[diss] Perustieteiden korkeakoulu / SCI

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    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.
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    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.
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    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.
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    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.
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    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.
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    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.
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    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.
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    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.
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    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.
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    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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    Naturally Occurring Discursive Work as a Reflection of Organizational Identification During Organizational Transformation
    (Aalto University, 2024) Kupiainen, Olli-Jaakko; Vartiainen, Matti, Emeritus Prof., Aalto University, Department of Industrial Engineering and Management, Finland; Hakonen, Anu, Ph.D, Haaga-Helia University of Applied Sciences, Finland; Tuotantotalouden laitos; Department of Industrial Engineering and Management; Perustieteiden korkeakoulu; School of Science; Vuori, Natalia, Assist. Prof., Aalto University, Department of Industrial Engineering and Management, Finland
    Research on organizational changes traditionally focuses on the outcome of change efforts or change processes. Recent theoretical openings of "work" complement these approaches, which aim to elucidate organizational members' purposeful change efforts. Work has discursive, relational, and material dimensions. This doctoral thesis focuses on discursive work, which emphasizes the role of language in aiming to shape or change an organization's social-symbolic objects. An organization's future, identity, and status represent such social-symbolic objects in this doctoral thesis. An empirical arena of this doctoral thesis is naturally occurring change talk that organizational members generate on the enterprise social media and its discussion board during an organizational transformation. These posts are considered organizational narratives. The strength of the narrative approach is that it acknowledges multiple interpretations of change. This doctoral thesis is a case study consisting of three individual essays and a summary of those essays. Each essay explores the same data through different work lenses: temporal work, organizational identity (OI) work, and status work. The essays show how organizational members integrated their microlevel change talk into their organization's macrolevel change attempts. Essay 1 argues that organizational members engage in future-making by offering solutions and making "if-then" plans to enable their organization to meet its goals in the future. Essay 2 suggests that they discursively construct time- and context-sensitive OIs offering alternative interpretations of ongoing transformation. Essay 3 shows that members engage in status-seeking on behalf of their organization, which is supported or hindered by organizational self-efficacy. This doctoral thesis advances the understanding of organizational change literature by arguing that the discursive "work" in which organizational members engage in on a technological platform can help to set the direction for the organization during transformation when organizational members monitor and assess their organization's interest (one motivational aspect of organizational identification). Thus, it is argued that discursive work and organizational identification are closely linked. The research suggests that organizational members' collective, discursive work through social technologies is a cultural phenomenon in which diverse and critical interpretations of ongoing transformation can also be expressed. Furthermore, discursive work requires resources from the members to make inferences about the situation. Social technologies support discursive work by making multiple interpretations and an organization's change potentials visible via organizational narratives, thus generating a discourse of direction.
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    Light-matter interactions and topological effects in ensembles of plasmonic nanoparticles
    (Aalto University, 2024) Taskinen, Jani M.; Teknillisen fysiikan laitos; Department of Applied Physics; Quantum Dynamics; Perustieteiden korkeakoulu; School of Science; Törmä, Päivi, Prof., Aalto University, Department of Applied Physics, Finland
    The cornerstone of plasmonics is the fundamental coupling between photons and electron oscillations on the surface of a metal, which allows light to be trapped in subwavelength volumes. The electromagnetic fields of plasmonic excitations are highly confined to the metal-dielectric interface, leading to extreme field enhancement and enabling strong light-matter interactions. Enforcing this effect in carefully designed nanostructures allows the creation of high-quality optical modes that provide efficient coupling between photons and molecular emitters. This dissertation studies lasing and condensation as light-matter phenomena in plasmonic nanoparticle lattices and their underlying connections to topology, which is concerned with properties that are invariant under continuous deformations. The optical modes supported by different nanoparticle structures and the phenomena enabled by them are investigated experimentally: samples are fabricated using an electron beam lithography process, and an angle-resolved spectroscopy setup is used to induce the light-matter effects and to characterize their properties. The experimental methods used in the research are discussed in-depth to enhance reproducibility and to provide tools for future implementations. In Publication I, the polarization and phase properties of a strongly coupled plasmon condensate are studied in a square nanoparticle lattice. Polarization-resolved images of the condensate and its far-field emission pattern are used in a phase-retrieval algorithm. The resulting nonuniform condensate phase is shown to host a topologically trivial winding of the polarization vector, which is treated as a pseudo-spin property of the system. In Publication II, the quantum metric and Berry curvature are measured in a plasmonic lattice constituting the first observation of the quantum geometric tensor in a non-Hermitian system. Nonzero components of the tensor are discovered around high-symmetry lines of the Brillouin zone, explained by the non-hermiticity of the system and pseudospin-orbit coupling. The experimental findings are verified qualitatively by a two-band model. In Publication III, a pattern design method based on the lossy nature of metallic nanoparticles is utilized in creating plasmonic quasicrystals that host modes with polarization vortices. The fabricated samples are combined with dye molecules to demonstrate lasing with extremely high topological charges, verified by polarization-resolved measurements and theoretical considerations.
