2,8% needed ED evaluation due to a fall. Decision support tools to help identify serious conditions and predict preventable falls in ED patients with dizziness are needed.
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
In this article we consider how the operator of an electric power system should activate bids on the regulating power market in order to minimize the expected operation cost. Important characteristics of the problem are reaction times of actors on the regulating market and ramp-rates for production changes in power plants. Neglecting these will in general lead to major underestimation of the opera
High-performance embedded systems require the execution of many applications on multicore platforms and are subject to stringent restrictions and constraints. The actors project approach provides temporal isolation through resource reservation over a multicore platform, adapting the available resources on the basis of the overall quality requirements. The architecture is fully operational on both
Non-Gaussian stochastic fields are introduced by means of integrals with respect to independently scattered stochastic measures distributed according to generalized Laplace laws. In particular, we discuss stationary second order random fields that, as opposed to their Gaussian counterpart, have a possibility of accounting for asymmetry and heavier tails. Additionally to this greater flexibility th
Abstract in UndeterminedRobustness of distributed routing policies is studied for dynamical networks, with respect to adversarial disturbances that reduce the link flow capacities. A dynamical network is modeled as a system of ordinary differential equations derived from mass conservation laws on a directed acyclic graph with a single origin-destination pair and a constant total outflow at the ori
We present the open-source software framework in JModelica.org for numerically solving large-scale dynamic optimization problems. The framework solves problems whose dynamic systems are described in Modelica, an open modeling language supported by several different tools. The framework implements a numerical method based on direct local collocation, of which the details are presented. The implemen
We consider the problem of gray-box identification of dynamic models for mechanical systems. In particular, the problem is approached by means of continuous-time system identification using subspace-based methods based on discrete-time input-output identification data. A method is developed, with the property that the structure of the model resulting from fundamental physical first principles is o
A method for determination of polycyclic aromatic hydrocarbons (PAHs) from aerosols was developed. Instead of conventionally used non-polar or slightly polar phenylmethylpolysiloxane column a highly polar, highly substituted, cyanopropyl column (VF-23 MS) was used for separation of PAHs. Based on hollow fiber micro-porous membrane liquid-liquid extraction (HF-MMLLE) a method was developed for samp
