Enablers of Remote Monitoring Technology Utilization in Availability Solutions

Digitaaliset teknologiat muuttavat nopeasti tapaa, jolla yritykset kilpailevat. Täten yritysten tulee vastata kilpailuun muuttamalla liiketoimintamallejaan vastamaan jatkuvasti muuttuvia asiakkaiden strategisia ongelmia ja vastaavasti luomaan kilpailuetua digitaalisia teknologioita hyödyntämällä. Etävalvontatekniikalla on havaittu olevan tärkeä rooli näiden uusien liiketoi-mintamallien ja arvolupauksien mahdollistamisessa. Vaikka yritykset ovatkin käyttäneet etä-valvontatekniikkaa palvelullistettujen liiketoimintamallien ja täten arvolupauksien toteutta-misessa varsin laajasti, kirjallisuus on näiden kahden välisen yhteyden tarkastelun kannalta pinnallista. Täten, mahdollisia keinoja tulee tarkastella ja panostaa tämän alueen teoreetti-sen perustan kehittämiseen. Tämä pro gradu pyrkii edistämään ymmärrystä mahdollisista uusista arvolupauksista digitaa-lisen palvelullistamisen mahdollistamana, ja siten osallistua tämän alueen teoreettisen pe-rustan kehittämiseen. Tämä pro gradu tutkii saatavuuden arvolupauksen arvoa tuottavia mekanismeja ja etävalvontatekniikan käytön hyötyjä, sekä näiden kahden yhteyden mahdol-listajia. Tutkimuksessa selvitetään, miten saatavuusratkaisut luovat mahdollisuuden uusien teknologioiden markkinoille viemiseen ja mitä tarvitaan, jotta etävalvontateknologia mahdol-listaa käytettävyyden arvolupauksen toteuttamisen. Tutkimuksessa käytettävä tapausorgani-saatio avaa erinomaisen väylän teorian kehittämiselle, sillä heidän matkansa edistyneiden palvelujen tarjoamiseen on vielä alussa. Tämä tutkimus on toteutettu käyttämällä kvalitatii-vista tapaustutkimus lähestymistapaa, joka koostuu asiakkaiden ja järjestelmätoimittajan haastatteluista. Tutkimus sisältää kuusi asiakashaastattelua arvoa luovien mekanismien to-dentamiseksi, sekä etävalvontateknologiaan kohdistuva asiantuntijahaastattelu analysoi-maan tarvittavia mahdollistajia. Tutkimuksessa havaitaan etenkin yhden arvonluontimekanismin olevan merkittävässä roolis-sa saatavuusratkaisujen tarjoamisessa sekä tarve organisaation uskottavalle kyvykkyydelle toteuttaa merkittävät arvolupaukset. Jotta etävalvontatekniikkaa voidaan hyödyntää saata-vuusratkaisujen tarjoamisessa ja riskien pienentämisessä, tulee organisaatiolla olla käytös-sään mahdollisuuden määritteleviä sekä laatua parantavia tekijöitä. Nämä havainnot rikas-tuttavat digitaalisen palvelullistamisen empiiristä perustaa, sekä edistää ymmärrystä palve-lullistettujen arvolupausten ja etävalvontatekniikan yhdistävistä tekijöistä

Massiv-Parallele Algorithmen zum Laden von Daten auf Moderner Hardware

While systems face an ever-growing amount of data that needs to be ingested, queried and analysed, processors are seeing only moderate improvements in sequential processing performance. This thesis addresses the fundamental shift towards increasingly parallel processors and contributes multiple massively parallel algorithms to accelerate different stages of the ingestion pipeline, such as data parsing and sorting.Systeme sehen sich mit einer stetig anwachsenden Menge an Daten konfrontiert, die geladen und analysiert, sowie Anfragen darauf bearbeitet werden müssen. Gleichzeitig nimmt die sequentielle Verarbeitungsgeschwindigkeit von Prozessoren nur noch moderat zu. Diese Arbeit adressiert den Wandel hin zu zunehmend parallelen Prozessoren und leistet mit mehreren massiv-parallelen Algorithmen einen Beitrag um unterschiedliche Phasen der Datenverarbeitung wie zum Beispiel Parsing und Sortierung zu beschleunigen

Elliptic Loci of SU(3) Vacua

The space of vacua of many four-dimensional, $\mathcal{N}=2$ supersymmetric gauge theories can famously be identified with a family of complex curves. For gauge group $SU(2)$, this gives a fully explicit description of the low-energy effective theory in terms of an elliptic curve and associated modular fundamental domain. The two-dimensional space of vacua for gauge group $SU(3)$ parametrizes an intricate family of genus two curves. We analyze this family using the so-called Rosenhain form for these curves. We demonstrate that two natural one-dimensional subloci of the space of $SU(3)$ vacua, $\mathcal{E}_u$ and $\mathcal{E}_v$, each parametrize a family of elliptic curves. For these elliptic loci, we describe the order parameters and fundamental domains explicitly. The locus $\mathcal{E}_u$ contains the points where mutually local dyons become massless, and is a fundamental domain for a classical congruence subgroup. Moreover, the locus $\mathcal{E}_v$ contains the superconformal Argyres-Douglas points, and is a fundamental domain for a Fricke group.Comment: 39 pages + Appendices, 5 figures, v2: minor changes and extended discussion on automorphism

