5,488 research outputs found

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Machine learning in solar physics

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    The application of machine learning in solar physics has the potential to greatly enhance our understanding of the complex processes that take place in the atmosphere of the Sun. By using techniques such as deep learning, we are now in the position to analyze large amounts of data from solar observations and identify patterns and trends that may not have been apparent using traditional methods. This can help us improve our understanding of explosive events like solar flares, which can have a strong effect on the Earth environment. Predicting hazardous events on Earth becomes crucial for our technological society. Machine learning can also improve our understanding of the inner workings of the sun itself by allowing us to go deeper into the data and to propose more complex models to explain them. Additionally, the use of machine learning can help to automate the analysis of solar data, reducing the need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a Living Review in Solar Physics (LRSP

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Using machine learning to predict pathogenicity of genomic variants throughout the human genome

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    Geschätzt mehr als 6.000 Erkrankungen werden durch Veränderungen im Genom verursacht. Ursachen gibt es viele: Eine genomische Variante kann die Translation eines Proteins stoppen, die Genregulation stören oder das Spleißen der mRNA in eine andere Isoform begünstigen. All diese Prozesse müssen überprüft werden, um die zum beschriebenen Phänotyp passende Variante zu ermitteln. Eine Automatisierung dieses Prozesses sind Varianteneffektmodelle. Mittels maschinellem Lernen und Annotationen aus verschiedenen Quellen bewerten diese Modelle genomische Varianten hinsichtlich ihrer Pathogenität. Die Entwicklung eines Varianteneffektmodells erfordert eine Reihe von Schritten: Annotation der Trainingsdaten, Auswahl von Features, Training verschiedener Modelle und Selektion eines Modells. Hier präsentiere ich ein allgemeines Workflow dieses Prozesses. Dieses ermöglicht es den Prozess zu konfigurieren, Modellmerkmale zu bearbeiten, und verschiedene Annotationen zu testen. Der Workflow umfasst außerdem die Optimierung von Hyperparametern, Validierung und letztlich die Anwendung des Modells durch genomweites Berechnen von Varianten-Scores. Der Workflow wird in der Entwicklung von Combined Annotation Dependent Depletion (CADD), einem Varianteneffektmodell zur genomweiten Bewertung von SNVs und InDels, verwendet. Durch Etablierung des ersten Varianteneffektmodells für das humane Referenzgenome GRCh38 demonstriere ich die gewonnenen Möglichkeiten Annotationen aufzugreifen und neue Modelle zu trainieren. Außerdem zeige ich, wie Deep-Learning-Scores als Feature in einem CADD-Modell die Vorhersage von RNA-Spleißing verbessern. Außerdem werden Varianteneffektmodelle aufgrund eines neuen, auf Allelhäufigkeit basierten, Trainingsdatensatz entwickelt. Diese Ergebnisse zeigen, dass der entwickelte Workflow eine skalierbare und flexible Möglichkeit ist, um Varianteneffektmodelle zu entwickeln. Alle entstandenen Scores sind unter cadd.gs.washington.edu und cadd.bihealth.org frei verfügbar.More than 6,000 diseases are estimated to be caused by genomic variants. This can happen in many possible ways: a variant may stop the translation of a protein, interfere with gene regulation, or alter splicing of the transcribed mRNA into an unwanted isoform. It is necessary to investigate all of these processes in order to evaluate which variant may be causal for the deleterious phenotype. A great help in this regard are variant effect scores. Implemented as machine learning classifiers, they integrate annotations from different resources to rank genomic variants in terms of pathogenicity. Developing a variant effect score requires multiple steps: annotation of the training data, feature selection, model training, benchmarking, and finally deployment for the model's application. Here, I present a generalized workflow of this process. It makes it simple to configure how information is converted into model features, enabling the rapid exploration of different annotations. The workflow further implements hyperparameter optimization, model validation and ultimately deployment of a selected model via genome-wide scoring of genomic variants. The workflow is applied to train Combined Annotation Dependent Depletion (CADD), a variant effect model that is scoring SNVs and InDels genome-wide. I show that the workflow can be quickly adapted to novel annotations by porting CADD to the genome reference GRCh38. Further, I demonstrate the integration of deep-neural network scores as features into a new CADD model, improving the annotation of RNA splicing events. Finally, I apply the workflow to train multiple variant effect models from training data that is based on variants selected by allele frequency. In conclusion, the developed workflow presents a flexible and scalable method to train variant effect scores. All software and developed scores are freely available from cadd.gs.washington.edu and cadd.bihealth.org

    Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)

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    The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment. FMER is a subset of image processing and it is a multidisciplinary topic to analysis. So, it requires familiarity with other topics of Artifactual Intelligence (AI) such as machine learning, digital image processing, psychology and more. So, it is a great opportunity to write a book which covers all of these topics for beginner to professional readers in the field of AI and even without having background of AI. Our goal is to provide a standalone introduction in the field of MFER analysis in the form of theorical descriptions for readers with no background in image processing with reproducible Matlab practical examples. Also, we describe any basic definitions for FMER analysis and MATLAB library which is used in the text, that helps final reader to apply the experiments in the real-world applications. We believe that this book is suitable for students, researchers, and professionals alike, who need to develop practical skills, along with a basic understanding of the field. We expect that, after reading this book, the reader feels comfortable with different key stages such as color and depth image processing, color and depth image representation, classification, machine learning, facial micro-expressions recognition, feature extraction and dimensionality reduction. The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment.Comment: This is the second edition of the boo

    The African Marine Litter Outlook

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    This open access book provides a cross-sectoral, multi-scale assessment of marine litter in Africa with a focus on plastics. From distribution, to impacts on environmental and human health, this book looks at what is known scientifically. It includes a policy analysis of the instruments that currently exist, and what is needed to help Africa tackle marine litter—including local and transboundary sources. Across 5 chapters, experts from Africa and beyond have put together a summary of the scientific knowledge currently known about marine litter in Africa. The context of the African continent and future projections form a backdrop on which the scientific knowledge is built. This scientific knowledge incorporates quantities, distributions, and pathways of litter into the marine environment, highlighting where the impacts of marine litter are most felt in Africa. These impacts have widespread effects, with ecological, social, economic, and human health repercussions. While containing detailed scientific information, this book provides a sound knowledge base for policymakers, NGOs and the broader public

    Pathophysiology of Spinal Cord Injury (SCI)

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    Spinal cord injury (SCI) leads to paralysis, sensory, and autonomic nervous system dysfunctions. However, the pathophysiology of SCI is complex, and not limited to the nervous system. Indeed, several other organs and tissue are also affected by the injury, directly or not, acutely or chronically, which induces numerous health complications. Although a lot of research has been performed to repair motor and sensory functions, SCI-induced health issues are less studied, although they represent a major concern among patients. There is a gap of knowledge in pre-clinical models studying these SCI-induced health complications that limits translational applications in humans. This reprint describes several aspects of the pathophysiology of spinal cord injuries. This includes, but is not limited to, the impact of SCI on cardiovascular and respiratory functions, bladder and bowel function, autonomic dysreflexia, liver pathology, metabolic syndrome, bones and muscles loss, and cognitive functions

    Leveraging Value-awareness for Online and Offline Model-based Reinforcement Learning

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    Model-based Reinforcement Learning (RL) lies at the intersection of planning and learning for sequential decision making. Value-awareness in model learning has recently emerged as a means to imbue task or reward information into the objective of model learn- ing, in order for the model to leverage specificity of a task. While finding success in theory as being superior to maximum likelihood estimation in the context of (online) model-based RL, value-awareness has remained impractical for most non-trivial tasks. This thesis aims to bridge the gap in theory and practice by applying the principle of value-awareness to two settings – the online RL setting and offline RL setting. First, within online RL, this thesis revisits value-aware model learning from the perspective of minimizing performance difference, obtaining a novel value-aware model learning objec- tive as a direct upper bound of it. Then, this thesis investigates and remedies the issue of stale value estimates that has so far been holding back the practicality of value-aware model learning. Using the proposed remedy, performance improvements are presented over maximum-likelihood based baselines and existing value-aware objectives, in several continuous control tasks, while also enabling existing value-aware objectives to become performant. In the offline RL context, this thesis takes a step back from model learning and ap- plies value-awareness towards better data augmentation. Such data augmentation, when applied to model-based offline RL algorithms, allows for leveraging unseen states with low epistemic uncertainty that have previously not been reachable within the assumptions and limitations of model-based offline RL. Value-aware state augmentations are found to enable better performance on offline RL benchmarks compared to existing baselines and non-value-aware alternatives.Ph.D

