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Journal articles on the topic 'De Carlo'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 April 2022

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1

TOKURA, Yoshiki. "Prof. Carlo Pincelli." Nishi Nihon Hifuka 73, no. 1 (2011): 93–94. http://dx.doi.org/10.2336/nishinihonhifu.73.93.

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2

Bortolotti, Uberto, Giovanni Stellin, and Alessandro Mazzucco. "Carlo A. carlon and the cavopulmonary anastomosis." Annals of Thoracic Surgery 52, no. 5 (1991): 1204–5. http://dx.doi.org/10.1016/0003-4975(91)91319-q.

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3

Herman, P. "Carlo Heip (1945-2013)." Scientia Marina 77, no. 1 (2013): 201–2. http://dx.doi.org/10.3989/scimar.03840.05a.

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4

Davidović, Branko, Duško Letić, and Aleksandar Jovanović. "MONTE CARLO SIMULATION IN INTRALOGISTICS." MEST Journal 2, no. 1 (2014): 87–93. http://dx.doi.org/10.12709/mest.02.02.01.09.

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5

Caflisch, Russel E. "Monte Carlo and quasi-Monte Carlo methods." Acta Numerica 7 (January 1998): 1–49. http://dx.doi.org/10.1017/s0962492900002804.

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Monte Carlo is one of the most versatile and widely used numerical methods. Its convergence rate, O(N−1/2), is independent of dimension, which shows Monte Carlo to be very robust but also slow. This article presents an introduction to Monte Carlo methods for integration problems, including convergence theory, sampling methods and variance reduction techniques. Accelerated convergence for Monte Carlo quadrature is attained using quasi-random (also called low-discrepancy) sequences, which are a deterministic alternative to random or pseudo-random sequences. The points in a quasi-random sequence are correlated to provide greater uniformity. The resulting quadrature method, called quasi-Monte Carlo, has a convergence rate of approximately O((logN)kN−1). For quasi-Monte Carlo, both theoretical error estimates and practical limitations are presented. Although the emphasis in this article is on integration, Monte Carlo simulation of rarefied gas dynamics is also discussed. In the limit of small mean free path (that is, the fluid dynamic limit), Monte Carlo loses its effectiveness because the collisional distance is much less than the fluid dynamic length scale. Computational examples are presented throughout the text to illustrate the theory. A number of open problems are described.

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6

Lanslots, Inge. "Gli ingranaggi della memoria del G8 2001 in Carlo Giuliani. Il ribelle di Genova." Forum Italicum: A Journal of Italian Studies 51, no. 1 (2017): 112–32. http://dx.doi.org/10.1177/0014585816682489.

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Il presente contributo propone l’analisi di Carlo Giuliani. Il ribelle di Genova di Francesco Barilli e Manuel De Carli (2011), che a dieci anni di distanza rielabora la memoria degli eventi del G8 2001 tramite quella della figura di Carlo Giuliani. Carlo Giuliani. Il ribelle di Genova, edito da BeccoGiallo, vuole essere una contronarrazione alla versione ufficiale dei fatti inscrivendosi nel sottogenere del docunovel, nel senso che si presenta come una narrazione grafica che si serve delle modalità di rappresentazione tipiche del documentario e della finzione. L’analisi del docunovel partirà dalla rappresentazione della figura di Carlo Giuliani la cui morte viene descritta come un evento tragico e traumatico tipico dell’ingiustizia che caratterizza la storia italiana contemporanea, ma che poi viene collegata ad altri eventi storici altrettanto traumatici. Si metteranno in rilievo le specificità contenutistiche e le strategie narratologiche del docunovel, ma anche l’importanza del fitto apparato paratestuale che iscrive la morte di Giuliani in un contesto storico prettamente italiano. La contestualizzazione porterà anche al confronto di Carlo Giuliani. Il ribelle di Genova con altre narrazioni fumettistiche nella misura in cui tutte sembrano rifarsi alla stessa iconografia. L’analisi del docunovel farà emergere le peculiarità del discorso commemorativo evocato dalla scomparsa di Carlo Giuliani e gli eventuali effetti performativi sul lettore.

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7

Duran, Maria Raquel Da Cruz. "Mitos gráficos e o tecer de sinestesias em linguagens/ Palavras em imagens: escritas, corpos e memórias, organizado por Carlos Fausto e Carlo Severi." Revista de Antropologia 60, no. 1 (2017): 324. http://dx.doi.org/10.11606/2179-0892.ra.2017.132083.

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8

Fleck, F. "Carlo Urbani." BMJ 326, no. 7393 (2003): 825. http://dx.doi.org/10.1136/bmj.326.7393.825.

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9

Ginzburg, Carlo. "Carlo Ginzburg." Postcolonial Studies 14, no. 3 (2011): 347. http://dx.doi.org/10.1080/13688790.2011.621405.

