Relevant bibliographies by topics / Multiscale Representation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Multiscale Representation'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 March 2023

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Journal articles on the topic "Multiscale Representation"

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Goldstein, Rhys, Azam Khan, Olivier Dalle, and Gabriel Wainer. "Multiscale representation of simulated time." SIMULATION 94, no. 6 (September 28, 2017): 519–58. http://dx.doi.org/10.1177/0037549717726868.

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To better support multiscale modeling and simulation, we present a multiscale time representation consisting of data types, data structures, and algorithms that collectively support the recording of past events and scheduling of future events in a discrete event simulation. Our approach addresses the drawbacks of conventional time representations: limited range in the case of 32- or 64-bit fixed-point time values; problematic rounding errors in the case of floating-point numbers; and the lack of a universally acceptable precision level in the case of brute force approaches. The proposed representation provides both extensive range and fine resolution in the timing of events, yet it stores and manipulates the majority of event times as standard 64-bit numbers. When adopted for simulation purposes, the representation allows a domain expert to choose a precision level for his/her model. This time precision is honored by the simulator even when the model is integrated with other models of vastly different time scales. Making use of C++11 programming language features and the Discrete Event System Specification formalism, we implemented a simulator to test the time representation and inform a discussion on its implications for collaborative multiscale modeling efforts.
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Jiang, Z., M. I. J. van Dijke, K. S. Sorbie, and G. D. Couples. "Representation of multiscale heterogeneity via multiscale pore networks." Water Resources Research 49, no. 9 (September 2013): 5437–49. http://dx.doi.org/10.1002/wrcr.20304.

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Knijnenburg, Theo A., Stephen A. Ramsey, Benjamin P. Berman, Kathleen A. Kennedy, Arian F. A. Smit, Lodewyk F. A. Wessels, Peter W. Laird, Alan Aderem, and Ilya Shmulevich. "Multiscale representation of genomic signals." Nature Methods 11, no. 6 (April 13, 2014): 689–94. http://dx.doi.org/10.1038/nmeth.2924.

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Pauly, Mark, Leif P. Kobbelt, and Markus Gross. "Point-based multiscale surface representation." ACM Transactions on Graphics 25, no. 2 (April 2006): 177–93. http://dx.doi.org/10.1145/1138450.1138451.

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Elizar, Elizar, Mohd Asyraf Zulkifley, Rusdha Muharar, Mohd Hairi Mohd Zaman, and Seri Mastura Mustaza. "A Review on Multiscale-Deep-Learning Applications." Sensors 22, no. 19 (September 28, 2022): 7384. http://dx.doi.org/10.3390/s22197384.

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In general, most of the existing convolutional neural network (CNN)-based deep-learning models suffer from spatial-information loss and inadequate feature-representation issues. This is due to their inability to capture multiscale-context information and the exclusion of semantic information throughout the pooling operations. In the early layers of a CNN, the network encodes simple semantic representations, such as edges and corners, while, in the latter part of the CNN, the network encodes more complex semantic features, such as complex geometric shapes. Theoretically, it is better for a CNN to extract features from different levels of semantic representation because tasks such as classification and segmentation work better when both simple and complex feature maps are utilized. Hence, it is also crucial to embed multiscale capability throughout the network so that the various scales of the features can be optimally captured to represent the intended task. Multiscale representation enables the network to fuse low-level and high-level features from a restricted receptive field to enhance the deep-model performance. The main novelty of this review is the comprehensive novel taxonomy of multiscale-deep-learning methods, which includes details of several architectures and their strengths that have been implemented in the existing works. Predominantly, multiscale approaches in deep-learning networks can be classed into two categories: multiscale feature learning and multiscale feature fusion. Multiscale feature learning refers to the method of deriving feature maps by examining kernels over several sizes to collect a larger range of relevant features and predict the input images’ spatial mapping. Multiscale feature fusion uses features with different resolutions to find patterns over short and long distances, without a deep network. Additionally, several examples of the techniques are also discussed according to their applications in satellite imagery, medical imaging, agriculture, and industrial and manufacturing systems.
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Maragos, P. "Pattern spectrum and multiscale shape representation." IEEE Transactions on Pattern Analysis and Machine Intelligence 11, no. 7 (July 1989): 701–16. http://dx.doi.org/10.1109/34.192465.

