20,340 research outputs found

    A fuzzy set theory-based fast fault diagnosis approach for rotators of induction motors

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    Induction motors have been widely used in industry, agriculture, transportation, national defense engineering, etc. Defects of the motors will not only cause the abnormal operation of production equipment but also cause the motor to run in a state of low energy efficiency before evolving into a fault shutdown. The former may lead to the suspension of the production process, while the latter may lead to additional energy loss. This paper studies a fuzzy rule-based expert system for this purpose and focuses on the analysis of many knowledge representation methods and reasoning techniques. The rotator fault of induction motors is analyzed and diagnosed by using this knowledge, and the diagnosis result is displayed. The simulation model can effectively simulate the broken rotator fault by changing the resistance value of the equivalent rotor winding. And the influence of the broken rotor bar fault on the motors is described, which provides a basis for the fault characteristics analysis. The simulation results show that the proposed method can realize fast fault diagnosis for rotators of induction motors

    Impact of plant growth promoting rhizobacteria (PGPR) on stress resistance of winter wheat (Triticum aestivum L.)

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    Wheat is one of the worldwide most cultivated crop and highly contribute to secure food production in different world regions. Although, it grows almost ubiquitous, its production is severely vulnerable to drought. Soil and rhizosphere microbial communities associated to plants come more and more into the focus of modern agrobiology research, as a solution to maintain productivity under drought, and reinforce sustainable production. Whereas numerous studies on wheat production and the beneficial influence of the soil microbiome under drought have been performed in arid and semiarid regions of the world, comparable studies in Central Europe are rare. This might change due to the ongoing climate crisis and expected less frequent precipitations during the vegetation season. So far, most studies that focus on acclimatization of the wheat rhizobiome to water deficit mostly consider, at best, two interacting factors, and lack to consider other biotic or abiotic drivers of rhizosphere microbial communities structure and function. Therefore, the aim of this thesis was to combine complementary analytical approaches to investigate drought-induced structural and functional changes in wheat rhizosphere bacterial communities and individual species in dependency of soil type, farming system, wheat cultivar and plant development stage, and to determine how these changes affect wheat performance as a consequence of possible climate change scenarios in Central Germany. The presented thesis starts with a general introduction and presentation of the project, followed by three consecutive chapters containing the main findings published in peer-reviewed articles. Starting with an experiment performed in the greenhouse (Chapter 1) and then moving to a realistic climate scenario under field conditions (Chapter 2 and 3), the three chapters demonstrate the sole and interacting effects of drought and farming system (Chapter 1-3), soil type and wheat cultivar (Chapter 1), as well as plant growth stages (Chapter 2 and 3) on bacterial communities and individual taxa of the wheat rhizobiome. The methods used reach from traditional cultivation and in-vitro bioassays (Chapter 3), over extracellular enzyme activity potentials (Chapter 1 and 2) to more advanced technologies such as metabarcoding (Chapter 1 and 2) and computational tools (Chapter 1 and 2), addressing single bacterial taxa as well