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10

Oransky, Ivan. "Carlo Urbani." Lancet 361, no. 9367 (2003): 1481. http://dx.doi.org/10.1016/s0140-6736(03)13107-8.

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11

Barryte, Bernard. "CARLO CRIVELLI." Art Book 12, no. 3 (2005): 29–31. http://dx.doi.org/10.1111/j.1467-8357.2005.00564.x.

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12

Richardson, Brian, and Nelia Saxby. "Carlo Dionisotti." Italian Studies 54, no. 1 (1999): 13–17. http://dx.doi.org/10.1179/its.1999.54.1.13.

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13

Brinkman, Anouk. "Carlo Leget." Pallium 14, no. 2 (2012): 2. http://dx.doi.org/10.1007/s12479-012-0020-5.

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14

Stevens, Denis. "Carlo Gesualdo." Musical Times 131, no. 1770 (1990): 410. http://dx.doi.org/10.2307/966618.

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15

Carme, Émile. "Carlo Cafiero." Ballast N° 3, no. 2 (2015): 128–57. http://dx.doi.org/10.3917/ball.003.0128.

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16

Alrammal, Muath, and Munir Naveed. "Monte-Carlo Based Reinforcement Learning (MCRL)." International Journal of Machine Learning and Computing 10, no. 2 (2020): 227–32. http://dx.doi.org/10.18178/ijmlc.2020.10.2.924.

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17

Sakota, Daisuke, and Setsuo Takatani. "Photon-cell interactive Monte Carlo simulation." Nippon Laser Igakkaishi 32, no. 4 (2012): 411–20. http://dx.doi.org/10.2530/jslsm.32.411.

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18

Hanif, Ayub, and Robert Elliott Smith. "State Space Modeling & Bayesian Inference with Computational Intelligence." New Mathematics and Natural Computation 11, no. 01 (2015): 71–101. http://dx.doi.org/10.1142/s1793005715500040.

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Recursive Bayesian estimation using sequential Monte Carlos methods is a powerful numerical technique to understand latent dynamics of nonlinear non-Gaussian dynamical systems. It enables us to reason under uncertainty and addresses shortcomings underlying deterministic systems and control theories which do not provide sufficient means of performing analysis and design. In addition, parametric techniques such as the Kalman filter and its extensions, though they are computationally efficient, do not reliably compute states and cannot be used to learn stochastic problems. We review recursive Bayesian estimation using sequential Monte Carlo methods highlighting open problems. Primary of these is the weight degeneracy and sample impoverishment problem. We proceed to detail synergistic computational intelligence sequential Monte Carlo methods which address this. We find that imbuing sequential Monte Carlos with computational intelligence has many advantages when applied to many application and problem domains.

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19

Musrati, Mufid Mohamed A., and Hanan Ettaher Dagez. "New Optimized Crossover Utilizes Exponential Monte Carlo." International Journal of Computer and Communication Engineering 3, no. 5 (2014): 384–87. http://dx.doi.org/10.7763/ijcce.2014.v3.354.

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20

Valdés, Nicolás. "Sobre la realidad mitificada de Carlo Levi." Philologia Hispalensis 1, no. 9 (1994): 173–85. http://dx.doi.org/10.12795/ph.1994.v09.i01.15.

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21

Ługowski, Piotr. "Carlo i Francesco Ceroniowie w świetle nowych źródeł archiwalnych." Biuletyn Historii Sztuki 81, no. 3 (2020): 481–97. http://dx.doi.org/10.36744/bhs.483.