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Bengtsson, A., and J. O. Eklundh. "Shape representation by multiscale contour approximation." IEEE Transactions on Pattern Analysis and Machine Intelligence 13, no. 1 (1991): 85–93. http://dx.doi.org/10.1109/34.67634.

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Jang, Dongik, Donghoh Kim, and Kyungmee O. Kim. "Multiscale representation for irregularly spaced data." Journal of the Korean Statistical Society 46, no. 4 (December 2017): 641–53. http://dx.doi.org/10.1016/j.jkss.2017.09.002.

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Sureau, F., F. Voigtlaender, M. Wust, J. L. Starck, and G. Kutyniok. "Learning sparse representations on the sphere." Astronomy & Astrophysics 621 (January 2019): A73. http://dx.doi.org/10.1051/0004-6361/201834041.

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Many representation systems on the sphere have been proposed in the past, such as spherical harmonics, wavelets, or curvelets. Each of these data representations is designed to extract a specific set of features, and choosing the best fixed representation system for a given scientific application is challenging. In this paper, we show that one can directly learn a representation system from given data on the sphere. We propose two new adaptive approaches: the first is a (potentially multiscale) patch-based dictionary learning approach, and the second consists in selecting a representation from among a parametrized family of representations, the α-shearlets. We investigate their relative performance to represent and denoise complex structures on different astrophysical data sets on the sphere.
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Lam, Ka Chun, Tsz Ching Ng, and Lok Ming Lui. "Multiscale Representation of Deformation via Beltrami Coefficients." Multiscale Modeling & Simulation 15, no. 2 (January 2017): 864–91. http://dx.doi.org/10.1137/16m1056614.

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Dissertations / Theses on the topic "Multiscale Representation"

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McGinty, Robert Davis. "Multiscale representation of polycrystalline inelasticity." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/16029.

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Athavale, Prashant Vinayak. "Novel integro-differential schemes for multiscale image representation." College Park, Md.: University of Maryland, 2009. http://hdl.handle.net/1903/9691.

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Thesis (Ph.D.) -- University of Maryland, College Park, 2009.
Thesis research directed by: Applied Mathematics & Statistics, and Scientific Computation Program. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Tymkovych, M. Y., О. Г. Аврунін, O. Gryshkov, K. G. Selivanova, V. Mutsenko, and B. Glasmacher. "Multiscale quantitative analysis of microscopic images of ice crystals." Thesis, The International Journal of Artificial Organs, 2019. http://openarchive.nure.ua/handle/document/9879.

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It was analyzed multiple images. The results in the first approximation show a 2-fold increase in speed when using our implementation of active contours. At the same time, the segmented areas of crystals correspond to the approach without the use of multi-scale image representation.
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Munoz, Esparza Domingo. "Multiscale modelling of atmospheric flows: towards improving the representation of boundary layer physics." Doctoral thesis, Universite Libre de Bruxelles, 2013. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209363.

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Atmospheric boundary layer flows are characterized by the coexistence of a broad range of scales. These scales cover from synoptic- (100-5000 km) and meso-scales (1-100 km) up to three-dimensional micro-scale turbulence (less than a few kilometers). This multiscale nature inherent to atmospheric flows clearly determines the behaviour of the atmospheric boundary layer, whose structure and evolution are of major importance for the wind energy community. This PhD thesis is focused on the development of a numerical methodology that allows to include contribution from all the above mentioned scales, with the purpose of improving the representation of boundary layer processes. The multiscale numerical methodology is developed based on a numerical weather prediction (NWP) model, the Weather Research and Forecasting (WRF) model.

