arXiv subjects

Xin Zhang

Publications and source records attributed to Xin Zhang.

At least 991 records · Page 55Linked to original sources

arXiv2020

Absence of Superconductivity in Nd$_{0.8}$Sr$_{0.2}$NiO$_x$ Thin Films without Chemical Reduction

The recently reported superconductivity 9-15 K in Nd0.8Sr0.2NiO2/SrTiO3 heterostructures that were fabricated by a soft-chemical topotactic reduction approach based on precursor Nd0.8Sr0.2NiO3 thin films deposited on SrTiO3 substrates, has excited an immediate surge of research interest. To explore an alternative physical path instead of chemical reduction for realizing superconductivity in this compound, using pulsed laser deposition, we systematically fabricated 63 Nd0.8Sr0.2NiOx (NSNO) thin films at a wide range of oxygen partial pressures on various different oxide substrates. Transport measurements did not find any signature of superconductivity in all the 63 thin-film samples. With reducing the oxygen content in the NSNO films by lowering the deposition oxygen pressure, the NSNO films are getting more resistive and finally become insulating. Furthermore, we tried to cap a 20-nm-thick amorphous LaAlO3 layer on a Nd0.8Sr0.2NiO3 thin film deposited at a high oxygen pressure of 150 mTorr to create oxygen vacancies on its surface and did not succeed in higher conductivity either. Our experimental results together with the recent report on the absence of superconductivity in synthesized bulk Nd0.8Sr0.2NiO2 crystals suggest that the chemical reduction approach could be unique for yielding superconductivity in NSNO/SrTiO3 heterostructures. However, SrTiO3 substrates could be reduced to generate oxygen vacancies during the chemical reduction process as well, which may thus partially contribute to conductivity.

cond-mat.supr-con↗

arXiv2020

Prospect for constraining holographic dark energy with gravitational wave standard sirens from the Einstein Telescope

We study the holographic dark energy (HDE) model by using the future gravitational wave (GW) standard siren data observed from the Einstein Telescope (ET) in this work. We simulate 1000 GW standard siren data based on a 10-year observation of the ET to make this analysis. We find that all the cosmological parameters in the HDE model can be tremendously improved by including the GW standard siren data in the cosmological fit. The GW data combined with the current cosmic microwave background anisotropies, baryon acoustic oscillations, and type Ia supernovae data will measure the cosmological parameters $Ω_{\rm m}$, $H_0$, and $c$ in the HDE model to be at the accuracies of 1.28%, 0.59%, and 3.69%, respectively. A comparison with the cosmological constant model and the constant-$w$ dark energy model shows that, compared to the standard model, the parameter degeneracies will be broken more thoroughly in a dynamical dark energy model. We find that the GW data alone can provide a fairly good measurement for $H_0$, but for other cosmological parameters the GW data alone can only provide rather weak measurements. However, due to the fact that the parameter degeneracies can be broken by the GW data, the standard sirens can play an essential role in improving the parameter estimation.

astro-ph.CO↗

arXiv2020

Higher derivatives driven symmetry breaking in holographic superconductors

In this paper, we construct a novel holographic superconductor from higher derivative (HD) gravity involving a coupling between the complex scalar field and the Weyl tensor. This HD coupling term provides a near horizon effective mass squared, which can violates IR Breitenlohner-Freedman (BF) bound by tuning the HD coupling and induces the instability of black brane such that the superconducting phase transition happens. We also study the properties of the condensation and the conductivity in the probe limit. We find that a wider extension of the superconducting energy gap ranging from 4.6 to 10.5 may provide a novel platform to model and interpret the phenomena in the real materials of high temperature superconductor.

hep-th↗

arXiv2020

PolSAR Image Classification Based on Dilated Convolution and Pixel-Refining Parallel Mapping network in the Complex Domain

Efficient and accurate polarimetric synthetic aperture radar (PolSAR) image classification with a limited number of prior labels is always full of challenges. For general supervised deep learning classification algorithms, the pixel-by-pixel algorithm achieves precise yet inefficient classification with a small number of labeled pixels, whereas the pixel mapping algorithm achieves efficient yet edge-rough classification with more prior labels required. To take efficiency, accuracy and prior labels into account, we propose a novel pixel-refining parallel mapping network in the complex domain named CRPM-Net and the corresponding training algorithm for PolSAR image classification. CRPM-Net consists of two parallel sub-networks: a) A transfer dilated convolution mapping network in the complex domain (C-Dilated CNN) activated by a complex cross-convolution neural network (Cs-CNN), which is aiming at precise localization, high efficiency and the full use of phase information; b) A complex domain encoder-decoder network connected parallelly with C-Dilated CNN, which is to extract more contextual semantic features. Finally, we design a two-step algorithm to train the Cs-CNN and CRPM-Net with a small number of labeled pixels for higher accuracy by refining misclassified labeled pixels. We verify the proposed method on AIRSAR and E-SAR datasets. The experimental results demonstrate that CRPM-Net achieves the best classification results and substantially outperforms some latest state-of-the-art approaches in both efficiency and accuracy for PolSAR image classification. The source code and trained models for CRPM-Net is available at: https://github.com/PROoshio/CRPM-Net.