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W 2. połowie XVII w. do Warszawy przybyła grupa architektów pochodzących z położonej nad jeziorem Lugano Valsoldy, w północnej Lombardii. Wśród nich znaleźli się Carlo i Francesco Ceroniowie. Postacie te za sprawą badań prof. Mariusza Karpowicza doczekały się samodzielnych opracowań. To on odnalazł w archiwum parafialnym w Albogasio Inferiore ich metryki, ustalił koligacje rodzinne, dotarł do kontraktu Francesca Ceroniego na prace przy kościele sakramentek oraz do testamentu Carla Ceroniego. Badania prowadzone przez autora tekstu dla Słownika architektów i budowniczych środowiska warszawskiego XV-XVIII wieku przyniosły szereg informacji biograficznych. Kwerenda w archiwum parafialnym w Albogasio Inferiore ujawniła nowe dokumenty źródłowe. Odnaleziono pełnomocnictwo Ceroniego dane w 1711 r. przed władzami Miasta Starej Warszawy dla Pietra Martiriego i Carla Antonia Bellottich na występowanie w jego imieniu przed władzami Valsoldy oraz zbiór dokumentów dotyczących konfliktu miedzy Carlem Ceronim a Carlem Fariną. Konflikt ten, o podłożu finansowym, skłonił Ceroniego do wyłączenia w testamencie rodziny Farina z udziału w spadku. Farina, który zabezpieczał w latach 1693-1699 włoskie interesy architekta, zarzucił Ceroniemu niespłacenie całości długu. Ceroni w liście z Warszawy z 16 maja 1714 roku skierowanym do kuzyna, burmistrza Valsoldy, przedstawił argumenty w których wykazał, że dług spłacił, a nawet poniósł szkody na wspólnych interesach z Carlem Fariną. Z tego samego listu wynika, że druga córka architekta, Lucia - wbrew dotychczasowym ustaleniom - dożyła pełnoletniości i w wieku 31 lat wstąpiła do zakonu (zm. 1717). Kwerenda w aktach notarialnych archiwum państwowego w Como pozwoliła odnaleźć kodycyl Franciszka Ceroniego oraz plenipotencję wystawioną przez spadkobierców Ceroniego architektom Carlo Antonio Bay i Giuseppe Rachetti na odzyskanie długów od Zofii i Dominika Combonich.  Sześć listów Carla Ceroniego do Samuela Pączkowskiego, administratora dóbr Krasińskich, odnalezionych w Archiwum Głównym Akt Dawnych w Warszawie przyniosło nowe informacje na temat prac prowadzonych w Węgrowie. Listy te każą zweryfikować czas trwania prac przy zespole kościoła i klasztoru węgrowskich reformatów, i wydłużyć je o co najmniej dziesięć lat. Przeprowadzona przez autora analiza źródeł pozwala stwierdzić, iż głównym właścicielem przedsiębiorstwa budowlanego był młodszy z braci Ceronich - Francesco. Carlo i Francesco Ceroniowie dorobili się znacznego majątku, który po ich śmierci przeszedł na własność mieszkańców Albogasio jako Eredita Ceroni. Jeszcze w XIX w. zyski z niego przeznaczane były na potrzeby mieszkańców miasteczka.

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22

Tesauro, Gerald. "MONTE-CARLO BACKGAMMON." ICGA Journal 30, no. 3 (2007): 183. http://dx.doi.org/10.3233/icg-2007-30317.

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23

Hartmann, Dap. "Monte-Carlo Galore!" ICGA Journal 32, no. 1 (2009): 41–42. http://dx.doi.org/10.3233/icg-2009-32106.

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24

Ziegel, Eric R., and C. Mooney. "Monte Carlo Simulation." Technometrics 40, no. 3 (1998): 267. http://dx.doi.org/10.2307/1271205.

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25

Van Houcke, Kris, Evgeny Kozik, N. Prokof’ev, and B. Svistunov. "Diagrammatic Monte Carlo." Physics Procedia 6 (2010): 95–105. http://dx.doi.org/10.1016/j.phpro.2010.09.034.

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26

Jirari, H., H. Kröger, X. Q. Luo, and K. J. M. Moriarty. "Monte Carlo Hamiltonian." Physics Letters A 258, no. 1 (1999): 6–14. http://dx.doi.org/10.1016/s0375-9601(99)00304-7.

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27

Mitas, Lubos. "Quantum Monte Carlo." Current Opinion in Solid State and Materials Science 2, no. 6 (1997): 696–700. http://dx.doi.org/10.1016/s1359-0286(97)80012-5.

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28

Lamb, H. "Interview: Carlo Rovelli." Engineering & Technology 13, no. 6 (2018): 66–67. http://dx.doi.org/10.1049/et.2018.0625.

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29

Stodółkiewicz, J. S. "Monte-Carlo Calculations." Symposium - International Astronomical Union 113 (1985): 361–72. http://dx.doi.org/10.1017/s0074180900147606.

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The evolution of a nonisolated globular cluster is presented. The binaries (both, tidally captured and formed in three-body interactions), outflow of mass from stellar envelopes and shocks are considered as sources of energy in the cluster.

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30

Ferrante, D. D., J. Doll, G. S. Guralnik, and D. Sabo. "Mollified Monte Carlo." Nuclear Physics B - Proceedings Supplements 119 (May 2003): 965–67. http://dx.doi.org/10.1016/s0920-5632(03)01732-8.

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31

Sullivan, Francis. "Learning Monte Carlo." Computing in Science & Engineering 19, no. 1 (2017): 86–87. http://dx.doi.org/10.1109/mcse.2017.11.

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32

Jacob, Maurice. "Gian‐Carlo Wick." Physics Today 46, no. 1 (1993): 85–86. http://dx.doi.org/10.1063/1.2808795.

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33

Bassi, Claudio. "Prof. Carlo Procacci." Pancreatology 4, no. 1 (2004): 63–64. http://dx.doi.org/10.1159/000077262.