Prior to the development of the multiscale numerical methodology, one-year of sonic anemometer and wind LiDAR measurements from the FINO1 offshore platform are analyzed. A comprehensive database of offshore measurements in the lowest 250 m of the boundary layer is developed after quality data check and correction for flow distortion effects by the measurement mast, allowing the characterization of the offshore conditions at FINO1. Spectral analysis of high frequency sonic anemometer measurements is used to estimate a robust averaing time for the turbulent fluxes that minimizes non-universal contributions from mesoscale structures but captures the contribution from boundary layer turbulence, employing the Ogive function concept. A stability classification of the measurements is carried out based on the Obukhov length. Results compare well to other surface layer observational studies while vertical wind speed profiles exhibit the expected stability-dependency.

Although NWP models have been extensively used for weather forecasting purposes, a comprehensive analysis of its suitability to meet the wind energy requirements needs to be carried out. The applicability of the WRF mesoscale model to reproduce offshore boundary layer characteristics is evaluated and validated against field measurements from FINO1. The ability of six planetary boundary layer (PBL) parameterizations to account for stability effects is analyzed. Overall, PBL parameterizations are rather accurate in reproducing the vertical structure of the boundary layer for convective and neutral stabilities. However, difficulties are found under stable stratifications, due to the general tendency of PBL formulations to be overdiffusive and therefore, not capable to develope the strong vertical gradients found in the observations. A low-level jet and a very shallow boundary layer cases are simulated to provide further insights into the limits of the parameterizations.

Large-eddy simulations (LES) based on averaged conditions from a convective episode at FINO1 are conducted to understand the mechanisms of transition and equilibration that occur in turbulent one-way nested simulations. The nonlinear backscatter and anisotropy subgrid scale model with a prognostic turbulent kinetic energy equation is found to be capable of providing similar results when performing one-way nested large-eddy simulations to a reference stand-alone domain using periodic lateral boundary conditions. A good agreement is obtained in terms of velocity shear and turbulent fluxes of heat and momentum, while velocity variances are overestimated. A considerable streamwise fetch is needed following each domain transition for appropriate energy levels to be reached at high wavelengths and for the solution to reach quasi-stationary results. A pile-up of energy is observed at low wavelengths on the first nested domain, mitigated by the inclusion of a second nested domain with higher resolution that allows the development of an appropriate turbulent energy cascade.

As the final step towards developing the multiscale capabilities of WRF, the specific problem of the transition from meso- to micro-scales in atmospheric models is addressed. The challenge is to generate turbulence on inner LES domain from smooth mesoscale inflow. Several new methods are proposed to trigger the development of turbulent features. The inclusion of adequate potential temperature perturbations near the inflow boundaries of the LES domain results in a very good agreement of mean velocity profiles, variances and turbulent fluxes, as well as velocity spectra, when compared to periodic stand-alone simulations. This perturbation method allows an efficient generation of fully developed turbulence and is tested under a broad range of atmospheric stabilities: convective, neutral and stable conditions, showing successful results in all the regimes.
Doctorat en Sciences de l'ingénieur
info:eu-repo/semantics/nonPublished

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Duong, Quang Thien. "Feasibility of agent-based modelling of articular cartilage including a conceptual representation of its structure." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/57989/1/Quang_Duong_Thesis.pdf.

