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Journal articles on the topic 'Deep structures'

Author: Grafiati

Published: 25 May 2024

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1

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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2

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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3

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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4

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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5

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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6

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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8

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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9

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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10

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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11

Orsucci, Franco F. "Evolution and deep structures in culture." Physics of Life Reviews 10, no. 2 (June 2013): 146–48. http://dx.doi.org/10.1016/j.plrev.2013.03.010.

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12

Endo, Midori, Haruo Seno, Shioekazu Haruki, Hiroshi Ishino, and Jiro Endo. "Pituitary function and deep brain structures." Biological Psychiatry 21, no. 2 (February 1986): 240–43. http://dx.doi.org/10.1016/0006-3223(86)90160-5.

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13

Schmiedel, Jörn M., and Ben Lehner. "Determining protein structures using deep mutagenesis." Nature Genetics 51, no. 7 (June 17, 2019): 1177–86. http://dx.doi.org/10.1038/s41588-019-0431-x.

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14

Leifer, Richard, Sunro Lee, and Jeffrey Durgee. "Deep structures: Real information requirements determination." Information & Management 27, no. 5 (November 1994): 275–85. http://dx.doi.org/10.1016/0378-7206(94)90022-1.

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Saad, Yasmeen, and Amin Habbeb AL-Khursan. "Deep Ultraviolet BGaN Quantum Dot structures." University of Thi-Qar Journal of Science 8, no. 2 (September 12, 2022): 104–15. http://dx.doi.org/10.32792/utq/utjsci.v8i2.871.

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This research studies boron-containing quantum dot (QD) structures that emit the ultraviolet. Ternary and quaternary lattice-matched structure:, , , , ,, , and and their TE and TM gain spectra, spontaneous emission, spontaneous polarization, and piezoelectric polarization have been examined in these structures. They have high TE and TM spectra under reducing boron content in the QD or barrier layer. The total polarization decreases for the Al-containing systems, which is preferred. Binary systems emit at 199nm, while the quaternary can have a peak wavelength near 235nm. Elongating of the wavelength to 290nm is possible with with high gain at a few boron contents.

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Goldenberg, Jacob, and David Mazursky. "When Deep Structures Surface: Design Structures That Can Repeatedly Surprise." Journal of Advertising 37, no. 4 (December 2008): 21–34. http://dx.doi.org/10.2753/joa0091-3367370402.

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TUDORACHE, VALENTIN-PAUL, LAZAR AVRAM, and NICULAE-NAPOLEON ANTONESCU. "Aspects on offshore drilling process in deep and very deep waters." Journal of Engineering Sciences and Innovation 5, no. 12 (June 3, 2020): 157–72. http://dx.doi.org/10.56958/jesi.2020.5.2.7.

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"Offshore is a broad concept and therefore in this article offshore refers to drilling wells of oil and gas in the hydrocarbons deposits located deep from the seabed. Oil and gas is drilled wells with help of different offshore structures, for example rigs and vessels. Offshore drilling is a complex process where a borehole is drilled through the seabed. Of course, offshore refers to energy activity located at a distance from the shore. Oil and natural gas is located below the bedrock, which makes it difficult to extract them. A limited amount of inland oil has driven oil industry to the seas to find more oil deposits. There are high financial markets in the offshore industry and that is why much money is being invested in new offshore structures all around the world. Offshore structures are constructed for many different purposes worldwide. The structures are expensive to construct but there is an opportunity to have significant financial profit. "

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18

Whittle, Joss, Rita Borgo, and Mark W. Jones. "Implementing generalized deep-copy in MPI." PeerJ Computer Science 2 (November 21, 2016): e95. http://dx.doi.org/10.7717/peerj-cs.95.

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In this paper, we introduce a framework for implementing deep copy on top of MPI. The process is initiated by passing just the root object of the dynamic data structure. Our framework takes care of all pointer traversal, communication, copying and reconstruction on receiving nodes. The benefit of our approach is that MPI users can deep copy complex dynamic data structures without the need to write bespoke communication or serialize/deserialize methods for each object. These methods can present a challenging implementation problem that can quickly become unwieldy to maintain when working with complex structured data. This paper demonstrates our generic implementation, which encapsulates both approaches. We analyze the approach with a variety of structures (trees, graphs (including complete graphs) and rings) and demonstrate that it performs comparably to hand written implementations, using a vastly simplified programming interface. We make the source code available completely as a convenient header file.

