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From calculations of quantum tunneling through the horizon of a metric (black hole, Rindler..) Hawking/Unruh temperature can be calculated! [http://arxiv.org/abs/arXiv:0710.0612 Ryan Kerner: Fermions Tunnelling from Black Holes] _ Using this method it is recently claimed that the so called Black hole information paradox can be solved:

At the moment we can use internet search machines to find knowledge, that was explicitly written on some webpage. But in my opinion the future of internet search will not be in finding better algorithms to search for this explicit knowledge.

The process of knowledge generation involves finding links or connections between already existing pieces of knowledge. There are some applications out there, that can help with that: PaperCube PathWay, see also CNET: New Mac Review: Keep Wikipedia research focused with Pathway Also helpful would be a Google Scholar API, but it does not exist yet:

From [http://mathworld.wolfram.com/Korteweg-deVriesEquation.html]: In fact, a transformation due to Gardner provides an algorithm for computing an infinite number of conserved densities of the KdV equation, which are connected to those of the so-called modified KdV equation through the Miura transformation v_x+v^2=u (Tabor 1989, p.

I thought for a long time, that the result of the study “The Limits to Growth” by the “Club of Rome” are clearly disproved. But then I investigated a little bit and found that it doesn’t seem to be the case.

The Netflix Prize will be over soon. As possibly best algorithms emerged: Support Vector Machine (SVN) Restricted Boltzmann Machine (RBM) k-nearest neighbors algorithm (KNN) Bibliography Was macht den perfekten Film aus?

Here I collect notes about a lot of things and topics I think are interesting. Physics Moving black holes: Since a black hole is a state of highest entropy, is a black hole moving at the speed of light the highest entropy transfer rate that can exist in nature?

Reset all local variables at the beginning of the loop (Except counters and similar of course). So you won’t mixup values from the old loop with the actual loop !

Fuzzy string comparison >>> import difflib >>> s=difflib.SequenceMatcher(None, "abcd","bcde") # Return a measure of the sequences’ similarity as a float in the range [0, 1]. # This is 1.0 if the sequences are identical, and 0.

Before the measurement the positions of the photons in space are not defined, thus the distance between them also makes no sense and without distance one cannot really speak about the speed of information transfer.