This work investigates the Fresnel-lens-based solar concentrator-receiver system in a multi-perspective manner to design, test and fabricate this concentrator with high-efficiency photon and heat outputs and a minimized effect of chromatic aberrations. First, a MATLAB®-incorporated algorithm optimizes both the flat-spot and the curved lens designs via a statistical ray-tracing methodology of the incident light, considering all of its incidence parameters. The target is to maximize the solar ray intensity on the receiver's aperture, and therefore, achieve the highest possible focal flux. The algorithm outputs prismatic and dimensional geometries of the Fresnel-lens concentrator, which are simulated by COMSOL® Multiphysics to validate the design. For the second part, a novel genetically-themed hierarchical algorithm (GTHA) has been investigated to design Fresnel-lens solar concentrators that match with the distinct energy input and spatial geometry of various thermal applications. Basic heat transfer analysis of each application decides its solar energy requirement. The GTHA incorporated in MATLAB® optimizes the concentrator characteristics to secure this energy demand, balancing a minimized geometry and a maximized efficiency. Two experimental applications were selected from literature to validate the optimization process, a solar welding system for H13 steel plates and a solar Stirling engine with an aluminum-cavity receiver attached to the heater section. In each case, a flat Fresnel-lens with a spot focus was algorithmically designed to supply the desired solar heat, and then a computer simulation of the optimized lens was conducted showing great comparability to the original experimental results. Thirdly, the prismatic geometry of the Fresnel lens was further optimized through a statistical approach that incorporates laws of light refraction and trigonometry. The proposed design produces high focal irradiance that is more suitable for thermal applications. The motivation was to enhance the tolerability of a flat Fresnel-lens concentrator to tracking errors, without the use of secondary optics or sophisticated, and normally costly, meticulous tracking equipment. A comparative simulation analysis was conducted for two case studies from literature, each with a different design method. Fresnel lenses optimized by this work enhanced the concentration acceptance product (CAP) significantly, compared to that in literature. Then, this work introduced an innovative code-based, detailed, and deterministic geometrical approach, which couples the optimization of the Fresnel lens primary optical element (POE) and the dome-shaped secondary optical element (SOE). The objective was to maximize the concentration acceptance product, while using the minimum SOE and receiver geometries at a given f-number and incidence angle (also referred to as the tracking error angle). The laws of polychromatic light refraction along with trigonometry and spherical geometry were utilized to optimize the POE grooves, SOE radius, receiver size, and SOE–receiver spacing. Two literature case studies were analyzed to verify this work's optimization, and the equivalent POEs designed by this work, with optimized SOEs, showed a significant enhancement in the CAP values compared to that of literature. Lastly, four methods for prototyping the Fresnel lens were discussed and experimentally tested; 3D printing, acrylic resin casting, direct CNC machining in acrylic and hot embossing. Once tested, the methods of CNC machining and hot embossing of acrylic proved to be the most promising in terms of cost, fabrication time, and concentration effectiveness. Future work will focus on enhancing the algorithmic design and improving the quality of lens fabrication.

Information technology forms an important part of the world and algorithmic trading has already become a common concept among traders. The High Frequency Trading (HFT) requires use of special hardware accelerators which are able to provide input response with sufficiently low latency. This master's thesis is focused on design and implementation of an architecture for order book building, which represents an essential part of HFT solutions targeted on financial exchanges. The goal is to use the FPGA technology to process information about an exchange's state with latency so low that the resulting solution is effectively usable in practice. The resulting architecture combines hardware and software in conjunction with fast lookup algorithms to achieve maximum performance without affecting the function or integrity of the order book.

L'objet de cette thèse est l'étude des aspects algorithmiques du vote par approbation. Il s'agit principalement d'une étude théorique des enjeux computationnels soulevés par le vote par approbation dans des contextes de décisions variés. Cependant, j'étudie aussi des questions plus proches de la théorie classique du choix social et je conduis de brèves études expérimentales.Dans un premier temps, l'étude se porte sur une famille générale de règles de vote pour les élections de comités et les référendums multiples à l'aide du vote par approbation. Dans un second temps, je porte mon attention sur un contexte plus général, le vote par approbation sur domaines combinatoires en se basant sur des préférences conditionnelles. Finalement, je me place dans le cadre du vote avec préférences incomplètes pour étudier les problèmes de vainqueurs possibles et nécessaires dans le vote par approbation. / The subject of this thesis is the study of computational aspects of approval voting. Most of the works are theoretical results about computational issues raised by approval voting, in many different settings. However, I also study some questions that are more related to classical choice theory, and some problems are investigated through experimental analysis.Firstly, I study a general family of rules for approval voting in the context of committee elections and multiple referenda. Secondly, I focus on a more general setting, approval voting in combinatorial domains, based on conditional preferences. Finally, I consider approval voting in the context of incomplete preferences, to study the possible and necessary winner problems.

