[diss] Perustieteiden korkeakoulu / SCI

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Modeling and Using Biographical Linked Data for Prosopographical Data Analysis
(Aalto University, 2024) Leskinen, Petri; Tuominen, Jouni, Dr., University of Helsinki & Aalto University, Finland; Tietotekniikan laitos; Department of Computer Science; Semantic Computing Research Group; Perustieteiden korkeakoulu; School of Science; Hyvönen, Eero, Prof., Aalto University, Department of Computer Science, Finland
Biographical data is used for identifying people, groups, and organizations and for conveying information about them. Biographical data describes life stories of people with the aim of getting a better understanding of their personality, actions, and interperson relations. The underlying texts can also be used for data analysis and distant reading once the documents are provided in a machine-readable format. Prosopographical analysis delves into the life stories of individuals within a defined group to identify shared characteristics and patterns. This dissertation presents and utilizes a comprehensive framework for managing and analyzing biographical data in Digital Humanities research. It includes data models, methods, and applications that enrich biographical content with links and reasoning to enhance the findability, accessibility, interoperability, and re-usability following the FAIR principles. Furthermore, the framework includes versatile tools for both individual biographical research and prosopographical research on groups of people. Linked Data together with event-based data model schemas are used in the published datasets to achieve the interoperability of heterogeneous data regarding historical people. Events are used as the glue combining information from various sources. The event-based modeling enables depicting historical narratives as data, which can be further enriched with the events of individual people and organizations. The research included in this dissertation follows the principles of the design science and action research. The research has been carried out in multiple research projects concentrating on biographical data: WarSampo (2015–), BiographySampo (2018–2021), Norssi High School Alumni (2017), AcademySampo (2019–2021), LetterSampo (2020–2022), and ParliamentSampo (2021–). The data publications and services, online portals, and published articles with analysis are represented as the results of the work accomplished for this thesis. Besides, this thesis tackles the practices of creating, modeling, and publishing Linked Data, as well as analyzing this biographical and prosopographical data by the means of network and data analysis.
Measurement and Control of Micromechanical Oscillators in the Quantum Regime
(Aalto University, 2024) Wang, Cheng; Mercier de Lépinay, Laure, Asst. Prof., Aalto University, Department of Applied Physics, Finland; Teknillisen fysiikan laitos; Department of Applied Physics; Quantum Nanomechanics Group; Perustieteiden korkeakoulu; School of Science; Sillanpää, Mika, Prof., Aalto University, Department of Applied Physics, Finland
Micro- and nanomechanical devices are key components ubiquitous in many real-world applications, and have become indispensable tools for fundamental studies in physics, in particular, macroscopic quantum phenomena of the motion of mechanical objects. However, a significant challenge in exploring the quantum nature of these devices arises from the thermal disturbances of the environment, which induce decoherence and obscure their quantum properties. Advancements in microfabrication, as well as cooling techniques such as direct refrigeration and cooling methods developed based on quantum control protocols, have propelled the study of macroscopic mechanical systems into the quantum regime. The micromechanical devices studied in this thesis are superconducting microwave circuits in which aluminum drumhead mechanical oscillators are strongly coupled to the circuit electromagnetic resonances. This circuit configuration, sometimes termed circuit electromechanics, can be viewed as a circuit realization of cavity optomechanics, which explores the interaction between the micromechancial motion and electromagnetic radiation. Important experimental verifications in eletromechanics have been recently demonstrated, including ground-state cooling, entanglement between two drumhead oscillators, and quantum squeezing of micromechanical motion. In this thesis, several endeavors aimed at achieving quantum control of micromechanical motion are experimentally investigated for the first time. We first implemented feedback control in circuit electromechanics and achieved feedback cooling of a 8 MHz oscillator near to its motional ground state, limited by added amplifier noise. We also explored the possibility of using feedback to stabilize an otherwise unstable regime in optomechanics. These attempts may establish the possibility of preparing non-classical motional quantum states using feedback. Next, we considered a noise-driven approach in optomechanics, in which we inject strong band-limited electromagnetic noise into the motional sideband of a multi-mode circuit optomechanical device. Ground-state cooling and optomechanical heating phenomena are investigated with different noise driving conditions. Additionally, we found an adiabatically driven regime of mechanical motion via narrowband field driving. The study holds the potential to yield some insights into the behavior of complex systems, when the system is driven close to its instability. Efficient quantum control over these systems and the ability to prepare them into target quantum states may open up the possibility of using these mechanical devices as valuable resources for future quantum applications.
