109,577 research outputs found
CAMPANILE ORCHESTRA EFRAÍN AMAYA, conductor PAUL ELLISON, soloist Sunday, April 17, 1988 8:00 p.m. in Hamman Hall
No full textPROGRAM: Soledad and the sea / Efraín Amaya -- Concerto in F major (op. 3 no. 9 ) / Antonio Vivaldi -- Concerto no. 3 for double bass, piano and strings / François Rabbath -- Symphony no. 2 in G major "London symphony" / Ralph Vaughan William
ASMC-SGR
No full text<p><strong>Multiple-point statistics Adaptive Sequential Monte Carlo to infer a challenging bimodal posterior</strong></p><p><i><strong>Overview</strong></i><br>These codes correspond to the article by Amaya et al. (2022). It is a Python 3.7 implementation of the Adaptive Sequential Monte Carlo (ASMC) method (Zhou et al., 2016; algorithm 4) to estimate the posterior probability density function (PDF) and the evidence (marginal likelihood) trough a Bayesian inversion. ASMC is a particle approach that relies on importance sampling over a sequence of intermediate distributions (power posteriors) that link the prior and the posterior PDF. Each power posterior is approximated by updating the particle importance weights and states using a small pre-defined number MCMC proposal steps. ASMC method adaptively tunes the sequence of power posteriors and performs resampling of particles when the variance of their importance weights becomes too large.</p><p>The test case is a synthetic groundwater transport problem from Laloy et al. (2016) with channelized categorical models representing the hydraulic conductivity spatial distribution.</p><p>This ASMC implementation (referred to as ASMC-SGR, algorithm 1 in Amaya et al. (2022)) includes:</p><p>-an adaptation of the Sequential Geostatistical Resampling algorithm from Laloy et al. (2016) to generate new model proposals troughout the inversion using the direct sampling approach (DS, Mariethoz et al., 2010). DS perfomrs multiple-point statistics (MPS) conditioned siumlations based on a training image, and it is implemented using the DeeSse algorithm (patented by the University of Neuchâtel, <a href="http://www.randlab.org/research/deesse/">http://www.randlab.org/research/deesse/</a>),</p><p>-an adaptation of the DREAMzs algorithm to perform MCMC steps approximating each power posterior (ter Braak and Vrugt, 2008; Vrugt, 2009; Laloy and Vrugt, 2012),</p><p>-the finite-volume open-source code MaFloT for transport simulations in porous media (Kunze & Lunati, 2012).</p><p>Clarification: In this codes beta indicates the inverse temperature of the power posterior, which in Amaya et al. (2021, 2022) is indicated as alpha.</p><p><i><strong>Files:</strong></i><br>run_asmc_sgr.py : control the user-defined parameters and run the inversion.</p><p>asmc_sgr.py : main ASMC-SGR code.</p><p>asmc_sgr_func.py : auxiliar functions called by asmc_sgr.py.</p><p>deesse: algorithm for MPS simulations (version 2016). It requires a license from University of Neuchâtel (<a href="http://www.randlab.org/research/deesse/">http://www.randlab.org/research/deesse/</a>).</p><p>data_03.mat: tracer concentration and data noise.</p><p>ti_rotated.gslib: training image.</p><p>run_DS.py: run DeeSse for the initial simulation (conly conditioned to cond_init.dat)</p><p>cond_init.dat: initial conditioning points corresponding to the facies at the pumping wells. '</p><p>test.unc: DeeSse parameters input file for the initial simulation. test.con: DeeSse parameters input file.