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🔗 Computer science 🔗 Statistics 🔗 Databases

In data mining and statistics, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis that seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two categories:

• Agglomerative: This is a "bottom-up" approach: Each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy.
• Divisive: This is a "top-down" approach: All observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.

In general, the merges and splits are determined in a greedy manner. The results of hierarchical clustering are usually presented in a dendrogram.

Hierarchical clustering has the distinct advantage that any valid measure of distance can be used. In fact, the observations themselves are not required: all that is used is a matrix of distances. On the other hand, except for the special case of single-linkage distance, none of the algorithms (except exhaustive search in ${\displaystyle {\mathcal {O}}(2^{n})}$) can be guaranteed to find the optimum solution.

🔗 Computer science 🔗 Statistics

A Boltzmann machine (also called stochastic Hopfield network with hidden units) is a type of stochastic recurrent neural network. It is a Markov random field. It was translated from statistical physics for use in cognitive science. The Boltzmann machine is based on stochastic spin-glass model with an external field, i.e., a Sherrington–Kirkpatrick model that is a stochastic Ising Model and applied to machine learning.

Boltzmann machines can be seen as the stochastic, generative counterpart of Hopfield networks. They were one of the first neural networks capable of learning internal representations, and are able to represent and (given sufficient time) solve combinatoric problems.

They are theoretically intriguing because of the locality and Hebbian nature of their training algorithm (being trained by Hebb's rule), and because of their parallelism and the resemblance of their dynamics to simple physical processes. Boltzmann machines with unconstrained connectivity have not proven useful for practical problems in machine learning or inference, but if the connectivity is properly constrained, the learning can be made efficient enough to be useful for practical problems.

They are named after the Boltzmann distribution in statistical mechanics, which is used in their sampling function. That's why they are called "energy based models" (EBM). They were invented in 1985 by Geoffrey Hinton, then a Professor at Carnegie Mellon University, and Terry Sejnowski, then a Professor at Johns Hopkins University.

🔗 Computer science

The Weissman score is an efficiency metric for lossless compression applications, which was developed for fictional use. It compares both required time and compression ratio of measured applications, with those of a de facto standard according to the data type. It was developed by Tsachy Weissman, a professor at Stanford University, and Vinith Mishra, a graduate student, at the request of producers for HBO's television series Silicon Valley, about a fictional tech start-up.

The formula is the following; where r is the compression ratio, T is the time required to compress, the overlined ones are the same metrics for a standard compressor, and alpha is a scaling constant.

Weissman score was used in Dropbox Tech Blog to explain real-world work on lossless compression.

🔗 Computer science

In computer science, Linda is a model of coordination and communication among several parallel processes operating upon objects stored in and retrieved from shared, virtual, associative memory. It was developed by Sudhir Ahuja at AT&T Bell Laboratories in collaboration with David Gelernter and Nicholas Carriero at Yale University in 1986.

🔗 Computer science 🔗 Mathematics

In computational complexity theory, the unique games conjecture (often referred to as UGC) is a conjecture made by Subhash Khot in 2002. The conjecture postulates that the problem of determining the approximate value of a certain type of game, known as a unique game, has NP-hard computational complexity. It has broad applications in the theory of hardness of approximation. If the unique games conjecture is true and P ≠ NP, then for many important problems it is not only impossible to get an exact solution in polynomial time (as postulated by the P versus NP problem), but also impossible to get a good polynomial-time approximation. The problems for which such an inapproximability result would hold include constraint satisfaction problems, which crop up in a wide variety of disciplines.

The conjecture is unusual in that the academic world seems about evenly divided on whether it is true or not.

🔗 Computer science

In computer programming, the Schwartzian transform is a technique used to improve the efficiency of sorting a list of items. This idiom is appropriate for comparison-based sorting when the ordering is actually based on the ordering of a certain property (the key) of the elements, where computing that property is an intensive operation that should be performed a minimal number of times. The Schwartzian transform is notable in that it does not use named temporary arrays.

The Schwartzian transform is a version of a Lisp idiom known as decorate-sort-undecorate, which avoids recomputing the sort keys by temporarily associating them with the input items. This approach is similar to memoization, which avoids repeating the calculation of the key corresponding to a specific input value. By comparison, this idiom assures that each input item's key is calculated exactly once, which may still result in repeating some calculations if the input data contains duplicate items.

