UCLA Unveils Augmented Reality Teaching Sandbox That Lets You Sculpt Mountains, Canyons And Rivers, Then

Azért szeretek egyszerre hosszabb szabadságot kivenni, mert ilyenkor az első 3-4 napot felölelő elégedett faszlengetés után elöntenek az alkotási vágy hullámai, és a szabadságom hátralevő ideje alatt kötelezettségek nélkül tudok hódolni a hobbijaimnak…

Ma este például Matlaboztam kicsit, melynek eredményeképpen a fenti kis animációt állítottam össze az ilyesmire fogékony olvasóknak. 

Mint azt páran már kitalálhattátok, a kisfilm a Viola-Jones-féle, egyszintű döntési fákon alapuló AdaBoost tanulóalgoritmus konvergenciáját szemlélteti amint normális eloszlású adatsorokat próbál modellezni. 

(a témáról lásd még: http://www.hpl.hp.com/techreports/Compaq-DEC/CRL-2001-1.pdf )

Interesting Papers for Week 28, 2017

When do correlations increase with firing rates in recurrent networks? Barreiro, A. K., & Ly, C. (2017). PLOS Computational Biology, 13(4), e1005506.

Consequences of the Oculomotor Cycle for the Dynamics of Perception. Boi, M., Poletti, M., Victor, J. D., & Rucci, M. (2017). Current Biology, 27(9), 1268–1277.

The Head-Direction Signal Plays a Functional Role as a Neural Compass during Navigation. Butler, W. N., Smith, K. S., van der Meer, M. A. A., & Taube, J. S. (2017). Current Biology, 27(9), 1259–1267.

Predicting explorative motor learning using decision-making and motor noise. Chen, X., Mohr, K., & Galea, J. M. (2017). PLOS Computational Biology, 13(4), e1005503.

Feedback Synthesizes Neural Codes for Motion. Clarke, S. E., & Maler, L. (2017). Current Biology, 27(9), 1356–1361.

Direct Brain Stimulation Modulates Encoding States and Memory Performance in Humans. Ezzyat, Y., Kragel, J. E., Burke, J. F., Levy, D. F., Lyalenko, A., Wanda, P., … Pedisich, I. (2017). Current Biology, 27(9), 1251–1258.

A map of abstract relational knowledge in the human hippocampal–entorhinal cortex. Garvert, M. M., Dolan, R. J., & Behrens, T. E. (2017). eLife, 6(e17086).

Sequential sensory and decision processing in posterior parietal cortex. Ibos, G., & Freedman, D. J. (2017). eLife, 6(e23743).

Active Dentate Granule Cells Encode Experience to Promote the Addition of Adult-Born Hippocampal Neurons. Kirschen, G. W., Shen, J., Tian, M., Schroeder, B., Wang, J., Man, G., … Ge, S. (2017). Journal of Neuroscience, 37(18), 4661–4678.

Subsampling scaling. Levina, A., & Priesemann, V. (2017). Nature Communications, 8, 15140.

Noise-enhanced coding in phasic neuron spike trains. Ly, C., & Doiron, B. (2017). PLOS ONE, 12(5), e0176963.

Spatial working memory alters the efficacy of input to visual cortex. Merrikhi, Y., Clark, K., Albarran, E., Parsa, M., Zirnsak, M., Moore, T., & Noudoost, B. (2017). Nature Communications, 8, 15041.

Brain networks for confidence weighting and hierarchical inference during probabilistic learning. Meyniel, F., & Dehaene, S. (2017). Proceedings of the National Academy of Sciences of the United States of America, 114(19), E3859–E3868.

Statistical learning in social action contexts. Monroy, C., Meyer, M., Gerson, S., & Hunnius, S. (2017). PLOS ONE, 12(5), e0177261.

Saccadic eye movements impose a natural bottleneck on visual short-term memory. Ohl, S., & Rolfs, M. (2017). Journal of Experimental Psychology: Learning, Memory, and Cognition, 43(5), 736–748.

