345 research outputs found

    Permutation Invariant Gaussian Matrix Models

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    Matrix models are ubiquitous in physics. Commonly arising due to the presence of gauge symmetries in a system, they play an important role in establishing results within the context of the AdS/CFT correspondence. They also capture the statistics of complex systems in a remarkably diverse range of fields within the framework of random matrix theory (RMT), these include nuclear physics, chaos, condensed matter physics, financial correlations and biological networks. Inspired by both of these applications, and motivated by the existence of physical matrix systems possessing discrete symmetries, is the study of Gaussian matrix models invariant under SN , the symmetric group of all permutations of N objects. We specialise the most general of these models to the case of symmetric matrices with vanishing diagonal elements. This model is used to study an ensemble of financial correlation matrices and as a tool to detect market states. This problem has a natural per- mutation symmetry, and the observables of interest are permutation invariant polynomials of the matrix variables (PIMOs). We find that the values of low order PIMOs are generally closely matched by the predictions by the model, and vectors of PIMOs are shown to be efficient indicators of the market state. Turning our attention to the general structure of permutation invariant Gaussian matrix (PIGM) models of general matrices of size N, we show that PIMOs of degree k are in one-to-one correspondence with equivalence classes of the diagrammatic partition algebra Pk(N). On a subspace of the 13-parameter space of general PIGM models there is an enhanced O(N) symmetry. At a special point within this subspace exists the simplest O(N) invariant action, which we furnish with an inner product on the PIMOs. We prove the large N factorisation of this inner product. Lastly, we study the implications of permutation symmetry for the state space and dynamics of quantum mechanical matrix systems. The general permutation invariant matrix quantum harmonic oscillator Hamiltonian is solved and families of interacting Hamiltonians, which are diagonalised by a representation theoretic basis for the permutation invariant subspace, are described. These include Hamiltonians for which low-energy states are SN invariant and can give rise to large ground state degeneracies related to the dimensions of partition algebras

    Rethinking Film Festivals in the Pandemic Era and After

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    ‘Rethinking Film Festivals’ explores how COVID-19 intervened in the film festival circuit by disrupting regular industry cycles and allowing new ways of reaching audiences to flourish. Films found other distribution channels, often online, while the festival format proved particularly vulnerable in a one-half-meter society. The search for a full-fledged substitute for live events and shared experiences involved all kinds of experimentation and an acceleration of digital developments that were already underway. The book documents how different festivals in different local contexts were affected differently by the pandemic and reacted differently to it as well. At the same time, the case studies confirm the interconnectedness and transnational relationships inherent in globalised media systems. Finally, the book seizes the momentum of the crisis to make the case for sustainable interventions: festivals must address their ecological footprint, decolonise their organisations, and ensure that their history and heritage is safeguarded for the future

