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Artigos de revistas sobre o assunto "Da Tang kai yuan li"

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Pochekaev, R. Yu, e I. V. Tutaev. "Some thoughts on historical and legal aspects regarding the fourth volume of the “Laws of the Great Ming dynasty” translated into Russian. [Review on:] Svistunova N. P. (transl.), Dmitriev S. V. (ed.). Laws of the Great Ming Dynasty with the Combined Commentary and Enclosed Decrees (Da Ming Liuy Tsi Tze Fu Li). Pt. IV. Moscow: Vostochnaya literature; 2019. 550 p." Orientalistica 3, n.º 4 (28 de dezembro de 2020): 1202–14. http://dx.doi.org/10.31696/2618-7043-2020-3-4-1202-1214.

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The article is a survey of the Russian translation of “Laws of the Great Ming dynasty” in four volumes published since 1997 to 2019. The introduction of this legal monument to the Russian scientific society is of great importance as it substantially expands contemporary idea on Chinese traditional legal system and meets a lack in the history of law ofChinain 14th–17th cc.To survey the legal monument there special legal scientific methods were used. Historical legal approach allowed to trace the creation and acting of this codification in the specific historical circumstances, value its urgency for the epoch of Ming dynasty (1368–1644). Comparative legal method gave an opportunity to compare this legal monument with other codifications of traditional Chinese law since the ancient times to the legislation of Qing, last dynasty of the imperialChina(1644–1911). Formal legal approach provided the analysis of the legal technique of the document, specific features of its structure and content, characteristic of legal terminology, etc.The analysis allowed to appreciate the “Laws of the Great Ming dynasty” at its high value as a source on history, state and law of medievalChina. It had similarities and differences with other sources of traditional Chinese law. Besides, it is of great importance for the further development of legislation of imperialChina.The codification is an important document on statehood and law of the Ming China as it contains valuable information on power system and competence of authorities, basic fields of legal relations in the medieval Chinese society. Its structure is traditional (based on the example of codification of Tang dynasty, 618–907), at the same time it has larger volume and regulates new fields of legal relations, takes into account changes in the internal and externaln status ofChinaafter the expelling the Mongolian Yuan dynasty (1271–1368) and foundation of “national” Ming dynasty. Some principles of domestic and foreign policy of Qing dynasty were legally fixed during the epoch of Ming.The analyzed legal monument is of great interest for researchers of the history ofChina, its state and law. In fact, each chapter as well as specific articles and supplement statements could be a subject of investigation. “Laws of the Great Ming dynasty” also could be used by lecturers of history of state and law and for students who study this discipline.
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Seredin, Pavel V., Dmitry L. Goloshchapov, Kirill A. Nikitkov, Vladimir M. Kashkarov, Yury A. Ippolitov e Vongsvivut Jitraporn (Pimm). "Применение синхротронной ИК-микроспектроскопии для анализа интеграции биомиметических композитов с нативной твердой тканью зуба человека". Kondensirovannye sredy i mezhfaznye granitsy = Condensed Matter and Interphases 21, n.º 2 (14 de junho de 2019): 262–77. http://dx.doi.org/10.17308/kcmf.2019.21/764.