as community level. Finalizing the thesis, a concluding synopsis compiles and critically reviews the gained results and formulates future study perspectives. In Chapter 1, we evaluated the impact of soil type (loamy vs. sandy), farming management (conventional vs. organic), wheat cultivar (non-demanding vs. demanding), and the interacting effects of these factors on wheat rhizobacterial community composition and function under extreme drought conditions. Water deficit exerted a strong pressure on rhizobacterial communities, and interacted with soil type and farming management, but not with the wheat cultivar types. In the sandy soil, we observed a strong drought-induced shift in community composition, with a decrease in species diversity and extracellulare enzyme production, while changes by drought were less prominent in the fertile loamy soil. A particular exception from this pattern was found for enzyme activities involved in carbon cycling in the sandy soil suggesting a positive plant-soil-feedback on enzyme activities by drought conditioning. In Chapter 2, two individual, but interrelated aims were pursued. First, we used the platform of the Global Change Experimental Facility (GCEF) to explore the impact of two farming practices (conventional vs. organic) and two climate treatments (ambient vs. future) on bacterial community composition and activity profiles of extracellulare enzymes involved in C,N and P cycles in the wheat rhizosphere at two different plant growth stages. The climate treatment in the GCEF had no effect on the rhizobacterial communities. Rhizobacterial community composition and functions significantly differed between vegetative and mature growth stages of the plants, in both conventional and organic farming. In a second step, we reused the data to explore further the accuracy of computational approaches, like Tax4Fun and PanFP, to predict functional profiles of bacterial communities based on 16S rDNA abundance data. To this end, we compared the measured enzyme activities with respective gene abundances in the community under different climate and farming treatments, and at the two plant development stages. This analysis revealed qualitative, but not necessarily quantitative concordances, i.e. we found effects of the different treatments on the measured enzyme activities reflected in the gene abundances. Chapter 3 is a complementary approach to Chapter 2 with a focus on individual bacterial species level. Culture-dependent methods were used to specifically isolate strong P-solubilizing bacteria from the rhizosphere of wheat, which were tested for their in-vitro drought tolerance. Among the more than 800 isolated species, Phyllobacterium, Pseudomonas and Streptomyces species dominated. While farming management and climate treatment had only minor effects on composition and functions of the isolates, the wheat growth stages had an impact, whereby a dominance of Pseudomonas species at the vegetative growth phase was replaced by dominance of Phyllobacterium species at the mature growth phase. Since P-solubilizing potential was paralleled by a high in vitro drought tolerance, Phyllobacterium species were characterized as promising plant growth promoting rhizobacteria (PGPR) of wheat under future drought conditions. In the synopsis part, we evaluated the multifactorial and multidisciplinary approaches and investigated to what extent the adaptations of bacterial communities in field and pot experiments coincided or differed. Overall, we found common and distinct adaptation processes of bacterial communities and individual species in the rhizosphere of wheat to drought, whereby single factors, but also interacting effects exerted a strong impact on these processes. This study underlines the importance of multifactorial approaches to reveal community- or species-specific plant-soil-feedbacks.:Contents 