These are my recent Pinboard.in links:
- “The recA/RAD51 gene family encodes a diverse set of recombinase proteins that effect homologous recombination, DNA-repair, and genome stability. The recA gene family is expressed in almost all species of Eubacteria, Archaea, and Eukaryotes, and even in some viruses. To date, efforts to resolve the deep evolutionary origins of this ancient protein family have been hindered, in part, by the high sequence divergence between families (i.e. ~30% identity between paralogous groups). Through (i) large taxon sampling, (ii) the use of a phylogenetic algorithm designed for measuring highly divergent paralogs, and (iii) novel Evolutionary Spatial Dynamics simulation and analytical tools, we obtained a robust, parsimonious and more refined phylogenetic history of the recA/RAD51 superfamily. Taken together, our model for the evolution of recA/RAD51 family provides a better understanding of ancient origin of recA proteins and multiple events leading to the diversification of recA homologs in eukaryotes, including the discovery of additional RAD51 sub-families.”
cladistics algorithms visualization deep-time statistics - “Mathematical models for systems of interacting agents using simple local rules have been proposed and shown to exhibit emergent swarming behavior. Most of these models are constructed by intuition or manual observations of real phenomena, and later tuned or verified to simulate desired dynamics. In contrast to this approach, we propose using a model that attempts to follow an averaged rule of the essential distance-dependent collective behavior of real pigeon flocks, which was abstracted from experimental data. By using a simple model to follow the behavioral tendencies of real data, we show that our model can exhibit emergent self-organizing dynamics such as flocking, pattern formation, and counter-rotating vortices. The range of behaviors observed in our simulations are richer than the standard models of collective dynamics, and should thereby give potential for new models of complex behavior.”
agent-based swarms boids algorithms emergent-design - “We describe a dynamic programming algorithm for computing the marginal distribution of discrete probabilistic programs. This algorithm takes a functional interpreter for an arbitrary probabilistic programming language and turns it into an efficient marginalizer. Because direct caching of sub-distributions is impossible in the presence of recursion, we build a graph of dependencies between sub-distributions. This factored sum-product network makes (potentially cyclic) dependencies between subproblems explicit, and corresponds to a system of equations for the marginal distribution. We solve these equations by fixed-point iteration in topological order. We illustrate this algorithm on examples used in teaching probabilistic models, computational cognitive science research, and game theory.”
recursion stochastic-programming simulation nudge Shareable: Hacking Home: Coliving Reinvents the Commune for a Networked Age
‘It was more than just a luxury home full of brilliant young minds. Dubbed “an intentional community”, The Rainbow Mansion was an experiment in a new type of cohabitation. The house began hosting hackathons and salons in its library, inviting Silicon Valley’s best and brightest to participate. “Right away it set itself in motion,” Schingler says. “It had this sort of accidental mystique about it.”’
cohousing collaboration nerd-culture- “An algorithm is presented to compute isolated values of the divisor summatory function in O(n^(1/3)) time and O (log n) space. The algorithm is elementary and uses a geometric approach of successive approximation combined with coordinate transformation.”
algorithms computational-geometry nudge-targets [1204.3650] Evolutionary Metadynamics: a Novel Method to Predict Crystal Structures
“A novel method for crystal structure prediction, based on metadynamics and evolutionary algorithms, is presented here. This technique can be used to produce efficiently both the ground state and metastable states easily reachable from a reasonable initial structure. We use the cell shape as collective variable and evolutionary variation operators developed in the context of the USPEX method [Oganov, Glass, textit{J. Chem. Phys.}, 2006, textbf{124}, 244704; Lyakhov textit{et al., Comp. Phys. Comm.}, 2010, textbf{181}, 1623; Oganov textit{et al., Acc. Chem. Res.}, 2011, textbf{44}, 227] to equilibrate the system as a function of the collective variables. We illustrate how this approach helps one to find stable and metastable states for Al$_2$SiO$_5$, SiO$_2$, MgSiO$_3$, and carbon. Apart from predicting crystal structures, the new method can also provide insight into mechanisms of phase transitions.”
evolutionary-algorithms search-algorithms physics nudge-targets condensed-matter