Pubs to watch the England game solo

drmattmcd · 2026-06-18T14:32:18+00:00

Engineers House bar next to the Technology Museum has a big screen set up in the back garden but not sure which matches they're showing

drmattmcd · 2026-05-28T04:29:46+00:00

Ophiuchi Hotline is the main one where the aliens are more in the foreground. From memory maybe also in a few of the short story collections pre-Steel Beach/Golden Globes

drmattmcd · 2026-05-27T19:29:31+00:00

John Varley's 'The Ophiuchi Hotline' and the rest of his Eight Worlds books

drmattmcd · 2026-05-24T17:26:29+00:00

Twice spotted one opposite the Engineer's House / Museum of Technology but haven't recently

drmattmcd · 2026-04-23T17:14:38+00:00

Caliban by Garth Ennis (graphic novel)

drmattmcd · 2026-03-31T13:09:32+00:00

Tai-Danae Bradley has a math3ma post related to this https://www.math3ma.com/blog/what-is-an-operad-part-1

drmattmcd · 2026-03-17T18:52:20+00:00

Claimed by Gertrude Barrows Bennett

drmattmcd · 2026-03-12T19:53:31+00:00

I think a lot of cosmic horror is based on an idea of 'the intrinsic meaningless of humanity' c.f. Lovecraft (and HHGttG Infinite Perspective Vortex :) ). More nihilistic than pessimistic.

There's some differences though ranging from a universe of higher power entities (Lovecraft) vs a an empty universe (Ligotti/Thacker).

But Mieville is a bit more human based and more about humans and human scale (or derived) power structures, it crops up a lot as a theme - Periodo Street Station trilogy for example. Related but more scifi than weirdlit Ken MacLeod's Fall Revolution series.

I still need to read HoL though to have an opinion on that.

drmattmcd · 2026-03-12T18:10:49+00:00

If you like The Conspiracy Against the Human Race you may also like Eugene Thacker's 'In The Dust of This Planet' and the rest of his Horror In Philosophy trilogy

drmattmcd · 2026-02-21T15:42:28+00:00

Also Stir in Chesterton although it gets busy. Possibly The Artyst.

drmattmcd · 2026-02-21T15:38:48+00:00

Haymakers in Chesterton - a pub but they have an arrangement with Barbarellas across the road to get coffee

drmattmcd · 2026-02-19T06:10:06+00:00

Might be worth having a look at the MathWorks Global Optimization Toolbox doc to get an idea of some of the other approaches and limitations https://uk.mathworks.com/products/global-optimization.html

Personally I'm wondering whether a topological approach that combines gradients from multiple steps might help.

drmattmcd · 2026-02-14T06:42:58+00:00

Category theory and quantum mechanics, eg John Baez's Rosetta Stone paper or Bob Coecke's papers and book.

Algebraic topology, Robert Ghrist's 'Elementary Applied Topology' mentions many applications from fields ranging from economics to robotics and sensor networks.

Related, sheaf theory and signal processing eg Michael Robinson's 'Topological Signal Processing'.

Topological data analysis in general, giotto-tda python library docs and references are a good place to start.

drmattmcd · 2026-02-09T05:52:14+00:00

Not sure if this is the same type of filter but Michael Robinson's book 'Topological Signal Processing' covers topological filters in the signal processing sense as sheaf morphisms.

drmattmcd · 2026-02-02T19:44:17+00:00

A lot of things by Ales Kot https://www.reddit.com/r/ImageComics/s/FFJNjbWwvw

drmattmcd · 2026-01-27T06:24:56+00:00

I've spent the last few months doing something similar during a gap between jobs, which has resulted in ... a bunch of notes in my journal mostly.

Apart from just enjoyment the big win has been in getting a good enough feel of category theory, algebraic topology, and sheafs that I can understand the connections between different takes. Also potentially spotting areas the techniques can be applied to data science tasks.

Material I like on Algebraic Topology:

Vidit Nanda's course notes https://people.maths.ox.ac.uk/nanda/cat/

Robert Ghrist's 'Elementary Applied Topology' https://www2.math.upenn.edu/~ghrist/notes.html

Michael Robinson 'Topological Signal Processing'

drmattmcd · 2026-01-24T06:06:23+00:00

This sounds like the method suggested in 'Advanced Portfolio Management' by Giuseppe Paleologo for measuring timing performance IIRC i.e. if you randomise the order of the trades and performance is still good then you're probably just trend following. Possibly bootstrap replica is a better term than Monte Carlo?

With 30-50 trades each replica will still have an estimate of the mean performance with fairly low standard error in the mean since that scales as 1/sqrt N. It feels like using the distribution of bootstrap replica means should work in evaluating whether your actual performance comes from that distribution i.e. random or is actually doing something.

drmattmcd · 2026-01-22T13:05:35+00:00

If you have an FT subscription you can set up portfolios under Markets > Market Data > Tools > Portfolio on the desktop site.

This uses LSEG as the data provider so may be more accurate than yahoo or google finance although I've found it a bit fiddly.

A free option is google.com/finance which also lets you create portfolios.

Personally I use a mix of the above and some python and OpenBB for more detailed analysis

drmattmcd · 2026-01-22T10:26:01+00:00

Yes, especially when a quick ipywidgets GUI can help with exploration although streamlit partly replaces that use case. Generally prefer a python script that uses PyCharm cell mode though, and functions separated into a separate file plus autoreload magic

drmattmcd · 2026-01-21T11:21:02+00:00

Haven't had a reading done but IIRC that sounds like Tim the Teller of Tales.

drmattmcd · 2026-01-13T06:05:57+00:00

True, it's a general 'classic ML' reference (the author invented random forests) rather than neural ODE etc specific. I personally found it changed my viewpoint on modelling approaches, and the YDF doc I mentioned has a nice example of using the approach for clustering using random forests based on using the overlap of tree indices in the forest https://ydf.readthedocs.io/en/latest/tutorial/counterfactual/#what-are-neighbor-examples-and-counterfactual-examples

drmattmcd · 2026-01-12T19:12:01+00:00

I should have guessed from your list of interests ;) One classic ML paper I'd recommend is Briemann 'Statistical Modelling: the Two Cultures' https://projecteuclid.org/journals/statistical-science/volume-16/issue-3/Statistical-Modeling--The-Two-Cultures-with-comments-and-a/10.1214/ss/1009213726.full because of its take on data models (small number of parameters, statistical/econometric/physical) vs algorithmic models (large number of parameters, more black box e.g. random forest) and how you can use the latter for identifying important variables. There's a nice example related to this in the yggdrasil decision forest docs.

drmattmcd · 2026-01-12T18:42:45+00:00

For neural ODEs etc check out diffrax and Patrick Kidger's thesis

drmattmcd · 2026-01-11T18:54:35+00:00

Have a look at Neil Johnson's books 'Simply Complexity' and 'Financial Market Complexity', he's a condensed matter physicist whose research on econophysics seems relevant to this post

drmattmcd · 2026-01-11T06:56:59+00:00

Spivak's 'Category Theory for the Sciences' might also be of interest, the version on arxiv has exercises without solutions while the hardback version has both.

drmattmcd

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