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В данной работе продемонстрирована возможность применения ИК-микроспектроскопии для многомерной визуализации и анализа интеграции с нативными твердыми тканями зуба человека нового поколения биомиметических материалов, воспроизводящих минералорганический комплекс эмали и дентина.На основе ИК-картирования интенсивности конкретной функциональной молекулярной группы с использованием синхротронного излучения найдены и визуализированы характеристические особенности биомиметического переходного слоя в межфазной области эмаль/стоматологический материал и определено расположение функциональных групп, отвечающих процессам интеграции биомиметического композита REFERENCES Rohr N., Fischer J. Tooth surface treatment strategies for adhesive cementation // The Journal of Advanced Prosthodontics, 2017, v. 9(2), pp. 85–92. https://doi.org/10.4047/jap.2017.9.2.85 Pereira C. N. de B., Daleprane B., Miranda G. L. P. de, Magalhães C. S. de, Moreira A. N. 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S., Weir M. D., Rodrigues L. K. A., Xu H. H. K. Novel calcium phosphate nanocomposite with caries-inhibition in a human in situ model // Dental Materials, 2013, v. 29(2), pp. 231–240. https://doi.org/10.1016/j.dental.2012.10.010 Wu X.-T., Mei M., Li Q.-L., Cao C., Chen-L., Xia R., Zhang Z.-H., Chu C. A Direct Electric Field-Aided Biomimetic Mineralization System for Inducing the Remineralization of Dentin Collagen Matrix // Materials, 2015, v. 8(12), pp. 7889–7899. https://doi.org/10.3390/ ma8115433 Barghamadi H., Atai M., Imani M., Esfandeh M. Effects of nanoparticle size and content on mechanical properties of dental nanocomposites: experimental versus modeling // Iranian Polymer Journal, 2015, v. 24. (10), pp. 837–848. https://doi.org/10.1007/s13726-015-0369-5 Wang H., Xiao Z., Yang J., Lu D., Kishen A., Li Y., Chen Z., Que K., Zhang Q., Deng X., Yang X., Cai Q., Chen N., Cong C., Guan B., Li T., Zhang X. Oriented and Ordered Biomimetic Remineralization of the Surface of Demineralized Dental Enamel Using HAP@ ACP Nanoparticles Guided by Glycine // Scientifi c Reports, 2017, v. 7(1), рр. 1-13. https://doi.org/10.1038/srep40701 Wu X., Zhao X., Li Y., Yang T., Yan X., Wang K. In situ synthesis carbonated hydroxyapatite layers on enamel slices with acidic amino acids by a novel twostep method // Materials Science & Engineering. C, Materials for Biological Applications, 2015, v. 54, pp. 150–157. httsp://doi.org/10.1016/j.msec.2015.05.006 Aljabo A., Abou Neel E. A., Knowles J. C., Young A. M. Development of dental composites with reactive fi llers that promote precipitation of antibacterial-hydroxyapatite layers // Materials Science and Engineering: C, 2016, v. 60, pp. 285–292. https://doi.org/10.1016/j.msec.2015.11.047 Wang P., Liu P., Peng H., Luo X., Yuan H., Zhang J., Yan Y. Biocompatibility evaluation of dicalcium phosphate/calcium sulfate/poly (amino acid) composite for orthopedic tissue engineering in vitro and in vivo // Journal of Biomaterials Science. Polymer Edition, 2016, v. 27(11), pp. 1170–1186. https://doi.org/10.1080/09205063.2016.1184123 Lübke A., Enax J., Wey K., Fabritius H.-O., Raabe D., Epple M. Composites of fl uoroapatite and methylmethacrylate-based polymers (PMMA) for biomimetic tooth replacement // Bioinspiration & Biomimetics, 2016, v. 11(3), pp. 035001. https://doi.org/10.1088/1748-3190/11/3/035001 Sa Y., Gao Y., Wang M., Wang T., Feng X., Wang Z., Wang Y., Jiang T. Bioactive calcium phosphate cement with excellent injectability, mineralization capacity and drug-delivery properties for dental bio- mimetic reconstruction and minimum intervention therapy. RSC Advances, 2016, v. 6(33), pp. 27349–27359. https://doi.org/10.1039/C6RA02488B Adachi T., Pezzotti G., Yamamoto T., Ichioka H., Boffelli M., Zhu W., Kanamura N. Vibrational algorithms for quantitative crystallographic analyses of hydroxyapatite-based biomaterials: II, application to decayed human teeth // Analytical and Bioanalytical Chemistry, 2015, v. 407(12), pp. 3343–3356. https://doi.org/10.1007/s00216-015-8539-z Mitić Ž., Stolić A., Stojanović S., Najman S., Ignjatović N., Nikolić G., Trajanović M. Instrumental methods and techniques for structural and physicochemical characterization of biomaterials and bone tissue: A review // Materials Science and Engineering: C, 2017, v. 79, pp. 930–949. https://doi.org/10.1016/j.msec.2017.05.127 Optical spectroscopy and computational methods in biology and medicine / Ed. by Barańska M., Dordrecht: Springer, 2014, 540 p. URL: http://link.springer.com/10.1007/978-94-007-7832-0 Hędzelek W., Marcinkowska A., Domka L., Wachowiak R. Infrared Spectroscopic Identifi cation of Chosen Dental Materials and Natural Teeth // Acta Physica Polonica A, 2008, v. 114(2), pp. 471–484. https://doi.org/10.12693/APhysPolA.114.471 Vongsvivut J., Perez-Guaita D., Wood B. R., Heraud P., Khambatta K., Hartnell D., Hackett M. J., Tobin M. J. Synchrotron macro ATR-FTIR microspectroscopy for high-resolution chemical mapping of single cells // The Analyst, 2019, v. 144(10), pp. 3226–3238. https://doi.org/10.1039/c8an01543k Seredin P., Goloshchapov D., Ippolitov Y., Vongsvivut P. Pathology-specifi c molecular profi les of saliva in patients with multiple dental caries—potential application for predictive, preventive and personalised medical services // EPMA Journal, 2018, v. 9(2), pp. 195–203. https://doi.org/10.1007/s13167-018-0135-9 Dusevich V., Xu C., Wang Y., Walker M. P., Gorski J. P. 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Vozmozhnost’ povyshenija biologicheskoj tropnosti svetootverzhdaemoj bondingovoj sistemy dlja adgezii tverdyh tkanej zuba k plombirovochnomu material [The possibility of increasing the biological tropism of the lightcuring bonding system for adhesion of hard tooth tissues to the filling material]. Volgogradskij nauchno-medicinskij zhurnal, 2010, v. 4 (28), pp. 31–34. URL: https://www.volgmed.ru/uploads/journals/articles/1293119124-bulletin-2010-4-815.pdf Seredin P., Goloshchapov D., Prutskij T., Ippolitov Y. Phase Transformations in a Human Tooth Tissue at the Initial Stage of Caries. PLoS ONE, 2015, v. 10(4), pp. 1–11. https://doi.org/10.1371/journal.pone.0124008 Seredin P. V., Goloshchapov D. L., Prutskij T., Ippolitov Yu. A. A Simultaneous Analysis of Microregions of Carious Dentin by the Methods of Laser- Induced Fluorescence and Raman Spectromicroscopy. Optics and Spectroscopy, 2018, v. 125(5), pp. 803–809. https://doi.org/10.1134/S0030400X18110267 Seredin P. V., Goloshchapov D. L., Prutskij T., Ippolitov Yu. A. Fabrication and characterisation of composites materials similar optically and in composition to native dental tissues. Results in Physics, 2017, v. 7, pp. 1086–1094. https://doi.org/10.1016/j.rinp.2017.02.025
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Lan, Luu Thi Phuong, Ellwood Brooks B., Tomkin Jonathan H., Nestell Galina P., Nestell Merlynd K., Ratcliffe Kenneth T., Rowe Harry et al. "Correlation and high-resolution timing for Paleo-tethys Permian-Triassic boundary exposures in Vietnam and Slovenia using geochemical, geophysical and biostratigraphic data sets". VIETNAM JOURNAL OF EARTH SCIENCES 40, n.º 3 (4 de junho de 2018): 253–70. http://dx.doi.org/10.15625/0866-7187/40/3/12617.

