n particular cases, GMDS failures (topological mistakes) continue to be a problem, but it is possible to detect whether that happens and then just filter those cases away. Having run experiments that try to overcome these occasional failures, I got nearly flawless classification with the FMM-based methods when it’s applied to simpler pairs, less successful when dealing with harder cases (an order of magnitude more failures, as shown in the ROC curve).

In order to reduce the recurrence of failures I am varying the size of the holes and the overall surface area, noting that still, even for corresponding surfaces, there are sometimes cases of GMDS failure.

More ROC curves were learned thereafter. Dealing yet again with cases that are hard, the following results were obtained by making further changes to the masks, even though ongoing experiments look at a wider range of attempts, over which a best fit or average get taken. By mixing the more successful approaches, better rates can be assured. By adding the simpler pairs, better rates can also be assured.

From this video description: “Due to the deactivation of “google videos” (i guess becose youtube is acepting long videos and google wants to keep all the vids over here) A lot of precious videos are being deleted in the next few days!”

OMORROW I shall do the eighth stage of the Mr. Fitness competition. So far I have gotten first place in almost every stage, but tomorrow’s stage is hard and I have done almost no practice towards it. It’s so much easier to just do stuff on the computer (photos below are from today and yes, I’ve stayed home all day).

fter persistence and a new type of mask put in place (with a little trick to penalise for miscorrespondence around the top) I have been getting good validation results again, with over 98% for the first 50 pairs I have tested (just one mistake some far). Some of the harder surface pairs have not been reached yet.

In an attempt to master GMDS as cross-identity discriminator I have further cut down the process, which now operates through a pipeline of reverse dilation, as shown in the first image beneath. The second shows a broader mask working upon false pairs and mostly failing, which is probably what we want. In terms of performance, as long as the true pairs are similar enough (some are far harder than others, e.g. expression variation), we are able to get decent results. Running this on the whole database again would not help beat results that we got around December, though. Several experimental modifications are hard to identify as means of reverting back to old versions. A lot of those were made to facilitate diffusion as a supported (surrogate) option.

I have reduced the number of points in GMDS to just 10 because it seemed as though it would significantly speed up the process without compromising too much in terms of performance. The experiments were then run on “hard” pairs, giving verification performance of almost 90%, depending on the method used.

In an attempt to understand how well one can do with just 10 samples in GMDS (because it is faster and insensitive to small changes) I took some of the hardest classification cases and ran repeated coarse GMDS on them, reaching a success rate of only 75% or thereabouts. This falls short because we are assuming that only for false pairs will we have improper correspondence, whereas for true pairs everything will be perfect. As shown in the images produced from the same false pair, sometimes a good correspondence is found, but usually not (symbolised by white colours in the dots, e.g. in the third example from the top, on the right hand side).

The example at the very bottom shows that even for different identities a decent correspondence can be found, which gives rather low stress values.

In order to mitigate or altogether annul the effect/artifact of flipping, I have made the two surfaces asymmetric, with centres of the eyes removed because there’s too much noise there, depending where the eyes look and how open they are (in some surfaces, the eyes are deliberately moved to challenge algorithms).

Several empirical results show that increasing the number of vertices, even doubling them, still seems not to help much in any way. So, I have isolated some difficult cases and I am trying to cut out the sources of ambiguity.

EVERAL years ago I wrote about an old backup procedure of mine. Sometimes people set up a job to make a backup, but what about removing backups that are too old to matter? If a directory/file needs deleting based on age, with wildcards one could run something like:

rm ~/some_file-`date -d "7 day ago" +%d%m%Y`*
rm ~/some_file-`date -d "6 day ago" +%d%m%Y`*

Or quick and dirty (risky if there’s a mixture of files in the said location):

find . -atime +7 -exec rm {} \;;

There’s nothing complicated to it. Once it’s done once, it can be modified thereafter.

EOPLE often fail to understand that a troll will strive to be inflammatory so as to push for censorship, then play the “victim” card. This is one of the most effective ways for the troll to discredit its target — to claim to be suppressed and then see how far it can be pushed. The solution to this is not easy; one is to let the troll do the trolling and another is to actually censor the troll. Does anyone have good advice on the matter?

ut crudely, government is a structural entity that helps others organise under the assumptions that without supervision from above people would act most ruthlessly among their species, unlike almost any other species. The following video from 2 months ago is about government. The key point is at the end — about Obama making it legal to assassinate US citizens whom he views as dangerous.