Density Functional Theory Studies of Magnetically Confined Fermi Gas

A theory is developed for magnetically confined Fermi gas at low temperature based on the density functional theory. The theory is illustrated by numerical calculation of density distributions of Fermi atoms $^{40}$K with parameters according to DeMarco and Jin's experiment[Science, 285(1999)1703]. Our results are in good agreement with the experiment. To check the theory, we also performed calculations using our theory at high temperature and compared very well to the result of classical limit.Comment: 6 page

Adversarial Network Bottleneck Features for Noise Robust Speaker Verification

In this paper, we propose a noise robust bottleneck feature representation which is generated by an adversarial network (AN). The AN includes two cascade connected networks, an encoding network (EN) and a discriminative network (DN). Mel-frequency cepstral coefficients (MFCCs) of clean and noisy speech are used as input to the EN and the output of the EN is used as the noise robust feature. The EN and DN are trained in turn, namely, when training the DN, noise types are selected as the training labels and when training the EN, all labels are set as the same, i.e., the clean speech label, which aims to make the AN features invariant to noise and thus achieve noise robustness. We evaluate the performance of the proposed feature on a Gaussian Mixture Model-Universal Background Model based speaker verification system, and make comparison to MFCC features of speech enhanced by short-time spectral amplitude minimum mean square error (STSA-MMSE) and deep neural network-based speech enhancement (DNN-SE) methods. Experimental results on the RSR2015 database show that the proposed AN bottleneck feature (AN-BN) dramatically outperforms the STSA-MMSE and DNN-SE based MFCCs for different noise types and signal-to-noise ratios. Furthermore, the AN-BN feature is able to improve the speaker verification performance under the clean condition

Coverage Optimal Empirical Likelihood Inference for Regression Discontinuity Design

This paper proposes an empirical likelihood inference method for a general framework that covers various types of treatment effect parameters in regression discontinuity designs (RDD) . Our method can be applied for standard sharp and fuzzy RDDs, RDDs with categorical outcomes, augmented sharp and fuzzy RDDs with covariates and testing problems that involve multiple RDD treatment effect parameters. Our method is based on the first-order conditions from local polynomial fitting and avoids explicit asymptotic variance estimation. We investigate both firstorder and second-order asymptotic properties and derive the coverage optimal bandwidth which minimizes the leading term in the coverage error expansion. In some cases, the coverage optimal bandwidth has a simple explicit form, which the Wald-type inference method usually lacks. We also find that Bartlett corrected empirical likelihood inference further improves the coverage accuracy. Easily implementable coverage optimal bandwidth selector and Bartlett correction are proposed for practical use. We conduct Monte Carlo simulations to assess finite-sample performance of our method and also apply it to two real datasets to illustrate its usefulness

Context-Patch Face Hallucination Based on Thresholding Locality-Constrained Representation and Reproducing Learning

Face hallucination is a technique that reconstruct high-resolution (HR) faces from low-resolution (LR) faces, by using the prior knowledge learned from HR/LR face pairs. Most state-of-the-arts leverage position-patch prior knowledge of human face to estimate the optimal representation coefficients for each image patch. However, they focus only the position information and usually ignore the context information of image patch. In addition, when they are confronted with misalignment or the Small Sample Size (SSS) problem, the hallucination performance is very poor. To this end, this study incorporates the contextual information of image patch and proposes a powerful and efficient context-patch based face hallucination approach, namely Thresholding Locality-constrained Representation and Reproducing learning (TLcR-RL). Under the context-patch based framework, we advance a thresholding based representation method to enhance the reconstruction accuracy and reduce the computational complexity. To further improve the performance of the proposed algorithm, we propose a promotion strategy called reproducing learning. By adding the estimated HR face to the training set, which can simulates the case that the HR version of the input LR face is present in the training set, thus iteratively enhancing the final hallucination result. Experiments demonstrate that the proposed TLcR-RL method achieves a substantial increase in the hallucinated results, both subjectively and objectively. Additionally, the proposed framework is more robust to face misalignment and the SSS problem, and its hallucinated HR face is still very good when the LR test face is from the real-world. The MATLAB source code is available at https://github.com/junjun-jiang/TLcR-RL