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Two Permian-Triassic boundary (PTB) successions, Lung Cam in Vietnam, and Lukač in Slovenia, have been sampled for high-resolution magnetic susceptibility, stable isotope and elemental chemistry, and biostratigraphic analyses. These successions are located on the eastern (Lung Cam section) and western margins (Lukač section) of the Paleo-Tethys Ocean during PTB time. Lung Cam, lying along the eastern margin of the Paleo-Tethys Ocean provides an excellent proxy for correlation back to the GSSP and out to other Paleo-Tethyan successions. This proxy is tested herein by correlating the Lung Cam section in Vietnam to the Lukač section in Slovenia, which was deposited along the western margin of the Paleo-Tethys Ocean during the PTB interval. It is shown herein that both the Lung Cam and Lukač sections can be correlated and exhibit similar characteristics through the PTB interval. Using time-series analysis of magnetic susceptibility data, high-resolution ages are obtained for both successions, thus allowing relative ages, relative to the PTB age at ~252 Ma, to be assigned. Evaluation of climate variability along the western and eastern margins of the Paleo-Tethys Ocean through the PTB interval, using d18O values indicates generally cooler climate in the west, below the PTB, changing to generally warmer climates above the boundary. A unique Black Carbon layer (elemental carbon present by agglutinated foraminifers in their test) below the boundary exhibits colder temperatures in the eastern and warmer temperatures in the western Paleo-Tethys Ocean.ReferencesBalsam W., Arimoto R., Ji J., Shen Z, 2007. Aeolian dust in sediment: a re-examination of methods for identification and dispersal assessed by diffuse reflectance spectrophotometry. International Journal of Environment and Health, 1, 374-402.Balsam W.L., Otto-Bliesner B.L., Deaton B.C., 1995. 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Teles, Samuel Gomes da Silva, Maria Cecília Simões Riscado de Castro, Sabrina Nogueira Dutra e Lídia Márcia Silva Santos. "Uso da saliva como um espécime alternativo para diagnóstico de COVID-19: uma revisão sistemática". ARCHIVES OF HEALTH INVESTIGATION 9, n.º 4 (6 de outubro de 2020). http://dx.doi.org/10.21270/archi.v9i4.5114.

