arXiv subjects

Xin Zhang

Publications and source records attributed to Xin Zhang.

At least 883 records · Page 49Linked to original sources

arXiv2021

Bias for the Trace of the Resolvent and Its Application on Non-Gaussian and Non-centered MIMO Channels

The mutual information (MI) of Gaussian multi-input multi-output (MIMO) channels has been evaluated by utilizing random matrix theory (RMT) and shown to asymptotically follow Gaussian distribution, where the ergodic mutual information (EMI) converges to a deterministic quantity. However, with non-Gaussian channels, there is a bias between the EMI and its deterministic equivalent (DE), whose evaluation is not available in the literature. This bias of the EMI is related to the bias for the trace of the resolvent in large RMT. In this paper, we first derive the bias for the trace of the resolvent, which is further extended to compute the bias for the linear spectral statistics (LSS). Then, we apply the above results on non-Gaussian MIMO channels to determine the bias for the EMI. It is also proved that the bias for the EMI is $-0.5$ times of that for the variance of the MI. Finally, the derived bias is utilized to modify the central limit theory (CLT) and calculate the outage probability. Numerical results show that the modified CLT significantly outperforms previous methods in approximating the distribution of the MI and improves the accuracy for the outage probability evaluation.

cs.IT↗

arXiv2021

Using a multi-messenger and multi-wavelength observational strategy to probe the nature of dark energy through direct measurements of cosmic expansion history

In the near future, the redshift drift observations in optical and radio bands will provide precise measurements on $H(z)$ covering the redshift ranges of $2<z<5$ and $0<z<0.3$. In addition, gravitational wave (GW) standard siren observations could make measurements on the dipole anisotropy of luminosity distance, which will also provide the $H(z)$ measurements in the redshift range of $0<z<3$. In this work, we propose a multi-messenger and multi-wavelength observational strategy to measure $H(z)$ based on the three next-generation projects, E-ELT, SKA, and DECIGO, and we wish to see whether the future $H(z)$ measurements could provide tight constraints on dark-energy parameters. The dark energy models we consider include $Λ$CDM, $w$CDM, CPL, HDE, and I$Λ$CDM models. It is found that E-ELT, SKA1, and DECIGO are highly complementary in constraining dark energy models. Although any one of these three data sets can only give rather weak constraints on each model we consider, the combination of them could significantly break the parameter degeneracies and give much tighter constraints on almost all the cosmological parameters. Moreover, we find that the combination of E-ELT, SKA1, DECIGO, and CMB could further improve the constraints on dark energy parameters, e.g., $σ(w_0)=0.024$ and $σ(w_a)=0.17$ in the CPL model, which means that these three promising probes will play a key role in helping reveal the nature of dark energy.

astro-ph.CO↗

arXiv2021

Novel Multiferroic Nanocomposite with High Pressure-Modulated Magnetoelectric Coupling

In this work, we have designed obtained a novel multiferroic nanocomposite using the high-pressure torsion (HPT) method. The crystal structure, phase composition, morphology, ferromagnetic (FM) and ferroelectric (FE) properties of the initial powders and "multiferroic-ferromagnetic" nanocomposites have been studied comprehensively. The initial powders and their composites show the perovskite and spinel crystalline phases for the FE and FM fractions, respectively. After HPT, the particle sizes of the initial powders are decreased significantly. It is shown that the novel nanocomposite consists of exchange-interacting FE and FM phases and demonstrates improved magnetic and electrical properties in low fields at room temperature. A colossal increase in residual polarization with an increase in external high-pressure is found in new composite. The obtained results make it possible to consider the novel nanocomposite as a new functional material for its use both in electronic devices for monitoring ultra-high-pressure and in integrated circuits of high-speed computing nanosystems with low switching energy. The HPT method is a promising method for obtaining new heterophase multiferroic nanosystems.

cond-mat.mtrl-sci↗

arXiv2021

Boundary driven $XYZ$ chain: Exact inhomogeneous triangular matrix product ansatz

We construct an explicit matrix product ansatz for the steady state of a boundary driven $XY\!Z$ spin-$\tfrac{1}{2}$ chain for arbitrary local polarizing channels at the chain's ends. The ansatz, where the Lax operators are written explicitly in terms of infinite-dimensional bidiagonal (triangular) site-dependent matrices, becomes exact either in the (Zeno) limit of infinite dissipation strength, or thermodynamic limit of infinite chain length. The solution is based on an extension of the newly discovered family of separable eigenstates of the model.

