Relevant bibliographies by topics / Deep structures

Academic literature on the topic 'Deep structures'

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Published: 25 May 2024

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Journal articles on the topic "Deep structures"

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Nizami Huseyn, Elcin. "ELECTROSTIMULATION OF BRAIN DEEP STRUCTURES IN PARKINSON'S DISEASE." SCIENTIFIC WORK 70, no. 09 (September 21, 2021): 14–19. http://dx.doi.org/10.36719/2663-4619/70/14-19.

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The study involved 56 patients with advanced and late stages of Parkinson's disease, which could be considered as potentially requiring neurosurgical treatment - electrical stimulation of deep brain structures. An algorithm has been developed for selecting patients with advanced and late stages of Parkinson's disease for neurological treatment-implantation of a system for electrical stimulation of deep brain structures in distant neurosurgical centers, which includes two stages for patients with limited mobility-outpatient and inpatient. The development of an algorithm for referral to neurological treatment has shortened the “path” of a patient with limited mobility from a polyclinic to a neurological center. Electro stimulation of deep brain structures in Parkinson's disease significantly improved the condition of patients - to increase functional activity by 55%, reduce the severity of motor disorders by 55%, and reduce the dose of levodopa drugs by half. Key words: Electrostimulation of deep brain structures, Parkinson's disease, patient selection
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Singh, Arunima. "Deep learning 3D structures." Nature Methods 17, no. 3 (March 2020): 249. http://dx.doi.org/10.1038/s41592-020-0779-y.

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Bowles, Martin L. "Recognizing Deep Structures in Organizations." Organization Studies 11, no. 3 (July 1990): 395–412. http://dx.doi.org/10.1177/017084069001100304.

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The understandings of classical science are now increasingly under threat from twentieth century physics. The machine model of organization, informed by classical science, is therefore in need of review. Analytical psychology is used as a framework for re-assessing and providing new insights into the nature and management of organizations in contemporary society.
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Zhou, Ding-Xuan. "Deep distributed convolutional neural networks: Universality." Analysis and Applications 16, no. 06 (November 2018): 895–919. http://dx.doi.org/10.1142/s0219530518500124.

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Deep learning based on structured deep neural networks has provided powerful applications in various fields. The structures imposed on the deep neural networks are crucial, which makes deep learning essentially different from classical schemes based on fully connected neural networks. One of the commonly used deep neural network structures is generated by convolutions. The produced deep learning algorithms form the family of deep convolutional neural networks. Despite of their power in some practical domains, little is known about the mathematical foundation of deep convolutional neural networks such as universality of approximation. In this paper, we propose a family of new structured deep neural networks: deep distributed convolutional neural networks. We show that these deep neural networks have the same order of computational complexity as the deep convolutional neural networks, and we prove their universality of approximation. Some ideas of our analysis are from ridge approximation, wavelets, and learning theory.
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Podoski, Jessica H., Thomas D. Smith, David C. Finnegan, Adam L. LeWinter, and Peter J. Gadomski. "UNMANNED AERIAL SYSTEM LIDAR SURVEY OF TWO BREAKWATERS IN THE HAWAIIAN ISLANDS." Coastal Engineering Proceedings, no. 36 (December 30, 2018): 23. http://dx.doi.org/10.9753/icce.v36.structures.23.

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The U.S. Army Corps of Engineers (USACE), Honolulu District (POH) is responsible for the operation and maintenance of 26 navigation projects within the State of Hawaii and the U.S. Pacific territories. The majority of these deep-draft and small-boat harbors include breakwaters that are consistently exposed to a substantial and varied Pacific Ocean wave climate, requiring POH to maintain a rigorous structure condition inspection program to ensure safe and efficient operations at all of its navigation projects. As part of its constant efforts to improve the quality and efficiency of this inspection program, POH has joined with the USACE Cold Regions Research and Engineering Laboratory (CRREL) Remote Sensing and GIS Center of Expertise to utilize an Unmanned LiDAR Scanning (ULS) system to collect LiDAR (Light Detection and Ranging) spatial data and co-registered imagery of breakwaters at Hilo Deep Draft Harbor on the island of Hawaii, and Kaumalapau Deep Draft Harbor on the island of Lanai.
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Hao, Xing, Guigang Zhang, and Shang Ma. "Deep Learning." International Journal of Semantic Computing 10, no. 03 (September 2016): 417–39. http://dx.doi.org/10.1142/s1793351x16500045.

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Deep learning is a branch of machine learning that tries to model high-level abstractions of data using multiple layers of neurons consisting of complex structures or non-liner transformations. With the increase of the amount of data and the power of computation, neural networks with more complex structures have attracted widespread attention and been applied to various fields. This paper provides an overview of deep learning in neural networks including popular architecture models and training algorithms.
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Eliava, Shalva, Oleg Shekhtman, and Mariya Varyukhina. "Microsurgical Angioarchitectonics of Deep Brain Structures and Deep Arterial Anastomoses." World Neurosurgery 126 (June 2019): e1092-e1098. http://dx.doi.org/10.1016/j.wneu.2019.02.213.