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Introdução: O padrão ouro atual para detectar o RNA de SARS-CoV-2 é por reação em cadeia da polimerase em tempo real de transcrição reversa (RT-rtPCR) em swabs nasofaríngeos (NPS). Por esse motivo, a demanda pelos NPS aumentou e sua escassez se tornou uma realidade em muitos países. Com isso o uso da saliva se mostra uma alternativa promissora na triagem epidemiológica além de ser de fácil coleta e não invasiva. Objetivo: realizar uma revisão sistemática da literatura para avaliar o uso da saliva como um espécime alternativo para a detecção de SARS-CoV-2. Metodologia: A pesquisa sistemática foi realizada em sete bancos de dados (PubMed, Cochrane Library, Lilacs, Scielo, Web of Science, Scopus e Google Scholar) usando a variação dos termos de pesquisa (COVID-19 OR SARS-CoV-2 OR 2019-nCoV) AND "Saliva" no ano de 2020, recuperando 5480 publicações. Resultados: Após a leitura dos títulos e resumos, 411 textos foram conduzidos para leitura integral e 30 publicações foram consideradas para avaliação qualitativa do artigo. Conclusão: A saliva se apresenta um método alternativo eficaz para a detecção de SARS-CoV-2 e diagnóstico de COVID-19.Descritores: Infecções por Coronavírus; Betacoronavirus; Saliva; Diagnóstico.ReferênciasHuang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395:497-506.Wang L, Wang Y, Ye D, Liu Q. A review of the 2019 Novel Coronavirus (COVID-19) based on current evidence. J Antimicrob Agents 2020;105948.Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N Engl J Med 2020;382:727-733.Coronaviridae Study Group of the International Committee on Taxonomy of V. 2020. The species Severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol. 2020;5:536-544.Lauer SA, Grantz KH, Bi Q, Jones FK, Zheng Q, Meredith HR, et al. The incubation period of Coronavirus Disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application. Ann Intern Med. 2020;172:577-82.To KK, Tsang OT, Chik-Yan Yip C, Chan KH, Wu TC, Chan JMC, et al. Consistent detection of 2019 novel coronavirus in saliva. Clin Infect Dis. 2020;149:5734265.Xu R, Cui B, Duan X, Zhang P, Zhou X, Yuan Q. Saliva: potential diagnostic value and transmission of 2019-nCoV. Int J Oral Sci. 2020;12:11.Khurshid Z, Asiri FYI, Al Wadaani H. Human Saliva: Non-Invasive Fluid for Detecting Novel Coronavirus (2019-nCoV). Int J Environ Res Public Health. 2020;17.Khurshid Z, Zohaib S, Najeeb S, Zafar MS, Slowey PD, Almas K. Human Saliva Collection Devices for Proteomics: An Update. Int J Mol Sci. 2016;17.Principais itens para relatar Revisões sistemáticas e Meta-análises: A recomendação PRISMA. Epidemiol. E Serviços Saúde 2015;24:335–42.Abdul MSM, Fatima U, Khanna SS, Bhanot R, Sharma A, Srivastava AP. Oral Manifestations of Covid-19-Are they the introductory symptoms?. J Adv Dent Sci Res. 2020;8:5.Azzi L, Carcano G, Dalla Gasperina D, Sessa F, Maurino V, Baj A. Two cases of COVID-19 with positive salivary and negative pharyngeal or respiratory swabs at hospital discharge: A rising concern. Oral Dis. 2020;00:1-3.Azzi L, Carcano G, Gianfagna F, Grossi P, Dalla Gasperina D, Genoni A, et al. Saliva is a reliable tool to detect SARS-CoV-2. J Infect. 2020;81:45-50.Becker D, Sandoval E, Amin A, De Hoff P, Leonetti N, Lim YW, et al. Saliva is less sensitive than nasopharyngeal swabs for COVID-19 detection in the community setting. medRxiv. 2020;20092338.Braz-Silva PH, Pallos D, Giannecchini S, To KKW. SARS-CoV-2: What can saliva tell us?. Oral Dis. 2020;13365.Chan JFW, Yip CCY, To KKW, Tang THC, Wong SCY, Leung KH, et al. Improved molecular diagnosis of COVID-19 by the novel, highly sensitive and specific COVID-19-RdRp/Hel real-time reverse transcription-PCR assay validated in vitro and with clinical specimens. J Clin Microbiol. 