Rethinking Economic Energy Policy Research – Developing Qualitative Scenarios to Identify Feasible Energy Policies

To accelerate deep decarbonisation in the energy sector, the discipline of economics should focus on identifying feasible instead of optimal policies. To do so, economic analysis should include four features: complexity (a), non-economic aspects (b),uncertainty (c) and stakeholders (d). The aim of this paper is to show that qualitative scenario analysis represents a promising alternative to conventional optimisation approaches and meets these requirements. This paper develops qualitative scenarios for the case study of gas infrastructure modifications with hydrogen and carbon capture and storage technologies in Germany. In the results, the six socio-economic qualitative scenarios are described in more detail. A comparison between the case study and a conventional approach reveals three limitations of the latter and highlights the value of qualitative scenario development. The authors distil the advantages of qualitative scenario analysis and discuss challenges and chances, that go beyond the case study. In conclusion, developing socio-economic scenarios has a large potential to improve economic policy assessment. It also allows to catch up with the rethinking of energy research taking place in other disciplines

Climatic thresholds for ecosystem stability

Die Heterogenität der Artenzusammensetzung, sowie der Waldökosystemgefüge der heutigen tropischen Flora im westlichen Zentralafrika ist Thema vieler Publikationen. Ein Großteil der Autoren gibt Veränderungen des Klimas, welche in Folge zu Störungen dieser Systeme führten, die Verantwortung für die heute bestehenden unterschiedlichen sukzessionalen Stadien tropischer Wälder. Zielsetzung dieser Arbeit ist es, anhand des Beispielbioms Westkongolesischer Tieflandregenwald (WCLR) den Einfluss klimatischer Parameter auf die Stabilität tropischer Waldökosysteme zu untersuchen. Der stochastische Wettergenerator MarkSim wird benutzt um Klimazeitserien mit quantifizierten klimatischen Parametern, wie die Gesamtniederschlagsmenge, die jährliche Variation des Niederschlags, die Verteilung des Niederschlags über das Jahr sowie die Art der Wolkendecke, zu generieren. Zu diesem Zeck wird MarkSim für Gabun, welches das WCLR-Biom beheimatet, adaptiert und validiert. Das für das WCLR-Biom parametrisierte mechanistische Ökosystemmodell Biome-BGC simuliert die Kreisläufte von Wasser, Energie, Kohlenstoff und Stickstoff durch unterschiedliche Bestandteile eines Waldökosystems, und wird zur Abschätzung der Stabilität solcher Systeme basierend auf den zuvor generierten Klimaten herangezogen. Die im Zuge dieser Arbeit entwickelten Methoden lassen sich auch auf andere Typen von Waldökosystemen übertragen und können als innovativer Ansatz zur Bewertung des Einflusses klimatischer Veränderungen auf diese Systeme erachtet werden.The heterogeneity in the composition of species and the mix of forest ecosystems of the present tropical flora in western Central Africa has been subject of many publications. Most of the authors agree on the idea that changing climatic conditions in the past have led to disturbances that subsequently caused different stages in plant succession in the present picture. This work's aim is to find out which climatic parameters have a significant impact on the stability of tropical forest ecosystems, such as the showcase biome of the Western Congolian Lowland Rainforest (WCLR). Using the stochastic weather generator MarkSim, climate time series with quantified meteorological parameters, such as the amount and year-to-year variation of annual rainfall, the distribution of rainfall within the year and the quality of the cloud cover, are generated. For this reason MarkSim is adapted and validated for sites in Gabon where the WCLR-biome is native. The mechanistic ecosystem model Biome-BGC, parametrized for the WCLR-biome, simulates the cycling of water, energy, carbon and nitrogen through different plant compartments and is applied to asses tropical forest ecosystem stability, based on the climate time series generated with MarkSim. The methods developed in the course of this work are applicable to other forest ecosystems and can be regarded as an innovative approach to assess the impact of climatic change

Interpretable PID Parameter Tuning for Control Engineering using General Dynamic Neural Networks: An Extensive Comparison

