Entries tagged paper | Hugonweb Annotated Link Bibliography

http://www.openmusiclabs.com/files/expotemp.pdf

https://www.pnas.org/doi/10.1073/pnas.2520385123

Bentz and Dutkiewicz analyze the statistical properties of geometric signs on $>200$ Central European artifacts. They find the complexity of the signs to be similar to that on the earliest protocuneiform tablets, but tens of thousands of years earlier.

See pictures and decoding below:

https://doi.org/10.3386/w28967

Gabaix, X. and Koijen. In search of the origins of financial fluctuations: the inelastic markets hypothesis. NBER Working Paper, 2021.

I found it via an article in The Economist: Is passive investment inflating a stockmarket bubble? by Buttonwood.

It describes how $1 invested in a broad-market index increases the price of the index by $3 to $8, and that this is due to inelasticity in the market due to most participants just buying and holding.

Also interesting is that $1 of stock buybacks leads to $\approx$ $2 of increase in stock price.

The important part for future research is that their financial model is dependent on easily-tracked flows of money in/out of the market, rather than e.g. sentiment.

https://doi.org/10.1007/978-3-030-19274-7_49

Highlights commands that can be used for command-line data analysis using UNIX pipes.

Including:

• less
• head
• tail
• cut
• grep
• sed
• awk
• sort
• uniq
• comm
• join
• tr
• wc
• diff
• comm

I'd add seq for testing things out.

https://arxiv.org/abs/1710.03462

A nice discussion of structure of arrays vs array of structures in the context of multi-particle simulations.

As of 2017, they show that structure of arrays performs better. The performance is especially great when the data fits in the CPU cache.

https://arxiv.org/abs/1807.04271

By Tang, Ewin

Interesting that the quantum algorithm inspired an equally performant classical one.

See also Tang's paper published in PRL: arXiv:1811.00414 (cs)

https://dl.acm.org/doi/abs/10.1145/3530258

A paper showing that there are problems quantum computers solve that aren't even solvable by a generalization of NP in classical computers.

Freely available here

https://www.frontiersin.org/news/2025/05/23/street-smarts-hawk-use-traffic-signals-hunting

A hawk learning that an audio pedestrian walk signal at a traffic light leads to backed up cars it can use as cover to approach prey.

The published paper is here: https://doi.org/10.3389/fetho.2025.1539103

https://doi.org/10.1002/adbi.202100994

Paper about 3D printers applied to microscopy. Some interesting projects and observations about 3D printing.

https://research.chalmers.se/publication/517894/file/517894_Fulltext.pdf

A guide to writing property-based tests. The author, John Hughes, co-wrote QuickCheck in Haskell, the first package for property-based testing.

Published conference paper: http://dx.doi.org/10.1007/978-3-030-47147-7_4

Some quotes:

"Avoid replicating your code in your tests," because it's lot of work and likely to contain the same errors as your code.

"Test your tests," because if your generator and shrinker produce invalid values, everything else will fail too.

Validity testing

Validity testing consists of defining a function to check the invariants of your datatypes, writing properties to test that your generators and shrinkers only produce valid results, and writing a property for each function under test that performs a single random call, and checks that the return value is valid.

Postcondition testing

A postcondition tests a single function, calling it with random arguments, and checking an expected relationship between its arguments and its result.

Metamorphic testing

A metamorphic property tests a single function by making (usually) two related calls, and checking the expected relationship between the two results.

Inductive testing

Inductive properties relate a call of the function-under-test to calls with smaller arguments. A set of inductive properties covering all possible cases together test the base case(s) and induction step(s) of an inductive proof-of-correctness. If all the properties hold, then we know the function is correct–inductive properties together make up a complete test.

Model-based testing

A model-based property tests a single function by making a single call, and comparing its result to the result of a related “abstract operation” applied to related abstract arguments. An abstraction functions maps the real, concrete arguments and results to abstract values, which we also call the “model”.

https://www.nature.com/articles/s41598-020-75843-7

Using the time of flight of relativistic cosmic ray muons to estimate relative position.

There is also a 2024 paper on a more practical, wireless system and another on time synchronization from 2022.

Here is the author, Hiroyuki K.M. Tanaka's inspirehep page.

https://doi.org/10.1038/s41586-024-08514-6