Computer Sciences

Finding the needles in a haystack of high-dimensional data sets

One of the challenges in the era of Big Data is dealing with many independent variables, also known as the "curse of dimensionality." Therefore, there is an urgent need to develop algorithms that can select subsets of features ...

Robotics

These robots can move your couch

To train robots how to work independently but cooperatively, researchers at the University of Cincinnati gave them a relatable task: Move a couch.

Computer Sciences

New AI-powered deep learning model to support medical diagnostics

A new deep-learning model can learn to identify diseases from medical scans faster and more accurately, according to new research by a team of University of Alberta computing scientists and the U of A spinoff company MEDO. ...

Computer Sciences

Finding control in hard-to-predict systems

Input one, output one; input two, output two; input three; output purple—what kind of system is this? Computer algorithms can exist as non-deterministic systems, in which there are multiple possible outcomes for each input. ...

Computer Sciences

T-GPS processes a graph with a trillion edges on a single computer

A KAIST research team has developed a new technology that enables the processing of a large-scale graph algorithm without storing the graph in the main memory or on disk. Named as T-GPS (Trillion-scale Graph Processing Simulation) ...

Computer Sciences

Algorithms improve how we protect our data

Daegu Gyeongbuk Institute of Science and Technology (DGIST) scientists in Korea have developed algorithms that more efficiently measure how difficult it would be for an attacker to guess secret keys for cryptographic systems. ...

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Algorithm

In mathematics, computing, linguistics, and related subjects, an algorithm is a finite sequence of instructions, an explicit, step-by-step procedure for solving a problem, often used for calculation and data processing. It is formally a type of effective method in which a list of well-defined instructions for completing a task, will when given an initial state, proceed through a well-defined series of successive states, eventually terminating in an end-state. The transition from one state to the next is not necessarily deterministic; some algorithms, known as probabilistic algorithms, incorporate randomness.

A partial formalization of the concept began with attempts to solve the Entscheidungsproblem (the "decision problem") posed by David Hilbert in 1928. Subsequent formalizations were framed as attempts to define "effective calculability" (Kleene 1943:274) or "effective method" (Rosser 1939:225); those formalizations included the Gödel-Herbrand-Kleene recursive functions of 1930, 1934 and 1935, Alonzo Church's lambda calculus of 1936, Emil Post's "Formulation 1" of 1936, and Alan Turing's Turing machines of 1936–7 and 1939.

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