367 research outputs found

    Structure and dynamics of nanoconfined water and aqueous solutions

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    This review is devoted to discussing recent progress on the structure, thermodynamic, reactivity, and dynamics of water and aqueous systems confined within different types of nanopores, synthetic and biological. Currently, this is a branch of water science that has attracted enormous attention of researchers from different fields interested to extend the understanding of the anomalous properties of bulk water to the nanoscopic domain. From a fundamental perspective, the interactions of water and solutes with a confining surface dramatically modify the liquid's structure and, consequently, both its thermodynamical and dynamical behaviors, breaking the validity of the classical thermodynamic and phenomenological description of the transport properties of aqueous systems. Additionally, man-made nanopores and porous materials have emerged as promising solutions to challenging problems such as water purification, biosensing, nanofluidic logic and gating, and energy storage and conversion, while aquaporin, ion channels, and nuclear pore complex nanopores regulate many biological functions such as the conduction of water, the generation of action potentials, and the storage of genetic material. In this work, the more recent experimental and molecular simulations advances in this exciting and rapidly evolving field will be reported and critically discussed

    Enhancing RGB-D SLAM Using Deep Learning

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    50 Years of quantum chromodynamics – Introduction and Review

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    Neural Scene Representations for 3D Reconstruction and Generative Modeling

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    With the increasing technologization of society, we use machines for more and more complex tasks, ranging from driving assistance to video conferencing, to exploring planets. The scene representation, i.e., how sensory data is converted to compact descriptions of the environment, is a fundamental property for enabling the success but also the safety of such systems. A promising approach for developing robust, adaptive, and powerful scene representations are learning-based systems that can adapt themselves from observations. Indeed, deep learning has revolutionized computer vision in recent years. In particular, better model architectures, large amounts of training data, and more powerful computing devices enabled deep learning systems with unprecedented performance, and they now set the state-of-the-art in many benchmarks, ranging from image classification, to object detection, to semantic segmentation. Despite these successes, the way these systems operate is still fundamentally different from human cognition. In particular, most approaches operate in the 2D domain, while humans understand that images are projections of the three-dimensional world. In addition, they often do not follow a compositional understanding of scenes, which is fundamental to human reasoning. In this thesis, our goal is to develop scene representations that enable autonomous agents to navigate and act robustly and safely in complex environments while reasoning compositionally in 3D. To this end, we first propose a novel output representation for deep learning-based 3D reconstruction and generative modeling. We find that, compared to previous representations, our neural field-based approach does not require 3D space to be discretized achieving reconstructions at arbitrary resolution with a constant memory footprint. Next, we develop a differentiable rendering technique to infer these neural field-based 3D shape and texture representations from 2D observations and find that this allows us to scale to more complex, real-world scenarios. Subsequently, we combine our novel 3D shape representation with a spatially and temporally continuous vector field to model non-rigid shapes in motion. We observe that our novel 4D representation can be used for various discriminative and generative tasks, ranging from 4D reconstruction to 4D interpolation, to motion transfer. Finally, we develop an object-centric generative model that can generate 3D scenes in a compositional manner and that allows for photorealistic renderings of generated scenes. We find that our model not only improves image fidelity but also enables more controllable scene generation and image synthesis than prior work while training only from raw, unposed image collections

    Naval Postgraduate School Academic Catalog - February 2023

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    LIPIcs, Volume 258, SoCG 2023, Complete Volume

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    LIPIcs, Volume 258, SoCG 2023, Complete Volum

    Generalised Kernel Representations with Applications to Data Efficient Machine Learning

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    The universe of mathematical modelling from observational data is a vast space. It consists a cacophony of differing paths, with doors to worlds with seemingly diametrically opposed perspectives that all attempt to conjure a crystal ball of both intuitive understanding and predictive capability. Among these many worlds is an approach that is broadly called kernel methods, which, while complex in detail, when viewed from afar ultimately reduces to a rather simple question: how close is something to something else? What does it mean to be close? Specifically, how can we quantify closeness in some reasonable and principled way? This thesis presents four approaches that address generalised kernel learning. Firstly, we introduce a probabilistic framework that allows joint learning of model and kernel parameters in order to capture nonstationary spatial phenomena. Secondly, we introduce a theoretical framework based on optimal transport that enables online kernel parameter transfer. Such parameter transfer involves the ability to re-use previously learned parameters, without re-optimisation, on newly observed data. This extends the first contribution which was unable operate in real-time due to the necessity of reoptimising parameters to new observations. Thirdly, we introduce a learnable Fourier based kernel embeddings that exploits generalised quantile representations for stationary kernels. Finally, a method for input warped Fourier kernel embeddings is proposed that allows nonstationary data embeddings using simple stationary kernels. By introducing theoretically cohesive and algorithmically intuitive methods this thesis opens new doors to removing traditional assumptions that have hindered adoption of the kernel perspective. We hope that the ideas presented will demonstrate a curious and inspiring view to the potential of learnable kernel embeddings