Distributional Security for OWF, PRF and Garbling
(Aalto University, 2024) Karanko, Pihla; Matematiikan ja systeemianalyysin laitos; Department of Mathematics and Systems Analysis; Perustieteiden korkeakoulu; School of Science; Brzuska, Chris, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland
Cryptography studies secure communication, including but not limited to: how to send messages safely over an untrusted channel, how to store and send passwords safely and how to outsource computations on confidential data to an untrusted cloud computing service. Cryptography as a field defines the adversarial model and the aim mathematically, and seeks to find the simplest, most efficient and trustworthy assumptions and cryptographic tools, called primitives (e.g. encryption), that are needed to reach the goal. Often, the goal might require two or more parties to send several messages (that consist of outputs of some primitives, e.g. encryption of some data or hash of a password) between themselves, such interactive setting is called a cryptographic protocol. One of the most elementary and well-studied cryptographic primitives is a one-way function (OWF). The only security a OWF guarantees is that a polynomial time adversary cannot invert it, except with a negligible probability, when the input to the OWF comes from the uniform distribution. However, there is also a weaker variant of the OWF definition, namely, a OWF where the adversary only fails to invert on a small (say, constant or polynomial) fraction of the inputs. Even though a weak OWF is weaker than a OWF, there are several constructions to obtain a OWF from a weak OWF. The simplest such construction is through parallel repetition, due to Yao [40]. In this thesis, we first study which input distributions to such parallel repetition of a weak OWF can yield a OWF. We show a lower bound on the entropy that the input needs to have. We also study input distributions to pseudorandom functions (PRF). A PRF is a function whose outputs are indistinguishable from random to an adversary who can query the function multiple times with any inputs. A weak PRF on the other hand is secure only when the inputs come from a (public) uniform distribution. A weak PRF can be amplified to a PRF by passing the inputs first to a random oracle (RO). RO is a very idealized (and hence not always realistic [11, 10]) model for a hash function, where all parties, adversaries included, have blackbox access to a truly random function (the RO). In the construction of a PRF from a weak PRF, the RO effectively randomizes the adversary's inputs to the PRF and hence the adversary's choices will not help. We study how to achieve a similar 'input randomization' as the RO provides using a more standard assumption, namely, extremely lossy functions (ELF). We also study how to replace the RO with an ELF in a (slightly modified) popular public key encryption construction, the Fujisaki-Okamoto transform. Finally, we study input distributions to garbling schemes. A garbling scheme is a way to encode a circuit and an input to the circuit in such a way that the circuit can be evaluated to obtain the output, but nothing else, about the circuit or the input, is leaked. Specifically, we are interested in an 5 adaptive setting where the adversarial evaluator gets the garbled circuit first and only after that chooses the input-to-be-garbled. We study whether it is possible to achieve short input encoding and still satisfy a strong, so called simulation based, security notion. Two lower bounds [3, 24] show that is this setting, in general, the input encoding length needs to depend on the circuit's output length or (pseudo-)entropy. We study where exactly these negative results stem from and we propose a new distributional simulation based adaptive security definition that does not suffer from the lower bounds but that still captures a strong and meaningful security requirement. We also establish a bootstrapping result: if a garbling scheme satisfies our new security definition for low depth circuits, then there exists a garbling scheme that satisfies the new security definition for arbitrary polynomial sized circuits.
Inkjet Printing for Low-Temperature Solid Oxide Fuel Cells: Comparative Fabrication Techniques and Microstructural Investigations
(Aalto University, 2024) Zarabi Golkhatmi, Sanaz ; Asghar, Muhammad Imran, Prof., Tampere University Finland, and Aalto University, Department of Applied Physics, Finland; Teknillisen fysiikan laitos; Department of Applied Physics; New Energy Technologies; Perustieteiden korkeakoulu; School of Science; Lund, Peter D., Prof., Aalto University, Department of Applied Physics, Finland
Solid oxide fuel cells (SOFCs) are emerging as a promising technology for clean energy generation, yet their market penetration is hampered by du-rability and stability challenges. This thesis addresses these challenges by focusing on new fabrication methods for SOFC components and their microstructure. It employs advanced basic materials in the manufacturing process, enabling much lower operating temperatures than traditional materials, which could potentially improve the longevity of the cells. The thesis introduces a unique inkjet printing technique, a mask-free, accurate, and contactless method for fabricating high-performance materials with customized microstructures. This is particularly beneficial for cathodes, where oxygen reduction reactions contribute to activation loss in SOFCs. The research involved developing and optimizing three distinct ink formulations: La0.6Sr0.4Co0.2Fe0.8O3 (LSCF), CuFe2O4, and CuFe2O4 – Gd:CeO2 (GDC) nanocomposite. These formulations were compared with other low-viscosity inks used in drop casting and spin coating. The created inks have undergone extensive evaluation, which includes particle size analysis, rheological characteristics, and thermal analysis, as well as microstructural investigations and electrochemical performance measurement. All inks demonstrated excellent jetting performance, with Z parameters indicating their suitability for the inkjet printing process. For instance, fresh LSCF ink and CuFe2O4 – GDC nanocomposite ink showed Z parameters of 2.77 and 5.5, respectively, at their printing temperature. Electrochemical performance analysis revealed improvements compared to drop casting and spin coating techniques with the same ink. Inkjet print-ing reduced the ohmic resistance of the LSCF symmetric cell from 1.05 Ω cm² to 0.37 Ω cm² at 550°C in an air atmosphere and decreased the mass diffusion resistance by 4.25 times compared to a drop-casted cell. Further comparisons using Electrochemical Impedance Spectroscopy (EIS) showed that inkjet printing could lower the area-specific resistance (ASR) of a 100-layer cell significantly from 19.59 Ω cm² to 5.99 Ω cm² under similar conditions. For the CuFe2O4 – GDC nanocomposite ink – Samba cartridge case at 650°C under H2 and air atmospheres, the best inkjet-printed complete fuel cell gave a Rohm and ASR of 0.96 and 1.12 Ω cm², respectively, using just 2.16 mg of deposited ink (1.63 mg cm-2). In contrast, a spin-coated cell with the same ink amount exhibited higher Rohm (3.2 Ω cm²) and ASR (37.82 Ω cm²). The drop-cast cell with 6.2 times more deposited ink showed even higher values (Rohm = 8.84 Ω cm² and ASR = 15.96 Ω cm²). These findings highlight the potential of inkjet printing for morphological control, improving gas transport, lowering ion transport losses, and speed-ing up charge transfer reactions. This resulted in improved electrochemical performance, emphasizing the potential of inkjet printing in tailoring cathode morphology for the development of high-performance materials in electrochemical energy conversion.
