La plasmonique vise à utiliser des nanostructures métalliques très petites devant la longueur d’onde pour manipuler la lumière. Les structures métalliques sont particulières parce qu’elles contiennent un plasma d’électrons libres qui conditionne complètement leur réponse optique. Notamment, lorsque la lumière se propage à proximité des métaux, sous forme de mode guidés comme les plasmons et les gap-palsmons, elle est souvent lente, présentant une vitesse de groupe faible. Dans ce travail, nous présentons une analyse physique qui permet de comprendre cette faible vitesse en considérant le fait que l’énergie se déplace à l’opposé de la lumière dans les métaux. Nous montrons que la vitesse de groupe est égale à la vitesse de l’énergie pour ces modes guidés, et proposons la notion de ralentissement plasmonique. Finalement, nous étudions comment cette « trainée plasmonique » rend une structure aussi simple qu’un coupleur à prisme sensible à la répulsion entre les électrons du plasma. / The field of plasmonics aims at manipulating light using deeply subwavelength nanostructures. Such structures present a peculiar optical response because of the free electron plasma they contain. Actually, when light propagates in the vicinity of metals, usually under the form of a guided mode, it presents a low group velocity. Such modes, like plasmons and gap-plasmons, are said to be slow. In this work we present a general physical analysis of this phenomenon by studying how the energy propagates in metals in a direction that is opposite to the propagation direction of the mode. We show that the group velocity and the energy velocity are the same, and finally introduce the concept of plasmonic drag. Finally, we study how slow guided modes make structures as simple as prism couplers sensitive to the repulsion between electrons inside the plasma.

Les épopées d’Homère et de Virgile, la Chanson de Roland sont marquées par une alternance qui peut paraître capricieuse. En fait, on la saisit beaucoup mieux dès lors qu’on prend en compte la présence des corps : ceux du jongleur, de l’aède, du récitant ; le corps du public. Postures, gestuelle, mouvements, regard, souffle, musique s’articulent à cette alternance pour en faire une véritable danse. Cette thèse étudie d’abord comment dansent chacun des temps principaux du récit dans ces épopées, en accordant la priorité à Homère et au Roland ; puis elle étudie comment cette danse des temps prend corps dans chacune des petites pièces dont nous décelons que sont composées les épopées anciennes comme le Roland : les laisses. / The Homeric and Virgilian epics, as well as the Chanson de Roland are full of tenseswitching, the use of which might seem capricious to the modern reader. It is in fact much better understood when bodies’ presence is taken into account — these bodies being the bard’s one as well as the audience’s. Postures, gestures, moves, eyes, breath, music are joint partners to tenseswitching, so that tenses really dance in epics. This study is firstly about how each one of the main narrative tenses dances in Homer and the Roland, and also in the Æneid. Then it studies the way tenses dance in each of the small pieces we find in the classical epics as well as in the Roland : the laisses.

We have examinined the problem of constructing efficient strategies for continuous-time dynamic asset allocation. In order to obtain efficient investment strategies; a stochastic optimal control approach was applied to find optimal transaction control. Two mathematical problems are formulized and studied: Model I; a dynamic programming approach that maximizes an isoelastic functional with respect to given underlying portfolio dynamics and Model II; a more sophisticated approach where a time-inconsistent state dependent mean-variance functional is considered. In contrast to the optimal controls for Model I, which are obtained by solving the Hamilton-Jacobi-Bellman (HJB) partial differential equation; the efficient strategies for Model II are constructed by attaining subgame perfect Nash equilibrium controls that satisfy the extended HJB equation, introduced by Björk et al. in [1]. Furthermore; comprehensive execution algorithms where designed with help from the generated results and several simulations are performed. The results reveal that optimality is obtained for Model I by holding a fix portfolio balance throughout the whole investment period and Model II suggests a continuous liquidation of the risky holdings as time evolves. A clear advantage of using Model II is concluded as it is far more efficient and actually takes time-inconsistency into consideration. / Vi har undersökt problemet som uppstår vid konstruktion av effektiva strategier för tidskontinuerlig dynamisk tillgångsallokering. Tillvägagångsättet för konstruktionen av strategierna har baserats på stokastisk optimal styrteori där optimal transaktionsstyrning beräknas. Två matematiska problem formulerades och betraktades: Modell I, en metod där dynamisk programmering används för att maximera en isoelastisk funktional med avseende på given underliggande portföljdynamik. Modell II, en mer sofistikerad metod som tar i beaktning en tidsinkonsistent och tillståndsberoende avvägning mellan förväntad avkastning och varians. Till skillnad från de optimala styrvariablerna för Modell I som satisfierar Hamilton-Jacobi-Bellmans (HJB) partiella differentialekvation, konstrueras de effektiva strategierna för Modell II genom att erhålla subgame perfekt Nashjämvikt. Dessa satisfierar den utökade HJB ekvationen som introduceras av Björk et al. i [1]. Vidare har övergripande exekveringsalgoritmer skapats med hjälp av resultaten och ett flertal simuleringar har producerats. Resultaten avslöjar att optimalitet för Modell I erhålls genom att hålla en fix portföljbalans mellan de riskfria och riskfyllda tillgångarna, genom hela investeringsperioden. Medan för Modell II föreslås en kontinuerlig likvidering av de riskfyllda tillgångarna i takt med, men inte proportionerligt mot, tidens gång. Slutsatsen är att det finns en tydlig fördel med användandet av Modell II eftersom att resultaten påvisar en påtagligt högre grad av effektivitet samt att modellen faktiskt tar hänsyn till tidsinkonsistens.

