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Simple first exercise applying an autoencoder to international relations data on nation characteristics. For, EITM 2019: http://eitm.emory.edu/
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Abstract--In this paper, a method for performance parameters estimation of Pockels high-voltage sensors by means of numerical optimization is presented. The proposed method integrates a theoretical model for simulation of Pockels sensors, and the optimization algorithm Pattern Search to estimate the model parameters in order to minimize the difference between the experimental and theoretical peak-peak output voltage. Two Pockels cells were built and tested for validation of the method, where model parameters estimated by means of numerical optimization were substituted in the theoretical model and compared with the experimental data for the peak-peak output voltage and output voltage in function of time, presenting a high degree of agreement between them, showing that the optimization resulted in a good solution.
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Exemplary code and data for figures generation (figures may not fully correspond to the published version). Additional code details: https://github.com/BUNPC/laserSpeckleImaging (runPulsatilityBasedIdentification.m)
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Radiotherapy is integral to the care of a majority of cancer patients. Despite differences in tumor responses to radiation (radioresponse), dose prescriptions are not currently tailored to individual patients. Recent large-scale cancer cell line databases hold the promise of unravelling the complex molecular arrangements underlying cellular response to radiation, which is critical for novel predictive biomarker discovery. Here, we present RadioGx, a computational platform for integrative analyses of radioresponse using radiogenomic databases. We first used RadioGx to investigate the robustness of radioresponse assays and indicators. We then combined radioresponse and genome-wide molecular data with established radiobiological models to identify molecular pathways that are relevant for individual tissue types and conditions. We also applied RadioGx to pharmacogenomic data to identify several classes of drugs whose effects correlate with radioresponse. RadioGx provides a unique computational toolbox to advance preclinical research for radiation oncology and precision medicine.
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This contains the data and code to reproduce the results of the experiments reported in "Scene recognition across variations in illumination caused by time of day". Study abstract: Variation in the prevailing illumination presents a challenge for the visual system when attempting to identify and retain information about the environment. Here, we investigated the effect of the illumination variation, caused by different times of day, on the capacity of human observers to recognize scenes. In Experiment 1, there was moderate evidence of illumination invariance when performing delayed scene recognition from dashcam clips of suburban neighbourhood locations. However, there was moderate to strong evidence in Experiments 2 and 3 of illumination dependence in a match-to-sample recognition task on static images of real and synthetic locations. In Experiment 4, there was weak evidence of similar scene recognition for two different test times of day. Overall, this study indicates that human observers can be affected by the prevailing illumination when performing scene recognition.
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This directory includes the Matlab functions implementing the Non-Negative Orthogonal Greedy (NNOG) algorithms NNOMP, NNOLS and SNNOLS together with a simple test program showing how to call the NNOG algorithms. This is a supplementary material to the paper Non-negative orthogonal greedy algorithms, by T.T. Nguyen, J. Idier, C. Soussen, and E.-H. Djermoune https://hal.archives-ouvertes.fr/hal-02049424 Contact information: charles.soussen@centralesupelec.fr
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Algorithm to reach target in minimum time while avoiding obstacles.
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This is the code for the simulations presented in paper "Restless bandits with controlled restarts: Indexability and computation of Whittle index" which has been appeared in CDC 2019.
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This code contains the NMF codes described in the paper (Section 6) "Inertial block mirror descent method for non-convex non-smooth optimization", Le Thi Khanh Hien, Nicolas Gillis and Panos Patrinos, 2019. The paper will be available on arXiv. The astract reads: "In this paper, we propose inertial versions of block coordinate descent methods for solving non-convex non-smooth composite optimization problems. We use the general framework of Bregman distance functions to compute the proximal maps. Our method not only allows using two different extrapolation points to evaluate gradients and adding the inertial force, but also takes advantage of randomly picking the block of variables to update. Moreover, our method does not require a restarting step, and as such, it is not a monotonically decreasing method. To prove the convergence of the whole generated sequence to a critical point, we modify the convergence proof recipe of Bolte, Sabach and Teboulle (Proximal alternating linearized minimization for non-convex and non-smooth problems, Math. Prog. 146(1):459--494, 2014), and combine it with auxiliary functions. We deploy the proposed methods to solve non-negative matrix factorization (NMF) problems and show that they compete favourably with the state-of-the-art NMF algorithms. "
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These files are used as part of a first semester physics course. They are intended to help students learn how to solve the type of physics problems they encounter in class using Jupyter Notebooks.
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