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Gooderham, David. "Deep calling unto deep: Pre-oedipal structures in children's texts." Childrens Literature in Education 25, no. 2 (June 1994): 113–23. http://dx.doi.org/10.1007/bf02355399.

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Kalygina, V. M., Yu S. Petrova, I. A. Prudaev, O. P. Tolbanov, and S. Yu Tsupiy. "Deep centers in TiO2-Si structures." Semiconductors 49, no. 8 (August 2015): 1012–18. http://dx.doi.org/10.1134/s1063782615080102.

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Kasztelanic, Rafał. "Multilevel structures in deep proton lithography." Journal of Micro/Nanolithography, MEMS, and MOEMS 7, no. 1 (January 1, 2008): 013006. http://dx.doi.org/10.1117/1.2841721.

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Dissertations / Theses on the topic "Deep structures"

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Lambert, C. P. "Multimodal segmentation of deep cortical structures." Thesis, University College London (University of London), 2012. http://discovery.ucl.ac.uk/1344055/.

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The organisation of the human cortex is characterised by macroscopically defined areas consisting of functionally distinct subunits, each connected to an array of local and distant targets forming distinctive networks. Classically, these structures have been parcellated according to ex vivo cytochemical and connectivity properties. However, the emergent flaw with this approach is the presence of significant inter-hemispheric and inter-individual anatomical variability. By exploiting several MRI modalities, a similar approach to sub-regional parcellation can be applied in vivo across large numbers of individuals. Using diffusion tensor imaging (DTI), probabilistic tractography can be used to generate a representation of the white matter pathways originating from or passing through a single voxel. By quantifying the degree of similarity between different tract distributions, regional parcellation can be achieved through several algorithms. These have previously been used on regions such as the thalamus and basal ganglia. However, due to computational limitations, it is normal practice to apply dimension reduction tactics prior to parcellation, thereby generating an upper bound on the degree of accuracy that can be achieved. I have set out to further this pre-existing framework by developing methods to analyse and cluster massive matrices without down-sampling data, thereby generating a prior free, bottom-up approach to regional parcellation based on regional connectivity. I have applied this approach to several areas including the sub-thalamic nucleus, amygdala and human brainstem. Several fundamental properties and limitations of the technique are revealed, and additional methods developed to further improve the white matter parcellation. This includes a novel method of multichannel segmentation, which was applied to the human brainstem and cortex. The new tissue classes were used both for quantitative analysis, and also to improve DTI based segmentation. Throughout, the findings are extrapolated to examine a variety of neuropathological scenarios, including symptom networks, pre-clinical diagnosis and therapeutic interventions such as deep brain stimulation.
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Xu, Yuan. "Statistical shape analysis for deep brain structures." Diss., Restricted to subscribing institutions, 2008. http://proquest.umi.com/pqdweb?did=1581917061&sid=11&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Billingsley, Richard John. "Deep Learning for Semantic and Syntactic Structures." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/12825.

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Deep machine learning has enjoyed recent success in vision and speech-to-text tasks, using deep multi-layered neural networks. They have obtained remarkable results particularly where the internal representation of the task is unclear. In parsing, where the structure of syntax is well studied and understood from linguistics, neural networks have so far not performed so well. State-of-the-art parsers use a tree-based graphical model that requires a large number of equivalent classes to represent each parse node and its phrase label. A recursive neural network (RNN) parser has been developed that works well on short sentences, but falls short of the state-of-the-art results on longer sentences. This thesis aims to investigate deep learning and improve parsing by examining how neural networks could perform state-of-the-art parsing by comparison with PCFG parsers. We hypothesize that a neural network could be configured to implement an algorithm parallel to PCFG parsers, and examine their suitability to this task from an analytic perspective. This highlights a missing term that the RNN parser is unable to model, and we identify the role of this missing term in parsing. We finally present two methods to improve the RNN parser by building upon the analysis in earlier chapters, one using an iterative process similar to belief propagation that yields a 0.38% improvement and another replacing the scoring method with a deeper neural model yielding a 0.83% improvement. By examining an RNN parser as an exemplar of a deep neural network, we gain insights to deep machine learning and some of the approximations it must make by comparing it with well studied non-neural parsers that achieve state-of-the-art results. In this way, our research provides a better understanding of deep machine learning and a step towards improvements in parsing that will lead to smarter algorithms that can learn more accurate representations of information and the syntax and semantics of text.
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Olowe, Adedayo Christianah. "Corrosion assessment and cathodic protection design parameters for steel structures in deep and ultra deep offshore waters." Thesis, University of Aberdeen, 2013. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=201965.