cs.CV↗

arXiv2020

Mitochondria in higher plants possess H2 evolving activity which is closely related to complex I

Hydrogenase occupy a central place in the energy metabolism of anaerobic bacteria. Although the structure of mitochondrial complex I is similar to that of hydrogenase, whether it has hydrogen metabolic activity remain unclear. Here, we show that a H2 evolving activity exists in higher plants mitochondria and is closely related to complex I, especially around ubiquinone binding site. The H2 production could be inhibited by rotenone and ubiquinone. Hypoxia could simultaneously promote H2 evolution and succinate accumulation. Redox properties of quinone pool, adjusted by NADH or succinate according to oxygen concentration, acts as a valve to control the flow of protons and electrons and the production of H2. The coupling of H2 evolving activity of mitochondrial complex I with metabolic regulation reveals a more effective redox homeostasis regulation mechanism. Considering the ubiquity of mitochondria in eukaryotes, H2 metabolism might be the innate function of higher organisms. This may serve to explain, at least in part, the broad physiological effects of H2.

q-bio.BM↗

arXiv2020

Noncollinear Spintronics and Electric-Field Control: A Review

Our world is composed of various materials with different structures, where spin structures have been playing a pivotal role in spintronic devices of the contemporary information technology. Apart from conventional collinear spin materials such as collinear ferromagnets and collinear antiferromagnetically coupled materials, noncollinear spintronic materials have emerged as hot spots of research attention owing to exotic physical phenomena. In this Review, we firstly introduce two types noncollinear spin structures, i.e., the chiral spin structure that yields real-space Berry phases and the coplanar noncollinear spin structure that could generate momentum-space Berry phases, and then move to relevant novel physical phenomena including topological Hall effect, anomalous Hall effect, multiferroic, Weyl fermions, spin-polarized current, and spin Hall effect without spin-orbit coupling in these noncollinear spin systems. Afterwards, we summarize and elaborate the electric-field control of the noncollinear spin structure and related physical effects, which could enable ultralow power spintronic devices in future. In the final outlook part, we emphasize the importance and possible routes for experimentally detecting the intriguing theoretically predicted spin-polarized current, verifying the spin Hall effect in the absence of spin-orbit coupling and exploring the anisotropic magnetoresistance and domain-wall-related magnetoresistance effects for noncollinear antiferromagnetic materials.

cond-mat.mes-hall↗

arXiv2019

Probabilistic Verification of Fairness Properties via Concentration

As machine learning systems are increasingly used to make real world legal and financial decisions, it is of paramount importance that we develop algorithms to verify that these systems do not discriminate against minorities. We design a scalable algorithm for verifying fairness specifications. Our algorithm obtains strong correctness guarantees based on adaptive concentration inequalities; such inequalities enable our algorithm to adaptively take samples until it has enough data to make a decision. We implement our algorithm in a tool called VeriFair, and show that it scales to large machine learning models, including a deep recurrent neural network that is more than five orders of magnitude larger than the largest previously-verified neural network. While our technique only gives probabilistic guarantees due to the use of random samples, we show that we can choose the probability of error to be extremely small.

cs.AI↗

arXiv2019

ResNetX: a more disordered and deeper network architecture

Designing efficient network structures has always been the core content of neural network research. ResNet and its variants have proved to be efficient in architecture. However, how to theoretically character the influence of network structure on performance is still vague. With the help of techniques in complex networks, We here provide a natural yet efficient extension to ResNet by folding its backbone chain. Our architecture has two structural features when being mapped to directed acyclic graphs: First is a higher degree of the disorder compared with ResNet, which let ResNetX explore a larger number of feature maps with different sizes of receptive fields. Second is a larger proportion of shorter paths compared to ResNet, which improves the direct flow of information through the entire network. Our architecture exposes a new dimension, namely "fold depth", in addition to existing dimensions of depth, width, and cardinality. Our architecture is a natural extension to ResNet, and can be integrated with existing state-of-the-art methods with little effort. Image classification results on CIFAR-10 and CIFAR-100 benchmarks suggested that our new network architecture performs better than ResNet.