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Articular cartilage is a complex structure with an architecture in which fluid-swollen proteoglycans constrained within a 3D network of collagen fibrils. Because of the complexity of the cartilage structure, the relationship between its mechanical behaviours at the macroscale level and its components at the micro-scale level are not completely understood. The research objective in this thesis is to create a new model of articular cartilage that can be used to simulate and obtain insight into the micro-macro-interaction and mechanisms underlying its mechanical responses during physiological function. The new model of articular cartilage has two characteristics, namely: i) not use fibre-reinforced composite material idealization ii) Provide a framework for that it does probing the micro mechanism of the fluid-solid interaction underlying the deformation of articular cartilage using simple rules of repartition instead of constitutive / physical laws and intuitive curve-fitting. Even though there are various microstructural and mechanical behaviours that can be studied, the scope of this thesis is limited to osmotic pressure formation and distribution and their influence on cartilage fluid diffusion and percolation, which in turn governs the deformation of the compression-loaded tissue. The study can be divided into two stages. In the first stage, the distributions and concentrations of proteoglycans, collagen and water were investigated using histological protocols. Based on this, the structure of cartilage was conceptualised as microscopic osmotic units that consist of these constituents that were distributed according to histological results. These units were repeated three-dimensionally to form the structural model of articular cartilage. In the second stage, cellular automata were incorporated into the resulting matrix (lattice) to simulate the osmotic pressure of the fluid and the movement of water within and out of the matrix; following the osmotic pressure gradient in accordance with the chosen rule of repartition of the pressure. The outcome of this study is the new model of articular cartilage that can be used to simulate and study the micromechanical behaviours of cartilage under different conditions of health and loading. These behaviours are illuminated at the microscale level using the socalled neighbourhood rules developed in the thesis in accordance with the typical requirements of cellular automata modelling. Using these rules and relevant Boundary Conditions to simulate pressure distribution and related fluid motion produced significant results that provided the following insight into the relationships between osmotic pressure gradient and associated fluid micromovement, and the deformation of the matrix. For example, it could be concluded that: 1. It is possible to model articular cartilage with the agent-based model of cellular automata and the Margolus neighbourhood rule. 2. The concept of 3D inter connected osmotic units is a viable structural model for the extracellular matrix of articular cartilage. 3. Different rules of osmotic pressure advection lead to different patterns of deformation in the cartilage matrix, enabling an insight into how this micromechanism influences macromechanical deformation. 4. When features such as transition coefficient were changed, permeability (representing change) is altered due to the change in concentrations of collagen, proteoglycans (i.e. degenerative conditions), the deformation process is impacted. 5. The boundary conditions also influence the relationship between osmotic pressure gradient and fluid movement at the micro-scale level. The outcomes are important to cartilage research since we can use these to study the microscale damage in the cartilage matrix. From this, we are able to monitor related diseases and their progression leading to potential insight into drug-cartilage interaction for treatment. This innovative model is an incremental progress on attempts at creating further computational modelling approaches to cartilage research and other fluid-saturated tissues and material systems.
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Budinich, Renato [Verfasser], Gerlind [Akademischer Betreuer] Plonka-Hoch, Gerlind [Gutachter] Plonka-Hoch, and Armin [Gutachter] Iske. "Adaptive Multiscale Methods for Sparse Image Representation and Dictionary Learning / Renato Budinich ; Gutachter: Gerlind Plonka-Hoch, Armin Iske ; Betreuer: Gerlind Plonka-Hoch." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2019. http://d-nb.info/1175625396/34.

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Duflot, Lesley-Ann. "Asservissement visuel direct utilisant les décompositions en shearlets et en ondelettes de l'image." Thesis, Rennes 1, 2018. http://www.theses.fr/2018REN1S018/document.

