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This Zip file contains snapshots from epochs 300 to 345 of CP_ResNet with rho=5 trained on the whole development set pf DCASE 2019 task 1A. More details on how to use this for prediction, can be found here https://github.com/kkoutini/cpjku_dcase19#loading-pretrained-models For more details: Koutini, Khaled, Hamid Eghbal-Zadeh, Matthias Dorfer, and Gerhard Widmer. "The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification." In 2019 27th European Signal Processing Conference (EUSIPCO), pp. 1-5. IEEE, 2019. Khaled Koutini, Hamid Eghbal-zadeh, Gerhard Widmer, "Acoustic Scene Classification and Audio Tagging with Receptive-Field-Regularized CNNs" Proceedings of the Detection and Classification of Acoustic Scenes and Events 2019 Workshop (DCASE2019)
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TheTransitClock real-time transit information system
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first release of hexagonal fan
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First public release.
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Release of Electromechanical Neuropathy Analyzer
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The .zip file contains all directories comprising data and scripts that were used for the study "Impact of Xylella fastidiosa subsp. pauca in European Olives: A Bio-Economic Analysis"
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A python script for analysing compression dilatometer data, allowing the user to plot the stress-strain data and for generating 'processing maps' of a material. The script was used to analyse data recorded in uniaxial compression for a Zr-2.5Nb alloy deformed at temperatures of 650C, 675C, 700C, 725C, 750C, 775C, 800C, 825C and 850C, at strain rates of 10-2.5, 10-2, 10-1.5, 10-1, 10-0.5 and 1 s-1, to 50% height reduction, using TA Instruments DIL 805 A/D/T Quenching and Deformation Dilatometer. The data for this experiment can be downloaded from; Daniel, Christopher Stuart, Peyton, Christian J., & Quinta da Fonseca, João. (2019). High Temperature Compression Studies of a Zr-2.5Nb Alloy using Deformation Dilatometer [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3374512 A critical assessment of the application of 'processing maps' is included in the accompanying paper; C.S. Daniel, P. Jedrasiak, C.J. Peyton, J. Quinta da Fonseca, H.R. Shercliff, l. Bradley, P.D. Honniball, Quantifying Processing Map Uncertainties by Modelling the Hot-Compression Behaviour of a Zr-2.5Nb Alloy, Zirconium in the Nuclear Industry: 19th International Symposium (2020)
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We present ScenTrees.jl, an open-source Julia package for generating scenario trees and scenario lattices which can be used, for example, for multistage stochastic optimization problems. It allows users to represent possible sequences of stochastic processes in form of a scenario tree in the case of a discrete time stochastic process and a scenario lattice for Markovian data processes. In extension, it also provides users with a platform for generating new and additional trajectories in case of a limited data using conditional density estimation.
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Signal Gathering Apparatus for Prosthetic Limbs with Machine Learning Algorithms
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1-D reflectometry fitting
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