Anti-Resonance and the 0.7 Anomaly in Conductance through a Quantum Point Contact

We investigate the transmission of electrons through a quantum point contact by using a quasi-one-dimensional model with a local bound state below the band bottom. While the complete transmission in lower channels gives rise to plateaus of conductance at multiples of $2e^{2}/h$, the electrons in the lowest channel are scattered by the local bound state when it is singly occupied. This scattering produces a wide zero-transmittance (anti-resonance) for a singlet formed by tunneling and local electrons, and has no effect on triplets, leading to an exact $0.75(2e^{2}/h)$ shoulder prior to the first $2e^{2}/h$ plateau. Formation of a Kondo singlet from electrons in the Fermi sea screens the local moment and reduces the effects of anti-resonance, complementing the shoulder from 0.75 to 1 at low temperatures.Comment: 4 pages, 3 figure

All You Need is Beyond a Good Init: Exploring Better Solution for Training Extremely Deep Convolutional Neural Networks with Orthonormality and Modulation

Deep neural network is difficult to train and this predicament becomes worse as the depth increases. The essence of this problem exists in the magnitude of backpropagated errors that will result in gradient vanishing or exploding phenomenon. We show that a variant of regularizer which utilizes orthonormality among different filter banks can alleviate this problem. Moreover, we design a backward error modulation mechanism based on the quasi-isometry assumption between two consecutive parametric layers. Equipped with these two ingredients, we propose several novel optimization solutions that can be utilized for training a specific-structured (repetitively triple modules of Conv-BNReLU) extremely deep convolutional neural network (CNN) WITHOUT any shortcuts/ identity mappings from scratch. Experiments show that our proposed solutions can achieve distinct improvements for a 44-layer and a 110-layer plain networks on both the CIFAR-10 and ImageNet datasets. Moreover, we can successfully train plain CNNs to match the performance of the residual counterparts. Besides, we propose new principles for designing network structure from the insights evoked by orthonormality. Combined with residual structure, we achieve comparative performance on the ImageNet dataset.Comment: Updating experiments; CVPR201

Market correlation structure changes around the Great Crash

We perform a comparative analysis of the Chinese stock market around the occurrence of the 2008 crisis based on the random matrix analysis of high-frequency stock returns of 1228 stocks listed on the Shanghai and Shenzhen stock exchanges. Both raw correlation matrix and partial correlation matrix with respect to the market index in two time periods of one year are investigated. We find that the Chinese stocks have stronger average correlation and partial correlation in 2008 than in 2007 and the average partial correlation is significantly weaker than the average correlation in each period. Accordingly, the largest eigenvalue of the correlation matrix is remarkably greater than that of the partial correlation matrix in each period. Moreover, each largest eigenvalue and its eigenvector reflect an evident market effect, while other deviating eigenvalues do not. We find no evidence that deviating eigenvalues contain industrial sectorial information. Surprisingly, the eigenvectors of the second largest eigenvalues in 2007 and of the third largest eigenvalues in 2008 are able to distinguish the stocks from the two exchanges. We also find that the component magnitudes of the some largest eigenvectors are proportional to the stocks' capitalizations.Comment: 6 pages including 5 figure

Thermal Characterization of Microscale Heat Convection under Rare Gas Condition by a Modified Hot Wire Method

As power electronics shrinks down to sub-micron scale, the thermal transport from a solid surface to environment becomes significant. Under circumstances when the device works in rare gas environment, the scale for thermal transport is comparable to the mean free path of molecules, and is difficult to characterize. In this work, we present an experimental study about thermal transport around a microwire in rare gas environment by using a steady state hot wire method. Unlike conventional hot wire technique of using transient heat transfer process, this method considers both the heat conduction along the wire and convection effect from wire surface to surroundings. Convection heat transfer coefficient from a platinum wire in diameter 25 um to air is characterized under different heating power and air pressures to comprehend the effect of temperature and density of gas molecules. It is observed that convection heat transfer coefficient varies from 14 Wm-2K-1 at 7 Pa to 629 Wm-2K-1 at atmosphere pressure. In free molecule regime, Nusselt number has a linear relationship with inverse Knudsen number and the slope of 0.274 is employed to determined equivalent thermal dissipation boundary as 7.03E10-4 m. In transition regime, the equivalent thermal dissipation boundary is obtained as 5.02E10-4 m. Under a constant pressure, convection heat transfer coefficient decreases with increasing temperature, and this correlation is more sensitive to larger pressure. This work provides a pathway for studying both heat conduction and heat convection effect at micro/nanoscale under rare gas environment, the knowledge of which is essential for regulating heat dissipation in various industrial applications

Multivariate Functional Regression Models for Epistasis Analysis

To date, most genetic analyses of phenotypes have focused on analyzing single traits or, analyzing each phenotype independently. However, joint epistasis analysis of multiple complementary traits will increase statistical power, and hold the key to understanding the complicated genetic structure of the complex diseases. Despite their importance in uncovering the genetic structure of complex traits, the statistical methods for identifying epistasis in multiple phenotypes remains fundamentally unexplored. To fill this gap, we formulate a test for interaction between two gens in multiple quantitative trait analysis as a multiple functional regression (MFRG) in which the genotype functions (genetic variant profiles) are defined as a function of the genomic position of the genetic variants. We use large scale simulations to calculate its type I error rates for testing interaction between two genes with multiple phenotypes and to compare its power with multivariate pair-wise interaction analysis and single trait interaction analysis by a single variate functional regression model. To further evaluate its performance, the MFRG for epistasis analysis is applied to five phenotypes and exome sequence data from the NHLBI Exome Sequencing Project (ESP) to detect pleiotropic epistasis. A total of 136 pairs of genes that formed a genetic interaction network showed significant evidence of epistasis influencing five traits. The results demonstrate that the joint interaction analysis of multiple phenotypes has much higher power to detect interaction than the interaction analysis of single trait and may open a new direction to fully uncovering the genetic structure of multiple phenotypes

What can we learn about GRB from the variability timescale related correlations?