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L'asservissement visuel est un procédé consistant à utiliser l'information visuelle obtenue par un capteur afin de commander un système robotique. Ces informations, appelées primitives visuelles peuvent être d'ordre 2D ou 3D. Le travail présenté ici porte sur une nouvelle approche 2D utilisant des primitives directes : les décompositions de l'image en ondelettes ou en shearlets. Ces représentations présentent en effet l'avantage de décrire l'image sous différentes formes, mettant l'accent soit sur les basses fréquences de l'image, soit sur les hautes fréquences selon plusieurs directions. Les zones de l'image contenant beaucoup d'information, comme les contours ou les points singuliers, possèdent alors de forts coefficients dans la transformée en ondelettes ou en shearlets de l'image, tandis que les zones uniformes possèdent des coefficients proches de zéro. Les travaux de cette thèse montrent la précision et la robustesse de l'approche utilisant la décomposition en shearlets dans le cadre de l'imagerie échographique. Néanmoins, sa contribution majeure est l'élaboration d'une commande permettant d'utiliser au choix les ondelettes ou les shearlets ainsi que la validation de cette méthode sur caméra monoculaire et sur capteur de type tomographie par cohérence optique dans différentes conditions d'utilisation. Cette méthode présente des performances significatives en termes de précision et de robustesse et ouvre la porte vers une utilisation couplée de l'asservissement visuel et de l'acquisition comprimée
A visual servoing scheme consists of a closed-loop control approach which uses visual information feedback to control the movement of a robotic system. This data, called visual features, can be 2D or 3D. This thesis deals with the development of a new generation of 2D direct visual servoing methods in which the signal control inputs are the coefficients of a multiscale image representation. Specially, we consider the use of multiscale image representations that are based on discrete wavelet and shearlet transformations. This kind of representations allows us to obtain several descriptions of the image based on either low or high frequencies levels. Indeed, high coefficients in the wavelet or in the shearlet transformation of the image correspond to image singularities. This thesis has begun with the development of a shearlet-based visual servoing for ultrasound imaging that has performed well in precision and robustness for this medical application. Nevertheless, the main contribution is a framework allowing us to use several multi-scale representations of the image. It was then tested with conventional white light camera and with an optical coherence tomography imaging system with nominal and unfavorable conditions. Then, the wavelet and the shearlet based methods showed their accuracy and their robustness in several conditions and led to the use of both visual servoing and compressed sensing as the main perspective of this work
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Wang, Han. "Méthodes de reconstruction d'images à partir d'un faible nombre de projections en tomographie par rayons x." Phd thesis, Université de Grenoble, 2011. http://tel.archives-ouvertes.fr/tel-00680100.

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Afin d'améliorer la sûreté (dose plus faible) et la productivité (acquisition plus rapide) du système de la tomographie par rayons X (CT), nous cherchons à reconstruire une image de haute qualitée avec un faible nombre de projections. Les algorithmes classiques ne sont pas adaptés à cette situation et la reconstruction est instable et perturbée par des artefacts. L'approche "Compressed Sensing" (CS) fait l'hypothèse que l'image inconnue est "parcimonieuse" ou "compressible", et la reconstruit via un problème d'optimisation (minimisation de la norme TV/L1) en promouvant la parcimonie. Pour appliquer le CS en CT, en utilisant le pixel/voxel comme base de representation, nous avons besoin d'une transformée parcimonieuse, et nous devons la combiner avec le "projecteur du rayon X" appliqué sur une image pixelisée. Dans cette thèse, nous avons adapté une base radiale de famille Gaussienne nommée "blob" à la reconstruction CT par CS. Elle a une meilleure localisation espace-fréquentielle que le pixel, et des opérations comme la transformée en rayons-X, peuvent être évaluées analytiquement et sont facilement parallélisables (sur plateforme GPU par exemple). Comparé au blob classique de Kaisser-Bessel, la nouvelle base a une structure multi-échelle : une image est la somme des fonctions translatées et dilatées de chapeau Mexicain radiale. Les images médicales typiques sont compressibles sous cette base. Ainsi le système de representation parcimonieuse dans les algorithmes ordinaires de CS n'est plus nécessaire. Des simulations (2D) ont montré que les algorithmes TV/L1 existants sont plus efficaces et les reconstructions ont des meilleures qualités visuelles que par l'approche équivalente basée sur la base de pixel-ondelettes. Cette nouvelle approche a également été validée sur des données expérimentales (2D), où nous avons observé que le nombre de projections en général peut être réduit jusqu'à 50%, sans compromettre la qualité de l'image.
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Chan, Alexander Mark. "Extracting Spatiotemporal Word and Semantic Representations from Multiscale Neurophysiological Recordings in Humans." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10251.

