Capital Flows to Developing Countries: The Allocation Puzzle

According to the consensus view in growth and development economics, cross country differences in per-capita income largely reflect differences in countries' total factor productivity. We argue that this view has powerful implications for patterns of capital flows: everything else equal, countries with faster productivity growth should invest more, and attract more foreign capital. We then show that the pattern of net capital flows across developing countries is not consistent with this prediction. If anything, capital seems to flow more to countries that invest and grow less. We argue that this result -- which we call the allocation puzzle -- constitutes an important challenge for economic research, and discuss some possible research avenues to solve the puzzle.

Sectors of solutions in three-dimensional gravity and Black Holes

We examine the connection between three dimensional gravity with negative cosmological constant and two-dimensional CFT via the Chern-Simons formulation. A set of generalized spectral flow transformations are shown to yield new sectors of solutions. One implication is that the microscopic calculation of the entropy of the Banados-Teitelboim-Zanelli (BTZ) black hole is corrected by a multiplicative factor with the result that it saturates the Bekenstein-Hawking expression.Comment: 28 pages, LaTeX; references adde

Resilience of Traffic Networks with Partially Controlled Routing

This paper investigates the use of Infrastructure-To-Vehicle (I2V) communication to generate routing suggestions for drivers in transportation systems, with the goal of optimizing a measure of overall network congestion. We define link-wise levels of trust to tolerate the non-cooperative behavior of part of the driver population, and we propose a real-time optimization mechanism that adapts to the instantaneous network conditions and to sudden changes in the levels of trust. Our framework allows us to quantify the improvement in travel time in relation to the degree at which drivers follow the routing suggestions. We then study the resilience of the system, measured as the smallest change in routing choices that results in roads reaching their maximum capacity. Interestingly, our findings suggest that fluctuations in the extent to which drivers follow the provided routing suggestions can cause failures of certain links. These results imply that the benefits of using Infrastructure-To-Vehicle communication come at the cost of new fragilities, that should be appropriately addressed in order to guarantee the reliable operation of the infrastructure.Comment: Accepted for presentation at the IEEE 2019 American Control Conferenc

From traffic and pedestrian follow-the-leader models with reaction time to first order convection-diffusion flow models

In this work, we derive first order continuum traffic flow models from a microscopic delayed follow-the-leader model. Those are applicable in the context of vehicular traffic flow as well as pedestrian traffic flow. The microscopic model is based on an optimal velocity function and a reaction time parameter. The corresponding macroscopic formulations in Eulerian or Lagrangian coordinates result in first order convection-diffusion equations. More precisely, the convection is described by the optimal velocity while the diffusion term depends on the reaction time. A linear stability analysis for homogeneous solutions of both continuous and discrete models are provided. The conditions match the ones of the car-following model for specific values of the space discretization. The behavior of the novel model is illustrated thanks to numerical simulations. Transitions to collision-free self-sustained stop-and-go dynamics are obtained if the reaction time is sufficiently large. The results show that the dynamics of the microscopic model can be well captured by the macroscopic equations. For non--zero reaction times we observe a scattered fundamental diagram. The scattering width is compared to real pedestrian and road traffic data

A Clustering-Based Algorithm for Data Reduction

Finding an efficient data reduction method for large-scale problems is an imperative task. In this paper, we propose a similarity-based self-constructing fuzzy clustering algorithm to do the sampling of instances for the classification task. Instances that are similar to each other are grouped into the same cluster. When all the instances have been fed in, a number of clusters are formed automatically. Then the statistical mean for each cluster will be regarded as representing all the instances covered in the cluster. This approach has two advantages. One is that it can be faster and uses less storage memory. The other is that the number of new representative instances need not be specified in advance by the user. Experiments on real-world datasets show that our method can run faster and obtain better reduction rate than other methods

A macroscopic scale model of bacterial flagellar bundling

Escherichia coli and other bacteria use rotating helical filaments to swim. Each cell typically has about four filaments, which bundle or disperse depending on the sense of motor rotation. To study the bundling process, we built a macroscopic scale model consisting of stepper-motor-driven polymer helices in a tank filled with a high-viscosity silicone oil. The Reynolds number, the ratio of viscous to elastic stresses, and the helix geometry of our experimental model approximately match the corresponding quantities of the full scale E. coli cells. We analyze digital video images of the rotating helices to show that the initial rate of bundling is proportional to the motor frequency and is independent of the characteristic relaxation time of the filament. We also determine which combinations of helix handedness and sense of motor rotation lead to bundling.Comment: 6 pages, 4 figures (3 in color). A supporting movie is published at the PNAS websit