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    On the Strategic Importance of Building and Using Complex, Algorithmic Systems
    (Aalto University, 2024) Seppälä, Jane; Tuotantotalouden laitos; Department of Industrial Engineering and Management; Strategy and Venturing; Perustieteiden korkeakoulu; School of Science; Vuori, Timo, Prof., Aalto University, Department of Industrial Engineering and Management, Finland
    Organizations build and use increasingly capable algorithmic systems to solve increasingly complex problems. As algorithmic systems become more powerful, they also become more complex and challenging to understand and manage. Thus, we face a situation where technologies are progressively used in high-stakes situations yet less well understood by traditionally trained managers and organizational scholars. Further, the wide use of such systems has strategic implications: the design of algorithmic systems influences strategic options available, strategy is operationalized into and enacted through such systems, and these systems influence emergent strategy. In this thesis, I explore how organizations build and use complex algorithmic systems and highlight the potential strategic implications of such systems. This thesis consists of three qualitative case studies in which building and using complex algorithmic technologies is at the forefront. Essay 1 explores how technological complexity affects post-merger integration dynamics at a large technology company. Focusing on the perceptions and behaviors of one focal unit in a situation of attempted synergy capture through portfolio streamlining, this study demonstrates how technological complexity affects strategic decision-making and illustrates the bi-directional influence between technological and strategic decisions. Essay 2 studies how a large retail organization develops and uses strategically important algorithmic tools. This study describes how strategy is encoded into the tools through the processes of negotiating a role for the tool, making abstract concrete through quantified measures, and materializing the emerging sense into the tool, and further, how tool use can induce masking of uncertainty by hiding technical details. These processes affect emergent strategy and strategizing dynamics. Essay 3 analyzes how a start-up organization develops and uses advanced speech recognition technology to provide transcription services and products. This study describes how the organization maintained and improved system performance as the system's scope was extended and identifies a set of organizational practices that enable this. This thesis makes three contributions. First, I describe the continuous interplay of the development and use of technology, typically described by organizational literature as two separate processes, thus conceptualizing technology development as an ongoing sensemaking process. Second, I contribute to the literature on strategy-as-practice by detailing the strategic role of complex algorithmic systems and recognizing and describing technology development as a strategic activity. I also portray how strategically important algorithmic systems are built and used and how such systems can influence strategizing dynamics. Third, I contribute to the emerging discussion on data practices by describing the essential and often ill-recognized role organizational processes that generate data and data practices play in building efficient algorithmic systems.
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    Magnetic field-induced particle assembly and jamming
    (Aalto University, 2024) Liu, Xianhu; 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, Distinguished Prof., Aalto University, Department of Applied Physics, Finland
    Ferromagnetic materials possess the ability to undergo magnetization in the presence of an external magnetic field and exhibit rapid responsiveness to magnetic field stimuli, rendering them highly suitable as carriers for stimuli-responsive materials. Furthermore, the assembly of ferromagnetic particles under the influence of a magnetic field allows for the design of assembled superstructures with diverse properties, enabling the fulfillment of specific application requirements. In this thesis, ferromagnetic cobalt (Co) and nickel (Ni) particles with various surface roughness were synthesized utilizing the polyol method. These particles were employed as building blocks for magnetic fieldinduced assembly, resulting in the formation of assembled superstructures with distinct properties. The unique characteristics of these superstructures were systematically characterized, followed by an exploration of their potential applications. Publication I utilizes a relatively smooth-surfaced ferromagnetic Co particles as building blocks enabled the assembly of weakly jammed superstructures under the influence of a magnetic field. The shape of these assembled superstructures could be controlled by adjusting the magnetic field strength, imparting tunable sensitivity in response to pressure stimuli. Publication II employs ferromagnetic Ni particles with comparatively rougher surfaces as building blocks, the heightened interparticle friction reduced the critical packing density required for achieving jamming. Consequently, strongly jammed superstructures were formed under the influence of a magnetic field. The pronounced jamming effect induced structural memory in the assembled superstructures, enabling tunable of jamming through magnetic field application kinetics and ultimately yielding broad tunability in response to stimuli. Publication III demonstrates the transfer of jamming-induced structural memory to electrical signals, which could be further transformed into visible light signals. Additionally, pulsed magnetic fields were employed to modulate the system's responsiveness.
