4,102 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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

    Machine learning applications in search algorithms for gravitational waves from compact binary mergers

    Get PDF
    Gravitational waves from compact binary mergers are now routinely observed by Earth-bound detectors. These observations enable exciting new science, as they have opened a new window to the Universe. However, extracting gravitational-wave signals from the noisy detector data is a challenging problem. The most sensitive search algorithms for compact binary mergers use matched filtering, an algorithm that compares the data with a set of expected template signals. As detectors are upgraded and more sophisticated signal models become available, the number of required templates will increase, which can make some sources computationally prohibitive to search for. The computational cost is of particular concern when low-latency alerts should be issued to maximize the time for electromagnetic follow-up observations. One potential solution to reduce computational requirements that has started to be explored in the last decade is machine learning. However, different proposed deep learning searches target varying parameter spaces and use metrics that are not always comparable to existing literature. Consequently, a clear picture of the capabilities of machine learning searches has been sorely missing. In this thesis, we closely examine the sensitivity of various deep learning gravitational-wave search algorithms and introduce new methods to detect signals from binary black hole and binary neutron star mergers at previously untested statistical confidence levels. By using the sensitive distance as our core metric, we allow for a direct comparison of our algorithms to state-of-the-art search pipelines. As part of this thesis, we organized a global mock data challenge to create a benchmark for machine learning search algorithms targeting compact binaries. This way, the tools developed in this thesis are made available to the greater community by publishing them as open source software. Our studies show that, depending on the parameter space, deep learning gravitational-wave search algorithms are already competitive with current production search pipelines. We also find that strategies developed for traditional searches can be effectively adapted to their machine learning counterparts. In regions where matched filtering becomes computationally expensive, available deep learning algorithms are also limited in their capability. We find reduced sensitivity to long duration signals compared to the excellent results for short-duration binary black hole signals

    Marchenko-Lippmann-Schwinger inversion

    Get PDF
    Seismic wave reflections recorded at the Earth’s surface provide a rich source of information about the structure of the subsurface. These reflections occur due to changes in the material properties of the Earth; in the acoustic approximation, these are the density of the Earth and the velocity of seismic waves travelling through it. Therefore, there is a physical relationship between the material properties of the Earth and the reflected seismic waves that we observe at the surface. This relationship is non-linear, due to the highly scattering nature of the Earth, and to our inability to accurately reproduce these scattered waves with the low resolution velocity models that are usually available to us. Typically, we linearize the scattering problem by assuming that the waves are singly-scattered, requiring multiple reflections to be removed from recorded data at great effort and with varying degrees of success. This assumption is called the Born approximation. The equation that describes the relationship between the Earth’s properties and the fully-scattering reflection data is called the Lippmann-Schwinger equation, and this equation is linear if the full scattering wavefield inside the Earth could be known. The development of Marchenko methods makes such wavefields possible to estimate using only the surface reflection data and an estimate of the direct wave from the surface to each point in the Earth. Substituting the results from a Marchenko method into the Lippmann-Schwinger equation results in a linear equation that includes all orders of scattering. The aim of this thesis is to determine whether higher orders of scattering improve the linear inverse problem from data to velocities, by comparing linearized inversion under the Born approximation to the inversion of the linear Lippmann-Schwinger equation. This thesis begins by deriving the linear Lippmann-Schwinger and Born inverse problems, and reviewing the theoretical basis for Marchenko methods. By deriving the derivative of the full scattering Green’s function with respect to the model parameters of the Earth, the gradient direction for a new type of least-squares full waveform inversion called Marchenko-Lippmann-Schwinger full waveform inversion is defined that uses all orders of scattering. By recreating the analytical 1D Born inversion of a boxcar perturbation by Beydoun and Tarantola (1988), it is shown that high frequency-sampling density is required to correctly estimate the amplitude of the velocity perturbation. More importantly, even when the scattered wavefield is defined to be singly-scattering and the velocity model perturbation can be found without matrix inversion, Born inversion cannot reproduce the true velocity structure exactly. When the results of analytical inversion are compared to inversions where the inverse matrices have been explicitly calculated, the analytical inversion is found to be superior. All three matrix inversion methods are found to be extremely ill-posed. With regularisation, it is possible to accurately determine the edges of the perturbation, but not the amplitude. Moving from a boxcar perturbation with a homogeneous starting velocity to a many-layered 1D model and a smooth representation of this model as the starting point, it is found that the inversion solution is highly dependent on the starting model. By optimising an iterative inversion in both the model and data domains, it is found that optimising the velocity model misfit does not guarantee improvement in the resulting data misfit, and vice versa. Comparing unregularised inversion to inversions with Tikhonov damping or smoothing applied to the kernel matrix, it is found that strong Tikhonov damping results in the most accurate velocity models. From the consistent under-performance of Lippmann-Schwinger inversion when using Marchenko-derived Green’s functions compared to inversions carried out with true Green’s functions, it is concluded that the fallibility of Marchenko methods results in inferior inversion results. Born and Lippmann-Schwinger inversion are tested on a 2D syncline model. Due to computational limitations, using all sources and receivers in the inversion required limiting the number of frequencies to 5. Without regularisation, the model update is uninterpretable due to the presence of strong oscillations across the model. With strong Tikhonov damping, the model updates obtained are poorly scaled, have low resolution, and low amplitude oscillatory noise remains. By replacing the inversion of all sources simultaneously with single source inversions, it is possible to reinstate all frequencies within our limited computational resources. These single source model updates can be stacked similarly to migration images to improve the overall model update. As predicted by the 1D analytical inversion, restoring the full frequency bandwidth eliminates the oscillatory noise from the inverse solution. With or without regularisation, Born and Lippmann-Schwinger inversion results are found to be nearly identical. When Marchenko-derived Green’s functions are introduced, the inversion results are worse than either the Born inversion or the Lippmann-Schwinger inversion without Marchenko methods. On this basis, one concludes that the inclusion of higher order scattering does not improve the outcome of solving the linear inverse scattering problem using currently available methods. Nevertheless, some recent developments in the methods used to solve the Marchenko equation hold some promise for improving solutions in future