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34

Cappé, O., A. Guillin, J. M. Marin, and C. P. Robert. "Population Monte Carlo." Journal of Computational and Graphical Statistics 13, no. 4 (2004): 907–29. http://dx.doi.org/10.1198/106186004x12803.

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35

Dai, Hongsheng, Murray Pollock, and Gareth Roberts. "Monte Carlo fusion." Journal of Applied Probability 56, no. 01 (2019): 174–91. http://dx.doi.org/10.1017/jpr.2019.12.

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AbstractIn this paper we propose a new theory and methodology to tackle the problem of unifying Monte Carlo samples from distributed densities into a single Monte Carlo draw from the target density. This surprisingly challenging problem arises in many settings (for instance, expert elicitation, multiview learning, distributed ‘big data’ problems, etc.), but to date the framework and methodology proposed in this paper (Monte Carlo fusion) is the first general approach which avoids any form of approximation error in obtaining the unified inference. In this paper we focus on the key theoretical underpinnings of this new methodology, and simple (direct) Monte Carlo interpretations of the theory. There is considerable scope to tailor the theory introduced in this paper to particular application settings (such as the big data setting), construct efficient parallelised schemes, understand the approximation and computational efficiencies of other such unification paradigms, and explore new theoretical and methodological directions.

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36

Pederiva, Francesco, and M. H. Kalos. "Fermion Monte Carlo." Computer Physics Communications 121-122 (September 1999): 440–45. http://dx.doi.org/10.1016/s0010-4655(99)00378-1.

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37

Jirari, H. "Monte Carlo Hamiltonian." Nuclear Physics B - Proceedings Supplements 83-84, no. 1-3 (2000): 953–55. http://dx.doi.org/10.1016/s0920-5632(00)00372-8.

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38

Jirari, H., H. Kröger, Chun-Qing Huang, Jun-Qin Jiang, X. Q. Luo, and K. J. M. Moriarty. "Monte Carlo Hamiltonian." Nuclear Physics B - Proceedings Supplements 83-84 (April 2000): 953–55. http://dx.doi.org/10.1016/s0920-5632(00)91855-3.

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39

Giersz, Mirek. "Monte-Carlo Simulations." Symposium - International Astronomical Union 174 (1996): 101–10. http://dx.doi.org/10.1017/s0074180900001431.

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The revision of the Stodółkiewicz's Monte-Carlo code is presented. It treats each superstar as a single star and follows the evolution and motion of all individual stellar objects. The first calculations, for equalmass N-body systems with three-body energy generation accordingly to Spitzer's formulae, show good agreement with the direct N-body calculations for N = 2000 and 10000 particles.

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40

Jiménez, Javier. "Monte Carlo science." Journal of Turbulence 21, no. 9-10 (2020): 544–66. http://dx.doi.org/10.1080/14685248.2020.1742918.

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41

Bardenet, Rémi. "Monte Carlo methods." EPJ Web of Conferences 55 (2013): 02002. http://dx.doi.org/10.1051/epjconf/20135502002.

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42

Kroese, Dirk P., and Reuven Y. Rubinstein. "Monte Carlo methods." Wiley Interdisciplinary Reviews: Computational Statistics 4, no. 1 (2011): 48–58. http://dx.doi.org/10.1002/wics.194.

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43

Kamtchueng, Christian. "Uncertain Monte Carlo." Wilmott 2013, no. 66 (2013): 54–63. http://dx.doi.org/10.1002/wilm.10234.

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44

Soisson, F. "Monte Carlo Simulations." EPJ Web of Conferences 14 (2011): 02003. http://dx.doi.org/10.1051/epjconf/20111402003.

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45

Glasserman, Paul. "Filtered Monte Carlo." Mathematics of Operations Research 18, no. 3 (1993): 610–34. http://dx.doi.org/10.1287/moor.18.3.610.

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46

CEPERLEY, D., and B. ALDER. "Quantum Monte Carlo." Science 231, no. 4738 (1986): 555–60. http://dx.doi.org/10.1126/science.231.4738.555.

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47

Newton, Paul K., and Kamran Aslam. "Monte Carlo Tennis." SIAM Review 48, no. 4 (2006): 722–42. http://dx.doi.org/10.1137/050640278.

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48

Dickman, B. H., and M. J. Gilman. "Monte Carlo optimization." Journal of Optimization Theory and Applications 60, no. 1 (1989): 149–57. http://dx.doi.org/10.1007/bf00938806.

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49

Koch, Karl-Rudolf. "Monte Carlo methods." GEM - International Journal on Geomathematics 9, no. 1 (2017): 117–43. http://dx.doi.org/10.1007/s13137-017-0101-z.

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50

Duane, Simon, A. D. Kennedy, Brian J. Pendleton, and Duncan Roweth. "Hybrid Monte Carlo." Physics Letters B 195, no. 2 (1987): 216–22. http://dx.doi.org/10.1016/0370-2693(87)91197-x.

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