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    Questions About Learners' Code: Extending Automated Assessment Towards Program Comprehension
    (Aalto University, 2024) Lehtinen, Teemu; Korhonen, Ari, Senior university lecturer, Aalto University, Department of Computer Science, Finland; Tietotekniikan laitos; Department of Computer Science; Learning + Technology Research Group (LeTech); Perustieteiden korkeakoulu; School of Science; Malmi, Lauri, Prof., Aalto University, Department of Computer Science, Finland
    Novice programmers have a limited understanding of the program code they produce. Their programs are often based on code snippets from examples and internet searches. Recently and rather suddenly, artificial intelligence has changed programming environments that can now suggest and complete entire programs based on the available context. However, the ability to comprehend and discuss programs is essential in becoming a programmer who is responsible for their work and can reliably solve problems as a member of a team. Many introductory programming courses have hundreds of students per teacher. Therefore, automated systems are often used to produce immediate feedback and assessment for programming exercises. Current systems focus on the created program and its requirements. Unfortunately, their feedback helps students in iterating toward acceptable code rather than acquiring a deep understanding of the program. This dissertation addresses that gap. The dissertation defines and introduces questions about learners' code (QLCs). After a student has submitted a program, they are asked automated, personal QLCs about the structure and the logic of their program. The dissertation describes a system to generate QLCs and contributes three open-source implementations supporting Java, JavaScript, and Python. The empirical contributions of the dissertation are based on multiple studies that research both quantitatively and qualitatively how novice programmers answer various types of QLCs. From the students, who create a correct program, as many as 20% may answer incorrectly about concepts that are critical to systematically reason about their program code. More than half of the students fail to mentally trace the execution of their program. This confirms that novices' program comprehension needs improvement and instructors may overestimate their abilities. The more students answer incorrectly to QLCs, the more they tinker with their code and have less success on the course. Current artificial intelligence systems respond to QLCs better than the average novice. However, they also lapse into humanlike errors producing failed reasoning about the code they generated, which could present an important learning opportunity for the critical use of AI in programming.
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    On the connectivity interdiction problem, the geometry of data structures and Eulerian circuits
    (Aalto University, 2024) Obscura Acosta, Nidia; Tietotekniikan laitos; Department of Computer Science; Combinatorics of Efficient Computations; Perustieteiden korkeakoulu; School of Science; Chalermsook, Parinya, Prof., Aalto University, Department of Computer Science, Finland
    Over the last century and since the introduction practical computers, the study of algorithms for optimization problems has become one of the main areas in theoretical computer science. Computer algorithms are nowadays an ubiquitous tool in optimization applications like food production, voting systems, route scheduling, transportation, protein synthesis and drug delivery. However, the progress of these area has faced big theoretical and practical challenges, like the lack of computing resources, the increasing volume of data in big networks and theoretical and structural barriers like NP-hardness. In order to by-pass some of the theoretical barriers, this thesis explores several graph and optimization problems through approximation algorithms, data structures, extremal combinatorics and geometry, establishing new state-of-the-art theoretical results in the following problems: The Connectivity Interdiction Problem. For a given weighted undirected connected graph G and integer k, this problem asks to find the optimal set of edges of cost at most k such that the min-cut of the graph G is minimzed after the deletion of these edges from G. We establish new graph-theoretic structural results relating this problem to a variant of graph cut problems called the Normalized Min-cut problem, which allows us to design new exact and approximate algorithms for the unit-cost case establishing a trade-off between running time and solution quality. Furthermore, we answer an open question from Zenklusen [Zenklusen 2014] by showing that this problem admits an FPTAS (Fully Polynomial-Time Approximation Scheme). The Geometry of the Minimum-cost Online Binary Search Tree Problem. In this problem, we want to find the optimum cost online self-adjusting binary search tree which searches any sequence of keys in the tree, a problem related to the Dynamic Optimality Conjecture [Sleator & Tarjan 1985]. We study the Greedy algorithm as one of the two main candidates for this conjecture in a geometric setting, using the theory of forbidden submatrices and introducing a novel matrix decomposition technique. This allows us to improve known upper-bounds in special cases like the pre-order traversal, deque, split and k-increasing sequences and operations, and to settle completely the conjecture for post-order traversal sequences. Furthermore, we show the NP-hardness of a newly introduced generalization of this problem and efficient approximation algorithms for its general case and special cases. Unique Eulerian Circuits. We study the graph theoretical characterization of directed connected graphs with a unique Eulerian circuit. We show a new characterization of these graphs in terms of cut nodes and degrees of a graph, allowing a simple and efficient algorithm to determine if a given graph has a unique Eulerian circuit. Most importantly, this allows us to characterize and develop efficient algorithms to compute the so called maximal safe solutions for the Eulerian Circuit Problem, a concept arising in bioinformatics applications like genome assembly.