Urgent scientifically-informed action is needed to stabilise the Earth System amidst anthropogenic climate change. Particularly, the notable transgression of the ‘biosphere integrity’ Planetary Boundary needs to be addressed. Modern Earth System Models struggle to accurately represent regional to local-scale climate features and biodiversity aspects. Recent developments allow to tackle these issues using Artificial Intelligence. This dissertation focuses on two main aspects: (i) deriving high spatio-temporal resolution climate data from coarser models; and (ii) integrating high-temporal-resolution climate data into Species Distribution Models. Three specific objectives were defined: Obj1 Improving Perfect Prognosis – Statistical Downscaling methods through modern Deep Learning algorithms. Obj2 Downscaling a high-resolution multivariate climate ensemble. Obj3 Employ the resulting dataset to improve Species Distribution Models’ projections. The objectives are connected to the three articles that support this cumulative dissertation. Its scope is limited to the Free State of Saxony, Germany, where local high-resolution climate data and high-quality observations of endangered vascular plant species were employed. From a broader perspective, these efforts should contribute to the overarching goal of bridging the gap between the scales of species distribution and climate models while establishing open-source, reproducible, and scalable containerised frameworks. Recent Deep Learning algorithms were leveraged to accomplish (i). The proposed frameworks enhance previous performance of Perfect Prognosis – Statistical Downscaling approaches, while ensuring repeatability. The key near-surface variables considered are precipitation, water vapour pressure, radiation, wind speed, and, maximum, mean and minimum temperature. The assumptions that support the Perfect Prognosis approach were thoroughly examined, confirming the robustness of the methods. The downscaled ensemble exhibits a novel output resolution of daily 1 km, which can serve as input for multiple climate impact studies, especially for local-scale decision-making and in topographically complex regions. Considerable methodological implementations were proposed and thoroughly analysed to achieve (ii). Despite notable limitations, Species Distribution Models are frequently used in climate change conservation planning. Thus, recent developments in climate data resolution could improve their usefulness and reliability, which have been previously constraint to coarse temporal aggregates in the projection domain. The presented framework provides fine-grained species suitability projections and satisfactory spatio-temporal transferability, albeit worrying trends. These improved projections are a step forward towards tailored conservation efforts. Limitations of Machine Learning methods and Species Distribution Models are addressed. Substantial avenues for future improvements are thoroughly discussed. As results suggest further reduction of suitable habitats, yet another call for swift action towards low-carbon societies is made. This requires maximising climate change mitigation and adaptation measures, along with a swift transition from short-term profit-driven policies to long-term sustainable development, but primarily, a collective shift in consciousness from anthropocentric positions to ecocentric policies and societies.:Contents Declaration of conformity........................................................ I Abstract....................................................................... III Zusammenfassung.................................................................. V Resumen........................................................................ VII Acknowledgments................................................................. IX List of Figures................................................................. XV List of Tables................................................................. XIX Symbols and Acronyms........................................................... XXI I Prelude & Foundations 1 1 Introduction................................................................... 3 1.1 Motivation – Human Impact on Earth....................................... 3 1.2 Earth System Modelling and Downscaling................................... 5 1.3 Biosphere’s Response to Recent Changes................................... 8 1.4 Species Distribution Models.............................................. 9 1.5 Objectives.............................................................. 10 1.6 Scope................................................................... 10 1.7 Outline................................................................. 10 2 Methodological Basis.......................................................... 13 2.1 Introduction to Artificial Intelligence Methods......................... 13 2.1.1 Artificial Intelligence........................................... 13 2.1.2 Machine Learning.................................................. 14 2.1.3 Deep Learning..................................................... 14 2.2 Downscaling Techniques.................................................. 15 2.2.1 Dynamical Downscaling............................................. 15 2.2.2 Statistical Downscaling........................................... 15 2.2.2.1 Model Output Statistics................................... 16 2.2.2.2 Perfect Prognosis......................................... 16 2.3 Species Distribution Models: Temporal Aspects........................... 17 2.4 Computational Framework................................................. 18 2.4.1 High-Performance Computing........................................ 18 2.4.2 Containers........................................................ 18 2.5 Remarks on Reproducibility.............................................. 19 II Articles’ Synthesis 21 3 Data.......................................................................... 23 3.1 Study Area.............................................................. 23 3.2 ReKIS................................................................... 24 3.3 ERA5.................................................................... 