Academic literature on the topic 'Deep'

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Journal articles on the topic "Deep"

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Takahashi, Satoshi. "Deep Color." Journal of The Institute of Image Information and Television Engineers 65, no. 9 (2011): 1308–11. http://dx.doi.org/10.3169/itej.65.1308.

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Wang, Yipu, and Stuart Perrin. "Deep Chinese Teaching and Learning Model Based on Deep Learning." International Journal of Languages, Literature and Linguistics 10, no. 1 (2024): 32–35. http://dx.doi.org/10.18178/ijlll.2024.10.1.479.

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Deep learning is a more situational and reflective way of learning that integrates complex knowledge and skills into intuitive thinking. As a language that closely combines sound, form and meaning, Chinese teaching and learning from the perspective of deep learning can help break through the limitations of the current teaching model that only focuses on certain language knowledge or cultural behaviors. This paper combines deep learning with international Chinese education, creates deep Chinese teaching and learning model including “four stages and ten steps”, and carries out practical application and teaching effect test. The results show that the deep Chinese teaching and learning model is conducive to improving students’ discourse presentation ability and comprehensive skills, cultivating the learners’ autonomous learning ability and intercultural communication competence, and strengthening the integration of language teaching and cultural teaching. At the same time, this model also has some limitations, need to be further adjusted and optimized.
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Yoshida, Koichiro. "Deep-seated mycosis." Medical Mycology Journal 55, no. 2 (2014): J55—J56. http://dx.doi.org/10.3314/mmj.55.j55.

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Honda, Hitoshi, Brian S Heist, and Ken Kikuchi. "Deep-seated mycosis." Medical Mycology Journal 56, no. 3 (2015): E21—E22. http://dx.doi.org/10.3314/mmj.56.e21.

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Watanabe, Akira. "Deep-seated mycosis." Medical Mycology Journal 56, no. 1 (2015): J1—J2. http://dx.doi.org/10.3314/mmj.56.j1.

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Honda, Hitoshi, Brian S Heist, and Ken Kikuchi. "Deep-seated mycosis." Medical Mycology Journal 56, no. 3 (2015): J105—J106. http://dx.doi.org/10.3314/mmj.56.j105.

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Yaguchi, Takashi. "Deep-seated mycosis." Medical Mycology Journal 56, no. 4 (2015): J137—J138. http://dx.doi.org/10.3314/mmj.56.j137.

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Mikamo, Hiroshige. "Deep-seated mycosis." Medical Mycology Journal 57, no. 1 (2016): J33—J34. http://dx.doi.org/10.3314/mmj.57.j33.

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Singh P, Barath Kumar, and Ravi Kumar Chittoria. "Deep Vein Thrombosis." Indian Journal of Medical and Health Sciences 9, no. 2 (December 15, 2022): 89–96. http://dx.doi.org/10.21088/ijmhs.2347.9981.9222.9.

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Deep vein thrombosis (DVT) and Pulmonary embolism (PE) are two symptoms of venous thromboembolism (VTE). Both in the community and in hospitals, VTE significantly increases morbidity and death. Anticoagulation is the cornerstone of DVT treatment, assuming there are no contraindications. Patients with DVT have further anticoagulation after initial anticoagulation in order to guard against subsequent recurrences, emboli, and thrombosis-related mortality. This article provides an overview of the management of lower extremity DVT.
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Rosenthal, Brock J. "How Deep Is Deep?" Marine Technology Society Journal 43, no. 5 (December 1, 2009): 8. http://dx.doi.org/10.4031/mtsj.43.5.2.

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Dissertations / Theses on the topic "Deep"

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Peralta, Yaddyra. "Deep Waters." FIU Digital Commons, 2012. http://digitalcommons.fiu.edu/etd/622.

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The purpose of this creative thesis was to explore the state of exile via the use of the contemporary lyric poem. Written primarily in free verse, with some poems written in the traditional forms of the sonnet, haiku and senryu, the thesis explored exile and its variant themes of colonization, assimilation, familial history, cultural and personal myth. The result was the discovery that the lyric poem is an ideal, productive and fluid medium through which a poet can consider and encounter the liminality of exile identity.
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Straube, Nicolas. "Deep divergence." Diss., Ludwig-Maximilians-Universität München, 2011. http://nbn-resolving.de/urn:nbn:de:bvb:19-138186.

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Joseph, Caberbe. "DEEP WITHIN." Master's thesis, University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2794.