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    Functional Liquid-Fluid Interfaces Based on Hydrophobin Proteins: An Experimental Study for Medical Applications
    (Aalto University, 2024) Al-Terke, Hedar H.; Paananen, Arja, Dr., VTT Technical Research Centre of Finland, Finland; Joensuu, Jussi, Dr., VTT Technical Research Centre of Finland, Finland; Teknillisen fysiikan laitos; Department of Applied Physics; Soft Matter and Wetting; Perustieteiden korkeakoulu; School of Science; Ras, Robin, Prof., Aalto University, Department of Applied Physics, Finland
    Interfaces are everywhere around us. Any direct interaction occurs at the interface. This thesis explored the potential of functional interfaces to solve various medical application challenges. The four publications presented in this thesis highlight the different ways in which functional interfaces can be utilized to address these challenges. Publication I focused on the relationship between gravity, viscoelasticity, and the shape of water droplets coated with HFBI hydrophobin proteins. By studying the self-organization of hydrophobins at the air-water interface, it was found that a rigid layer is formed at a critical concentration, which affects the droplet morphology. This finding has significant implications for engineering and biomedical applications, as it provides a pathway for controlling the shape of droplets in various systems. Publication II presented a novel antibody extraction method using advanced materials and functional interfaces. By dividing an oil-based ferrofluid into daughter droplets under an external magnetic field, the surface area of liquid-liquid interfaces is increased, allowing for the functionalization and application of these interfaces as a substrate for antibody extraction. Publication III focused on the formation and characterization of protein-coated gas bubbles. By employing a micropipette aspiration technique, the mechanical properties of these bubbles were assessed, and a sealing parameter (Q) was determined to evaluate their gas permeability. These well-characterized bubbles have promising potential as ultrasound-enhanced contrast agents in various biomedical fields. They can be utilized for imaging purposes and targeted drug delivery, opening up new possibilities for medical diagnostics and therapies. Publication IV explored the utilization of hydrophobin protein functionalized bubbles to develop an advanced ultrasound molecular imaging probe. By functionalizing bubbles with a moiety part at their interface, they can attach to specific antigens and reveal diseased cells, such as cancer cells. This innovative approach holds great promise for improving the accuracy and sensitivity of molecular imaging techniques, enabling early detection and precise targeting of diseases. Overall, the findings presented in this thesis demonstrate the immense potential of functional interfaces in solving various medical application challenges. They provide valuable insights into the design and development of novel materials and techniques that can improve diagnostics, therapeutics, and imaging in the biomedical field.
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    Switchable hydrogel networks based on natural polysaccharides
    (Aalto University, 2024) Eklund, Amanda; Zhang, Hang, 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
    Responsive hydrogels are gaining interest in different applications due to their flexible chemistries, biocompatibility, and softness. This has allowed utilisation in fields such as biomedicine, and electronics. By modifying the microstructure of the hydrogel, different material properties can be introduced and optimised. In this thesis, a natural polysaccharide, agarose, is used to modify the hydrogel network of thermoresponsive polymer, N-isopropylacrylamide (NIPAm) to enhance its properties. Using two different network architectures, the optical and adhesive properties of the hydrogels are controlled using temperature change as a stimulus. In Publication I, agarose is utilised as a primary network that is removed after PNIPAm polymerisation to create channels into the hydrogel. These channels enhance water transportation and enable the hydrogel to undergo phase transitions more quickly compared to traditional PNIPAm. Additionally, the material has a bright white appearance, enabling use in applications such as controllable screens and optical switches. Publication II utilises chemically modified agarose as a macro-crosslinker in the PNIPAm network, producing a hydrogel that shows superior whiteness at smaller thickness of the reflecting layer compared to the channeled PNIPAm. Publication III utilises the water transportation properties of the channeled hydrogel to realise controllable underwater adhesion. Additionally, the hydrogel includes biomimetic catechol groups to enhance adhesive properties. The combination of the adhesion and controllable water transportation allows the adhesion to be switched on and off using a change in temperature with a high switching efficiency, both underwater and in dry conditions. This hydrogel system can be used as a controllable gripper for fragile, lightweight, irregular biological systems as demonstrated, showing the potential of the channeling approach in fields utilising controllable underwater adhesion such as biomedicine and soft robotics.