</p><p>Sequential Geostatistical Resampling (SGR) scripts: SetInput.py InitializeInput.py GenCond.py</p><p>Matlab scripts to run hydrological simulations with MaFloT: Diffusion.m Dispersion.m DisplayVariable.m Initialize.m InputFile.m InputFile_Original.m MaFloT.m Output.m PresMat.m runMaFloT.m Transport.m UpMat.m Velocity.m</p><p><i><strong>Citation:</strong></i><br>Amaya, M., Linde, N., & Laloy, E. (2022). Hydrogeological multiple-point statistics inversion by adaptive sequential Monte Carlo. Advances in Water Resources, 166, 104252.</p><p><i><strong>References:</strong></i><br>Amaya, M., Linde, N., & Laloy, E. (2022). Hydrogeological multiple-point statistics inversion by adaptive sequential Monte Carlo. Advances in Water Resources, 166, 104252.</p><p>Amaya, M., Linde, N., & Laloy, E. (2021). Adaptive sequential Monte Carlo for posterior inference and model selection among complex geological priors. Geophysical Journal International, 226(2), 1220-1238.</p><p>Künze, R., & Lunati, I. (2012). An adaptive multiscale method for density-driven instabilities. Journal of Computational Physics, 231(17), 5557-5570.</p><p>Laloy, E., & Vrugt, J. A. (2012). High‐dimensional posterior exploration of hydrologic models using multiple‐try DREAM (ZS) and high‐performance computing. Water Resources Research, 48(1).</p><p>Laloy, E., Linde, N., Jacques, D., & Mariethoz, G. (2016). Merging parallel tempering with sequential geostatistical resampling for improved posterior exploration of high-dimensional subsurface categorical fields. Advances in water resources, 90, 57-69.</p><p>Mariethoz, G., Renard, P., & Straubhaar, J. (2010). The direct sampling method to perform multiple‐point geostatistical simulations. Water Resources Research, 46(11).</p><p>Ter Braak, C. J., & Vrugt, J. A. (2008). Differential evolution Markov chain with snooker updater and fewer chains. Statistics and Computing, 18(4), 435-446.</p><p>Vrugt, J. A., ter Braak, C., Diks, C., Robinson, B. A., Hyman, J. M., & Higdon, D. (2009). Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling. International Journal of Nonlin ear Sciences and Numerical Simu- lation, 10(3), 273–290.</p><p>Zhou, Y., Johansen, A. M., & Aston, J. A., (2016). Toward automatic model comparison: an adaptive sequential Monte Carlo approach, Journal of Computational and Graphical Statistics,69925(3), 701–726.</p>
Traducir la guerra: el caso de La Perrona y La guerra de Amaya de Vicente Muñoz Puelles
No full textL'articolo parte dalle traduzioni, per motivi accademici, di due opere dello scrittore spagnolo Vicente Muñoz Puelles: il racconto per bambini La perrona (2006) e il romanzo per ragazzi La guerra de Amaya. La riflessione verte sul significato della traduzione, ai giorni nostri, di testi dedicati alla Guerra civile spagnola destinati a un giovane lettore, lontano nel tempo e nello spazio da quell'epoca storica. Poiché si tratta di due romanzi fortemente connotati dal punto di vista culturale, il traduttore è chiamato a svolgere il compito di mediatore interculturale affinché il suo destinatario possa avvicinarsi a quel mondo e assimilare nuove conoscenze. In questo processo di comunicazione interculturale, saranno determinanti le strategie adottate, tenendo presenti l'età del destinatario (in un caso un bambino, nell'altro un adolescente) e, di conseguenza, il diverso bagaglio culturale.A partir de la experiencia de traducción al italiano –por motivos académicos– de dos novelas de Vicente Muñoz Puelles, La perrona (2006) y La guerra de Amaya (2010), este estudio pretende reflexionar sobre qué sentido tiene en nuestros tiempos traducir textos dedicados a la Guerra Civil española para un joven lector, lejano en el tiempo y en el espacio de aquella época. Tratándose de dos novelas caracterizadas por su fuerte connotación cultural, el traductor debe funcionar como mediador intercultural para que su destinatario se acerque a aquel mundo e incorpore conocimientos nuevos. En este proceso de comunicación intercultural, determinantes van a ser las estrategias adoptadas, teniendo en cuenta la edad del receptor (en un caso infantil, en el otro juvenil) y por consiguiente su diferente bagaje cultural
