Applications of the principle of least effort in data transformation

This file was last viewed in Adobe Acrobat Pro.I am interested in applying the Principle of Least Effort to data transformation in an effort to solve three important challenges in using video games as a testbed for the study of inverse reinforcement learning. These challenges are as follows: the very large state space created by high granularity of time, the very large range of feature values provided by quantitative features, and the difficulty to measure similarity of trajectories. Through exploring The Principle of Least Effort from the Social Sciences, I propose a form of data transformation that categorizes data according to the familiarity and preferences of the modeled user. Furthermore I will also show how this approach can be used to create a similarity comparison between trajectories in accordance to The Principle of Least Effort. For the collection of test data I have utilized a reinforcement learning agent to play the minigame BuildMarines for StarCraft II as provided by DeepMind

Progress toward a universal biomedical data translator

Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based "Translator" system capable of integrating existing biomedical data sets and "translating" those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system's architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems

Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science

<h2>What's Changed</h2> <ul> <li>Documentation and repo hierarchy refactoring by @sierra-moxon in https://github.com/biolink/biolink-model/pull/1418</li> </ul> <p>Summary: 4.0.0 is a major release that includes many changes to the documentation for Biolink Model as well as the reorganization of the repository to support the new documentation structure and comply with LinkML best practices. The model itself has not changed significantly, but the documentation has been updated to reflect the current state of the model, and includes new visualizations of the model, additional text-based documentation, and a new gh-pages documentation layout.</p> <p><strong>Full Changelog</strong>: https://github.com/biolink/biolink-model/compare/v3.6.0...v4.0.0</p>Please cite the following works when using this software