This thesis describes the development and setup of hybrid index structures. They are access methods for retrieval techniques in hybrid data spaces which are formed by one or more relational or normalised columns in conjunction with one non-relational or non-normalised column. Examples for these hybrid data spaces are, among others, textual data combined with geographical ones or data from enterprise content management systems. However, all non-relational data types may be stored as well as image feature vectors or comparable types. Hybrid index structures are known to function efficiently regarding retrieval operations. Unfortunately, little information is available about reorganisation operations which insert or update the row tuples. The fundamental research is mainly executed in simulation based environments. This work is written ensuing from a previous thesis that implements hybrid access structures in realistic database surroundings. During this implementation it has become obvious that retrieval works efficiently. Yet, the restructuring approaches require too much effort to be set up, e.g., in web search engine environments where several thousands of documents are inserted or modified every day. These search engines rely on relational database systems as storage backends. Hence, the setup of these access methods for hybrid data spaces is required in real world database management systems. This thesis tries to apply a systematic approach for the optimisation of the rearrangement algorithms inside realistic scenarios. Thus, a measurement and evaluation scheme is created which is repeatedly deployed to an evolving state and a model of hybrid index structures in order to optimise the regrouping algorithms to make a setup of hybrid index structures in real world information systems possible. Thus, a set of input corpora is selected which is applied to the test suite as well as an evaluation scheme. To sum up, it can be said that this thesis describes input sets, a test suite including an evaluation scheme as well as optimisation iterations on reorganisation algorithms reflecting a theoretical model framework to provide efficient reorganisations of hybrid index structures supporting multimedia search criteria.

Mémoire en recherche-création / Creative dissertation / Cette thèse de maîtrise traite du processus créatif et de la recherche qui y est associée afin de produire deux performances en direct dans le domaine de la musique électroacoustique. À l’aide de ces deux œuvres, mon intention était de concevoir une pratique artistique qui réunit plusieurs modes autour de la gestuelle et du son, influencée par des algorithmes. Une tentative approfondie d’extraire un processus de composition à partir des réactions de la matière en vibration englobe une grande partie de la recherche. Cette recherche découle de ma transition d’une pratique artistique basée sur la représentation (soucieuse des haut-parleurs, manettes et boutons) vers une pratique imprégnée par la performance (soucieuse de la transformation continuelle du son en relation avec les modulations de la matière). Tout au long de cette recherche, j’ai mené un examen approfondi des rythmes au-delà de la pure mesure de leurs expressions musicales, pour considérer les nombreuses notions du rythme qui se dévoilent dans les interactions quotidiennes de l’expérience vécue. Les micro-rythmes perçus par l’oreille comme des textures, les gestes répétitifs perçus par l’œil comme un mouvement linéaire et les rythmes observés lors de circonstances sociales communes, comme la cadence de la conversation sont, parmi les caractéristiques du rythme qui ont suscité mon intérêt. Le tout se situe dans un récit historique éclairé qui étudie l’influence de l’algorithme et de la matière tout au long de la musique et de l’art sonore du XXe siècle. La recherche conceptuelle est enrichie par des expériences exhaustives en composition algorithmique, analyse gestuelle et modélisation gestuelle. Dans chacun de ces domaines, bien que soutenue par des lectures fondamentales en philosophie et en art, une approche primaire de la création s’est faite dans un processus « réfléchir-en-faisant » qui ont généré de nombreuses ex- périences tant avec la matière physique qu’avec la conception d’instruments numériques. Au- delà de la création des performances qui constituent la base des résultats de cette recherche, un vaste ensemble d’outils interopérables d’analyse gestuelle en temps réel, de modélisation, de composition algorithmique et de traitement du son a été développé et publié pour l’environnement Max/MSP. / This master’s thesis concerns the creative process and related research for the production of two live performances in the domain of electroacoustic music. Across the creation of the two works, my intention has been to develop a unified multi-modal gesture, sound, and algorithm influenced performance practise. Encompassing the largest portion of the research is an earnest attempt to derive compositional process from the behavior of vibrating matter. This research is precipitated by my movement from an artistic practice based on representation (concerned with speakers, knobs, and buttons) towards a practice steeped in performance (concerned with the continuous transformation of sound correlated to material modulation). Across this research, an in-depth investigation was conducted into rhythms beyond their purely metric musical manifestations, and into the numerous alternative notions of rhythm which are revealed through daily interactions and lived experience. Rhythmic artifacts of interest have included micro-rhythms perceived by the ear as textures, repetitive gestures perceived by the eye as linear motion, and rhythms observed in ordinary social situations such as the cadence of conversation. This is all situated within an informed historical narrative which considers the influence of the algorithm and material primarily across 20th century music and sound art. The conceptual research is augmented by extensive experiments in algorithmic composition, gesture analysis, and gesture mapping. In each of these areas, though tied to fundamental readings in philosophy and art, a primary approach to creation has been thinking-through-making, which has led to extensive experimentation with both physical materials and digital instrument design. Beyond the performance creations which form the basis of this research output, a large set of interoperable tools for real-time gesture analysis, mapping, algorithmic composition, and sound processing was developed and published for the Max/MSP environment.