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With the recent advent of neuroimaging techniques, the majority of the research studying the neural basis of language processing has focused on the localization of various lexical and semantic functions. Unfortunately, the limited time resolution of functional neuroimaging prevents a detailed analysis of the dynamics involved in word recognition, and the hemodynamic basis of these techniques prevents the study of the underlying neurophysiology. Compounding this problem, current techniques for the analysis of high-dimensional neural data are mainly sensitive to large effects in a small area, preventing a thorough study of the distributed processing involved for representing semantic knowledge. This thesis demonstrates the use of multivariate machine-learning techniques for the study of the neural representation of semantic and speech information in electro/magneto-physiological recordings with high temporal resolution. Support vector machines (SVMs) allow for the decoding of semantic category and word-specific information from non-invasive electroencephalography (EEG) and magnetoenecephalography (MEG) and demonstrate the consistent, but spatially and temporally distributed nature of such information. Moreover, the anteroventral temporal lobe (avTL) may be important for coordinating these distributed representations, as supported by the presence of supramodal category-specific information in intracranial recordings from the avTL as early as 150ms after auditory or visual word presentation. Finally, to study the inputs to this lexico-semantic system, recordings from a high density microelectrode array in anterior superior temporal gyrus (aSTG) are obtained, and the recorded spiking activity demonstrates the presence of single neurons that respond specifically to speech sounds. The successful decoding of word identity from this firing rate information suggests that the aSTG may be involved in the population coding of acousto-phonetic speech information that is likely on the pathway for mapping speech-sounds to meaning in the avTL. The feasibility of extracting semantic and phonological information from multichannel neural recordings using machine learning techniques provides a powerful method for studying language using large datasets and has potential implications for the development of fast and intuitive communication prostheses.
Engineering and Applied Sciences
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McCarty, James. "Multiscale Modeling and Thermodynamic Consistency between Soft-Particle Representations of Macromolecular Liquids." Thesis, University of Oregon, 2014. http://hdl.handle.net/1794/17906.

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Coarse-graining and multi-scale approaches are rapidly becoming important tools for computer simulations of large complex molecular systems. Such theoretical models are powerful tools because they allow one to probe the essential features of a complex, many-bodied system on length and time scales over which emergent phenomena may occur. Because of the computational advantages and fundamental insight made available through coarse-grained methods, a vast array of various phenomenological potentials to describe coarse-grained interactions have been developed; nonetheless, the ability of these potentials to provide quantitative information about several different properties of the same system is not evident. On a theoretical level, it is not well-understood how small correlations in the long-range structure propagate through the coarse-graining procedure into the effective potential and lead to incorrect thermodynamics. Taking an alternative approach, this dissertation will discuss an analytical coarse-graining method for synthetic polymer chains of specific chemical structure, where a group of atoms on a polymer chain are represented by a variable number of soft interacting effective sites. The approach is based in liquid-state theory, providing a theoretical framework to address questions of thermodynamic consistency. It will be shown that the proposed method of coarse-graining maintains thermodynamic consistency for a variety of polymer models. In a multi-scale modeling scheme simulations of the same system represented by several different levels of detail may be joined to provide a complete description of the system at all length and time scales of interest. This dissertation includes previously published and unpublished co-authored material.
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Books on the topic "Multiscale Representation"

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Mallat, Stephane. Complete signal representation with multiscale edges. New York: Courant Institute of Mathematical Sciences, New York University, 1989.

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Kutyniok, Gitta. Shearlets: Multiscale Analysis for Multivariate Data. Boston: Birkhäuser Boston, 2012.

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Zhong, Sifen, and Stéphane Mallat. Complete Signal Representation with Multiscale Edges. Creative Media Partners, LLC, 2018.

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Kutyniok, Gitta, and Demetrio Labate. Shearlets: Multiscale Analysis for Multivariate Data. Springer, 2012.

