Moskva is a competent thriller set in 1980s Moscow. Along with a time and place, it also shares with Gorky Park, a hero who can take enormous amounts of psychic and physical pain as he strides, limps and finally crawls through a landscape littered by bodies.

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It gets classified here correctly as N but also as numerical, punctuation, or verb. Maybe too rare a word or something? Numerical and punctuation are still odd.


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Here, the tag was initially on level surface with the bottom down as you might expect. At point 1, I flipped it upside down (belly up). This moved Z acceleration value from 1 to -1 while X and Y stayed constant. At point 2 I turned the tag on its side. At points 3 and 4 I rotated it on its side a bit each time.

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Considering the brisk reaction time we have been getting on test samples, I fully expect this GB to materialize at a faster pace than previous GBs. I don’t forsee a problem having this ready for the Christmas season. Nevertheless, this is not a guarantee.


A strict or severe abstraction that is subject to prohibitions on the free use of colour as the agent of space, as in the line of descent from Mondrian of the 1930s, and geometric Abstraction in general, will inevitably result in an impoverished art in some direction or another. Hence the wide range of Hofmann’s exploring of painterly means (always subject to the disciplines of the picture plane), and what some regard as the profligacy of his multi-styled approach. But this is precisely why his art has been so influential on a younger group of painters in this country. He closes down nothing, and opens up possibilities for others.

Tokenization could also be of more interest. Finnish language has a lot of compound words, some of which are visible in the above examples. For example, “kuorma-auto”, and “linja-auto” for the wikipedia example. Or the different “liikelaitos” combinations for the city of Oulu version. Further n-grams (combinations of words) would be useful to investigate further. For example, “energia” in the city example could easily be related to the city power company called “Oulun Energia”. Many similar examples likely can be found all over any language and domain vocabulary.


Well, reading the discussion and other answers on the StackOverflow page seems to suggest not to stem before POS tagging. And the wikipedia pages on stemming and lemmatization describe the difference as in Lemmatization requiring the context (the POS tag) to properly function. Which makes sense, since words can have multiple meaning depending on their context (part of speech). So therefore we should probably conclude that it is better to not stem or lemmatize before training a POS tagger (or using it I guess). But common sense never stopped us before, so lets try it.

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The seed files should be the same format as the output produced by PICT. Lets say I want to try all types with all file systems, using smallest size.


Moskva begins with the disappearance of the British ambassador's daughter, something that just does not happen to a foreign dignitary's family. Major Tom Fox is stationed in Moscow after his military intelligence work in Northern Ireland. Major Tom (yes, that is referenced in the book) is in a cooling off period from his earlier assignment but cannot help getting caught up in the search for the missing girl.

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TF-IDF stands for term frequency (TF) – inverse document frequency (IDF). For example, if the word “bob” appears often in a document, it has a high term frequency for that document. Generally, one might consider such a word to describe that document well (or the concepts in the document). However, if the same word also appears commonly in all the documents (in the “corpus”), it is not really specific to that document, and not very representative of that document vs all other documents in the corpus. So IDF is used to modify the TF so that words that appear often in a document but less often in others in the corpus get a higher weight. And if the word appears often across many documents, it gets a lower weight.


OKB-1 was moved to East Germany, where merged into the existing industrial association. Aircraft received the designation VEB-152 and its development continued.

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And to preemptively counter the “Leica’s are all about the lenses” squad, here’s some truth. Every brand, by the 1990s, had mastered the creation of amazing lenses. Shooting any late model AF SLR will allow you to mount excellent glass of whatever focal length you want.


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Install InfluxDB somewhere with a network connection accessible from the Pi to have a place for the data. Really very simple to do (even for me), so no big instructions here, the link is good.

Anyway, for illustration, see the lines below. The highlighted blue parts are where the silence happened.


Additionally, we have had some really interesting suggestions on design, materials usage etc. These ideas deserve adequate attention, and are being discussed with Neal and the manufacturer of the X6-SE v2. Likewise, it is nice to see the willingness and excitement of BLFers wanting to help and push this SE light set along. The more people we have participating, the better value and specifications we can achieve.

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To an extent, consensus taste is still albeit unconsciously dominated by the promotional tropes of post-painterly Abstraction, even though the works themselves are denied current agency. There is an inbuilt bias in favour of that smoothly immaculate suppression of the evidence of human agency, the suppression of brushwork that shapes and models surfaces in a spatial way so eloquently described by Patrick Heron in “From Late Matisse” – for fear that it might suggest a figurative space.


Switching to a female character part way through the game does nothing to improve matters, aside from changing the view slightly. And seeing as you're never going to play the damn thing, I may as well tell you that Aurora Watching also drops the old DNA-manipulation/ zombie mutant clanger - a kiss of death that we were willing to overlook in Far Cry, but never want to see in another videogame as long as we live.

If that sounds like your idea of fun then by all means ignore us and buy Aurora Watching - it's only 20 quid, after all. Otherwise, stick with proper games like Splinter Cell.


After this, I ran the same analysis on a bunch of document sets I have from different Finnish organizations. I won’t be putting the exact data for those documents online here, but I will show some statistics on the runs and the models produced, as well as my feeling from looking at the topics generated and the stats.

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And this amazing value proposition still stands when we compare any late-1990s/early-2000s AF SLR even against less expensive popular cameras, like the Nikon F3 or a Yashica T4. It’s just that you’ll be able to buy fewer bananas. The point is that these unpopular and dorky 35mm autofocus SLR cameras are an unbelievable value, the best value in photography today, in fact. And that’s independent of whether or not you like bananas.


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My first mistake in this regard was to think of LDA as a single-core solution. I implemented the original algorithm some times back, and did not see it becoming anything else. But the online version seems to batch it in pieces, which I guess makes it more parallelizable. And the Gensim docs also nicely describe how running this online algorithm now also merges the results in a way that you don’t necessarily need to run large numbers of passes (iterations) over the corpus to converge on a better model. Chunksize 10000 in the above code seems to cause this merge after each 10000 docs, and with Wikipedia having about 4 million articles, this amounts for quite a few merges. Maybe somewhat equal to iterations of old.

