wwydmanski @wwydmanski@qoto.org

I'm grateful to be a part of @pawel_labaj's team, and I'm happy to say that we were granted the Award of the City of Kraków for the efforts against the SARS-CoV-2 pandemic.

Happy to be a part of this!
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RT @LChoshen
Data augmentation? Look no further.

Framework of 100+ "transformations" (augmentations\paraphrasing functions\filters)
Many types:emojis, linguistic... see Fig
Extendable!
A vast effort, constructed by almost a hundred authors!
https://arxiv.org/abs/2112.02721
#scientivism
https://twitter.com/LChoshen/status/1468209223427411972

As an unordered service, we have also thoroughly disinfected two random spots on each of our swabbing locations.
#gCSD2021 with @metasub is nearly over!
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RT @pawel_labaj
Most of @MCB_UJ @JagiellonskiUni teams are done with @metasub #gCSD2021 in @krakow_pl Thank You All!!!
https://twitter.com/pawel_labaj/status/1406982443077357573

#gCSD2021 #samplingday with @metasub @MCB_UJ

Similarity of concepts of a knowledge graph is a frequently encountered problem in text analysis for which Zhu et al. propose an approach that goes beyond the casual path calculation.
If you need more resolution of your metrics you should give it a try:

https://core.ac.uk/download/pdf/148683663.pdf

The dynamic time warping is a technique that is often used for alignment of time series data, with many usages in areas such as biometry.

Surprisingly, it can also be used in computing text similarity!

Let Matuschek et al. tell you about it:
https://www.researchgate.net/publication/221524439_Measuring_text_similarity_with_dynamic_time_warping/figures

The validation data for the "Literature #AI for Drug Induced Liver Injury" #CAMDAchallenge is live!

Feel free to join! If you've got any questions you can ask them here or on our forum.

http://data.camda.info/contest_dataset

#NLP #ML #DILI

Jay Alammar's overview of BERT-based models shows us some not so obvious conclusions - like the fact that the best way of getting a contextual word embedding isn't taking the output layer of the model.

http://jalammar.github.io/illustrated-bert/

Our article with @SzymonSzott about deep reinforcement learning for contention window optimization is available in the IEEE Xplore repository.

#AI #RL #WiFi
https://ieeexplore.ieee.org/document/9417575

ICML 2021 is going to show us a new self-supervised embedding algorithm called Barlow Twins. It's name is ofc a reference to the siamese networks, but the key novelty is lack of contrastive samples.

Preprint by Zbontar et al.:
https://arxiv.org/abs/2103.03230

Local random walk is a basis for many #MachineLearning algorithms. However, there is an interesting alternative - maximum entropy random walk.

Overview from TFML by J.Duda: https://tfml.gmum.net/assets/files/2015/Duda.pdf

Application in medical imaging by Galinsky&Frank: https://par.nsf.gov/servlets/purl/10063685

If you've had some problems with accessing the #CAMDAchallenge data then everything should be fixed by now.

You will find the direct link at:
http://data.camda.info/contest_dataset

Topics of our challenges include: #COVID19 #NLP #Bioinformatics #CancerResearch