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Статті в журналах з теми "Multiplicatif gaussien"
Lemańczyk, M. "Multiplicative Gaussian cocycles." Aequationes Mathematicae 61, no. 1-2 (February 1, 2001): 162–78. http://dx.doi.org/10.1007/s000100050168.
Повний текст джерелаRobert, Raoul, and Vincent Vargas. "Gaussian multiplicative chaos revisited." Annals of Probability 38, no. 2 (March 2010): 605–31. http://dx.doi.org/10.1214/09-aop490.
Повний текст джерелаShamov, Alexander. "On Gaussian multiplicative chaos." Journal of Functional Analysis 270, no. 9 (May 2016): 3224–61. http://dx.doi.org/10.1016/j.jfa.2016.03.001.
Повний текст джерелаLacoin, Hubert, Rémi Rhodes, and Vincent Vargas. "Complex Gaussian Multiplicative Chaos." Communications in Mathematical Physics 337, no. 2 (April 22, 2015): 569–632. http://dx.doi.org/10.1007/s00220-015-2362-4.
Повний текст джерелаKODOGIANNIS, VASSILIS S., MAHDI AMINA, and ILIAS PETROUNIAS. "A CLUSTERING-BASED FUZZY WAVELET NEURAL NETWORK MODEL FOR SHORT-TERM LOAD FORECASTING." International Journal of Neural Systems 23, no. 05 (August 7, 2013): 1350024. http://dx.doi.org/10.1142/s012906571350024x.
Повний текст джерелаDahab, R., D. Hankerson, F. Hu, M. Long, J. Lopez, and A. Menezes. "Software multiplication using Gaussian normal bases." IEEE Transactions on Computers 55, no. 8 (August 2006): 974–84. http://dx.doi.org/10.1109/tc.2006.132.
Повний текст джерелаBarral, Julien, Xiong Jin, Rémi Rhodes, and Vincent Vargas. "Gaussian Multiplicative Chaos and KPZ Duality." Communications in Mathematical Physics 323, no. 2 (August 3, 2013): 451–85. http://dx.doi.org/10.1007/s00220-013-1769-z.
Повний текст джерелаSafieh, Malek, and Jürgen Freudenberger. "Montgomery Reduction for Gaussian Integers." Cryptography 5, no. 1 (February 1, 2021): 6. http://dx.doi.org/10.3390/cryptography5010006.
Повний текст джерелаWang, Kang-Kang, Hui Ye, Ya-Jun Wang, and Ping-Xin Wang. "Impact of Time Delay and Non-Gaussian Noise on Stochastic Resonance and Stability for a Stochastic Metapopulation System Driven by a Multiplicative Periodic Signal." Fluctuation and Noise Letters 18, no. 03 (July 16, 2019): 1950017. http://dx.doi.org/10.1142/s0219477519500172.
Повний текст джерелаGuo, Yong-Feng, Ya-Jun Shen, Bei Xi, and Jian-Guo Tan. "Colored correlated multiplicative and additive Gaussian colored noises-induced transition of a piecewise nonlinear bistable model." Modern Physics Letters B 31, no. 28 (October 10, 2017): 1750256. http://dx.doi.org/10.1142/s0217984917502566.
Повний текст джерелаДисертації з теми "Multiplicatif gaussien"
Allez, Romain. "Chaos multiplicatif Gaussien, matrices aléatoires et applications." Phd thesis, Université Paris Dauphine - Paris IX, 2012. http://tel.archives-ouvertes.fr/tel-00780270.
Повний текст джерелаRemy, Guillaume. "Intégrabilité du chaos multiplicatif gaussien et théorie conforme des champs de Liouville." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE051/document.
Повний текст джерелаThroughout this PhD thesis we will study two probabilistic objects, Gaussian multiplicative chaos (GMC) measures and Liouville conformal field theory (LCFT). GMC measures were first introduced by Kahane in 1985 and have grown into an extremely important field of probability theory and mathematical physics. Very recently GMC has been used to give a probabilistic definition of the correlation functions of LCFT, a theory that first appeared in Polyakov’s 1981 seminal work, “Quantum geometry of bosonic strings”. Once the connection between GMC and LCFT is established, one can hope to translate the techniques of conformal field theory in a probabilistic framework to perform exact computations on the GMC measures. This is precisely what we develop for GMC on the unit circle. We write down the BPZ equations which lead to non-trivial relations on the GMC. Our final result is an exact probability density for the total mass of the GMC measure on the unit circle. This proves a conjecture of Fyodorov and Bouchaud stated in 2008. Furthermore, it turns out that the same techniques also work on a more difficult model, the GMC on the unit interval, and thus we also prove conjectures put forward independently by Ostrovsky and by Fyodorov, Le Doussal, and Rosso in 2009. The last part of this thesis deals with the construction of LCFT on a domain with the topology of an annulus. We follow the techniques introduced by David-Kupiainen- Rhodes-Vargas although novel ingredients are required as the annulus possesses two boundaries and a non-trivial moduli space. We also provide more direct proofs of known results
Huang, Yichao. "Chaos multiplicatif gaussien et applications à la gravité quantique de Liouville." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066623/document.