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Carbon steel finds much application for use in industries including civil; manufacturing; oil and gas; as well as, renewable energy. Common examples for usage of steel include water pipelines; oil pipelines; bridges; etc. The main advantages of steel over other engineering materials are its strength and affordability. However, steel undergoes corrosion which is a degradation mechanism that occurs as a result of the electrochemical interaction between steel and its environment. There are two main options to control corrosion, aside from material selection techniques, namely, the use of protective coating systems to isolate the steel from the environment; or the use of cathodic protection. Cathodic protection involves the use of galvanic anodes or impressed current system to prevent steel corrosion. Currently the oil and gas industry accounts for the major share of consumption of galvanic anodes for the protection of steel in engineering applications. Recent incursions into deep water depths by the Oil and Gas industry in the last decade or so has brought to the fore the need to understand better the performance of steel at deep and ultra deep water depths; as well as to develop an understanding of how cathodic protection works at these water depths. So far, the bulk of industry experience lies in shallow waters and current international cathodic protection design guidelines are based on data collated at these shallow water depths. It is the objective of this research work to assess the corrosion properties of steel with deep seawater parameters and determine design current density requirements for effective cathodic protection of steel at deep and ultra deep water depths offshore.
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Grice, James Robert. "Prediction of extreme wave-structure interactions for multi-columned structures in deep water." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:dd7320c1-7121-4ea7-827f-527af9405e9a.

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With a continuing and rising demand for hydrocarbons, the energy companies are installing infrastructure ever further offshore, where such infrastructure is often exposed to extreme waves. This thesis explores some aspects of wave-structure interaction, particularly the maximum water surface elevation increase in severe storms due to these local interactions. The effects on wave-structure interactions of column cross-sectional shape are investigated using linear and second-order wave diffraction theory. For multi-column structures, the excitation of locally resonant wave modes (near-trapping) is studied for several column cross-sectional shapes, and a simple method for estimating the surface elevation mode shape is given. The structure of the quadratic transfer functions for second-order sum wave elevation is investigated and an approximation assuming these QTFs are flat perpendicular to the leading diagonal is shown to be adequate for the first few lowest frequency modes. NewWave-type focused wave groups can be used as a more realistic model of extreme ocean waves. A Net Amplification Factor based on the NewWave model is given as an efficient tool for finding the incident frequencies most likely to cause a violent wave-structure interaction and where these violent responses are likely to occur. Statistics are collected from Monte Carlo type simulations of random waves to verify the use of the Net Amplification Factor. Going beyond linear calculations, surface elevation statistics are collected to second-order and a `designer' wave is found to model the most extreme surface elevation responses. A `designer' wave can be identified at required levels of return period to help to understand the relative size of harmonic components in extreme waves. The methods developed with a fixed body are then applied to an identical hull which is freely floating, and the responses between the fixed and moving cases are compared. The vertical heave motion of a semi-submersible in-phase with the incident wave crests is shown to lead to a much lower probability of water-deck impact for the same hull shape restrained vertically. The signal processing methods developed are also applied to a single column to allow comparison with experimental results. Individual harmonic components of the hydrodynamic force are identified up to at least the fifth harmonic. Stokes scaling is shown to hold even for the most violent interactions. It is also shown that the higher harmonic components of the hydrodynamic force can be reconstructed from just the fundamental force time history, and a transfer function in the form of a single phase and an amplitude for each harmonic. The force is also reconstructed well to second-order from the surface elevation time history using diffraction transfer functions. Finally, possible causes of damage to a platform high above mean water level in the North Sea are investigated.
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Dikdogmus, Halil. "RISER CONCEPTS FOR DEEP WATERS." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for marin teknikk, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-18528.

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Oil and gas exploration and production activities in deep and ultra deep waters in hostile environments necessitates the need to develop innovative riser systems capable of ensuring transfer of fluids from the seabed to a floating vessel and vice versa, with little or no issues with respect to influences of environmental loads and vessel motions.The design of the riser system must focus on different types of loading and load effects than for traditional water-depth. A variety of different riser concepts are proposed, both with respect to geometric shape and selection of materials.In the last few years, steel catenary risers have been a preferred riser solution for deep-water field developments due to its simple engineering concept, cost effective, flexibility in using different host platform and flexibility in geographical and environmental conditions. In this report, a case study considering a steel catenary riser operating in 1000 m water depth was conducted. The riser was subjected to extreme environmental conditions and static and dynamic response analyses were performed by the computer program RIFLEX.Last, parametric study is carried out to investigate the effects of parameter variation based on some parameters like current profiles, mesh density, wall thickness and so on. These parameters have significant effect on the structural response, especially in the touch down region.
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Romagna, Pinter Patricia. "Reappraising the Numidian system (Miocene, southern Italy) deep-water sandstone fairways confined by tectonised substrate." Thesis, University of Aberdeen, 2017. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=238534.

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Oyallon, Edouard. "Analyzing and introducing structures in deep convolutional neural networks." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE060.