ASMC-SURR-HF
No full text<p><strong>Adaptive Sequential Monte Carlo python codes combined with surrogate solvers to speed-up Bayesian inversion</strong></p><p><i><strong>Overview:</strong></i><br>This codes correspond to the article by Amaya et al. <i>(Multifidelity adaptive sequential Monte Carlo applied to geophysical inversion. Submitted to Geophysical Journal International)</i>. It is a Python 3.7 implementation of the Adaptive Sequential Monte Carlo (ASMC) method (Zhou et al., 2016; algorithm 4) to estimate<br>the posterior probability density function (PDF) and the evidence (marginal likelihood) in Bayesian inversions. ASMC is a particle approach that relies on importance sampling over a sequence<br>of intermediate distributions (power posteriors) that link the prior and the posterior PDF. Each power posterior is approximated by updating the particle importance weights and states using a small<br>pre-defined number MCMC proposal steps. ASMC method adaptively tunes the sequence of power posteriors and performs resampling of particles when the variance of their importance weights<br>becomes too large.</p><p>This particular implementation relies on polinomial chaoes expansion (PCE) surrogates, although other types of surrogate solver can be used.<br>The PCE surrogates are trained using Uqlab <a href="https://www.uqlab.com/">https://www.uqlab.com/</a> .</p><p><i><strong>Test case:</strong></i><br>The test case is a synthetic ground penetrating radar tomography modified from Meles et al. (2022).</p><p><i><strong>Codes</strong></i><br>run_asmc_surr.py : control the user-defined parameters and run the inversion.</p><p>asmc_surr.py : main asmc code.</p><p>asmc_func_surr.py : contain the auxiliar functions called by asmc.py.</p><p>##Note that there is a file missing called PCADATA.m (too big for the repositories), which contains the coefficients learnt for the model principal component decomposition of this example.<br>In case you would like to reproduce the exact decomposition and test case, please contact me (<a href="mailto:[email protected]">[email protected]</a>).</p><p><i><strong>Performing the inversion</strong></i><br>Modify the user-defined parameters and run run_asmc_surr.py.</p><p><i><strong>Citation :</strong></i><br>Amaya, M., Linde, N., & Laloy, E.. Multifidelity adaptive sequential Monte Carlo applied to geophysical inversion. Submitted to Geophysical Journal International (under review).</p><p><i><strong>References:</strong></i><br>Amaya, M., Linde, N., Laloy, E.. Multifidelity adaptive sequential Monte Carlo applied to geophysical inversion. Submitted to Geophysical Journal International (under review).</p><p>Amaya, M., Linde, N., & Laloy, E. (2021). Adaptive sequential Monte Carlo for posterior inference and model selection among complex geological priors. Geophysical Journal International, 226(2), 1220-1238.</p><p>Laloy, E., & Vrugt, J. A. (2012). High‐dimensional posterior exploration of hydrologic models using multiple‐try DREAM (ZS) and high‐performance computing. Water Resources Research, 48(1).</p><p>Marelli, S. & Sudret, B., 2014. UQLab: A framework for uncertainty quantification in Matlab. In: Beer, M., Au, S. and Hall, J.W., Eds., Vulnerability, Uncertainty, and Risk: Quantification, Mitigation, and Management, pp. 2554–2563.</p><p>Marelli, S., Lu¨then, N., & Sudret, B., 2022. UQLab user manual – Polynomial chaos expansions, Tech. rep., Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, Report UQLab-V2.0-104.