Articles tagged with Random Walks
Physicists develop new method to efficiently determine all possible first-passage times and their probabilities, capturing randomness of both walker and environment. This approach builds on existing ideas and could improve predictive analyses in fields such as biology, migration systems, and financial markets.
Researchers at the University of Arizona found that rock ants follow a methodical search strategy, combining systematized meandering with random movement to efficiently explore new areas. This unique behavior may provide insights into the evolution of exploration strategies in other species.
Sandia researchers have demonstrated that neuromorphic computers can solve more complex problems than artificial intelligence and may earn a place in high-performance computing. The findings show that neuromorphic simulations can track X-rays, disease spreading, information flowing through social networks, and financial markets.
Researchers at Carnegie Mellon University developed an efficient new way to quickly analyze complex geometric models by using Monte Carlo methods. This approach eliminates the need to divide shapes into meshes, reducing errors and increasing computation speed.
Researchers at Uppsala University developed a new computer algorithm that simulates polymer dynamics hundreds of times faster than traditional methods. This breakthrough has the potential to revolutionize fields such as inkjet printing and materials science.
Researchers at Washington University in St. Louis developed mathematical tools that determine when randomness emerges in stochastic systems, describing the kinetics before dissolving into randomness. The tools have potential to predict onset of chaos in nanoparticles to checking accounts.
Researchers at Lawrence Berkeley National Laboratory developed HipMCL, an algorithm that can cluster large biological networks containing millions of nodes and edges. The new method allows biologists to make sense of big science data using massively parallel supercomputers.
A mathematical model has been developed to explain how new ideas emerge, using Heaps' law and complex networks to analyze the discovery process. The study reveals that innovations often arise in clusters and are strongly correlated, providing insights for effective interventions to nurture sustainable growth.
Researchers from MIT's Computer Science and Artificial Intelligence Laboratory propose a theoretical framework for estimating population density in networks, which converges quickly and is more accurate than random sampling. This approach has practical applications in analyzing social networks, robot swarms, and ad hoc networks.
Researchers found that tiger sharks can navigate long distances using directed walks, while thresher sharks also exhibit this behavior. Blacktip reef sharks, on the other hand, swim randomly within their small home ranges.
Young canaries learn atypical songs but recast them into adult canary syntax as they mature. As they approach sexual maturity, rules interfere with the freedom of youth, leading to a reprogramming of their songs.