    Modern data analytics in the cloud era

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    Cloud Computing ist die dominante Technologie des letzten Jahrzehnts. Die Benutzerfreundlichkeit der verwalteten Umgebung in Kombination mit einer nahezu unbegrenzten Menge an Ressourcen und einem nutzungsabhĂ€ngigen Preismodell ermöglicht eine schnelle und kosteneffiziente Projektrealisierung fĂŒr ein breites Nutzerspektrum. Cloud Computing verĂ€ndert auch die Art und Weise wie Software entwickelt, bereitgestellt und genutzt wird. Diese Arbeit konzentriert sich auf Datenbanksysteme, die in der Cloud-Umgebung eingesetzt werden. Wir identifizieren drei Hauptinteraktionspunkte der Datenbank-Engine mit der Umgebung, die verĂ€nderte Anforderungen im Vergleich zu traditionellen On-Premise-Data-Warehouse-Lösungen aufweisen. Der erste Interaktionspunkt ist die Interaktion mit elastischen Ressourcen. Systeme in der Cloud sollten ElastizitĂ€t unterstĂŒtzen, um den Lastanforderungen zu entsprechen und dabei kosteneffizient zu sein. Wir stellen einen elastischen Skalierungsmechanismus fĂŒr verteilte Datenbank-Engines vor, kombiniert mit einem Partitionsmanager, der einen Lastausgleich bietet und gleichzeitig die Neuzuweisung von Partitionen im Falle einer elastischen Skalierung minimiert. DarĂŒber hinaus fĂŒhren wir eine Strategie zum initialen BefĂŒllen von Puffern ein, die es ermöglicht, skalierte Ressourcen unmittelbar nach der Skalierung auszunutzen. Cloudbasierte Systeme sind von fast ĂŒberall aus zugĂ€nglich und verfĂŒgbar. Daten werden hĂ€ufig von zahlreichen Endpunkten aus eingespeist, was sich von ETL-Pipelines in einer herkömmlichen Data-Warehouse-Lösung unterscheidet. Viele Benutzer verzichten auf die Definition von strikten Schemaanforderungen, um TransaktionsabbrĂŒche aufgrund von Konflikten zu vermeiden oder um den Ladeprozess von Daten zu beschleunigen. Wir fĂŒhren das Konzept der PatchIndexe ein, die die Definition von unscharfen Constraints ermöglichen. PatchIndexe verwalten Ausnahmen zu diesen Constraints, machen sie fĂŒr die Optimierung und AusfĂŒhrung von Anfragen nutzbar und bieten effiziente UnterstĂŒtzung bei Datenaktualisierungen. Das Konzept kann auf beliebige Constraints angewendet werden und wir geben Beispiele fĂŒr unscharfe Eindeutigkeits- und Sortierconstraints. DarĂŒber hinaus zeigen wir, wie PatchIndexe genutzt werden können, um fortgeschrittene Constraints wie eine unscharfe Multi-Key-Partitionierung zu definieren, die eine robuste Anfrageperformance bei Workloads mit unterschiedlichen Partitionsanforderungen bietet. Der dritte Interaktionspunkt ist die Nutzerinteraktion. Datengetriebene Anwendungen haben sich in den letzten Jahren verĂ€ndert. Neben den traditionellen SQL-Anfragen fĂŒr Business Intelligence sind heute auch datenwissenschaftliche Anwendungen von großer Bedeutung. In diesen FĂ€llen fungiert das Datenbanksystem oft nur als Datenlieferant, wĂ€hrend der Rechenaufwand in dedizierten Data-Science- oder Machine-Learning-Umgebungen stattfindet. Wir verfolgen das Ziel, fortgeschrittene Analysen in Richtung der Datenbank-Engine zu verlagern und stellen das Grizzly-Framework als DataFrame-zu-SQL-Transpiler vor. Auf dieser Grundlage identifizieren wir benutzerdefinierte Funktionen (UDFs) und maschinelles Lernen (ML) als wichtige Aufgaben, die von einer tieferen Integration in die Datenbank-Engine profitieren wĂŒrden. Daher untersuchen und bewerten wir AnsĂ€tze fĂŒr die datenbankinterne AusfĂŒhrung von Python-UDFs und datenbankinterne ML-Inferenz.Cloud computing has been the groundbreaking technology of the last decade. The ease-of-use of the managed environment in combination with nearly infinite amount of resources and a pay-per-use price model enables fast and cost-efficient project realization for a broad range of users. Cloud computing also changes the way software is designed, deployed and used. This thesis focuses on database systems deployed in the cloud environment. We identify three major interaction points of the database engine with the environment that show changed requirements compared to traditional on-premise data warehouse solutions. First, software is deployed on elastic resources. Consequently, systems should support elasticity in order to match workload requirements and be cost-effective. We present an elastic scaling mechanism for distributed database engines, combined with a partition manager that provides load balancing while minimizing partition reassignments in the case of elastic scaling. Furthermore we introduce a buffer pre-heating strategy that allows to mitigate a cold start after scaling and leads to an immediate performance benefit using scaling. Second, cloud based systems are accessible and available from nearly everywhere. Consequently, data is frequently ingested from numerous endpoints, which differs from bulk loads or ETL pipelines in a traditional data warehouse solution. Many users do not define database constraints in order to avoid transaction aborts due to conflicts or to speed up data ingestion. To mitigate this issue we introduce the concept of PatchIndexes, which allow the definition of approximate constraints. PatchIndexes maintain exceptions to constraints, make them usable in query optimization and execution and offer efficient update support. The concept can be applied to arbitrary constraints and we provide examples of approximate uniqueness and approximate sorting constraints. Moreover, we show how PatchIndexes can be exploited to define advanced constraints like an approximate multi-key partitioning, which offers robust query performance over workloads with different partition key requirements. Third, data-centric workloads changed over the last decade. Besides traditional SQL workloads for business intelligence, data science workloads are of significant importance nowadays. For these cases the database system might only act as data delivery, while the computational effort takes place in data science or machine learning (ML) environments. As this workflow has several drawbacks, we follow the goal of pushing advanced analytics towards the database engine and introduce the Grizzly framework as a DataFrame-to-SQL transpiler. Based on this we identify user-defined functions (UDFs) and machine learning inference as important tasks that would benefit from a deeper engine integration and investigate approaches to push these operations towards the database engine