Optimization of relief networks in humanitarian logistics often exemplifies the need for solutions that are feasible given a hard constraint on time. For instance, the distribution of medical countermeasures immediately following a biological disaster event must be completed within a short time-frame. When these supplies are not distributed within the maximum time allowed, the severity of the disaster is quickly exacerbated. Therefore emergency response plans that fail to facilitate the transportation of these supplies in the time allowed are simply not acceptable. As a result, all optimization solutions that fail to satisfy this criterion would be deemed infeasible. This creates a conflict with the priority optimization objective in most variants of the generic vehicle routing problem (VRP). Instead of efficiently maximizing usage of vehicle resources available to construct a feasible solution, these variants ordinarily prioritize the construction of a minimum cost set of vehicle routes. Research presented in this dissertation focuses on the design and analysis of efficient computational methods for optimizing high-consequence variants of the VRP for relief networks. The conflict between prioritizing the minimization of the number of vehicles required or the minimization of total travel time is demonstrated. The optimization of the time and capacity constraints in the context of minimizing the required vehicles are independently examined. An efficient meta-heuristic algorithm based on a continuous spatial partitioning scheme is presented for constructing a minimized set of vehicle routes in practical instances of the VRP that include critically high-cost penalties. Multiple optimization priority strategies that extend this algorithm are examined and compared in a large-scale bio-emergency case study. The algorithms designed from this research are implemented and integrated into an existing computational framework that is currently used by public health officials. These computational tools enhance an emergency response planner's ability to derive a set of vehicle routes specifically optimized for the delivery of resources to dispensing facilities in the event of a bio-emergency.

AI spås få lika stor påverkan på samhället som elektricitet haft och avancemangen inom till exempel maskininlärning och neurala nätverk har tagit AI in i sektorer som rättsväsende, rekrytering och hälso- och sjukvård. Men AI-system är, precis som människor, känsliga för olika typer av snedvridningar, vilket kan leda till orättvisa beslut. En alarmerande mängd studier och rapporter visar att AI i flera fall speglar, sprider och förstärker befintliga snedvridningar i samhället i form av fördomar och värderingar vad gäller könsstereotyper och könsdiskriminering. Algoritmer som används i bildigenkänning baserar sina beslut på stereotyper om vad som är manligt och kvinnligt, röstigenkänning är mer trolig att korrekt känna igen manliga röster jämfört med kvinnliga röster och röstassistenter som Microsoft:s Cortona eller Apple:s Siri förstärker befintlig könsdiskriminering i samhällen. Syftet med denna studie är att undersöka hur könsdiskriminering kan uppstå i AI-system generellt, hur relationen mellan gender bias och AI-system ser ut samt hur ett företag som arbetar med utveckling av AI resonerar kring relationen mellan gender bias och AI-utveckling. Studiens syfte uppfylls genom en litteraturgenomgång samt djupintervjuer med nyckelpersoner som på olika sätt arbetar med AI-utveckling på KPMG. Resultaten visar att bias i allmänhet och gender bias i synnerhet finns närvarande i alla steg i utvecklingen av AI och kan uppstå på grund av en mängd olika faktorer, inklusive men inte begränsat till mångfald i utvecklingsteamen, utformningen av algoritmer och beslut relaterade till hur data samlas in, kodas, eller används för att träna algoritmer. De lösningar som föreslås handlar dels om att adressera respektive orsaksfaktor som identifierats, men även att se problemet med gender bias och könsdiskriminering i AI-system från ett helhetsperspektiv. Essensen av resultaten är att det inte räcker att ändra någon av parametrarna om inte systemets struktur samtidigt ändras. / Recent advances in, for example, machine learning and neural networks have taken artificial intelligence into disciplines such as justice, recruitment and health care. As in all fields subject to AI, correct decisions are crucial and there is no room for discriminatory conclusions. However, AI-systems are, just like humans, subject to various types of distortions, which can lead to unfair decisions. An alarming number of studies and reports show that AI in many cases reflects and reinforces existing gender bias in society. Algorithms used in image recognition base their decisions on character stereotypes of male and female. Voice recognition is more likely to correctly recognize male voices compared to female voices, and earlier 2019 the United Nations released a study showing that voice assistants, such as Microsoft's Cortona or Apple's Siri, reinforce existing gender bias. The purpose of this study is to investigate how gender discrimination can appear in AI-systems, and what constitutes the relationship between gender bias, gender discrimination and AI-systems. Furthermore it addresses how a company that works with the development of AI reason concerning the relationship between gender bias, gender discrimination and AI development. The study contains a thorough literature review, as well as in-depth interviews with key persons working with various aspects of AI development at KPMG.  The results show that bias in general, and gender bias in particular, are present at all stages of AI development. It can occur due to a variety of factors, including but not limited to the lack of diversity in the workforce, the design of algorithms and the decisions related to how data is collected, encoded and used to train algorithms. The solutions proposed are partly about addressing the identified factors, but also about looking at the problem from a holistic perspective. The significance of seeing and understanding the links between gender bias in society and gender bias in AI-systems, as well as reconsidering how each factor depends on and correlates with other ones, is emphasized. The essence of the results is that it is not enough to alter any of the parameters unless the structure of the system is changed as well.

