Omkar2118/datasets huggingface dataset:
PredNet Grayscale Model Weights

A pretrained PredNet neural network, used in EIGen to generate grayscale illusions.

Introducing paper: Evolutionary Generation of Visual Motion Illusions
PredNet Color Model Weights

A pretrained PredNet neural network, used in EIGen to generate color illusions.
Multiview Manipulation Data
Full name: Multiview Manipulation Expert Data and Trained Models

Accompanying expert data and trained models for 2021 IROS paper on Multiview Manipulation.

Introducing paper: Seeing All the Angles: Learning Multiview Manipulation Policies for Contact-Rich Tasks from Demonstrations
NLC2CMD

The NLC2CMD Competition hosted at NeurIPS 2020 aimed to bring the power of natural
language processing to the command line. Participants were tasked with building models
that can transform descriptions of command line tasks in English to their Bash syntax.

Introducing paper: NeurIPS 2020 NLC2CMD Competition: Translating Natural Language to Bash Commands
2018 n2c2 (Track 2) - Adverse Drug Events and Medication Extraction
Full name: 2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records

Abstract
Objective
This article summarizes the preparation, organization, evaluation, and results of Track 2 of the 2018 National NLP Clinical Challenges shared task. Track 2 focused on extraction of adverse drug events (ADEs) from clinical records and evaluated 3 tasks: concept extraction, relation classification, and end-to-end systems. We perform an analysis of the results to identify the state of the art in these tasks, learn from it, and build on it.

Materials and Methods
For all tasks, teams were given raw text of narrative discharge summaries, and in all the tasks, participants proposed deep learning–based methods with hand-designed features. In the concept extraction task, participants used sequence labelling models (bidirectional long short-term memory being the most popular), whereas in the relation classification task, they also experimented with instance-based classifiers (namely support vector machines and rules). Ensemble methods were also popular.

Results
A total of 28 teams participated in task 1, with 21 teams in tasks 2 and 3. The best performing systems set a high performance bar with F1 scores of 0.9418 for concept extraction, 0.9630 for relation classification, and 0.8905 for end-to-end. However, the results were much lower for concepts and relations of Reasons and ADEs. These were often missed because local context is insufficient to identify them.

Conclusions
This challenge shows that clinical concept extraction and relation classification systems have a high performance for many concept types, but significant improvement is still required for ADEs and Reasons. Incorporating the larger context or outside knowledge will likely improve the performance of future systems.
SPARTQA -
Full name: SPAtial Reasoning on Textual Question Answering.

We take advantage of the ground truth of NLVR images, design CFGs to generate stories, and use spatial reasoning rules to ask and answer spatial reasoning questions. This automatically generated data is called SpaRTQA.   https://aclanthology.org/2021.naacl-main.364/

Introducing paper: SPARTQA: A Textual Question Answering Benchmark for Spatial Reasoning
Moon Phases
Full name: moon phases and derived

Dates with Moon phases extended days until next phase (1992/1/4 to 2027/12/20)

Incorporate lunar data to your research.
The moon affects multiple physical things on the earth, such as the ocean tides, the behavior of living organisms as well as humans

Moon Phases data

0 = New Moon
1 = first day after New Moon
2 = second day after New Moon
. . .

10 = First Quarter
11 = first day after First Quarter
12 = second day after First Quarter
. . .

20 = Full Moon
21 = first day after Full Moon
22 = second day after Full Moon
. . .

30 = Third Quarter
31 = first day after Third Quarter
32 = second day after Third Quarter
. . .

Introducing paper: Lunatic Stocks: Moon Phases as Irregular Sampling Features for Pattern Recognition in the Stock Markets
CLIPS
Full name: Corpora e Lessici dell'Italiano Parlato e Scritto

CLIPS, ovvero Corpora e Lessici dell'Italiano Parlato e Scritto, è uno degli otto progetti (Progetto n. 2) del Cluster C18 "LINGUISTICA COMPUTAZIONALE: RICERCHE MONOLINGUI E MULTILINGUI" (Legge 488), finanziato dal Ministero dell'Istruzione, dell'Università e della Ricerca (MIUR).