24 3.4 CORDEX.................................................................. 24 3.5 Species Occurrences..................................................... 25 3.6 WorldClim............................................................... 26 4 Methodological Implementations................................................ 27 4.1 Advancing Statistical Downscaling....................................... 27 4.1.1 Transfer Function Calibration.................................... 27 4.1.2 Evaluation....................................................... 29 4.1.3 Repeatability.................................................... 29 4.2 Downscaling a Multivariate Ensemble..................................... 30 4.2.1 Transfer Function Adaptations.................................... 30 4.2.2 Validation....................................................... 30 4.2.3 Perfect Prognosis Assumptions Evaluation......................... 31 4.3 Integrating High-Temporal-Resolution into SDMs.......................... 32 4.3.1 Climate Data..................................................... 32 4.3.1.1 Predictor Sets.......................................... 32 4.3.1.2 Temporal Approaches..................................... 33 4.3.2 SDM Implementation............................................... 33 4.3.3 Spatio-Temporal Thinning & Trimming.............................. 33 4.3.4 Meta-analysis.................................................... 34 4.3.5 Pseudo-Reality Assessment........................................ 34 4.3.6 Spatio-Temporal Transferability.................................. 34 5 Results & Discussions......................................................... 35 5.1 Advancing Statistical Downscaling....................................... 35 5.1.1 Performance Improvement.......................................... 35 5.1.2 Repeatability.................................................... 36 5.1.3 Transfer Function Suitability.................................... 38 5.2 Downscaling a Multivariate Ensemble..................................... 39 5.2.1 Transfer Function performance.................................... 39 5.2.2 Bias-Correction.................................................. 40 5.2.3 Pseudo-Reality................................................... 42 5.2.4 Projections...................................................... 43 5.3 Integrating High-Temporal-Resolution into SDMs.......................... 45 5.3.1 Predictor Set Evaluation for H2k................................. 45 5.3.2 Temporal Approach Comparison..................................... 46 5.3.3 Spatio-Temporal Transferability.................................. 47 5.3.4 Suitability Projections.......................................... 47 III Insights 51 6 Summary....................................................................... 53 6.1 Article A1.............................................................. 53 6.2 Article A2.............................................................. 54 6.3 Article A3.............................................................. 56 7 Conclusions and Outlook....................................................... 59 References 65 Articles 81 A1 Repeatable high-resolution statistical downscaling through deep learning..... 83 A2 Downscaling CORDEX Through Deep Learning to Daily 1 km Multivariate Ensemble in Complex Terrain............................................................. 103 A3 Integrating High-Temporal-Resolution Climate Projections into Species Distribu- tion Model..................................................................... 127 / Um das Erdsystem angesichts des anthropogenen Klimawandels zu stabilisieren, sind Maßnahmen auf Basis wissenschaftlicher Erkenntnisse dringend erforderlich. Insbesondere muss die drastisch Überschreitung der planetaren Grenze ‘Integrität der Biosphäre’ angegangen werden. Bisher haben aber Modelle des Erdsystems Schwierigkeiten, regionale bis lokale Klimamerkmale und Aspekte der Biodiversität genau abzubilden. Aktuelle Entwicklungen ermöglichen es, diese Herausforderungen mithilfe von Künstlicher Intelligenz anzugehen. Diese Dissertation konzentriert sich auf zwei Hauptaspekte: (i) die Ableitung von Klimadaten mit hoher räumlicher und zeitlicher Auflösung aus groberen Modellen und (ii) die Integration von Klimadaten mit hoher zeitlicher Auflösung in Modelle zur Artverbreitung. Es wurden drei konkrete Ziele definiert: Ziel1 Verbesserung von Perfect Prognosis – Statistische Downscaling-Methoden durch moderne Deep Learning-Algorithmen Ziel2 Downscaling eines hochauflösenden multivariaten Klimaensembles Ziel3 Verwendung des resultierenden Datensatzes zur Verbesserung von Prognosen in Modellen zur Artverbreitung Diese Ziele werden in drei wissenschaftlichen Artikeln beantwortet, auf die diese kumulative Dissertation sich stützt. Der Anwendungsbereich erstreckt sich auf den Freistaat Sachsen, Deutschland, wo lokale hochauflösende Klimadaten und hochwertige Beobachtungen gefährdeter Gefäßpflanzenarten verwendet wurden. In einer breiteren Perspektive tragen diese Bemühungen dazu bei, die Kluft zwischen regionalen sowie zeitlichen Skalen der Artverbreitung und Klimamodellen zu überbrücken und gleichzeitig Open-Source-, reproduzierbare und skalierbare containerisierte Frameworks zu etablieren. Aktuelle Deep Learning-Algorithmen wurden eingesetzt, um Hauptaspekt (i) zu erreichen. Die vorgeschlagenen Frameworks verbessern die bisherige Leistung von Perfect Prognosis – Statistische Downscaling-Ansätzen und gewährleisten gleichzeitig die Wiederholbarkeit. Die wichtigsten bodennahen Variablen, die berücksichtigt werden, sind Niederschlag, Wasserdampfdruck, Strahlung, Windgeschwindigkeit sowie Maximal-, Durchschnitts- und Minimaltemperatur. Die Annahmen, die den Perfect Prognosis-Ansatz unterstützen, wurden analysiert und bestätigen die Robustheit der Methoden. Das downscaled Ensemble weist eine neuartige Auflösung von 1 km auf Tagesbasis auf, welches als Grundlage für mehrere Studien zu den Auswirkungen des Klimawandels dienen kann, insbesondere für Entscheidungsfindung auf lokaler Ebene und in topografisch komplexen Regionen. Es wurden umfassende methodische Implementierungen vorgeschlagen und analysiert, um Hauptaspekt (ii) zu erreichen. Trotz großer Einschränkungen werden