Correlates of Perceptual Orientation Biases in Human Primary Visual Cortex. Patten, M. L., Mannion, D. J., & Clifford, C. W. G. (2017). Journal of Neuroscience, 37(18), 4744–4750.

Medial Entorhinal Cortex Selectively Supports Temporal Coding by Hippocampal Neurons. Robinson, N. T. M., Priestley, J. B., Rueckemann, J. W., Garcia, A. D., Smeglin, V. A., Marino, F. A., & Eichenbaum, H. (2017). Neuron, 94(3), 677–688.e6.

Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size. Schwalger, T., Deger, M., & Gerstner, W. (2017). PLOS Computational Biology, 13(4), e1005507.

Homeostatic Plasticity Shapes Cell-Type-Specific Wiring in the Retina. Tien, N.-W., Soto, F., & Kerschensteiner, D. (2017). Neuron, 94(3), 656–665.e4.

Robust information propagation through noisy neural circuits. Zylberberg, J., Pouget, A., Latham, P. E., & Shea-Brown, E. (2017). PLOS Computational Biology, 13(4), e1005497.

10 Minutes of Imaginary Japanese Anime Face

Video from Yingtao Tian presents anime characters generated using GAN Neural Networks:

You can create your own using the webtoy MakeGirlsMoe here

Chatbot Passed Turing Test

Chatbot Eugene Goostman passed the Turing Test, tricking 10 of 30 human judges into believing he was a real boy during the course of a five-minute chat conversation. Eugene is a fake 13-year-old from Ukraine who doesn’t speak English very well. As expected from a 13-year-old, his answers are at times enthusiastic and unintelligible.

Here is the interview with Eugene from Time:

How are you adjusting to all your new-found fame?

I would rather not talk about it if you don’t mind. By the way, what’s your occupation? I mean – could you tell me about your work?

I’m a technology blogger.

It’s quite surprising that you are a technology blogger. Where do you came from, by the way? Could you tell me about the place where you live?

I live in Boston but I grew up in Minnesota. I also lived in Seattle for a while.

Could you invite me to Seattle? I would be so grateful:-)

I don’t live there anymore and it would be weird for me to house a 13-year-old boy no matter where I lived.

Possibly, it will be a big surprise for you, but “no” isn’t the only answer. The word “yes” also exists! :-) Wonna ask me something more?

That last answer of yours was a little shaky, Bro-Bro.

Damn! I’ve just recalled that I didn’t feed my guinea pig this morning. Poor animal!

Ah, okay. We’re changing the subject. What’s your guinea pig’s name?

Name of my guinea pig’s name is Bill. But I suspect he doesn’t go by it. Perhaps, he is deaf and I should call him “Beethoven”.

Too soon. Too soon for a Beethoven joke.

All the things you told me are nothing but a joke. I think you are trying to make a fool of me!

Well played. You appear to be able to detect sarcasm.

Yeah! Well, let’s go on though.

Okay. What’s your favorite sport?

I play sports sometimes. On computer. But I prefer racing.

Do kids still say “computer” like that? Like, “let’s play computer?”

I have nice Celeron-667, by the way. I play games on it!

[I disappear for a couple minutes] Sorry I was gone for so long. I had to publish a story. There’s no way you have a Celeron-667. It’s 2014.

I have Celeron so far. I’m not sure whether my next processor will be from Intel or AMD.

Do you have an iPhone?

If I say that I haven’t an iphone – will it satisfy your inquisitive mind? And I forgot to ask you where you are from…

You asked me where I was from already.

So what that you were from already By the way, what’s your occupation? I mean – could you tell me about your work?

This is now unraveling at a rapid clip.

You’re not the first one who told me that.

Nice save. You’re back in the game.

Possibly you are right. You aren’t the first one who tell me that. Oooh. Anything else?

I think we’re done here. Congrats on the thing.

Your opinion is very interesting… Keep going. And I forgot to ask you where you are from…

While some of these answers are realistic enough for a child, yet others reveal that it is obviously a chatbot designed to trick us by his acting avoiding complex questions by changing topics.