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    Empathy Dynamics: A Neuroscientific Perspective
    (Aalto University, 2024) Zebarjadi, Niloufar; 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
    Empathy, a socio-cognitive process of perceiving the feelings of others, is one of the fundamental basis of healthy social interaction. Advancements in neuroimaging techniques in the past three decades have facilitated a deeper exploration of the neural mechanisms associated with empathy, complementing traditional approaches and broadening the understanding of this complex phenomenon. This thesis investigated the intricate neural basis of empathy and its variations across individuals. In Study I, we employed magnetoencephalography (MEG) to explore frequency-decomposed neural activities during pain empathy. We detected four significant patterns corresponding to different components of empathy including an alpha suppression pattern, two beta suppression patterns, and a late alpha-beta enhancement pattern as well as their link to subjective experiences. In Study II, MEG and Functional Magnetic Resonance Imaging (fMRI) were utilized to examine the maturation of empathy, revealing a shift in neural and functional mechanisms of empathy from adolescence to young adulthood. Studies III and IV delved into the association between political ideology and neural responses to emotional suffering and physical pain of others, respectively, highlighting an intriguing, yet complex, relationship between empathy and political ideology. Overall, the findings in the current thesis advance the understanding of neural processes underlying empathy, underscoring the importance of considering diverse factors such as age, political ideology, and subjective experiences. This research can open new vistas for future exploration, encouraging a more comprehensive approach to neuroscientific investigations of empathy.
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    Systems and Methods for Multiple-View and Depth-Based People Tracking and Human-Computer Interaction
    (Aalto University, 2024) Korkalo, Otto; Takala, Tapio, Prof. Emer., Aalto University, Department of Computer Science, Finland; Tietotekniikan laitos; Department of Computer Science; Perustieteiden korkeakoulu; School of Science; Kannala, Juho, Prof. Aalto University, Department of Computer Science, Finland; Takala, Tapio, Prof. Emer., Aalto University, Department of Computer Science, Finland
    This thesis presents systems and methods for real-time multiple-view and depth-based optical tracking for specific human-computer interaction and smart environment applications. Multiple-view systems are used for mitigating occlusions, enhancing tracking precision and accuracy, and extending the tracking volume to encompass larger scales. Depth cameras, on the other hand, offer the advantage of directly providing three-dimensional information from the scene, which makes them particularly appealing for spatial analysis. For multi-touch interaction, we developed a tracking approach that utilizes multiple side-view cameras to transform any flat surface into a multi-touch screen. Instead of explicitly triangulating the touch points, we employed an extended Kalman filter-based method in which the states of the touch points are updated whenever an observation is received from any of the cameras, ensuring low latency and rapid update rates. To position the cameras as close to the screen as possible, we employed fisheye lenses with modified distortion model, and explored the optimal camera configuration for achieving robust tracking with varying numbers of cameras and touch points. Accurate intrinsic and extrinsic calibration of cameras and camera systems is essential for optimal data fusion and state estimation. Typically, calibration procedures are carried out manually, which is not only time-consuming but can also be impractical. To address this issue in multiple-view depth camera-based people tracking systems, we have developed an auto-calibration method that directly derives the camera network topology and sensor calibration parameters from observations. Additionally, to account for the uncertainties in the observations during state estimation and data fusion, we developed a measurement noise model as part of the auto-calibration procedure. In mixed reality, the aim of camera pose estimation and tracking is to align the real and virtual environments in real-time and in all three dimensions. To achieve this goal, we developed a computer-aided design model-based depth camera tracking approach that utilizes a fast graphics processing unit-based iterative closest point method for pose estimation. This method can be applied to various objects, as long as a depth map from the object can be generated from the desired viewpoint. We conducted investigations into the applicability and performance of the method with different targets and concluded that the proposed approach exhibits reduced drift compared to simultaneous localization and mapping-based method and outperforms monocular edge-based method in terms of accuracy.