2020;58:5.Chen L, Zhao J, Peng J, Li X, Deng X, Geng Z, et al. Detection of 2019-nCoV in saliva and characterization of oral symptoms in COVID-19 patients. Lancet. 2020;3556665.Cheng VC, Wong SC, Chen JH, Yip CC, Chuang VW, Tsang OT, et al. Escalating infection control response to the rapidly evolving epidemiology of the Coronavirus disease 2019 (COVID-19) due to SARS-CoV-2 in Hong Kong. Infect Control Hosp Epidemiol. 2020;41:493-498.Han P, Ivanovski S. Saliva—Friend and Foe in the COVID-19 Outbreak. Diagn. 2020;10:290.Iwasaki S, Fujisawa S, Nakakubo S, Kamada K, Yamashita Y, Fukumoto T, et al. Comparison of SARS-CoV-2 detection in nasopharyngeal swab and saliva. J Infect. 2020;20:30349.Krajewska J, Krajewski W, Zub K, Zatoński T. COVID-19 in otolaryngologist practice: a review of current knowledge. Eur Arch Otorhinolaryngol. 2020;1-13.Lalli MA, Chen X, Langmade SJ, Fronick CC, Sawyer CS, Burcea LC, et al. Rapid and extraction-free detection of SARS-CoV-2 from saliva with colorimetric LAMP. medRxiv. 2020;7273276.Li X, Geng M, Peng Y, Meng L, Lu S. Molecular immune pathogenesis and diagnosis of COVID-19. J Pharm Anal. 2020;10:101-108.Li H, Liu SM, Yu XH, Tang SL, Tang CK. Coronavirus disease 2019 (COVID-19): current status and future perspective. Int J Antimicrob Agents. 2020;105951.McCormick-Baw C, Morgan K, Gaffney D, Cazares Y, Jaworski K, Byrd A, et al. Saliva as an Alternate Specimen Source for Detection of SARS-CoV-2 in Symptomatic Patients Using Cepheid Xpert Xpress SARS-CoV-2. J Clin Microbiol. 2020;01109-20.Pasomsub E, Watcharananan SP, Boonyawat K, Janchompoo P, Wongtabtim G, Suksuwan W, et al. Saliva sample as a non-invasive specimen for the diagnosis of coronavirus disease-2019 (COVID-19): a cross-sectional study. Clin Microbiol Infect. 2020;20302780.Sabino-Silva R, Jardim ACG, Siqueira WL. Coronavirus COVID-19 impacts to dentistry and potential salivary diagnosis. Clinical oral investigations. 2020;1-3.Sapkota D, Thapa SB, Hasséus B, Jensen JL. Saliva testing for COVID-19?. BDJ. 2020;228:658-659.Sharma S, Kumar V, Chawla A, Logani A. Rapid detection of SARS-CoV-2 in saliva: Can an endodontist take the lead in point-of-care COVID-19 testing?. Int Endod J. 2020;13317.Tang YW, Schmitz JE, Persing DH, Stratton CW. Laboratory Diagnosis of COVID-19: Current Issues and Challenges. J Clin Microbiol. 2020;58(6).Tatikonda SS, Reshu P, Hanish A, Konkati S, Madham S. A Review of Salivary Diagnostics and Its Potential Implication in Detection of Covid-19. Cureus. 2020;12(4).To KKW, Tsang OTY, Leung WS, Tam AR, Wu TC, Lung DC, et al. Temporal profiles of viral load in posterior oropharyngeal saliva samples and serum antibody responses during infection by SARS-CoV-2: an observational cohort study. Lancet Infect Dis. 2020;20:565-574.Vinayachandran D, Saravanakarthikeyan B. Salivary diagnostics in COVID-19: Future research implications. J Dent Sci. 2020;7177105.Williams E, Bond K, Zhang B, Putland M, Williamson DA. Saliva as a non-invasive specimen for detection of SARS-CoV-2. J Clin Microbiol. 2020;00776-20.Wyllie AL, Fournier J, Casanovas-Massana A, Campbell M, Tokuyama M, Vijayakumar P, et al. Saliva is more sensitive for SARS-CoV-2 detection in COVID-19 patients than nasopharyngeal swabs. Medrxiv. 2020;20067835.Yoon JG, Yoon J, Song JY, Yoon SY, Lim CS, Seong H, et al. Clinical Significance of a High SARS-CoV-2 Viral Load in the Saliva. J Korean Med Sci. 2020;35(20).Zheng S, Yu F, Fan J, Zou Q, Xie G, Yang X, et al. Saliva as a Diagnostic Specimen for SARS-CoV-2 by a PCR-Based Assay: A Diagnostic Validity Study. Lancet. 2020;3543605.Zhong F, Liang Y, Xu J, Chu M, Tang G, Hu F, et al. Continuously High Detection Sensitivity of Saliva, Viral Shedding in Salivary Glands and High Viral Load in Patients with COVID-19. Lancet. 2020;3576869.
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5

