135 research outputs found

    Attention is All They Need: Exploring the Media Archaeology of the Computer Vision Research Paper

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    The success of deep learning has led to the rapid transformation and growth of many areas of computer science, including computer vision. In this work, we examine the effects of this growth through the computer vision research paper itself by analyzing the figures and tables in research papers from a media archaeology perspective. We ground our investigation both through interviews with veteran researchers spanning computer vision, graphics and visualization, and computational analysis of a decade of vision conference papers. Our analysis focuses on elements with roles in advertising, measuring and disseminating an increasingly commodified "contribution." We argue that each of these elements has shaped and been shaped by the climate of computer vision, ultimately contributing to that commodification. Through this work, we seek to motivate future discussion surrounding the design of the research paper and the broader socio-technical publishing system

    GREAT3 results I: systematic errors in shear estimation and the impact of real galaxy morphology

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    We present first results from the third GRavitational lEnsing Accuracy Testing (GREAT3) challenge, the third in a sequence of challenges for testing methods of inferring weak gravitational lensing shear distortions from simulated galaxy images. GREAT3 was divided into experiments to test three specific questions, and included simulated space- and ground-based data with constant or cosmologically-varying shear fields. The simplest (control) experiment included parametric galaxies with a realistic distribution of signal-to-noise, size, and ellipticity, and a complex point spread function (PSF). The other experiments tested the additional impact of realistic galaxy morphology, multiple exposure imaging, and the uncertainty about a spatially-varying PSF; the last two questions will be explored in Paper II. The 24 participating teams competed to estimate lensing shears to within systematic error tolerances for upcoming Stage-IV dark energy surveys, making 1525 submissions overall. GREAT3 saw considerable variety and innovation in the types of methods applied. Several teams now meet or exceed the targets in many of the tests conducted (to within the statistical errors). We conclude that the presence of realistic galaxy morphology in simulations changes shear calibration biases by ∌1\sim 1 per cent for a wide range of methods. Other effects such as truncation biases due to finite galaxy postage stamps, and the impact of galaxy type as measured by the S\'{e}rsic index, are quantified for the first time. Our results generalize previous studies regarding sensitivities to galaxy size and signal-to-noise, and to PSF properties such as seeing and defocus. Almost all methods' results support the simple model in which additive shear biases depend linearly on PSF ellipticity.Comment: 32 pages + 15 pages of technical appendices; 28 figures; submitted to MNRAS; latest version has minor updates in presentation of 4 figures, no changes in content or conclusion

    Graph Neural Network Flavour Tagging and Boosted Higgs Measurements at the LHC

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    This thesis presents investigations into the challenges of, and potential improvements to, b-jet identification (b-tagging) at the ATLAS experiment at the Large Hadron Collider (LHC). The presence of b-jets is a key signature of many interesting physics processes such as the production of Higgs bosons, which preferentially decay to a pair of b-quarks. In this thesis, a particular focus is placed on the high transverse momentum regime, which is a critical region in which to study the Higgs boson and the wider Standard Model, but also a region within which b-tagging becomes increasingly difficult. As b-tagging relies on the accurate reconstruction of charged particle trajectories (tracks), the tracking performance is investigated and potential improvements are assessed. Track reconstruction becomes increasingly difficult at high transverse momentum due to the in- creased multiplicity and collimation of tracks, and also due to the presence of displaced tracks from the decay of a long-flying b-hadron. The investigations reveal that the quality selections applied during track reconstruction are suboptimal for b-hadron decay tracks inside high transverse momentum b-jets, motivating future studies into the optimisation of these selections. Two novel approaches are developed to improve b-tagging performance. Firstly, an algorithm which is able to classify the origin of tracks is used to select a more optimal set of tracks for input to the b-tagging algorithms. Secondly, a graph neural network (GNN) jet flavour tagging algorithm has been developed. This algorithm directly accepts jets and tracks as inputs, making a break from previous algorithms which relied on the outputs of intermediate taggers. The model is trained to simultaneously predict the jet flavour, track origins, and the spatial track-pair compatibility, and demonstrates marked improvements in b-tagging performance both at low and high transverse momenta. The closely related task of c-jet identification also benefits from this approach. Analysis of high transverse momentum H → bb decays, where the Higgs boson is produced in association with a vector boson, was performed using 139 fb−1 of 13 TeV proton-proton collision data from Run 2 of the LHC. This analysis provided first measurements of the V H, H → bb process in two high transverse momentum regions, and is described with a particular focus on the background modelling studies performed by the author