Modelle zur Artverbreitung häufig in der Klimaschutzplanung eingesetzt. Daher könnten aktuelle Entwicklungen in der Klimadatenauflösung deren Nützlichkeit und Zuverlässigkeit verbessern, die bisher auf grobe zeitliche Aggregatformen im Projektionsbereich beschränkt waren. Das vorgestellte Framework bietet feingliedrige Prognosen zur Eignung von Arten und zufriedenstellende räumlich-zeitliche Übertragbarkeit, trotz besorgniserregender Trends. Diese verbesserten Prognosen sind ein Schritt in Richtung maßgeschneiderter Naturschutzmaßnahmen. Einschränkungen von Machine Learning-Methoden und Modellen zur Artverbreitung werden untersucht. Substanzielle Möglichkeiten zur zukünftigen Verbesserung werden ausführlich erörtert. Da die Ergebnisse darauf hinweisen, dass geeignete Lebensräume weiter abnehmen, wird erneut zum schnellen Handeln in Richtung kohlenstoffarmer Gesellschaften aufgerufen. Dies erfordert die Maximierung von Maßnahmen zur Bekämpfung des Klimawandels und zur Anpassung, zusammen mit einem raschen Übergang von kurzfristig Profitorientierten Politiken zu langfristiger nachhaltiger Entwicklung, aber vor allem zu einem kollektiven Bewusstseinswandel von anthropozentrischen Positionen zu ökozentrischen Politiken und Gesellschaften.:Contents Declaration of conformity........................................................ I Abstract....................................................................... III Zusammenfassung.................................................................. V Resumen........................................................................ VII Acknowledgments................................................................. IX List of Figures................................................................. XV List of Tables................................................................. XIX Symbols and Acronyms........................................................... XXI I Prelude & Foundations 1 1 Introduction................................................................... 3 1.1 Motivation – Human Impact on Earth....................................... 3 1.2 Earth System Modelling and Downscaling................................... 5 1.3 Biosphere’s Response to Recent Changes................................... 8 1.4 Species Distribution Models.............................................. 9 1.5 Objectives.............................................................. 10 1.6 Scope................................................................... 10 1.7 Outline................................................................. 10 2 Methodological Basis.......................................................... 13 2.1 Introduction to Artificial Intelligence Methods......................... 13 2.1.1 Artificial Intelligence........................................... 13 2.1.2 Machine Learning.................................................. 14 2.1.3 Deep Learning..................................................... 14 2.2 Downscaling Techniques.................................................. 15 2.2.1 Dynamical Downscaling............................................. 15 2.2.2 Statistical Downscaling........................................... 15 2.2.2.1 Model Output Statistics................................... 16 2.2.2.2 Perfect Prognosis......................................... 16 2.3 Species Distribution Models: Temporal Aspects........................... 17 2.4 Computational Framework................................................. 18 2.4.1 High-Performance Computing........................................ 18 2.4.2 Containers........................................................ 18 2.5 Remarks on Reproducibility.............................................. 19 II Articles’ Synthesis 21 3 Data.......................................................................... 23 3.1 Study Area.............................................................. 23 3.2 ReKIS................................................................... 24 3.3 ERA5.................................................................... 24 3.4 CORDEX.................................................................. 24 3.5 Species Occurrences..................................................... 25 3.6 WorldClim............................................................... 26 4 Methodological Implementations................................................ 27 4.1 Advancing Statistical Downscaling....................................... 27 4.1.1 Transfer Function Calibration.................................... 27 4.1.2 Evaluation....................................................... 29 4.1.3 Repeatability.................................................... 29 4.2 Downscaling a Multivariate Ensemble..................................... 30 4.2.1 Transfer Function Adaptations.................................... 30 4.2.2 Validation....................................................... 30 4.2.3 Perfect Prognosis Assumptions Evaluation......................... 31 4.3 Integrating High-Temporal-Resolution into SDMs.......................... 32 4.3.1 Climate Data..................................................... 32 4.3.1.1 Predictor Sets.......................................... 32 4.3.1.2 Temporal Approaches..................................... 33 4.3.2 SDM Implementation............................................... 33 4.3.3 Spatio-Temporal Thinning & Trimming.............................. 33 4.3.4 Meta-analysis.................................................... 34 4.3.5 Pseudo-Reality Assessment........................................ 34 4.3.6 Spatio-Temporal Transferability.................................. 34 5 Results & Discussions......................................................... 35 5.1 Advancing Statistical Downscaling....................................... 35 5.1.1 Performance Improvement.......................................... 35 5.1.2 Repeatability.................................................... 36 5.1.3 Transfer Function Suitability.................................... 38 5.2 Downscaling a Multivariate Ensemble..................................... 39 5.2.1 Transfer Function performance.................................... 39 5.2.2 Bias-Correction.................................................. 