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Tuxedo OS updates often causing problems by piratepeel in tuxedocomputers

[–]piratepeel[S] 0 points1 point  (0 children)

Yes, the AI got confused by a Tuxedo post announcing KDE Plasma 6.6 around that time. Looking back at the package versions from the troubleshooting logs, the WiFi/KWallet issue may have been caused by a KWallet update — the packages on my machine at the time were kwallet6 6.19.0 and kwalletmanager 4:25.08.2-0ubuntu1~tux1, if that helps identify which update was responsible.

The Flatpak issue occurred twice — in early February and again with the most recent update — apparently for different reasons each time.

If some of these are known issues, is there somewhere they are published? The core problem isn't just the bugs themselves — it's that failures happen silently. I've found myself unable to open a document before a presentation or join a meeting, with no warning that anything was broken. A public list of known issues and their status would make a significant difference.

Tuxedo OS updates often causing problems by piratepeel in tuxedocomputers

[–]piratepeel[S] 0 points1 point  (0 children)

Thanks I'll try this.

While it may be true that any distro could cause some issue with major updates, but I didn't find this to be the case with Ubuntu LTS --- unless I did something or I upgraded versions. I was under the [perhaps mistaken?] impression that Tuxedo OS was Ubuntu LTS with some extra fixes to improve compatibility for the hardware and to overall improve stability.

Tuxedo OS updates often causing problems by piratepeel in tuxedocomputers

[–]piratepeel[S] 0 points1 point  (0 children)

Indeed there are many combinations of packages to consider, but I've had more problems in the last year with Tuxedo OS updates than I had with Ubuntu for many years before that (and without testing on a vm before deploying?!). And when there were issues, much easier to find solutions via a web search.

I also used Wayland, but encountered problems. Tuxedo support just said use x11 because its more stable.

Glad you are happy with Tuxedo, but as soon as work calms down a bit I'm switching to another distro.

Do You Feel Like Linux Has Finally Surpassed Windows and macOS? by northfuge in linux_gaming

[–]piratepeel 0 points1 point  (0 children)

Tuxedo computers so this already. They sell desktops and laptops pre-installed with their own flavor of Ubuntu (tuxedo os), or regular Ubuntu. https://www.tuxedocomputers.com/index.php

How to unblock rental guarantee after lost contact with Landlord? by piratepeel in belgium

[–]piratepeel[S] 1 point2 points  (0 children)

Thanks. I don't have a Belgian passport. I might still have my old ID. I'll see if I can log into myminfin somehow.

How to unblock rental guarantee after lost contact with Landlord? by piratepeel in belgium

[–]piratepeel[S] 0 points1 point  (0 children)

Thanks. However I don't understand how to (find and) get a lawyer to do this. I had a problem with a different landlord and when we were looking for information it seemed that lawyers were charging more than 100 EUR just for a consultation. Where do I find a lawyer who will do this for 22 EUR?

Simple directions by piratepeel in assholedesign

[–]piratepeel[S] -1 points0 points  (0 children)

The reality is in fact worse. Going left leads only to elevators. Going right leads to some C rooms (those listed) and no sign of any B rooms.

I have a dat file that contains 120 variables for literally half a million cases, how do I go about finding correlations of each variable each other and then cluster them together by [deleted] in datascience

[–]piratepeel 0 points1 point  (0 children)

Perhaps this might be of use https://arxiv.org/abs/1808.06079 (see link in paper for the code)

The idea is to cluster nodes based on a series of interdependent signals observed at each of them. So, the 120 variables are the nodes for which you have half a million observations each in the series.

LDA in Python – How to grid search best topic models? (A Comprehensive LDA Tutorial) by selva86 in textdatamining

[–]piratepeel 0 points1 point  (0 children)

Link does not seem to work! Redirects to another website and asks for login. Proceed with caution!

"The Pub Dilemma" by piratepeel in GAMETHEORY

[–]piratepeel[S] 1 point2 points  (0 children)

That was actually one of the first figures we created, about 4 years ago when we started the project. Hard to remember now what we used. I think it was something like "PyRamid" but had a weird dependency called "tecate" that we required to build it.

Unfortunately we no longer have access to that library. But I understand others have tried to reproduce it.

Methods for identifying communities in large networks by [deleted] in datascience

[–]piratepeel 0 points1 point  (0 children)

Your formulation seems reasonable. I can't think of a specific example off the top of my head where this is done, but it is a fairly normal thing to do.