Повний текст джерелаIn this thesis, we study the theory of Liouville Quantum Gravity via probabilist approach, introduced in the seminal paper of Polyakov in 1981, using path integral formalism on 2d surfaces. To define this path integral with exponential interaction, we started from the theory of Gaussian Multiplicative Chaos in order to define exponential of log-correlated Gaussian fields. In the first part, we generalise the construction of Liouville Quantum Gravity on the Riemann sphere to another geometry, the one of the unit disk. The novelty of this work, in collaboration with R.Rhodes and V.Vargas, is to analyse carefully the boundary term in the path integral formalism and its interaction with the bulk measure. We establish rigorously formulae from Conformal Field Theory in Physics, such as conformal covariance, KPZ relation, conformal anomaly and Seiberg bounds. A relaxed Seiberg bound in the unit volume case of Liouville Quantum Gravity on the disk is also announced and studied. In the second part of this thesis, we compare this construction in the spirit of Polyakov to another approach to the Liouville Quantum Gravity. In collaboration with two other young researchers, J.Aru and X.Sun, we give a correspondance between these two approaches in a simple but conceptually important case, namely the one on the Riemann sphere with three marked points. Using technics coming from these two approches, we give a new way of regularisation procedure that eventually allow us to link these two pictures
Sauzedde, Isao. "Windings of the planar Brownian motion and Green’s formula." Thesis, Sorbonne université, 2021. http://www.theses.fr/2021SORUS437.
Повний текст джерелаWe study the windings of the planar Brownian motion around points, following the previous works of Wendelin Werner in particular. In the first chapter, we motivate this study by the one of smoother curves. We prove in particular a Green formula for Young integration, without simplicity assumption for the curve. In the second chapter, we study the area of the set of points around which the Brownian motion winds at least N times. We give an asymptotic estimation for this area, up to the second order, both in the almost sure sense and in the Lp spaces, when N goes to infinity.The third chapter is devoted to the proof of a result which shows that the points with large winding are distributed in a very balanced way along the trajectory. In the fourth chapter, we use the results from the two previous chapters to give a new Green formula for the Brownian motion. We also study the averaged winding around randomly distributed points in the plan. We show that, almost surely for the trajectory, this averaged winding converges in distribution, not toward a constant (which would be the Lévy area), but toward a Cauchy distribution centered at the Lévy area. In the last two chapters, we apply the ideas from the previous chapters to define and study the Lévy area of the Brownian motion, when the underlying area measure is not the Lebesgue measure anymore, but instead a random and highly irregular measure. We deal with the case of the Gaussian multiplicative chaos in particular, but the tools can be used in a much more general framework
Astoquillca, Aguilar Jhon Kevin. "Gaussian Multiplicative Chaos." Master's thesis, Pontificia Universidad Católica del Perú, 2020. http://hdl.handle.net/20.500.12404/17752.
Повний текст джерелаThe Kolmogorov-Obukhov-Mandelbrot theory of energy dissipation in turbulence developed was established to study the chaotic behavior of fluids. In the absence of a rigorous mathematical basis, Kahane introduced the Gaussian multiplicative chaos as a random object inspired by the additive chaos theory developed by Wiener. In this thesis we developed random theory in the spaces of Radon measures in order to rigorously define Gaussian multiplicative chaos. We follow Kahane’s paper and weaken some conditions to provide an accessible and selfcontained introduction.
Watson, Stephen M. "Frequency demodulation in the presence of multiplicative speckle noise." Thesis, University of Nottingham, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.246382.
Повний текст джерелаTian, Kuanhou. "Some properties of a class of stochastic heat and wave equations with multiplicative Gaussian noises." Thesis, Loughborough University, 2015. https://dspace.lboro.ac.uk/2134/19611.
Повний текст джерелаShao, Qiliang. "FPGA Realization of Low Register Systolic Multipliers over GF(2^m)." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1481808131971019.
Повний текст джерелаVestin, Albin, and Gustav Strandberg. "Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.
Повний текст джерелаJarrett, Nicholas Walton Daniel. "Nonlinear Prediction in Credit Forecasting and Cloud Computing Deployment Optimization." Diss., 2015. http://hdl.handle.net/10161/9974.
Повний текст джерелаThis thesis presents data analysis and methodology for two prediction problems. The first problem is forecasting midlife credit ratings from personality information collected during early adulthood. The second problem is analysis of matrix multiplication in cloud computing.
The goal of the credit forecasting problem is to determine if there is a link between personality assessments of young adults with their propensity to develop credit in middle age. The data we use is from a long term longitudinal study of over 40 years. We do find an association between credit risk and personality in this cohort Such a link has obvious implications for lenders but also can be used to improve social utility via more efficient resource allocation
We analyze matrix multiplication in the cloud and model I/O and local computation for individual tasks. We established conditions for which the distribution of job completion times can be explicitly obtained. We further generalize these results to cases where analytic derivations are intractable.
We develop models that emulate the multiplication procedure, allowing job times for different deployment parameter settings to be emulated after only witnessing a subset of tasks, or subsets of tasks for nearby deployment parameter settings.