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Shearlets Multiscale Analysis For Multivariate Data. Birkh User, 2012.

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Do, Minh N., and Yue M. Lu. Multidimensional Filter Banks and Multiscale Geometric Representations. Now Publishers, 2012.

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Book chapters on the topic "Multiscale Representation"

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Jähne, Bernd. "Multiscale Representation." In Digital Image Processing, 125–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04781-1_5.

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Jähne, Bernd. "Multiscale Representation." In Digital Image Processing, 121–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-03477-4_5.

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Resnikoff, Howard L., and Raymond O. Wells. "Multiscale Representation of Geometry." In Wavelet Analysis, 266–79. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-0593-7_11.

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Saragadam, Vishwanath, Jasper Tan, Guha Balakrishnan, Richard G. Baraniuk, and Ashok Veeraraghavan. "MINER: Multiscale Implicit Neural Representation." In Lecture Notes in Computer Science, 318–33. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20050-2_19.

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Chen, Fang, and David Suter. "Multiscale image representation and edge detection." In Computer Vision — ACCV'98, 49–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63931-4_197.

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Koepfler, Georges, and Lionel Moisan. "Geometric Multiscale Representation of Numerical Images." In Scale-Space Theories in Computer Vision, 339–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48236-9_30.

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El Rube, I., N. Alajlan, M. Kamel, M. Ahmed, and G. Freeman. "Efficient Multiscale Shape-Based Representation and Retrieval." In Lecture Notes in Computer Science, 415–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11559573_52.

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You, Xinhua, Bin Fang, Xinge You, Zhenyu He, Dan Zhang, and Yuan Yan Tang. "Skeleton Representation of Character Based on Multiscale Approach." In Computational Intelligence and Security, 1060–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11596981_158.

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Ranjan, Uma S., and K. R. Ramakrishnan. "A Stochastic Scale Space for Multiscale Image Representation." In Scale-Space Theories in Computer Vision, 441–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48236-9_40.

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Alpay, Daniel, Palle Jorgensen, Izchak Lewkowicz, and Itzik Marziano. "Representation Formulas for Hardy Space Functions Through the Cuntz Relations and New Interpolation Problems." In Multiscale Signal Analysis and Modeling, 161–82. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4145-8_7.

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Conference papers on the topic "Multiscale Representation"

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Forte, Peter, and Darrel Greenhill. "Multiscale 2D shape representation." In Photonics for Industrial Applications, edited by Robert A. Melter and Angela Y. Wu. SPIE, 1995. http://dx.doi.org/10.1117/12.198615.

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Zhou, Honglu, Shuyuan Xu, Zuohui Fu, Gerard de Melo, Yongfeng Zhang, and Mubbasir Kapadia. "HID: Hierarchical Multiscale Representation Learning for Information Diffusion." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/468.

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Abstract:
Multiscale modeling has yielded immense success on various machine learning tasks. However, it has not been properly explored for the prominent task of information diffusion, which aims to understand how information propagates along users in online social networks. For a specific user, whether and when to adopt a piece of information propagated from another user is affected by complex interactions, and thus, is very challenging to model. Current state-of-the-art techniques invoke deep neural models with vector representations of users. In this paper, we present a Hierarchical Information Diffusion (HID) framework by integrating user representation learning and multiscale modeling. The proposed framework can be layered on top of all information diffusion techniques that leverage user representations, so as to boost the predictive power and learning efficiency of the original technique. Extensive experiments on three real-world datasets showcase the superiority of our method.
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Alami, Wassim, and Gregory Dudek. "Multiscale object representation using surface patches." In Photonics for Industrial Applications, edited by David P. Casasent. SPIE, 1994. http://dx.doi.org/10.1117/12.188884.

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Scharcanski, Jacob, Jeff K. Hovis, and Helen C. Shen. "Color texture representation using multiscale feature boundaries." In Applications in Optical Science and Engineering, edited by Petros Maragos. SPIE, 1992. http://dx.doi.org/10.1117/12.131434.