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Cette thèse étudie des propriétés empiriques des réseaux de neurones convolutifs profonds, et en particulier de la transformée en Scattering. En effet, l’analyse théorique de ces derniers est difficile et représente jusqu’à ce jour un défi : les couches successives de neurones ont la capacité de réaliser des opérations complexes, dont la nature est encore inconnue, via des algorithmes d’apprentissages dont les garanties de convergences ne sont pas bien comprises. Pourtant, ces réseaux de neurones sont de formidables outils pour s’attaquer à une grande variété de tâches difficiles telles la classification d’images, ou plus simplement effectuer des prédictions. La transformée de Scattering est un opérateur mathématique, non-linéaire dont les spécifications sont inspirées par les réseaux convolutifs. Dans ce travail, elle est appliquée sur des images naturelles et obtient des résultats compétitifs avec les architectures non-supervisées. En plaçant un réseau de neurones convolutifs supervisés à la suite du Scattering, on obtient des performances compétitives sur ImageNet2012, qui est le plus grand jeux de donnée d’images étiquetées accessibles aux chercheurs. Cela nécessite d’implémenter un algorithme efficace sur carte graphique. Dans un second temps, cette thèse s’intéresse aux propriétés des couches à différentes profondeurs. On montre qu’un phénomène de réduction de dimensionnalité progressif à lieu et on s’intéresse aux propriétés de classifications supervisées lorsqu’on varie des hyper paramètres de ces réseaux. Finalement, on introduit une nouvelle classe de réseaux convolutifs, dont les opérateurs sont structurés par des groupes de symétries du problème de classification
This thesis studies empirical properties of deep convolutional neural networks, and in particular the Scattering Transform. Indeed, the theoretical analysis of the latter is hard and until now remains a challenge: successive layers of neurons have the ability to produce complex computations, whose nature is still unknown, thanks to learning algorithms whose convergence guarantees are not well understood. However, those neural networks are outstanding tools to tackle a wide variety of difficult tasks, like image classification or more formally statistical prediction. The Scattering Transform is a non-linear mathematical operator whose properties are inspired by convolutional networks. In this work, we apply it to natural images, and obtain competitive accuracies with unsupervised architectures. Cascading a supervised neural networks after the Scattering permits to compete on ImageNet2012, which is the largest dataset of labeled images available. An efficient GPU implementation is provided. Then, this thesis focuses on the properties of layers of neurons at various depths. We show that a progressive dimensionality reduction occurs and we study the numerical properties of the supervised classification when we vary the hyper parameters of the network. Finally, we introduce a new class of convolutional networks, whose linear operators are structured by the symmetry groups of the classification task
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Astolfi, Pietro. "Toward the "Deep Learning" of Brain White Matter Structures." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/337629.

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In the brain, neuronal cells located in different functional regions communicate through a dense structural network of axons known as the white matter (WM) tissue. Bundles of axons that share similar pathways characterize the WM anatomy, which can be investigated in-vivo thanks to the recent advances of magnetic resonance (MR) techniques. Diffusion MR imaging combined with tractography pipelines allows for a virtual reconstruction of the whole WM anatomy of in-vivo brains, namely the tractogram. It consists of millions of WM fibers as 3D polylines, each approximating thousands of axons. From the analysis of a tractogram, neuroanatomists can characterize well-known white matter structures and detect anatomically non-plausible fibers, which are artifacts of the tractography and often constitute a large portion of it. The accurate characterization of tractograms is pivotal for several clinical and neuroscientific applications. However, such characterization is a complex and time-consuming process that is difficult to be automatized as it requires properly encoding well-known anatomical priors. In this thesis, we propose to investigate the encoding of anatomical priors with a supervised deep learning framework. The ultimate goal is to reduce the presence of artifactual fibers to enable a more accurate automatic process of WM characterization. We devise the problem by distinguishing between volumetric and non-volumetric representations of white matter structures. In the first case, we learn the segmentation of the WM regions that represent relevant anatomical waypoints not yet classified by WM atlases. We investigate using Convolutional Neural Networks (CNNs) to exploit the volumetric representation of such priors. In the second case, the goal is to learn from the 3D polyline representation of fibers where the typical CNN models are not suitable. We introduce the novelty of using Geometric Deep Learning (GDL) models designed to process data having an irregular representation. The working assumption is that the geometrical properties of fibers are informative for the detection of tractogram artifacts. As a first contribution, we present StemSeg that extends the use of CNNs to detect the WM portion representing the waypoints of all the fibers for a specific bundle. This anatomical landmark, called stem, can be critical for extracting that bundle. We provide the results of an empirical analysis focused on the Inferior Fronto-Occipital Fasciculus (IFOF). The effective segmentation of the stem improves the final segmentation of the IFOF, outperforming with a significant gap the reference state of the art. As a second and major contribution, we present Verifyber, a supervised tractogram filtering approach based on GDL, distinguishing between anatomically plausible and non-plausible fibers. The proposed model is designed to learn anatomical features directly from the fiber represented as a 3D points sequence. The extended empirical analysis on healthy and clinical subjects reveals multiple benefits of Verifyber: high filtering accuracy, low inference time, flexibility to different plausibility definitions, and good generalization. Overall, this thesis constitutes a step toward characterizing white matter using deep learning. It provides effective ways of encoding anatomical priors and an original deep learning model designed for fiber.
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Yang, Yuzhe S. M. Massachusetts Institute of Technology. "On exploiting structures for deep learning algorithms with matrix estimation." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127319.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 113-118).
Despite recent breakthroughs of deep learning, the intrinsic structures within tasks have not yet been fully explored and exploited for better performance. This thesis proposes to harness the structured properties of deep learning tasks using matrix estimation (ME). Motivated by the theoretical guarantees and appealing results, we apply ME to study the following two important learning problems: 1. Adversarial robustness. Deep neural networks are vulnerable to adversarial attacks. This thesis proposes ME-Net, a defense method that leverages ME. In ME-Net, images are preprocessed using two steps: first pixels are randomly dropped from the image; then, the image is reconstructed using ME. We show that this process destroys the adversarial structure of the noise, while re-enforcing the global structure in the original image. Comparing ME-Net with state-of-the-art defense mechanisms shows that ME-Net consistently outperforms prior techniques, improving robustness against both black-box and white-box attacks. 2. Value-based planning and deep reinforcement learning (RL). This thesis proposes to exploit the underlying low-rank structures of the state-action value function, i.e., Q function. We verify empirically the existence of low-rank Q functions in the context of control and deep RL tasks. As our key contribution, by leveraging ME, we propose a generic framework to exploit the underlying low-rank structure in Q functions. This leads to a more efficient planning procedure for classical control, and additionally, a simple scheme that can be applied to any value-based RL techniques to consistently achieve better performance on "low-rank" tasks. The results of this thesis demonstrate the value of using matrix estimation to capture the internal structures of deep learning tasks, and highlight the benefits of leveraging structure for analyzing and improving modern learning algorithms.
by Yuzhe Yang.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Books on the topic "Deep structures"

