Niko's Project Corner

Bruteforcing Countdown numbers game with CUDA

Youtu­ber An­other Roof posed an in­ter­est­ing ques­tion on his video "The Sur­pris­ing Maths of Britain's Old­est* Game Show". The chal­lenge was stated in the video's de­scrip­tion: "I want to see a list of the per­cent­age of solv­able games for ALL op­tions of large num­bers. Like I did for the 15 op­tions of the form {n, n+25, n+50, n+75}, but for all of them. The op­tions for large num­bers should be four dis­tinct num­bers in the range from 11 to 100. As I said there are 2555190 such op­tions so this will re­quire a clever bit of code, but I think it’s pos­si­ble!". His ref­er­ence Python im­ple­men­ta­tion would have taken 1055 days (25000 hours) of CPU time (when all four large num­bers are used in the game), but with CUDA and a RTX 4080 card it could be solved in just 0.8 hours, or 31000x faster!

Cheating at Bananagrams with real-time AI, part 1

Ba­nana­grams is a real-time word game in which par­tic­ipants race to build their own cross­words. It re­quires com­pre­hen­sive vo­cab­ulary, fast think­ing and good de­ci­sion mak­ing. Some times sit­ua­tions arise which re­quire an ei­ther-or de­ci­sion: do I scrap my par­tial so­lu­tion and do a fresh start or not? Or you may be left with one un­us­able ex­tra let­ter, which you can put back into the pile and pick up three ran­dom new ones. This first ar­ti­cle of the topic de­scribes a two-phase ar­ti­fi­cial neu­ral net­work which de­tects and iden­ti­fies the tiles from an im­age. The sec­ond ar­ti­cle will de­scribe how to gen­er­ate a valid cross­word from given let­ters.

Introduction to Stable Diffusion's parameters

Sta­ble Dif­fu­sion is an im­age gen­er­ation net­work, which was re­leased to the pub­lic in 2022. It is based on a dif­fu­sion pro­cess, in which the model gets a noisy im­age as an in­put and it tries to gen­er­ate a noise-free im­age as an out­put. This pro­cess can be guided by de­scrib­ing the tar­get im­age in plain En­glish (aka txt2img), and op­tion­ally even giv­ing it a tar­get im­age (aka. img2img). This ar­ti­cle doesn't de­scribe how the model works and how to run it your­self, in­stead this is more of a tu­to­rial on how var­ious pa­ram­eters af­fect the re­sult­ing im­age. Non-tech­ni­cal peo­ple can use these im­age gen­er­at­ing AIs via web­pages such as Ar­tis­tic.wtf (my and my friend's pro­ject), Craiyon.com, Mid­jour­ney.com and others.

Matching puzzle pieces together

Some peo­ple en­joy solv­ing puz­zles in the old fash­ioned way, but en­gi­neers would like to au­to­mate te­dious tasks like that. This is a well-suited task for su­per­vised learn­ing, but nat­urally it re­quires train­ing data. Gath­er­ing it from real-life puz­zles would be time-con­sum­ing as well, so I opted for gen­er­at­ing it in­stead. This gives a lot of con­trol on the data, but the re­sult­ing sys­tem might not even work with real-life in­puts. There are also sev­eral dif­fer­ent styles of puz­zles, but in this pro­ject each "base-piece" is a rect­an­gle of iden­ti­cal size. An ex­am­ple 3 × 3 puz­zle is shown in the thumb­nail.

Single channel speech / music separation

Hu­mans are nat­urally ca­pa­ble of sep­arat­ing an ob­ject from the back­ground of an im­age, or speech from mu­sic on an au­dio clip. Photo edit­ing is an easy task, but per­son­ally I don't know how to re­move mu­sic from the back­ground. A first ap­proach would be to use band-pass fil­ters, but it wouldn't re­sult in a sat­is­fac­tory end re­sult since there is so much over­lap be­tween the fre­quen­cies. This ar­ti­cle de­scripbes a su­per­vised learn­ing ap­proach on solv­ing this prob­lem.

Image and video clustering with an autoencoder

This ar­ti­cle de­scribes a neu­ral net­work which au­to­mat­ically pro­jects a large col­lec­tion of video frames (or im­ages) into 2D co­or­di­nates, based on their con­tent and sim­ilar­ity. It can be used to find con­tent such as ex­plo­sions from Arnold's movies, or car sce­nes from Bonds. It was orig­inally de­vel­oped to or­ga­nize over 6 hours of Go­Pro footage from Åre bike trip from the sum­mer of 2020, and cre­ate a high-res poster which shows the beau­ti­ful and vary­ing land­scape (Fig­ure 9).

Helsinki Deblur Challenge 2021

The Finnish In­verse Prob­lems So­ci­ety (FIPS) or­ga­nized the Helsinki De­blur Chal­lenge 2021 dur­ing the sum­mer and fall of 2021. The chal­lenge is to "de­blur" (de­con­volve) im­ages of a known amount of blur, and run the re­sult­ing im­age through and OCR al­go­rithm. De­blur-re­sults are scored based on how well the pytesser­act OCR al­go­rithm is able to read the text. They also kindly pro­vided un­blurred ver­sions of the pic­tures, so we can train neu­ral net­works us­ing any su­per­vised learn­ing meth­ods at hand. The net­work de­scribed in this ar­ti­cle isn't of­fi­cially reg­is­tered to the con­test, but since the eval­ua­tion dataset is also pub­lic we can run the statis­tics our­selves. Hy­per­pa­ram­eter tun­ing got a lot more dif­fi­cult once it started tak­ing 12 - 24 hours to train the model. I might re-visit this pro­ject later, but here its sta­tus de­scribed as of De­cem­ber 2021. Had the cur­rent best net­work been sub­mit­ted to the chal­lenge, it would have ranked 7th out of the 10 (nine plus this one) par­tic­ipants. There is al­ready a long list of known pos­si­ble im­prove­ments at the end of this ar­ti­cle, so stay tuned for fu­ture ar­ti­cles.