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19

Nørgaard, Jørgen Quvang Harck, and Thomas Lykke Andersen. "DISTRIBUTION OF WAVE LOADS FOR DESIGN OF CROWN WALLS IN DEEP AND SHALLOW WATER." Coastal Engineering Proceedings 1, no. 34 (October 28, 2014): 47. http://dx.doi.org/10.9753/icce.v34.structures.47.

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20

Schnotz, Wolfgang, and Christiane Baadte. "Surface and deep structures in graphics comprehension." Memory & Cognition 43, no. 4 (December 3, 2014): 605–18. http://dx.doi.org/10.3758/s13421-014-0490-2.

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21

Zhang, Hongbin, Teng Long, Yixuan Zhang, Nuno M. Fortunato, Chen Shen, and Oliver Gutfleisch. "CCDCGAN: deep learning prediction of crystal structures." Acta Crystallographica Section A Foundations and Advances 77, a2 (August 14, 2021): C75. http://dx.doi.org/10.1107/s0108767321096045.

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22

Watts, C. "In Vivo Atlas of Deep Brain Structures." British Journal of Neurosurgery 17, no. 1 (February 1, 2003): 110. http://dx.doi.org/10.1080/0268869031000093906.

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23

Aaro, Sven. "The deep structures of the Finspång Massif." Geoexploration 23, no. 3 (September 1985): 437. http://dx.doi.org/10.1016/0016-7142(85)90045-6.

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24

McBride, Allan, and Robert K. Toburen. "Deep Structures: Polpop Culture on Primetime Television." Journal of Popular Culture 29, no. 4 (March 1996): 181–200. http://dx.doi.org/10.1111/j.0022-3840.1996.00181.x.

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25

Thomas, M., and G. Dhillon. "Interpreting Deep Structures of Information Systems Security." Computer Journal 55, no. 10 (November 30, 2011): 1148–56. http://dx.doi.org/10.1093/comjnl/bxr118.

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26

Zhang, Xun, Yantao Du, Weiwei Sun, and Xiaojun Wan. "Transition-Based Parsing for Deep Dependency Structures." Computational Linguistics 42, no. 3 (September 2016): 353–89. http://dx.doi.org/10.1162/coli_a_00252.

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Derivations under different grammar formalisms allow extraction of various dependency structures. Particularly, bilexical deep dependency structures beyond surface tree representation can be derived from linguistic analysis grounded by CCG, LFG, and HPSG. Traditionally, these dependency structures are obtained as a by-product of grammar-guided parsers. In this article, we study the alternative data-driven, transition-based approach, which has achieved great success for tree parsing, to build general dependency graphs. We integrate existing tree parsing techniques and present two new transition systems that can generate arbitrary directed graphs in an incremental manner. Statistical parsers that are competitive in both accuracy and efficiency can be built upon these transition systems. Furthermore, the heterogeneous design of transition systems yields diversity of the corresponding parsing models and thus greatly benefits parser ensemble. Concerning the disambiguation problem, we introduce two new techniques, namely, transition combination and tree approximation, to improve parsing quality. Transition combination makes every action performed by a parser significantly change configurations. Therefore, more distinct features can be extracted for statistical disambiguation. With the same goal of extracting informative features, tree approximation induces tree backbones from dependency graphs and re-uses tree parsing techniques to produce tree-related features. We conduct experiments on CCG-grounded functor–argument analysis, LFG-grounded grammatical relation analysis, and HPSG-grounded semantic dependency analysis for English and Chinese. Experiments demonstrate that data-driven models with appropriate transition systems can produce high-quality deep dependency analysis, comparable to more complex grammar-driven models. Experiments also indicate the effectiveness of the heterogeneous design of transition systems for parser ensemble, transition combination, as well as tree approximation for statistical disambiguation.

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27

Combettes, Patrick L., and Jean-Christophe Pesquet. "Deep Neural Network Structures Solving Variational Inequalities." Set-Valued and Variational Analysis 28, no. 3 (February 13, 2020): 491–518. http://dx.doi.org/10.1007/s11228-019-00526-z.

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28

Mailer, Alexander. "An Analytical Visual Representation of Non-Rectangular Deep Space Structures." International Journal of Space Structures 6, no. 4 (December 1991): 315–24. http://dx.doi.org/10.1177/026635119100600409.

