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# Installation conda create -n deep_texture python=3.6 source activate deep_texture conda install numpy pillow conda install keras-gpu conda install keras # if GPUs are not available pip install git+https://github.com/keras-team/keras-applications.git@d506dc82d0 # downgrade keras-application ## usage import deep_texture (prep, dnn) = deep_texture.setup_texture(arch = 'nasnet', layer = 'normal_concat_11', cbp_dir = '/tmp') dtr = deep_texture.calc_features_file("./test.png", prep, dnn)
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Integrating data from multiple sources with the aim to identify records that correspond to the same entity is required in many real-world applications including healthcare, national security, and businesses. However, privacy and confidentiality concerns impede the sharing of personal identifying values to conduct linkage across different organizations. Privacy-preserving record linkage (PPRL) techniques have been developed to tackle this problem by performing clustering based on the similarity between encoded record values, such that each cluster contains (similar) records corresponding to one single entity. When employing PPRL on databases from multiple parties, one major challenge is the prohibitively large number of similarity comparisons required for clustering, especially when the number and size of databases are large. While there have been several private blocking methods proposed to reduce the number of comparisons, they fall short in providing an efficient and effective solution for linking multiple large databases. Further, all of these methods are largely dependent on data. In this paper, we propose a novel private blocking method for efficiently linking multiple databases by exploiting the data characteristics in the form of probabilistic signatures and introduce a local blocking evaluation step for validating blocking methods without knowing the ground-truth. Experimental results show the efficacy of our method in comparison to several state-of-the-art methods.
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Abstract Motivation Antibodies are widely used experimental reagents to test expression of proteins. However, they might not always provide the intended tests because they do not specifically bind to the target proteins that their providers designed them for, leading to unreliable and irreproducible research results. While many proposals have been developed to deal with the problem of antibody specificity, they may not scale well to deal with the millions of antibodies that have ever been designed and used in research. In this study, we investigate the feasibility of automatically extracting statements about antibody specificity reported in the literature by text mining, and generate reports to alert scientist users of problematic antibodies. Results We developed a deep neural network system called Antibody Watch and tested its performance on a corpus of more than two thousand articles that report uses of antibodies. We leveraged the Research Resource Identifiers (RRID) to precisely identify antibodies mentioned in an input article and the BERT language model to classify if the antibodies are reported as nonspecific, and thus problematic, as well as inferred the coreference to link statements of specificity to the antibodies that the statements referred to. Our evaluation shows that Antibody Watch can accurately perform both classification and linking with F-scores over 0.8, given only thousands of annotated training examples. The result suggests that with more training, Antibody Watch will provide useful reports about antibody specificity to scientists.
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ATC-Anno is an annotation tool for the transcription and semantic annotation of air traffic control utterances. It was developed at the Spoken Language Systems (LSV) group at Saarland University. The latest version of the tool can always be found on the LSV GitHub account. If you use the tool in your research, please cite the associated paper: Marc Schulder, Johannah O'Mahony, Yury Bakanouski, Dietrich Klakow (2020). ATC-Anno: Semantic Annotation for Air Traffic Control with Assistive Auto-Annotation. In Proceedings of the International Conference on Language Resources and Evaluation (LREC), Marseilles, France.
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MATLAB codes used to model arsenic(III) remediation using a composite TiO2-Fe2O3 sorbent in batch and continuous-flow systems, using a modified form of the pseudo-second order (PSO) adsorption kinetic model. This data supports the manuscript provisionally titled 'A kinetic adsorption model to inform the design of arsenic(III) treatment plants using photocatalyst-sorbent materials'
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Tools for interacting with the publicly available California Delta Fish Salvage Database, including continuous deployment of data access, analysis, and presentation.
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This patch fixes an issue affecting the "nngt" backend (for users not using any of the standard graph libraries) that led to weights set after edge creation (using set_weights) to be incorrectly updated in some cases, notably when using NNGT with NEST. (note that the Travis test for graph-tool on Python 2 fails solely because Tiago is no longer supporting it and removed the package from his repo, it is not linked to any issue with this release)
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sasartifact-master.zip has been obtained from https://gricad-gitlab.univ-grenoble-alpes.fr/verimag/reproducible-research/sasartifact (sha:d78a48f1) artifacts.zip has been obtained from a Gitlab CI/CP pipeline of this gitlab repository
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See https://viresclient.readthedocs.io/en/latest/release_notes.html
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RADWave is Python package that provides a mechanism to access altimeter datasets through web-enabled data services (THREDDS). The package capabilities are illustrated based on the the Australian Ocean Data Network (AODN database that spans from 1985-present and that has already been calibrated and validated by [Ribal and Young, 2019]. RADWave allows to query over a range of spatial and temporal scales altimeter parameters in specific geographical regions and subsequently calculates significant wave heights, periods, group velocities, average wave energy densities and wave energy fluxes.
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