3 Preface 5 Bibliographic description 6 Zusammenfassung 9 Summary 13 Introduction 16 When extreme events become the new normal 17 Feedback to agricultural production and need for management adaptation 20 Difficulties in exploring the soil microbiome and identification of plant beneficial microbial taxa 22 Our approach with wheat 24 Bibliography 27 ֎ Chapter 1 31 Interactions Between Soil Properties, Agricultural Management and Cultivar Type Drive Structural and Functional Adaptations of the Wheat Rhizosphere Microbiome To Drought 31 Supplemental Tables 51 Supplemental Figures 55 ╬ Chapter 2 59 Can We Estimate Functionality of Soil Microbial Communities from Structure-Derived Predictions? A Reality Test in Agricultural Soils 59 Supplementary Tables 79 Supplemental Figures 84 Supplemental Material 1: 87 Variation in edaphic parameters according to experimental factors 87 Supplemental Material 2 88 Effect of abiotic soil parameters on bacterial community structure and function 88 Supplemental Material 3 90 Indicator species analysis 90 ۝ Chapter 3 95 Shifts Between and Among Populations of Wheat Rhizosphere Pseudomonas, Streptomyces and Phyllobacterium Suggest Consistent Phosphate Mobilization at Different Wheat Growth Stages Under Abiotic Stress 95 Supplementary Figures 112 Supplementary Tables 117 Synopsis 152 Multidisciplinary approaches combine advantages of cultivation-based and high throughput community-based methods 155 Multifactorial approaches to gain a more holistic understanding of plant-microbe interactions in pot experiments 157 Transferability of findings gained in the pot experiment to field conditions 159 Towards a wheat core microbiome? 161 Study limitations and outlook 163 Bibliography 164 Acknowledgements 169 Curriculum Vitae 171 Personal details 171 Education 171 Work experience 172 Research and Mentoring experience 172 Extracurricular activities 173 List of publications and Presentations 174 Publications in peer-reviewed journals: 174 Oral Presentations: 175 Poster Presentations: 175 Statutory declaration 176 Eidesstattliche ErklĂ€rung 177 Author contributions 178Weizen ist eine der weltweit am hĂ€ufigsten angebauten Kulturpflanzen und trĂ€gt zur Sicherung der Nahrungsmittelproduktion in verschiedenen Regionen der Welt bei. Obwohl er fast ĂŒberall angebaut werden kann, ist die Produktion durch Trockenheit limitiert. Daher rĂŒcken mehr und mehr die mikrobiellen Gemeinschaften im Boden und in der RhizosphĂ€re in den Mittelpunkt der modernen agrarbiologischen Forschung, um die ProduktivitĂ€t bei Trockenheit aufrechtzuerhalten und eine nachhaltige Produktion zu fördern. WĂ€hrend bereits zahlreiche Studien ĂŒber die Weizenproduktion und den positiven Einfluss des Bodenmikrobioms in ariden und semiariden Regionen der Welt durchgefĂŒhrt wurden, sind vergleichbare Studien in Mitteleuropa selten. Dies könnte sich aufgrund der anhaltenden Klimakrise und der zu erwartenden ausbleibenden SommerniederschlĂ€ge Ă€ndern. Dabei haben die meisten Studien, die sich mit der Akklimatisierung des Weizenrhizobioms an Wasserdefizite befasst haben, bestenfalls den Einfluss von Trockenheit und ein oder zwei weiteren biotischen oder abiotischen Einflussfaktoren, die zudem miteinander interagieren können, auf die Struktur und Funktion der mikrobiellen Gemeinschaften in der RhizosphĂ€re untersucht. Ziel dieser Arbeit war es daher, verschiedene komplementĂ€re Analysemethoden zu kombinieren, um trockenheitsbedingte strukturelle und funktionelle VerĂ€nderungen in den bakteriellen Gemeinschaften und auch einzelner Arten in der WeizenrhizosphĂ€re, in AbhĂ€ngigkeit von Bodentyp, Landnutzungssystem, Weizensorte und Pflanzenentwicklungsstadium zu untersuchen, und zu ermitteln, wie sich diese VerĂ€nderungen auf die ProduktivitĂ€t des Weizens als Folge möglicher Szenarien des Klimawandels in Mitteldeutschland