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Architecturally speaking, space structures refer to the “deep” totality of the built space not only to its envelope. Designed architectural space is, so far, predominantly structured according to rectangular geometry. The premise of this paper is that a major reason for the popularity of rectangular space structuring is the limited capacity designers have had, until recently, to visualize the high complexity of non-rectangular space structures. The paper describes an experiment designed to compare emerging Computer Aided Design and Drafting (CADD), procedures with traditional procedures applied in an architectural design study using a non-rectangular polyhedral geometry. The findings of the experiment point out that computer-aided visualization can generate a more efficient and more convenient procedure to address, in practical design terms, non-rectangular deep space structures. However, the efficiency of such procedures is conditioned by a close collaboration between architects and computer scientists.

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29

Shang, Zhiyuan, Zhonghua Yao, Jian Liu, Linli Xu, Yan Xu, Binzheng Zhang, Ruilong Guo, and Yong Wei. "Automated Classification of Auroral Images with Deep Neural Networks." Universe 9, no. 2 (February 12, 2023): 96. http://dx.doi.org/10.3390/universe9020096.

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Terrestrial auroras are highly structured that visualize the perturbations of energetic particles and electromagnetic fields in Earth’s space environments. However, the identification of auroral morphologies is often subjective, which results in confusion in the community. Automated tools are highly valuable in the classification of auroral structures. Both CNNs (convolutional neural networks) and transformer models based on the self-attention mechanism in deep learning are capable of extracting features from images. In this study, we applied multiple algorithms in the classification of auroral structures and performed a comparison on their performances. Trans-former and ConvNeXt models were firstly used in the analysis of auroras in this study. The results show that the ConvNeXt model can have the highest accuracy of 98.5% among all of the applied algorithms. This study provides a direct comparison of deep learning tools on the application of classifying auroral structures and shows promising capability, clearly demonstrating that auto-mated tools can help to minimize the bias in future auroral studies.

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30

Christensen, Nicole Færch, Mads Sønderstrup Røge, Jonas Bjerg Thomsen, Thomas Lykke Andersen, Hans Falk Burcharth, and Jorgen Quvang Harck Nørgaard. "OVERTOPPING ON RUBBLE MOUND BREAKWATERS FOR LOW STEEPNESS WAVES IN DEEP AND DEPTH LIMITED CONDITIONS." Coastal Engineering Proceedings 1, no. 34 (October 30, 2014): 6. http://dx.doi.org/10.9753/icce.v34.structures.6.

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31

BALLESTEROS, MIGUEL, BERND BOHNET, SIMON MILLE, and LEO WANNER. "Data-driven deep-syntactic dependency parsing." Natural Language Engineering 22, no. 6 (August 18, 2015): 939–74. http://dx.doi.org/10.1017/s1351324915000285.

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Abstract‘Deep-syntactic’ dependency structures that capture the argumentative, attributive and coordinative relations between full words of a sentence have a great potential for a number of NLP-applications. The abstraction degree of these structures is in between the output of a syntactic dependency parser (connected trees defined over all words of a sentence and language-specific grammatical functions) and the output of a semantic parser (forests of trees defined over individual lexemes or phrasal chunks and abstract semantic role labels which capture the frame structures of predicative elements and drop all attributive and coordinative dependencies). We propose a parser that provides deep-syntactic structures. The parser has been tested on Spanish, English and Chinese.

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32

Gebrekidan, Semere B., and Steffen Marburg. "Deep reinforcement learning for optimal sound absorbing structures design." Journal of the Acoustical Society of America 154, no. 4_supplement (October 1, 2023): A232. http://dx.doi.org/10.1121/10.0023377.

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Deep learning algorithms have demonstrated a tremendous success in designing structures that surpass human capabilities. Based on the recent achievements of deep reinforcement learning in surpassing human capabilities, this paper focuses on implementing these algorithms to design optimal configurations of solid and porous materials that achieve a broadband absorption within the frequency range of 300 Hz to 3000 Hz. We employ model-free approaches, specifically deep Q-learning, double deep Q-learning, and dueling deep Q-learning algorithms, to predict material configurations that optimize absorption without requiring expertise knowledge. From a 230×30 different material combinations, the deep reinforcement algorithms learn to predict configurations that yield optimal absorption in few hundred steps. We discuss further the superior performance of a dueling deep learning algorithm compared to the other two deep learning approaches and a heuristic approach, such as genetic algorithm. The proposed model-free algorithms enable the prediction of absorption performance for any material configurations without the need for expertise.

