16,881 research outputs found

    Deep Learning Models on CPUs: A Methodology for Efficient Training

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    GPUs have been favored for training deep learning models due to their highly parallelized architecture. As a result, most studies on training optimization focus on GPUs. There is often a trade-off, however, between cost and efficiency when deciding on how to choose the proper hardware for training. In particular, CPU servers can be beneficial if training on CPUs was more efficient, as they incur fewer hardware update costs and better utilizing existing infrastructure. This paper makes several contributions to research on training deep learning models using CPUs. First, it presents a method for optimizing the training of deep learning models on Intel CPUs and a toolkit called ProfileDNN, which we developed to improve performance profiling. Second, we describe a generic training optimization method that guides our workflow and explores several case studies where we identified performance issues and then optimized the Intel Extension for PyTorch, resulting in an overall 2x training performance increase for the RetinaNet-ResNext50 model. Third, we show how to leverage the visualization capabilities of ProfileDNN, which enabled us to pinpoint bottlenecks and create a custom focal loss kernel that was two times faster than the official reference PyTorch implementation

    Green Carbon Footprint for Model Inference Serving via Exploiting Mixed-Quality Models and GPU Partitioning

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    This paper presents a solution to the challenge of mitigating carbon emissions from large-scale high performance computing (HPC) systems and datacenters that host machine learning (ML) inference services. ML inference is critical to modern technology products, but it is also a significant contributor to datacenter compute cycles and carbon emissions. We introduce Clover, a carbon-friendly ML inference service runtime system that balances performance, accuracy, and carbon emissions through mixed-quality models and GPU resource partitioning. Our experimental results demonstrate that Clover is effective in substantially reducing carbon emissions while maintaining high accuracy and meeting service level agreement (SLA) targets. Therefore, it is a promising solution toward achieving carbon neutrality in HPC systems and datacenters

