Weighted Bergman Projection on the Hartogs Triangle

We prove the $L^p$ regularity of the weighted Bergman projection on the Hartogs triangle, where the weights are powers of the distance to the singularity at the boundary. The restricted range of $p$ is proved to be sharp. By using a two-weight inequality on the upper half plane with Muckenhoupt weights, we can consider a slightly wider class of weights.Comment: The article has been revised. There are 23 pages in tota

High Lundquist Number Simulations of Parker\u27s Model of Coronal Heating: Scaling and Current Sheet Statistics Using Heterogeneous Computing Architectures

Parker\u27s model [Parker, Astrophys. J., 174, 499 (1972)] is one of the most discussed mechanisms for coronal heating and has generated much debate. We have recently obtained new scaling results for a 2D version of this problem suggesting that the heating rate becomes independent of resistivity in a statistical steady state [Ng and Bhattacharjee, Astrophys. J., 675, 899 (2008)]. Our numerical work has now been extended to 3D using high resolution MHD numerical simulations. Random photospheric footpoint motion is applied for a time much longer than the correlation time of the motion to obtain converged average coronal heating rates. Simulations are done for different values of the Lundquist number to determine scaling. In the high-Lundquist number limit (S \u3e 1000), the coronal heating rate obtained is consistent with a trend that is independent of the Lundquist number, as predicted by previous analysis and 2D simulations. We will present scaling analysis showing that when the dissipation time is comparable or larger than the correlation time of the random footpoint motion, the heating rate tends to become independent of Lundquist number, and that the magnetic energy production is also reduced significantly. We also present a comprehensive reprogramming of our simulation code to run on NVidia graphics processing units using the Compute Unified Device Architecture (CUDA) and report code performance on several large scale heterogenous machines

The Optimal Size of Stochastic Hodgkin-Huxley Neuronal Systems for Maximal Energy Efficiency in Coding of Pulse Signals

The generation and conduction of action potentials represents a fundamental means of communication in the nervous system, and is a metabolically expensive process. In this paper, we investigate the energy efficiency of neural systems in a process of transfer pulse signals with action potentials. By computer simulation of a stochastic version of Hodgkin-Huxley model with detailed description of ion channel random gating, and analytically solve a bistable neuron model that mimic the action potential generation with a particle crossing the barrier of a double well, we find optimal number of ion channels that maximize energy efficiency for a neuron. We also investigate the energy efficiency of neuron population in which input pulse signals are represented with synchronized spikes and read out with a downstream coincidence detector neuron. We find an optimal combination of the number of neurons in neuron population and the number of ion channels in each neuron that maximize the energy efficiency. The energy efficiency depends on the characters of the input signals, e.g., the pulse strength and the inter-pulse intervals. We argue that trade-off between reliability of signal transmission and energy cost may influence the size of the neural systems if energy use is constrained.Comment: 22 pages, 10 figure

Magnetic miniband and magnetotransport property of a graphene superlattice

The eigen energy and the conductivity of a graphene sheet subject to a one-dimensional cosinusoidal potential and in the presence of a magnetic field are calculated. Such a graphene superlattice presents three distinct magnetic miniband structures as the magnetic field increases. They are, respectively, the triply degenerate Landau level spectrum, the nondegenerate minibands with finite dispersion and the same Landau level spectrum with the pristine graphene. The ratio of the magnetic length to the period of the potential function is the characteristic quantity to determine the electronic structure of the superlattice. Corresponding to these distinct electronic structures, the diagonal conductivity presents very strong anisotropy in the weak and moderate magnetic field cases. But the predominant magnetotransport orientation changes from the transverse to the longitudinal direction of the superlattice. More interestingly, in the weak magnetic field case, the superlattice exhibits half-integer quantum Hall effect, but with large jump between the Hall plateaux. Thus it is different from the one of the pristine graphene.Comment: 7 pages, 5 figure

KBGAN: Adversarial Learning for Knowledge Graph Embeddings

We introduce KBGAN, an adversarial learning framework to improve the performances of a wide range of existing knowledge graph embedding models. Because knowledge graphs typically only contain positive facts, sampling useful negative training examples is a non-trivial task. Replacing the head or tail entity of a fact with a uniformly randomly selected entity is a conventional method for generating negative facts, but the majority of the generated negative facts can be easily discriminated from positive facts, and will contribute little towards the training. Inspired by generative adversarial networks (GANs), we use one knowledge graph embedding model as a negative sample generator to assist the training of our desired model, which acts as the discriminator in GANs. This framework is independent of the concrete form of generator and discriminator, and therefore can utilize a wide variety of knowledge graph embedding models as its building blocks. In experiments, we adversarially train two translation-based models, TransE and TransD, each with assistance from one of the two probability-based models, DistMult and ComplEx. We evaluate the performances of KBGAN on the link prediction task, using three knowledge base completion datasets: FB15k-237, WN18 and WN18RR. Experimental results show that adversarial training substantially improves the performances of target embedding models under various settings.Comment: To appear at NAACL HLT 201

Corporate Equality and Equity Prices: Doing Well While Doing Good?

Two competing hypotheses, value enhancing and value discounting, state that implementing socially responsible corporate policies can have positive or negative effects on firm value. This paper tests how a specific type of social responsibility–corporate equality–affects firm value. Corporate equality is measured by the corporate equality index (CEI). This index quantifies how companies treat their gay, lesbian, bisexual, and transgender employees, consumers, and investors. Using a sample of CEI-rated, publicly traded firms in the U.S., we find that, between 2002 and 2006, firms with a higher degree of corporate equality have higher stock returns and higher market valuation (Q). We provide suggestive, causal evidence that corporate equality enhances firm value through better performance in product markets and labor markets: Firms with a higher degree of corporate equality also tend to have larger sales, higher profit margins, higher employee productivity, and attract more employees. These results are robust to the inclusion of unobserved firm-heterogeneities. Overall, our results support the value-enhancing effects of corporate social responsibility.Corporate equality; social responsibility; socially responsible investment; stock returns; performance.