40 5.2.3 Pseudo-Reality................................................... 42 5.2.4 Projections...................................................... 43 5.3 Integrating High-Temporal-Resolution into SDMs.......................... 45 5.3.1 Predictor Set Evaluation for H2k................................. 45 5.3.2 Temporal Approach Comparison..................................... 46 5.3.3 Spatio-Temporal Transferability.................................. 47 5.3.4 Suitability Projections.......................................... 47 III Insights 51 6 Summary....................................................................... 53 6.1 Article A1.............................................................. 53 6.2 Article A2.............................................................. 54 6.3 Article A3.............................................................. 56 7 Conclusions and Outlook....................................................... 59 References 65 Articles 81 A1 Repeatable high-resolution statistical downscaling through deep learning..... 83 A2 Downscaling CORDEX Through Deep Learning to Daily 1 km Multivariate Ensemble in Complex Terrain............................................................. 103 A3 Integrating High-Temporal-Resolution Climate Projections into Species Distribu- tion Model..................................................................... 127 / Acción urgente científicamente informada es necesaria para estabilizar el sistema terrestre en medio del cambio climático antropogénico. En particular, la notable transgresión del límite planetario de ’integridad de la biosfera’ debe abordarse. Los modernos modelos del sistema terrestre tienen dificultades para representar con precisión las características climáticas a escala regional y local, así como los aspectos de la biodiversidad. Desarrollos recientes permiten abordar estos problemas mediante la inteligencia artificial. Esta disertación se enfoca en dos aspectos principales: (i) derivar datos climáticos de alta resolución espacio-temporal a partir de modelos más gruesos; y (ii) integrar datos climáticos de alta resolución temporal en modelos de distribución de especies. Se definieron tres objetivos específicos: Obj1 Mejorar los métodos de pronóstico perfecto – reducción de escala estadística mediante algoritmos modernos de aprendizaje profundo. Obj2 Generar un conjunto climático multivariado de alta resolución. Obj3 Emplear el conjunto de datos resultante para mejorar las proyecciones de los modelos de distribución de especies. Los objetivos están vinculados a los tres artículos que respaldan esta disertación acumulativa. Su alcance se limita al Estado Libre de Sajonia, Alemania, donde se emplearon datos climáticos locales de alta resolución y observaciones de alta calidad de especies de plantas vasculares en peligro de extinción. Desde una perspectiva más amplia, estos esfuerzos deberían contribuir a la meta general de cerrar la brecha entre las escalas de la distribución de especies y los modelos climáticos, mientras que se establecen marcos de trabajo contenedorizados de código abierto, reproducibles y escalables. Algoritmos recientes de aprendizaje profundo fueron aprovechados para lograr (i). Los marcos de trabajo propuestos mejoran el rendimiento previo de los métodos de pronóstico perfecto – reducción de escala estadística, al tiempo que garantizan la repetibilidad. Las variables clave de la superficie cercana consideradas son precipitación, presión de vapor de agua, radiación, velocidad del viento, así como la temperatura máxima, media y mínima. Se examinaron meticulosamente las suposiciones que respaldan el método de pronóstico perfecto, confirmando la robustez de las propuestas. El conjunto reducido de escala exhibe una novedosa resolución diaria de 1 km, el cual puede servir como insumo para múltiples estudios de impacto climático, especialmente para la toma de decisiones a nivel local y en regiones topográficamente complejas. Se propusieron y analizaron minuciosamente considerables implementaciones metodológicas para lograr (ii). A pesar de sus notables limitaciones, los modelos de distribución de especies son utilizados con frecuencia en la planificación de la conservación debido al cambio climático. Por lo tanto, los desarrollos recientes en la resolución de datos climáticos podrían mejorar su utilidad y confiabilidad, ya que antes se limitaban a agregados temporales gruesos en el caso de las proyecciones. El marco de trabajo presentado proporciona proyecciones de idoneidad de especies detalladas y una transferibilidad espacio-temporal satisfactoria, aunque con tendencias preocupantes. Estas proyecciones mejoradas son un paso adelante en los esfuerzos de conservación a la medida. Se abordan las limitaciones de los métodos de aprendizaje automático y de los modelos de distribución de especies. Se discuten a fondo posibilidades sustanciales para futuras mejoras. Dado que los resultados sugieren una mayor reducción de hábitats adecuados, se hace otro llamado a la acción rápida hacia sociedades bajas en carbono. Esto requiere maximizar las medidas de mitigación y adaptación al cambio climático, junto con una transición rápida de políticas orientadas a beneficios a corto plazo hacia un desarrollo sostenible a largo plazo, pero principalmente, un cambio colectivo de conciencia, desde posiciones antropocéntricas hacia políticas y sociedades ecocéntricas.:Contents Declaration of conformity........................................................ I Abstract....................................................................... III Zusammenfassung.................................................................. V Resumen........................................................................ VII Acknowledgments................................................................. IX List of Figures................................................................. XV List of Tables................................................................. XIX Symbols and Acronyms........................................................... XXI I Prelude & Foundations 1 1 Introduction................................................................... 3 1.1 Motivation – Human Impact on Earth....................................... 