    Deep integrative information extraction from scientific literature

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    Doctor of PhilosophyDepartment of Computer ScienceWilliam H HsuThis dissertation presents deep integrative methods from both visual and textual perspectives to address the challenges of extracting information from documents, particularly scientific literature. The number of publications in the academic literature has soared. Published literature includes large amounts of valuable information that can help scientists and researchers develop new directions in their fields of interest. Moreover, this information can be used in many applications, among them scholar search engines, relevant paper recommendations, and citation analysis. However, the increased production of scientific literature makes the process of literature review laborious and time-consuming, especially when large amounts of data are stored in heterogeneous unstructured formats, both numerical and image-based text, both of which are challenging to read and analyze. Thus, the ability to automatically extract information from the scientific literature is necessary. In this dissertation, we present integrative information extraction from scientific literature using deep learning approaches. We first investigated a vision-based approach for understanding layout and extracting metadata from scanned scientific literature images. We tried convolutional neural network and transformer-based approaches to document layout. Furthermore, for vision-based metadata information extraction, we proposed a trainable recurrent convolutional neural network that integrated scientific document layout detection and character recognition to extract metadata information from the scientific literature. In doing so, we addressed the problem of existing methods that cannot combine the techniques of layout extraction and text recognition efficiently because different publishers use different formats to present information. This framework requires no additional text features added into the network during the training process and will generate text content and appropriate labels of major sections of scientific documents. We then extracted key-information from unstructured texts in the scientific literature using technologies based on Natural Language Processing (NLP). Key-information could include the named entity and the relationship between pairs of entities in the scientific literature. This information can help provide researchers with key insights into the scientific literature. We proposed the attention-based deep learning method to extract key-information with limited annotated data sets. This method enhances contextualized word representations using pre-trained language models like a Bidirectional Encoder Representations from Transformers (BERT) that, unlike conventional machine learning approaches, does not require hand-crafted features or training with massive data. The dissertation concludes by identifying additional challenges and future work in extracting information from the scientific literature

    GREAT3 results – I. Systematic errors in shear estimation and the impact of real galaxy morphology

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    We present first results from the third GRavitational lEnsing Accuracy Testing (GREAT3) challenge, the third in a sequence of challenges for testing methods of inferring weak gravitational lensing shear distortions from simulated galaxy images. GREAT3 was divided into experiments to test three specific questions, and included simulated space- and ground-based data with constant or cosmologically varying shear fields. The simplest (control) experiment included parametric galaxies with a realistic distribution of signal-to-noise, size, and ellipticity, and a complex point spread function (PSF). The other experiments tested the additional impact of realistic galaxy morphology, multiple exposure imaging, and the uncertainty about a spatially varying PSF; the last two questions will be explored in Paper II. The 24 participating teams competed to estimate lensing shears to within systematic error tolerances for upcoming Stage-IV dark energy surveys, making 1525 submissions overall. GREAT3 saw considerable variety and innovation in the types of methods applied. Several teams now meet or exceed the targets in many of the tests conducted (to within the statistical errors). We conclude that the presence of realistic galaxy morphology in simulations changes shear calibration biases by ∌1 per cent for a wide range of methods. Other effects such as truncation biases due to finite galaxy postage stamps, and the impact of galaxy type as measured by the SĂ©rsic index, are quantified for the first time. Our results generalize previous studies regarding sensitivities to galaxy size and signal-to-noise, and to PSF properties such as seeing and defocus. Almost all methods’ results support the simple model in which additive shear biases depend linearly on PSF ellipticity
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