Learning to train neural networks for real-world control problems

Over the past three years, our group has concentrated on the application of neural network methods to the training of controllers for real-world systems. This presentation describes our approach, surveys what we have found to be important, mentions some contributions to the field, and shows some representative results. Topics discussed include: (1) executing model studies as rehearsal for experimental studies; (2) the importance of correct derivatives; (3) effective training with second-order (DEKF) methods; (4) the efficacy of time-lagged recurrent networks; (5) liberation from the tyranny of the control cycle using asynchronous truncated backpropagation through time; and (6) multistream training for robustness. Results from model studies of automotive idle speed control serve as examples for several of these topics

In-situ Analysis of Laminated Composite Materials by X-ray Micro-Computed Tomography and Digital Volume Correlation

The complex mechanical behaviour of composite materials, due to internal heterogeneity and multi-layered composition impose deeper studies. This paper presents an experimental investigation technique to perform volume kinematic measurements in composite materials. The association of X-ray micro-computed tomography acquisitions and Digital Volume Correlation (DVC) technique allows the measurement of displacements and deformations in the whole volume of composite specimen. To elaborate the latter, composite fibres and epoxy resin are associated with metallic particles to create contrast during X-ray acquisition. A specific in situ loading device is presented for three-point bending tests, which enables the visualization of transverse shear effects in composite structures

A new fireworm (Amphinomidae) from the Cretaceous of Lebanon identified from three-dimensionally preserved myoanatomy

© 2015 Parry et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. The attached file is the published version of the article

A fast sparse block circulant matrix vector product

In the context of computed tomography (CT), iterative image reconstruction techniques are gaining attention because high-quality images are becoming computationally feasible. They involve the solution of large systems of equations, whose cost is dominated by the sparse matrix vector product (SpMV). Our work considers the case of the sparse matrices being block circulant, which arises when taking advantage of the rotational symmetry in the tomographic system. Besides the straightforward storage saving, we exploit the circulant structure to rewrite the poor-performance SpMVs into a high-performance product between sparse and dense matrices. This paper describes the implementations developed for multi-core CPUs and GPUs, and presents experimental results with typical CT matrices. The presented approach is up to ten times faster than without exploiting the circulant structure.Romero Alcalde, E.; Tomás Domínguez, AE.; Soriano Asensi, A.; Blanquer Espert, I. (2014). A fast sparse block circulant matrix vector product. En Euro-Par 2014 Parallel Processing. Springer. 548-559. doi:10.1007/978-3-319-09873-9_46S548559Bian, J., Siewerdsen, J.H., Han, X., Sidky, E.Y., Prince, J.L., Pelizzari, C.A., Pal, X.: Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam ct. Physics in Medicine and Biology 55, 6575–6599 (2010)Dalton, S., Bell, N.: CUSP: A C++ templated sparse matrix library version 0.4.0 (2014), http://cusplibrary.github.com/Feldkamp, L., Davis, L., Kress, J.: Practical cone-beam algorithm. Journal of the Optical Society of America 1, 612–619 (1984)Ganine, V., Legrand, M., Michalska, H., Pierre, C.: A sparse preconditioned iterative method for vibration analysis of geometrically mistuned bladed disks. Computers & Structures 87(5-6), 342–354 (2009)Hara, A.K., Paden, R.G., Silva, A.C., Kujak, J.L., Lawder, H.J., Pavlicek, W.: Iterative reconstruction technique for reducing body radiation dose at CT: Feasibility study. American Journal of Roentgenology 193, 764–771 (2009)Heroux, M.A., Bartlett, R.A., Howle, V.E., Hoekstra, R.J., Hu, J.J., Kolda, T.G., Lehoucq, R.B., Long, K.R., Pawlowski, R.P., Phipps, E.T., Salinger, A.G., Thornquist, H.K., Tuminaro, R.S., Willenbring, J.M., Williams, A., Stanley, K.S.: An overview of the Trilinos project. ACM Trans. Math. Softw. 31(3), 397–423 (2005)Im, E.J., Yelick, K., Vuduc, R.: Sparsity: Optimization framework for sparse matrix kernels. International Journal of High Performance Computing Applications 18(1), 135–158 (2004)Jones, E., Oliphant, T., Peterson, P., et al.: SciPy: Open source scientific tools for Python (2001), http://www.scipy.org/Kaveh, A., Rahami, H.: Block circulant matrices and applications in free vibration analysis of cyclically repetitive structures. Acta Mechanica 217(1-2), 51–62 (2011)Kourtis, K., Goumas, G., Koziris, N.: Optimizing sparse matrix-vector multiplication using index and value compression. In: Proceedings of the 5th Conference on Computing Frontiers, CF 2008, pp. 87–96. ACM, New York (2008)Krotkiewski, M., Dabrowski, M.: Parallel symmetric sparse matrix–vector product on scalar multi-core CPUs. Parallel Computing 36(4), 181–198 (2010)Lee, B., Vuduc, R., Demmel, J., Yelick, K.: Performance models for evaluation and automatic tuning of symmetric sparse matrix-vector multiply. In: International Conference on Parallel Processing, ICPP 2004, vol. 1, pp. 169–176 (2004)Leroux, J.D., Selivanov, V., Fontaine, R., Lecomte, R.: Accelerated iterative image reconstruction methods based on block-circulant system matrix derived from a cylindrical image representation. In: Nuclear Science Symposium Conference Record, NSS 2007, vol. 4, pp. 2764–2771. IEEE (2007)NVIDIA: CUSPARSE library (2014), https://developer.nvidia.com/cusparsePan, X., Sidky, E.Y., Vannier, M.: Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction? Inverse Problems 25, 123009 (2008)Rodríguez-Alvarez, M.J., Soriano, A., Iborra, A., Sánchez, F., González, A.J., Conde, P., Hernández, L., Moliner, L., Orero, A., Vidal, L.F., Benlloch, J.M.: Expectation maximization (EM) algorithms using polar symmetries for computed tomography CT image reconstruction. Computers in Biology and Medicine 43(8), 1053–1061 (2013)Sheep, L., Vardi, Y.: Maximum likelihood reconstruction for emmision tomography. IEEE Transactions on Medical Imaging 1, 113–122 (1982)Sidky, E.Y., Pan, X.: Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization. Physics in Medicine and Biology 53, 4777–4807 (2008)Soriano, A., Rodríguez-Alvarez, M.J., Iborra, A., Sánchez, F., Carles, M., Conde, P., González, A.J., Hernández, L., Moliner, L., Orero, A., Vidal, L.F., Benlloch, J.M.: EM tomographic image reconstruction using polar voxels. Journal of Instrumentation 8, C01004 (2013)Thibaudeau, C., Leroux, J.D., Pratte, J.F., Fontaine, R., Lecomte, R.: Cylindrical and spherical ray-tracing for ct iterative reconstruction. In: 2011 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), pp. 4378–4381 (2011)Vuduc, R., Demmel, J.W., Yelick, K.A.: OSKI: A library of automatically tuned sparse matrix kernels. Journal of Physics: Conference Series 16(1), 521 (2005)Vuduc, R.W., Moon, H.-J.: Fast sparse matrix-vector multiplication by exploiting variable block structure. In: Yang, L.T., Rana, O.F., Di Martino, B., Dongarra, J. (eds.) HPCC 2005. LNCS, vol. 3726, pp. 807–816. Springer, Heidelberg (2005)Williams, S., Oliker, L., Vuduc, R., Shalf, J., Yelick, K., Demmel, J.: Optimization of sparse matrix-vector multiplication on emerging multicore platforms. Parallel Computing 35(3), 178–194 (2009

