Bookmarks

Interesting Links/bookmarks for personal use or sharing with others
Bookmarked Great Expectations (Serapis Classics) (7switch.com)
An ebook published using TiddlyWiki
An interesting example of a book published using TiddlyWiki as an ebook platform. It also enables highlighting and annotations to boot! I’m curious how well it works with Hypothes.is given their anchoring schemes?
Bookmarked TiddlyBlink — TiddlyWiki with bi-directional linking (giffmex.org)

TiddlyBlink is an adaptation of TiddlyWiki with the goal of helping you see connections between your ideas, and move quickly from one idea to another. It was inspired by the bi-directional linking found in Roam (https://roamresearch.com/), but built with capabilities already available in TiddlyWiki (https://tiddlywiki.com). See my example file here.

If he hasn’t seen this, it seems like the sort of thing that Jack Baty would appreciate.

I wonder if he’s considered using webmention.io to work with his TiddlyWiki? I’ve set it up with my MediaWiki set up, but still need to tinker with it on a public TiddlyWiki.

Bookmarked TiddlyWiki — a non-linear personal web notebook (tiddlywiki.com)

Have you ever had the feeling that your head is not quite big enough to hold everything you need to remember?

Welcome to TiddlyWiki, a unique non-linear notebook for capturing, organising and sharing complex information.

Use it to keep your to-do list, to plan an essay or novel, or to organise your wedding. Record every thought that crosses your brain, or build a flexible and responsive website.

Unlike conventional online services, TiddlyWiki lets you choose where to keep your data, guaranteeing that in the decades to come you will still be able to use the notes you take today.

Bookmarked Old Fashioned by Tom MacWrightTom MacWright (oldfashioned.tech)

This is a website that I made about cocktails. I'm not a huge cocktail nerd (drinking is bad, probably), but think that they're cool. And the world's pretty bad right now and making this has been calming.

It gave me a chance to both tinker with technology I usually don't use (Elm), and explore some of the cool properties of cocktails: notably that they're pretty similar and have standardized ingredients, so they can be described in relationship to each other.

So some of it might seem funky. By default, the list is sorted by 'feasibility': as you add ingredients that you have, it'll put recipes that you can make (or barely make) closer to the top. Also, click on 'Grid' for a wacky adjacency grid of cocktails and their ingredients.

Also, for vim fans, there’s j & k support.

IndieWeb for trying times!

hat tip:

Bookmarked Neil's Noodlemaps by Neil Mather (commonplace.doubleloop.net)

Welcome! This is my digital commonplace book. I started it (in this format) in October 2019.

It is a companion to my blog. They are the Garden and the Stream.

Please feel free to click around here and explore. Don't expect too much in the way coherence or permanence… it is a lot of half-baked ideas, badly organised. The very purpose is for snippets to percolate and morph and evolve over time, and it's possible (quite likely) that pages will move around.

That said, I make it public in the interest of info-sharing, and occassionally it is quite useful to have a public place to refer someone to an idea-in-progress of mine.

Some more info on the whats and the whys.

According to Neil, this is using “emacs with Org mode and Org-roam and publishing it as static HTML from org-mode. My holy grail would be something like TiddlyWiki but in emacs.”

I’ll have to take a look at this sort of set up while I’m looking at wikis. I’m sort of partial to TiddlyWiki myself so far.

Bookmarked Explainable Artificial Intelligence and Machine Learning: A reality rooted perspective by Frank Emmert-Streib, Olli Yli-Harja, Matthias Dehmer (arXiv.org)
We are used to the availability of big data generated in nearly all fields of science as a consequence of technological progress. However, the analysis of such data possess vast challenges. One of these relates to the explainability of artificial intelligence (AI) or machine learning methods. Currently, many of such methods are non-transparent with respect to their working mechanism and for this reason are called black box models, most notably deep learning methods. However, it has been realized that this constitutes severe problems for a number of fields including the health sciences and criminal justice and arguments have been brought forward in favor of an explainable AI. In this paper, we do not assume the usual perspective presenting explainable AI as it should be, but rather we provide a discussion what explainable AI can be. The difference is that we do not present wishful thinking but reality grounded properties in relation to a scientific theory beyond physics.
Bookmarked Application of information theory in systems biology by Shinsuke Uda (SpringerLink)
Over recent years, new light has been shed on aspects of information processing in cells. The quantification of information, as described by Shannon’s information theory, is a basic and powerful tool that can be applied to various fields, such as communication, statistics, and computer science, as well as to information processing within cells. It has also been used to infer the network structure of molecular species. However, the difficulty of obtaining sufficient sample sizes and the computational burden associated with the high-dimensional data often encountered in biology can result in bottlenecks in the application of information theory to systems biology. This article provides an overview of the application of information theory to systems biology, discussing the associated bottlenecks and reviewing recent work.
Bookmarked Nonadditive Entropies Yield Probability Distributions with Biases not Warranted by the Data by Ken Dill (academia.edu)
Different quantities that go by the name of entropy are used in variational principles to infer probability distributions from limited data. Shore and Johnson showed that maximizing the Boltzmann-Gibbs form of the entropy ensures that probability distributions inferred satisfy the multiplication rule of probability for independent events in the absence of data coupling such events. Other types of entropies that violate the Shore and Johnson axioms, including nonadditive entropies such as the Tsallis entropy, violate this basic consistency requirement. Here we use the axiomatic framework of Shore and Johnson to show how such nonadditive entropy functions generate biases in probability distributions that are not warranted by the underlying data.