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This is part of clij release 1.5.6 https://github.com/clij/clij/releases/tag/1.5.6
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0.10.5 make PartSeg PEP517 compatible. fix multiple files widget on Windows (path normalisation) Changes: a8b22adc3b583c375d0a6689a38880cb64f4b01e bump to 0.10.5 a2049068884cccc5bd2e4dc3d63ee424b36238ad fix multiple files widget for pyside, fix warning on load files 45f1f4324034b36483405c1c351e4aae5f00b8ba use test extra in test wheel 4e60c7bfae0f36e4c6369c005c407b5ab401f2aa move qt instalation to extras requires 4bc202f2f1aea2a0dd327696bcfc11546d285f5e move test for python 3.6 and 3.8 to github actions de19a781b9d943457495a15d32cb4c3ecced87df fix PARTSEG-38 PARTSEG-39 7dff5ca12953957774a1a447bf44cd93d7be6a13 fixes names, add raw mask in mask file 82c4e40b918864af85149e2a17e68dba11f80c90 bump requirements a5cd88c8dde57e2d21f7e002b518f15ce39d6a6e add gitpod configuration c38aecf168bfaaf38310664132c3cd51dafe0221 go to mainline cibuildwheel See More 2e90c950f648ce5d771e2caea1a39456344e56a3 update changelog This list of changes was auto generated.
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The first release of the updated code for our manuscript 'Synchronization and resilience in the Kuramoto white matter network model with adaptive state-dependent delays'.
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Data and code for analysing fish data for growth
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Acceptance of the GMD paper introducing downscaleR.keras
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Recently, advances in neural network approaches have achieved many successes in both sentiment classification and probabilistic topic modelling. On the one hand, latent topics derived from the global context of documents could be helpful in capturing more accurate word semantics and hence could potentially improve the sentiment classification accuracy. On the other hand, the word-level attention vectors obtained during the learning of sentiment classifiers could carry word-level polarity information and can be used to guide the discovery of topics in topic modelling. This paper proposes a multi-task learning framework which jointly learns a sentiment classifier and a topic model by making the word-level latent topic distributions in the topic model to be similar to the word-level attention vectors in the classifier through mutual learning. Experimental results on the Yelp and IMDB datasets verify the superior performance of the proposed framework over strong baselines on both sentiment classification accuracy and topic modelling evaluation results including perplexity and topic coherence measures. The proposed framework also extracts more interpretable topics compared to other conventional topic models and neural topic models.
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OceanDistributions v0.1.1 Diff since v0.1.0 various fixes in pkg setup, test, etc
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Econometrics v0.2.6 Diff since v0.2.5 Updated compat for StatsBase Merged pull requests: CompatHelper: bump compat for "StatsBase" to "0.33" (#38) (@github-actions[bot])
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Adoption and impact of non-pharmaceutical interventions for COVID-19
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Bug fixes: Various small bug fixes. Internal changes: Refactoring the code structure, moving to more submodules. The API and functionality is unchanged. One can now directly import from the madminer namespace, e.g. as from madminer import MadMiner (but the old from madminer.core import MadMiner will still work).
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