</p><p>Meles, G. A., N. Linde, and S. Marelli (2022), Bayesian tomography with prior-knowledge based parametrization and surrogate modelling, Geophysical Journal International, 231(1),673–691.</p><p>Ter Braak, C. J., & Vrugt, J. A. (2008). Differential evolution Markov chain with snooker updater and fewer chains. Statistics and Computing, 18(4), 435-446.</p><p>Vrugt, J. A., ter Braak, C., Diks, C., Robinson, B. A., Hyman, J. M., & Higdon, D. (2009). Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling. International Journal of Nonlinear Sciences and Numerical Simu- lation, 10(3), 273–290.</p><p>Zhou, Y., Johansen, A. M., & Aston, J. A., (2016). Toward automatic model comparison: an adaptive sequential Monte Carlo approach, Journal of Computational and Graphical Statistics,69925(3), 701–726.</p>
Carotenoid Composition Of Brazilian Fruits And Vegetables
No full textBrazil has a wide diversity of food sources of carotenoids. The updated Brazilian database consists of more than 270 items of fruits, vegetables and their prepared and processed products. The database demonstrates variations due to variety, maturity, production technique, climate and processing. Many of these foods are not found in the US and European databases. Good to rich sources (>20 μg/g) of β-carotene are: acerola, bocaiúva, mango 'Extreme' and tucumã. Sources of both α-carotene and β-carotene are buriti, carrot, Cucurbita moschata 'Menina Brasileira', 'Baianinha' and 'Goianinha', and red palm oil. Commercially produced and uncultivated or semi-cultivated leafy vegetables, C. maxima 'Jerimum Caboclo' and the hybrid Tetsukabuto, cooked broccoli are sources of lutein and β-carotene. The edible Tropaeolum majus flower is especially rich in lutein. Although many fruits have β-cryptoxanthin as principal carotenoid (e.g. caja, nectarine, peach, orange-fleshed papaya, tree tomato), the levels are below 20 μg/g. Good to rich sources of lycopene are guava and guava products, papaya, pitanga and pitanga juice, tomato and tomato products, and watermelon. Sources of zeaxanthin are rare; although the principal carotenoid of piqui, the amount is low, lower than that found in buriti.744409416Agostini, T.S., Cecchi, H.M., Godoy, H.T., Composição de carotenóides no marolo in natura e em produtos de preparo caseiro (1996) Ciênc. Tecnol. Aliment, 16, pp. 67-71Almeida-Muradian, L.B., Penteado, M.V.C., Carotenoid and provitamin A value of some Brazilian sweet potato cultivars (Ipomoea batatas Lam.) (1992) Rev. Farm. Bioquím. Univ, 28, pp. 145-154. , São PauloArima, H.K., Rodriguez-Amaya, D.B., Carotenoid composition and vitamin A value of a squash and a pumpkin from Northeastern Brasil (1990) Arch. Latinoamer. Nutr., 40, pp. 284-292Azevedo-Meleiro, C.H., Rodriguez-Amaya, D.B., Carotenoids of endive and New Zealand spinach as affected by maturity, season and minimal processing (2005) J. Food Comp. Anal, 18, pp. 845-855Azevedo-Meleiro, C.H., Rodriguez-Amaya, D.B., Carotenoid composition of kale as influenced by maturity, season and minimal processing (2005) J. Sci. Food Agric., 85, pp. 591-597Azevedo-Meleiro, C.H., Rodriguez-Amaya, D.B., Varietal and interspecific variation and profiling of the carotenoids of squashes and pumpkins (2006) J. Agric Food Chem., , Submitted toBianchini, R., Penteado, M.V.C., Carotenóides de pimentões amarelos (Capsicum annuum L.). Caracterização e verificação de mudanças com o cozimento (1998) Ciênc. Tecnol. Aliment, 18, pp. 283-288Cecchi, H.M., Rodriguez-Amaya, D.B., Carotenoid composition and vitamin A value of fresh and pasteurized cashew-apple (Anacardium occidentale L.) (1981) Juice. J. Food Sci., 46, pp. 