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Petersen, Ib Damgaard. Deep structures in international politics. København, Danmark: Institute of Political Studies, University of Copenhagen, 1987.

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Lucerna, Sebastiano, Francesco M. Salpietro, Concetta Alafaci, and Francesco Tomasello. In Vivo Atlas of Deep Brain Structures. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56381-2.

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Pinto, Pedro, Chang-Yu Ou, and Hany Shehata, eds. Innovative Solutions for Deep Foundations and Retaining Structures. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34190-9.

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Maccarini, Andrea M. Deep Change and Emergent Structures in Global Society. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13624-6.

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1957-, Lucerna S., ed. In vivo atlas of deep brain structures: With 3D reconstructions. Berlin: Springer, 2002.

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Engineers, Institution of Structural. Design and construction of deep basements including cut-and-cover structures. London: The Institution, 2004.

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Engineers, Institution of Structural, ed. Design and construction of deep basements including cut-and-cover structures. London: Institution of Structural Engineers, 2004.

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Inc, BarCharts, ed. Anatomy 2: Includes deep and posterior anatomy plus many new structures. [Boca Raton, Fla.]: BarCharts, Inc., 2005.

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Andrew St. Lawrence John Wickens. The Trinity and anthropology: The philosophical deep structures of Karl Rahner's theology. Birmingham: University of Birmingham, 1994.

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1943-, Stecker Michael, ed. Structures in space: Hidden secrets of the deep sky : the Stecker files. New York: Springer, 2000.

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Book chapters on the topic "Deep structures"

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Strenski, Ivan. "Falsifying Deep Structures." In Religion in Relation, 57–74. London: Palgrave Macmillan UK, 1993. http://dx.doi.org/10.1007/978-1-349-11866-3_4.

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Caracas, Razvan. "Crystal Structures of Core Materials." In Deep Earth, 55–68. Hoboken, NJ: John Wiley & Sons, Inc, 2016. http://dx.doi.org/10.1002/9781118992487.ch5.

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Osipyan, Hasmik, Bosede Iyiade Edwards, and Adrian David Cheok. "Neural Network Structures." In Deep Neural Network Applications, 29–55. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9780429265686-3.

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Wicks, June K., and Thomas S. Duffy. "Crystal Structures of Minerals in the Lower Mantle." In Deep Earth, 69–87. Hoboken, NJ: John Wiley & Sons, Inc, 2016. http://dx.doi.org/10.1002/9781118992487.ch6.

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Ben-Menahem, Ari. "Deep Principles – Complex Structures." In Historical Encyclopedia of Natural and Mathematical Sciences, 5081–986. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-68832-7_9.

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Eppelbaum, Lev, Izzy Kutasov, and Arkady Pilchin. "Investigating Deep Lithospheric Structures." In Lecture Notes in Earth System Sciences, 269–391. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-34023-9_6.

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McCawley, James D. "On what is Deep about Deep Structures." In Cognition and the Symbolic Processes, 125–28. London: Routledge, 2024. http://dx.doi.org/10.4324/9781003482833-5.