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As a contemporary photographer, I focus most on light and color to bring out the uniqueness of my images. Photography is about lighting and I manipulate lights to raise questions in my viewers. Manipulating light is my way of being curious about how it may change mood physically and emotionally. Inspired by classical paintings, I have developed a body of photographs that can be admired by anyone. Although the main focus of my work is light and color, this body of work is also intended to empower those with little confidence in themselves and those who have been rejected, abused, or mistrusted.
M.F.A.
Department of Art
Arts and Humanities
Studio Art and the Computer MFA
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Krotevych, K. "Deep web." Thesis, Sumy State University, 2015. http://essuir.sumdu.edu.ua/handle/123456789/40487.

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We got accustomed to the fact that all the information on the Internet instantly could be found by search engines. They know everything about everyone. But is it really so? It turns out there are areas in WWW, neither Google nor Yandex have access. Moreover, according to most experts, their size is hundreds of times greater than the size of the rest of the internet. This secret web called deep web.
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Wood, Rebecca. "Deep Surface." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1427899904.

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Peterson, Grant. "Deep time /." abstract, 2008. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1455664.

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Thesis (M.A.)--University of Nevada, Reno, 2008.
"May, 2008." Library also has microfilm. Ann Arbor, Mich. : ProQuest Information and Learning Company, [2009]. 1 microfilm reel ; 35 mm. Online version available on the World Wide Web.
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Traxl, Dominik. "Deep graphs." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2017. http://dx.doi.org/10.18452/17785.

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Netzwerk Theorie hat sich als besonders zweckdienlich in der Darstellung von Systemen herausgestellt. Jedoch fehlen in der Netzwerkdarstellung von Systemen noch immer essentielle Bausteine um diese generell zur Datenanalyse heranzuziehen zu können. Allen voran fehlt es an einer expliziten Assoziation von Informationen mit den Knoten und Kanten eines Netzwerks und einer schlüssigen Darstellung von Gruppen von Knoten und deren Relationen auf verschiedenen Skalen. Das Hauptaugenmerk dieser Dissertation ist der Einbindung dieser Bausteine in eine verallgemeinerte Rahmenstruktur gewidmet. Diese Rahmenstruktur - Deep Graphs - ist in der Lage als Bindeglied zwischen einer vereinheitlichten und generalisierten Netzwerkdarstellung von Systemen und den Methoden der Statistik und des maschinellen Lernens zu fungieren (Software: https://github.com/deepgraph/deepgraph). Anwendungen meiner Rahmenstruktur werden dargestellt. Ich konstruiere einen Regenfall Deep Graph und analysiere raumzeitliche Extrem-Regenfallcluster. Auf Grundlage dieses Graphs liefere ich einen statistischen Beleg, dass die Größenverteilung dieser Cluster einem exponentiell gedämpften Potenzgesetz folgt. Mit Hilfe eines generativen Sturm-Modells zeige ich, dass die exponentielle Dämpfung der beobachteten Größenverteilung durch das Vorhandensein von Landmasse auf unserem Planeten zustande kommen könnte. Dann verknüpfe ich zwei hochauflösende Satelliten-Produkte um raumzeitliche Cluster von Feuer-betroffenen Gebieten im brasilianischen Amazonas zu identifizieren und deren Brandeigenschaften zu charakterisieren. Zuletzt untersuche ich den Einfluss von weißem Rauschen und der globalen Kopplungsstärke auf die maximale Synchronisierbarkeit von Oszillatoren-Netzwerken für eine Vielzahl von Oszillatoren-Modellen, welche durch ein breites Spektrum an Netzwerktopologien gekoppelt sind. Ich finde ein allgemeingültiges sigmoidales Skalierungsverhalten, und validiere dieses mit einem geeignetem Regressionsmodell.
Network theory has proven to be a powerful instrument in the representation of complex systems. Yet, even in its latest and most general form (i.e., multilayer networks), it is still lacking essential qualities to serve as a general data analysis framework. These include, most importantly, an explicit association of information with the nodes and edges of a network, and a conclusive representation of groups of nodes and their respective interrelations on different scales. The implementation of these qualities into a generalized framework is the primary contribution of this dissertation. By doing so, I show how my framework - deep graphs - is capable of acting as a go-between, joining a unified and generalized network representation of systems with the tools and methods developed in statistics and machine learning. A software package accompanies this dissertation, see https://github.com/deepgraph/deepgraph. A number of applications of my framework are demonstrated. I construct a rainfall deep graph and conduct an analysis of spatio-temporal extreme rainfall clusters. Based on the constructed deep graph, I provide statistical evidence that the size distribution of these clusters is best approximated by an exponentially truncated powerlaw. By means of a generative storm-track model, I argue that the exponential truncation of the observed distribution could be caused by the presence of land masses. Then, I combine two high-resolution satellite products to identify spatio-temporal clusters of fire-affected areas in the Brazilian Amazon and characterize their land use specific burning conditions. Finally, I investigate the effects of white noise and global coupling strength on the maximum degree of synchronization for a variety of oscillator models coupled according to a broad spectrum of network topologies. I find a general sigmoidal scaling and validate it with a suitable regression model.
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Jönsson, Jennifer Annie Patricia. "Deep Impression." Thesis, Högskolan i Borås, Akademin för textil, teknik och ekonomi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-22025.