    Program and Proceedings: The Nebraska Academy of Sciences 1880-2023. 142th Anniversary Year. One Hundred-Thirty-Third Annual Meeting April 21, 2023. Hybrid Meeting: Nebraska Wesleyan University & Online, Lincoln, Nebraska

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    AERONAUTICS & SPACE SCIENCE Chairperson(s): Dr. Scott Tarry & Michaela Lucas HUMANS PAST AND PRESENT Chairperson(s): Phil R. Geib & Allegra Ward APPLIED SCIENCE & TECHNOLOGY SECTION Chairperson(s): Mary Ettel BIOLOGY Chairpersons: Lauren Gillespie, Steve Heinisch, and Paul Davis BIOMEDICAL SCIENCES Chairperson(s): Annemarie Shibata, Kimberly Carlson, Joseph Dolence, Alexis Hobbs, James Fletcher, Paul Denton CHEM Section Chairperson(s): Nathanael Fackler EARTH SCIENCES Chairpersons: Irina Filina, Jon Schueth, Ross Dixon, Michael Leite ENVIRONMENTAL SCIENCE Chairperson: Mark Hammer PHYSICS Chairperson(s): Dr. Adam Davis SCIENCE EDUCATION Chairperson: Christine Gustafson 2023 Maiben Lecturer: Jason Bartz 2023 FRIEND OF SCIENCE AWARD TO: Ray Ward and Jim Lewi

    Design of a perception system for the Formula Student Driverless competition: from vehicle sensorization to SLAM

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    openFormula Student Driverless is an international racing competition held among universities, where the vehicles must complete a set of trials without any human intervention. Together with RaceUP, the Formula Student team of the University of Padova, this thesis represents the beginning of the project to build an autonomous prototype to compete in the Driverless Cup in the 2024 season. Three important aspects of an autonomous system design will be tackled: vehicle sensorization, perception, and simultaneous localization and mapping (SLAM), with the main focus on the development of the last one. The proposed approach for the back-end is based on the optimization of a factor graph, holding information about car poses and landmarks positions, by exploiting spatial and kinematic constraints between its vertices. The full back-end pipeline has been tested thoroughly, step by step, allowing to obtain satisfactory results on the different virtual tracks used for testing. Using both modern and classical techniques, we can process information produced by the stereo camera and the LIDAR, to be able to localize the colored cones delimiting the track. The estimation of cones positions serves then as input for other important modules of the car, such as the control part and the SLAM pipeline. Finally, a complete dataset has been acquired by properly sensorizing RaceUP's last year's car: having real data represents a helpful resource to make experiments and validate the system, even without the availability of the actual vehicle prototype.Formula Student Driverless is an international racing competition held among universities, where the vehicles must complete a set of trials without any human intervention. Together with RaceUP, the Formula Student team of the University of Padova, this thesis represents the beginning of the project to build an autonomous prototype to compete in the Driverless Cup in the 2024 season. Three important aspects of an autonomous system design will be tackled: vehicle sensorization, perception, and simultaneous localization and mapping (SLAM), with the main focus on the development of the last one. The proposed approach for the back-end is based on the optimization of a factor graph, holding information about car poses and landmarks positions, by exploiting spatial and kinematic constraints between its vertices. The full back-end pipeline has been tested thoroughly, step by step, allowing to obtain satisfactory results on the different virtual tracks used for testing. Using both modern and classical techniques, we can process information produced by the stereo camera and the LIDAR, to be able to localize the colored cones delimiting the track. The estimation of cones positions serves then as input for other important modules of the car, such as the control part and the SLAM pipeline. Finally, a complete dataset has been acquired by properly sensorizing RaceUP's last year's car: having real data represents a helpful resource to make experiments and validate the system, even without the availability of the actual vehicle prototype

    CITIES: Energetic Efficiency, Sustainability; Infrastructures, Energy and the Environment; Mobility and IoT; Governance and Citizenship

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    This book collects important contributions on smart cities. This book was created in collaboration with the ICSC-CITIES2020, held in San José (Costa Rica) in 2020. This book collects articles on: energetic efficiency and sustainability; infrastructures, energy and the environment; mobility and IoT; governance and citizenship
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