Mixed-integer formulations for large-scale energy-environmental optimization
(Aalto University, 2024) Herrala, Olli; Ekholm, Tommi, Res. Prof., Finnish Meteorological Institute, Finland; Matematiikan ja systeemianalyysin laitos; Department of Mathematics and Systems Analysis; Gamma-opt research group; Perustieteiden korkeakoulu; School of Science; Oliveira, Fabricio, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland
To support decision making in complex systems, different mathematical programming approaches have been developed for both modelling the decision process and finding the best decisions or policies. This dissertation focuses on decision making in the context of climate change mitigation, considering two different perspectives on the topic. The first is a global perspective of research and development of negative emission technologies and their effect on the optimal emission levels in the next 50 years. The second is a more localized perspective of regulating a competitive power market, aiming to efficiently reduce the emissions of electricity production. The structures contained within these problems render them incompatible with existing solution methods. First, many of the uncertainties in climate change mitigation, such as the cost of reducing emissions in the future, depend on the earlier decisions such as the level of research investment. While different types of decision-dependent uncertainty have been researched before, combining these types in a way that would allow for accurate modeling of the decision process has not been possible. Second, a bilevel hierarchical structure of a power market with a transmission system operator and electricity producers has received significant attention in the literature, but these methods are not directly applicable to problems with a third hierarchical level representing, in our case, the international regulator. This dissertation enables more realistic modeling of decision-dependent uncertainty and hierarchical decision making by reformulating the problems using mixed-integer programming (MIP). Using mixed-integer optimization as the main solution framework allows us to utilize the vast developments in solving mixed-integer models. Additionally, implementing explicit risk measures in MIP models has received significant attention in the literature, and these developments can be applied to the proposed models with minor adjustments. However, the computational efficiency of solving mixed-integer models depends not only on the solution method, but also the formulation used. The articles in this dissertation discuss and compare three different MIP formulations for limited memory influence diagrams, and two single-level reformulations for trilevel equilibrium models. Despite their simplified nature, the case studies in this dissertation provide policy insights on cost-benefit optimal emission trajectories and the effect of a carbon tax on the Nordic electricity market. Such models can help justify decisions when developing policies in complex and controversial contexts such as climate change mitigation. While the focus of the dissertation is on energy and environment, the methodological developments in this dissertation are equally applicable to a variety of problems in fields such as healthcare, systems monitoring and traffic planning.
Dynamic yet Persistent: Investigating Digital Traces of Human Behaviour
(Aalto University, 2024) Heydari, Sara; Tietotekniikan laitos; Department of Computer Science; Complex Systems; Perustieteiden korkeakoulu; School of Science; Saramäki, Jari, Prof., Aalto University, Department of Computer Science, Finland
We are citizens of the digital age, an age in which our smallest everyday actions leave digital traces behind. These traces can be used to reconstruct and analyse the patterns of human behaviour at both individual and societal levels. The growing abundance of quantitative data on almost every aspect of human behaviour is driving a paradigm shift from traditional social sciences to computational social sciences. This thesis utilises a wide range of digital traces of human behaviour to study and model the structure of social networks, as well as population-level patterns of commuting and travelling. A longitudinal study of these patterns reveals that while both personal social networks and population commuting networks are dynamic and undergo gradual changes and abrupt external disturbances, they also exhibit persistence in certain aspects and retain some of their distinctive features. Our work advances the knowledge of personal networks by identifying their universal and individual features through the study of a large population and multiple communication media. To clarify the generative mechanisms behind the observed universal patterns and individual variations within them, we present an ego-network model that connects the structure of ego-networks to the communication strategies of egos. Furthermore, we utilise rich and high-resolution digital communication logs and computational methods to validate the predictions of sociological theories that predate the emergence of computational social science as a field. Particularly, we show that the temporal manifestation of social ties can indicate the extent of their multiplexity (i.e., whether multiple social contexts underlie the tie) and that the multiplexity of ties impacts their functional features, such as their role in network connectivity. In addition to social networks, this thesis enriches our understanding of human mobility patterns by building predictive models that employ digital traces data. These models are capable of real-time estimation of country-wide mobility flows, even amidst rapid and significant distortions in mobility patterns. Taken together, my results highlight the potential of digital trace data and computational methods for advancing our understanding of various aspects of human behaviour. It applies these insights to predict human behaviour, identify universal and individual human traits, and contribute to bridging the new field of computational social science with the rich tradition of classical social science.
Changes in private healthcare supply, demand, and service utilisation during the COVID-19 pandemic
(Aalto University, 2024) Niemenoja, Oskar; Lillrank, Paul, Prof. Emer., Aalto University, Department of Industrial Engineering and Management, Finland; Taimela, Simo, Docent, University of Helsinki, Finland; Bono, Petri, Docent, University of Helsinki, Finland; Tuotantotalouden laitos; Department of Industrial Engineering and Management; Perustieteiden korkeakoulu; School of Science; Saarinen, Lauri, Asst. Prof., Aalto University, Department of Industrial Engineering and Management, Finland
Pandemics have wide-reaching effects on health service utilisation and diagnostic activity. Health service usage declined markedly during the COVID-19 pandemic across most diagnosis classes, especially those unrelated to the pandemic directly. These changes may potentially introduce pressure on the healthcare system in the future. This thesis analyses how the Finnish private healthcare service utilisation, diagnostic activity, and booking patterns were affected between January 2020 and June 2022 during the COVID-19 pandemic. The study had access to a novel, comprehensive dataset on weekly Finnish private healthcare usage collected from routine electronic medical record data spanning the wide range of health service usage and diagnostic activity. Data from before the COVID-19 pandemic enabled us to create an estimate on the health service utilisation in the hypothetical case without the effect of the pandemic, which was compared to observed time series data. Of the three manuscripts that make up this thesis, one studied nationwide service utilisation, while two focused on the capital region of Uusimaa. The manuscripts covered data from 833 444, 632 466 and 900 572 patients, respectively. We estimated that service utilisation rates for private healthcare usage decreased by one-fourth across different diagnosis classes during the early pandemic. Only some diagnosis classes recovered to pre-pandemic utilisation levels towards the end of the observation period, while some remained at permanently reduced levels. Upper respiratory system-related diseases displayed a notable increase in diagnostic activity during the initial weeks of the pandemic, as well as late in the observation period. We postulate that the decrease in service utilisation was driven by a decrease in service demand, to which supply reacted. Cancellations were a contributing factor to this decrease only during the first weeks of the pandemic, after which disengagement from within the services was passive. Digital services offered a rapidly scaleable service channel to regulate access to healthcare services. We highlight the importance of healthcare providers and policymakers collecting and utilising high-quality, up-to-date data when managing systemic healthcare utilisation shocks such as pandemics.