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D’sa, Bouvier, Sohaila Fatima, and Nazima Haider. "Ganglioneuroma Involving Deep Facial Structures - A Case Report." Journal of Cancer and Tumor International 6, no. 4 (January 27, 2018): 1–5. http://dx.doi.org/10.9734/jcti/2017/38725.

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34

Ogadzhanov, Viktor A. "Deep meridional structures of the East-European platform." Izvestiya of Saratov University. Earth Sciences 22, no. 3 (August 24, 2022): 185–90. http://dx.doi.org/10.18500/1819-7663-2022-22-3-185-190.

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Using a complex of remote and surface geophysical methods in the eastern part of the East European Platform, regional meridional heterogeneitieswere revealed; it isshown that these heterogeneitiescan becaused by low-density and magnetically active rocks in thecrystalline crust and mantle. The totality of geological and geophysical data indicates the relationship of meridional heterogeneities with transcontinental deep faults.

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35

Golding, Vaughn Peter, Zahra Gharineiat, Hafiz Suliman Munawar, and Fahim Ullah. "Crack Detection in Concrete Structures Using Deep Learning." Sustainability 14, no. 13 (July 2, 2022): 8117. http://dx.doi.org/10.3390/su14138117.

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Infrastructure, such as buildings, bridges, pavement, etc., needs to be examined periodically to maintain its reliability and structural health. Visual signs of cracks and depressions indicate stress and wear and tear over time, leading to failure/collapse if these cracks are located at critical locations, such as in load-bearing joints. Manual inspection is carried out by experienced inspectors who require long inspection times and rely on their empirical and subjective knowledge. This lengthy process results in delays that further compromise the infrastructure’s structural integrity. To address this limitation, this study proposes a deep learning (DL)-based autonomous crack detection method using the convolutional neural network (CNN) technique. To improve the CNN classification performance for enhanced pixel segmentation, 40,000 RGB images were processed before training a pretrained VGG16 architecture to create different CNN models. The chosen methods (grayscale, thresholding, and edge detection) have been used in image processing (IP) for crack detection, but not in DL. The study found that the grayscale models (F1 score for 10 epochs: 99.331%, 20 epochs: 99.549%) had a similar performance to the RGB models (F1 score for 10 epochs: 99.432%, 20 epochs: 99.533%), with the performance increasing at a greater rate with more training (grayscale: +2 TP, +11 TN images; RGB: +2 TP, +4 TN images). The thresholding and edge-detection models had reduced performance compared to the RGB models (20-epoch F1 score to RGB: thresholding −0.723%, edge detection −0.402%). This suggests that DL crack detection does not rely on colour. Hence, the model has implications for the automated crack detection of concrete infrastructures and the enhanced reliability of the gathered information.

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36

Saad, Yasmeen, and Amin Habbeb Al-Khursan. "Deep ultraviolet spectra from BGaN quantum dot structures." Materials Science in Semiconductor Processing 142 (May 2022): 106484. http://dx.doi.org/10.1016/j.mssp.2022.106484.

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37

Bai, Dongshun, Michelle Fowler, Curtis Planje, and Xie Shao. "Planarization of Deep Structures Using Self-Leveling Materials." International Symposium on Microelectronics 2012, no. 1 (January 1, 2012): 000079–83. http://dx.doi.org/10.4071/isom-2012-ta32.

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To achieve device integration that will allow the manufacture of smaller, more functional, and more efficient microelectronics, the industry increasingly requires materials to fill and planarize devices with deep structures. Brewer Science has developed several new self-leveling materials to address these planarization needs. These newly developed materials are designed to be either temporary materials that can be removed after their use in processing steps or permanent materials that can stay in a device for its lifetime. These new materials can be applied easily by means of a spin-coating process. They are unique because they can fill and planarize high-aspect-ratio trenches and vias hundreds of microns deep. Some of the materials are photosensitive and can be patterned using photolithography. All of the photosensitive materials in this paper can be developed with industry-accepted solvents and some with an aqueous TMAH solution. Because of their good thermal stability, high transparency, and excellent planarization properties, these materials have potential applications for microelectromechanical systems (MEMS), 3-D integrated circuits, light-emitting diodes (LEDs), semiconductors, flat-panel displays, and related microelectronic and optoelectronic devices. This paper will discuss the properties of these new materials and will present the filling and leveling results obtained in several applications.

