Probabilistic models, such as hidden Markov models or Bayesian networks, are commonly used to model biological data. Much of their popularity can be attributed to the existence of efficient and robust ...
Abstract: The problem of fault localization, which needs to find the optimal hypothesis from all possible fault hypotheses, is theoretically proven to be NP-hard. In this paper, we propose a ...
The phenomenal success of our integrated circuits managed to obscure an awkward fact: they’re not always the best way to solve problems. The features of modern computers—binary operations, separated ...
Most recent advances in artificial intelligence—such as mobile apps that convert speech to text—are the result of machine learning, in which computers are turned loose on huge data sets to look for ...
Abstract: Soil freeze-thaw (F/T) states are a key indicator of the Arctic climate, highlighting the need for their accurate retrieval. Global Navigation Satellite System-Reflectometry (GNSSR) offers a ...
Hosted on MSN
Fermat's little theorem as an algorithm, when probability replaces certainty in primality testing
A deterministic proof seems within reach, until composite numbers start masquerading as primes. This video traces how Fermat's theorem becomes a probabilistic algorithm, and why embracing uncertainty ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results