Modern automation systems rely on closed loop control, wherein a controller interacts with a controlled process, based on observations. These systems are increasingly complex, yet most controllers are linear Proportional-Integral-Derivative (PID) controllers. PID controllers perform well on linear and near-linear systems but their simplicity is at odds with the robustness required to reliably control complex processes. Modern machine learning offers a way to extend PID controllers beyond their linear capabilities by using neural networks. However, such an extension comes at the cost of losing stability guarantees and controller interpretability. In this paper, we examine the utility of extending PID controllers with recurrent neural networks-namely, General Dynamic Neural Networks (GDNN); we show that GDNN (neural) PID controllers perform well on a range of control systems and highlight how they can be a scalable and interpretable option for control systems. To do so, we provide an extensive study using four benchmark systems that represent the most common control engineering benchmarks. All control benchmarks are evaluated with and without noise as well as with and without disturbances. The neural PID controller performs better than standard PID control in 15 of 16 tasks and better than model-based control in 13 of 16 tasks. As a second contribution, we address the lack of interpretability that prevents neural networks from being used in real-world control processes. We use bounded-input bounded-output stability analysis to evaluate the parameters suggested by the neural network, thus making them understandable. This combination of rigorous evaluation paired with better interpretability is an important step towards the acceptance of neural-network-based control approaches. It is furthermore an important step towards interpretable and safely applied artificial intelligence

Towards Cross-Provider Analysis of Transparency Information for Data Protection

Transparency and accountability are indispensable principles for modern data protection, from both, legal and technical viewpoints. Regulations such as the GDPR, therefore, require specific transparency information to be provided including, e.g., purpose specifications, storage periods, or legal bases for personal data processing. However, it has repeatedly been shown that all too often, this information is practically hidden in legalese privacy policies, hindering data subjects from exercising their rights. This paper presents a novel approach to enable large-scale transparency information analysis across service providers, leveraging machine-readable formats and graph data science methods. More specifically, we propose a general approach for building a transparency analysis platform (TAP) that is used to identify data transfers empirically, provide evidence-based analyses of sharing clusters of more than 70 real-world data controllers, or even to simulate network dynamics using synthetic transparency information for large-scale data-sharing scenarios. We provide the general approach for advanced transparency information analysis, an open source architecture and implementation in the form of a queryable analysis platform, and versatile analysis examples. These contributions pave the way for more transparent data processing for data subjects, and evidence-based enforcement processes for data protection authorities. Future work can build upon our contributions to gain more insights into so-far hidden data-sharing practices.Comment: technical repor

Event-based Backpropagation for Analog Neuromorphic Hardware

Neuromorphic computing aims to incorporate lessons from studying biological nervous systems in the design of computer architectures. While existing approaches have successfully implemented aspects of those computational principles, such as sparse spike-based computation, event-based scalable learning has remained an elusive goal in large-scale systems. However, only then the potential energy-efficiency advantages of neuromorphic systems relative to other hardware architectures can be realized during learning. We present our progress implementing the EventProp algorithm using the example of the BrainScaleS-2 analog neuromorphic hardware. Previous gradient-based approaches to learning used "surrogate gradients" and dense sampling of observables or were limited by assumptions on the underlying dynamics and loss functions. In contrast, our approach only needs spike time observations from the system while being able to incorporate other system observables, such as membrane voltage measurements, in a principled way. This leads to a one-order-of-magnitude improvement in the information efficiency of the gradient estimate, which would directly translate to corresponding energy efficiency improvements in an optimized hardware implementation. We present the theoretical framework for estimating gradients and results verifying the correctness of the estimation, as well as results on a low-dimensional classification task using the BrainScaleS-2 system. Building on this work has the potential to enable scalable gradient estimation in large-scale neuromorphic hardware as a continuous measurement of the system state would be prohibitive and energy-inefficient in such instances. It also suggests the feasibility of a full on-device implementation of the algorithm that would enable scalable, energy-efficient, event-based learning in large-scale analog neuromorphic hardware

Rabbi Jehuda Lebh Versione, Notis, Paraphrasi, Emendatione Textus, Interstinctione, dictorumque S.S. in margine notatione illustratus, Quem Consensu

http://tartu.ester.ee/record=b1714675~S1*es

A dynamic programming heuristic for vehicle routing with time-dependent travel times and required breaks.

For the intensively studied vehicle routing problem (VRP), two real-life restrictions have received only minor attention in the VRP-literature: traffic congestion and driving hours regulations. Traffic congestion causes late arrivals at customers and long travel times resulting in large transport costs. To account for traffic congestion, time-dependent travel times should be considered when constructing vehicle routes. Next, driving hours regulations, which restrict the available driving and working times for truck drivers, must be respected. Since violations are severely fined, also driving hours regulations should be considered when constructing vehicle routes, even more in combination with congestion problems. The objective of this paper is to develop a solution method for the VRP with time windows (VRPTW), time-dependent travel times, and driving hours regulations. The major difficulty of this VRPTW extension is to optimize each vehicle’s departure times to minimize the duty time of each driver. Having compact duty times leads to cost savings. However, obtaining compact duty times is much harder when time-dependent travel times and driving hours regulations are considered. We propose a restricted dynamic programming (DP) heuristic for constructing the vehicle routes, and an efficient heuristic for optimizing the vehicle’s departure times for each (partial) vehicle route, such that the complete solution algorithm runs in polynomial time. Computational experiments demonstrate the trade-off between travel distance minimization and duty time minimization, and illustrate the cost savings of extending the depot opening hours such that traveling before the morning peak and after the evening peak becomes possible