    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 Erklärung 177 Author contributions 178Weizen ist eine der weltweit am häufigsten angebauten Kulturpflanzen und trägt zur Sicherung der Nahrungsmittelproduktion in verschiedenen Regionen der Welt bei. Obwohl er fast überall angebaut werden kann, ist die Produktion durch Trockenheit limitiert. Daher rücken mehr und mehr die mikrobiellen Gemeinschaften im Boden und in der Rhizosphäre in den Mittelpunkt der modernen agrarbiologischen Forschung, um die Produktivität bei Trockenheit aufrechtzuerhalten und eine nachhaltige Produktion zu fördern. Während bereits zahlreiche Studien über die Weizenproduktion und den positiven Einfluss des Bodenmikrobioms in ariden und semiariden Regionen der Welt durchgeführt wurden, sind vergleichbare Studien in Mitteleuropa selten. Dies könnte sich aufgrund der anhaltenden Klimakrise und der zu erwartenden ausbleibenden Sommerniederschläge ändern. 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 Rhizosphäre untersucht. Ziel dieser Arbeit war es daher, verschiedene komplementäre Analysemethoden zu kombinieren, um trockenheitsbedingte strukturelle und funktionelle Veränderungen in den bakteriellen Gemeinschaften und auch einzelner Arten in der Weizenrhizosphäre, in Abhängigkeit von Bodentyp, Landnutzungssystem, Weizensorte und Pflanzenentwicklungsstadium zu untersuchen, und zu ermitteln, wie sich diese Veränderungen auf die Produktivität des Weizens als Folge möglicher Szenarien des Klimawandels in Mitteldeutschland auswirken. Die vorliegende Arbeit leitet mit einer allgemeinen Einführung 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 Gewächshaus (Kapitel 1) und weiterführend 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), über extrazelluläre Enzymaktivitätspotenziale (Kapitel 1 und 2), bis hin zu fortschrittlicheren Technologien, wie Metabarcoding (Kapitel 1 und 2) und computergestützten Vorhersagen (Kapitel 1 und 2). Zum Abschluss der Arbeit werden in einer abschließenden Synopsis die gewonnenen Ergebnisse zusammengetragen und kritisch betrachtet, sowie Ideen für zukünftige 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 Rhizosphäre von Weizen unter extremen Trockenheitsbedingungen. Das Wasserdefizit übte einen starken Druck auf die Rhizosphärenbakteriengemeinschaften aus und stand in Wechselwirkung mit dem Bodentyp und der Bewirtschaftung, nicht aber mit den Weizensorten. In den Sandböden beobachteten wir eine starke trockenheitsbedingte Veränderung der Zusammensetzung der Gemeinschaft mit einem Rückgang der Artenvielfalt und der extrazellulären Enzymproduktion, während die Veränderungen durch die Trockenheit in den fruchtbaren Lehmböden weniger stark ausgeprägt waren. Eine besondere Ausnahme von diesem Muster wurde für Enzymaktivitäten gefunden, die am Kohlenstoffkreislauf im Sandboden beteiligt sind, was auf eine positive Rückkopplung zwischen Pflanze und Bodengemeinschaften unter Trockenheit hindeutet. In Kapitel 2 wurden zwei einzelne, jedoch miteinander verknüpfte 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. zukünftig) auf die Zusammensetzung der Bakteriengemeinschaft und die Aktivitätsprofile extrazellulärer Enzyme, die an den C-, N- und P-Zyklen in der Rhizosphäre von Weizen beteiligt sind, in zwei verschiedenen Pflanzenwachstumsstadien zu untersuchen. Die Klimabehandlung in der GCEF hatte keinen Einfluss auf die Rhizosphärenbakteriengemeinschaften. Die Zusammensetzung und die Funktionen der Rhizosphärenbakteriengemeinschaften 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 Ansätze wie Tax4Fun und PanFP zur Vorhersage funktioneller Profile von Bakteriengemeinschaften auf der Grundlage von 16S rDNA-Daten zu überprüfen. Zu diesem Zweck verglichen wir die gemessenen Enzymaktivitäten mit den jeweiligen Genhäufigkeiten 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 Enzymaktivitäten, die sich auch in den Genhäufigkeiten widerspiegeln. Kapitel 3 stellt einen ergänzenden Ansatz zu Kapitel 2 dar, wobei der Schwerpunkt auf einzelnen Bakterienarten liegt. Mit kulturabhängigen Methoden wurden gezielt stark Phosphat-solubilisierende Bakterien aus der Rhizosphäre von Weizen isoliert und auf ihre In-vitro-Trockenheitstoleranz getestet. Unter den mehr als 800 isolierten Arten dominierten Phyllobacterium-, Pseudomonas- und Streptomyces-Arten. Während 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) für Weizen unter zukünftigen Trockenheitsbedingungen charakterisiert. In der Synopsis dieser Arbeit bewerteten wir die multifaktoriellen und multidisziplinären Ansätze, und untersuchten, inwieweit die Anpassungen der Bakteriengemeinschaften in Feld- und Topfversuchen übereinstimmen oder sich unterscheiden. Insgesamt fanden wir allgemeine, aber auch differenzielle Anpassungsprozesse von Bakteriengemeinschaften und einzelnen Arten in der Rhizosphäre von Weizen an die Trockenheit, wobei einzelne Faktoren, aber auch interagierende Effekte einen starken Einfluss auf diese Prozesse ausübten. Diese Studie unterstreicht damit die Bedeutung multifaktorieller Ansätze, um gemeinschafts- oder artspezifische Rückkopplungen 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 Erklärung 177 Author contributions 17

    Detecting Anomalous Microflows in IoT Volumetric Attacks via Dynamic Monitoring of MUD Activity