Coloured pigments do not behave the way spectral light does when fused together. Blue and red light combine to create violet, but blue and red pigments, even when overlaid in transparent layers, do not, but rather produce a murky brownish black. The Delaunays tend to avoid this by placing simultaneous contrasts side-by-side in accordance with classic neo-impressionist principles, or else interspaced with adjacent hues on the spectrum.


I like spies and intrigue, especially Cold War. This was set in my childhood era, and yet nothing felt familiar or true. The story is convoluted and choppy in places, and was hard to finish.

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He was buried in The Vagankov Cemetery in Moscow. He was awarded the Order of Lenin (17/06/1961), two Orders of the Red Banner of Labour (1944, 12/07/1957), the Order of the Red Star (31/10/1941), the Medal for Labor Valor (21/06/1943), and other medals.

Running these, you should see all sorts of live captured bluetooth data printed on the console. As I mentioned, I had issues with various versions of the hci-tools. Even if you get no errors running these two commands (hcitool and hcidump), it does not mean the hci-tools would not have issues. I initially got errors trying to run these two commands. After various fixes, they would start but not log anything. So no errors but no data either. Only after installing the Raspbian Stretch hci-tool versions on top of otherwise plain Raspbian Jessie install they started to print all the BT traffic, and I figured they were finally working.


First, lemmatize all your data (I prefer it over stemming but it can be more resource intensive). Clean all your data from the typical stopwords for your language, but also for your dataset and domain. Run the models and analysis several times, and keep refining your list of removed words to clean also based on your use case, your dataset and your domain. Also likely need to consider domain specific lemmatization rules as I already discussed with POS tagging.

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Suddenly, and as if from nowhere, there came a radical shift in aesthetic to a cooler, blander and simpler statement of pictorial priority, appearing in the same galleries that had formerly promoted them. Concept was everything, essentially a design concept, and execution carried out using methods that had formerly been the territory of designers and graphic artists, minimising brushwork and individual touch. The St. Ives artists were most affected, to unsettling effect, and many were side-lined in favour of the new aesthetic. Although from another point of view, the Davie may seem a lot richer and more adventurous, this was not the conclusion drawn by the art-world at the time, however.

Simultaneity, or simultanism, became the watchword, and it has been very influential on later colourists. But it is not the colour contrasts themselves that induce the sensation of movement they sought, so much as the arcs and whorls of curvilinear drawing with which they disposed their colours.

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So this is all nice and shiny, but the examples are actually quite simple test scenarios. There are no complex dependencies between them, not complex state that defines what parameters and values are available, and so on. It mostly seems to vary around what combinations of software or system configurations should be used in testing.


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So, this is where we are at right now. I will be adding updates to this OP as received. I hope to have this available by Christmas, since it will make an awesome gift set.

He does not project an image into fictive space as Matta does. He has learned from Miro that the surface is an expressive force in its own right, as are the varied properties of the medium itself, if used sensitively. And the brilliant suppleness and eloquence of his line drawing is in a different class from the flashy cartoonish art of Matta, (shades of Pixar). Lee Krasner was right to reject his art as insufficiently “plastic” in conception.


Since these have a very small number of documents when compare to Wikipedia, I ran the Gensim LDA model generator for them in the online mode using batch size of 1000. Separately with 10 iterations and 100 iterations to get some comparable data on impact of iteration counts. Listing all 3×3 grids for the 17 document sets would be a bit much to show here. So after looking at them, I figured they were mostly similar but with maybe a few minor differences.

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With logging enabled, Gensim prints some texts about “topic diff” between each batch and merge. This seems to indicate how much the topic model changed between the runs. So I plotted the topic diff for the wikipedia run (when generating the LDA models), to see how much the topics drift during the run.

Newer versions are not in the Raspbian repositories. Downloaded the sources for the packages online, after lots of messing around, finally got them to compile. Overall, a bit more complicated than I was looking for, and could not get them to work. No idea why really, and no time/resources to debug the source code. Others had some issues as well, so no go for me. Nice. I though RPi was supposed to be easy and nice way to do all this stuff for dummies like me. Obviously, I thought wrong.


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Such a solution, providing avoiding the fatal coincidence of the vector of inertial forces with the direction of the main, main human blood vessels, gave a key advantage in the portability of aerobatic overloads. Note that all this without the use of any anti-overload suits. Subsequently, the direction of work on the study of overload tolerance and the search for the best flight manager and astronaut became one of the leading and successful accelerations of our enterprise in the physiological laboratory.

Output: The output from the tagger, formatted as word_tag. Word = the word given to the tagger as input for that part of the sencence, tag = the POS tag assigned by the tagger for that word.


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Sam Tucker has spent his life in the army. He was involved in the first Iraq war where he was decorated. He was court marshaled as a result of an incident when returning to Iraq in the second conflict and after a prison sentence, was dishonorably discharged. With no experience except the army he joined an organization run by a Russian ex- KGB officer who assonated people for a fee. As successful as Sam was, he came to the attention of Interpol and Scotland Yards special Branch. Life became more complicated when Sam fell in love with the ex mistress of his ex-KGB boss and they decided to marry. The story details Sams contracts and his involvement with his mistress, his boss, the police and the British Army and has a surprising conclusion.

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The entire "fighter theme" at that moment was concentrated in two organizations - MiG and Yak. Alekseev with the engine M-251 ( I-218 ), as well as fighter escorts designed by Alekseev and Pashinin.


This is quite simple, it just measures that the given hash-string starts with a given number of zeroes. In this case the measure of difficulty is the number of zeroes the hash should start with. In a more general case, I believe the difficulty can be a number under which the hash should fall (2nd top answer at the time of writing this). That gives you much more granularity to define the difficulty. But, again, no need to go overly complex on things in a Naivecoin tutorial.