cs.CV↗

arXiv2019

CP asymmetry in $τ\to K_Sπν_τ$ decays within the Standard Model and beyond

Motivated by the $2.8σ$ discrepancy observed between the BaBar measurement and the Standard Model prediction of the CP asymmetry in $τ\to K_Sπν_τ$ decays, as well as the prospects of future measurements at Belle II, we revisit this observable in this paper. Firstly, we reproduce the known CP asymmetry due to $K^0 -\bar{K}^0$ mixing by means of the reciprocal basis, which is convenient when a $K_{S(L)}$ is involved in the final state. As the $Kπ$ tensor form factor plays a crucial role in generating a non-zero direct CP asymmetry that can arise only from the interference of vector and tensor operators, we then present a dispersive representation of this form factor, with its phase obtained in the context of chiral theory with resonances, which fulfills the requirements of unitarity and analyticity. Finally, the $τ\to K_Sπν_τ$ decays are analyzed both within a model-independent low-energy effective theory framework and in a scalar leptoquark scenario. It is observed that the CP anomaly can be accommodated in the model-independent framework, even at the $1σ$ level, together with the constraint from the branching ratio of $τ^-\to K_Sπ^-ν_τ$ decay; it can be, however, marginally reconciled only at the $2σ$ level, due to the specific relation between the scalar and tensor operators in the scalar leptoquark scenario. Once the combined constraints from the branching ratio and the decay spectrum of this decay are taken into account, these possibilities are however both excluded, even without exploiting further the stronger bounds from the (semi-)leptonic kaon decays under the assumption of lepton-flavour universality, as well as from the neutron electric dipole moment and $D-\bar{D}$ mixing under the assumption of $SU(2)$ invariance of the weak interactions.

hep-ph↗

arXiv2019

Equitable partition of plane graphs with independent crossings into induced forests

The cluster of a crossing in a graph drawing in the plane is the set of the four end-vertices of its two crossed edges. Two crossings are independent if their clusters do not intersect. In this paper, we prove that every plane graph with independent crossings has an equitable partition into $m$ induced forests for any $m\geq 8$. Moreover, we decrease this lower bound 8 for $m$ to 6, 5, 4 and 3 if we additionally assume that the girth of the considering graph is at least 4, 5, 6 and 26, respectively.

math.CO↗

arXiv2019

Communication-Efficient Network-Distributed Optimization with Differential-Coded Compressors

Network-distributed optimization has attracted significant attention in recent years due to its ever-increasing applications. However, the classic decentralized gradient descent (DGD) algorithm is communication-inefficient for large-scale and high-dimensional network-distributed optimization problems. To address this challenge, many compressed DGD-based algorithms have been proposed. However, most of the existing works have high complexity and assume compressors with bounded noise power. To overcome these limitations, in this paper, we propose a new differential-coded compressed DGD (DC-DGD) algorithm. The key features of DC-DGD include: i) DC-DGD works with general SNR-constrained compressors, relaxing the bounded noise power assumption; ii) The differential-coded design entails the same convergence rate as the original DGD algorithm; and iii) DC-DGD has the same low-complexity structure as the original DGD due to a {\em self-noise-reduction effect}. Moreover, the above features inspire us to develop a hybrid compression scheme that offers a systematic mechanism to minimize the communication cost. Finally, we conduct extensive experiments to verify the efficacy of the proposed DC-DGD and hybrid compressor.

cs.DC↗

arXiv2019

Finite-Time 4-Expert Prediction Problem

We explicitly solve the nonlinear PDE that is the continuous limit of dynamic programming of \emph{expert prediction problem} in finite horizon setting with $N=4$ experts. The \emph{expert prediction problem} is formulated as a zero sum game between a player and an adversary. By showing that the solution is $\mathcal{C}^2$, we are able to show that the strategies conjectured in arXiv:1409.3040G form an asymptotic Nash equilibrium. We also prove the "Finite vs Geometric regret" conjecture proposed in arXiv:1409.3040G for $N=4$, and and show that this conjecture in fact follows from the conjecture that the comb strategies are optimal.

math.PR↗

arXiv2019

Unique solvability of elliptic problems associated with two-phase incompressible flows in unbounded domains

This paper shows the unique solvability of elliptic problems associated with two-phase incompressible flows, which are governed by the two-phase Navier-Stokes equations, in unbounded domains such as the whole space separated by a compact interface and the whole space separated by a non-compact interface. As a by-product of the unique solvability of elliptic problems, we obtain the Helmholtz-Weyl decomposition for two-phase incompressible flows.