    ADDRESSING FOOD INSECURITY IN FAMILIES LIVING BELOW THE FEDERAL POVERTY LEVEL IN DURHAM COUNTY

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    Households at or below the Federal Poverty Level (FPL) are at risk for food insecurity. By improving education and awareness for healthy food access, families that reside in food insecure areas tend to experience a disproportionate amount of barriers linked to poor health outcomes. In noting this, Durham County also reflects a higher incidence of skipping meals and indulging in poor nutrition behaviors. Our proposed intervention is a longitudinal cohort study of dietitian led nutrition education. Eligible program participants will be followed through from implementation until August 30, 2025. The evaluation measures will be done via the Hunger Vital SignTM survey and the Federal Food Security Determination Survey. A total of the points will be used to obtain the point and percentage of difference at every survey administration for each individual household. Success will be measured by decreasing point scores. This indicates decreased food insecurity amongst our target population.Master of Public Healt

    Advances in flexible manipulation through the application of AI-based techniques

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    282 p.Objektuak hartu eta uztea oinarrizko bi eragiketa dira ia edozein aplikazio robotikotan. Gaur egun, "pick and place" aplikazioetarako erabiltzen diren robot industrialek zeregin sinpleak eta errepikakorrak egiteko duten eraginkortasuna dute ezaugarri. Hala ere, sistema horiek oso zurrunak dira, erabat kontrolatutako inguruneetan lan egiten dute, eta oso kostu handia dakarte beste zeregin batzuk egiteko birprogramatzeak. Gaur egun, industria-ingurune desberdinetako zereginak daude (adibidez, logistika-ingurune batean eskaerak prestatzea), zeinak objektuak malgutasunez manipulatzea eskatzen duten, eta oraindik ezin izan dira automatizatu beren izaera dela-eta. Automatizazioa zailtzen duten botila-lepo nagusiak manipulatu beharreko objektuen aniztasuna, roboten trebetasun falta eta kontrolatu gabeko ingurune dinamikoen ziurgabetasuna dira.Adimen artifizialak (AA) gero eta paper garrantzitsuagoa betetzen du robotikaren barruan, robotei zeregin konplexuak betetzeko beharrezko adimena ematen baitie. Gainera, AAk benetako esperientzia erabiliz portaera konplexuak ikasteko aukera ematen du, programazioaren kostua nabarmen murriztuz. Objektuak manipulatzeko egungo sistema robotikoen mugak ikusita, lan honen helburu nagusia manipulazio-sistemen malgutasuna handitzea da AAn oinarritutako algoritmoak erabiliz, birprogramatu beharrik gabe ingurune dinamikoetara egokitzeko beharrezko gaitasunak emanez

    Measurable Safety of Automated Driving Functions in Commercial Motor Vehicles

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    With the further development of automated driving, the functional performance increases resulting in the need for new and comprehensive testing concepts. This doctoral work aims to enable the transition from quantitative mileage to qualitative test coverage by aggregating the results of both knowledge-based and data-driven test platforms. The validity of the test domain can be extended cost-effectively throughout the software development process to achieve meaningful test termination criteria
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