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I mostly enjoyed this for the vivid portrayal of 80s Russia and, in the flashbacks, of the battles for Stalingrad and Berlin in WW2. I'm afraid however that I found the plot impenetrable.


In general, there are a number of parameters to play with in LDAvis, and I don’t pretend to know all of/about them. For example, you can cycle through the topics using the controls on the top as well. A handy tool for topic exploration.

I finally received my Ruuvitag sensors from the Kickstarter campaign a few weeks back. Everyone else seemed to get theirs months before (boohoo) but finally they did arrive. So now that I had them, what could I do with them? After a few weeks of finishing Lennu Run, I had the time to try them.


The above code creates the special transaction called the coinbase transaction. In proof-of-work type solutions, such as in the Naivecoin tutorial, this is added to each mined block. So when a miner finds a hash for a new block (thus “creating” it), they get the reward for the block. This reward is paid as the coinbase transaction. Each block can have one, and the miner can create it in the block. I would expect the miner then puts their own address in the transaction as the receiver.

I find these topics for Wikipedia to be pretty good and clear topics. More data obviously gives better topics. I am still running the cohesion metrics for these for Wikipedia. Even if u_mass is supposed to be faster, it took me 4 days to run it just for the 25 topics on Wikipedia. So it would take me weeks to run it for all the 25-200 sized topic counts. If I ever finish it, maybe I will post some update.


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But "life is not always as you expected". A lot of mistakes, a lot of wrong things. Among all of these "bugs", these are 2 important things that failed the event: 1. The Internet Connection: The biggest problem was the poor Internet connection. It's just too slow when 10 persons tried to connect to the wi-fi. I intended to introduce some great tools such as: + TogetherJS: transform your website into a collaborative environment + Thimble: an online HTML editor + Popcorn: an online media builder + Modpad: a collaborative notes taking tool based on Etherpad We could only see through some of the tools.

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OK, so what “n”, what values might “err” take and under what circumstances, etc? I had plenty of such experiences in building my little app.

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Instead, look at this – I have gone ahead and made a demonstrative physical representation of each machine’s specs by meticulously listing the core specifications of both the Leica M3 and the Minolta Maxxum 5 in competing text documents. I have then increased the font size for dramatic effect, and printed the spec sheets onto standard 8 x 10 printer paper. I have used a fancy font for the M3, and a dorky font for the Minolta, which I think is appropriate.

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Even if there are no classes etc, there is something called an “interface”. Haven’t quite figured it out, but wanted to hack the logging a bit and had to try to figure it out.


For some reason the brackets are to the left. I sometime read somewhere that Golang reads from left to right.

PreviousHash: Should match the Hash value of the previous block in the blockchain. Useful to verify chain integrity I am sure.


I found the explanations related to how these work very confusing to start with. Eventually I got it, but it is not the most intuitive thing. Especially with the terminology of Transaction (Tx), TxIn, TxOut. Especially since the TxIn and TxOut are mostly the same thing, but expressed in a different way. Well, that is my undestanding anyway.

Needed to forward some TCP connections to two different locations (one stream to two destinations). Had trying out Golang on my todolist for a while. So decided to give it a Go. Previously, I have implemented a similar TCP forwarding tool in Java. Installing the full JVM to run some simple TCP forwarding seemed a bit silly. So figured I could just try having a Go at it as well.


R: There are a lot more names: Fay Lansner, Alice Baber, Chryssa, Marti Edelheit, and we’re still leaving some important ones out. Why don’t we just make up a list of artists and hand it in and forget about this whole thing.

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The show was conceived from the beginning as an argument against Flaubert, Tolstoi, Dostoyevski, etc. With the exception of Defoe and Ibsen, we have always been losers. It has been part of woman’s glamour as an artistic subject that she was seen as pathetic, passive, in short—the superb victim. Most artists are gentlemen and treat us with compassion—liberals are always compassionate—but their verdict is inexorable. Anna Karenina doesn’t leave her up-tight husband to become commissioner of railroads. She leaves only when she finds another host to live off. Tolstoi calls that bizarre practicality a romantic nature. Flaubert may say he is Madame Bovary, but she never locked herself into a room to write unpopular novels or went to court to defend them. I was determined to present women without pathos or helplessness. Since a life style is the ability to recognize in the morning the same person who went to bed at night, it can be said to be a person’s most important decision.


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The hci-tools complained about not finding the BT device. So install a bunch of BT packages for Pi. No more errors. But running the command line tools, I expect they should dump BT traffic out.

Noland championed the notion of “making” a painting, rather than painting one, an emphasis on the literal, the canvas, the support, decisions as to where the literal spreading of paint began and ended in the shape of the support; design decisions, in short. This literalism of facture was combined with “opticality”, the suppression of tactility in favour of an optical buzz, pure visual sensation without physical overtones, but differing markedly from Op-art, in that the relationships of colour remained harmonious and did not blur and confuse the eye with flickering effects and hazy mirages.


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Type 3 refers to models where there was no big difference in final topic diff in 10 vs 100 iterations. This was just the models for document sets 7 and 15. These are also the two smallest document sets (least docs). Maybe smaller sets converge better with fewer iterations?

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Here is one more thing I have to take note when messing around with Heroku: SSH Key After installing a fresh copy of Xubuntu 13/10 (Saucy Salamander), I created a new virtualenv with all the requirement. I made some changes in the Django app. And, finally I intended to push the updates to Heroku: $ git push heroku master But, it said that I don't have permission. OK, I don't have the right ssh key on my new OS. So, I fixed this by: 1. Generate new pair keys: $ ssh-keygen -t rsa 2. Add the new key to Heroku: $ heroku keys:add Done.


Exploring associations between medical terms. Perhaps helpful identify new links you did not think of before? Likely would match other similar domains as well.

Baseforming the sentence to tag with Voikko has a chicken and egg problem (as mentioned in the Wikipedia links I put high above). You can get multiple baseforms for a word, depending on what POS the word is. If you need to define this to do POS tagging, then how do you pick which one to use? For example “keksiä” in Finnish refers to “innovating” but could also mean “cookie”. Here, I just used the first baseform of a word given by Voikko, which for “keksiä” just happens to be the one for “cookie”. When the correct one in this case would be the “innovation” one.