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The scope of this thesis is to reveal the hidden dimensions of fashion. With the aim to stress the worth of participation and the individual experience of fashion. This work is questioning what we see, and later what is actually there. Through a thorough investigation of the knit technique the relationship of loop and thread (pause and activity) is the focus of this paper. Enhancing the significant qualities of the knitted technique, where material and shape is born simultaneously, the result presented holds a variety of results. With the aim to discuss multiple dimensions this knit investigation is presented in a fashion context. Styled with technical sportswear this work is challenging knitwear -as well as sportswear. By clashing sports connotated materials with the knitted wool, both fields are expanded and new options and expression are presented. The motive of this investigation is to further state the worth of fashion. To create a space for the experience of fashion, stating the various result that is not depending on the presentation on body. This work questions the pre-set truths and conventions of what fashion could be, and our ability to judge what is presented for us.
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Lynch, Cassie A. "Korangan: Deep Time and Deep Transformation in Noongar Country." Thesis, Curtin University, 2020. http://hdl.handle.net/20.500.11937/81989.

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Recent research suggests that Indigenous stories that feature 'cold times' and rising seas are in fact eyewitness accounts of the last ice age and the rise in sea-level that followed it. Building on this notion, this research explores whether writing fiction in the scale of deep time can be employed to explore colonial pasts, the contested present and radical futures.
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Backstad, Sebastian. "Federated Averaging Deep Q-NetworkA Distributed Deep Reinforcement Learning Algorithm." Thesis, Umeå universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-149637.

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In the telecom sector, there is a huge amount of rich data generated every day. This trend will increase with the launch of 5G networks. Telco companies are interested in analyzing their data to shape and improve their core businesses. However, there can be a number of limiting factors that prevents them from logging data to central data centers for analysis.  Some examples include data privacy, data transfer, network latency etc. In this work, we present a distributed Deep Reinforcement Learning (DRL) method called Federated Averaging Deep Q-Network (FADQN), that employs a distributed hierarchical reinforcement learning architecture. It utilizes gradient averaging to decrease communication cost. Privacy concerns are also satisfied by training the agent locally and only sending aggregated information to the centralized server. We introduce two versions of FADQN: synchronous and asynchronous. Results on the cart-pole environment show 80 times reduction in communication without any significant loss in performance. Additionally, in case of asynchronous approach, we see a great improvement in convergence.
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Books on the topic "Deep"

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Deep, deep down. [Bath]: Galaxy Plus, 2013.

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Preston-Gannon, Frann. Deep deep sea. London: Pavilion, 2014.

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Deep, deep down. London: Simon & Schuster Children's, 2011.

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Sharma, Bhagavatikumar. Deep se deep jale. Ahmedabad: Adarsh Prakashan, 2001.

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Woodward, John. The deep, deep ocean. Redding, Conn: Brown Bear Books, 2010.

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Woodward, John. The deep, deep ocean. Redding, Conn: Brown Bear Books, 2010.

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Woodward, John. The deep, deep ocean. London: Franklin Watts, 2009.

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ill, Zemke Deborah, ed. The deep deep puddle. New York: Dial Books for Young Readers, 2013.

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Hill, Laban Carrick. Deep powder, deep trouble. New York: Hyperion Paperbacks for Children, 1998.

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Gaewsky, Henry P. Deep, deep, the jungle sleeps. Kearney, NE: Morris Pub., 2001.

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Book chapters on the topic "Deep"

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Webb, Stephen. "Deep sea, deep snow … deep space." In New Eyes on the Universe, 159–78. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-2194-8_7.

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O’Dea Bradley, Daniel. "Deep Calls to Deep." In Anacarnation and Returning to the Lived Body with Richard Kearney, 86–106. New York: Routledge, 2022. http://dx.doi.org/10.4324/9781003285649-8.

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Bährle-Rapp, Marina. "deep." In Springer Lexikon Kosmetik und Körperpflege, 143. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71095-0_2697.

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Gooch, Jan W. "Deep." In Encyclopedic Dictionary of Polymers, 198. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_3356.

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Matthews, D. H. "Deep Reflections and Deep Drilling." In Exploration of the Deep Continental Crust, 73–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-45616-9_10.

