Over the past decade or so, a scientific debate has emerged around whether rapid Arctic warming could be affecting extreme weather in the mid-latitudes. Much of this work focuses on the jet stream – the narrow current of strong winds encircling the globe around 40-50 degrees North.
KSL is a sparse math library written in the C programming language that is targeted to real-time kinematics, dynamics, contact detection, robotics and 3D visualization applications.
MIT 2.003SC Engineering Dynamics, Fall 2011 View the complete course: http://ocw.mit.edu/2-003SCF11 Instructor: J. Kim Vandiver License: Creative Commons BY-...
The Dynamic Linked Data Observatory is a framework to monitor Linked Data over an extended period of time. The core goal of our work is to collect frequent, continuous snapshots of a subset of the Web of Data that is interesting for further study and experimentation, with an aim to capture raw data about the dynamics of Linked Data. The resulting corpora will be made openly and continuously available to the Linked Data research community.
GROMACS is a versatile package to perform molecular dynamics, i.e. simulate the Newtonian equations of motion for systems with hundreds to millions of particles.
A new analysis of data from del.ici.ous shows that, while the number of tags grows roughly in proportion to the growth of content, users can unwittingly provide bridges between networks of seemingly unrelated concepts.
Damián H. Zanette, Marcelo A. Montemurro
We investigate the origin of Zipf's law for words in written texts by means of a stochastic dynamical model for text generation. The model incorporates both features related to the general structure of languages and memory effects inherent to the production of long coherent messages in the communication process. It is shown that the multiplicative dynamics of our model leads to rank-frequency distributions in quantitative agreement with empirical data. Our results give support to the linguistic relevance of Zipf's law in human language.
S. Hoffmann, and C. Lessig. (2022)cite arxiv:2202.01897Comment: Submitted to "Environmental Data Science", Cambridge University Press. Revised version. Journal-version of "Towards Representation Learning for Atmospheric Dynamics. arXiv:2109.09076".