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Go also comes with a pretty nice logging package. Surprisingly it is called “log”.


Secondly, what use cases did I find looking at topic modelling use cases online? Actually, it seems really hard to find concrete actual reports of uses for topic models. Quora has usually been promising but not so much this time. So I looked at reports in the published research papers instead, trying to see if any companies were involved as well.

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This is as near as one can get to “non-referential” colour – quasi-abstract inter-relational colour. But as oppositions of hue tend to create light between them, there is always the tendency for this “light” to evoke the light falling on the surface of objects in natural sunlight (despite all efforts that may be made to suppress the phenomenon).

I still wanted to try the use of the baseform column in the Treebank so I ran all the words (baseform col and col1) in the Treebank through Voikko to see if it would recognize them. Recorded all the misses and sorted them highest occurence count to lowest. This showed me that the Treebank has its own “oddities”.


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A Leica M3 with a basic lens costs about $1,200 on eBay today. Current eBay Buy it Now listings have the Minolta Maxxum 5 with a standard lens priced anywhere from $35 to $45. This, I should insert here, is absurd. For the same amount of money that you’d spend on a Leica M3 with a lens, you can buy a Minolta Maxxum 5 AND enough bananas to fill the bed of a Ford F-150. Or with your leftover cash you could buy 162 rolls of Kodak Tri-X (that’s 3/11 years worth of film if you shoot one roll of film per week).


Manker and samples: I wanted to let everybody know that samples are being assembled as we speak! Manker has been chosen to help design and produce the X6-SE v2. We chose Manker because of a strong combination of their technical expertise, design and quick turn-around times.

I am willing to bet that it is as simple as a number change on a document like this to change Conquest ticket count. But yea congrats PGI, I think there is less in this patch than any other patch you have ever released.


A related question that came to my mind: Does it matter if you stem/lemmatize your words you give as input to the tagger to train and test? I could not find a good answer on Google. One question on Stack Overflow about stemming vs POS tagging. And the response seems to be not to give an answer but riddles.

Traditional machine learning approaches also require a more fixed size set of input features. Since documents are of varying length, this can be a bit of an issue.


E: Well, in writing and poetry a few of them managed to survive, as Miss Nochlin points out. I was discussing this with the poet David Shapiro and he read me an excerpt from a letter of Rimbaud’s: “When the infinite servitude of women shall have ended, she will be able to live by and for herself; man—hitherto abominable—will give her her freedom, and she too will be a poet. Women will discover the unknown. Will her world be different from ours? She will discover strange, unfathomable things—repulsive, delicious.

PowerSchool is a huge student information system which is used worldwide in International Schools. It's not open-source but It provides us a lot of features.


Police officers, detectives and prosecutors are poorly equipped and poorly paid, earning $100 to $400 a month, depending on their rank. As a result, they are demoralized and susceptible to the corruption that gnaws at every layer of Russian society - from health care to education to routine traffic stops.

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So with that, another alternative is again to just turn the R and S into hex-strings or Base56 encoded strings, combine them as a single byte-array and hex-string or base56 that, or whatever. But just those two values need to be passed to capture the signature.

Previously I wrote about Building a Finnish POS tagger. This post is to elaborate a bit on training with OpenNLP, which I skimmed last time, put the code for it out, and do some additional tests on it.


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Despite Apollinaire’s advocacy, after 1913-14 Delaunay and Kupka did not pursue the idea of a “pure Abstraction” with rigour, if at all, preferring to explore the expressive potential of simultaneity in colour relationships in a way which drew them back to figuration. A more rigorous engagement with “pure Abstraction” was left to Mondrian, but this involved the suppression of the space-making properties of colour, entrapping primary colour of minimal sensuosity within a prison of black bars, and a commitment to a radical “flatness”. And since colour is the main preoccupation of this Musée imaginaire, Mondrian will be less prominent than other painters who may be have been influenced by him.


But they do make for some weird looking code at when starting to Go. As do many other things actually. I guess it is the Go approach to try to be “simple” and terse.

First off, the Raspbian desktop changed so I no longer could even find where to configure wireless connections (wifi/bluetooth). Somehow my previous configs still seemed OK as wifi worked so just SSH in and try it. “hcitool” and “hcidump” both were also installed and new enough versions. I also upgraded the kernel with rpi-update just to be sure.


The number of topics LDA produces is given as a parameter, and is always a bit of a puzzle for me how to pick the best number for topics. Googling for it, I found various references to using “perplexity” to choose the best number of topics. I still have not found a good “for dummies” explanation for what that really means in practice for LDA, or how to implement it. Maybe some of the libs out there will do it for me? Python seems all the rage in data science these days, because whatever. So after a few search, gensim it is.

I made “mymodels” and “myoutputs” directories under the directory I cloned the git and compile the code to. Just so I could keep some order to my stuffs. So this is why the following example commands work.


I think artists transgress all boundaries and should not be segregated according to the comfortable academic niches supplied by curators, let alone by society. Man, woman, black, white, big tits, big penises, Italian, Jewish.

I also tried to set the sensor in the door shelf on its side, as it is round, so it would roll a bit when the door is opened and give some acceleration readings. The graph showing one such event is below. So if I managed to get the sensor to roll, the event is bigger. But again this does not happen all the time.


But would it be so bad to say “a byte array” instead of “array of bytes”? At least that would not break the minds of programmers who used most of the mainstream languages out there.

I have worked plenty with model-based testing myself (see OSMO), and actually have talked to some people who have done combinations of combinatorial input generation and model-based testing. I can see how this could be interested, to identify a set of injection points for parameters and values in a MBT model, and use a combinatorial test data generator to build data sets for those injection points. Likely doing some more of this in practice would reveal good insights on what works and what could be done to make the match even better.