147-149Godoy, H.T., Rodriguez-Amaya, D.B., Carotenoid composition of commercial mangos from Brazil (1989) Lebensm. Wiss. Technol., 22, pp. 100-103Godoy, H.T., Rodriguez-Amaya, D.B., Occurrence of cis-isomers of provitamin A in Brazilian fruits (1994) J. Agric. Food Chem., 42, pp. 1306-1313Godoy, H.T., Rodriguez-Amaya, D.B., Carotenoid composition and vitamin A value of Brazilian loguat (Eriobotrya japonica L.) (1995) Arch. Latinoamer. Nutr., 45, pp. 336-339Godoy, H.T., Rodriguez-Amaya, D.B., Buriti (Mauritia vinifera Mart.) uma fonte riquíssima de pró-vitamina A (1995) Arq. Biol. Tecnol, 38, pp. 109-120Godoy, H.T., Rodriguez-Amaya, D.B., Composição de carotenóides em nectarina (Prunus persica) brasileira (1998) Rev. Inst. Adolfo Lutz, 57, pp. 73-79Hiani, P.A., Penteado, M.V.C., Carotenóides e valores de vitamina A do fruto e da farinha de bocaiúva (Acrocomia mokayáyba Barb. Rodr.) do Estado de Mato Grosso do Sul (1989) Rev. Farm. Bioquím. Univ. SãRo Paulo, 25, pp. 158-168Huber, L.S., Kobori, C.N., Kimura, M., Rodriguez-Amaya, D.B., Determination of the principal carotenoids of Brazilian tomato products by HPLC. V Brazilian Meeting on Chemistry of Food and Beverages (2004) Sao Carlos, Sao Paulo, Brasil, p. 64. , 1-4 decKimura, M., Rodriguez-Amaya, D.B., Yokoyama, S.M., Cultivar differences and geographic effects an the carotenoid composition and vitamin A value of papaya (1991) Lebens. Wissen. Technol., 24, pp. 415-418Kimura, M., Rodriguez-Amaya, D.B., Carotenoid composition of hydroponic leafy vegetables (2003) J. Agric. Food Chem., 51, pp. 2603-2607Mercadante, A.Z., Rodriguez-Amaya, D.B., Carotenoid composition and vitamin A value of some native Brazilian green leafy vegetables (1990) Int. J. Food Sci. Technol., 25, pp. 213-219Mercadante, A.Z., Rodriguez-Amaya, D.B., Effects of ripening, cultivar differences, and processing on the carotenoid composition of mango (1998) J. Agric. Food Chem., 46, pp. 128-130Niizu, P.Y., Rodriguez-Amaya, D.B., Melancia como fonte de licopeno (2003) Rev. Inst. Adolfo Lutz, 62, pp. 195-199Niizu, P.Y., Rodriguez-Amaya, D.B., New data on the carotenoid composition of raw salad vegetables (2005) J. Food Comp. Anal., 18, pp. 739-749Niizu, P.Y., Rodriguez-Amaya, D.B., Flowers and leaves of Tropaeolum majus L. as rich sources of lutein (2005) J. Food Sci., 70, pp. S605-S609Oliveira, G.P.R., Rodriguez-Amaya, D.B., Zeaxanthin and lutein contents in four brands of canned corn. V Brazilian Meeting on Chemistry of Food and Beverages (2004) Sao Carlos, Sao Paulo, Brasil, p. 74. , 1-4 decPadula, M., Rodriguez-Amaya, D.B., Characterisation of the carotenoids and assessment of the vitamin A value of Brasilian guava (1986) Food Chem., 20, pp. 11-19Porcu, O.M., Rodriguez-Amaya, D.B., Variation in the carotenoid composition of acerola and its processed products (2006) J. Sci. Food Agric., , accepted for publicationPorcu, O.M., Rodriguez-Amaya, D.B., Pink-fleshed guava and guava products as rich sources of lycopene (2006) Effect of Industrial Processing. Submitted to Food Chem.Porcu, O.M., Rodriguez-Amaya, D.B., Carotenoids of fresh and processed pitanga (Eugenia uniflora L.) (2006) Food Sci. Technol. Inter, , Submitted toRodriguez-Amaya, D.B., Latin American food sources of carotenoids (1999) Arch. Latinoamer. Nutr., 49, pp. 74S-84SRodriguez-Amaya, D.B., Bobbio, P.A., Bobbio, F.O., Carotenoid composition and vitamin A value of the Brazilian fruit Cyphomandra betacea (1983) Food Chem., 12, pp. 61-65Rodriguez-Amaya, D.B., Kimura, M., Carotenóides e valor de vitamina A em cajá (Spondias lutea) (1989) Cienc. Tecnol. Aliment, 9, pp. 148-162Rodriguez-Amaya, D.B., Kimura, M., Godoy, H.T., Amaya-Farfan, J., Updated Brazilian database on food