From Scans to Reality: Effects of Preprocessing and Daily Behavioral Patterns on fMRI Brain Connectivity
(Aalto University, 2024) Triana Hoyos, Ana Maria; Glerean, Enrico, D.Sc., Aalto University, Department of Neuroscience and Biomedical Engineering, Finland; Sams, Mikko, Prof. Emeritus, Aalto University, Department of Neuroscience and Biomedical Engineering, Finland; Tietotekniikan laitos; Department of Computer Science; Complex Systems; Perustieteiden korkeakoulu; School of Science; Saramäki, Jari, Prof., Aalto University, Department of Computer Science, Finland
Functional magnetic resonance imaging (fMRI) has significantly advanced our understanding of the brain. By combining data from hundreds of individuals, neuroscientists have been able to reliably identify general trends of the large-scale network organization of the brain in controls and patients. Consequently, there is optimism that fMRI connectivity could be used to identify biomarkers for prognosis and diagnosis and targets for interventions. However, the clinical application of fMRI techniques remains limited, partly because of the heterogeneity in findings from between-group comparisons and the challenge of group-to-individual generalization. Therefore, it is critical to investigate and understand the underlying factors contributing to these challenges, which might include fMRI preprocessing and the impact of environmental and behavioral neuromodulators on a day-to-day basis. In this thesis, I explore these sources of variability to understand their impact on fMRI brain connectivity. This includes the impact of preprocessing methods and the role of environmental and behavioral neuromodulators -- or external factors -- as well as examining the integration of fMRI with digital phenotyping to provide a comprehensive understanding of the brain. In the first study, I demonstrate that spatial smoothing has unpredictable effects when comparing resting-state fMRI data between groups. In the second study, I focus on developing software tools for preprocessing, analyzing, and visualizing digital phenotyping data, aiming to quantify the influence of external factors more effectively. In the third study, I review how the combination of brain MRI and digital phenotyping devices enables the monitoring of external factors in real-world scenarios, offering a more precise method to quantify these variables. Finally, in the fourth study, I show that external factors, including sleep, physical activity, mood, respiration rate, and heart rate variability, are significantly correlated with functional connectivity, a relationship that persists up to fifteen days prior. This thesis demonstrates that both preprocessing choices and external factors uniquely influence fMRI brain connectivity, often in unexpected ways. It supports merging digital phenotyping with MRI data to bridge brain research and real-life experiences, extending brain research from scanners to reality. My findings reveal temporal co-variations between external factors and brain connectivity, crucial for understanding mental health disorders that exhibit week-to-week variability. Thus, integrating brain connectivity analysis with insights into environmental and behavioral neuromodulators propels environmental neuroscience forward and supports the development of precision healthcare, making significant strides towards personalized medicine and the understanding of individual variability.
Discovering Ice and Water Structures with High-Resolution AFM, Atomistic Modeling, and Machine Learning
(Aalto University, 2024) Priante, Fabio; Foster, Adam Stuart, Prof., Aalto University, Department of Applied Physics, Finland; Teknillisen fysiikan laitos; Department of Applied Physics; Surfaces and Interfaces at the Nanoscale (SIN); Perustieteiden korkeakoulu; School of Science; Foster, Adam Stuart, Prof., Aalto University, Department of Applied Physics, Finland
Since its invention several decades ago, Atomic Force Microscopy (AFM) has become an irreplaceable technique for the investigation of matter at the nanoscale. Specifically, the development of three-dimensional AFM (3D-AFM) enabled the observation of hydration structures in solid-liquid interfaces, while the use of tip functionalization in ultra-high vacuum has been crucial for reaching atomically resolved imaging of individual molecules. However, in both scenarios, only completely flat structures can be fully characterized by AFM. In more threedimensional samples, interpreting the measurements can be challenging, as only partial structural information is available. In this thesis, atomistic simulation and machine learning techniques are combined to tackle this problem in various systems, in all of which water molecules have central importance. First, a structure discovery workflow is developed for the case of ice nanoclusters on Au(111) and Cu(111) surfaces, centered about the use of neural network potentials. Then, molecular dynamics simulations are carried out to uncover the atomistic structure of cellulose-Iα and α-chitin nanocrystals surfaces in water. Finally, a high-throughput workflow is developed to identify the arrangement of solid-binding peptides assemblies on a graphite surface.
Quantum transport and phase transitions in superconducting systems
(Aalto University, 2024) Subero Rengel, Diego Armando; Teknillisen fysiikan laitos; Department of Applied Physics; Pico Group; Perustieteiden korkeakoulu; School of Science; Pekola, Jukka P., Prof., Aalto University, Department of Applied Physics, Finland
This dissertation focuses on charge and thermal transport phenomena in mesoscopic superconducting circuits, particularly emphasizing the effects of Coulomb blockade on photonic heat transport at cryogenic temperatures. We investigate tunneling in mesoscopic systems with Coulomb blockade effects, the role of the electromagnetic environment on small Josephson junctions (JJs), and the impact of these factors on the bolometry of microwave photons through superconducting circuits. We propose and test a variant of the Maxwell demon experiment, the gambling demon. Unlike the standard Maxwell demon, the gambling demon decides, based on the acquired information, whether to stop the process following a customary gambling condition. Within this context, we derive and verify second-law-like inequalities accounting for the average work done when gambling is involved. For experimental verification, we use a single electron box connected capacitively to an electrometer, where an electrostatic potential governs the dynamics of electron tunneling into a metallic island. Our findings align closely with theoretical predictions, showing remarkable accuracy within 0.5%. We present results on photon-mediated heat transport through a superconducting circuit. We exploit the Johnson-Nyquist archetype to do that, where two thermal reservoirs are connected via a frequency-dependent transmission line. Here, two different frequency-dependent transmission lines are studied: one is a Cooper pair transistor controlled by an electric field, and the other is a superconducting quantum interference device (SQUID) controlled by a magnetic field. The first experiment with the Cooper pair transistor demonstrates a precise control of the thermal conductance close to its quantum limit with the gate voltage. The second experiment examines the environmental back-action effect on photon-mediated heat transport, revealing that while strong fluctuations produced by the environment affect charge transport through the SQUID as expected, they do not impact heat transport. This indicates that, unlike in the DC charge transport experiment, the Josephson effect survived regardless of the strength of the dissipation, which is a complementary experiment to test the anticipated dissipative phase transition by Schmid and Bulgadaev. In this scenario, we have performed a DC charge transport experiment through a JJ connected to a voltage source via an Ohmic resistor with resistance either greater or smaller than the superconducting resistance quantum RQ~6.5 kΩ to revisit the debated dissipative phase transition in a JJ. Our results support the existence of this transition, evidenced by a distinct dip in electrical conductance at zero voltage bias in the JJ connected to an environmental resistance exceeding RQ. Conversely, in devices where the environmental resistance is less than RQ, a conductance peak appears at zero voltage bias, indicative of the Josephson effect.