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This is a bleak, atmospheric and literary thriller set in 1980s Moscow. It begins with the discovery of a young boy's frozen body by the Kremlin wall. Major Tom Fox is temporarily based at the British Embassy when the Ambassador, Sir Edward Masterton's step daughter, Alex, goes missing. With his personal life a mess, Tom is separating from his wife, Caro, and struggling to come to terms with the death of his daughter, Rebecca.

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Actually quite nice and simple way to get it done. Just another part that needs a different thinking. You set up some Go-routines (as in threads), have them wait on channels, pull jobs from the channels when available, and the run them in the Go-routine(s), and wait for more on the channel. Possibly return values through a channel as well.

The first cameras were primitive light-tight boxes made to suspend a lens a certain distance away from a focal plane of film. These simple boxes of cardboard or wood quickly evolved into beautiful assemblies of shiny metal. Over the next fifty years, cameras became smaller, incrementally more automated, more reliable, and easier to use. By the mid-1950s, cameras still required a dedicated user who knew what he or she was doing in order to make a proper image, but the cameras of the 1950s were overall very good cameras.


The boy had a difficult childhood, Alekseyev at an early age he lost his parents. Early orphaned and brought up in an orphanage, then adopted by a working family Alekseevyh - distant relatives of his deceased parents. In 1926 he graduated from a seven-year school in Moscow. From 1926 he worked at the Proletarian Labor factory in Moscow. Since 1929 - a student of the evening department.

Stalin about the possibility of problems with engine production. All these fighters were twin-engine. So, the difficulties of the post-war years affected the fate of these most interesting developments. Note that the twin-engine circuit for machines of this class has become ubiquitous in later times. Despite the fact that all work on these aircraft was phased out due to the closure of the plant, a number of bold design and technical solutions were successfully implemented in the development of other aircraft designers.


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People on the internets talk about using on “stretch” version of Raspbian which uses newer versions. Stretch seems to be some kind of a testing branch of the OS distribution. There is some mention about just taking the BLE packages from there and leaving the rest as the Jessie version (the current version of Raspbian). Others complain about potential to mess the system up. So why don’t I just upgrade my RPi to stretch as a whole?

The above code uses thresholds and minimum co-occurrence counts to avoid combining every possible word with every other possible word. So only word-pairs and triplets that commonly are found to occur together are used (replaced) in the document.


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Gromov Flight Testing Institute (City of Shchukovskiy in Moscow Region). In this position, he headed the direction of work on the creation of aircraft laboratories, aircraft fueling systems in flight and anti -loading facilities.

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The tags have been assigned by human experts who provide the Treebank. The whole Treebank file is parsed and output similar to above is generated by the Python script.


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With the arrival of this machine on the fronts of the Great Patriotic War, superiority over fascist aviation was won. At that time, solving the problem of further improving the flight characteristics of the new LA-7 fighte, by installing an additional, booster jet engine.

So here spit() takes an integer sum value as parameter and returns two integer values named X and Y. Again, what was wrong with the return value on the left? Same complaints as I had with the array declaration.


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There has almost always been a conflict in Ayres’s work between painterly fusion of this kind, and decorative pattern. But for a few years from around 1978 to 1982 this conflict was resolved in favour of a kind of all-over unity of sombre rich colour harmony and a fully integrated surface.

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Unsympathetic characters in a tale that dragged at times. Am sure the story could have been better told.


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Similar issues surrounding literalism and spatial illusion through colour are posed by the paintings of Morris Louis and Jules Olitski. Olitski’s remark that he wanted to “hang the weather” does convey the vaporous spatial illusionism he induced by overlaying sprayed colour on colour.

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Type 2 refers to models where the 25 topics line has a noticeable “jiggly” effect to it. Maybe this is between the iterations (or “passes”)? Not sure how Gensim restarts iterations, so could have something to do with it. Topics for document sets 5 and 8 had the biggest such effects, as also shown in the Type 2 figure above for document set 5. For document sets 1 and 4, the effect was smaller but still seemed to be there.


Practical examples of applying machine learning seem to be a bit difficult to find. So I tried to create one for a presentation I was doing on testing and data analytics. I made a review of works in the area, and just chose one for illustrate. This one tries to predict a target category to assign for an issue report. I used ARM mBed OS as a test target since it has issues available on Github and there were some people who work with it attending the presentation.

Note: Please do NOT edit your previous post! When that happens, it makes it difficult to determine what is new and what isn’t.


Overall, I would say the Ruuvi tags are a nice way to meter your house and surrounding areas. Quite a bit of setup to get all this to work still. Ruuvitag was actually very easy to take into use and update. Just the Raspbian bluetooth side was a load of trouble.

Earlier this month, the police publicly played down a bombing outside the apartment of a journalist who wrote a sensational behind-the-scenes account of Mr. Putin at the Kremlin. They said the bomb was directed at an empty apartment, not the journalist's across the hall - as if that were somehow a lesser deal.


He was born in Moscow in 1903, immigrated to Germany where he graduated from the Polytechnic Institute in the city of Schermitz, and worked as an aviation engineer in Dessau. Apparently, he was a good specialist because, despite his "emigre" past, he headed an OKB section and was highly paid, receiving 5000 rubles a month.

That is to say, I have no idea what they mean. Luckily I am really only interested in the POS tag as input to other algoritms so don’t really care what they are as long as they are correct and help to differentiate the words in context. Of course, with my lack of the language nuances and academic details of all those tags, I am not very good at judging the correctness of the taggings above.


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Revol Rossel – once a virtuoso violinist with a glittering future – is now a humble state militia cop, forced to investigate desperate crimes in this desperate era. But when five frozen corpses are found neatly arranged between railway lines, Rossel is faced with the most puzzling – and most dangerous – case of his career. His hunt for the truth leads him to the dark heart of Leningrad's musical establishment, and, ultimately, to the highest levels of the Kremlin itself. It's a world he knows intimately. A world where his dreams were shattered.

