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Search found 7 results on 1 pages for 'computability'.

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• Complex behavior generated by simple computation

  - by Yuval A

  Stephen Wolfram gave a fascinating talk at TED about his work with Mathematica and Wolfram Alpha. Amongst other things, he pointed out how very simple computations can yield extremely complex behaviors. (He goes on to discuss his ambition for computing the entire physical universe. Say what you will, you gotta give the guy some credit for his wild ideas...) As an example he showed several cellular automata. What other examples of simple computations do you know of that yield fascinating results?

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• what practical proofs are there about the Turing completeness of neural nets? what nns can execute c

  - by Albert

  I'm interested in the computational power of neural nets. It is generally accepted that recurrent neural nets are Turing complete. Now I was searching for some papers which proofs this. What I found so far: Turing computability with neural nets, Hava T. Siegelmann and Eduardo D. Sontag, 1991 I think this is only interesting from a theoretical point of view because it needs to have the neuron activity of infinite exactness (to encode the state somehow as a rational number). S. Franklin and M. Garzon, Neural computability This needs an unbounded number of neurons and also doesn't really seem to be that much practical. (Note that another question of mine tries to point out this kind of problem between such theoretical results and the practice.) I'm searching mostly for some neural net which really can execute some code which I can also simulate and test in practice. Of course, in practice, they would have some kind of limited memory. Does anyone know something like this?

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• How important is Discrete Mathematics for a Computer Scientist?

  - by mort

  As the title says, How important is Discrete Mathematics for a Computer Scientist? Background: I'm pursuing a Master's degree with a focus on fundamentals such as Algorithms, Complexity and Computability Theory and Programming Languages to get a good foundation for working in the field of Parallel Computing. Some more background: My university grants a lot of freedom in the choices of courses for my Master's degree. It's officially called "Software Engineering", but due to a the broad range of electives, a different focus is possible. Interestingly, none of the electives is a lecture in Math! I'm thinking about doing a course about Discrete Mathematics that would take half a semester to complete successfully, even if I can't use it for my degree. So with this question I'm trying to find out if the effort is justifiable.

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• Cuda driver, CPU/GPU performances issue

  - by elect

  I implemented a RNS Montgomery exponentiation in Cuda and on cpu for comparison. Everything nice everything fine. It runs on just one SM. However I am going to tell you some strange regression in both cpu/gpu performances. During the devoloping, about two month ago, I was using Cuda 5 preview on Ubuntu 11.04 64b. In this time, I reach the following performances: cpu 460ms gpu 120ms Then one day when I turn on the pc, the graphical environment didnt start. I dont know which was the problem, however I switched to the console and installed again the Cuda driver. At the following boot performances changed: cpu 310ms gpu 80ms I was like Q.Q...uhm ok, nice to see this, but I was wondering how that could be possible However, I went then in holiday for 10 days and I continued developing and optimizing on my notebook (but not the same part of the code, some additional stuff) When I was back, I just updated the source files, and performances came back to 460/120ms.. I couldnt believe it, I tried to install Cuda 5 RC, updating the video driver too... nothing changed... I checked Debug/Release, Cuda computability, but the problem seems being somewhere else.. Looking around the net I found this, I am pretty sure it must have something to do with the driver, because the performance change affected both cpu and gpu Do you have some tips/ideas/suggestions?

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• Windows threading: _beginthread vs _beginthreadex vs CreateThread C++

  - by Lirik

  What's a better way to start a thread? I'm trying to determine what are the advantages/disadvantages of _beginthread, _beginthreadex and CreateThread. All of these functions return a thread handle to a newly created thread, I already know that CreateThread provides a little extra information when an error occurs (it can be checked by calling GetLastError)... but what are some things I should consider when I'm using these functions? I'm working with a windows application, so cross-platform computability is already out of the question. I have gone through the msdn documentation and I just can't understand, for example, why anybody would decide to use _beginthread instead of CreateThread or vice versa. Cheers! Update: OK, thanks for all the info, I've also read in a couple of places that I can't call WaitForSingleObject() if I used _beginthread(), but if I call _endthread() in the thread shouldn't that work? What's the deal there?

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• Excel file reading with 2007 office connection string.

  - by p-vasuu

  Actually in my system having 2007 office then i am reading the 2003 .xls file with using the 2007 connection string string ConnectionString = "Provider=Microsoft.ACE.OLEDB.12.0;Data Source=" + Filename + ";Extended Properties=\"Excel 8.0;HDR=YES;\""; data is not reading. But if the first row first column data length is lessthen 255 then the following first columns data is cutting up to 255 character. If the First row first column is morethan the 255 character then the following first columns data is reading fine. Is there any back word computability is there?

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• how useful is Turing completeness? are neural nets turing complete?

  - by Albert

  While reading some papers about the Turing completeness of recurrent neural nets (for example: Turing computability with neural nets, Hava T. Siegelmann and Eduardo D. Sontag, 1991), I got the feeling that the proof which was given there was not really that practical. For example the referenced paper needs a neural network which neuron activity must be of infinity exactness (to reliable represent any rational number). Other proofs need a neural network of infinite size. Clearly, that is not really that practical. But I started to wonder now if it does make sense at all to ask for Turing completeness. By the strict definition, no computer system nowadays is Turing complete because none of them will be able to simulate the infinite tape. Interestingly, programming language specification leaves it most often open if they are turing complete or not. It all boils down to the question if they will always be able to allocate more memory and if the function call stack size is infinite. Most specification don't really specify this. Of course all available implementations are limited here, so all practical implementations of programming languages are not Turing complete. So, what you can say is that all computer systems are just equally powerful as finite state machines and not more. And that brings me to the question: How useful is the term Turing complete at all? And back to neural nets: For any practical implementation of a neural net (including our own brain), they will not be able to represent an infinite number of states, i.e. by the strict definition of Turing completeness, they are not Turing complete. So does the question if neural nets are Turing complete make sense at all? The question if they are as powerful as finite state machines was answered already much earlier (1954 by Minsky, the answer of course: yes) and also seems easier to answer. I.e., at least in theory, that was already the proof that they are as powerful as any computer.

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