Constant-Time LCA Retrieval
... ◦ Depth-first traversal can be done in O( n ) space and time complexity. ◦ L is of size O( n ) and thus it’s creation and initialization can be done in O( n ) space and time complexity. ◦ To find lca(u,v) we need the first occurrence of u and v in L. This could be stored in a table of size O( n ). T ...
... ◦ Depth-first traversal can be done in O( n ) space and time complexity. ◦ L is of size O( n ) and thus it’s creation and initialization can be done in O( n ) space and time complexity. ◦ To find lca(u,v) we need the first occurrence of u and v in L. This could be stored in a table of size O( n ). T ...
MN101E51/52 Series - Panasonic Semiconductor
... (operating power supply voltage and operating environment etc.). Especially, please be careful not to exceed the range of absolute maximum rating on the transient state, such as power-on, power-off and mode-switching. Otherwise, we will not be liable for any defect which may arise later in your equi ...
... (operating power supply voltage and operating environment etc.). Especially, please be careful not to exceed the range of absolute maximum rating on the transient state, such as power-on, power-off and mode-switching. Otherwise, we will not be liable for any defect which may arise later in your equi ...
probability
... What is “random variation” in the distribution of a population? Examples: Toasting time, Temperature settings, etc.… POPULATION 1: Little to no variation (e.g., product manufacturing) ...
... What is “random variation” in the distribution of a population? Examples: Toasting time, Temperature settings, etc.… POPULATION 1: Little to no variation (e.g., product manufacturing) ...
Continuous Random Variables and Reliability Analysis
... • Also called infant mortality phase or reliability growth phase • Caused by undetected hardware/software defects that are being fixed resulting in reliability growth • Can cause significant prediction errors if steadystate failure rates are used • Availability models can be constructed and solved t ...
... • Also called infant mortality phase or reliability growth phase • Caused by undetected hardware/software defects that are being fixed resulting in reliability growth • Can cause significant prediction errors if steadystate failure rates are used • Availability models can be constructed and solved t ...
The TSP phase transition - Computer Science and Engineering
... The traveling salesman problem is one of the simplest and most famous combinatorial problems in computer science. Given a list of II cities, and a matrix giving the distance between each pair of cities, the traveling salesman problem is to determine if you can visit all II cities and return to the s ...
... The traveling salesman problem is one of the simplest and most famous combinatorial problems in computer science. Given a list of II cities, and a matrix giving the distance between each pair of cities, the traveling salesman problem is to determine if you can visit all II cities and return to the s ...
Streaming algorithms for embedding and computing edit distance in
... string left to right, push opening parenthesis on a stack and match them against closing parenthesis. Whenever there is a mismatch remove at random either the closing parenthesis or the opening one. This algorithm can be applied also to approximately compute the edit distance of strings by pushing a ...
... string left to right, push opening parenthesis on a stack and match them against closing parenthesis. Whenever there is a mismatch remove at random either the closing parenthesis or the opening one. This algorithm can be applied also to approximately compute the edit distance of strings by pushing a ...
stochastic local search. - International Center for Computational Logic
... I Considers variables in the selected clauses sorted according to their score. I If the best variable is not the most recently flipped one, it is flipped, otherwise, it is flipped with a probability 1 − p, while in the remaining cases, the second-best variable is flipped. I Is in many cases substant ...
... I Considers variables in the selected clauses sorted according to their score. I If the best variable is not the most recently flipped one, it is flipped, otherwise, it is flipped with a probability 1 − p, while in the remaining cases, the second-best variable is flipped. I Is in many cases substant ...
Hardware random number generator
In computing, a hardware random number generator (TRNG, True Random Number Generator) is an apparatus that generates random numbers from a physical process, rather than a computer program. Such devices are often based on microscopic phenomena that generate low-level, statistically random ""noise"" signals, such as thermal noise, the photoelectric effect, and other quantum phenomena. These processes are, in theory, completely unpredictable, and the theory's assertions of unpredictability are subject to experimental test. A hardware random number generator typically consists of a transducer to convert some aspect of the physical phenomena to an electrical signal, an amplifier and other electronic circuitry to increase the amplitude of the random fluctuations to a measurable level, and some type of analog to digital converter to convert the output into a digital number, often a simple binary digit 0 or 1. By repeatedly sampling the randomly varying signal, a series of random numbers is obtained. The main application for electronic hardware random number generators is in cryptography, where they are used to generate random cryptographic keys to transmit data securely. They are widely used in Internet encryption protocols such as Secure Sockets Layer (SSL).Random number generators can also be built from ""random"" macroscopic processes, using devices such as coin flipping, dice, roulette wheels and lottery machines. The presence of unpredictability in these phenomena can be justified by the theory of unstable dynamical systems and chaos theory. Even though macroscopic processes are deterministic under Newtonian mechanics, the output of a well-designed device like a roulette wheel cannot be predicted in practice, because it depends on the sensitive, micro-details of the initial conditions of each use. Although dice have been mostly used in gambling, and in more recent times as ""randomizing"" elements in games (e.g. role playing games), the Victorian scientist Francis Galton described a way to use dice to explicitly generate random numbers for scientific purposes in 1890.Hardware random number generators generally produce a limited number of random bits per second. In order to increase the data rate, they are often used to generate the ""seed"" for a faster Cryptographically secure pseudorandom number generator, which then generates the pseudorandom output sequence.