Scientific Computing: Terrorist networks form a structural hierarchy based on one-way information flow called a directed acyclic graph (DAG) that makes them similar to many companies and some social media networks. Counterterrorism is interested in stopping the flow of information. A group of mathematicians at Ryerson University in Toronto adapted a previous model of terrorist networks to examine the best counterterrorism actions. They used a specific DAG known as a partially ordered set, in which not all of the nodes are connected; the higher nodes represented the leaders and the lower nodes represented foot soldiers. They used two extreme variations of the network—in one, each node had a regular number of connections, and in the other, the number of connections followed a power law. By applying a stochastic model of action to both the counterterrorist and terrorist networks, the researchers reinforced the idea that disrupting information at lower levels is less efficient than disrupting it at high levels. However, by accurately mapping the members and connections of an actual terrorist network, counterterrorist efforts could determine the most effective places for attempting to stop information flow.
BBC: Entropy is a measure of the number of internal arrangements that a system can exhibit. For a closed system, entropy inevitably increases, as defined by the second law of thermodynamics. A related quantity, causal path entropy, measures the number of possible arrangements a system could have on its way to possible future states. By providing a route toward greater complexity, causal path entropy could conceivably serve as a model for how intelligence develops. With that idea in mind, Alex Wissner-Gross of Harvard University and MIT and Cameron Freer of the University of Hawaii at Manoa have proposed the existence of a force that works to maximize causal path entropy. They created simulations of simple physical systems that accounted for that force and found that the effects were profound—a particle in a box moved to the center and a pendulum on a sliding pivot moved to an inverted position that would be unstable without the force. They then created physical models that reproduced standard animal intelligence tests. The models behaved in ways analogous to the development of tool use and of social cooperation, both characteristics of basic levels of cognition. However, whereas the models are suggestive of a connection between entropy and intelligence, they are descriptive rather than explanatory and don’t give any evidence of the actual existence of a force that maximizes future possible arrangements of a system.
MIT Technology Review: Online shopping sites make use of various algorithms to suggest items for you to purchase based on what you and other users have purchased in the past. One effect of such recommendations is that some locales or products suffer from the sudden influx of people directed to them by the recommendation. To try to avoid this problem, a team of researchers led by Stanislao Gualdi of the University of Fribourg in Switzerland has applied a feature of particle physics. At the atomic level, particles tend to occupy the most energetically favorable states; but the number of particles that can occupy any given state depends on the type of particle. Gualdi drew a parallel between this concept and that of commercial products, which can be shared by either many or just a few people. The team developed a model that can limit the number of users allowed for a given product. When testing their model against empirical data of DVD rentals, they found that limiting rentals ensures that a wider range of DVDs get rented. And the more DVDs rented, the broader the range and accuracy of the ensuing recommendations. The overall effect was a healthier rental system. However, whether the model would work for actual retailers, who focus on maximizing their profits, is uncertain.
Nature: Absolute zero corresponds to the theoretical state in which the average energy of a system of particles is zero. During the normal state of a gas, the majority of the particles are at energies near the average, with just a few at higher energy levels. Theorists predicted in the 1950s that if a gas could be created in which the situation was reversed—the majority of the particles had higher energy levels—then the temperature could drop to below absolute zero. Ulrich Schneider, with Ludwig-Maximilians University in Munich, and his colleagues appear to have done just that. Using lasers and magnetic fields, the researchers arranged a stable lattice structure out of a quantum gas of potassium atoms. Quickly adjusting the magnetic field caused the atoms to attract rather than repel each other, and they shifted from their lowest-energy state to a high-energy state. Normally, that would cause the lattice to collapse, but the researchers used the lasers to make it too difficult for the atoms to leave their positions. The result is a gas that has a temperature just a few billionths of a degree below absolute zero. The experiment opens the way to potential stable states of exotic materials, and the theoretical behavior of other systems at sub-absolute-zero temperatures may provide some answers about cosmological phenomena such as dark energy.
Wired: Determining the origin of a bit of information, or of a disease outbreak, has often required the backwards, step-by-step tracing of the transmission. However, a new process can calculate the most likely source using only a fraction of the information required by earlier methods. Researchers at ETH Zürich recently estimated, and then combined, the most likely paths of transmission to individual nodes within a network. When they applied their technique to a known cholera outbreak in South Africa in 2000, the researchers could narrow down the source by using information about the presence of cholera in just 20% of the network’s nodes — in this case, communities in the region. The same technique was used to determine the identity of the leader of a terrorist group and the spread of contamination in a subway system.
Nature: Simultaneous dependencies within large data sets can be effectively invisible to statistical analysis. David Reshef of the Broad Institute of MIT and Harvard in Massachusetts, Yakir Reshef at the Weizmann Institute of Science in Rehovot, Israel, and their colleagues have devised a method called the maximal information coefficient (MIC) to find superimposed correlations between variables and measure how tight each relationship is. The MIC is calculated by plotting data on a graph and looking for all the ways of dividing up the graph into blocks or grids that capture the largest possible number of data points. The team applied their method to data concerning global health, gene expression, major-league baseball, and human gut microbiota to identify both known and novel dependencies.