811 research outputs found

    The infrared structure of perturbative gauge theories

    Get PDF
    Infrared divergences in the perturbative expansion of gauge theory amplitudes and cross sections have been a focus of theoretical investigations for almost a century. New insights still continue to emerge, as higher perturbative orders are explored, and high-precision phenomenological applications demand an ever more refined understanding. This review aims to provide a pedagogical overview of the subject. We briefly cover some of the early historical results, we provide some simple examples of low-order applications in the context of perturbative QCD, and discuss the necessary tools to extend these results to all perturbative orders. Finally, we describe recent developments concerning the calculation of soft anomalous dimensions in multi-particle scattering amplitudes at high orders, and we provide a brief introduction to the very active field of infrared subtraction for the calculation of differential distributions at colliders. © 2022 Elsevier B.V

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Processing-Structure-Property Relationships in Ni-based Superalloy René 41

    Full text link
    The cast and wrought Nickel based superalloy René 41 combines excellent strength, toughness, and corrosion properties. Its mechanical properties outperform all similar competitor aerospace alloys such as Waspaloy and Haynes 282. However, processing of René 41 remains challenging due to cracking and inhomogeneous grain size distributions, resulting in poor yield, limiting more wide-spread application. Therefore, despite having been developed in the 1950s, René 41 is not yet widely applied, and published research is limited. However, trends towards higher efficiency aircraft engines have now reignited interest in René 41 as a candidate material in next generation engines due to its excellent property profile. This necessitates new research to reduce the knowledge gaps in the processing-microstructure-property relationships of René 41. Correlative high-resolution microscopy is successfully applied to identify previously ambiguous secondary phases in René 41. This approach reveals complete space group, as well as high quality compositional information. These insights are applied to the development of an experimental precipitation study, and to update a current thermodynamic database for superalloys. The updated database has improved predictive quality regarding phase stability and composition of the grain boundary carbides M6C and M23C6. Applying these results to kinetic simulations also demonstrates higher predictive power. Such improved simulations are required to optimize the precipitation behaviour and predict material properties after processing. Comparison to literature data shows that the updated database yields improvements for other alloys besides René 41 as well, such as Haynes 282, Waspaloy and alloys in the Nimonic series. Further, dynamic restoration processes are studied based on laboratory scale hot-working experiments. Targeted design of the temperature profiles, allows the effect of nm scale γ’ precipitates on microstructural evolution to be isolated. Implementing the gained insights in simulations and phenomenological models advances the capabilities of modern software tools, providing better insights into microstructural processes. The results presented in this thesis will thus advance the understanding of the microstructural evolution in René 41 and the descriptive capabilities of modern thermodynamic simulation packages. This will facilitate higher yield in processing and enable future alloy design for next generation aerospace applications

    The long-range Falicov-Kimball model and the amorphous Kitaev model: Quantum many-body systems I have known and loved

    Get PDF
    Large systems of interacting objects can give rise to a rich array of emergent behaviours. Make those objects quantum and the possibilities only expand. Interacting quantum many-body systems, as such systems are called, include essentially all physical systems. Luckily, we don't usually need to consider this full quantum many-body description. The world at the human scale is essentially classical (not quantum), while at the microscopic scale of condensed matter physics we can often get by without interactions. Strongly correlated materials, however, do require the full description. Some of the most exciting topics in modern condensed matter fall under this umbrella: the spin liquids, the fractional quantum Hall effect, high temperature superconductivity and much more. Unfortunately, strongly correlated materials are notoriously difficult to study, defying many of the established theoretical techniques within the field. Enter exactly solvable models, these are interacting quantum many-body systems with extensively many local symmetries. The symmetries give rise to conserved charges. These charges break the model up into many non-interacting quantum systems which are more amenable to standard theoretical techniques. This thesis will focus on two such exactly solvable models. The first, the Falicov-Kimball (FK) model is an exactly solvable limit of the famous Hubbard model which describes itinerant fermions interacting with a classical Ising background field. Originally introduced to explain metal-insulator transitions, it has a rich set of ground state and thermodynamic phases. Disorder or interactions can turn metals into insulators and the FK model features both transitions. We will define a generalised FK model in 1D with long-range interactions. This model shows a similarly rich phase diagram to its higher dimensional cousins. We use an exact Markov Chain Monte Carlo method to map the phase diagram and compute the energy resolved localisation properties of the fermions. This allows us to look at how the move to 1D affects the physics of the model. We show that the model can be understood by comparison to a simpler model of fermions coupled to binary disorder. The second, the Kitaev Honeycomb (KH) model, was the one of the first solvable 2D models with a Quantum Spin Liquid (QSL) ground state. QSLs are generally expected to arise from Mott insulators, when frustration prevents magnetic ordering all the way to zero temperature. The QSL state defies the traditional Landau-Ginzburg-Wilson paradigm of phases being defined by local order parameters. It is instead a topologically ordered phase. Recent work generalising non-interacting topological insulator phases to amorphous lattices raises the question of whether interacting phases like the QSLs can be similarly generalised. We extend the KH model to random lattices with fixed coordination number three generated by Voronoi partitions of the plane. We show that this model remains solvable and hosts a chiral amorphous QSL ground state. The presence of plaquettes with an odd number of sides leads to a spontaneous breaking of time reversal symmetry. We unearth a rich phase diagram displaying Abelian as well as a non-Abelian QSL phases with a remarkably simple ground state flux pattern. Furthermore, we show that the system undergoes a phase transition to a conducting thermal metal state and discuss possible experimental realisations.Open Acces