    Real-Time Hybrid Visual Servoing of a Redundant Manipulator via Deep Reinforcement Learning

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    Fixtureless assembly may be necessary in some manufacturing tasks and environ-ments due to various constraints but poses challenges for automation due to non-deterministic characteristics not favoured by traditional approaches to industrial au-tomation. Visual servoing methods of robotic control could be effective for sensitive manipulation tasks where the desired end-effector pose can be ascertained via visual cues. Visual data is complex and computationally expensive to process but deep reinforcement learning has shown promise for robotic control in vision-based manipu-lation tasks. However, these methods are rarely used in industry due to the resources and expertise required to develop application-specific systems and prohibitive train-ing costs. Training reinforcement learning models in simulated environments offers a number of benefits for the development of robust robotic control algorithms by reducing training time and costs, and providing repeatable benchmarks for which algorithms can be tested, developed and eventually deployed on real robotic control environments. In this work, we present a new simulated reinforcement learning envi-ronment for developing accurate robotic manipulation control systems in fixtureless environments. Our environment incorporates a contemporary collaborative industrial robot, the KUKA LBR iiwa, with the goal of positioning its end effector in a generic fixtureless environment based on a visual cue. Observational inputs are comprised of the robotic joint positions and velocities, as well as two cameras, whose positioning reflect hybrid visual servoing with one camera attached to the robotic end-effector, and another observing the workspace respectively. We propose a state-of-the-art deep reinforcement learning approach to solving the task environment and make prelimi-nary assessments of the efficacy of this approach to hybrid visual servoing methods for the defined problem environment. We also conduct a series of experiments ex-ploring the hyperparameter space in the proposed reinforcement learning method. Although we could not prove the efficacy of a deep reinforcement approach to solving the task environment with our initial results, we remain confident that such an ap-proach could be feasible to solving this industrial manufacturing challenge and that our contributions in this work in terms of the novel software provide a good basis for the exploration of reinforcement learning approaches to hybrid visual servoing in accurate manufacturing contexts

    Automated UAV and Satellite Image Analysis For Wildlife Monitoring.

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    Very high resolution satellites and unmanned aerial vehicles (UAVs) are revolutionising our ability to monitor wildlife, especially species in remote and inaccessible regions. However, given the rapid increase in data acquisition, computer-automated approaches are urgently needed to count wildlife in the resultant imagery. In this thesis, we investigate the application of convolutional neural networks (CNNs) to the task of detecting vulnerable seabird populations in satellite and UAV imagery. In our first application we train a U-Net CNN to detect wandering albatrosses in 31-cm resolution WorldView-3 satellite imagery. We compare results across four different island colonies using a leave-one-island-out cross validation, achieving a mean average precision (mAP) score of 0.669. By collecting new data on inter-observer variation in albatross counts, we show that our U-Net results fall within the range of human accuracy for two islands, with misclassifications at other sites being simple to filter manually. In our second application we detect Abbott’s boobies nesting in forest canopy, using UAV Structure from Motion (SfM) imagery. We focus on overcoming occlusion from branches by implementing a multi-view detection method. We first train a Faster R-CNN model to detect Abbott’s booby nest sites (mAP=0.518) and guano (mAP=0.472) in the 2D UAV images. We then project Faster R-CNN detections onto the 3D SfM model, cluster multi-view detections of the same objects using DBSCAN, and use cluster features to classify proposals into true and false positives (comparing logistic regression, support vector machine, and multilayer perceptron models). Our best-performing multi-view model successfully detects nest sites (mAP=0.604) and guano (mAP=0.574), and can be incorporated with expert review to greatly expedite analysis time. Both methods have immediate real-world application for future surveys of the target species, allowing for more frequent, expansive, and lower-cost monitoring, vital for safeguarding populations in the long-term
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