Ericsson has used topic models to map incoming bug reports to specific components. To make resolving bugs easier and faster by automatically assigning them to (correct) teams for resolution. Large historical datasets of bug reports and their assignments to components are used to learn the topic models. Topic distributions of incoming bug reports are used to give probability rankings for the bug report describing a specific component, in comparison to topic distributions of previous bug reports for that component. Topic distributions are also used as explanatory data to present to the expert looking at the classification results. Later, different approaches are reported at Ericsson as well. So just to remind that topic models are not the answer to everything, even if useful components and worth a try in places.


Try a number of different topic distributions and parameters if possible, look at the models manually both in text and visually, and pick a nice configuration. Depends really if the topics are used for human consumption as such or just as some form of automated input.

The “gripping, romantic, and dazzlingly original” (Cosmopolitan) Arkady Renko book that started it all: the #1 bestseller Gorky Park, an espionage classic that begins the series, by Martin Cruz Smith, “the master of the international thriller” (The New York Times). It begins with a triple murder in a Moscow amusement center: three corpses found frozen in the snow, faces and fingers missing. Chief homicide investigator Arkady Renko is brilliant, sensitive, honest, and cynical about everything except his profession. To identify the victims and uncover the truth, he must battle the KGB, FBI, and the New York City police as he pursues a rich, ruthless, and well-connected American fur dealer.


Perhaps more interestingly, I figured it would be nice to try something a bit different as well. So I was hoping to use the sensors not just to capture temperature, humidity and air pressure. But also use the accelometer to capture some events.

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Of the two Delaunays, Sonia seems to have been the more gifted painter. Robert’s colour is vitiated by an unhappy blend of pseudo-science, mysticism emanating from Apollinaire’s flights of fancy, and naturalistic thinking, and a surfeit of ideas incapable of pictorial realisation.


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Trained on: voikko = The tagger was trained on column 1 of the treebank, but before training all words in the sentence were baseformed using Voikko. Similar to “Tagger: Voikko” but for training data.

I think lemmatization in these cases I showed here makes sense. I have no interest in just knowing that a singular form of a word is related to a plural form of the same word. But I guess in some use cases that could be valid. Of course, for proper lemmatization you might also wish to first do POS tagging to be able to choose the correct baseforms from all the options presented. In this case I just took the first baseform from the list Voikko gives for each word.


A simpler, yet interesting topic to do would be to integrate the PICT type generator directly with the test environment. Run the combinatorial generator from this during the test runs, and have it randomize the combinations in a bit different ways during different runs. While still maintaining the overall combinatorial coverage.

Even the smaller topics here seem much better now with the increase in passes over the corpus. So perhaps the real difference just comes from having enough passes over the data, giving the algorithms more time and data to refine the models. At least I would not try without multiple passes based on comparing the results here of 1 vs 20 passes.


Abcrit #96. Alan Gouk writes on Key Paintings of the 20th Century; a Musée Imaginaire, Part 2 Comments Feed

Lavochkin from evacuation to Khimki near Moscow, he was appointed chief of the Design Bureau-21 in Gorky (basics), which led the development of jet fighters. In those years, this was a fact of very high confidence. Approval for such a position occurred, most often, with the approval of the Supreme Commander. Under his leadership, four experimental types were created. None of them went into mass production, but a number of bold design and technical solutions were successfully implemented in the development other aircraft designers.

I mean, people's lives are at stake, it's still the cold war, it's the USSR, it's winter, and the KGB are involved, so what do you do? Get shit drunk all the time so your wits and reflexes are dull.


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There under his leadership fighter jets were created on the basis of the development of the designers taken out of Germany, who had previously worked for the aviation companies Ah Junkers and Henkel. But the aircraft created in this Design Bureau creation of a fighter and the bomber, including the one with the reverse sweep wing, were also not launched into mass production. Work with the Germans was characterized by different approaches and standards in the design, which made the work very unproductive in general.

As stated previously, the box is optional only for those of you getting the SS/Cu set(s). The production box will be larger than the one pictured, and house both lights. We also plan (not guaranteed) to have a matching Serial Number on the box, either on a Stainless Steel badge, or other means! This will obviously add cost, and that is why we are making it optional.


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For parsing command line arguments, Go comes with a reasonably nice looking “flag” package. But it is quite limited in not making it possible to create long and short versions of the parameter names. Also, customizing the help prints is a bit of a hassle. Maybe that is why there seem to be oh so many command line parsing libraries for Go?


Which would likely lead to resource leaking as new connections would keep getting created but never closed. Since the for loop does not exit and thus defer is not called.

Running the 2-way generator produces 61 tests. So the number of combinations generated was finally reduced by one with the additional constraint.


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There is a bunch of interesting information to be had from these measurements and dashboards by moving the tags around the places they are in, measuring temperature, humidity, pressure etc around different places in appliances and stuffs. And figuring out why the temperatures seem too high only to find there must be something wrong but not with the tag.

We had to give an estimate on how many Serial Numbers for boxes there would be to be able to order the Serial Number badges. Manker/BangGood was gracious enough to order extra boxes for sale so that others could still have a box. There should be plenty enough boxes for all that want them.


On Thursday, a court convicted a police officer of accepting a bribe to provide a residency document to one of 41 Chechen terrorists who took hundreds of hostages in a Moscow theater in October 2002. The biggest surprise was not that he took a bribe, but that he was convicted of it.

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So I got myself a Raspberry Pi 3 box with all the twinkies to go with it. To act as the Bluetooth hub to collect the measurements from the actual sensor tags and push into the actual database. I run InfluxDB on another host in my network, along with Grafana.


This demo “service” I created works by first training a predictive model based on a set of previous issue reports. I downloaded the reports from the issue repository. The amount of data available there was so small, I just downloaded the issues manually using the Github API that let me download the data for 100 issues at once. Automating the download should be pretty easy if needed. The amount of data is small, and there are a large number of categories to predict, so not the best for results, but serves as an example to illustrate the concept.