cond-mat.stat-mech↗

arXiv2021

Multipole analysis on gyroscopic precession in $f(R)$ gravity with irreducible Cartesian tensors

In $f(R)$ gravity, the metric, presented in the form of the multipole expansion, for the external gravitational field of a spatially compact supported source up to $1/c^3$ order is provided, where $c$ is the velocity of light in vacuum. The metric consists of General Relativity-like part and $f(R)$ part, where the latter is the correction to the former in $f(R)$ gravity. At the leading pole order, the metric can reduce to that for a point-like or ball-like source. For the gyroscope moving around the source without experiencing any torque, the multipole expansions of its spin's angular velocities of gravitoelectric-type precession, gravitomagnetic-type precession, $f(R)$ precession, and Thomas precession are all derived. The first two types of precession are collectively called General Relativity-like precession, and the $f(R)$ precession is the correction in $f(R)$ gravity. At the leading pole order, these expansions can recover the results for the gyroscope moving around a point-like or ball-like source. If the gyroscope has a nonzero four-acceleration, its spin's total angular velocity of precession up to $1/c^3$ order in $f(R)$ gravity is the same as that in General Relativity.

gr-qc↗

arXiv2021

CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning

Labels are costly and sometimes unreliable. Noisy label learning, semi-supervised learning, and contrastive learning are three different strategies for designing learning processes requiring less annotation cost. Semi-supervised learning and contrastive learning have been recently demonstrated to improve learning strategies that address datasets with noisy labels. Still, the inner connections between these fields as well as the potential to combine their strengths together have only started to emerge. In this paper, we explore further ways and advantages to fuse them. Specifically, we propose CSSL, a unified Contrastive Semi-Supervised Learning algorithm, and CoDiM (Contrastive DivideMix), a novel algorithm for learning with noisy labels. CSSL leverages the power of classical semi-supervised learning and contrastive learning technologies and is further adapted to CoDiM, which learns robustly from multiple types and levels of label noise. We show that CoDiM brings consistent improvements and achieves state-of-the-art results on multiple benchmarks.

cs.LG↗

arXiv2021

Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition

Crowdsourcing is regarded as one prospective solution for effective supervised learning, aiming to build large-scale annotated training data by crowd workers. Previous studies focus on reducing the influences from the noises of the crowdsourced annotations for supervised models. We take a different point in this work, regarding all crowdsourced annotations as gold-standard with respect to the individual annotators. In this way, we find that crowdsourcing could be highly similar to domain adaptation, and then the recent advances of cross-domain methods can be almost directly applied to crowdsourcing. Here we take named entity recognition (NER) as a study case, suggesting an annotator-aware representation learning model that inspired by the domain adaptation methods which attempt to capture effective domain-aware features. We investigate both unsupervised and supervised crowdsourcing learning, assuming that no or only small-scale expert annotations are available. Experimental results on a benchmark crowdsourced NER dataset show that our method is highly effective, leading to a new state-of-the-art performance. In addition, under the supervised setting, we can achieve impressive performance gains with only a very small scale of expert annotations.

cs.CL↗

arXiv2021

How can gravitational-wave standard sirens and 21 cm intensity mapping jointly provide a precise late-universe cosmological probe?

In the next decades, the gravitational-wave (GW) standard siren observations and the neutral hydrogen 21-cm intensity mapping (IM) surveys, as two promising cosmological probes, will play an important role in precisely measuring cosmological parameters. In this work, we make a forecast for cosmological parameter estimation with the synergy between the GW standard siren observations and the 21-cm IM surveys. We choose the Einstein Telescope (ET) and the Taiji observatory as the representatives of the GW detection projects and choose the Square Kilometre Array (SKA) phase I mid-frequency array as the representative of the 21-cm IM experiments. In the simulation of the 21-cm IM data, we assume perfect foreground removal and calibration. We find that the synergy of the GW standard siren observations and the 21-cm IM survey could break the cosmological parameter degeneracies. The joint ET+Taiji+SKA data give $σ(H_0)=0.28\ {\rm km\ s^{-1}\ Mpc^{-1}}$ in the $Λ$CDM model, $σ(w)=0.028$ in the $w$CDM model, which are better than the results of $Planck$+BAO+SNe, and $σ(w_0)=0.077$ and $σ(w_a)=0.295$ in the CPL model, which are comparable with the results of $Planck$+BAO+SNe. In the $Λ$CDM model, the constraint precision of $H_0$ and $Ω_{\rm m}$ is less than or rather close to 1%, indicating that the magnificent prospects for precision cosmology with these two promising cosmological probes are worth expecting.