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    IoT networks are increasingly becoming target of sophisticated new cyber-attacks. Anomaly-based detection methods are promising in finding new attacks, but there are certain practical challenges like false-positive alarms, hard to explain, and difficult to scale cost-effectively. The IETF recent standard called Manufacturer Usage Description (MUD) seems promising to limit the attack surface on IoT devices by formally specifying their intended network behavior. In this paper, we use SDN to enforce and monitor the expected behaviors of each IoT device, and train one-class classifier models to detect volumetric attacks. Our specific contributions are fourfold. (1) We develop a multi-level inferencing model to dynamically detect anomalous patterns in network activity of MUD-compliant traffic flows via SDN telemetry, followed by packet inspection of anomalous flows. This provides enhanced fine-grained visibility into distributed and direct attacks, allowing us to precisely isolate volumetric attacks with microflow (5-tuple) resolution. (2) We collect traffic traces (benign and a variety of volumetric attacks) from network behavior of IoT devices in our lab, generate labeled datasets, and make them available to the public. (3) We prototype a full working system (modules are released as open-source), demonstrates its efficacy in detecting volumetric attacks on several consumer IoT devices with high accuracy while maintaining low false positives, and provides insights into cost and performance of our system. (4) We demonstrate how our models scale in environments with a large number of connected IoTs (with datasets collected from a network of IP cameras in our university campus) by considering various training strategies (per device unit versus per device type), and balancing the accuracy of prediction against the cost of models in terms of size and training time.Comment: 18 pages, 13 figure

    Norman M. Klein's »Bleeding Through: Layers of Los Angeles«; Bleeding Through: Layers of Los Angeles 1920-1986«, 2003

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    In 2003, Norman M. Klein's docufable »Bleeding Through« raised questions of urban aesthetics and memory as part of the multimedia documentary »Bleeding Through: Layers of Los Angeles, 1920-1986.« Now, 20 years later, this important text is reissued along with several essays addressing its central themes, such as the aesthetics and politics of urban memory, the development of Los Angeles since the 20th century, the role of urban imaginaries in US politics, or media evolution in the 21st century. The volume also features a long interview with Klein and two docufables from Klein's celebrated study »The History of Forgetting: Los Angeles and the Erasure of Memory«, one being the kernel of the novella, the other imagining Walter Benjamin in L.A. Finally, the book contains links to two films featuring much of the multimedia material contained in the first edition

    Automatic architecture selection for hierarchical mixture of experts models

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    Hierarchical mixture of experts (HME) is a powerful tree-structured modeling technique based on the divide and conquer principle. HME model trees consist of two types of nodes - gate nodes, which are responsible for splitting a large complex problem into several smaller subproblems, and expert nodes, which perform the corresponding subproblemsolving. Selecting the number of such nodes as well as the order in which they are arranged is, however, a non-trivial task. A commonly used approach involves fitting several architectures and using methods such as cross-validation to pick the best one. As well as being computationally intensive, this method first requires one to pick the set of architectures to consider. For complex models with a large number of architectural elements, this leads to an unmanageable number of potential options. Pre-setting model architecture also requires choosing initial parameter values, which becomes progressively more challenging as parameter dimensionality increases. The latter challenges could be addressed by growing trees during the model fitting process instead of selecting the architecture in advance. It is thus evident that HME models suffer from a lack of a flexible and adaptive way of performing automatic architecture selection. The work presented in this thesis proposes automatic architecture selection methods for HME models, which allow for adding and removing tree nodes as well as adjusting the order in which they are arranged. As part of the development, three Bayesian parameter sampling strategies are proposed and systematically evaluated resulting in a recommended strategy. An adaptation of the Reversible Jump (RJ) algorithm is then used to grow and prune HME model trees. The main downfall of the RJ, which lies in low acceptance rates, is addressed by the addition of a novel reversible jump proposal algorithm. A new Gate Swaps (GS) algorithm is then proposed to tackle the problem of changing the order in which the existing tree nodes are arranged. Both algorithms are evaluated on two real-life problems with a particular focus on the Glasgow rental property prices data. It is shown that HME models fitted using the proposed RJ GS MCMC yield accurate predictions as well as provide an exceptionally high level of model interpretability, which is unusual amongst other machine learning methods

    Knowledge-based Modelling of Additive Manufacturing for Sustainability Performance Analysis and Decision Making