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To preprocess the documents for word2vec, I split the documents to sentences to give the words a more meaningful context (a sentence vs just any surrounding words). There are other similar techniques, such as Glove, that may work better with more global “context” than a sentence. But anyway this time I was playing with Word2vec, which I think is also interesting for many things. It also has lots of implementations and popularity.


When I hear seeding in test generation, I think about the seed value for a random number generator. Because often those are used to help generate tests. Well, with PICT it actually means you can predine a set of combinations that need to be a part of the final test set.

For example, topic2 here has small numbers but still all items seem related to grey market economy. Similarly, topic7 has small numbers but the words are mostly related to arts and culture.


After trying for a bit and asking some questions on the Ruuvi Slack, I figured it does not work quite this way. The accelerometer only captures changes in the movement speed. And even at the high-frequency setting, the sensor is only capturing the data twice a second. So if you start to pull the door open in between, nothing shows in the accelerometer graph. The tag is steadily moving before and after the initial pull, but not accelerating any more. So I guess unless the measurement polling happens just at the time of the initial pull, the curve just stays flat.

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Stemming and lemmatization are two basic transformations that are often used in NLP. Stemming is a process of cutting the ending of a word to get simple version that matches all different forms of the word. The result is not always a real “word”. For example, “argue”, “arguing”, “argus” could all stem to “argu”. Lemmatization on the other hand produces more “real” words (the Wikipedia link describes it as producing the dictionary base forms).

Elsewhere, the Al is predictably primitive and inconsistent, with enemies failing to hear gun battles right outside their windows, yet spotting you with unerring ability at unlikely distances across the map. You can also have great fun with the civilians in the game, running in front of them to put them on alert, then ducking behind them for ten seconds until they forget you ever existed. Then running in front of them again, obviously, just to blow their little minds.


Gensim seems to have some perplexity options and a bunch of weird formulas to apply. Is it so hard to write some simple docs and explain these things? I guess nobody pays people to do it, and doing for free would just go against the goal of making oneself important. Sort of makes sense, and applies to most OSS software I have used. Or maybe I am just bad at using stuff.

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This is how we photo geeks are getting our information and advice about photo stuff in 2021? From someone who’s never shot the camera that he’s reviewing? This does not establish confidence.


The murder of another member of his party eight months earlier, however, remains a mystery. A trial also began at last in the case of Galina V. Starovoitova, a prominent liberal politician who was shot to death outside her apartment in St. Petersburg more than five years ago. There are six suspects, but whoever ordered the crime has not yet been found.

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Random experiments in software engineering

I actually found the Gensim implementations are quite nicely optimized not to take huge amounts of memory, and they also pretty much make use of all the cores in a system. Except perhaps the topic cohesion ones that seemed to run single core still. Perhaps because they seem relatively new?

The format of the topic list I used here is “topicX=word1[count] word2[count]”, where X is the number of the topic, word1 is the first word in the topic, word2 the second, and so on. The [count] is how many times the word was associated with the topic in different documents. Consider it the strength, weight, or whatever of the word in the topic.


Army intelligence officer Tom Fox has only been in the city for a few days when the British Ambassador's teenage daughter disappears and he is charged with finding her. The last thing the Soviets want is a foreigner sticking his nose where it doesn't belong, especially when there's a sadistic killer on the loose that they'd prefer to pretend doesn't exist.

Comparing these figures with the ones before for topic counts 25-200, the lower number of topics generally scored better here. Just for a quick comparison, most of these 2-20 sizes have the highest score close to -0/5 to -0/7, while the best scores for 25-200 were closer to -1/0. The difference being againg topic 15, which trolls us again with a value close to -0/8 at 3 and 150 topics.


His line does not cut into depth so much as skate or slide over the fictive spaces afforded. He further complicates things by filling in this jig-saw of interlocking shapes with colour and tone, seemingly, at least to a rational eye, arbitrarily, without any discernible plastic logic.

To see how much more abstract Miro is than Picasso, see this installation shot of their two pictures side by side. Clement Greenberg, in perhaps his greatest piece of critical writing, described the difference between Picasso and Klee thus: “ The difference is that he (Picasso) sees the picture as a wall, while Klee sees it as a page”.


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I have updated the Standby list, but it has been difficult with the number of posts showing up, and the quantity of PMs I am receiving. So, if I somehow missed you, or you want to be added to the list.

Remember, the types are just something I made up myself. I chose Type 1 to refer to models where there was a big difference from 10 iterations to 100 iterations in the final topic diff for the 25 topic run. In the example Type 1 figures here (for doc type 6), the 10 iteration run gets to around 0/25 final diff. In my set for type 1, document sets 2, 16, and 17 had the biggest diff of about 0/5 in the end after 10 iterations. Document sets 3, 6, 9, 12, 13, and 14 were close to 0/2 diff after 10 iterations. Document sets 10 and 11 were close to 0/1 diff for 10 iterations. Each of these was close to 0 final diff after 100 iterations.


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There is a limited number of such labeled data items, as many of the downloaded issues do not have this label assigned. The print at the end of the above code shows the total number of items with the “component” label given, and the number in this dataset is 1078.

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E: There’s no doubt, in terms of the numbers of women painters in this country, that women are not bought by collectors to the same extent that men are. On a given level, let’s say, in a given age-group, they’re not exhibited as adequately in museums, are not given teaching posts at the universities. I’ve taught and lectured at various universities around the country in the past 12 years and it’s noticeable how very few women art-teachers there are on the college level. So you’re right—there is prejudice in terms of the whole PR mechanism—but the question we’re discussing is not recognition; the question is the idea of being an artist.


I almost gave up on this book after a couple of chapters, but am so pleased I stuck with it, as it took right off after then. The storyline became totally engrossing, and I found it tough to put the book down most nights. I have seen the word 'bleak' used to describe this book and I can't think of one more appropriate; however, cold, anxious, frustrating and atmospheric also come to mind, perfect for crime noir in 80's Moscow.

