From nuclear structure to neutron stars

Recent progress in quantum Monte Carlo with modern nucleon-nucleon interactions have enabled the successful description of properties of light nuclei and neutron-rich matter. As a demonstration, we show that the agreement between theoretical calculations of the charge form factor of 12C and the experimental data is excellent. Applying similar methods to isospin-asymmetric systems allows one to describe neutrons confined in an external potential and homogeneous neutron-rich matter. Of particular interest is the nuclear symmetry energy, the energy cost of creating an isospin asymmetry. Combining these advances with recent observations of neutron star masses and radii gives insight into the equation of state of neutron-rich matter near and above the saturation density. In particular, neutron star radius measurements constrain the derivative of the symmetry energy.Comment: 14 pages, 8 figures, Proceedings of the International Nuclear Physics Conference (INPC), 2-7 June 2013, Firenze, Ital

A Galactic Halo Origin of the Neutrinos Detected by IceCube

Recent IceCube results suggest that the first detection of very high energy astrophysical neutrinos have been accomplished. We consider these results at face value in a Galactic origin context. Emission scenarios from both the Fermi bubble and broader halo region are considered. We motivate that such an intensity of diffuse neutrino emission could be Galactic in origin if it is produced from an outflow into the halo region. This scenario requires cosmic ray transport within the outflow environment to be different to that inferred locally within the disk and that activity in the central part of the Galaxy accelerates cosmic rays to trans-"knee" energies before they escape into an outflow. The presence of a large reservoir of gas in a very extended halo around the Galaxy, recently inferred from X-ray observations, implies that relatively modest acceleration power of $10^{39}$ erg s$^{-1}$ in PeV energy cosmic rays may be sufficient to explain the observed neutrino flux. Such a luminosity is compatible with that required to explain the observed intensity of cosmic rays around the "knee".Comment: 7 pages, 2 figure

Explicit model predictive control accuracy analysis

Model Predictive Control (MPC) can efficiently control constrained systems in real-time applications. MPC feedback law for a linear system with linear inequality constraints can be explicitly computed off-line, which results in an off-line partition of the state space into non-overlapped convex regions, with affine control laws associated to each region of the partition. An actual implementation of this explicit MPC in low cost micro-controllers requires the data to be "quantized", i.e. represented with a small number of memory bits. An aggressive quantization decreases the number of bits and the controller manufacturing costs, and may increase the speed of the controller, but reduces accuracy of the control input computation. We derive upper bounds for the absolute error in the control depending on the number of quantization bits and system parameters. The bounds can be used to determine how many quantization bits are needed in order to guarantee a specific level of accuracy in the control input.Comment: 6 pages, 7 figures. Accepted to IEEE CDC 201

The returns to participation in the non-farm sector in rural Rwanda

In this paper, we investigate the differences in outcomes (earnings and consumption) between individuals (households) who participate in the non-farm sector and those who do not. We use propensity score matching methods, where we create appropriate comparison groups of individuals and households. First we find that non-farm self-employed individuals in rural Rwanda have significantly higher earnings than farm workers and non-farm formal employees. Second, we show that the benefits to non-farm self-employment are much higher among the non-poor than among the poor. Third, we show that diversified households, those with a farm and a non-farm enterprise, are less likely to be poor. Finally, farm households who do not participate in the market have significantly lower consumption levels than households that do. However, the benefits to market participation appear to matter less for the poor than for the non-poor. We find little difference in expenditures between market participants and non-market participants, for comparable households in the bottom 40% of the expenditure distribution.Environmental Economics&Policies,Public Health Promotion,Health Monitoring&Evaluation,Decentralization,Housing&Human Habitats,Livestock&Animal Husbandry,Crops&Crop Management Systems,Health Monitoring&Evaluation,Environmental Economics&Policies,Housing&Human Habitats

What do foreigners want? Evidence from targets in bank cross-border M&As

Given the recent traumatic events in the world’s banking industry it is important to understand what drives bankers to create larger and larger, often multinational, banking groups. In this paper we investigate whether the targets in cross-border bank M&As are materially different from those banks targeted in domestic M&A deals. To address this question we use a sample of over 24,000 banks from more than 100 countries. We begin by estimating the probability that a bank will be a M&A target; this probability is based upon both bank specific and country specific characteristics. The sample also naturally includes banks that were not involved in any M&A deal, this set of banks acts as a control sample for the study. We then estimate a multinomial model that distinguishes between (i) targets in domestic operations, (ii) targets in cross-border operations and (iii) non-targets. The main message of the paper is that, with few exceptions, domestic and foreign investors target similar banks. In particular, contrary to what one might expect, bank size does not affect differently the probability of being a domestic or a cross-border target, but it has a positive and highly significant effect in both cases. What differs between national and international M&As are the characteristics of the countries where banks operate. On average, banking systems characterized by lower leverage, higher cost inefficiency and lower liquidity are more likely to be targets of cross-border acquisitions, while none of this characteristics affects the likelihood of being acquired domestically.M&As, M&Asbank internationalisation

Dense 3D Object Reconstruction from a Single Depth View

In this paper, we propose a novel approach, 3D-RecGAN++, which reconstructs the complete 3D structure of a given object from a single arbitrary depth view using generative adversarial networks. Unlike existing work which typically requires multiple views of the same object or class labels to recover the full 3D geometry, the proposed 3D-RecGAN++ only takes the voxel grid representation of a depth view of the object as input, and is able to generate the complete 3D occupancy grid with a high resolution of 256^3 by recovering the occluded/missing regions. The key idea is to combine the generative capabilities of autoencoders and the conditional Generative Adversarial Networks (GAN) framework, to infer accurate and fine-grained 3D structures of objects in high-dimensional voxel space. Extensive experiments on large synthetic datasets and real-world Kinect datasets show that the proposed 3D-RecGAN++ significantly outperforms the state of the art in single view 3D object reconstruction, and is able to reconstruct unseen types of objects.Comment: TPAMI 2018. Code and data are available at: https://github.com/Yang7879/3D-RecGAN-extended. This article extends from arXiv:1708.0796

Learning with Training Wheels: Speeding up Training with a Simple Controller for Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) has been applied successfully to many robotic applications. However, the large number of trials needed for training is a key issue. Most of existing techniques developed to improve training efficiency (e.g. imitation) target on general tasks rather than being tailored for robot applications, which have their specific context to benefit from. We propose a novel framework, Assisted Reinforcement Learning, where a classical controller (e.g. a PID controller) is used as an alternative, switchable policy to speed up training of DRL for local planning and navigation problems. The core idea is that the simple control law allows the robot to rapidly learn sensible primitives, like driving in a straight line, instead of random exploration. As the actor network becomes more advanced, it can then take over to perform more complex actions, like obstacle avoidance. Eventually, the simple controller can be discarded entirely. We show that not only does this technique train faster, it also is less sensitive to the structure of the DRL network and consistently outperforms a standard Deep Deterministic Policy Gradient network. We demonstrate the results in both simulation and real-world experiments.Comment: Published in ICRA2018. The code is now available at https://github.com/xie9187/AsDDP