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38

Leaman, D. E. "Application of magnetic methods to deep basin structures." Exploration Geophysics 28, no. 1-2 (March 1997): 97–105. http://dx.doi.org/10.1071/eg997097.

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Micheli, Fiorenza, Charles H. Peterson, Lauren S. Mullineaux, Charles R. Fisher, Susan W. Mills, Gorka Sancho, Galen A. Johnson, and Hunter S. Lenihan. "PREDATION STRUCTURES COMMUNITIES AT DEEP-SEA HYDROTHERMAL VENTS." Ecological Monographs 72, no. 3 (August 2002): 365–82. http://dx.doi.org/10.1890/0012-9615(2002)072[0365:pscads]2.0.co;2.

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40

Xuecheng, Yuan. "On Deep Structures of the Xikang-Yunnan Axis." Acta Geologica Sinica - English Edition 2, no. 3 (May 29, 2009): 211–25. http://dx.doi.org/10.1111/j.1755-6724.1989.mp2003001.x.

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41

Christova, C., and S. B. Nikolova. "The Aegean region: deep structures and seismological properties." Geophysical Journal International 115, no. 3 (December 1993): 635–53. http://dx.doi.org/10.1111/j.1365-246x.1993.tb01485.x.

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42

Tsang, Y. L., and J. M. Aitken. "Junction breakdown instabilities in deep trench isolation structures." IEEE Transactions on Electron Devices 38, no. 9 (1991): 2134–38. http://dx.doi.org/10.1109/16.83741.

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Gombia, E., R. Mosca, D. Pal, A. Motta, L. Nasi, A. Bosacchi, and S. Franchi. "Deep level investigation on n-In0.35Ga0.65As/GaAs structures." Solid-State Electronics 42, no. 2 (March 1998): 211–15. http://dx.doi.org/10.1016/s0038-1101(97)00225-6.

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44

Seghier, D., and H. P. Gislason. "Shallow and deep defects in AlxGa1−xN structures." Physica B: Condensed Matter 401-402 (December 2007): 335–38. http://dx.doi.org/10.1016/j.physb.2007.08.181.

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45

Gorev, Nikolai B., Inna Kodzhespirova, Evgeny N. Privalov, Levan Khvedelidze, Nina Khuchua, Giorgi G. Peradze, Michael S. Shur, and Kevin Stevens. "Non-destructive deep trap diagnostics of epitaxial structures." Solid-State Electronics 47, no. 9 (September 2003): 1569–75. http://dx.doi.org/10.1016/s0038-1101(03)00079-0.

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46

Lee, S., and R. Bashir. "Modeling and characterization of deep trench isolation structures." Microelectronics Journal 32, no. 4 (April 2001): 295–300. http://dx.doi.org/10.1016/s0026-2692(00)00148-8.

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47

Kubo, Jeffrey M., Hossein Khiabanian, Ian P. Dell'Antonio, David Wittman, and J. Anthony Tyson. "DARK MATTER STRUCTURES IN THE DEEP LENS SURVEY." Astrophysical Journal 702, no. 2 (August 18, 2009): 980–88. http://dx.doi.org/10.1088/0004-637x/702/2/980.

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48

Belyavsky, V. I., Yu V. Kopaev, N. V. Kornyakov, Yu A. Pomerantsev, and S. V. Shevtsov. "Deep impurity states in semiconductor quantum well structures." Semiconductor Science and Technology 13, no. 5 (May 1, 1998): 460–67. http://dx.doi.org/10.1088/0268-1242/13/5/004.

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49

Sarigül, M., and M. Avci. "Comparison of Different Deep Structures for Fish Classification." International Journal of Computer Theory and Engineering 9, no. 5 (2017): 362–66. http://dx.doi.org/10.7763/ijcte.2017.v9.1167.

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Vaughan, Alan P. M., Christian Seiler, Andrew J. Bladon, Jennifer F. Ellis, and Sugeng Widodo. "Neoproterozoic basement structures control New Guinea deep structure." Applied Earth Science 125, no. 2 (April 2, 2016): 98. http://dx.doi.org/10.1080/03717453.2016.1166677.

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