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Falcao, Alexandre X., and Bruno S. Cunha. "Multiscale shape representation by image foresting transform." In Medical Imaging 2001, edited by Milan Sonka and Kenneth M. Hanson. SPIE, 2001. http://dx.doi.org/10.1117/12.430984.

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Scheunders, Paul. "Multiscale edge representation applied to image fusion." In International Symposium on Optical Science and Technology, edited by Akram Aldroubi, Andrew F. Laine, and Michael A. Unser. SPIE, 2000. http://dx.doi.org/10.1117/12.408573.

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Torres-Madronero, Maria C., and Miguel Velez-Reyes. "Unsupervised unmixing analysis based on multiscale representation." In SPIE Defense, Security, and Sensing, edited by Sylvia S. Shen and Paul E. Lewis. SPIE, 2012. http://dx.doi.org/10.1117/12.920698.

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Thao, Truong My Thu, Syed Yazdani Samar, Pottier Bernard, Rodin Vincent, and Huynh Xuan Hiep. "Multiscale Geographic Exploration, Observation, Simulation, and Representation." In 2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS). IEEE, 2019. http://dx.doi.org/10.1109/macs48846.2019.9024777.

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Shen, Yangmei, Hongkai Xiong, and Wenrui Dai. "Multiscale dictionary learning for hierarchical sparse representation." In 2017 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2017. http://dx.doi.org/10.1109/icme.2017.8019435.

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Wu, Qian, Rong Zhang, and Dawei Xu. "Hyperspectral image representation using learned multiscale dictionaries." In 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2014. http://dx.doi.org/10.1109/whispers.2014.8077501.

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Reports on the topic "Multiscale Representation"

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Bassu, Devasis. Fast Multiscale Algorithms for Information Representation and Fusion. Fort Belvoir, VA: Defense Technical Information Center, April 2013. http://dx.doi.org/10.21236/ada608426.

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Bassu, Devasis. Fast Multiscale Algorithms for Information Representation and Fusion. Fort Belvoir, VA: Defense Technical Information Center, January 2011. http://dx.doi.org/10.21236/ada538312.

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Bassu, Devasis. Fast Multiscale Algorithms for Information Representation and Fusion. Fort Belvoir, VA: Defense Technical Information Center, July 2012. http://dx.doi.org/10.21236/ada565467.

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Bassu, Devasis. Fast Multiscale Algorithms for Information Representation and Fusion. Fort Belvoir, VA: Defense Technical Information Center, October 2012. http://dx.doi.org/10.21236/ada570238.

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Bassu, Devasis. Fast Multiscale Algorithms for Information Representation and Fusion. Fort Belvoir, VA: Defense Technical Information Center, January 2013. http://dx.doi.org/10.21236/ada574842.

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Bassu, Devasis. Fast Multiscale Algorithms for Information Representation and Fusion. Fort Belvoir, VA: Defense Technical Information Center, April 2011. http://dx.doi.org/10.21236/ada543835.

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Bassu, Devasis. Fast Multiscale Algorithms for Information Representation and Fusion. Fort Belvoir, VA: Defense Technical Information Center, July 2011. http://dx.doi.org/10.21236/ada549173.

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Bassu, Devasis. Fast Multiscale Algorithms for Information Representation and Fusion. Fort Belvoir, VA: Defense Technical Information Center, October 2011. http://dx.doi.org/10.21236/ada553728.

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Bassu, Devasis. Fast Multiscale Algorithms for Information Representation and Fusion. Fort Belvoir, VA: Defense Technical Information Center, April 2012. http://dx.doi.org/10.21236/ada560967.

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Niyogi, Devdutta S. Utilizing CLASIC observations and multiscale models to study the impact of improved Land surface representation on modeling cloud- convection. Office of Scientific and Technical Information (OSTI), June 2013. http://dx.doi.org/10.2172/1082727.

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