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A BIG thanks to Bugs for keeping the list updated for me! Tending the list can be a huge time killer, so I appreciate him and all the other talents he possesses! Thanks also to Dale and Jmac for answering questions and helping the other BLFers with their concerns and questions.


Random experiments in software engineering Comments Feed

This dumps the whole LDAvis thing into a HTML file you can then load up any time later and play with. Nice thing about this is that it can be run on a headless remote server, and produces a single HTML file (a bit large but anyway). This HTML file can then be downloaded and opened from a local file. So no webserver needed anywhere, and the interactive visualization can be shared as a single file.

And here, if “Size” equals 4 or 5 (so is >3), “Value” is always “c” or d”. The PICT docs state “String comparison is lexicographical and case-insensitive by default”. So [> “b”] just refers to letters coming after “b”, which equals “c” and “d” in the choices in this model. It seems a bit odd to define such comparisons against text in a model, but I guess it can help make a model more readable if you can represent values as numbers or strings, and define constraints on them in a similar way.


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Across the broad spectrum of modernist pursuits, there are the extremes. On the one hand the impetus towards an unvisualisable and unsayable future, epitomised by Schoenberg’s ” I breathe the air of an unknown planet”, and carried to ludicrous extremes by the maniacal phase of the later Stockhausen. On the other, there is the atavistic attempt to revisit the ancient past, to recreate the sound-world of the pagan pre-Christian origins of ancient religion, even as they survive in the earliest liturgical music, of the Orthodox Russian Church, for instance; a ritualised timeless stasis, as attempted by the post-serial Stravinsky.


E: And what about Gertrude Stein, the greatest discoverer of them all? I find artists are always generous to other artists. Jeanne Reynal bought Gorky, de Kooning, Noguchi, Rothko before any museum when they were broke and needed the money. The first paintings Bill sold were to artists and poets: Edwin Denby, Allen Tate, Rudy Burckhardt, Biala and Alain, Saul Steinberg—and Saul bought Richard Lindner before the collectors got to him. In fact, most of the artists I know own other artist’s work.

In 200 topics, music takes the first spot. In 200, the second is about novels (book), third football, and finally movies come fourth.


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A visit to Gorky Central Park in Minsk will be a refreshing one. This historical park has been built exclusively for fun and cheer. Whether you visit it for a few hours or for the whole day, you will come out happy and relaxed. With plenty of open spaces and several rides and slides, Gorky Central Park attracts tourists as well as local populace. There are some great rides like the big Ferris wheel that is visible from a distance, wild train that takes you through the periphery of the park, Russian swing, Ferris wheel for kids, Children’s chain carousel, Calypso, and several trampolines. You can also try your aim at pneumatic shooting or ride on the pleasure boat. In a nutshell, your day will be well spent in this park.


I used to reel back in horror at the tactics of women who burn bras, claim in WITCH and SCUM manifestos that men are “unfit even for stud service,” and introduce raw eggs and Tampax inscribed with messages about “equal rights” into museum galleries. I never really thought of women as victims of a mysterious fatality. Like Simone de Beauvoir, I refused to conclude that their ovaries condemn them to live forever on their knees. I believed that personal creditability was achieved by acid intelligence, enlightened will and superior effort. Since I never subscribed to the mystique of motherhood or to the cult of routine domesticity, I found myself by my middle thirties in the privileged position of being able to do largely as I pleased. Nevertheless, I have always been uneasy in an amorphous and unfocused way about the relatively small role of women in world-history, and plagued by the seemingly unaccountable lack of first-rate women artists. I have always assumed that this arose from the collusion which women on the whole practice by sacrificing aspects of their identity and development in return for “security” and the fulfillment of a stereotyped idea of femininity. From the beginning women face a conflict of roles which men by and large are spared: they are deluded into thinking that dependence and subordination offer in built rewards. That attitude is moreover authorized and encouraged by the society in which they live, and it is reinforced still further by the notion that womanhood remains incomplete and thwarted without a child.

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From 1971: Eight Artists Reply: Why Have There Been No Great Women Artists

In the LDAvis figure here for 25 topics, the cluster of four smaller ones on the right are related to Asian countries. In the topic word list below for 25 topics, these are topics 4, 14,16, and 20. The numbering is just different because they are ordered differently. The LDAvis figure above for 200 topics also has a cluster of small ones on the left, with many of those for countries/states but also some for other topics such as chess, church, weightlifting and more. I am sure this would also be an interesting topic to study, why PCA grounds them together.

To the extent that a painting evinces a coherent formal structure, it will tend toward a rhyming correspondence with structures outside painting, more than mere analogy, and much more than metaphor, but always falling short of coaxing the viewer into a naturalistic reading of pictorial space. In the end the painting should “ stand intact as a created independent object,” a work of plastic art. Simultaneity, transparency of colour, and “push and pull”, the reciprocal pressure of saturated hues, employing the full range of contrast of which the means is capable, all of this is in play in Hofmann’s oeuvre.


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I turned around and went back to Spring Street, producing my next body of work. My energies in the world were for those not yet born. I laminated myself against the walls with my paintings. Cézanne didn’t live on institutional acceptance.

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In the above I am using RandomForest classifier, with a set of parameters previously tuned. I am also using 5-fold cross validation, meaning the data is split into 5 different parts. The parts are “stratified”, meaning each fold has about the same percentage of each target label as the original set. This is why I removed the labels with less that 10 instances in the beginning, to have at least 2 for each class. Which is till super-tiny but thats what this example is about.


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The novel itself was initially difficult to get into and gather momentum. Some parts were ambiguous and events occurred without adequate explanation to the reader.

There is clearly a debt to Braque in this counterpoint of line and plane, but where, in Braque, there seems to lie behind this decorative play an attempt to grasp concrete spatial experience out there in the real world, this grasp on spatial experience is more tenuous in Nicholson, and experienced by the artist at a further remove. Whereas the carving conception of physical contact with the board by way of incisive line takes precedence. Nicholson is in some ways the more abstract, and the more “optical”, but at a price in palpable experience.