LocustDB: a relational database for the transcriptome and biology of the migratory locust (Locusta migratoria)

BACKGROUND: The migratory locust (Locusta migratoria) is an orthopteran pest and a representative member of hemimetabolous insects for biological studies. Its transcriptomic data provide invaluable information for molecular entomology and pave a way for the comparative research of other medically, agronomically, and ecologically relevant insects. We developed the first transcriptomic database of the locust (LocustDB), building necessary infrastructures to integrate, organize, and retrieve data that are either currently available or to be acquired in the future. DESCRIPTION: LocustDB currently hosts 45,474 high-quality EST sequences from the locust, which were assembled into 12,161 unigenes. It, through user-friendly web interfaces, allows investigators to freely access sequence data, including homologous/orthologous sequences, functional annotations, and pathway analysis, based on conserved orthologous groups (COG), gene ontology (GO), protein domain (InterPro), and functional pathways (KEGG). It also provides information from comparative analysis based on data from the migratory locust and five other invertebrate species, including the silkworm, the honeybee, the fruitfly, the mosquito and the nematode. The website address of LocustDB is . CONCLUSION: LocustDB starts with the first transcriptome information for an orthopteran and hemimetabolous insect and will be extended to provide a framework for incorporating in-coming genomic data of relevant insect groups and a workbench for cross-species comparative studies

Inference on Individual Treatment Effects in Nonseparable Triangular Models

In nonseparable triangular models with a binary endogenous treatment and a binary instrumental variable, Vuong and Xu (2017) show that the individual treatment effects (ITEs) are identifiable. Feng, Vuong and Xu (2019) show that a kernel density estimator that uses nonparametrically estimated ITEs as observations is uniformly consistent for the density of the ITE. In this paper, we establish the asymptotic normality of the density estimator of Feng, Vuong and Xu (2019) and show that despite their faster rate of convergence, ITEs' estimation errors have a non-negligible effect on the asymptotic distribution of the density estimator. We propose asymptotically valid standard errors for the density of the ITE that account for estimated ITEs as well as bias correction. Furthermore, we develop uniform confidence bands for the density of the ITE using nonparametric or jackknife multiplier bootstrap critical values. Our uniform confidence bands have correct coverage probabilities asymptotically with polynomial error rates and can be used for inference on the shape of the ITE's distribution

Shear viscosity of neutron-rich nucleonic matter near its liquid-gas phase transition

Within a relaxation time approach using free nucleon-nucleon cross sections modified by the in-medium nucleon masses that are determined from an isospin- and momentum-dependent effective nucleon-nucleon interaction, we investigate the specific shear viscosity ($\eta/s$) of neutron-rich nucleonic matter near its liquid-gas phase transition. It is found that as the nucleonic matter is heated at fixed pressure or compressed at fixed temperature, its specific shear viscosity shows a valley shape in the temperature or density dependence, with the minimum located at the boundary of the phase transition. Moreover, the value of $\eta/s$ drops suddenly at the first-order liquid-gas phase transition temperature, reaching as low as $4\sim5$ times the KSS bound of $\hbar/4\pi$. However, it varies smoothly for the second-order liquid-gas phase transition. Effects of the isospin degree of freedom and the nuclear symmetry energy on the value of $\eta/s$ are also discussed.Comment: 6 pages, 5 figure

Energy dependence of pion in-medium effects on \pi^-/\pi^+ ratio in heavy-ion collisions

Within the framework of a thermal model with its parameters fitted to the results from an isospin-dependent Boltzmann-Uehling-Uhlenbeck (IBUU) transport model, we study the pion in-medium effect on the charged-pion ratio in heavy-ion collisions at various energies. We find that due to the cancellation between the effects from pion-nucleon s-wave and p-wave interactions in nuclear medium, the \pi^-/\pi^+ ratio generally decreases after including the pion in-medium effect. The effect is larger at lower collision energies as a result of narrower pion spectral functions at lower temperatures.Comment: 4 pages, 4 figures, 1 table, minor modifications, version to appear in Physical Review