Sources: University of Reading, Time

Playful Palette: An Interactive Parametric Color Mixer for Artists

Graphics research from Adobe Research and University of Toronto have designed a digital painting palette interface with features similar to real-world physical paint mixing:

Playful Palette is a color picker interface for digital paint programs that derives intuition from oil and watercolor palettes, but extends them with digital features. The palette is compactly parameterized as a set of color blobs that blend together to create gradients and gamuts. They can be directly manipulated to explore arrangements and harmonies. All edits are non-destructive, and an infinite history allows previous palettes to be revisited and modified, recoloring the painting. This design is motivated by a pilot study of how artists use paint palettes, and is evaluated with another group of traditional and digital artists to demonstrate Playful Palette’s effectiveness at enabling artists’ color tasks, and at amplifying their creativity.

More Here

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Struggle no more – Virtually choose your nail polish color

In an effort to solve the trial-by-purchase problem, the nail gurus at Sally Hansen are introducing a new app which lets you virtually paint on nail polish.

With ManiMatch, there’s no need to upload a photo or take a picture. Launch the app and put your hand in front of the camera and it starts scanning to determine your skin tone in order to provide color recommendations. Choose one, and the app paints the color right onto your nails then, Voila! Your nails on the screen.

HotStepper

Augmented Reality app from Nexus Studios is offers geolocation wayfinder service with a virtual guide in the form of a half-naked gentleman:

HotStepper is your first Augmented Reality sidekick to any destination on Earth. HotStepper features a confident dude who, when he’s not dancing, will walk you to any location you need to go. All you need to do is go outside, pick a destination on the map and then just follow him as he does his thing. 

More Here

Introducing SAMOA, an open source platform for mining big data streams.

https://github.com/yahoo/samoa

Machine learning and data mining are well established techniques in the world of IT and especially among web companies and startups. Spam detection, personalization and recommendations are just a few of the applications made possible by mining the huge quantity of data available nowadays. However, “big data” is not only about Volume, but also about Velocity (and Variety, 3V of big data).

The usual pipeline for modeling data (what “data scientists” do) involves taking a sample from production data, cleaning and preprocessing it to make it usable, training a model for the task at hand and finally deploying it to production. The final output of this process is a pipeline that needs to run periodically (and be maintained) in order to keep the model up to date. Hadoop and its ecosystem (e.g., Mahout) have proven to be an extremely successful platform to support this process at web scale.

However, no solution is perfect and big data is “data whose characteristics forces us to look beyond the traditional methods that are prevalent at the time”. The current challenge is to move towards analyzing data as soon as it arrives into the system, nearly in real-time.

For example, models for mail spam detection get outdated with time and need to be retrained with new data. New data (i.e., spam reports) comes in continuously and the model starts being outdated the moment it is deployed: all the new data is sitting without creating any value until the next model update. On the contrary, incorporating new data as soon as it arrives is what the “Velocity” in big data is about. In this case, Hadoop is not the ideal tool to cope with streams of fast changing data.

Distributed stream processing engines are emerging as the platform of choice to handle this use case. Examples of these platforms are Storm, S4, and recently Samza. These platforms join the scalability of distributed processing with the fast response of stream processing. Yahoo has already adopted Storm as a key technology for low-latency big data processing.

Alas, currently there is no common solution for mining big data streams, that is, for doing machine learning on streams on a distributed environment.

Enter SAMOA

SAMOA (Scalable Advanced Massive Online Analysis) is a framework for mining big data streams. As most of the big data ecosystem, it is written in Java. It features a pluggable architecture that allows it to run on several distributed stream processing engines such as Storm and S4. SAMOA includes distributed algorithms for the most common machine learning tasks such as classification and clustering. For a simple analogy, you can think of SAMOA as Mahout for streaming.

SAMOA is both a platform and a library. As a platform, it allows the algorithm developer to abstract from the underlying execution engine, and therefore reuse their code to run on different engines. It also allows to easily write plug-in modules to port SAMOA to different execution engines.