Recently, two empirical correlations related to the minimum variability timescale ($\rm MTS$) of the lightcures are discovered in gamma-ray bursts (GRBs). One is the anti-correlation between $\rm MTS$ and Lorentz factor $\Gamma$, the other is the anti-correlation between the $\rm MTS$ and gamma-ray luminosity $L_\gamma$. Both the two correlations might be used to explore the activity of the central engine of GRBs. In this paper we try to understand these empirical correlations by combining two popular black hole (BH) central engine models (namely, Blandford \& Znajek mechanism and neutrino-dominated accretion flow). By taking the $\rm MTS$ as the timescale of viscous instability of the neutrino-dominated accretion flow (NDAF), we find that these correlations favor the scenario in which the jet is driven by Blandford-Znajek (BZ) mechanism.Comment: 6 pages, 3 figures, accepted for publication in Ap

Chern Kondo Insulator in an Optical Lattice

We propose to realize and observe Chern Kondo insulators in an optical superlattice with laser-assisted $s$ and $p$ orbital hybridization and synthetic gauge field, which can be engineered based on the recent cold atom experiments. Considering a double-well square optical lattice, the localized $s$ orbitals are decoupled from itinerant $p$ bands and are driven into a Mott insulator due to strong Hubbard interaction. Raman laser beams are then applied to induce tunnelings between $s$ and $p$ orbitals, and generate a staggered flux simultaneously. Due to the strong Hubbard interaction of $s$ orbital states, we predict the existence of a critical Raman laser-assisted coupling, beyond which the Kondo screening is achieved and then a fully gapped Chern Kondo phase emerges, with the topology characterized by integer Chern numbers. Being a strongly correlated topological state, the Chern Kondo phase is different from the single-particle quantum anomalous Hall state, and can be identified by measuring the band topology and double occupancy of $s$ orbitals. The experimental realization and detection of the predicted Chern Kondo insulator are also proposed.Comment: 5 pages, 4 figures, plus Supplementary Materia

Joint multifractal analysis based on the partition function approach: Analytical analysis, numerical simulation and empirical application

Many complex systems generate multifractal time series which are long-range cross-correlated. Numerous methods have been proposed to characterize the multifractal nature of these long-range cross correlations. However, several important issues about these methods are not well understood and most methods consider only one moment order. We study the joint multifractal analysis based on partition function with two moment orders, which was initially invented to investigate fluid fields, and derive analytically several important properties. We apply the method numerically to binomial measures with multifractal cross correlations and bivariate fractional Brownian motions without multifractal cross correlations. For binomial multifractal measures, the explicit expressions of mass function, singularity strength and multifractal spectrum of the cross correlations are derived, which agree excellently with the numerical results. We also apply the method to stock market indexes and unveil intriguing multifractality in the cross correlations of index volatilities.Comment: 19 pages, 5 figure

Unveiling correlations between financial variables and topological metrics of trading networks: Evidence from a stock and its warrant

Traders adopt different trading strategies to maximize their returns in financial markets. These trading strategies not only results in specific topological structures in trading networks, which connect the traders with the pairwise buy-sell relationships, but also have potential impacts on market dynamics. Here, we present a detailed analysis on how the market behaviors are correlated with the structures of traders in trading networks based on audit trail data for the Baosteel stock and its warrant at the transaction level from 22 August 2005 to 23 August 2006. In our investigation, we divide each trade day into 48 time windows with a length of five minutes, construct a trading network within each window, and obtain a time series of over 1,100 trading networks. We find that there are strongly simultaneous correlations between the topological metrics (including network centralization, assortative index, and average path length) of trading networks that characterize the patterns of order execution and the financial variables (including return, volatility, intertrade duration, and trading volume) for the stock and its warrant. Our analysis may shed new lights on how the microscopic interactions between elements within complex system affect the system's performance.Comment: 3 tables and 5 page

CA3Net: Contextual-Attentional Attribute-Appearance Network for Person Re-Identification

Person re-identification aims to identify the same pedestrian across non-overlapping camera views. Deep learning techniques have been applied for person re-identification recently, towards learning representation of pedestrian appearance. This paper presents a novel Contextual-Attentional Attribute-Appearance Network (CA3Net) for person re-identification. The CA3Net simultaneously exploits the complementarity between semantic attributes and visual appearance, the semantic context among attributes, visual attention on attributes as well as spatial dependencies among body parts, leading to discriminative and robust pedestrian representation. Specifically, an attribute network within CA3Net is designed with an Attention-LSTM module. It concentrates the network on latent image regions related to each attribute as well as exploits the semantic context among attributes by a LSTM module. An appearance network is developed to learn appearance features from the full body, horizontal and vertical body parts of pedestrians with spatial dependencies among body parts. The CA3Net jointly learns the attribute and appearance features in a multi-task learning manner, generating comprehensive representation of pedestrians. Extensive experiments on two challenging benchmarks, i.e., Market-1501 and DukeMTMC-reID datasets, have demonstrated the effectiveness of the proposed approach