The relationship between the structural and orthogonal compressive properties of trabecular bone

In this study, cubes of trabecular bone with a wide range of structural properties were scanned on a micro-computed tomography system to produce complete three-dimensional digitizations from which morphological and architectural parameters could be measured in a nondestructive manner. The cubes were then mechanically tested in uniaxial compression in three orthogonal directions and to failure in one direction to find the orthogonal tangent elastic moduli and ultimate strengths. After testing, the cubes were weighed and ashed to determine the apparent and ash densities. A high correlation between the basic stereologic measurements was found, indicating that there is a relationship between the amount of bone and number of trabeculae in cancellous bone. Regression analysis was used to estimate the modulus and ultimate strength; these regressions accounted for 68-90% of the variance in these measures. These relationships were dependent on the metaphyseal type and donor, with the modulus also dependent on the direction of testing. This indicates that the properties of the individual trabeculae, as well as their amount and organization, may be important in predicting the mechanical properties of cancellous bone.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/31681/1/0000617.pd

Controlled assembly of SNAP-PNA-fluorophore systems on DNA templates to produce fluorescence resonance energy transfer

The SNAP protein is a widely used self-labeling tag that can be used for tracking protein localization and trafficking in living systems. A model system providing controlled alignment of SNAP-tag units can provide a new way to study clustering of fusion proteins. In this work, fluorescent SNAP-PNA conjugates were controllably assembled on DNA frameworks forming dimers, trimers, and tetramers. Modification of peptide nucleic acid (PNA) with the O6-benzyl guanine (BG) group allowed the generation of site-selective covalent links between PNA and the SNAP protein. The modified BG-PNAs were labeled with fluorescent Atto dyes and subsequently chemo-selectively conjugated to SNAP protein. Efficient assembly into dimer and oligomer forms was verified via size exclusion chromatography (SEC), electrophoresis (SDS-PAGE), and fluorescence spectroscopy. DNA directed assembly of homo- and hetero-dimers of SNAP-PNA constructs induced homo- and hetero-FRET, respectively. Longer DNA scaffolds controllably aligned similar fluorescent SNAP-PNA constructs into higher oligomers exhibiting homo-FRET. The combined SEC and homo-FRET studies indicated the 1:1 and saturated assemblies of SNAP-PNA-fluorophore:DNA formed preferentially in this system. This suggested a kinetic/stoichiometric model of assembly rather than binomially distributed products. These BG-PNA-fluorophore building blocks allow facile introduction of fluorophores and/or assembly directing moieties onto any protein containing SNAP. Template directed assembly of PNA modified SNAP proteins may be used to investigate clustering behavior both with and without fluorescent labels which may find use in the study of assembly processes in cells

Optimization of Parameters for High-Resolution X-Ray Computed Tomography

In recent years there have been efforts to increase the amount of structural information available in x-ray computed tomography (CT) images. These efforts have proceeded in several directions: increasing the number of resolution elements within a given imaging field; increasing the size of the imaging field for a given resolution element size; and decreasing the imaging field with the number of resolution elements held fixed or increased. This paper will discuss problems common to all of these approaches, with examples referred to two specific CT systems designed for 0.05-mm resolution over a 5-cm field and 2-mm resolution over a 2-meter field. The first problem is that of achieving the desired spatial resolution; the solution is application-dependent and must be discussed individually for each case. The second problem is that of achieving acceptable image noise levels.</p