3 1.2 Earth System Modelling and Downscaling................................... 5 1.3 Biosphere’s Response to Recent Changes................................... 8 1.4 Species Distribution Models.............................................. 9 1.5 Objectives.............................................................. 10 1.6 Scope................................................................... 10 1.7 Outline................................................................. 10 2 Methodological Basis.......................................................... 13 2.1 Introduction to Artificial Intelligence Methods......................... 13 2.1.1 Artificial Intelligence........................................... 13 2.1.2 Machine Learning.................................................. 14 2.1.3 Deep Learning..................................................... 14 2.2 Downscaling Techniques.................................................. 15 2.2.1 Dynamical Downscaling............................................. 15 2.2.2 Statistical Downscaling........................................... 15 2.2.2.1 Model Output Statistics................................... 16 2.2.2.2 Perfect Prognosis......................................... 16 2.3 Species Distribution Models: Temporal Aspects........................... 17 2.4 Computational Framework................................................. 18 2.4.1 High-Performance Computing........................................ 18 2.4.2 Containers........................................................ 18 2.5 Remarks on Reproducibility.............................................. 19 II Articles’ Synthesis 21 3 Data.......................................................................... 23 3.1 Study Area.............................................................. 23 3.2 ReKIS................................................................... 24 3.3 ERA5.................................................................... 24 3.4 CORDEX.................................................................. 24 3.5 Species Occurrences..................................................... 25 3.6 WorldClim............................................................... 26 4 Methodological Implementations................................................ 27 4.1 Advancing Statistical Downscaling....................................... 27 4.1.1 Transfer Function Calibration.................................... 27 4.1.2 Evaluation....................................................... 29 4.1.3 Repeatability.................................................... 29 4.2 Downscaling a Multivariate Ensemble..................................... 30 4.2.1 Transfer Function Adaptations.................................... 30 4.2.2 Validation....................................................... 30 4.2.3 Perfect Prognosis Assumptions Evaluation......................... 31 4.3 Integrating High-Temporal-Resolution into SDMs.......................... 32 4.3.1 Climate Data..................................................... 32 4.3.1.1 Predictor Sets.......................................... 32 4.3.1.2 Temporal Approaches..................................... 33 4.3.2 SDM Implementation............................................... 33 4.3.3 Spatio-Temporal Thinning & Trimming.............................. 33 4.3.4 Meta-analysis.................................................... 34 4.3.5 Pseudo-Reality Assessment........................................ 34 4.3.6 Spatio-Temporal Transferability.................................. 34 5 Results & Discussions......................................................... 35 5.1 Advancing Statistical Downscaling....................................... 35 5.1.1 Performance Improvement.......................................... 35 5.1.2 Repeatability.................................................... 36 5.1.3 Transfer Function Suitability.................................... 38 5.2 Downscaling a Multivariate Ensemble..................................... 39 5.2.1 Transfer Function performance.................................... 39 5.2.2 Bias-Correction.................................................. 40 5.2.3 Pseudo-Reality................................................... 42 5.2.4 Projections...................................................... 43 5.3 Integrating High-Temporal-Resolution into SDMs.......................... 45 5.3.1 Predictor Set Evaluation for H2k................................. 45 5.3.2 Temporal Approach Comparison..................................... 46 5.3.3 Spatio-Temporal Transferability.................................. 47 5.3.4 Suitability Projections.......................................... 47 III Insights 51 6 Summary....................................................................... 53 6.1 Article A1.............................................................. 53 6.2 Article A2.............................................................. 54 6.3 Article A3.............................................................. 56 7 Conclusions and Outlook....................................................... 59 References 65 Articles 81 A1 Repeatable high-resolution statistical downscaling through deep learning..... 83 A2 Downscaling CORDEX Through Deep Learning to Daily 1 km Multivariate Ensemble in Complex Terrain............................................................. 103 A3 Integrating High-Temporal-Resolution Climate Projections into Species Distribu- tion Model..................................................................... 127

