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Bead sort - Wikipedia, the free encyclopedia
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The bead sort operation can be compared to the manner in which beads slide on parallel poles, such as on an abacus. However, each pole may have a distinct number of beads. Initially, it may be helpful to imagine the beads suspended on vertical poles. In Step 1, such an arrangement is displayed using n=5 rows of beads on m=4 vertical poles. The numbers to the right of each row indicate the number that the row in question represents; rows 1 and 2 are representing the positive integer 3 (because they each contain three beads) while the top row represents the positive integer 2 (as it only contains two beads).[1]
28 Oct 08
Suggestly - the smart domain search tool
http://codeismightier.com/2008/10/generating-domain-names/
17 Oct 08
MIT’s Introduction to Algorithms, Lecture 12: Skip Lists - good coders code, great reuse
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Skip lists are an efficient data structure that can be used in place of balanced
trees. Skip lists use probabilistic balancing rather than strictly enforce
balancing and as a result the algorithms for insertion and deletion in skip
lists are much simpler to implement and significantly faster than the equivalent
algorithms for balanced search trees (see lectures
9 and 10 for search trees).
07 Aug 08
Want to Remember Everything You'll Ever Learn? Surrender to This Algorithm
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Piotr Wozniak's quest for anonymity has been successful. Nobody along this string of little beach resorts recognizes him as the inventor of a technique to turn people into geniuses. A portion of this technique, embodied in a software program called SuperMemo, has enthusiastic users around the world. They apply it mainly to learning languages, and it's popular among people for whom fluency is a necessity — students from Poland or other poor countries aiming to score well enough on English-language exams to study abroad. A substantial number of them do not pay for it, and pirated copies are ubiquitous on software bulletin boards in China, where it competes with knockoffs like SugarMemo.
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SuperMemo is based on the insight that there is an ideal moment to practice what you've learned. Practice too soon and you waste your time. Practice too late and you've forgotten the material and have to relearn it. The right time to practice is just at the moment you're about to forget. Unfortunately, this moment is different for every person and each bit of information. Imagine a pile of thousands of flash cards. Somewhere in this pile are the ones you should be practicing right now. Which are they?
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24 Jan 08
Bit Twiddling Hacks
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When totaling the number of operations for algorithms here, any C operator is
counted as one operation. Intermediate assignments, which need not be written to
RAM, are not counted. Of course, this operation counting approach only serves as
an approximation of the actual number of machine instructions and CPU time. All
operations are assumed to take the same amount of time, which is not true in
reality, but CPUs have been heading increasingly in this direction over time.
There are many nuances that determine how fast a system will run a given sample
of code, such as cache sizes, memory bandwidths, instruction sets, etc. In the
end, benchmarking is the best way to determine whether one method is really
faster than another, so consider the techniques below as possibilities to test
on your target architecture.
21 Jan 08
80. Robots Evolve And Learn How to Lie | Robots | DISCOVER Magazine
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Floreano and his colleagues outfitted robots with light sensors, rings of blue
light, and wheels and placed them in habitats furnished with glowing “food
sources” and patches of “poison” that recharged or drained their batteries.
Their neural circuitry was programmed with just 30 “genes,” elements of software
code that determined how much they sensed light and how they responded when they
did. The robots were initially programmed both to light up randomly and to move
randomly when they sensed light. -
By the 50th generation, the robots had learned to communicate—lighting up, in
three out of four colonies, to alert the others when they’d found food or
poison. The fourth colony sometimes evolved “cheater” robots instead, which
would light up to tell the others that the poison was food, while they
themselves rolled over to the food source and chowed down without emitting so
much as a blink.
28 Nov 07
Clustering - Introduction
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So, the goal of clustering is to
determine the intrinsic grouping in a set of unlabeled data. But how to decide
what constitutes a good clustering? It can be shown that there is no absolute
“best” criterion which would be independent of the final aim of the clustering.
Consequently, it is the user which must supply this criterion, in such a way
that the result of the clustering will suit their needs.
05 Apr 07
New Algorithms from UCSD Improve Automated Image Labeling [Jacobs School of Engineering: News & Events]
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Electrical engineers from UC San Diego are making progress on a different kind of image search engine – one that analyzes the images themselves. This approach may be folded into next-generation image search engines for the Internet; and in the shorter term, could be used to annotate and search commercial and private image collections.
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