This dissertation explores the analysis and creation of compositions from the standpoint of texture and momentum. It is comprised of four chapters. The first presents a number of concepts as tools for analysis, including textural typography and transformation, perception of time and psychological engagement of an audience, and respiration as a metaphor for musical momentum. The second and third chapters apply these tools to Gerard Grisey's "Periodes" and "Partiels," and Brian Ferneyhough's "Lemma-Icon-Epigram." The fourth explores specific methodologies used in composing my dissertation piece, "Phase," including the application of number systems ranging from formal to local levels.

The world is right now in a global transition from a fossil fuel dependency towards an electrified society based on green and renewable energy. Investments in power grid capacity are therefore needed to meet the increased future demand which this transition implicates. One part of this is the expansion of intermittent energy sources, such as wind and solar power. Even though these sources have benefits in form of cheap and green energy, they have other characteristics that need to be addressed. Per definition, intermittent power sources cannot produce energy on demand since they are dependent on weather conditions such as wind and sun. This induces a second problem which is that it can be hard to predict the production from intermittent power sources, especially wind, which increases the volatility in the power market. Because of these characteristics, the expansion of wind power has increased the volume traded on the intraday power market. The intermittent energy surge, emphasizes the need of a good trading strategy for balance responsible parties to handle the increased trading volume and volatility. The prupose of this report is to introduce the elements which affect intraday power trading, formulate the fundamentals of a power trading strategy and thereafter explore how predictive models can be used in such a strategy. This includes predicting regulating and intraday market prices using linear regression models, neural networks and LSTM-models. Furthermore, the report highlights underlying properties which affects the predictive power of a prediction model used to forecast wind power production. Regulating prices can be predicted well using both linear regression models and more complex deep learning models based on weather and market data. Both approaches are better than using a simple model based on the latest regulating and market price, since the simple model tends to fall short in a volatile market. Overall, the deep learning models performs the best.  The difference in result when predicting the volume weighted average price on the intraday market, using linear regression and machine learning, are not as substantial. In fact, the linear models tends to outperform the machine learning models in some instaces. The conclusion when analyzing how underlying properties affect wind power prediction models is that how far ahead the model predicts is not the key factor affecting predictive power. Instead, the production volume predicted has a larger effect.

When modelling risk in the banking book, a simple capital level approach can fail to capture the interactions between different risk measures or risk classes since they are modelled separately. In this thesis we propose a model for projecting the book value of a run-off balance sheet portfolio of fixed and variable rate loans, while also calculating net interest income, economic value of equity, capital requirement and capital cost within the same model. Using adjoint algorithmic differentiation, we also retrieve the sensitivities of each measure and the balance sheet towards a term structure of zero rates, for the lifetime of the portfolio. The model is an attempt at a holistic approach to modelling interest rate risk in the banking book, and its design allows for extensions to other financial risk classes such as credit risk and liquidity risk. / Vid modellering av risker i bankboken kan en enkel kapitalmodell misslyckas med att fånga interaktionerna mellan olika riskmått eller riskklasser eftersom de modelleras separat. I den här masteruppsatsen föreslår vi en modell för att projicera balansräkningen framåt i tiden för en portfölj av lån med fast och rörlig ränta, och beräknar samtidigt Räntenetto, Ekonomiskt Värde av Eget Kapital, kapitalkrav och kapitalkostnad inom samma modell. Med hjälp av adjoint algoritmisk derivering beräknar vi även känsligheterna för varje mått samt bokfört värde av portföljen mot en terminstruktur av nollräntor under portföljens livstid. Modellen är ett försök att holistisk modellera ränterisk i bankboken, och dess utformning möjliggör utvidgning till andra finansiella riskklasser som kreditrisk och likviditetsrisk.