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    Additiivista valmistusta on pidetty käyttökelpoisena monimutkaisissa geometrioissa, topologisesti optimoiduissa kappaleissa ja kappaleissa joita on muuten vaikea valmistaa perinteisillä valmistusprosesseilla. Eduista huolimatta, yksi additiivisen valmistuksen vallitsevista haasteista on ollut heikko kyky tuottaa toimivia osia kilpailukykyisillä tuotantomäärillä perinteisen valmistuksen kanssa. Mallintaminen ja simulointi ovat tehokkaita työkaluja, jotka voivat auttaa lyhentämään suunnittelun, rakentamisen ja testauksen sykliä mahdollistamalla erilaisten tuotesuunnitelmien ja prosessiskenaarioiden nopean analyysin. Perinteisten ja edistyneiden valmistusteknologioiden mahdollisuudet ja rajoitukset määrittelevät kuitenkin rajat uusille tuotekehityksille. Siksi on tärkeää, että suunnittelijoilla on käytettävissään menetelmät ja työkalut, joiden avulla he voivat mallintaa ja simuloida tuotteen suorituskykyä ja siihen liittyvän valmistusprosessin suorituskykyä, toimivien korkea arvoisten tuotteiden toteuttamiseksi. Motivaation tämän väitöstutkimuksen tekemiselle on, meneillään oleva kehitystyö uudenlaisen korkean lämpötilan suprajohtavan (high temperature superconducting (HTS)) magneettikokoonpanon kehittämisessä, joka toimii kryogeenisissä lämpötiloissa. Sen monimutkaisuus edellyttää monitieteisen asiantuntemuksen lähentymistä suunnittelun ja prototyyppien valmistuksen aikana. Tutkimus hyödyntää tietopohjaista mallinnusta valmistusprosessin analysoinnin ja päätöksenteon apuna HTS-magneettien mekaanisten komponenttien suunnittelussa. Tämän lisäksi, tutkimus etsii mahdollisuuksia additiivisen valmistuksen toteutettavuuteen HTS-magneettikokoonpanon tuotannossa. Kehitetty lähestymistapa käyttää fysikaalisiin kokeisiin perustuvaa tuote-prosessi-integroitua mallinnusta tuottamaan kvantitatiivista ja laadullista tietoa, joka määrittelee prosessi-rakenne-ominaisuus-suorituskyky-vuorovaikutuksia tietyille materiaali-prosessi-yhdistelmille. Tuloksina saadut vuorovaikutukset integroidaan kaaviopohjaiseen malliin, joka voi auttaa suunnittelutilan tutkimisessa ja täten auttaa varhaisessa suunnittelu- ja valmistuspäätöksenteossa. Tätä varten testikomponentit valmistetaan käyttämällä kahta metallin additiivista valmistus prosessia: lankakaarihitsaus additiivista valmistusta (wire arc additive manufacturing) ja selektiivistä lasersulatusta (selective laser melting). Rakenteellisissa sovelluksissa yleisesti käytetyistä metalliseoksista (ruostumaton teräs, pehmeä teräs, luja niukkaseosteinen teräs, alumiini ja kupariseokset) testataan niiden mekaaniset, lämpö- ja sähköiset ominaisuudet. Lisäksi tehdään metalliseosten mikrorakenteen karakterisointi, jotta voidaan ymmärtää paremmin valmistusprosessin parametrien vaikutusta materiaalin ominaisuuksiin. Integroitu mallinnustapa yhdistää kerätyn kokeellisen tiedon, olemassa olevat analyyttiset ja empiiriset vuorovaikutus suhteet, sekä muut tietopohjaiset mallit (esim. elementtimallit, koneoppimismallit) päätöksenteon tukijärjestelmän muodossa, joka mahdollistaa optimaalisen materiaalin, valmistustekniikan, prosessiparametrien ja muitten ohjausmuuttujien valinnan, lopullisen 3d-tulosteun komponentin halutun rakenteen, ominaisuuksien ja suorituskyvyn saavuttamiseksi. Valmistuspäätöksenteko tapahtuu todennäköisyysmallin, eli Bayesin verkkomallin toteuttamisen kautta, joka on vankka, modulaarinen ja sovellettavissa muihin valmistusjärjestelmiin ja tuotesuunnitelmiin. Väitöstyössä esitetyn mallin kyky parantaa additiivisien valmistusprosessien suorituskykyä ja laatua, täten edistää kestävän tuotannon tavoitteita.Additive manufacturing (AM) has been considered viable for complex geometries, topology optimized parts, and parts that are otherwise difficult to produce using conventional manufacturing processes. Despite the advantages, one of the prevalent challenges in AM has been the poor capability of producing functional parts at production volumes that are competitive with traditional manufacturing. Modelling and simulation are powerful tools that can help shorten the design-build-test cycle by enabling rapid analysis of various product designs and process scenarios. Nevertheless, the capabilities and limitations of traditional and advanced manufacturing technologies do define the bounds for new product development. Thus, it is important that the designers have access to methods and tools that enable them to model and simulate product performance and associated manufacturing process performance to realize functional high value products. The motivation for this dissertation research stems from ongoing development of a novel high temperature superconducting (HTS) magnet assembly, which operates in cryogenic environment. Its complexity requires the convergence of multidisciplinary expertise during design and prototyping. The research applies knowledge-based modelling to aid manufacturing process analysis and decision making in the design of mechanical components of the HTS magnet. Further, it explores the feasibility of using AM in the production of the HTS magnet assembly. The developed approach uses product-process integrated modelling based on physical experiments to generate quantitative and qualitative information that define process-structure-property-performance interactions for given material-process combinations. The resulting interactions are then integrated into a graph-based model that can aid in design space exploration to assist early design and manufacturing decision-making. To do so, test components are fabricated using two metal AM processes: wire and arc additive manufacturing and selective laser melting. Metal alloys (stainless steel, mild steel, high-strength low-alloyed steel, aluminium, and copper alloys) commonly used in structural applications are tested for their mechanical-, thermal-, and electrical properties. In addition, microstructural characterization of the alloys is performed to further understand the impact of manufacturing process parameters on material properties. The integrated modelling approach combines the collected experimental data, existing analytical and empirical relationships, and other data-driven models (e.g., finite element models, machine learning models) in the form of a decision support system that enables optimal selection of material, manufacturing technology, process parameters, and other control variables for attaining desired structure, property, and performance characteristics of the final printed component. The manufacturing decision making is performed through implementation of a probabilistic model i.e., a Bayesian network model, which is robust, modular, and can be adapted for other manufacturing systems and product designs. The ability of the model to improve throughput and quality of additive manufacturing processes will boost sustainable manufacturing goals