Thinh, Nguyen Hong, Tran Hoang Tung e Le Vu Ha. "Depth-aware salient object segmentation". VNU Journal of Science: Computer Science and Communication Engineering 36, n.º 2 (7 de outubro de 2020). http://dx.doi.org/10.25073/2588-1086/vnucsce.217.

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Object segmentation is an important task which is widely employed in many computer vision applications such as object detection, tracking, recognition, and retrieval. It can be seen as a two-phase process: object detection and segmentation. Object segmentation becomes more challenging in case there is no prior knowledge about the object in the scene. In such conditions, visual attention analysis via saliency mapping may offer a mean to predict the object location by using visual contrast, local or global, to identify regions that draw strong attention in the image. However, in such situations as clutter background, highly varied object surface, or shadow, regular and salient object segmentation approaches based on a single image feature such as color or brightness have shown to be insufficient for the task. This work proposes a new salient object segmentation method which uses a depth map obtained from the input image for enhancing the accuracy of saliency mapping. A deep learning-based method is employed for depth map estimation. Our experiments showed that the proposed method outperforms other state-of-the-art object segmentation algorithms in terms of recall and precision. KeywordsSaliency map, Depth map, deep learning, object segmentation References[1] Itti, C. Koch, E. Niebur, A model of saliency-based visual attention for rapid scene analysis, IEEE Transactions on pattern analysis and machine intelligence 20(11) (1998) 1254-1259.[2] Goferman, L. Zelnik-Manor, A. Tal, Context-aware saliency detection, IEEE transactions on pattern analysis and machine intelligence 34(10) (2012) 1915-1926.[3] Kanan, M.H. Tong, L. Zhang, G.W. Cottrell, Sun: Top-down saliency using natural statistics, Visual cognition 17(6-7) (2009) 979-1003.[4] Liu, Z. Yuan, J. Sun, J. Wang, N. Zheng, X. Tang, H.-Y. Shum, Learning to detect a salient object, IEEE Transactions on Pattern analysis and machine intelligence 33(2) (2011) 353-367.[5] Perazzi, P. Krähenbühl, Y. Pritch, A. Hornung, Saliency filters: Contrast based filtering for salient region detection, in: Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, IEEE, 2012, pp. 733-740.[6] M. Cheng, N.J. Mitra, X. Huang, P.H. Torr, S.M. Hu, Global contrast based salient region detection, IEEE Transactions on Pattern Analysis and Machine Intelligence 37(3) (2015) 569-582.[7] Borji, L. Itti, State-of-the-art in visual attention modeling, IEEE transactions on pattern analysis and machine intelligence 35(1) (2013) 185-207.[8] Simonyan, A. Vedaldi, A. Zisserman, Deep inside convolutional networks: Visualising image classification models and saliency maps, arXiv preprint arXiv:1312.6034.[9] Li, Y. Yu, Visual saliency based on multiscale deep features, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 5455-5463.[10] Liu, J. Han, Dhsnet: Deep hierarchical saliency network for salient object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 678-686.[11] Achanta, S. Hemami, F. Estrada, S. Susstrunk, Frequency-tuned saliency detection model, CVPR: Proc IEEE, 2009, pp. 1597-604.Fu, J. Cheng, Z. Li, H. Lu, Saliency cuts: An automatic approach to object segmentation, in: Pattern Recognition, 2008. ICPR 2008. 19th International Conference on, IEEE, 2008, pp. 1-4Borenstein, J. Malik, Shape guided object segmentation, in: Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, Vol. 1, IEEE, 2006, pp. 969-976.Jiang, J. Wang, Z. Yuan, T. Liu, N. Zheng, S. Li, Automatic salient object segmentation based on context and shape prior., in: BMVC. 6 (2011) 9.Ciptadi, T. Hermans, J.M. Rehg, An in depth view of saliency, Georgia Institute of Technology, 2013.Desingh, K.M. Krishna, D. Rajan, C. Jawahar, Depth really matters: Improving visual salient region detection with depth., in: BMVC, 2013.Li, J. Ye, Y. Ji, H. Ling, J. Yu, Saliency detection on light field, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 2806-2813.Koch, S. Ullman, Shifts in selective visual attention: towards the underlying neural circuitry, in: Matters of intelligence, Springer, 1987, pp. 115-141.Laina, C. Rupprecht, V. Belagiannis, F. Tombari, N. Navab, Deeper depth prediction with fully convolutional residual networks, in: 3D Vision (3DV), 2016 Fourth International Conference on, IEEE, 2016, pp. 239-248.Bruce, J. Tsotsos, Saliency based on information maximization, in: Advances in neural information processing systems, 2006, pp. 155-162.Ren, X. Gong, L. Yu, W. Zhou, M. Ying