In dieser Arbeit untersuche ich aus einer typologischen Perspektive die Bedeutung von Tempus, Modalität und Aspekt (TMA) in Nafsan (South Efate), einer ozeanischen Sprache Vanuatus. Ich konzentriere mich auf die Bedeutung des perfektiven Aspekts und der Realis/Irrealis-Modalität in Nafsan und anderen ozeanischen Sprachen, als Fallstudien zur Untersuchung der sprach-übergreifenden Merkmale dieser TMA-Kategorien. Um ihre Bedeutungen in Nafsan zu analysieren, untersuche ich die Grammatik von Nafsan (Thieberger, 2006) und den Korpus von Thieberger (1995–2018), gefolgt von meiner Feldarbeit (Krajinovic, 2017b). Meine Analysen zeigen, dass Perfekt in Nafsan alle Funktionen hat, die für das Perfekt im Englischen typisch sind, mit Ausnahme der zusätzlichen Bedeutung von Zustandsänderungen. Die Verwendung des Nafsan-Perfekts liefert einen Beitrag zu der Debatte über die sprachübergreifende Gültigkeit von Iamitive, definiert durch die Bedeutung von Zustandsänderungen (Olsson, 2013). Basierend auf den Daten aus Nafsan und anderen ozeanischen Sprachen zeige ich, dass die von Klein (1994) vorgeschlagene semantische Definition des Perfekts ausreichend ist, um zusätzliche Funktionen des Perfekts zu berücksichtigen, ohne eine neue Iamitive-Kategorie zu etablieren. Was die Unterscheidung zwischen Realis und Irrealis betrifft, so habe ich festgestellt, dass die Kategorie Realis in Nafsan semantisch unterbewertet ist, wie sie in Irrealis-Kontexten auftreten kann, die mit der Bedeutung von Realis unvereinbar sein sollten. Ich schlage vor, dass “Realis” gelegentlich Realis-Bedeutungen durch pragmatischen Wettbewerb mit Irrealis erhaltet. Indem ich das “branching-times’’ Modell annehme, das den Ausdruck von Modalität und zeitlichem Bezug vereint (Prince, 2018), zeige ich, dass Nafsan und mehrere andere ozeanische Sprachen Beweise dafür liefern, dass Irrealis als Modalitätskategorie, die sich auf nicht-aktuelle Welten bezieht, eine semantisch sinnvolle Kategorie ist. / In this thesis I study the meaning of tense, mood, and aspect (TMA) expressions in Nafsan (South Efate), an Oceanic language of Vanuatu, from a typological perspective. I focus on the meanings of the perfect aspect and realis/irrealis mood in Nafsan and other Oceanic languages, as case studies for investigating the cross-linguistic features of these TMA categories, frequently disputed in the literature. In order to analyze their meanings in Nafsan, I studied the Nafsan grammar (Thieberger, 2006) and corpus by Thieberger (1995–2018), followed by storyboard and questionnaire elicitation in my fieldwork (Krajinovic, 2017b). I found that the Nafsan perfect has all the functions considered to be typical of the English-style perfect, except for the additional meaning of change of state. I place the analysis of the Nafsan perfect in the debate about the cross-linguistic validity of the newly proposed category of iamitives, defined by the meaning of change of state akin to `already' and lacking experiential and universal perfect functions (Olsson, 2013). Based on the data from Nafsan and other Oceanic languages, I show that, when language-internal processes are considered, the semantic definition of perfect proposed by Klein (1994) is sufficient to account for additional perfect functions, without the need to posit the new iamitive category. Regarding the realis/irrealis distinction, I have found that the “realis” category is semantically underspecified in Nafsan, as it can occur in irrealis contexts that should be incompatible with realis meanings. I propose that “realis” in Nafsan only occasionally receives realis meanings through pragmatic competition with the irrealis category. By adopting a branching-times model that unites the expression of modality and temporal reference (Prince, 2018), I also show that Nafsan and several other Oceanic languages provide evidence that irrealis as a mood category referring to non-actual worlds is a semantically meaningful category.

This work develops a discontinuous Galerkin finite element discretization of non- linear hyperbolic conservation equations with efficient and robust high order stabilization built on an entropy-based artificial viscosity approximation. The solutions of equations are represented by elementwise polynomials of an arbitrary degree p > 0 which are continuous within each element but discontinuous on the boundaries. The discretization of equations in time is done by means of high order explicit Runge-Kutta methods identified with respective Butcher tableaux. To stabilize a numerical solution in the vicinity of shock waves and simultaneously preserve the smooth parts from smearing, we add some reasonable amount of artificial viscosity in accordance with the physical principle of entropy production in the interior of shock waves. The viscosity coefficient is proportional to the local size of the residual of an entropy equation and is bounded from above by the first-order artificial viscosity defined by a local wave speed. Since the residual of an entropy equation is supposed to be vanishingly small in smooth regions (of the order of the Local Truncation Error) and arbitrarily large in shocks, the entropy viscosity is almost zero everywhere except the shocks, where it reaches the first-order upper bound. One- and two-dimensional benchmark test cases are presented for nonlinear hyperbolic scalar conservation laws and the system of compressible Euler equations. These tests demonstrate the satisfactory stability properties of the method and optimal convergence rates as well. All numerical solutions to the test problems agree well with the reference solutions found in the literature. We conclude that the new method developed in the present work is a valuable alternative to currently existing techniques of viscous stabilization.

Definované markery tvoří základ určování polohy kamery pro velké množství aplikací s rozšířenou realitou, v případě že jsou přísné požadavky na rychlost a robustnost. Tato práce popisuje účinnou metodu pro určení pózy kamery pomocí Uniformního pole markerů a několik realistických aplikací na bázi popsané metody. Metoda je velice výpočetně levná a poskytuje spolehlivou detekci pro několik výpočetních platforem, včetně běžných chytrých telefonů. Markery jako část zobrazené informace na monitorech jsou použité v této práci pro určení relativní orientaci mezi poskytovatelem obsahu a užívatelským zařízením, sloužícím pro výběr prvků užívatelského rozhraní při  interakci a migraci úkolů. Ve filmařském průmyslu poskytuje popsaná metoda pro zjištění polohy kamery jako součást klíčovaní pozadí filmářům živý náhled virtuální scény. Výsledky ukazují, že popsaná metoda pro detekci pole markerů má srovnatelnou úspěšnost a přesnost v porovnání s ostatními metodami na bázi markerů a je několikrát rýchlejší. Aplikace zahrnuté v této práci podle výsledků testů jsou životaschopné - rychlejší a levnější - alternativy k existujícím řešením.