carotenoids. Factors affecting carotenoid composition (2005) J. Food Comp. Anal, , Submitted toAcute, S.M.C., Rodriguez-Amaya, D.B., Carotenoid composition of cooked green vegetables from restaurants (2003) Food Chem., 83, pp. 595-600Tavares, C.A., Rodriguez-Amaya, D.B., Carotenoid composition of Brazilian tomatoes and tomato products (1994) Lebens. Wissen. Technol., 27, pp. 219-224Trujillo-Quijano, J.A., Rodriguez-Amaya, D.B., Esteves, W., Plonis, G.F., Carotenoid composition and vitamin A values of oils from four Brazilian palm fruits (1990) Fat Sci. Technol., 6, pp. 222-22
Desafíos económicos de la Argentina reciente
No full textFil: Amaya Guerrero, Romina G. Universidad Nacional de Quilmes. Departamento de Economía y Administración; Argentina“Desafíos económicos de la Argentina reciente” trata de una asignatura electiva que se propone acercar a los y las estudiantes a las problemáticas de la economía Argentina, que atravesó nuestro país desde la Dictadura de 1976, desde la historia económica, pero en diálogo con aportes teóricos ligados a esas cuestiones. El objetivo de la asignatura es que los y las estudiantes aborden la historia económica del período reciente, problematizando sobre los principales desafíos económicos que atraviesa nuestro país en el período reciente, a partir de la caracterización general del período, de aportes teóricos sobre los ejes económicos planteados y de las políticas económicas que se llevaron adelante
Modelo de transferencia de tecnología: el caso de una universidad pública del norte de México
No full textLas Instituciones de Educación Superior (IES) son las responsables principales de la creación de conocimiento; el cual se obtiene a través de sus tres funciones sustantivas: la docencia, la investigación, y la divulgación. Esta última función, es particularmente relevante pues se puede relacionar con la transferencia y, por ende, con el crecimiento económico, ya que implica que el conocimiento generado llega a quien lo pueda convertir en productos, así como desarrollar proyectos tecnológicos que, eventualmente, podrían ayudar a la generación de innovaciones tecnológicas.Para que los desarrollos tecnológicos de las IES transiten hacia el mercado se requiere relacionarse con sectores externos, así como saber cuáles son sus necesidades para poder ofrecer soluciones a sus problemas. Para ello es necesario que las IES consideren una serie de actividades que van desde desarrollar estrategias para estrechar los lazos de colaboración a través de una vinculación enfocada a dar solución a los sectores externos, principalmente del productivo, hasta generar acuerdo de transferencia de tecnología, e incluso la formación de nuevas empresas de base tecnológica. Una instancia mencionada en la literatura, que podría ayudar a este tipo de actividades, son las oficinas de transferencia tecnológica (OTT). Varios autores han analizado las estructuras, los actores, y las funciones a desarrollar por la OTT para apoyar e incentivar a los académicos de la IES a transferir tanto tecnología como conocimiento. Sin embargo, aún se presentan problemas para la adopción de modelos de OTT ya establecidas en otros contextos
Plan de mercadeo para Amaya Soluciones Industriales S.A.S
No full textAmaya Soluciones Industriales SAS es una empresa que se encuentra en el subsector de la metalmecánica, su portafolio se concentra en tres unidades de negocio como lo son Iluminación industrial, productos de cableado estructurado y servicio de metalmecánica con una experiencia de 16 años en el mercado, logrando generar una cartera de clientes importantes para su mantenimiento, sin embargo, se identifican escenarios importantes para su crecimiento.