Yang, Exploiting global priors for rgb-d saliency detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2015, pp. 25-32.Fang, J. Wang, M. Narwaria, P. Le Callet, W. Lin, Saliency detection for stereoscopic images., IEEE Trans. Image Processing 23(6) (2014) 2625-2636.Hou, L. Zhang, Saliency detection: A spectral residual approach, in: Computer Vision and Pattern Recognition, 2007. CVPR’07. IEEE Conference on, IEEE, 2007, pp. 1-8.Guo, Q. Ma, L. Zhang, Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform, in: Computer vision and pattern recognition, 2008. cvpr 2008. ieee conference on, IEEE, 2008, pp. 1-8.Fang, W. Lin, B.S. Lee, C.T. Lau, Z. Chen, C.W. Lin, Bottom-up saliency detection model based on human visual sensitivity and amplitude spectrum, IEEE Transactions on Multimedia 14(1) (2012) 187-198.Lang, T.V. Nguyen, H. Katti, K. Yadati, M. Kankanhalli, S. Yan, Depth matters: Influence of depth cues on visual saliency, in: Computer vision-ECCV 2012, Springer, 2012, pp. 101-115.Zhang, G. Jiang, M. Yu, K. Chen, Stereoscopic visual attention model for 3d video, in: International Conference on Multimedia Modeling, Springer, 2010, pp. 314-324.Wang, M.P. Da Silva, P. Le Callet, V. Ricordel, Computational model of stereoscopic 3d visual saliency, IEEE Transactions on Image Processing 22(6) (2013) 2151-2165.Peng, B. Li, W. Xiong, W. Hu, R. Ji, Rgbd salient object detection: A benchmark and algorithms, in: European Conference on Computer Vision (ECCV), 2014, pp. 92-109.Wu, L. Duan, L. Kong, Rgb-d salient object detection via feature fusion and multi-scale enhancement, in: CCF Chinese Conference on Computer Vision, Springer, 2015, pp. 359-368.Xue, Y. Gu, Y. Li, J. Yang, Rgb-d saliency detection via mutual guided manifold ranking, in: Image Processing (ICIP), 2015 IEEE International Conference on, IEEE, 2015, pp. 666-670.Katz, A. Adler, Depth camera based on structured light and stereo vision, uS Patent App. 12/877,595 (Mar. 8 2012).Chatterjee, G. Molina, D. Lelescu, Systems and methods for determining depth from multiple views of a scene that include aliasing using hypothesized fusion, uS Patent App. 13/623,091 (Mar. 21 2013).Matthies, T. Kanade, R. Szeliski, Kalman filter-based algorithms for estimating depth from image sequences, International Journal of Computer Vision 3(3) (1989) 209-238.Y. Schechner, N. Kiryati, Depth from defocus vs. stereo: How different really are they?, International Journal of Computer Vision 39(2) (2000) 141-162.Delage, H. Lee, A.Y. Ng, A dynamic bayesian network model for autonomous 3d reconstruction from a single indoor image, in: Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, Vol. 2, IEEE, 2006, pp. 2418-2428.Saxena, M. Sun, A.Y. Ng, Make3d: Learning 3d scene structure from a single still image, IEEE transactions on pattern analysis and machine intelligence 31(5) (2009) 824-840.Hedau, D. Hoiem, D. Forsyth, Recovering the spatial layout of cluttered rooms, in: Computer vision, 2009 IEEE 12th international conference on, IEEE, 2009, pp. 1849-1856.Liu, S. Gould, D. Koller, Single image depth estimation from predicted semantic labels, in: Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, IEEE, 2010, pp. 1253-1260.Ladicky, J. Shi, M. Pollefeys, Pulling things out of perspective, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 89-96.K. Nathan Silberman, Derek Hoiem, R. Fergus, Indoor segmentation and support inference from rgbd images, in: ECCV, 2012.Liu, J. Yuen, A. Torralba, Sift flow: Dense correspondence across scenes and its applications, IEEE transactions on pattern analysis and machine intelligence 33(5) (2011) 978-994.Konrad, M. Wang, P. Ishwar, 2d-to-3d image conversion by learning depth from examples, in: Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, IEEE, 2012, pp. 16-22.Liu, C. Shen, G. Lin, Deep convolutional neural fields for depth estimation from a single image, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 5162-5170.Wang, X. Shen, Z. Lin, S. Cohen, B. Price, A.L. Yuille, Towards unified depth and semantic prediction from a single image, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 2800-2809.Geiger, P. Lenz, C. Stiller, R. Urtasun, Vision meets robotics: The kitti dataset, International Journal of Robotics Research (IJRR).Achanta, S. Süsstrunk, Saliency detection using maximum symmetric surround, in: Image processing (ICIP), 2010 17th IEEE international conference on, IEEE, 2010, pp. 2653-2656.E. Rahtu, J. Kannala, M. Salo, J. Heikkilä, Segmenting salient objects from images and videos, in: Computer Vision-ECCV 2010, Springer, 2010, pp. 366-37.
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Livros sobre o assunto "Da Tang kai yuan li"