Fast Qubit Control with a Quantum-Circuit Refrigerator
(Aalto University, 2024) Mörstedt, Timm Fabian; Kundu, Suman, Dr., Department of Applied Physics, Aalto University; Teknillisen fysiikan laitos; Department of Applied Physics; QCD Labs; Perustieteiden korkeakoulu; School of Science; Möttönen, Mikko, Prof., Department of Applied Physics, Aalto University
Superconducting circuits have emerged as powerful building blocks on the path toward a useful quantum computer. However, fast and accurate control over these circuits remains one of the key challenges. In particular, the fast initialization of superconducting qubits is a growing requirement in this era of constantly increasing qubit lifetimes. In this thesis, we investigate different means of qubit control in the context of dissipation engineering. We use a quantum-circuit refrigerator (QCR), an on-chip microcooler based on one or two normal-metal–insulator–superconductor junctions, to create a tunable environment for superconducting circuits. We present and compare two different realizations of this device, the double-junction QCR directly coupled to a transmon qubit and the single-junction QCR coupled to the qubit via a superconducting resonator. Beyond qubit reset, we explore other properties of the QCR, including the cooling and creation of exceptional points in superconducting resonators and the generation of thermal states in superconducting qubits. Through single-shot readout experiments, we gain insight into the quantum state of the qubit and its dynamics in response to different control signals. Combining the results of these experiments, we discuss the possible realization of a quantum heat engine using a QCR as a two-way tunable environment, extending the scope of applications toward the fundamental study of open quantum systems. This thesis sheds light on the versatile world of quantum-circuit refrigeration and presents novel insights, experiments, and applications. At the intersection of circuit quantum electrodynamics and quantum thermodynamics, the QCR promises further possibilities for advancement and increased understanding of the behavior and control of superconducting quantum systems.
Neural oscillations underlying the expression and modulation of intergroup bias
(Aalto University, 2024) Kluge, Annika; Levy, Jonathan, Dr., Aalto University, Department of Neuroscience and Biomedical Engineering, Finland; Neurotieteen ja lääketieteellisen tekniikan laitos; Department of Neuroscience and Biomedical Engineering; Empathy Building Neuro-lab; Perustieteiden korkeakoulu; School of Science; Jääskeläinen, Iiro, Prof., Aalto University, Department of Neuroscience and Biomedical Engineering, Finland
Humans can be distinguished from robots by their ability to select all images containing a car regardless of the make, model, or picture angle. This basic human function, effortlessly sorting complicated information into clear categories, enables us to navigate in the constantly changing world. However, the tendency to categorize others into social groups can lead to stereotyping, prejudice, intergroup bias, and in worse cases discrimination and violent conflicts. After decades of research on intergroup relations using surveys to assess explicit self-reports and psychological tests to uncover automatic implicit processes, neuroscientific studies have found neural markers for processes that are not necessarily captured with the traditional measures of intergroup bias. This thesis presents work that uses magnetoencephalography (MEG) to examine the expression of intergroup bias in neural oscillations and its dependence on the urgency and conflict level of the intergroup context. Neural oscillations are examined in three different settings: political polarization in Israel, immigration support in Finland, and negativity against covid-19 vaccine hesitancy in Finland. The findings shed light on distinct context-dependent neural intergroup bias processes. In Israeli politics, two neural mechanisms activate asymmetrically for political leftists and rightists and relate to explicit and implicit behavioral assessments respectively. Investigating young Finns' reactions to stereotypical Muslim faces shows that while explicit and implicit psychological measures are unable to capture the subtle prejudice in this sample, a biased neural reaction related to face processing surfaces. Two vaccination datasets reveal how quickly the neural bias can change, with distinct neural processes emerging during the pandemic and in its aftermath. After pinpointing the neural mechanisms that activate during intergroup bias processes, this thesis investigates the question of whether and how these mechanisms can be modulated using prejudice-reducing intergroup interventions. One such intervention that is tested in this thesis is paradoxical thinking: exposing people to ideas that are consistent with their existing beliefs but taken to a wildly exaggerated, even absurd, level. The studies find that the paradoxical thinking intervention effectively reduces neural bias and moreover, the neural processes activating during the intervention can predict the change in explicitly reported attitudes towards the outgroup. Overall, the results of this thesis increase knowledge of the neural underpinnings of intergroup bias and propose strategies for reducing said bias.