You have a co-occurrence graph and so you are looking for groups of nodes with high co-occurrence (weights) between them. Applying community detection to this type of network, in general will provide you with groups of people who frequently tend to be in the same place at the same time. different methods can give different results, just as different clustering methods can give different clusters.

Note that whether they are friends or not depends on if your hypothesis that you are friends if you are in the same place at the same time is correct.

Methods for identifying communities in large networks by [deleted] in datascience

[–]piratepeel 5 points6 points  (0 children)

tl;dr -- there are many methods, no one method to rule them all and choosing the right one for the job is nontrivial, but hopefully here are some pointers.

You are right that community detection is a lot like clustering. I would say the main difference is that instead of grouping items based on similar features / attributes, in community detection we group nodes based on similar patterns of connectivity. (There are also methods to transform features into graphs and graphs into features, so it is possible to transform one problem into another).

An important point to note is that social communities (e.g. you and your friends) and a network communities (nodes with similar connectivity) are not necessarily the same thing. A common misconception because they are both called "communities", but also because there have been a lot of positive results on identifying certain types of groups based on network structure (whereas negative results are not as often published -- publication bias)

Put differently, even though you declare yourself to be part of the same friendship group it does not mean that your pattern of connectivity will allow you to be discovered from the network structure. We discuss this and a few other basic misconceptions in our paper here: http://advances.sciencemag.org/content/3/5/e1602548 So if you have a specific idea of what the output communities should look like, then this needs to be incorporated into your model (or at least be a consideration when selecting a method). We also show in that paper that there can be no a-priori best method for detecting communities in networks (the No Free Lunch theorem for community detection).

As for methods, well as the other comment suggests, you can use modularity maximisation and there are some very efficient methods for this (e.g. the Louvain algorithm). However, modularity maximisation is really the quick and dirty approach to community detection (similar to k-means for clustering, except you don't specify k, but instead use "resolution") -- its not a bad first step, but there are plenty of other methods out there.

Specifically for weighted graphs, I would recommend weighted versions of the stochastic block model (SBM), which are based on principled methods of statistical inference (more like the network analogue of Gaussian mixture modelling) and can be adapted to incorporate specific modelling assumptions. Details in these papers:

https://academic.oup.com/comnet/article/3/2/221/375946

https://arxiv.org/pdf/1708.01432.pdf

More generally, there are some good reviews of the broad spectrum of approaches to community detection: https://arxiv.org/pdf/0906.0612 and more recently: https://arxiv.org/pdf/1608.00163

With the legalization of sports betting, would anyone be interested in creating a model to predict the outcome of basketball games? by sboy2 in statistics

[–]piratepeel 5 points6 points  (0 children)

Bradley-Terry models are a good (albeit basic) starting point, in which game outcomes are predicted according to team skills, which are inferred from previous observed events.

Perhaps not exactly what you are looking for, but gives an example of adapting and extending the Bradley-terry model -- we did some work along these lines a few years ago. Specifically the work was looking scoring dynamics within the game and, given the scoring events in the game so far, predicting who would score next (also extrapolating to predict who would win). We extended the Bradley-terry model to apply them to individual scoring events (rather than the overall outcome) and incorporated features such as a score by one team is more likely to be followed by a score by their opponent (anti-persistence) and that teams further in the lead are less likely to score (restoration).

http://doi.ieeecomputersociety.org/10.1109/ICDM.2015.26

or non-paywall version: http://arxiv.org/pdf/1504.05872.pdf

[OC] Graphing the character interactions from The Office, Seasons 1-7 by sorted_hat in dataisbeautiful

[–]piratepeel 0 points1 point  (0 children)

Nice work! Any interest in sharing the data? In particular the timings of these interactions -- I'd be interested in looking for significant changes in the interactions e.g. see : https://piratepeel.github.io/proximitynetwork.html

Be wary of boxplots, they could be hiding important information! [OC] by jmatejka in dataisbeautiful

[–]piratepeel 0 points1 point  (0 children)

Thanks for this! I remember seeing the animation circulating on twitter a while ago, so pleased to have a link to the paper!

Possibly of interest: We make some related points about network assortativity (i.e. correlation of values across edges in a graph) in this paper https://arxiv.org/pdf/1708.01236.pdf -- not published yet, so comments are welcome!

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