auswirken. Die vorliegende Arbeit leitet mit einer allgemeinen EinfĂŒhrung und Vorstellung des Projekts ein, gefolgt von drei aufeinanderfolgenden Kapiteln, die die wichtigsten Ergebnisse enthalten, die in von Fachleuten begutachteten Artikeln veröffentlicht wurden. Beginnend mit einem Experiment im GewĂ€chshaus (Kapitel 1) und weiterfĂŒhrend zu einem realistischen Klimaszenario unter Feldbedingungen (Kapitel 2 und 3), beschreiben die drei Kapitel die alleinigen und interagierenden Auswirkungen von Trockenheit und Anbausystem (Kapitel 1-3), Bodentyp und Weizensorte (Kapitel 1), sowie Pflanzenwachstumsstadien (Kapitel 2 und 3) auf Bakteriengemeinschaften und einzelne Taxa des Weizenrhizobioms. Die verwendeten Methoden reichen dabei von der traditionellen Kultivierung und In-vitro-Bioassays (Kapitel 3), ĂŒber extrazellulĂ€re EnzymaktivitĂ€tspotenziale (Kapitel 1 und 2), bis hin zu fortschrittlicheren Technologien, wie Metabarcoding (Kapitel 1 und 2) und computergestĂŒtzten Vorhersagen (Kapitel 1 und 2). Zum Abschluss der Arbeit werden in einer abschließenden Synopsis die gewonnenen Ergebnisse zusammengetragen und kritisch betrachtet, sowie Ideen fĂŒr zukĂŒnftige Studien formuliert. In Kapitel 1 untersuchten wir die Auswirkungen des Bodentyps (lehmig vs. sandig), der Bewirtschaftung (konventionell vs. ökologisch), der Weizensorte (anspruchslos vs. anspruchsvoll) und die Wechselwirkungen zwischen diesen Faktoren auf die Zusammensetzung und Funktion der Bakteriengemeinschaft in der RhizosphĂ€re von Weizen unter extremen Trockenheitsbedingungen. Das Wasserdefizit ĂŒbte einen starken Druck auf die RhizosphĂ€renbakteriengemeinschaften aus und stand in Wechselwirkung mit dem Bodentyp und der Bewirtschaftung, nicht aber mit den Weizensorten. In den Sandböden beobachteten wir eine starke trockenheitsbedingte VerĂ€nderung der Zusammensetzung der Gemeinschaft mit einem RĂŒckgang der Artenvielfalt und der extrazellulĂ€ren Enzymproduktion, wĂ€hrend die VerĂ€nderungen durch die Trockenheit in den fruchtbaren Lehmböden weniger stark ausgeprĂ€gt waren. Eine besondere Ausnahme von diesem Muster wurde fĂŒr EnzymaktivitĂ€ten gefunden, die am Kohlenstoffkreislauf im Sandboden beteiligt sind, was auf eine positive RĂŒckkopplung zwischen Pflanze und Bodengemeinschaften unter Trockenheit hindeutet. In Kapitel 2 wurden zwei einzelne, jedoch miteinander verknĂŒpfte Ziele verfolgt. Erstens nutzten wir die Plattform der Global Change Experimental Facility (GCEF), um die Auswirkungen von zwei Anbaupraktiken (konventionell vs. ökologisch) und zwei Klimabehandlungen (ambient vs. zukĂŒnftig) auf die Zusammensetzung der Bakteriengemeinschaft und die AktivitĂ€tsprofile extrazellulĂ€rer Enzyme, die an den C-, N- und P-Zyklen in der RhizosphĂ€re von Weizen beteiligt sind, in zwei verschiedenen Pflanzenwachstumsstadien zu untersuchen. Die Klimabehandlung in der GCEF hatte keinen Einfluss auf die RhizosphĂ€renbakteriengemeinschaften. Die Zusammensetzung und die Funktionen der RhizosphĂ€renbakteriengemeinschaften unterschieden sich signifikant zwischen dem vegetativen und dem generativen Wachstumsstadium der Pflanzen, sowohl im konventionellen als auch im ökologischen Landbau. In einem zweiten Schritt nutzten wir die gewonnenen Daten, um die Genauigkeit rechnerischer AnsĂ€tze wie Tax4Fun und PanFP zur Vorhersage funktioneller Profile von Bakteriengemeinschaften auf der Grundlage von 16S rDNA-Daten zu ĂŒberprĂŒfen. Zu diesem Zweck verglichen wir die gemessenen EnzymaktivitĂ€ten mit den jeweiligen GenhĂ€ufigkeiten in der Gemeinschaft unter verschiedenen Klima- und Anbaubedingungen und in den beiden Entwicklungsstadien der Pflanzen. Diese Analyse ergab qualitative, aber nicht unbedingt quantitative Übereinstimmungen, d. h. wir fanden Auswirkungen der verschiedenen Behandlungen auf die gemessenen EnzymaktivitĂ€ten, die sich auch in den GenhĂ€ufigkeiten