El presente trabajo tiene como objetivo desarrollar un plan de mercadeo como propuesta para la fidelización de los clientes actuales y lograr atraer a clientes potenciales, buscando así un impacto positivo en las ventas actuales de la empresa.
Para el desarrollo de la propuesta se tuvo en cuenta la percepción actual de los clientes como también la perspectiva de la gerencia de la empresa, esto se desarrolló por medio de encuestas y entrevistas aplicadas durante los meses de abril y mayo del presente año, logrando así identificar las fortalezas y debilidades de Amaya Soluciones Industriales SAS.
La estructura del presente documento se desarrolla inicialmente con la contextualización y diagnóstico, la identificación de la problemática, el planteamiento de objetivos, el desarrollo de la propuesta y los indicadores de gestión y control del plan.Amaya Soluciones Industriales SAS is a company that is in the metalworking subsector, its portfolio is concentrated in three business units such as industrial lighting, structured cabling products and metalworking service with an experience of 16 years in the market, managing to generate a portfolio of important customers for maintenance, however, important scenarios for growth are identified. The objective of this work is to develop a marketing plan as a proposal for the loyalty of current customers and to attract potential customers, thus seeking a positive impact on the company's current sales. For the development of the proposal we took into account the current perception of customers as well as the perspective of the company's management, this was developed through surveys and interviews applied during the months of April and May of this year, thus identifying the strengths and weaknesses of Amaya Soluciones Industriales SAS. The structure of this document is initially developed with the contextualization and diagnosis, the identification of the problem, the statement of objectives, the development of the proposal and the management and control indicators of the plan.Profesional en MercadeoPregradoMercade
ASMC
No full text<p><strong>Adaptive Sequential Monte Carlo python codes for posterior inference and evidence computation</strong></p><p><i><strong>Overview:</strong></i><br>Python 3.7 implementation of the Adaptive Sequential Monte Carlo method presented in Amaya et al. (2021), a particle approach to infer the posterior probability density function and compute de evidence (marginal likelihood) introduced by in Zhou et al (2016) [algorithm 4]. This method relies on importance sampling over a sequence of intermediate distributions, linking the prior and the posterior PDF. Each distribution is approximated by updating the particle weights and states, using a small pre-defined number MCMC proposal steps. ASMC method adaptively tunes the sequence of distributions and performs resampling of particles when the variance of the particle weights becomes too large.</p><p>This implementation (referred to as ASMC-DREAM) uses the code presented by Laloy et al. (2018) for GAN-based probabilistic inversion using DREAMzs MCMC sampler to propose the MCMC steps (ter Braak and Vrugt, 2008; Vrugt, 2009; Laloy and Vrugt, 2012). The associated synthetic cross-hole ground penetrating radar (GPR) tomography data first-arrival times are calculated using the time2d algorithm by Podvin & Lecomte (1991).</p><p><i><strong>Test cases:</strong></i><br>CM1: binary channelized training image (CM1) (Zahner et al., 2016)<br>CM2: tri-categorical training image representing braided-river aquifer deposits (Pirot et al., 2015).</p><p><i><strong>Codes and files:</strong></i><br>run_asmc.py : control the user-defined parameters and run the inversion.</p><p>asmc.py : main code.</p><p>asmc_func.py : contain the auxiliar functions called by asmc.py.