The modeling framework developed sheds new light on the problem of determining expected job completion time for sparse matrix multiplication.
Dissertation
Книги з теми "Multiplicatif gaussien"
Rhodes1, Rémi, and Vincent Vargas2. Gaussian multiplicative chaos and Liouville quantum gravity. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198797319.003.0012.
Повний текст джерелаЧастини книг з теми "Multiplicatif gaussien"
Saksman, Eero, and Christian Webb. "On the Riemann Zeta Function and Gaussian Multiplicative Chaos." In Advancements in Complex Analysis, 473–96. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40120-7_12.
Повний текст джерелаChiou, C. W., Y. S. Sun, C. M. Lee, Y. L. Chiu, J. M. Lin, and C. Y. Lee. "Problems on Gaussian Normal Basis Multiplication for Elliptic Curve Cryptosystem." In Advances in Intelligent Systems and Computing, 201–7. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23207-2_20.
Повний текст джерелаWang, Zhen, Xiaozhe Wang, and Shuqin Fan. "Concurrent Error Detection Architectures for Field Multiplication Using Gaussian Normal Basis." In Information Security, Practice and Experience, 96–109. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12827-1_8.
Повний текст джерелаFedorov, Eugene, Tetyana Utkina, and Tetyana Neskorodeva. "A Voice Signal Filtering Methods for Speaker Biometric Identification." In Recent Advances in Biometrics [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.101975.
Повний текст джерелаPawlowsky-Glahn, Vera, and Richardo A. Olea. "Cokriging." In Geostatistical Analysis of Compositional Data. Oxford University Press, 2004. http://dx.doi.org/10.1093/oso/9780195171662.003.0011.
Повний текст джерелаТези доповідей конференцій з теми "Multiplicatif gaussien"
Muravev, Nikita, and Aleksandr Petiushko. "Certified Robustness via Randomized Smoothing over Multiplicative Parameters of Input Transformations." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/467.
Повний текст джерелаYe, Yalan, Zhi-lin Zhang, Shaozhi Wu, and Xiaobin Zhou. "Improved Multiplicative Orthogonal-Group Based ICA for Separating Mixed Sub-Gaussian and Super-Gaussian Sources." In 2006 International Conference on Communications, Circuits and Systems. IEEE, 2006. http://dx.doi.org/10.1109/icccas.2006.284649.
Повний текст джерелаTian, Xuemei, Xingiun Wu, and Guoqiang Bai. "Towards Low Space Complexity Design of Gaussian Normal Basis Multiplication." In 2019 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC). IEEE, 2019. http://dx.doi.org/10.1109/edssc.2019.8754096.
Повний текст джерелаKiyono, Ken, Zbigniew R. Struzik, and Yoshiharu Yamamoto. "Characterisation of non-Gaussian fluctuations in multiplicative log-normal models." In NOISE AND FLUCTUATIONS: 19th International Conference on Noise and Fluctuations; ICNF 2007. AIP, 2007. http://dx.doi.org/10.1063/1.2759758.
Повний текст джерелаPhalakarn, Kittiphon, and Athasit Surarerks. "A Matrix Decomposition Method for Odd-Type Gaussian Normal Basis Multiplication." In 2018 3rd International Conference on Computer and Communication Systems (ICCCS). IEEE, 2018. http://dx.doi.org/10.1109/ccoms.2018.8463251.
Повний текст джерелаPalahina, Elena, and Volodymyr Palahin. "Signal detection in additive-multiplicative non-Gaussian noise using higher order statistics." In 2016 26th International Conference Radioelektronika (RADIOELEKTRONIKA). IEEE, 2016. http://dx.doi.org/10.1109/radioelek.2016.7477367.
Повний текст джерелаTrujillo-Olaya, Vladimir, Jaime Velasco-Medina, and Julio C. Lopez-Hernandez. "Efficient Hardware Implementations for the Gaussian Normal Basis Multiplication Over GF(2163)." In 2007 3rd Southern Conference on Programmable Logic. IEEE, 2007. http://dx.doi.org/10.1109/spl.2007.371722.
Повний текст джерелаSokouti, Massoud, and Ali Zakerolhosseini. "Increasing the speed of QTRU using the Gaussian and Brent equations multiplication." In 2014 22nd Iranian Conference on Electrical Engineering (ICEE). IEEE, 2014. http://dx.doi.org/10.1109/iraniancee.2014.6999653.
Повний текст джерелаCiblat, P., and M. Ghogho. "ZIV-ZAKAI bound for harmonic retrieval in multiplicative and additive gaussian noise." In 2005 Microwave Electronics: Measurements, Identification, Applications. IEEE, 2005. http://dx.doi.org/10.1109/ssp.2005.1628658.
Повний текст джерелаArtyushenko, V. M., V. I. Volovach, and M. V. Shakursky. "The demodulation signal under the influence of additive and multiplicative non-Gaussian noise." In 2016 IEEE East-West Design & Test Symposium (EWDTS). IEEE, 2016. http://dx.doi.org/10.1109/ewdts.2016.7807704.
Повний текст джерела