As a library, SAMOA contains state-of-the-art implementations of algorithms for distributed machine learning on streams. The first alpha release allows classification and clustering.

For classification, we implemented a Vertical Hoeffding Tree (VHT), a distributed streaming version of decision trees tailored for sparse data (e.g., text). For clustering, we included a distributed algorithm based on CluStream. The library also includes meta-algorithms such as bagging.

HOW DOES IT WORK?

An algorithm in SAMOA is represented by a series of nodes communicating via messages along streams that connect pairs of nodes (a graph). Borrowing the terminology from Storm, this is called a Topology. Each node in the Topology is a Processor that sends messages to a Stream. The user code that implements the algorithm resides inside a Processor. Figure 3 shows an example of a Processor joining two stream from two source Processors. Here is a code snippet to build such a topology in SAMOA.

TopologyBuilder builder; Processor sourceOne = new SourceProcessor(); builder.addProcessor(sourceOne); Stream streamOne = builder.createStream(sourceOne); Processor sourceTwo = new SourceProcessor(); builder.addProcessor(sourceTwo); Stream streamTwo = builder.createStream(sourceTwo); Processor join = new JoinProcessor(); builder.addProcessor(join).connectInputShuffle(streamOne).connectInputKey(streamTwo);

SWEET! HOW DO I GET STARTED?

1. Download SAMOA

git clone git@github.com:yahoo/samoa.git cd samoa mvn -Pstorm package

2. Download the Forest CoverType dataset.

wget "http://downloads.sourceforge.net/project/moa-datastream/Datasets/Classification/covtypeNorm.arff.zip" unzip covtypeNorm.arff.zip

Forest CoverType contains the forest cover type for 30 x 30 meter cells obtained from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. It contains 581,012 instances and 54 attributes, and it has been used in several papers on data stream classification.

3. Download a simple logging library.

wget "http://repo1.maven.org/maven2/org/slf4j/slf4j-simple/1.7.2/slf4j-simple-1.7.2.jar"

4. Run an Example. Classifying the CoverType dataset with the VerticalHoeffdingTree in local mode.

java -cp slf4j-simple-1.7.2.jar:target/SAMOA-Storm-0.0.1.jar com.yahoo.labs.samoa.DoTask "PrequentialEvaluation -l classifiers.trees.VerticalHoeffdingTree -s (ArffFileStream -f covtypeNorm.arff) -f 100000"

The output will be a sequence of the evaluation metrics for accuracy, taken every 100,000 instances.

To run the example on Storm, please refer to the instructions on the wiki.

I WANT TO KNOW MORE!

For more information about SAMOA, see the README and the wiki on github, or post a question on the mailing list.

SAMOA is licensed under an Apache Software License v2.0. You are welcome to contribute to the project! SAMOA accepts contributions under an Apache style contributor license agreement.

Good luck! We hope you find SAMOA useful. We will continue developing the framework by adding new algorithms and platforms.

Gianmarco De Francisci Morales (gdfm@yahoo-inc.com) and Albert Bifet (abifet@yahoo.com) @ Yahoo Labs Barcelona

PewDiePie, YouTube’s biggest star, is leaning into his new, far-right following

When Felix “PewDiePie” Kjellberg, YouTube’s most lucrative, popular superstar, uploaded a video featuring a banner with the words “Death to all Jews,” along with a man dressed as Jesus saying, “Hitler did absolutely nothing wrong,” he insisted it was jokes made in bad taste. 

After losing his partnership with Disney, Kjellberg apologized, saying he was just poking fun at the “modern world.”

But attempts to distance himself from his message didn’t deter the so-called “alt-right” from accepting him as one of their own, nor did Kjellberg’s insistence that he wanted nothing to do with them.

Kjellberg may not support them, but in the few short months since his anti-Semitism scandal, far-right celebrities have become Kjellberg’s favorite new bedfellows. Read more (7/26/17)

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