The idiom is named after Randal L. Schwartz, who first demonstrated it in Perl shortly after the release of Perl 5 in 1994. The term "Schwartzian transform" applied solely to Perl programming for a number of years, but it has later been adopted by some users of other languages, such as Python, to refer to similar idioms in those languages. However, the algorithm was already in use in other languages (under no specific name) before it was popularized among the Perl community in the form of that particular idiom by Schwartz. The term "Schwartzian transform" indicates a specific idiom, and not the algorithm in general.

For example, to sort the word list ("aaaa","a","aa") according to word length: first build the list (["aaaa",4],["a",1],["aa",2]), then sort it according to the numeric values getting (["a",1],["aa",2],["aaaa",4]), then strip off the numbers and you get ("a","aa","aaaa"). That was the algorithm in general, so it does not count as a transform. To make it a true Schwartzian transform, it would be done in Perl like this:

🔗 Computing 🔗 Computer science 🔗 Biology 🔗 Chemistry 🔗 Genetics

DNA computing is a branch of computing which uses DNA, biochemistry, and molecular biology hardware, instead of the traditional silicon-based computer technologies. Research and development in this area concerns theory, experiments, and applications of DNA computing. The term "molectronics" has sometimes been used, but this term has already been used for an earlier technology, a then-unsuccessful rival of the first integrated circuits; this term has also been used more generally, for molecular-scale electronic technology.

🔗 Computer science

Graffiti is an essentially single-stroke shorthand handwriting recognition system used in PDAs based on the Palm OS. Graffiti was originally written by Palm, Inc. as the recognition system for GEOS-based devices such as HP's OmniGo 100 and 120 or the Magic Cap-line and was available as an alternate recognition system for the Apple Newton MessagePad, when NewtonOS 1.0 could not recognize handwriting very well. Graffiti also runs on the Windows Mobile platform, where it is called "Block Recognizer", and on the Symbian UIQ platform as the default recognizer and was available for Casio's Zoomer PDA.

The software is based primarily on a neography of upper-case characters that can be drawn blindly with a stylus on a touch-sensitive panel. Since the user typically cannot see the character as it is being drawn, complexities have been removed from four of the most difficult letters. "A" "F", "K" and "T" all are drawn without any need to match up a cross-stroke.

Some letters can be drawn with strokes other than the "official" ones. Two examples of these alternative strokes are the letters "V" (drawn the same only from right to left) and "X" (drawn the same as the letter "K" except reversed from right to left). These alternative strokes are frequently recognized with greater reliability.

🔗 Computer science 🔗 Mathematics

In computer science and graph theory, the Canadian traveller problem (CTP) is a generalization of the shortest path problem to graphs that are partially observable. In other words, the graph is revealed while it is being explored, and explorative edges are charged even if they do not contribute to the final path.

This optimization problem was introduced by Christos Papadimitriou and Mihalis Yannakakis in 1989 and a number of variants of the problem have been studied since. The name supposedly originates from conversations of the authors who learned of a difficulty Canadian drivers had: traveling a network of cities with snowfall randomly blocking roads. The stochastic version, where each edge is associated with a probability of independently being in the graph, has been given considerable attention in operations research under the name "the Stochastic Shortest Path Problem with Recourse" (SSPPR).

🔗 Video games 🔗 Computer science 🔗 Science

A human-based computation game or game with a purpose (GWAP) is a human-based computation technique of outsourcing steps within a computational process to humans in an entertaining way (gamification).

Luis von Ahn first proposed the idea of "human algorithm games", or games with a purpose (GWAPs), in order to harness human time and energy for addressing problems that computers cannot yet tackle on their own. He believes that human intellect is an important resource and contribution to the enhancement of computer processing and human computer interaction. He argues that games constitute a general mechanism for using brainpower to solve open computational problems. In this technique, human brains are compared to processors in a distributed system, each performing a small task of a massive computation. However, humans require an incentive to become part of a collective computation. Online games are used as a means to encourage participation in the process.

The tasks presented in these games are usually trivial for humans, but difficult for computers. These tasks include labeling images, transcribing ancient texts, common sense or human experience based activities, and more. Human-based computation games motivate people through entertainment rather than an interest in solving computation problems. This makes GWAPs more appealing to a larger audience. GWAPs can be used to help build the semantic web, annotate and classify collected data, crowdsource general knowledge, and improving other general computer processes. GWAPs have a vast range of applications in variety of areas such as security, computer vision, Internet accessibility, adult content filtering, and Internet search. In applications such as these, games with a purpose have lowered the cost of annotating data and increased the level of human participation.