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

    Get PDF
    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Identifying Sources Of Error In Computer Navigated Total Knee Arthroplasties Using A Metric On SE(3) and Sensitivity Analyses

    Get PDF
    Throughout the procedure of a computer-navigated total knee arthroplasty (TKA), there are many opportunities for sources of error to be introduced. Identifying these errors can improve surgical outcomes. There is also a lack of accessible methods in available literature for clinicians to perform research in this area using engineering analysis techniques. This thesis aims to provide a greater understanding of the sources of error that can occur pre-bone cut. Possible sources of error include the bony landmark selections and the placement of the cut guide. Using artificial bone models and a 3D point capture system concurrently with a computer-navigation system, the data points collected during the procedure are mimicked. It was found that variability of point selection varied between landmarks with some being more precise than others. Bone reference frames can be calculated using these landmark points. By painting the surface of the saw blade, the cut plane values, and a reference frame for the cuts, can also be estimated. These frames are easily represented with homogeneous transformation matrices. One method of comparing transformation matrices is with a metric on SE(3), simplified in this thesis to be the Frobenius norm. It was found that bone reference frames with the highest metric were the ones with the highest error in femur or tibia center points. It was also found that there was no clear correlation between the bone reference frame error and cut plane error, implying that other sources must be taken into account. Sensitivity analyses were performed to observe the outcome error of the bone reference frame and cut plane in regards to error in the landmark selection. The results from this support other results in this thesis: that landmark points used for the origin of the reference frames have the greatest effect on the system output. The methods in this thesis can easily be applied to other computer-navigated systems for analysis

    Knowledge extraction from unstructured data

    Get PDF
    Data availability is becoming more essential, considering the current growth of web-based data. The data available on the web are represented as unstructured, semi-structured, or structured data. In order to make the web-based data available for several Natural Language Processing or Data Mining tasks, the data needs to be presented as machine-readable data in a structured format. Thus, techniques for addressing the problem of capturing knowledge from unstructured data sources are needed. Knowledge extraction methods are used by the research communities to address this problem; methods that are able to capture knowledge in a natural language text and map the extracted knowledge to existing knowledge presented in knowledge graphs (KGs). These knowledge extraction methods include Named-entity recognition, Named-entity Disambiguation, Relation Recognition, and Relation Linking. This thesis addresses the problem of extracting knowledge over unstructured data and discovering patterns in the extracted knowledge. We devise a rule-based approach for entity and relation recognition and linking. The defined approach effectively maps entities and relations within a text to their resources in a target KG. Additionally, it overcomes the challenges of recognizing and linking entities and relations to a specific KG by employing devised catalogs of linguistic and domain-specific rules that state the criteria to recognize entities in a sentence of a particular language, and a deductive database that encodes knowledge in community-maintained KGs. Moreover, we define a Neuro-symbolic approach for the tasks of knowledge extraction in encyclopedic and domain-specific domains; it combines symbolic and sub-symbolic components to overcome the challenges of entity recognition and linking and the limitation of the availability of training data while maintaining the accuracy of recognizing and linking entities. Additionally, we present a context-aware framework for unveiling semantically related posts in a corpus; it is a knowledge-driven framework that retrieves associated posts effectively. We cast the problem of unveiling semantically related posts in a corpus into the Vertex Coloring Problem. We evaluate the performance of our techniques on several benchmarks related to various domains for knowledge extraction tasks. Furthermore, we apply these methods in real-world scenarios from national and international projects. The outcomes show that our techniques are able to effectively extract knowledge encoded in unstructured data and discover patterns over the extracted knowledge presented as machine-readable data. More importantly, the evaluation results provide evidence to the effectiveness of combining the reasoning capacity of the symbolic frameworks with the power of pattern recognition and classification of sub-symbolic models
    corecore