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Mirtskhulava, Lela. "Deep Learning Applications in Predicting Polymer Properties." In Advanced Polymer Structures, 161–71. New York: Apple Academic Press, 2023. http://dx.doi.org/10.1201/9781003352181-16.

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Jishun, Ren, Jiang Chunfa, Zhang Zhengkun, and Qin Deyu. "Deep Fractures and Deep-Seated Structures in China." In Geotectonic Evolution of China, 126–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-61574-0_5.

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Bédaride, Paul, and Claire Gardent. "Deep Semantics for Dependency Structures." In Computational Linguistics and Intelligent Text Processing, 277–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19400-9_22.

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Conference papers on the topic "Deep structures"

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Chikersal, Prerna, Maria Tomprou, Young Ji Kim, Anita Williams Woolley, and Laura Dabbish. "Deep Structures of Collaboration." In CSCW '17: Computer Supported Cooperative Work and Social Computing. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/2998181.2998250.

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GAWRONSKI, W., B. BIENKIEWICZ, and R. HILL. "Wind-induced dynamics of the deep space network antennas." In 33rd Structures, Structural Dynamics and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1992. http://dx.doi.org/10.2514/6.1992-2458.

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Scalise, Carmen, and Kevin Fitzpatrick. "Chicago Deep Tunnel Design and Construction." In Structures Congress 2012. Reston, VA: American Society of Civil Engineers, 2012. http://dx.doi.org/10.1061/9780784412367.132.

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Han, Jie, and Ken Akins. "Use of Geogrid-Reinforced and Pile-Supported Earth Structures." In International Deep Foundations Congress 2002. Reston, VA: American Society of Civil Engineers, 2002. http://dx.doi.org/10.1061/40601(256)48.

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Birrcher, David, Robin Tuchscherer, Matthew Huizinga, and Oguzhan Bayrak. "Depth Effect in Reinforced Concrete Deep Beams." In Structures Congress 2009. Reston, VA: American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/41031(341)175.

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Bi, Zhnegfa, and Xinming Wu. "Implicit structural modeling of geological structures with deep learning." In First International Meeting for Applied Geoscience & Energy. Society of Exploration Geophysicists, 2021. http://dx.doi.org/10.1190/segam2021-3583427.1.

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Phoon, Kok-Kwang, and Fred H. Kulhawy. "Drilled Shaft Design for Transmission Structures Using LRFD and MRFD." In International Deep Foundations Congress 2002. Reston, VA: American Society of Civil Engineers, 2002. http://dx.doi.org/10.1061/40601(256)70.

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Wu, Xiong-Jian, and W. G. Price. "The Behaviour of Shallow Draft Offshore Structures and Service Vessels in Deeper Water." In Development In Deep Waters. RINA, 1986. http://dx.doi.org/10.3940/rina.ddw.1986.17.

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Bouadi, Hakim, Eric Green, and Narendra Gosain. "Evaluation and Repair of a Deep Transfer Girder." In Structures Congress 2005. Reston, VA: American Society of Civil Engineers, 2005. http://dx.doi.org/10.1061/40753(171)255.

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Tomaszkiewicz, Karolina, and Tomasz Owerko. "Deep machine learning in bridge structures durability analysis." In 5th Joint International Symposium on Deformation Monitoring. Valencia: Editorial de la Universitat Politècnica de València, 2022. http://dx.doi.org/10.4995/jisdm2022.2022.13884.

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According to Eurocode 0 structural durability is next to ultimate and serviceability one of the basic criteria in the structural design process. This article discusses the subject of concrete cracks observation in bridge structures, as one of the factors determining their durability. The durability of bridge structures is important due to both social, economic aspects and also the defense aspects of countries. Cracking of the reinforced concrete structures is a natural effect in concrete. The aim in the design and construction of structures is not to prevent the formation of cracks, but to limit their width to acceptable values. At the same time, there is a need for structure tests that allow for non-contact, fast measurements and algorithms that allow for efficient analysis of large amounts of measurement data. Deep machine learning algorithms can be used here. They can be used to analyse data which are acquired by means of photogrammetric methods (especially helpful during construction to inventory concealed works). Moreover, they can also be applied to standard data acquisition methods, consisting in photographing objects damage during works acceptance or periodic inspections. This paper discusses the application of deep machine learning to assess the condition of bridge structures based on photographs of object damage. The use of this method makes it possible to observe the rate and extent of damage development. Consequently, this method makes it possible to predict the development of damage in time and space in order to prevent failures and take structures out of service.
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Reports on the topic "Deep structures"

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Harris, L. B., P. Adiban, and E. Gloaguen. The role of enigmatic deep crustal and upper mantle structures on Au and magmatic Ni-Cu-PGE-Cr mineralization in the Superior Province. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328984.