Satellite crash course

Since hear­ing the news of a fal­ing Chi­nese rocket booster Long March 5B and be­ing re­minded that Earth's sur­face is about 70% ocean, I got in­ter­ested on how the or­bital pa­ram­eters af­fect the odds of crash­ing to ocean vs. ground. I was nearly fin­ished with the pro­ject when I re­al­ized that Earth is ro­tat­ing un­der the satel­lite, thus in­val­idat­ing all the re­sults! This doesn't take or­bits' ec­cen­tric­ity into ac­count ei­ther, but I've heard that the ath­mo­spheric drag has a den­dency of re­duc­ing it to zero as the or­bit falls. Any­way, I found the var­ious "straight" paths around the globe in­ter­est­ing and de­cided to pub­lish these re­sults any­way. In con­clu­sion there are paths which spend only 9 % on top of land (in­clud­ing lakes) and 91% on top of ocean or up-to 57% on top of land and 43% on top of ocean.

Chess video search engine

Youtube has a quite good search func­tion­al­ity based on video ti­tles, de­scrip­tions and maybe even sub­ti­tles but it doesn't go into ac­tual video con­tents and provide ac­cu­rate times­tamps for users' searches. An youtu­ber "Agad­ma­tor" has a very pop­ular chan­nel (1.1 mil­lion sub­scribers, 454 mil­lion video views at the time of writ­ing) which show­cases ma­jor chess games from past and re­cent tour­na­ments and on­line games. Here a search en­gine is in­tro­duced which an­alyzes the videos, rec­og­nizes chess pieces and builds a database of all of the po­si­tions on the board ready to be searched. It keeps track of the ex­act times­tamps of the videos in which the queried po­si­tion oc­curs so it is able to provide di­rect links to rel­evant videos.

An efficient schema for hierarchical data on Elasticsearch

Many busi­nesses gen­er­ate rich datasets from which valu­able in­sights can be dis­cov­ered. A ba­sic start­ing point is to an­alyze sep­arate events such as item sales, tourist at­trac­tion vis­its or movies seen. From these a time se­ries (to­tal sales / item / day, to­tal vis­its / tourist spot / week) or ba­sic met­rics (his­togram of movie rat­ings) can be ag­gre­gated. Things get a lot more in­ter­est­ing when in­di­vid­ual data points can be linked to­gether by a com­mon id, such as items be­ing bought in the same bas­ket or by the same house hold (iden­ti­fied by a loy­alty card), the spots vis­ited by a tourist group through out their jour­ney or movie rat­ings given by a speci­fic user. This richer data can be used to build rec­om­men­da­tion en­gi­nes, iden­tify sub­sti­tute prod­ucts or ser­vices and do clus­ter­ing anal­ysis. This ar­ti­cle de­scribes a schema for Elas­tic­search which sup­ports ef­fi­cient fil­ter­ing and ag­gre­ga­tions, and is au­to­mat­ically com­pat­ible with new data val­ues.

Caching and perf. monitoring with Redis and Python

When im­ple­ment­ing real-time APIs most of the time server load can greatly be re­duced by caching fre­quently ac­cessed and rarely mod­ified data, or re-us­able cal­cu­la­tion re­sults. Luck­ily Python has sev­eral fea­tures which make it easy to add new con­structs and wrap­pers to the lan­guage, for ex­am­ple thanks to *args, **kwargs func­tion ar­gu­ments, first-class func­tions, dec­ora­tors and so fort. Thus it doesn't take too much ef­fort to im­ple­ment a @cached dec­ora­tor with business-specific logic on cache invalidation. Redis is the perfect fit for the job thanks to its high performance, binary-friendly key-value store with TTL and different data eviction policies and support for other data structures which make it trivial to store additional key metrics there.

Scalable analytics with Docker, Spark and Python

Tra­di­tion­ally data sci­en­tists in­stalled soft­ware pack­ages di­rectly to their ma­chi­nes, wrote code, trained mod­els, saved re­sults to lo­cal files and ap­plied mod­els to new data in batch pro­cess­ing style. New data-driven prod­ucts re­quire rapid de­vel­op­ment of new mod­els, scal­able train­ing and easy in­te­gra­tion to other as­pects of the busi­ness. Here I am propos­ing one (per­haps al­ready well-known) cloud-ready ar­chi­tec­ture to meet these re­quire­ments.

Very fuzzy searching with Elasticsearch

I en­coun­tered an in­ter­est­ing ques­tion at Stack Over­flow about fuzzy search­ing of hashes on Elas­tic­search and de­cided to give it a go. It has na­tive sup­port for fuzzy text searches but due to per­for­mance rea­sons it only sup­ports an edit dis­tance up-to 2. In this con­text the max­imum al­lowed dis­tance was eight so an al­ter­na­tive so­lu­tion was needed. A so­lu­tion was found from lo­cal­ity-sen­si­tive hash­ing.