Tuning atomic scale magnetism with artificial nanostructures
(Aalto University, 2024) Aapro, Markus; Kezilebieke, Shawulienu, Prof., University of Jyväskylä, Finland; Teknillisen fysiikan laitos; Department of Applied Physics; Atomic Scale Physics; Perustieteiden korkeakoulu; School of Science; Liljeroth, Peter, Prof., Aalto University, Department of Applied Physics, Finland
In a world where the demand for quantum technology is rapidly increasing, scanning tunneling microscopy (STM) remains one of the few experimental techniques capable of not only imaging and measuring atomic scale systems, but assembling artificial nanostructures and lattices at atomic precision. The emergent properties of increasingly complex quantum systems can be designed and characterised by assembling structures from individual atoms and molecules. Some of the most interesting building blocks for such lattices have magnetic properties: by coupling spin systems into lattices, a rich tapestry of physics becomes accessible for experimentation and applications. Despite the promising theoretical predictions, the interplay of artificial nanostructures and atomic-scale magnets remains relatively unexplored. This thesis discusses recent experimental efforts to understand magnetic impurities coupled to a conduction bath, how machine learning techniques can be utilized in atom manipulation, and finally the behaviour of magnetic impurities inside artificial nanostructures. A magnetic impurity coupled to a conduction bath gives rise to the Kondo effect, whereby the magnetic moment of the impurity is screened by conduction electrons. This many-body effect results in a resonance with an intrinsic temperature dependence. We experimentally verify a new model for this temperature dependence, and demonstrate the importance of various broadening factors in the analysis of the spectral features. Our work provides a widely applicable model for verifying the Kondo nature of a resonance at the Fermi level, and how to accurately determine the energy scale determining the low-temperature dynamics of such systems, i.e. the Kondo temperature. We then proceed to explore how deep reinforcement learning (DRL) methods can be applied to lateral atom manipulation. A DRL algorithm is designed and trained to find suitable manipulation parameters for moving Ag and Co atoms on a Ag(111) surface. The trained model is capable of adjusting to changing conditions, and combined with path planning algorithms forms the basis for an autonomous nanostructure assembly system.Finally, we combine Kondo systems and atom manipulations by studying magnetic impurities inside quantum corrals, closed structures built from individual atoms. By confining the surface state electrons of the underlying Ag(111) substrate, we tune the conduction bath environment of Co atoms and H2-Pc molecules and observe changes in their low-energy excitations. The presented results pave the way for further studies combining magnetic impurities and artificial lattices built atom by atom.
Engineering quantum matter with generative machine learning
(Aalto University, 2024) Koch, Rouven Alexander; Teknillisen fysiikan laitos; Department of Applied Physics; Correlated Quantum Materials (CQM) group; Perustieteiden korkeakoulu; School of Science; Lado, Jose L., Prof., Aalto University, Department of Applied Physics, Finland
Quantum matter presents a rich landscape of emergent phenomena and exotic properties that are rare in natural compounds. This includes many-body systems such as topological insulators and unconventional superconductors. Understanding and characterizing these systems presents significant challenges due to their complexity and exotic behavior. In this dissertation, we explore the intersection of condensed matter theory, quantum matter, and artificial intelligence (AI). We demonstrate how machine learning (ML) can be used as a powerful tool for untangling complex problems in quantum many-body physics and go beyond conventional methods. Generative ML methods allow us to design complex quantum materials efficiently, optimize experimental parameters, uncover hidden correlations of quantum many-body systems, and bring together experiments and theoretical models. With this thesis, we aim to provide a complementary strategy to design exotic quantum phenomena, making a step towards future technological advancements in correlated quantum materials, materials science, and quantum computing.
Energy system resilience to extreme disruptions: reexamining impacts and their assessment
(Aalto University, 2024) Jasiūnas, Justinas; Lund, Peter D., Prof., Aalto University, Department of Applied Physics, Finland; Teknillisen fysiikan laitos; Department of Applied Physics; New Energy Technologies; Perustieteiden korkeakoulu; School of Science; Lund, Peter D., Prof., Aalto University, Department of Applied Physics, Finland
The functioning of modern societies relies on an undisrupted energy supply, which is subject to a large number and variety of physical and nonphysical threats. The most severe disruptions, despite their rarity, are responsible for a major share of disrupted supply. Furthermore, cost-driving factors depend on disruption severity, which calls for knowledge about the magnitude of unprecedented but possible future disruptions. This work starts with a broad mapping of the landscape of threats to energy systems, later narrowed down to extreme weather threats and vulnerabilities of the Finnish electricity system. As the largest cause of electricity supply interruptions in Finland, windstorms are chosen for impact modeling in the rest of this thesis. To capture the magnitude of unprecedented windstorm impacts, a new fragility-based spatio-temporal impact model was developed with a unique combination of national scale and medium voltage grid detail. The development of this model necessitated rethinking relevant aspects and suitable approaches and the development of new methods across multiple impact chain steps. The most significant new modeling contribution is the synthetic grid generation method utilizing distribution grid operator (DSO) specific data in a country with many relatively small DSOs. This generation method combines spatially mapped grid component data with assumed standard feeder topology. The second most distinct methodological contribution is severity-dependent fixing time distribution derivation using a two-level fitting procedure. The first level of this procedure includes fitting fixing time distributions of faults in a storm and calm periods considered meteorologically independent events. The model is applied to Finland's three most impactful historical and historically unprecedented but meteorologically plausible windstorm cases. The model recreates lost load profiles for historical windstorms with errors of around 20%, despite omitting many windstorm impact driving environmental factors. The historically unprecedented windstorm's wind gust field is obtained by scaling the field of the historically most impactful windstorm upwards by 24%, a value obtained with the extreme-value-theory-based method. The lost load from 24% higher wind gust speeds increases tenfold. Impacts are limited by the significant cabling of powerlines done since 2011, which, despite high costs, would largely pay off during the unprecedented windstorm. That said, the cost of such an event requires a reevaluation of cost rates considering time dependency, critical services, and impacts on smaller economy and population segments.