    Ecos de la academia: Revista de la Facultad de Educación, Ciencia y Tecnología - FECYT Nro 4

    Get PDF
    Ecos de la academia, Revista de la Facultad de Educación Ciencia y Tecnología es una publicación científica de la Universidad Técnica del Norte, con revisión por pares a doble ciego que publica artículos en idioma español, quichua, portugués e inglés. Se edita con una frecuencia semestral con dos números por año.En ella se divulgan trabajos originales e inéditos generados por los investigadores, docentes y estudiantes de la FECYT, y contribuciones de profesionales de instituciones docentes e investigativas dentro y fuera del país, con calidad, originalidad y relevancia en las áreas de ciencias sociales y tecnología aplicada.Los orígenes de la fotografía en la segunda ciudad de Cataluña: Reus, 1839-1903. Hábitos de consumo y uso de medios digitales en los estudiantes de la Universidad Técnica del Norte. Gastronomía, historia y cultura afrodescendiente de las comunidades Chota y Salinas en Imbabura, Ecuador. Los organizadores gráficos: elementos y procedimientos básicos para su diseño. Análisis del desempeño profesional del graduado de la carrera de Licenciatura en Inglés de la Universidad Técnica del Norte. Uso del software Aleks como complemento en la asignatura de Fundamentos de Matemáticas del curso de nivelación EPN-SENECYT. La educación de postgrado y la enseñanza de Redes Neuronales Artificiales como herramienta versátil para egresados. Home is an uneasty place: Afroperipheralism anda diasporic sensibilities in Wayde Compton’s “The Instrumental”. Respuesta de la carrera de Educación Básica a las necesidades sociales en la Zona 1 del Ecuador. Programa SaludArte: Salud, Alimentación y Movimiento entran a las escuelas para mejorar la calidad educativa. Tendencias de consumo turístico de los Millennials en la ciudad de Ibarra. Los Grupos de Investigación como estrategias para desarrollo de la investigación científica en las instituciones de educación superior ecuatorianas. Paradigmas y modelos pedagógicos de los postulados científicos en el espacio de aula en la Universidad Técnica de Ambato. Predicting academic performance in traditional environments at higher-education institutions using data mining: A review. El Proyecto de Investigación “Muros que hablan. Un recorrido por los graffitis de Imbabura”. Construcción de la marca ciudad. Normas de presentación de artículos científicos en la revista Ecos de la Academia