1

Meili Dapu tong xiang hui., ed. Meili Dapu tong xiang hui qing zhu chuang hui wu shi zhou nian jin xi ji nian ji Dapu you zhi yuan da li tang luo cheng kai mu te ji. Miri, Sarawak: Thai Poo Community Association Miri, 2003.

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2

Luo, Yaoxian. 0~1 sui zuo nao you nao duo yuan zhi neng da kai fa. Beijing: Zhong guo ren kou chu ban she, 2012.

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3

Nan kai da xue Zhou Enlai zheng fu guan li xue yuan xue shu lun cong bian wei hui., ed. Nan kai da xue Zhou Enlai zheng fu guan li xue yuan xue shu lun cong (2006-2007). Tianjin: Tianjin ren min chu ban she, 2007.

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4

Jing, Xu, ed. Kai fa, bao hu, jue qi: Xi bu da kai fa yu min zu di qu sheng tai zi yuan de bao hu li yong. Guiyang: Guizhou min zu chu ban she, 2004.

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5

1941-, Tang Shaoyun, Tang Shaoyun 1941- e Xiamen da xue, eds. Mei li Xia da: Tang Shaoyun Xiamen da xue xiao yuan feng jing you hua ji = A campus between mountains and sea oil : paintings of Xiamen University. Xiamen Shi: Xiamen da xue chu ban she, 2011.

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6

translator, Wang Chunyi, ed. Yong yuan li zhao huan qi ji: Da kai ni de nei zai li liang, zhuan hua sheng ming neng liang de 108 ge xin ling lian xi. Taibei Shi: Shi bao wen hua chu ban qi ye gu fen you xian gong si, 2016.

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7

bu, Jiao tong. An quan jia shi cong zhei li kai shi: Shi yong che xing A2:qi che lie che B2:da xing huo che. Beijing: Ren min jiao tong chu ban she, 2005.

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8

Zimei, Gao, ed. Ru he rang gu ke li bu kai ni: 5 da li liang, 28 tang ke rang ni cheng wei bu ke huo que de qi ye = Indispensable : how to become the company that your customers can't live without. Taibei Shi: Lian pu chu ban, 2007.

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9

Chao, Xiaoli. Yan hai fa da di qu nong cun fu nü ren li zi yuan kai fa yan jiu: Research on the human resource development of rural women in developed coastal areas. Hangzhou Shi: Zhejiang da xue chu ban she, 2013.

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10

Qinghai Sheng qing zhu gai ge kai fang 40 zhou nian li lun yan tao hui (2018 Xining, Qinghai Sheng, China). Wei da de bian ge: Qinghai Sheng she hui ke xue yuan ji nian gai ge kai fang si shi zhou nian li lun yan tao hui wen ji = Forty years of historic transformations : a Qinghai Academy of Social Sciences tribute to China's reform and opening-up. Beijing: She hui ke xue wen xian chu ban she, 2018.

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