394 research outputs found

    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

    A Comprehensive Review of Data-Driven Co-Speech Gesture Generation

    Full text link
    Gestures that accompany speech are an essential part of natural and efficient embodied human communication. The automatic generation of such co-speech gestures is a long-standing problem in computer animation and is considered an enabling technology in film, games, virtual social spaces, and for interaction with social robots. The problem is made challenging by the idiosyncratic and non-periodic nature of human co-speech gesture motion, and by the great diversity of communicative functions that gestures encompass. Gesture generation has seen surging interest recently, owing to the emergence of more and larger datasets of human gesture motion, combined with strides in deep-learning-based generative models, that benefit from the growing availability of data. This review article summarizes co-speech gesture generation research, with a particular focus on deep generative models. First, we articulate the theory describing human gesticulation and how it complements speech. Next, we briefly discuss rule-based and classical statistical gesture synthesis, before delving into deep learning approaches. We employ the choice of input modalities as an organizing principle, examining systems that generate gestures from audio, text, and non-linguistic input. We also chronicle the evolution of the related training data sets in terms of size, diversity, motion quality, and collection method. Finally, we identify key research challenges in gesture generation, including data availability and quality; producing human-like motion; grounding the gesture in the co-occurring speech in interaction with other speakers, and in the environment; performing gesture evaluation; and integration of gesture synthesis into applications. We highlight recent approaches to tackling the various key challenges, as well as the limitations of these approaches, and point toward areas of future development.Comment: Accepted for EUROGRAPHICS 202

    Self-supervised spontaneous latent-based facial expression sequence generation

    Get PDF
    In this paper, we investigate the spontaneity issue in facial expression sequence generation. Current leading methods in the field are commonly reliant on manually adjusted conditional variables to direct the model to generate a specific class of expression. We propose a neural network-based method which uses Gaussian noise to model spontaneity in the generation process, removing the need for manual control of conditional generation variables. Our model takes two sequential images as input, with additive noise, and produces the next image in the sequence. We trained two types of models: single-expression, and mixed-expression. With single-expression, unique facial movements of certain emotion class can be generated; with mixed expressions, fully spontaneous expression sequence generation can be achieved. We compared our method to current leading generation methods on a variety of publicly available datasets. Initial qualitative results show our method produces visually more realistic expressions and facial action unit (AU) trajectories; initial quantitative results using image quality metrics (SSIM and NIQE) show the quality of our generated images is higher. Our approach and results are novel in the field of facial expression generation, with potential wider applications to other sequence generation tasks

    Learning-based methods for planning and control of humanoid robots

    Get PDF
    Nowadays, humans and robots are more and more likely to coexist as time goes by. The anthropomorphic nature of humanoid robots facilitates physical human-robot interaction, and makes social human-robot interaction more natural. Moreover, it makes humanoids ideal candidates for many applications related to tasks and environments designed for humans. No matter the application, an ubiquitous requirement for the humanoid is to possess proper locomotion skills. Despite long-lasting research, humanoid locomotion is still far from being a trivial task. A common approach to address humanoid locomotion consists in decomposing its complexity by means of a model-based hierarchical control architecture. To cope with computational constraints, simplified models for the humanoid are employed in some of the architectural layers. At the same time, the redundancy of the humanoid with respect to the locomotion task as well as the closeness of such a task to human locomotion suggest a data-driven approach to learn it directly from experience. This thesis investigates the application of learning-based techniques to planning and control of humanoid locomotion. In particular, both deep reinforcement learning and deep supervised learning are considered to address humanoid locomotion tasks in a crescendo of complexity. First, we employ deep reinforcement learning to study the spontaneous emergence of balancing and push recovery strategies for the humanoid, which represent essential prerequisites for more complex locomotion tasks. Then, by making use of motion capture data collected from human subjects, we employ deep supervised learning to shape the robot walking trajectories towards an improved human-likeness. The proposed approaches are validated on real and simulated humanoid robots. Specifically, on two versions of the iCub humanoid: iCub v2.7 and iCub v3

    An information field theory approach to Bayesian state and parameter estimation in dynamical systems