    Insights into temperature controls on rockfall occurrence and cliff erosion

    Get PDF
    A variety of environmental triggers have been associated with the occurrence of rockfalls however their role and relative significance remains poorly constrained. This is in part due to the lack of concurrent data on rockfall occurrence and cliff face conditions at temporal resolutions that mirror the variability of environmental conditions, and over durations for large enough numbers of rockfall events to be captured. The aim of this thesis is to fill this data gap, and then to specifically focus on the role of temperature in triggering rockfall that this data illuminates. To achieve this, a long-term multiannual 3D rockfall dataset and contemporaneous Infrared Thermography (IRT) monitoring of cliff surface temperatures has been generated. The approaches used in this thesis are undertaken at East Cliff, Whitby, which is a coastal cliff located in North Yorkshire, UK. The monitored section is ~ 200 m wide and ~65 m high, with a total cliff face area of ~9,592 m². A method for the automated quantification of rockfall volumes is used to explore data collected between 2017–2019 and 2021, with the resulting inventory including > 8,300 rockfalls from 2017–2019 and > 4,100 rockfalls in 2021, totalling > 12,400 number of rockfalls. The analysis of the inventory demonstrates that during dry conditions, increases in rockfall frequency are coincident with diurnal surface temperature fluctuations, notably at sunrise, noon and sunset in all seasons, leading to a marked diurnal pattern of rockfall. Statistically significant relationships are observed to link cliff temperature and rockfall, highlighting the response of rock slopes to absolute temperatures and changes in temperature. This research also shows that inclement weather constitutes the dominant control over the annual production of rockfalls but also quantifies the period when temperature controls are dominant. Temperature-controlled rockfall activity is shown to have an important erosional role, particularly in periods of iterative erosion dominated by small size rockfalls. As such, this thesis provides for the first high-resolution evidence of temperature controls on rockfall activity, cliff erosion and landform development

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

    Get PDF
    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Relatively Absolute : Relative and Absolute Chronologies in the Neolithic of Southeast Europe

    Get PDF
    Зборник радова на тему апсолутне и релативне хронологије неолитског периода у југоисточној Европи. Географски покрива области од Грчке до Хрватске, а хронолошки период између 7000 и 4500 године пре нове ере. У зборнику су приказани најновији приступи и резултати радиокарбонских анализа и статистички и типолошки модели који побољшавају прецизност резултата

    Tiny Machine Learning Environment: Enabling Intelligence on Constrained Devices

    Get PDF
    Running machine learning algorithms (ML) on constrained devices at the extreme edge of the network is problematic due to the computational overhead of ML algorithms, available resources on the embedded platform, and application budget (i.e., real-time requirements, power constraints, etc.). This required the development of specific solutions and development tools for what is now referred to as TinyML. In this dissertation, we focus on improving the deployment and performance of TinyML applications, taking into consideration the aforementioned challenges, especially memory requirements. This dissertation contributed to the construction of the Edge Learning Machine environment (ELM), a platform-independent open-source framework that provides three main TinyML services, namely shallow ML, self-supervised ML, and binary deep learning on constrained devices. In this context, this work includes the following steps, which are reflected in the thesis structure. First, we present the performance analysis of state-of-the-art shallow ML algorithms including dense neural networks, implemented on mainstream microcontrollers. The comprehensive analysis in terms of algorithms, hardware platforms, datasets, preprocessing techniques, and configurations shows similar performance results compared to a desktop machine and highlights the impact of these factors on overall performance. Second, despite the assumption that TinyML only permits models inference provided by the scarcity of resources, we have gone a step further and enabled self-supervised on-device training on microcontrollers and tiny IoT devices by developing the Autonomous Edge Pipeline (AEP) system. AEP achieves comparable accuracy compared to the typical TinyML paradigm, i.e., models trained on resource-abundant devices and then deployed on microcontrollers. Next, we present the development of a memory allocation strategy for convolutional neural networks (CNNs) layers, that optimizes memory requirements. This approach reduces the memory footprint without affecting accuracy nor latency. Moreover, e-skin systems share the main requirements of the TinyML fields: enabling intelligence with low memory, low power consumption, and low latency. Therefore, we designed an efficient Tiny CNN architecture for e-skin applications. The architecture leverages the memory allocation strategy presented earlier and provides better performance than existing solutions. A major contribution of the thesis is given by CBin-NN, a library of functions for implementing extremely efficient binary neural networks on constrained devices. The library outperforms state of the art NN deployment solutions by drastically reducing memory footprint and inference latency. All the solutions proposed in this thesis have been implemented on representative devices and tested in relevant applications, of which results are reported and discussed. The ELM framework is open source, and this work is clearly becoming a useful, versatile toolkit for the IoT and TinyML research and development community
    corecore