math.AP↗

arXiv2019

Dark energy versus modified gravity: Impacts on measuring neutrino mass

In this paper, we make a comparison for the impacts of smooth dynamical dark energy, modified gravity, and interacting dark energy on the cosmological constraints on the total mass of active neutrinos. For definiteness, we consider the $Λ$CDM model, the $w$CDM model, the $f(R)$ model, and two typical interacting vacuum energy models, i.e., the I$Λ$CDM1 model with $Q=βHρ_{\rm c}$ and the I$Λ$CDM2 model with $Q=βHρ_Λ$. In the cosmological fits, we use the Planck 2015 temperature and polarization data, in combination with other low-redshift observations including the baryon acoustic oscillations, the type Ia supernovae, the Hubble constant measurement, and the large-scale structure observations, such as the weak lensing as well as the redshift-space distortion. Besides, the Planck lensing measurement is also employed in this work. We find that, the $w$CDM model favors a higher upper limit on the neutrino mass compared to the $Λ$CDM model, while the upper limit in the $f(R)$ model is similar with that of $Λ$CDM model. For the interacting vacuum energy models, the I$Λ$CDM1 model favors a higher upper limit on neutrino mass, while the I$Λ$CDM2 model favors an identical neutrino mass with the case of $Λ$CDM.

astro-ph.CO↗

arXiv2019

Air-Writing Translater: A Novel Unsupervised Domain Adaptation Method for Inertia-Trajectory Translation of In-air Handwriting

As a new way of human-computer interaction, inertial sensor based in-air handwriting can provide a natural and unconstrained interaction to express more complex and richer information in 3D space. However, most of the existing in-air handwriting work is mainly focused on handwritten character recognition, which makes these work suffer from poor readability of inertial signal and lack of labeled samples. To address these two problems, we use unsupervised domain adaptation method to reconstruct the trajectory of inertial signal and generate inertial samples using online handwritten trajectories. In this paper, we propose an AirWriting Translater model to learn the bi-directional translation between trajectory domain and inertial domain in the absence of paired inertial and trajectory samples. Through semantic-level adversarial training and latent classification loss, the proposed model learns to extract domain-invariant content between inertial signal and trajectory, while preserving semantic consistency during the translation across the two domains. We carefully design the architecture, so that the proposed framework can accept inputs of arbitrary length and translate between different sampling rates. We also conduct experiments on two public datasets: 6DMG (in-air handwriting dataset) and CT (handwritten trajectory dataset), the results on the two datasets demonstrate that the proposed network successes in both Inertia-to Trajectory and Trajectory-to-Inertia translation tasks.

cs.CV↗

arXiv2019

Neural Machine Reading Comprehension: Methods and Trends

Machine reading comprehension (MRC), which requires a machine to answer questions based on a given context, has attracted increasing attention with the incorporation of various deep-learning techniques over the past few years. Although research on MRC based on deep learning is flourishing, there remains a lack of a comprehensive survey summarizing existing approaches and recent trends, which motivated the work presented in this article. Specifically, we give a thorough review of this research field, covering different aspects including (1) typical MRC tasks: their definitions, differences, and representative datasets; (2) the general architecture of neural MRC: the main modules and prevalent approaches to each; and (3) new trends: some emerging areas in neural MRC as well as the corresponding challenges. Finally, considering what has been achieved so far, the survey also envisages what the future may hold by discussing the open issues left to be addressed.

cs.CL↗

arXiv2019

Improving cosmological parameter estimation with the future 21 cm observation from SKA

Future observations of 21 cm emission from neutral hydrogen survey will become a promising approach to probe the large scale structure of Universe. In this paper, we investigate the impacts of Square Kilometer Array (SKA) 21 cm observation on the estimation of cosmological parameters. We use the simulated data of the baryonic acoustic oscillation (BAO) measurements based on the future SKA experiment with the intensity mapping (IM) technique to do the analysis. For the current observations, we use the latest cosmic microwave background (CMB) observation from {\it Planck} 2018, the optical BAO measurements, and the Type Ia supernovae (SN) observation (Pantheon compilation). We find that the SKA mock data could break the degeneracy between the matter density and the Hubble constant, further improving the cosmological constraints to a great extent. We also find that the constraint on the equation of state parameters of dark energy could be significantly improved by including the SKA mock data into the cosmological global fit.

astro-ph.CO↗

arXiv2019

The $\mathcal{R}-$bounded operator families arising from the study of the barotropic compressible flows with free surface

In this paper, we study some model problem associated to the free boundary value problem of the barotropic compressible Navier-Stokes equations in general smooth domain with taking surface tension into account. To obtain the maximal $L_p-L_q$ regularity property of the model problem, we prove the existence of $\mathcal{R}-$bounded operator families of the resolvent problem via Weis' theory on operator valued Fourier multipliers.

math.AP↗