    Full text link
    Dynamical system state estimation and parameter calibration problems are ubiquitous across science and engineering. Bayesian approaches to the problem are the gold standard as they allow for the quantification of uncertainties and enable the seamless fusion of different experimental modalities. When the dynamics are discrete and stochastic, one may employ powerful techniques such as Kalman, particle, or variational filters. Practitioners commonly apply these methods to continuous-time, deterministic dynamical systems after discretizing the dynamics and introducing fictitious transition probabilities. However, approaches based on time-discretization suffer from the curse of dimensionality since the number of random variables grows linearly with the number of time-steps. Furthermore, the introduction of fictitious transition probabilities is an unsatisfactory solution because it increases the number of model parameters and may lead to inference bias. To address these drawbacks, the objective of this paper is to develop a scalable Bayesian approach to state and parameter estimation suitable for continuous-time, deterministic dynamical systems. Our methodology builds upon information field theory. Specifically, we construct a physics-informed prior probability measure on the function space of system responses so that functions that satisfy the physics are more likely. This prior allows us to quantify model form errors. We connect the system's response to observations through a probabilistic model of the measurement process. The joint posterior over the system responses and all parameters is given by Bayes' rule. To approximate the intractable posterior, we develop a stochastic variational inference algorithm. In summary, the developed methodology offers a powerful framework for Bayesian estimation in dynamical systems

    The impact of the stellar evolution of single and binary stars on the global, dynamical evolution of dense star clusters across cosmic time

    Get PDF
    Sternhaufen im Universum stellen dichte, selbstgravitierende und typischerweise dynamisch kollidierende Umgebungen dar, die aus Tausenden bis Millionen von Sternen bestehen. Sie bevölkern galaktische Scheiben, Halos und sogar galaktische Zentren im gesamten Kosmos und bilden eine grundlegende Einheit in einer Hierarchie der kosmischen Strukturbildung. Außerdem sind sie in der Regel viel dichter als ihre Wirtsgalaxie, was sie zu unglaublich faszinierenden astronomischen Objekten macht. Anders als ihre Umgebung erleben Sterne und kompakte Objekte in Sternhaufen häufige dynamische Streuungen, bilden dynamische Doppelsterne, verschmelzen unter Aussendung von Gravitationswellen, werden durch Dreikörperdynamik herausgeschleudert und stoßen in seltenen Fällen sogar direkt zusammen. Infolgedessen sind Sternhaufen Fabriken aller exotischen Doppelsterne, von z.B. Thorne-Zytkow-Objekten und kataklysmischen Variablen bis hin zu kompakten Doppelsternen, beispielsweise Doppelsterne, die aus schwarzen Löchern und Neutronensternen bestehen. Darüber hinaus fangen mit zunehmender Teilchenzahl einzigartige Gravitationseffekte von kollidierenden Vielteilchensystemen an die frühe Entwicklung des Haufens zu dominieren, die zu zusammenziehenden und zunehmend schneller rotierenden Kernen der Sternhaufen führen, die bevorzugt massereiche Sterne und kompakte Objeckte sowie Doppelsterne enthalten, und einem sich ausdehnenden Halo aus Sternen und kompakten Objekten geringerer Masse. Sternhaufen sind daher nicht nur ein Labor für die Gravitationsvielteilchenphysik, sondern auch für die Sternentwicklung von Einzel- und Doppelsternen sowie hierarchischen Sternensystemen höherer Ordnung. Alle diese physikalischen Prozesse können nicht isoliert betrachtet werden - sie verstärken sich in Sternhaufen gegenseitig und viele passieren auf ähnlichen Zeitskalen. In dieser Arbeit möchte ich den Einfluss der Sternentwicklung auf die globale Dynamik von Sternhaufen mit Hilfe von direkten gravitativen N-Körper und Hénon-Typ Monte-Carlo Simulationen von Sternhaufen genauer studieren. Ich konzentriere mich auf die Entwicklung von metallarmen Sternpopulationen (Population II), die in Kugelsternhaufen und extrem metallarme Sternpopulationen (Population III), die die ältesten Sternpopulationen im Universum bilden