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Aeromagnetic and ground gravity data for the Canadian Superior Province, filtered to extract long wavelength components and converted to pseudo-gravity, highlight deep, N-S trending regional-scale, rectilinear faults and margins to discrete, competent mafic or felsic granulite blocks (i.e. at high angles to most regional mapped structures and sub-province boundaries) with little to no surface expression that are spatially associated with lode ('orogenic') Au and Ni-Cu-PGE-Cr occurrences. Statistical and machine learning analysis of the Red Lake-Stormy Lake region in the W Superior Province confirms visual inspection for a greater correlation between Au deposits and these deep N-S structures than with mapped surface to upper crustal, generally E-W trending, faults and shear zones. Porphyry Au, Ni, Mo and U-Th showings are also located above these deep transverse faults. Several well defined concentric circular to elliptical structures identified in the Oxford Stull and Island Lake domains along the S boundary of the N Superior proto-craton, intersected by N- to NNW striking extensional fractures and/or faults that transect the W Superior Province, again with little to no direct surface or upper crustal expression, are spatially associated with magmatic Ni-Cu-PGE-Cr and related mineralization and Au occurrences. The McFaulds Lake greenstone belt, aka. 'Ring of Fire', constitutes only a small, crescent-shaped belt within one of these concentric features above which 2736-2733 Ma mafic-ultramafic intrusions bodies were intruded. The Big Trout Lake igneous complex that hosts Cr-Pt-Pd-Rh mineralization west of the Ring of Fire lies within a smaller concentrically ringed feature at depth and, near the Ontario-Manitoba border, the Lingman Lake Au deposit, numerous Au occurrences and minor Ni showings, are similarly located on concentric structures. Preliminary magnetotelluric (MT) interpretations suggest that these concentric structures appear to also have an expression in the subcontinental lithospheric mantle (SCLM) and that lithospheric mantle resistivity features trend N-S as well as E-W. With diameters between ca. 90 km to 185 km, elliptical structures are similar in size and internal geometry to coronae on Venus which geomorphological, radar, and gravity interpretations suggest formed above mantle upwellings. Emplacement of mafic-ultramafic bodies hosting Ni-Cr-PGE mineralization along these ringlike structures at their intersection with coeval deep transverse, ca. N-S faults (viz. phi structures), along with their location along the margin to the N Superior proto-craton, are consistent with secondary mantle upwellings portrayed in numerical models of a mantle plume beneath a craton with a deep lithospheric keel within a regional N-S compressional regime. Early, regional ca. N-S faults in the W Superior were reactivated as dilatational antithetic (secondary Riedel/R') sinistral shears during dextral transpression and as extensional fractures and/or normal faults during N-S shortening. The Kapuskasing structural zone or uplift likely represents Proterozoic reactivation of a similar deep transverse structure. Preservation of discrete faults in the deep crust beneath zones of distributed Neoarchean dextral transcurrent to transpressional shear zones in the present-day upper crust suggests a 'millefeuille' lithospheric strength profile, with competent SCLM, mid- to deep, and upper crustal layers. Mechanically strong deep crustal felsic and mafic granulite layers are attributed to dehydration and melt extraction. Intra-crustal decoupling along a ductile décollement in the W Superior led to the preservation of early-formed deep structures that acted as conduits for magma transport into the overlying crust and focussed hydrothermal fluid flow during regional deformation. Increase in the thickness of semi-brittle layers in the lower crust during regional metamorphism would result in an increase in fracturing and faulting in the lower crust, facilitating hydrothermal and carbonic fluid flow in pathways linking SCLM to the upper crust, a factor explaining the late timing for most orogenic Au. Results provide an important new dataset for regional prospectively mapping, especially with machine learning, and exploration targeting for Au and Ni-Cr-Cu-PGE mineralization. Results also furnish evidence for parautochthonous development of the S Superior Province during plume-related rifting and cannot be explained by conventional subduction and arc-accretion models.
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Clay, C. S. Acoustic Reverberation in Wedge Structures at the Transitions from Deep to Shallow Water. Fort Belvoir, VA: Defense Technical Information Center, August 1997. http://dx.doi.org/10.21236/ada328801.

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VIGIL, MANUEL GILBERT. Design of Largest Shaped Charge: Generation of Very Large Diameter, Deep Holes in Rock and Concrete Structures. Office of Scientific and Technical Information (OSTI), April 2003. http://dx.doi.org/10.2172/810682.

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Bernau, Jeremiah A., Charles G. Oviatt, Donald L. Clark, and Brenda B. Bowen. Sediment Logs Compiled From the Great Salt Lake Desert, Western Utah, With a Focus on the Bonneville Salt Flats Area. Utah Geological Survey, June 2023. http://dx.doi.org/10.34191/ofr-754.

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Sediment logs from exposures, excavations, cores, and wells are valuable records that can be used to interpret depositional records, aquifer extents, and geologic structures. Here we present sediment logs compiled from the Great Salt Lake Desert (GSLD) in western Utah with a specific focus on the Bonneville Salt Flats area. The logs are grouped into three categories (shallow, alluvial fan, and deep) that may be used to explore different aspects of GSLD deposition.
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Vito, L. F. Di, G. Mannucci, G. Demofonti, G. Cumino, A. Izquierdo, F. Daguerre, H. Quintanille, and M. Tivelli. CGX-00-003 Tenaris Double Joint for Deep Water Applications Subjected to Large Cyclic Plastic Strains. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), August 1994. http://dx.doi.org/10.55274/r0011808.