</p><p>Eikonal_solver.py : forward solver (function for times2d)</p><p>.pht : SGAN</p><p>gen_from_z.py, generator.py, generate.py and generators.py: contain the SGAN generators</p><p>Z_trumodel_vector.npy: Latent space parameter values for the reference model.</p><p>datatruemodel_sigma1.npy : first arrival times obtained with times2d for the reference model plus sigma=1ns gaussian random noise.</p><p>noise_vector_sigma1.npy : the noise that was added to the first arrival times.</p><p>forward_setup_0 folder: to use for forward solver parallel computation, contains the times2d codes.</p><p><i><strong>SGAN specifications:</strong></i><br>The SGAN generator for prior proposals runs with Pytorch Deep Learning Library (we used Pytorch 1.0.1 version).</p><p><i><strong>Running the inversion:</strong></i><br>Modify the user-defined parameters and run run_asmc.py.</p><p><i><strong>Citation:</strong></i><br>Amaya, M., Linde, N., & Laloy, E. (2021). Adaptive sequential Monte Carlo for posterior inference and model selection among complex geological priors. Geophysical Journal International, 226(2), 1220-1238</p><p><i><strong>References:</strong></i><br>_Amaya, M., Linde, N., & Laloy, E. (2021). Adaptive sequential Monte Carlo for posterior inference and model selection among complex geological priors. Geophysical Journal International, 226(2), 1220-1238.</p><p>Laloy, E., & Vrugt, J. A. (2012). High‐dimensional posterior exploration of hydrologic models using multiple‐try DREAM (ZS) and high‐performance computing. Water Resources Research, 48(1).</p><p>Laloy, E., Hérault, R., Jacques, D., and Linde, N. (2018). Training-image based geostatistical inversion using a spatial generative adversarial neural network. Water Resources Research, 54, 381–406.</p><p>Pirot, G., Straubhaar, J., & Renard, P., (2015). A pseudo genetic model of coarse braided-river deposits, Water Resources Research,51(12), 9595–9611.</p><p>Podvin, P. & Lecomte, I., (1991). Finite difference computation of traveltimes in very contrasted velocity models: a massively parallel approach and its associated tools, Geophysical Journal International,105(1), 271–284</p><p>Ter Braak, C. J., & Vrugt, J. A. (2008). Differential evolution Markov chain with snooker updater and fewer chains. Statistics and Computing, 18(4), 435-446.</p><p>Vrugt, J. A., ter Braak, C., Diks, C., Robinson, B. A., Hyman, J. M., & Higdon, D. (2009). Accelerating Markov chain Monte Carlo simulation by differential evolution with self-adaptive randomized subspace sampling. International Journal of Nonlin ear Sciences and Numerical Simu- lation, 10(3), 273–290.</p><p>Zahner, T., Lochbühler, T., Mariethoz, G., & Linde, N., (2016). Image synthesis with graph cuts: a fast model proposal mechanism in probabilistic inversion,Geophysical Journal International,693204(2), 1179–1190</p><p>Zhou, Y., Johansen, A. M., & Aston, J. A., (2016). Toward automatic model comparison: an adaptive sequential Monte Carlo approach, Journal of Computational and Graphical Statistics,69925(3), 701–726.</p>
Bibliographie Hilarion G. Petzold 1958 – 2009 mit Anhang als Einführung
No full textDieses Archiv enthält die Gesamtbibliographie der Werke des Autors nebst einiger Texte „Über H. G. Petzold“ im Schlussteil der Bibliographie sowie einen Anhang mit einer Einführung in die Architektur des Werkes in seinem wissenslogischen Aufbau als Ausarbeitung seines „Tree of Science Modells“ (2007).This archive contains the complete bibliography of the author and some texts about H. G. Petzold, moreover an epilogue with an introduction to the architecture of the works in its epistemological structure and composition and as an elaborations of Petzold’s „Tree of Science Modell (2007).https://www.fpi-publikation.de/polyloge/01-2009-petzold-h-g-gesamtbibliographie-h-g-petzold-1958-2009-updating-november2009/peerReviewedpublishedVersio
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