196,369 research outputs found
J. Jáuregui, M. E. Olavarría & V. M. Franco Pellotier, eds., Cultura y comunicación. Edmund Leach in memoriam
No full textGalinier Jacques. J. Jáuregui, M. E. Olavarría & V. M. Franco Pellotier, eds., Cultura y comunicación. Edmund Leach in memoriam. In: L'Homme, 1997, tome 37 n°144. pp. 162-163
Nutini, H. G & J. M. Roberts. — Bloodsucking Witchcraft. An Epistemological Study of Anthropomorphic Supernaturalism in Rural Tlaxcala
No full textGalinier Jacques. Nutini, H. G & J. M. Roberts. — Bloodsucking Witchcraft. An Epistemological Study of Anthropomorphic Supernaturalism in Rural Tlaxcala. In: Journal de la Société des Américanistes. Tome 80, 1994. pp. 322-325
J.-M. Sallmann, s. dir., avec S. Gruzinski, A. Molinie Fioravanti, C. Salazar, Visions indiennes, visions baroques : les métissages de l'inconscient
No full textGalinier Jacques. J.-M. Sallmann, s. dir., avec S. Gruzinski, A. Molinie Fioravanti, C. Salazar, Visions indiennes, visions baroques : les métissages de l'inconscient. In: L'Homme, 1993, tome 33 n°126-128. La remontée de l'Amazone. pp. 540-542
T. Schweizer, M. Schweizer & W. Kokot, Eds., Handbuch Der Ethnologie. — W. Schmied-Kowarzik & J. Sagl, eds., Graudfragen der Ethnologie. Beiträge zur gegenwärtigen Theorie-Diskussion
No full textGalinier Jacques. T. Schweizer, M. Schweizer & W. Kokot, Eds., Handbuch Der Ethnologie. — W. Schmied-Kowarzik & J. Sagl, eds., Graudfragen der Ethnologie. Beiträge zur gegenwärtigen Theorie-Diskussion. In: L'Homme, 1996, tome 36 n°137. Chine : facettes d'identité. pp. 241-243
Lagarriga, I., J. Galinier & M. Perrin (coord.). — Chamanismo en Latinoamérica : una revisión conceptual.
No full textBuchillet Dominique. Lagarriga, I., J. Galinier & M. Perrin (coord.). — Chamanismo en Latinoamérica : una revisión conceptual.. In: Journal de la Société des Américanistes. Tome 83, 1997. pp. 348-350
A hybrid interior point - Deep learning approach for poisson image deblurring
No full textIn this paper we address the problem of deconvolution of an image corrupted with Poisson noise by reformulating the restoration process as a constrained minimization of a suitable regularized data fidelity function. The minimization step is performed by means of an interior-point approach, in which the constraints are incorporated within the objective function through a barrier penalty and a forward-backward algorithm is exploited to build a minimizing sequence. The key point of our proposed scheme is that the choice of the regularization, barrier and step-size parameters defining the interior point approach is automatically performed by a deep learning strategy. Numerical tests on Poisson corrupted benchmark datasets show that our method can obtain very good performance when compared to a state-of-the-art variational deblurring strategy
Deep Neural Networks for Inverse Problems with Pseudodifferential Operators: An Application to Limited-Angle Tomography
No full textWe propose a novel convolutional neural network (CNN), called \Psi DONet, designed for learning pseudodifferential operators (\Psi DOs) in the context of linear inverse problems. Our starting point is the iterative soft thresholding algorithm (ISTA), a well-known algorithm to solve sparsity-promoting
minimization problems. We show that, under rather general assumptions on the forward operator, the unfolded iterations of ISTA can be interpreted as the successive layers of a CNN, which in turn provides fairly general network architectures that, for a specific choice of the parameters involved, allow us to reproduce ISTA, or a perturbation of ISTA for which we can bound the coefficients of the filters. Our case study is the limited-angle X-ray transform and its application to limited-angle computed tomography (LA-CT). In particular, we prove that, in the case of LA-CT, the operations of upscaling, downscaling, and convolution, which characterize our \Psi DONet and most deep learning schemes, can be exactly determined by combining the convolutional nature of the limited-angle Xray transform and basic properties defining an orthogonal wavelet system. We test two different implementations of \Psi DONet on simulated data from limited-angle geometry, generated from the ellipse data set. Both implementations provide equally good and noteworthy preliminary results, showing the potential of the approach we propose and paving the way to applying the same idea to other convolutional operators which are \Psi DOs or Fourier integral operators
Dr. Duane M. Jackson, Morehouse College, July 2011
No full textThis video is a conversation with Dr. Duane M. Jackson. Dr. Jackson talks about his paper, "Recall and the Serial Position Effect: The Role of Primacy and Recency on Accounting Students' Performance." Jackie Daniel, AUC Woodruff Library, is the interviewer
Deep neural networks for inverse problems with pseudodifferential operators: an application to limited-angle tomography
No full textWe propose a novel convolutional neural network (CNN), called PsiDONet, designed for learning pseudodifferential operators (PsiDOs) in the context of linear inverse problems. Our starting point is the Iterative Soft Thresholding Algorithm (ISTA), a well-known algorithm to solve sparsity-promoting minimization problems. We show that, under rather general assumptions on the forward operator, the unfolded iterations of ISTA can be interpreted as the successive layers of a CNN, which in turn provides fairly general network architectures that, for a specific choice of the parameters involved, allow to reproduce ISTA, or a perturbation of ISTA for which we can bound the coefficients of the filters. Our case study is the limited-angle X-ray transform and its application to limited-angle computed tomography (LA-CT). In particular, we prove that, in the case of LA-CT, the operations of upscaling, downscaling and convolution, which characterize our PsiDONet and most deep learning schemes, can be exactly determined by combining the convolutional nature of the limited angle X-ray transform and basic properties defining an orthogonal wavelet system. We test two different implementations of PsiDONet on simulated data from limited angle geometry, generated from the ellipse data set. Both implementations provide equally good and noteworthy preliminary results, showing the potential of the approach we propose and paving the way to applying the same idea to other convolutional operators which are PsiDOs or Fourier integral operators
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