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The evaluation of the defect tolerance assessment for girth welded joints of seamless pipes for off-shore applications when subjected to large cyclic plastic strains. The reeling laying technique, which is considered to be the most severe from this point of view, has been considered and studied in depth in order to determine how the several plastic strain cycles suffered by the joint during the laying could affect the defect tolerability. Advanced Engineering Critical Assessment methods have been considered in the analysis as the BS 7910 FAD approach implemented with the corrections recommended by more recent studies (such as DNV-OS-F101) about the structures subjected to large plastic deformations. Then the reliability and conservativeness of the setup ECA procedure have been discussed on the basis of a dedicated large scale segment tests program performed on girth weld joints realized in house by Tenaris on X65 grade seamless pipe for deepwater applications. The paper demonstrated the good behavior of the Tenaris Double Joint by both toughness and tensile properties point of view by the light of the more recent and advanced ECA methodologies.
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Bourhrous, Amal, Shivan Fazil, and Dylan O’Driscoll. Post-conflict Reconstruction in the Nineveh Plains of Iraq: Agriculture, Cultural Practices and Social Cohesion. Stockholm International Peace Research Institute, November 2022. http://dx.doi.org/10.55163/raep9560.

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The atrocities committed by the Islamic State (IS) between 2014 and 2017 left deep scars on the Nineveh Plains in northern Iraq. IS deliberately targeted ethnic and religious communities with the aim of erasing the traces of diversity, pluralism and coexistence that have long characterized the region. To prevent people from living as Assyrians, Chaldeans, Kaka’i, Shabaks, Syriacs, Turkmen and Yazidis, IS destroyed sites of cultural and religious significance to these communities and devastated their livelihoods, including their crop and livestock farming activities. Using a people-centered approach, this SIPRI Research Policy Paper stresses the need for a holistic approach to post-conflict reconstruction in the Nineveh Plains that not only focuses on rebuilding the physical environment and economic structures, but also pays adequate attention to restoring the ability of communities to engage in cultural and religious practices, and to mending social and intercommunity relations. The paper highlights the interconnectedness of physical environments, economic structures, cultural practices and social dynamics. It stresses the need to address the impacts of the IS occupation while taking into account other pressing challenges such as climate change and water scarcity.
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Ng, Andrew Y., and Christopher D. Manning. Discovery of Deep Structure from Unlabeled Data. Fort Belvoir, VA: Defense Technical Information Center, November 2014. http://dx.doi.org/10.21236/ada614158.

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Heaney, Kevin. Spatial Structure of Deep Water Acoustic Propagation. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada533364.

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Dafoe, L. T., K. Dickie, and G. L. Williams. Stratigraphy of western Baffin Bay: a review of existing knowledge and some new insights. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/321846.

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Sedimentary basins within the Labrador-Baffin Seaway are the product of rifting between Greenland and the paleo-North American Plate. Rifting started in the Early Cretaceous, with seafloor spreading initiated in the Paleocene and ending near the Eocene-Oligocene boundary. A change in the spreading direction in the latest Paleocene resulted in transform offsets in the Davis Strait and along fracture zones in Baffin Bay, with deformation in northern Baffin Bay during the Eurekan Orogeny. Since the stratigraphy of western Baffin Bay is poorly constrained, analogues are used from the well studied Labrador and West Greenland margins and exposures on nearby Bylot Island. The generally northwest-trending basement structures are infilled with Cretaceous strata, which are overlain by a seaward-thickening wedge of post-rift Paleocene to Middle Miocene sedimentary rocks. Finally, a thick Middle Miocene and younger interval blankets the deep water and oceanic crust, with clinoforms locally developed on the shelf.
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Copeland, Ronald, and James Lewis. Technical assessment of the Old, Mississippi, Atchafalaya, and Red (OMAR) Rivers: Mississippi River HEC-6T model. Engineer Research and Development Center (U.S.), August 2022. http://dx.doi.org/10.21079/11681/45160.

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The HEC-6T 1D numerical sedimentation model was used to evaluate the long-term and system-wide sedimentation effects of modifying the operation schedule at the Old River Control Complex (ORCC). The changes evaluated were the increase and decrease of the percentages of flow diverted from the Mississippi River at the ORCC and the modification of the distributions through the four ORCC structures. Also evaluated were dredging scenarios that removed sediment from the bed of the Mississippi River at the ORCC. Sedimentation effects for several operation and dredging scenarios were compared to a Base Condition that represented the existing operation protocols. The predictive simulations extended for 50 years. The model was used to calculate and compare sand transport past various gages along the Mississippi River and in the Outflow Channel. Dredging volumes at the deep-draft crossings and in Southwest Pass and above Head of Passes were compared. Differences in water-surface profiles in the vicinity of ORCC were determined.
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