    AN EMPIRICAL STUDY OF CONCURRENT FEATURE USAGE IN GO

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    The Go language includes support for running functions or methods concurrently as goroutines, which are lightweight threads managed directly by the Go language runtime. Go is probably best known for the use of a channel-based, message-passing concurrency mechanism, based on Hoare's Communicating Sequential Processes (CSP), for inter-thread communication. However, Go also includes support for traditional concurrency features, such as mutexes and condition variables, that are commonly used in other languages. In this paper, we analyze the use of these traditional concurrency features, using a corpus of Go programs used in earlier work to study the use of message-passing concurrency features in Go. The goal of this work is to better support developers in using traditional concurrency features, or a combination of traditional and message-passing features, in Go

    FineIBT: Fine-grain Control-flow Enforcement with Indirect Branch Tracking

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    We present the design, implementation, and evaluation of FineIBT: a CFI enforcement mechanism that improves the precision of hardware-assisted CFI solutions, like Intel IBT and ARM BTI, by instrumenting program code to reduce the valid/allowed targets of indirect forward-edge transfers. We study the design of FineIBT on the x86-64 architecture, and implement and evaluate it on Linux and the LLVM toolchain. We designed FineIBT's instrumentation to be compact, and incur low runtime and memory overheads, and generic, so as to support a plethora of different CFI policies. Our prototype implementation incurs negligible runtime slowdowns (\approx0%-1.94% in SPEC CPU2017 and \approx0%-1.92% in real-world applications) outperforming Clang-CFI. Lastly, we investigate the effectiveness/security and compatibility of FineIBT using the ConFIRM CFI benchmarking suite, demonstrating that our nimble instrumentation provides complete coverage in the presence of modern software features, while supporting a wide range of CFI policies (coarse- vs. fine- vs. finer-grain) with the same, predictable performance
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