Neuroimaging cortical proprioceptive processing with evoked movements
(Aalto University, 2024) Nurmi, Timo; Piitulainen, Harri, Prof., University of Jyväskylä, Finland; Neurotieteen ja lääketieteellisen tekniikan laitos; Department of Neuroscience and Biomedical Engineering; Sensorimotor Systems Neuroscience (MOTOR) group; Perustieteiden korkeakoulu; School of Science; Parkkonen, Lauri, Prof., Aalto University, Department of Neuroscience and Biomedical Engineering, Finland
Motor function such as a person grasping an apple depends on functional motor efference. Motor efference means downstream neural, electrochemical signalling, where the motor regions of the brain send neural signals via the spinal cord for the appropriate muscles to contract and relax. Often overlooked aspect of motor function, however, is the sensory afference, where the feedback from the sensory organs is processed in the brain to plan and correct movement. Sensory afference includes proprioception which is the position, force and movement sense of the body. Signals from the proprioceptors residing mainly in the muscles inform the brain about the positional configuration of the body to initiate and adjust appropriate movements. Cortical proprioception is mainly processed in the somatosensory cortices. Cortical proprioception can be studied with neuroimaging methods in conjunction with evoked (passive) movements. Behavioral methods can also be used to study proprioception. This thesis consists of three publications (PI–PIII) studying cortical proprioception using functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) with evoked movements of the index fingers and ankles. The evoked movements stimulating the proprioceptors were produced with pneumatic devices. First (PI) and second publications (PII) studied how varying kinematic parameters such as movement frequency and range of evoked index finger movements affected cortical proprioceptive responses in fMRI and MEG. The third publication (PIII) examined how cortical proprioceptive processing differed between adolescents with and without cerebral palsy and how these differences related to sensorimotor performance (i.e. motor and sensory abilities). Movement frequency ≥ 3 Hz and range ≥ 5 mm of the index finger elicited strongest cortical proprioceptive responses in fMRI (PI). In contrast, movement range did not have an effect on cortical proprioceptive response strength in MEG (PII). Adolescents with CP had stronger cortical proprioceptive responses of the somatosensory cortices in their more affected hemisphere to index finger stimulation compared to adolescents without CP (PIII). Moreover, worse sensorimotor performance was associated with stronger cortical proprioceptive responses regardless whether the participant had CP or not (PIII). These studies demonstrate that using evoked movements with neuroimaging is a viable tool to study cortical proprioception. The effect of kinematic stimulation parameters on cortical proprioceptive processing can be studied using evoked movements. Neuroimaging with evoked movements also revealed that proprioceptive processing differs between adolescents with and without CP and these differences are associated with sensorimotor performance or motor ability (PIII). Sensory afference in general and cortical proprioception in particular is a critical part of motor function and should be studied further with neuroimaging and evoked movements.
On Improving QoE of Remote Rendered Graphics
(Aalto University, 2024) Illahi, Gazi Karam; Siekkinen, Matti, DSc. (Tech.), Aalto University, Department of Computer Science, Finland; Tietotekniikan laitos; Department of Computer Science; Perustieteiden korkeakoulu; School of Science; Ylä-Jääski, Antti, Prof., Aalto University, Department of Computer Science, Finland
A new class of interactive multimedia experiences leverages real-time remote rendering with video encoding to provide high quality visual experiences on low end devices, the so called thin-clients. The basic architecture entails off-loading some or all the rendering calculations of a complex computer graphics scene to a remote server, often a cloud graphics server, which renders the scene, encodes it and sends it to a client as video. The video is then decoded by the thin-client and displayed to a user. Cloud gaming and Cloud Virtual Reality (VR) are two example use cases of such experiences. These applications have two principal constraints: downstream bandwidth and motion to photon (M2P) latency. Quality of experience (QoE) of such applications can be improved by reducing the downstream bandwidth needed for a given visual quality of the encoded video and by reducing the perceived M2P latency; that is the perceived latency between user action and corresponding frame update at the client. In this thesis, we investigate avenues to improve QoE of remotely rendered graphics applications by addressing the above constraints. We evaluate the feasibility of leveraging the characteristics of the Human Visual System (HVS) to reduce the downstream bandwidth needed for streaming high quality graphics videos. Specifically, we investigate the phenomenon of foveation in the context of real time video encoding and evaluate different parameterizations and schemes of foveated video encoding (FVE). We also investigate whether synergies exist between FVE and foveated rendering (FR). To address the challenge of low latency requirements for interactive remotely rendered graphics applications, we investigate Machine Learning (ML) based approaches to predict human motion kinematics used to render a scene by a rendering engine. Specifically, we investigate head pose and gaze prediction using past pose and gaze data. Accurate head pose and gaze information are critical for field of view (FoV) rendering and foveated encoding or rendering respectively. The investigated approaches focus on light weight data ingest and low latency inference in order to preclude introduction of additional latency in the rendering and media delivery pipeline.
Adaptive OSS: Principles and Design of an Adaptive OSS for 5G Networks
(Aalto University, 2024) Mfula, Harrison; Nurminen, Jukka K., Prof., University of Helsinki, Finland; Tietotekniikan laitos; Department of Computer Science; Perustieteiden korkeakoulu; School of Science; Ylä-Jääski, Antti, Prof., Department of Computer Science, Aalto University, Finland
In recent years, the rise and continued popularity of connected applications has resulted in explosive growth in the demand for wireless broadband services of high speed, massive capacity, and ultra-low latency, such as video-on-demand services, Internet of Things, and mission critical applications. 5G technology is designed to provide the required connectivity in these applications. As a consequence of its continued success, seamless connectivity has become synonymous to a human right. Suffice to say, at the moment, due to the vast potential benefits of 5G technology, there is a kind of gold rush driving rapid worldwide deployments of 5G networks that has led to a significant gap in investment, research, and development of suitable operation support system (OSS) solutions for daily operation, monitoring and control of 5G networks. Furthermore, as the number of 5G deployments continue to rise, high data traffic volumes and stakeholder expectation of seamless connectivity from anything to anything has become the norm. In this regard, the need for suitable OSS solutions has become critical. This dissertation fills the identified gap in the following way, first, we design a scalable architecture that enables batch and stream processing of high throughput, high volume, and ultra-low-latency data driven OSS solutions to effectively support existing and 5G OSS use cases. Building on the resulting architecture, we extend existing, and in some cases develop new SON algorithms to meet 5G requirements. Particularly, we develop adaptive algorithms which focus on self-configuration, self-optimization, self-healing, and SON-coordination use cases. Furthermore, we introduce solutions for transitioning from the current mainly proprietary OSS hardware to vendor agnostic cloud-native dynamic infrastructure. Lastly, we make digitization of OSS operations more efficient. Specifically, we develop an artificial intelligence based solution (AIOps) for conducting OSS operations efficiently at cloud scale. Using the findings and proposed solutions in this dissertation, vendors, and service providers can design and implement suitable solutions that meet stringent business and technical requirements of applications running on top of 5G networks and beyond.
