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• Journal article
  Curcin V, Guo Y, Gilardoni F,

  Scientific Workflow Applied to Nano-and Material Sciences

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• Journal article
  Mo Y, Wang S, Dai C, Zhou R, Teng Z, Bai W, Guo Yet al.,

  Efficient Deep Representation Learning by Adaptive Latent Space Sampling

  Supervised deep learning requires a large amount of training samples withannotations (e.g. label class for classification task, pixel- or voxel-wisedlabel map for segmentation tasks), which are expensive and time-consuming toobtain. During the training of a deep neural network, the annotated samples arefed into the network in a mini-batch way, where they are often regarded ofequal importance. However, some of the samples may become less informativeduring training, as the magnitude of the gradient start to vanish for thesesamples. In the meantime, other samples of higher utility or hardness may bemore demanded for the training process to proceed and require moreexploitation. To address the challenges of expensive annotations and loss ofsample informativeness, here we propose a novel training framework whichadaptively selects informative samples that are fed to the training process.The adaptive selection or sampling is performed based on a hardness-awarestrategy in the latent space constructed by a generative model. To evaluate theproposed training framework, we perform experiments on three differentdatasets, including MNIST and CIFAR-10 for image classification task and amedical image dataset IVUS for biophysical simulation task. On all threedatasets, the proposed framework outperforms a random sampling method, whichdemonstrates the effectiveness of proposed framework.

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• Journal article
  Amador J, Oehmichen A, Molina-Solana M,

  Characterizing Political Fake News in Twitter by its Meta-Data

  This article presents a preliminary approach towards characterizing politicalfake news on Twitter through the analysis of their meta-data. In particular, wefocus on more than 1.5M tweets collected on the day of the election of DonaldTrump as 45th president of the United States of America. We use the meta-dataembedded within those tweets in order to look for differences between tweetscontaining fake news and tweets not containing them. Specifically, we performour analysis only on tweets that went viral, by studying proxies for users'exposure to the tweets, by characterizing accounts spreading fake news, and bylooking at their polarization. We found significant differences on thedistribution of followers, the number of URLs on tweets, and the verificationof the users.

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