widerspiegeln. Kapitel 3 stellt einen ergĂ€nzenden Ansatz zu Kapitel 2 dar, wobei der Schwerpunkt auf einzelnen Bakterienarten liegt. Mit kulturabhĂ€ngigen Methoden wurden gezielt stark Phosphat-solubilisierende Bakterien aus der RhizosphĂ€re von Weizen isoliert und auf ihre In-vitro-Trockenheitstoleranz getestet. Unter den mehr als 800 isolierten Arten dominierten Phyllobacterium-, Pseudomonas- und Streptomyces-Arten. WĂ€hrend Anbaumanagement und Klimabehandlung nur geringe Auswirkungen hatten, wirkten sich die Wachstumsstadien des Weizens signifikant auf die Zusammensetzung und Funktionen der Isolate aus, wobei eine Dominanz von Pseudomonas-Arten in der vegetativen Wachstumsphase durch eine Dominanz von Phyllobacterium-Arten in der generativen Wachstumsphase ersetzt wurde. Da das Potenzial zur P-Solubilisierung mit einer hohen in vitro-Trockenheitstoleranz einherging, wurden Phyllobacterium-Arten als vielversprechende pflanzenwachstumsfördernde Rhizobakterien (PGPR) fĂŒr Weizen unter zukĂŒnftigen Trockenheitsbedingungen charakterisiert. In der Synopsis dieser Arbeit bewerteten wir die multifaktoriellen und multidisziplinĂ€ren AnsĂ€tze, und untersuchten, inwieweit die Anpassungen der Bakteriengemeinschaften in Feld- und Topfversuchen ĂŒbereinstimmen oder sich unterscheiden. Insgesamt fanden wir allgemeine, aber auch differenzielle Anpassungsprozesse von Bakteriengemeinschaften und einzelnen Arten in der RhizosphĂ€re von Weizen an die Trockenheit, wobei einzelne Faktoren, aber auch interagierende Effekte einen starken Einfluss auf diese Prozesse ausĂŒbten. Diese Studie unterstreicht damit die Bedeutung multifaktorieller AnsĂ€tze, um gemeinschafts- oder artspezifische RĂŒckkopplungen zwischen Pflanze und Boden zu untersuchen.:Contents 3 Preface 5 Bibliographic description 6 Zusammenfassung 9 Summary 13 Introduction 16 When extreme events become the new normal 17 Feedback to agricultural production and need for management adaptation 20 Difficulties in exploring the soil microbiome and identification of plant beneficial microbial taxa 22 Our approach with wheat 24 Bibliography 27 ֎ Chapter 1 31 Interactions Between Soil Properties, Agricultural Management and Cultivar Type Drive Structural and Functional Adaptations of the Wheat Rhizosphere Microbiome To Drought 31 Supplemental Tables 51 Supplemental Figures 55 ╬ Chapter 2 59 Can We Estimate Functionality of Soil Microbial Communities from Structure-Derived Predictions? A Reality Test in Agricultural Soils 59 Supplementary Tables 79 Supplemental Figures 84 Supplemental Material 1: 87 Variation in edaphic parameters according to experimental factors 87 Supplemental Material 2 88 Effect of abiotic soil parameters on bacterial community structure and function 88 Supplemental Material 3 90 Indicator species analysis 90 ۝ Chapter 3 95 Shifts Between and Among Populations of Wheat Rhizosphere Pseudomonas, Streptomyces and Phyllobacterium Suggest Consistent Phosphate Mobilization at Different Wheat Growth Stages Under Abiotic Stress 95 Supplementary Figures 112 Supplementary Tables 117 Synopsis 152 Multidisciplinary approaches combine advantages of cultivation-based and high throughput community-based methods 155 Multifactorial approaches to gain a more holistic understanding of plant-microbe interactions in pot experiments 157 Transferability of findings gained in the pot experiment to field conditions 159 Towards a wheat core microbiome? 161 Study limitations and outlook 163 Bibliography 164 Acknowledgements 169 Curriculum Vitae 171 Personal details 171 Education 171 Work experience 172 Research and Mentoring experience 172 Extracurricular activities 173 List of publications and Presentations 174 Publications in peer-reviewed journals: 174 Oral Presentations: 175 Poster Presentations: 175 Statutory declaration 176 Eidesstattliche ErklĂ€rung 177 Author contributions 17

