Two-neutron transfer reactions and shape phase transitions in the microscopically-formulated interacting boson model

Two-neutron transfer reactions are studied within the interacting boson model based on the nuclear energy density functional theory. Constrained self-consistent mean-field calculations with the Skyrme energy density functional are performed to provide microscopic input to completely determine the Hamiltonian of the IBM. Spectroscopic properties are calculated only from the nucleonic degrees of freedom. This method is applied to study the $(t,p)$ and $(p,t)$ transfer reactions in the assorted set of rare-earth nuclei $^{146-158}$Sm, $^{148-160}$Gd, and $^{150-162}$Dy, where spherical-to-axially-deformed shape phase transition is suggested to occur at the neutron number $N\approx 90$. The results are compared with those from the purely phenomenological IBM calculations, as well as with the available experimental data. The calculated $(t,p)$ and $(p,t)$ transfer reaction intensities, from both the microscopic and phenomenological IBM frameworks, signal the rapid nuclear structural change at particular nucleon numbers.Comment: 12 pages, 12 figures, 2 table

Scale disparities and magnetohydrodynamics in the Earth’s core

Fluid motions driven by convection in the Earth’s fluid core sustain geomagnetic ­ fields by magnetohydrodynamic dynamo processes. The dynamics of the core is critically influenced by the combined effects of rotation and magnetic ­ fields. This paper attempts to illustrate the scale-related difficulties in modelling a convection-driven geodynamo by studying both linear and nonlinear convection in the presence of imposed toroidal and poloidal ­ fields. We show that there exist three extremely large disparities, as a direct consequence of small viscosity and rapid rotation of the Earth’s fluid core, in the spatial, temporal and amplitude scales of a convection-driven geodynamo. We also show that the structure and strength of convective motions, and, hence, the relevant dynamo action, are extremely sensitive to the intricate dynamical balance between the viscous, Coriolis and Lorentz forces; similarly, the structure and strength of the magnetic field generated by the dynamo process can depend very sensitively on the fluid flow. We suggest, therefore, that the zero Ekman number limit is strongly singular and that a stable convection-driven strong-­field geodynamo satisfying Taylor’s constraint may not exist. Instead, the geodynamo may vacillate between a strong ­field state, as at present, and a weak ­ field state, which is also unstable because it fails to convect sufficient heat

An ELU Network with Total Variation for Image Denoising

In this paper, we propose a novel convolutional neural network (CNN) for image denoising, which uses exponential linear unit (ELU) as the activation function. We investigate the suitability by analyzing ELU's connection with trainable nonlinear reaction diffusion model (TNRD) and residual denoising. On the other hand, batch normalization (BN) is indispensable for residual denoising and convergence purpose. However, direct stacking of BN and ELU degrades the performance of CNN. To mitigate this issue, we design an innovative combination of activation layer and normalization layer to exploit and leverage the ELU network, and discuss the corresponding rationale. Moreover, inspired by the fact that minimizing total variation (TV) can be applied to image denoising, we propose a TV regularized L2 loss to evaluate the training effect during the iterations. Finally, we conduct extensive experiments, showing that our model outperforms some recent and popular approaches on Gaussian denoising with specific or randomized noise levels for both gray and color images.Comment: 10 pages, Accepted by the 24th International Conference on Neural Information Processing (2017

Contextual advertising

Contextual advertising entails the display of relevant ads based on the content that consumers view, exploiting the potential that consumers' content preferences are indicative of their product preferences. This paper studies the strategic aspects of such advertising, considering an intermediary who has access to a content base, sells advertising space to advertisers who compete in the product market, and provides the targeting technology. The results show that contextual targeting impacts advertiser profit in two ways: First, advertising through relevant content topics helps advertisers reach consumers with a strong preference for their product. Second, heterogeneity in consumers' content preferences can be leveraged to reduce product market competition, especially when competition is intense. The intermediary has incentives to strategically design its targeting technology, sometimes at the cost of the advertisers. When product market competition is moderate, the intermediary offers accurate targeting such that the consumers see the most relevant ads. When competition is high, the intermediary lowers the targeting accuracy such that the consumers see less relevant ads. Doing so intensifies competition and encourages advertisers to bid for multiple content topics in order to prevent their competitors from reaching consumers. In some cases, this may lead to an asymmetric equilibrium where one advertiser bids high even for the content topic that is more relevant to its competitor. © 2012 INFORMS

Large-scale Reservoir Simulations on IBM Blue Gene/Q

This paper presents our work on simulation of large-scale reservoir models on IBM Blue Gene/Q and studying the scalability of our parallel reservoir simulators. An in-house black oil simulator has been implemented. It uses MPI for communication and is capable of simulating reservoir models with hundreds of millions of grid cells. Benchmarks show that our parallel simulator are thousands of times faster than sequential simulators that designed for workstations and personal computers, and the simulator has excellent scalability