Parabolic bounded mean oscillation and Muckenhoupt weights
(Aalto University, 2024) Myyryläinen, Kim; Kinnunen, Juha, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland; Matematiikan ja systeemianalyysin laitos; Department of Mathematics and Systems Analysis; Nonlinear PDE research group; Perustieteiden korkeakoulu; School of Science; Kinnunen, Juha, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland
This thesis further develops the parabolic theory of functions of bounded mean oscillation (BMO) and Muckenhoupt weights motivated by one-sided maximal functions and a doubly nonlinear parabolic partial differential equation of p-Laplace type. The definition of parabolic BMO consists of two conditions on the mean oscillation of a function, one in the past and the other one in the future with a time lag between the estimates. Various parabolic John–Nirenberg inequalities, which give exponential decay estimates for the oscillation of a function, are shown in the natural geometry of the partial differential equation. We extend and complement the existing theory for the parabolic Muckenhoupt Aq weights and obtain a complete theory for the limiting parabolic Muckenhoupt A1 class including factorization and characterization results. In particular, an uncentered parabolic maximal function with a time lag is applied leading to a more streamlined theory. Weighted norm inequalities are shown for the parabolic maximal function which allows us to establish parabolic versions of the Jones factorization and the Coifman–Rochberg characterization. The other endpoint class of parabolic Muckenhoupt A∞ weights is also discussed and new characterizations are discovered in terms of quantitative absolute continuity with a time lag. Furthermore, this is considered from the perspective of parabolic reverse Hölder inequalities. We obtain several characterizations and self-improving properties for the weights satisfying a parabolic reverse Hölder inequality and study their connection to parabolic Muckenhoupt weights. Parabolic Muckenhoupt weights satisfy the parabolic reverse Hölder inequality, whereas the reverse direction is investigated in terms of a parabolic doubling condition with a time lag. Essential tools in the parabolic theory include delicate parabolic Calderón–Zygmund decompositions, good lambda estimates, covering and chaining arguments. In addition to parabolic BMO, different function spaces of BMO type are studied in the setting of metric measure spaces with a doubling measure. We consider the John–Nirenberg space defined via medians and a weak version of the Gurov–Reshetnyak class. Moreover, we show the corresponding John–Nirenberg inequalities and discuss their consequences. The John–Nirenberg lemma for the median-type John–Nirenberg space gives a polynomial decay estimate for the oscillation of a function. On the other hand, the John–Nirenberg lemma for the weak Gurov–Reshetnyak class provides a specific decay estimate.
Investigations of Ionic Functional Soft Matter
(Aalto University, 2024) Gustavsson, Lotta; Peng, Bo, Dr., Aalto University, Department of Applied Physics, Finland; Teknillisen fysiikan laitos; Department of Applied Physics; Molecular Materials; Perustieteiden korkeakoulu; School of Science; Ikkala, Olli, Prof., Aalto University, Department of Applied Physics, Finland
Electrostatic interactions play an important role in functional self-assembled structures of both natural and synthetic origin. Such processes are complex and reflect the importance of balancing competing interactions which is crucial in the development of materials for new technological advancements. This thesis presents studies on electrostatic forces in different environments and how they can be harvested in functional and motile materials. The first part of the thesis presents new liquid-crystalline materials based on ionic surfactants and their complexation. Publication I studied the thermotropic liquid crystallinity of zwitterionic amphiphilic molecules. The observed melting points were high and thus the compounds were plasticized using a low-melting ionic liquid, which led to decreased transition temperatures, ionic liquid-crystalline complex formation, and ion-conduction. In Publication II, the complexation of cellulose nanocrystals (CNCs) by a nonionic-anionic surfactant led to nematic liquid-crystalline phase formation both in organic solvent toluene and in the bulk state. The suppression of the chirality of CNCs is of high technological relevance as it would widen the applicability of CNCs as, e.g., reinforcements and optical polarizers. The electrostatic interactions were accounted as the driving force for the material properties and structural characteristics in both publications. In the second part of the thesis, electrostatic interactions were used to integrate functional responsiveness in materials. In Publication III, a hydrogel consisting of zwitterionic and nonionic units was demonstrated as a taste-recognizing material. The sensing was based on the interactions between the hydrogel's repeating units and the small-molecular tastant molecules, leading to volumetric and electrical responses depending on the (non)ionic nature of the tastant. In Publication IV, a polyampholyte was used as a ligand to prepare fluorescent gold nanoclusters with pH-responsive photoluminescence, where the protonation of the tertiary amine groups led to enhanced photoluminescence in acidic medium. This feature was further used in the bioimaging of lysosomes. Finally, Publication V demonstrated that a low-magnitude electric field can lead to a controlled locomotion of surfactant-stabilized aqueous droplets. The results suggest that the droplet propulsion occurs through non-equilibrium mechanisms at the zwitterion-covered droplet interface. The results of this thesis contribute to the understanding of ionic interactions and how they can be used in the development of functional responsive materials through equilibrium and nonequilibrium mechanisms.

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