    Transformer fault diagnosis based on probabilistic neural networks combined with vibration and noise characteristics

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    When the transformer is running, the vibration which is generated in the core and winding will spread outward through the medium of metal, oil, and air. The magnetic field of the core changes with the variation of the transformer excitation source and the state of the core, so the corresponding vibration and noise change. Therefore, the vibration and noise of the transformer contain a lot of information. If the information can be associated with the fault characteristics of the transformer, it is significant to evaluate the running state of the transformer through the vibration and noise signal, which improve the intelligence, safety, and stability of the transformer operation. Based on this, modeling and simulation of transformer multi-point grounding, DC bias, and short-circuit between silicon steel sheets fault are first carried out in this paper, and vibration and noise distribution of transformer under different faults are given. Second, a fault diagnosis method based on transformer vibration and noise characteristics is proposed. In the process of implementation, vibration and noise signals under multi-point grounding, DC bias, and short-circuit between silicon steel sheets are taken as the sample data, and the probabilistic neural network algorithm is used to effectively predict the transformer fault. Finally, the effectiveness of the proposed scheme is verified by identifying the simulation faults-the proposed fault diagnosis method based on PNN can be effectively applied to transformer

    Bildung in der digitalen Transformation

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    Die Coronapandemie und der durch sie erzwungene zeitweise Übergang von PrĂ€senz- zu Distanzlehre haben die Digitalisierung des Bildungswesens enorm vorangetrieben. Noch deutlicher als vorher traten dabei positive wie negative Aspekte dieser Entwicklung zum Vorschein. WĂ€hrend den Hochschulen der Wechsel mit vergleichsweise geringen Reibungsverlusten gelang, offenbarten sich diese an Schulen weitaus deutlicher. Trotz aller Widrigkeiten erscheint eines klar: Die zeitweisen VerĂ€nderungen werden Nachwirkungen zeigen. Eine völlige RĂŒckkehr zum Status quo ante ist kaum noch vorstellbar. Zwei Fragen bestimmen vor diesem Hintergrund die Doppelgesichtigkeit des Themas der 29. Jahrestagung der Gesellschaft fĂŒr Medien in der Wissenschaft (GMW). Erstens: Wie ‚funktioniert‘ Bildung in der sich derzeit ereignenden digitalen Transformation und welche Herausforderungen gibt es? Und zweitens: Befindet sich möglicherweise Bildung selbst in der Transformation? BeitrĂ€ge zu diesen und weiteren Fragen vereint der vorliegende Tagungsband

    Improved wolf swarm optimization with deep-learning-based movement analysis and self-regulated human activity recognition

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    A wide variety of applications like patient monitoring, rehabilitation sensing, sports and senior surveillance require a considerable amount of knowledge in recognizing physical activities of a person captured using sensors. The goal of human activity recognition is to identify human activities from a collection of observations based on the behavior of subjects and the surrounding circumstances. Movement is examined in psychology, biomechanics, artificial intelligence and neuroscience. To be specific, the availability of pervasive devices and the low cost to record movements with machine learning (ML) techniques for the automatic and quantitative analysis of movement have resulted in the growth of systems for rehabilitation monitoring, user authentication and medical diagnosis. The self-regulated detection of human activities from time-series smartphone sensor datasets is a growing study area in intelligent and smart healthcare. Deep learning (DL) techniques have shown enhancements compared to conventional ML methods in many fields, which include human activity recognition (HAR). This paper presents an improved wolf swarm optimization with deep learning based movement analysis and self-regulated human activity recognition (IWSODL-MAHAR) technique. The IWSODL-MAHAR method aimed to recognize various kinds of human activities. Since high dimensionality poses a major issue in HAR, the IWSO algorithm is applied as a dimensionality reduction technique. In addition, the IWSODL-MAHAR technique uses a hybrid DL model for activity recognition. To further improve the recognition performance, a Nadam optimizer is applied as a hyperparameter tuning technique. The experimental evaluation of the IWSODL-MAHAR approach is assessed on benchmark activity recognition data. The experimental outcomes outlined the supremacy of the IWSODL-MAHAR algorithm compared to recent models

    ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT

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    Large language models (LLMs) such as ChatGPT have recently demonstrated significant potential in mathematical abilities, providing valuable reasoning paradigm consistent with human natural language. However, LLMs currently have difficulty in bridging perception, language understanding and reasoning capabilities due to incompatibility of the underlying information flow among them, making it challenging to accomplish tasks autonomously. On the other hand, abductive learning (ABL) frameworks for integrating the two abilities of perception and reasoning has seen significant success in inverse decipherment of incomplete facts, but it is limited by the lack of semantic understanding of logical reasoning rules and the dependence on complicated domain knowledge representation. This paper presents a novel method (ChatABL) for integrating LLMs into the ABL framework, aiming at unifying the three abilities in a more user-friendly and understandable manner. The proposed method uses the strengths of LLMs' understanding and logical reasoning to correct the incomplete logical facts for optimizing the performance of perceptual module, by summarizing and reorganizing reasoning rules represented in natural language format. Similarly, perceptual module provides necessary reasoning examples for LLMs in natural language format. The variable-length handwritten equation deciphering task, an abstract expression of the Mayan calendar decoding, is used as a testbed to demonstrate that ChatABL has reasoning ability beyond most existing state-of-the-art methods, which has been well supported by comparative studies. To our best knowledge, the proposed ChatABL is the first attempt to explore a new pattern for further approaching human-level cognitive ability via natural language interaction with ChatGPT

    Quantifying the Expressive Capacity of Quantum Systems: Fundamental Limits and Eigentasks

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    The expressive capacity of quantum systems for machine learning is limited by quantum sampling noise incurred during measurement. Although it is generally believed that noise limits the resolvable capacity of quantum systems, the precise impact of noise on learning is not yet fully understood. We present a mathematical framework for evaluating the available expressive capacity of general quantum systems from a finite number of measurements, and provide a methodology for extracting the extrema of this capacity, its eigentasks. Eigentasks are a native set of functions that a given quantum system can approximate with minimal error. We show that extracting low-noise eigentasks leads to improved performance for machine learning tasks such as classification, displaying robustness to overfitting. We obtain a tight bound on the expressive capacity, and present analyses suggesting that correlations in the measured quantum system enhance learning capacity by reducing noise in eigentasks. These results are supported by experiments on superconducting quantum processors. Our findings have broad implications for quantum machine learning and sensing applications.Comment: 7 + 21 pages, 4 + 12 figures, 1 tabl

    Colour technologies for content production and distribution of broadcast content

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    The requirement of colour reproduction has long been a priority driving the development of new colour imaging systems that maximise human perceptual plausibility. This thesis explores machine learning algorithms for colour processing to assist both content production and distribution. First, this research studies colourisation technologies with practical use cases in restoration and processing of archived content. The research targets practical deployable solutions, developing a cost-effective pipeline which integrates the activity of the producer into the processing workflow. In particular, a fully automatic image colourisation paradigm using Conditional GANs is proposed to improve content generalisation and colourfulness of existing baselines. Moreover, a more conservative solution is considered by providing references to guide the system towards more accurate colour predictions. A fast-end-to-end architecture is proposed to improve existing exemplar-based image colourisation methods while decreasing the complexity and runtime. Finally, the proposed image-based methods are integrated into a video colourisation pipeline. A general framework is proposed to reduce the generation of temporal flickering or propagation of errors when such methods are applied frame-to-frame. The proposed model is jointly trained to stabilise the input video and to cluster their frames with the aim of learning scene-specific modes. Second, this research explored colour processing technologies for content distribution with the aim to effectively deliver the processed content to the broad audience. In particular, video compression is tackled by introducing a novel methodology for chroma intra prediction based on attention models. Although the proposed architecture helped to gain control over the reference samples and better understand the prediction process, the complexity of the underlying neural network significantly increased the encoding and decoding time. Therefore, aiming at efficient deployment within the latest video coding standards, this work also focused on the simplification of the proposed architecture to obtain a more compact and explainable model

    Scaling up integrated photonic reservoirs towards low-power high-bandwidth computing

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