bookmark | Chris Aldrich

🔖 PaperBadger

Bookmarked PaperBadger by Mozilla Science (GitHub)

Issuing badges to credit authors for their work on academic papers https://badges.mozillascience.org/

Exploring the use of digital badges for crediting contributors to scholarly papers for their work

As the research environment becomes more digital, we want to test how we can use this medium to help bring transparency and credit for individuals in the publication process.

This work is a collaboration with publishers BioMed Central (BMC), Ubiquity Press (UP) and the Public Library of Science (PLoS); the biomedical research foundation, The Wellcome Trust; the software and technology firm Digital Science; the registry of unique researcher identifiers, ORCID; and the Mozilla Science Lab.

h/t to Greg McVerry via https://chat.indieweb.org/dev/2018-04-04#t1522869725219200

🔖 [1803.05316] Seven Sketches in Compositionality: An Invitation to Applied Category Theory

Bookmarked Seven Sketches in Compositionality: An Invitation to Applied Category Theory by Brendan Fong, David I. Spivak (arxiv.org)

This book is an invitation to discover advanced topics in category theory through concrete, real-world examples. It aims to give a tour: a gentle, quick introduction to guide later exploration. The tour takes place over seven sketches, each pairing an evocative application, such as databases, electric circuits, or dynamical systems, with the exploration of a categorical structure, such as adjoint functors, enriched categories, or toposes. No prior knowledge of category theory is assumed. [.pdf]

This is the textbook that John Carlos Baez is going to use for his online course in Applied Category Theory.

🔖 [1803.09745] English verb regularization in books and tweets | arXiv

Bookmarked [1803.09745] English verb regularization in books and tweets by Tyler J. Gray, Andrew J. Reagan, Peter Sheridan Dodds, Christopher M. Danforth (arxiv.org)

The English language has evolved dramatically throughout its lifespan, to the extent that a modern speaker of Old English would be incomprehensible without translation. One concrete indicator of this process is the movement from irregular to regular (-ed) forms for the past tense of verbs. In this study we quantify the extent of verb regularization using two vastly disparate datasets: (1) Six years of published books scanned by Google (2003--2008), and (2) A decade of social media messages posted to Twitter (2008--2017). We find that the extent of verb regularization is greater on Twitter, taken as a whole, than in English Fiction books. Regularization is also greater for tweets geotagged in the United States relative to American English books, but the opposite is true for tweets geotagged in the United Kingdom relative to British English books. We also find interesting regional variations in regularization across counties in the United States. However, once differences in population are accounted for, we do not identify strong correlations with socio-demographic variables such as education or income. [.pdf]

🔖 [1803.08823] A high-bias, low-variance introduction to Machine Learning for physicists | arXiv

Bookmarked A high-bias, low-variance introduction to Machine Learning for physicists by Pankaj Mehta, Marin Bukov, Ching-Hao Wang, Alexandre G.R. Day, Clint Richardson, Charles K. Fisher, David J. Schwab (arxiv.org)

Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, and generalization before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Python notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton-proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists maybe able to contribute. (Notebooks are available at this https URL )

🔖 The True Believer: Thoughts on the Nature of Mass Movements by Eric Hoffer

Bookmarked The True Believer: Thoughts on the Nature of Mass Movements by Eric Hoffer (Perennial Classics)

A stevedore on the San Francisco docks in the 1940s, Eric Hoffer wrote philosophical treatises in his spare time while living in the railroad yards. The True Believer -- the first and most famous of his books -- was made into a bestseller when President Eisenhower cited it during one of the earliest television press conferences.Completely relevant and essential for understanding the world today, The True Believer is a visionary, highly provocative look into the mind of the fanatic and a penetrating study of how an individual becomes one.
The famous bestseller with “concise insight into what drives the mind of the fanatic and the dynamics of a mass movement” (Wall St. Journal) by Presidential Medal of Freedom recipient Eric Hoffer, The True Believer is a landmark in the field of social psychology, and even more relevant today than ever before in history. Called a “brilliant and original inquiry” and “a genuine contribution to our social thought” by Arthur Schlesinger, Jr., The True Believer is mandatory reading for anyone interested in the machinations by which an individual becomes a fanatic.

h/t

The GOP narrative is very much focused on "pulling yourself up by your bootstraps" and anti-victimhood, yet mobilizes its base by sharing messages that white males are oppressed and persecuted, christians are oppressed and persecuted, and gun owners are oppressed and persecuted.

— Susan Fowler (@susanthesquark) March 24, 2018

Messages of oppression and persecution are really dangerous, especially when they are being used for political ends. This is why.

— Susan Fowler (@susanthesquark) March 24, 2018

Hoffer's book The True Believer addresses this really well (one of my favorite books).

— Susan Fowler (@susanthesquark) March 24, 2018

🔖 WPCampus 2018 Call for Speakers

Bookmarked Call for Speakers (WPCampus 2018 Conference: Where WordPress Meets Higher Education)

WPCampus is looking for stories, how-tos, hypotheticals, demos, case studies and more for our third annual in-person conference focused on WordPress in higher education. This year’s event will take place July 12-14, 2018, at Washington University in St. Louis, Missouri.

The call for speakers will close at midnight PDT on April 7, 2018.
The planning committee will begin their selection process and be in touch shortly thereafter.

h/t to @wpcampusorg

We don’t care what software you use but we know you’re awesome and we’d love to learn from you. If you’re passionate about #WordPress or #highered, #WPCampus would love to hear more this summer in St. Louis. Our call for speakers is open until April 7. https://t.co/EeoaLqGCSo pic.twitter.com/n8Mgnymw1L

— WPCampus (@wpcampusorg) March 23, 2018

🔖 Efficient Algorithms for Searching the Minimum Information Partition in Integrated Information Theory

Bookmarked Efficient Algorithms for Searching the Minimum Information Partition in Integrated Information Theory by Jun Kitazono, Ryota Kanai, Masafumi Oizumi (MDPI)

The ability to integrate information in the brain is considered to be an essential property for cognition and consciousness. Integrated Information Theory (IIT) hypothesizes that the amount of integrated information ( Φ ) in the brain is related to the level of consciousness. IIT proposes that, to quantify information integration in a system as a whole, integrated information should be measured across the partition of the system at which information loss caused by partitioning is minimized, called the Minimum Information Partition (MIP). The computational cost for exhaustively searching for the MIP grows exponentially with system size, making it difficult to apply IIT to real neural data. It has been previously shown that, if a measure of Φ satisfies a mathematical property, submodularity, the MIP can be found in a polynomial order by an optimization algorithm. However, although the first version of Φ is submodular, the later versions are not. In this study, we empirically explore to what extent the algorithm can be applied to the non-submodular measures of Φ by evaluating the accuracy of the algorithm in simulated data and real neural data. We find that the algorithm identifies the MIP in a nearly perfect manner even for the non-submodular measures. Our results show that the algorithm allows us to measure Φ in large systems within a practical amount of time.

h/t Christoph Adami, Erik Hoel, and @kanair

Français

English