astro-ph.CO↗

arXiv2021

Tunable intrinsic ferromagnetic topological phases in bulk van der Waals crystal MnSb6Te10

Intrinsic ferromagnetism is a crucial ingredient to realize quantum anomalous Hall effect in quasi two dimensional materials, thus the search of intrinsic ferromagnetic topological materials is one of the most concerned issues in the field of topological phases of matter. In this work, combining magnetotransport measurements, first principles calculations, and angle-resolved photoemission spectroscopy studies, we find that in MnSb6Te10, the n = 2 member of the MnSb2Te4/(Sb2Te3)n family, the strong magnetic competition realizes a fragile ferromagnetic ground state, which whereas easily enters into ferrimagnetic and the Z_2 antiferromagnetic topological insulator phase with warming to higher temperature. Interestingly, the system stays in an inversion-symmetry-protected axion insulator phase in the ferromagnetic ground state as well as in the external magnetic field driven spin-polarized FM phase and can be converted into a Weyl semimetal with multiple Weyl nodes in the valence bands with hole doping, which are manifested by the measured notable intrinsic anomalous Hall effect. Our work thus provides an intrinsic magnetic topological material which is highly tunable into versatile topological phases by temperature, magnetic field, as well as carrier doping.

cond-mat.mtrl-sci↗

arXiv2021

The Gravitational-Wave Physics II: Progress

It has been a half-decade since the first direct detection of gravitational waves, which signifies the coming of the era of the gravitational-wave astronomy and gravitational-wave cosmology. The increasing number of the detected gravitational-wave events has revealed the promising capability of constraining various aspects of cosmology, astronomy, and gravity. Due to the limited space in this review article, we will briefly summarize the recent progress over the past five years, but with a special focus on some of our own work for the Key Project ``Physics associated with the gravitational waves'' supported by the National Natural Science Foundation of China. In particular, (1) we have presented the mechanism of the gravitational-wave production during some physical processes of the early Universe, such as inflation, preheating and phase transition, and the cosmological implications of gravitational-wave measurements; (2) we have put constraints on the neutron star maximum mass according to GW170817 observations; (3) we have developed a numerical relativity algorithm based on the finite element method and a waveform model for the binary black hole coalescence along an eccentric orbit.

gr-qc↗

arXiv2021

Antiferromagnetism in Ni-Based Superconductors

Due to the lack of any magnetic order down to 1.7 K in the parent bulk compound NdNiO2, the recently discovered 9-15 K superconductivity in the infinite-layer Nd0.8Sr0.2NiO2 thin films has provided an exciting playground for unearthing new superconductivity mechanisms. In this letter, we report the successful synthesis of a series of superconducting Nd0.8Sr0.2NiO2 thin films ranging from 8 to 40 nm. We observe the large exchange bias effect between the superconducting Nd0.8Sr0.2NiO2 films and a thin ferromagnetic layer, which suggests the existence of the antiferromagnetic order. Furthermore, the existence of the antiferromagnetic order is evidenced by X-ray magnetic linear dichroism measurements. These experimental results are fundamentally critical for the current field.

cond-mat.supr-con↗

arXiv2021

Radiogenic neutron background in reactor neutrino experiments

We report a novel correlated background in the antineutrino detection using the inverse beta decay reaction. Spontaneous fissions and $(α,n)$ reactions in peripheral materials of the antineutrino detector, such as borosilicate glass of photomultipliers, produce fast neutrons and prompt gamma rays. If the shielding from the material to the detector target were not thick enough, neutrons and gammas could enter the target volume and mimic antineutrino signals. This paper revisits the yields and energy spectra of neutrons produced in B$(α,n)$N and F$(α,n)$Na reactions. A Geant4 based simulation has been carried out using a simplified detector geometry for the present generation reactor neutrino experiments. The background rates in these experiments are estimated. If this background was not taken into account, the value of the neutrino mixing angle $\sin^22θ_{13}$ would be underestimated. We recommend that Daya Bay, RENO, Double Chooz, and JUNO, carefully examine the masses and radiopurity levels of detector materials that are close to the target and rich in boron and fluorine.