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Tindale, Christopher W. "Deep Diversity and Deep Disagreement." In The Anthropology of Argument, 158–73. New York, NY : Routledge, 2021.: Routledge, 2020. http://dx.doi.org/10.4324/9781003107637-9.

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Maher, Sean. "Deep Fakes." In Deep Fakes, 1–22. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003173397-1.

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Stern, Daniel, Andrew Connolly, Peter Eisenhardt, Richard Elston, Brad Holden, Piero Rosati, S. Adam Stanford, Hyron Spinrad, Paolo Tozzi, and Katherine Wu. "First Results from the SPICES Survey." In Deep Fields, 76–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/10854354_14.

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Kellermann, K. I., E. B. Fomalont, P. Rosati, and P. Shaver. "Radio Observations of the Chandra Deep Field South." In Deep Fields, 139–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/10854354_26.

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Rosati, Piero, Adam Stanford, Chris Lidman, Vincenzo Mainieri, and Peter Eisenhardt. "A Deep Look at the Most Distant X-Ray Clusters." In Deep Fields, 179–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/10854354_50.

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Conference papers on the topic "Deep"

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Wijethilake, Navodini, Mithunjha Anandakumar, Cheng Zheng, Peter T. C. So, Murat Yildirim, and Dushan N. Wadduwage. "DEEP2: Deep Learning Powered De-scattering with Excitation Patterning (DEEP)." In Optics and the Brain. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/brain.2023.bw3b.3.

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We present DEEP2, a computational multiphoton microscope to image through scattering tissue. In DEEP2, temporally focused structured light excites deep tissue in wide-field, and deep learning reconstructs clean images from scattered measurements.
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"DEEP-ML 2019 Program Committee." In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). IEEE, 2019. http://dx.doi.org/10.1109/deep-ml.2019.00007.

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"DEEP-ML 2019 Organizing Committee." In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). IEEE, 2019. http://dx.doi.org/10.1109/deep-ml.2019.00006.

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"Keynote Abstracts." In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). IEEE, 2019. http://dx.doi.org/10.1109/deep-ml.2019.00008.

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"[Title page i]." In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). IEEE, 2019. http://dx.doi.org/10.1109/deep-ml.2019.00001.

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"[Title page iii]." In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). IEEE, 2019. http://dx.doi.org/10.1109/deep-ml.2019.00002.

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"[Copyright notice]." In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). IEEE, 2019. http://dx.doi.org/10.1109/deep-ml.2019.00003.

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"Table of contents." In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). IEEE, 2019. http://dx.doi.org/10.1109/deep-ml.2019.00004.

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"Message from the DEEP-ML 2019 Chairs." In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). IEEE, 2019. http://dx.doi.org/10.1109/deep-ml.2019.00005.

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Kaskavalci, Halil Can, and Sezer Goren. "A Deep Learning Based Distributed Smart Surveillance Architecture using Edge and Cloud Computing." In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). IEEE, 2019. http://dx.doi.org/10.1109/deep-ml.2019.00009.

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Reports on the topic "Deep"

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Eriksen, Charles C. Deep Glider Development. Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada629140.

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Lyle A. Johnson Jr. FORT UNION DEEP. Office of Scientific and Technical Information (OSTI), September 2002. http://dx.doi.org/10.2172/822148.

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Lyle A. Johnson Jr. FORT UNION DEEP. Office of Scientific and Technical Information (OSTI), March 2002. http://dx.doi.org/10.2172/822149.

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Eriksen, Charles C. Deep Glider Development. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada619126.

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Taylor, A. E., M. M. Burgess, A. S. Judge, and V. S. Allen. Deep ground temperatures. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2000. http://dx.doi.org/10.4095/211920.

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Draelos, Timothy John, Nadine E. Miner, Christopher C. Lamb, Craig Michael Vineyard, Kristofor David Carlson, Conrad D. James, and James Bradley Aimone. Neurogenesis Deep Learning: Extending deep networks to accommodate new classes. Office of Scientific and Technical Information (OSTI), December 2016. http://dx.doi.org/10.2172/1505351.

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Chu, P. C. Geophysics of Deep Convection and Deep Water Formation in Oceans. Fort Belvoir, VA: Defense Technical Information Center, January 1987. http://dx.doi.org/10.21236/ada480128.

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Pillsbury, Dale. Expendable Deep Ocean Mooring. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada292161.

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Brunish, Wendee M. Deep Dive Quad Charts. Office of Scientific and Technical Information (OSTI), September 2012. http://dx.doi.org/10.2172/1050478.

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Heaney, Kevin. Deep Water Ocean Acoustics. Fort Belvoir, VA: Defense Technical Information Center, April 2015. http://dx.doi.org/10.21236/ada615119.

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