Současné vizuální markerové systémy mají jednu zásadní nevýhodu oproti tzv. markerless přístupům - pohyb kamery je omezen na oblast pokrytou markery. V každém snímku musí být marker dostatečně velký, aby jej bylo možné identifikovat a vypočítat pozici a rotaci kamery. Zároveň musí být dostatečně malý, aby se celý (nebo alespoň jeho podstatná část) vešel do záběru kamery. Avšak tyto požadavky jsou protichůdné. Tato práce nabízí řešení tohoto problému za pomoci konceptu Marker Fields. Jde o strukturu, jejíž přítomnost je možné v obraze kamery snadno detekovat a identifikovat část, na kterou se kamera právě dívá, a to na základě jakékoli (malé) podoblasti s definovanou velikostí. Aby bylo možné podoblasti identifikovat zblízka i zdálky, nejsou od sebe odděleny, ale do velké míry se překrývají. V této práci jsou vysvětleny různé implementace konceptu marker fields, spolu s jejich zamýšleným použitím a výhodami a nevýhodami. Jako důkaz použitelnosti marker fields v reálném světě, se druhá největší část této práce věnuje popisu jejich reálných aplikací.

Магистерская диссертация посвящена проблеме подготовки учебного издания по компьютерной верстке, отвечающего требованиям специалистов и удовлетворяющего потребности читателей. Цель исследования – на основе редакторского анализа разработать рекомендации для учебного пособия по верстке. Объектом исследования стали пособия по компьютерной верстке. Предметом – структурно-содержательные особенности пособий по компьютерной верстке. Магистерская диссертация «Редакторский анализ пособий по компьютерной верстке» состоит из введения, двух глав, заключения и библиографического списка, включающего 71 наименование. В первой главе диссертации учебное пособие охарактеризовано как вид издания, определен ряд критериев, отличающих учебное пособие от учебника. В ходе работы с научными источниками были выделены и проанализированы требования специалистов к структуре, содержанию, языку и стилю учебных изданий, а также выявлены важные характеристики учебной книги с точки зрения читательской аудитории. На основе сравнительного анализа этих точек зрения сделаны выводы о роли, которой наделяют учебные издания специалисты и читатели. Вторая глава содержит характеристику структурно-содержательные особенности шестнадцати пособий по компьютерной верстке, представленных на рынке и доступных для обучающихся. Выявленные особенности соотнесены с требованиями к учебным изданиям, сформулированными в первой главе. На основе собранного материала представлен список рекомендаций по подготовке учебного пособия по компьютерной верстке. Результаты исследования могут быть использованы как при создании учебного пособия по компьютерной верстке по заказу кафедры Издательского дела, так и для подготовки современных и качественных изданий по другим дисциплинам. / Master’s thesis is devoted to the matter of preparation of desktop publishing study aid which meets specialists’ requirements and satisfies readers’ needs. The purpose of the research is to develop recommendations for desktop publishing study aid basing on editorial analysis. Research object is desktop publishing study aids. Subject matter is structural and content features of the desktop publishing study aids. Master’s thesis “Editorial analysis of desktop publishing study aids” consists of introduction, two chapters, conclusion and bibliography containing 71 source materials. First chapter characterizes study aid as a types of publication, defines a set of criteria differentiating study aid from textbook. During the process of working with scientific sources, specialists’ requirements for structure, content, language and style of study publications were identified and analyzed; in addition significant features of study aid from reader’s perspective were identified as well. Basing on the comparative analysis of these points of view conclusions on the role assigned to the study aids by both specialists and readers were drawn. Second chapter comprises characteristic of structural and content features of sixteen desktop publishing study aids found on the market and available for students. Detected features are compared to the requirements for study aids formulated in the first chapter. The list of recommendations for preparation of desktop publishing study aid is formulated in accordance with the gathered material. The research results may be used for both creating desktop publishing study aid on request by Faculty of Publishing and preparation contemporary high-quality publications dedicated to other disciplines.

Plasmonic properties and the related novel applications are studied on various types of metallic nano-structures in one, two, or three dimensions. For 1D nanostructure, the motion of free electrons in a metal-film with nanoscale thickness is confined in its normal dimension and free in the other two. Describing the free-electron motion at metal-dielectric surfaces, surface plasmon polariton (SPP) is an elementary excitation of such motions and is well known. When further perforated with periodic array of holes, periodicity will introduce degeneracy, incur energy-level splitting, and facilitate the coupling between free-space photon and SPP. We applied this concept to achieve a plasmonic perfect absorber. The experimentally observed reflection dip splitting is qualitatively explained by a perturbation theory based on the above concept. If confined in 2D, the nanostructures become nanowires that intrigue a broad range of research interests. We performed various studies on the resonance and propagation of metal nanowires with different materials, cross-sectional shapes and form factors, in passive or active medium, in support of corresponding experimental works. Finite- Difference Time-Domain (FDTD) simulations show that simulated results agrees well with experiments and makes fundamental mode analysis possible. Confined in 3D, the electron motions in a single metal nanoparticle (NP) leads to localized surface plasmon resonance (LSPR) that enables another novel and important application: plasmon-heating. By exciting the LSPR of a gold particle embedded in liquid, the excited plasmon will decay into heat in the particle and will heat up the surrounding liquid eventually. With sufficient exciting optical intensity, the heat transfer from NP to liquid will undergo an explosive process and make a vapor envelop: nanobubble. We characterized the size, pressure and temperature of the nanobubble by a simple model relying on Mie calculations and continuous medium assumption. A novel effective medium method is also developed to replace the role of Mie calculations. The characterized temperature is in excellent agreement with that by Raman scattering. If fabricated in an ordered cluster, NPs exhibit double-resonance features and the double Fano-resonant structure is demonstrated to most enhance the four-wave mixing efficiency.