1,136 research outputs found

    Strategies and performance of the CMS silicon tracker alignment during LHC Run 2

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    The strategies for and the performance of the CMS silicon tracking system alignment during the 2015–2018 data-taking period of the LHC are described. The alignment procedures during and after data taking are explained. Alignment scenarios are also derived for use in the simulation of the detector response. Systematic effects, related to intrinsic symmetries of the alignment task or to external constraints, are discussed and illustrated for different scenarios

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Tools for efficient Deep Learning

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    In the era of Deep Learning (DL), there is a fast-growing demand for building and deploying Deep Neural Networks (DNNs) on various platforms. This thesis proposes five tools to address the challenges for designing DNNs that are efficient in time, in resources and in power consumption. We first present Aegis and SPGC to address the challenges in improving the memory efficiency of DL training and inference. Aegis makes mixed precision training (MPT) stabler by layer-wise gradient scaling. Empirical experiments show that Aegis can improve MPT accuracy by at most 4\%. SPGC focuses on structured pruning: replacing standard convolution with group convolution (GConv) to avoid irregular sparsity. SPGC formulates GConv pruning as a channel permutation problem and proposes a novel heuristic polynomial-time algorithm. Common DNNs pruned by SPGC have maximally 1\% higher accuracy than prior work. This thesis also addresses the challenges lying in the gap between DNN descriptions and executables by Polygeist for software and POLSCA for hardware. Many novel techniques, e.g. statement splitting and memory partitioning, are explored and used to expand polyhedral optimisation. Polygeist can speed up software execution in sequential and parallel by 2.53 and 9.47 times on Polybench/C. POLSCA achieves 1.5 times speedup over hardware designs directly generated from high-level synthesis on Polybench/C. Moreover, this thesis presents Deacon, a framework that generates FPGA-based DNN accelerators of streaming architectures with advanced pipelining techniques to address the challenges from heterogeneous convolution and residual connections. Deacon provides fine-grained pipelining, graph-level optimisation, and heuristic exploration by graph colouring. Compared with prior designs, Deacon shows resource/power consumption efficiency improvement of 1.2x/3.5x for MobileNets and 1.0x/2.8x for SqueezeNets. All these tools are open source, some of which have already gained public engagement. We believe they can make efficient deep learning applications easier to build and deploy.Open Acces

    Modern meat: the next generation of meat from cells

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    Modern Meat is the first textbook on cultivated meat, with contributions from over 100 experts within the cultivated meat community. The Sections of Modern Meat comprise 5 broad categories of cultivated meat: Context, Impact, Science, Society, and World. The 19 chapters of Modern Meat, spread across these 5 sections, provide detailed entries on cultivated meat. They extensively tour a range of topics including the impact of cultivated meat on humans and animals, the bioprocess of cultivated meat production, how cultivated meat may become a food option in Space and on Mars, and how cultivated meat may impact the economy, culture, and tradition of Asia

    International consensus statement on allergy and rhinology: Allergic rhinitis – 2023

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    Background In the 5 years that have passed since the publication of the 2018 International Consensus Statement on Allergy and Rhinology: Allergic Rhinitis (ICAR-Allergic Rhinitis 2018), the literature has expanded substantially. The ICAR-Allergic Rhinitis 2023 update presents 144 individual topics on allergic rhinitis (AR), expanded by over 40 topics from the 2018 document. Originally presented topics from 2018 have also been reviewed and updated. The executive summary highlights key evidence-based findings and recommendation from the full document. Methods ICAR-Allergic Rhinitis 2023 employed established evidence-based review with recommendation (EBRR) methodology to individually evaluate each topic. Stepwise iterative peer review and consensus was performed for each topic. The final document was then collated and includes the results of this work. Results ICAR-Allergic Rhinitis 2023 includes 10 major content areas and 144 individual topics related to AR. For a substantial proportion of topics included, an aggregate grade of evidence is presented, which is determined by collating the levels of evidence for each available study identified in the literature. For topics in which a diagnostic or therapeutic intervention is considered, a recommendation summary is presented, which considers the aggregate grade of evidence, benefit, harm, and cost. Conclusion The ICAR-Allergic Rhinitis 2023 update provides a comprehensive evaluation of AR and the currently available evidence. It is this evidence that contributes to our current knowledge base and recommendations for patient evaluation and treatment

    A Tale of Two Approaches: Comparing Top-Down and Bottom-Up Strategies for Analyzing and Visualizing High-Dimensional Data

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    The proliferation of high-throughput and sensory technologies in various fields has led to a considerable increase in data volume, complexity, and diversity. Traditional data storage, analysis, and visualization methods are struggling to keep pace with the growth of modern data sets, necessitating innovative approaches to overcome the challenges of managing, analyzing, and visualizing data across various disciplines. One such approach is utilizing novel storage media, such as deoxyribonucleic acid~(DNA), which presents efficient, stable, compact, and energy-saving storage option. Researchers are exploring the potential use of DNA as a storage medium for long-term storage of significant cultural and scientific materials. In addition to novel storage media, scientists are also focussing on developing new techniques that can integrate multiple data modalities and leverage machine learning algorithms to identify complex relationships and patterns in vast data sets. These newly-developed data management and analysis approaches have the potential to unlock previously unknown insights into various phenomena and to facilitate more effective translation of basic research findings to practical and clinical applications. Addressing these challenges necessitates different problem-solving approaches. Researchers are developing novel tools and techniques that require different viewpoints. Top-down and bottom-up approaches are essential techniques that offer valuable perspectives for managing, analyzing, and visualizing complex high-dimensional multi-modal data sets. This cumulative dissertation explores the challenges associated with handling such data and highlights top-down, bottom-up, and integrated approaches that are being developed to manage, analyze, and visualize this data. The work is conceptualized in two parts, each reflecting the two problem-solving approaches and their uses in published studies. The proposed work showcases the importance of understanding both approaches, the steps of reasoning about the problem within them, and their concretization and application in various domains

    2022 GREAT Day Program

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    SUNY Geneseo’s Sixteenth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1016/thumbnail.jp
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