    2022-2023 Xavier University Undergraduate and Graduate University Catalog

    Get PDF
    https://www.exhibit.xavier.edu/coursecatalog/1275/thumbnail.jp

    Low-Cost Bayesian Methods for Fixing Neural Networks' Overconfidence

    Get PDF
    Well-calibrated predictive uncertainty of neural networks—essentially making them know when they do not know—is paramount in safety-critical applications. However, deep neural networks are overconfident in the region both far away and near the training data. In this thesis, we study Bayesian neural networks and their extensions to mitigate this issue. First, we show that being Bayesian, even just at the last layer and in a post-hoc manner via Laplace approximations, helps mitigate overconfidence in deep ReLU classifiers. Then, we provide a cost-effective Gaussian-process extension to ReLU Bayesian neural networks that provides a guarantee that ReLU nets will never be overconfident in the region far from the data. Furthermore, we propose three ways of improving the calibration of general Bayesian neural networks in the regions near the data by (i) refining parametric approximations to the Bayesian neural networks’ posteriors with normalizing flows, (ii) training the uncertainty of Laplace approximations, and (iii) leveraging out-of-distribution data during training. We provide an easy-to-use library, laplace-torch, to facilitate the modern arts of Laplace approximations in deep learning. It gives users a way to turn a standard pre-trained deep net into a Bayesian neural network in a cost-efficient manner

    A Digital Triplet for Utilizing Offline Environments to Train Condition Monitoring Systems for Rolling Element Bearings

    Get PDF
    Manufacturing competitiveness is related to making a quality product while incurring the lowest costs. Unexpected downtime caused by equipment failure negatively impacts manufacturing competitiveness due to the ensuing defects and delays caused by the downtime. Manufacturers have adopted condition monitoring (CM) techniques to reduce unexpected downtime to augment maintenance strategies. The CM adoption has transitioned maintenance from Breakdown Maintenance (BM) to Condition-Based Maintenance (CbM) to anticipate impending failures and provide maintenance actions before equipment failure. CbM is the umbrella term for maintenance strategies that use condition monitoring techniques such as Preventive Maintenance (PM) and Predictive Maintenance (PdM). Preventive Maintenance involves providing periodic checks based on either time or sensory input. Predictive Maintenance utilizes continuous or periodic sensory inputs to determine the machine health state to predict the equipment failure. The overall goal of the work is to improve bearing diagnostic and prognostic predictions for equipment health by utilizing surrogate systems to generate failure data that represents production equipment failure, thereby providing training data for condition monitoring solutions without waiting for real world failure data. This research seeks to address the challenges of obtaining failure data for CM systems by incorporating a third system into monitoring strategies to create a Digital Triplet (DTr) for condition monitoring to increase the amount of possible data for condition monitoring. Bearings are a critical component in rotational manufacturing systems with wide application to other industries outside of manufacturing, such as energy and defense. The reinvented DTr system considers three components: the physical, surrogate, and digital systems. The physical system represents the real-world application in production that cannot fail. The surrogate system represents a physical component in a test system in an offline environment where data is generated to fill in gaps from data unavailable in the real-world system. The digital system is the CM system, which provides maintenance recommendations based on the ingested data from the real world and surrogate systems. In pursuing the research goal, a comprehensive bearing dataset detailing these four failure modes over different collection operating parameters was created. Subsequently, the collections occurred under different operating conditions, such as speed-varying, load-varying, and steadystate. Different frequency and time measures were used to analyze and identify differentiating criteria between the different failure classes over the differing operating conditions. These empirical observations were recreated using simulations to filter out potential outliers. The outputs of the physical model were combined with knowledge from the empirical observations to create ”spectral deltas” to augment existing bearing data and create new failure data that resemble similar frequency criteria to the original data. The primary verification occurred on a laboratory-bearing test stand. A conjecture is provided on how to scale to a larger system by analyzing a larger system from a local manufacturer. From the subsequent analysis of machine learning diagnosis and prognosis models, the original and augmented bearing data can complement each other during model training. The subsequent data substitution verifies that bearing data collected under different operating conditions and sizes can be substituted between different systems. Ostensibly, the full formulation of the digital triplet system is that bearing data generated at a smaller size can be scaled to train predictive failure models for larger bearing sizes. Future work should consider implementing this method for other systems outside of bearings, such as gears, non-rotational equipment, such as pumps, or even larger complex systems, such as computer numerically controlled machine tools or car engines. In addition, the method and process should not be restricted to only mechanical systems and could be applied to electrical systems, such as batteries. Furthermore, an investigation should consider further data-driven approximations to specific bearing characteristics related to the stiffness and damping parameters needed in modeling. A final consideration is for further investigation into the scalability quantities within the data and how to track these changes through different system levels
    • …
    corecore