Optimised constellation for the paper High-Cardinality Geometrical Constellation Shaping for the Nonlinear Fibre Channel. Each file is a constellation optimised for the SNR in dB mentioned in the filename, containing the coordinates of the constellation points as comma-separated values. Each column represents a dimension and each row is a separate constellation point. The bit labels for the generalised mutual information (GMI) are implied and follow natural mapping, the first row is 0,..,0,0 the second 0,...0,1 the third 0,..,1,0 the fourth 0,...,1,1 etc and the last 1,...,1,1. The file named gmi.txt is the GMI for the resulting constellations.

Analysis of ‘HR Analytics: Job Change of Data Scientists’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context and Content

A company which is active in Big Data and Data Science wants to hire data scientists among people who successfully pass some courses which conduct by the company. Many people signup for their training. Company wants to know which of these candidates are really wants to work for the company after training or looking for a new employment because it helps to reduce the cost and time as well as the quality of training or planning the courses and categorization of candidates. Information related to demographics, education, experience are in hands from candidates signup and enrollment.

This dataset designed to understand the factors that lead a person to leave current job for HR researches too. By model(s) that uses the current credentials,demographics,experience data you will predict the probability of a candidate to look for a new job or will work for the company, as well as interpreting affected factors on employee decision.

The whole data divided to train and test . Target isn't included in test but the test target values data file is in hands for related tasks. A sample submission correspond to enrollee_id of test set provided too with columns : enrollee _id , target

Note: - The dataset is imbalanced. - Most features are categorical (Nominal, Ordinal, Binary), some with high cardinality. - Missing imputation can be a part of your pipeline as well.

# Features #
- enrollee_id : Unique ID for candidate

• city: City code

• city_ development _index : Developement index of the city (scaled)

• gender: Gender of candidate

• relevent_experience: Relevant experience of candidate

• enrolled_university: Type of University course enrolled if any

• education_level: Education level of candidate

• major_discipline :Education major discipline of candidate

• experience: Candidate total experience in years

• company_size: No of employees in current employer's company

• company_type : Type of current employer

• last_new_job: Difference in years between previous job and current job

• training_hours: training hours completed

• target: 0 – Not looking for job change, 1 – Looking for a job change

Inspiration

• Predict the probability of a candidate will work for the company
• Interpret model(s) such a way that illustrate which features affect candidate decision # Please refer to the following task for more details: https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists/tasks?taskId=3015

--- Original source retains full ownership of the source dataset ---

KAMEL comprises knowledge about 234 relations from Wikidata with a large training, validation, and test dataset. We make sure that all facts are also present in Wikipedia so that they have been seen during the pre-training procedure of the LMs we are probing. Most importantly we overcome the limitations of existing probing datasets by (1) having a larger variety of knowledge graph relations, (2) it contains single- and multi-token entities, (3) we use relations with literals, and (4) have alternative labels for entities. (5) Furthermore, we created an evaluation procedure for higher cardinality relations, which was missing in previous works, and (6) make sure that the dataset can be used for causal LMs.