physics.ins-det↗

arXiv2021

CXR-Net: An Encoder-Decoder-Encoder Multitask Deep Neural Network for Explainable and Accurate Diagnosis of COVID-19 pneumonia with Chest X-ray Images

Accurate and rapid detection of COVID-19 pneumonia is crucial for optimal patient treatment. Chest X-Ray (CXR) is the first line imaging test for COVID-19 pneumonia diagnosis as it is fast, cheap and easily accessible. Inspired by the success of deep learning (DL) in computer vision, many DL-models have been proposed to detect COVID-19 pneumonia using CXR images. Unfortunately, these deep classifiers lack the transparency in interpreting findings, which may limit their applications in clinical practice. The existing commonly used visual explanation methods are either too noisy or imprecise, with low resolution, and hence are unsuitable for diagnostic purposes. In this work, we propose a novel explainable deep learning framework (CXRNet) for accurate COVID-19 pneumonia detection with an enhanced pixel-level visual explanation from CXR images. The proposed framework is based on a new Encoder-Decoder-Encoder multitask architecture, allowing for both disease classification and visual explanation. The method has been evaluated on real world CXR datasets from both public and private data sources, including: healthy, bacterial pneumonia, viral pneumonia and COVID-19 pneumonia cases The experimental results demonstrate that the proposed method can achieve a satisfactory level of accuracy and provide fine-resolution classification activation maps for visual explanation in lung disease detection. The Average Accuracy, the Precision, Recall and F1-score of COVID-19 pneumonia reached 0.879, 0.985, 0.992 and 0.989, respectively. We have also found that using lung segmented (CXR) images can help improve the performance of the model. The proposed method can provide more detailed high resolution visual explanation for the classification decision, compared to current state-of-the-art visual explanation methods and has a great potential to be used in clinical practice for COVID-19 pneumonia diagnosis.

eess.IV↗

arXiv2021

Synthesizing Queries via Interactive Sketching

We propose a novel approach to program synthesis, focusing on synthesizing database queries. At a high level, our proposed algorithm takes as input a sketch with soft constraints encoding user intent, and then iteratively interacts with the user to refine the sketch. At each step, our algorithm proposes a candidate refinement of the sketch, which the user can either accept or reject. By leveraging this rich form of user feedback, our algorithm is able to both resolve ambiguity in user intent and improve scalability. In particular, assuming the user provides accurate inputs and responses, then our algorithm is guaranteed to converge to the true program (i.e., one that the user approves) in polynomial time. We perform a qualitative evaluation of our algorithm, showing how it can be used to synthesize a variety of queries on a database of academic publications.

cs.PL↗

arXiv2021

An Explicitly Solvable Energy-Conserving Algorithm for Pitch-Angle Scattering in Magnetized Plasmas

We develop an Explicitly Solvable Energy-Conserving (ESEC) algorithm for the Stochastic Differential Equation (SDE) describing the pitch-angle scattering process in magnetized plasmas. The Cayley transform is used to calculate both the deterministic gyromotion and stochastic scattering, affording the algorithm to be explicitly solvable and exactly energy conserving. An unusual property of the SDE for pitch-angle scattering is that its coefficients diverge at the zero velocity and do not satisfy the global Lipschitz condition. Consequently, when standard numerical methods, such as the Euler-Maruyama (EM), are applied, numerical convergence is difficult to establish. For the proposed ESEC algorithm, its energy-preserving property enables us to overcome this obstacle. We rigorously prove that the ESEC algorithm is order 1/2 strongly convergent. This result is confirmed by detailed numerical studies. For the case of pitch-angle scattering in a magnetized plasma with a constant magnetic field, the numerical solution is benchmarked against the analytical solution, and excellent agreements are found.

physics.plasm-ph↗

arXiv2021

Safe Human-Interactive Control via Shielding

Ensuring safety for human-interactive robotics is important due to the potential for human injury. The key challenge is defining safety in a way that accounts for the complex range of human behaviors without modeling the human as an unconstrained adversary. We propose a novel approach to ensuring safety in these settings. Our approach focuses on defining backup actions that we believe human always considers taking to avoid an accident -- e.g., brake to avoid rear-ending the other agent. Given such a definition, we consider a safety constraint that guarantees safety as long as the human takes the appropriate backup actions when necessary to ensure safety. Then, we propose an algorithm that overrides an arbitrary given controller as needed to ensure that the robot is safe. We evaluate our approach in a simulated environment, interacting with both real and simulated humans.

cs.RO↗

arXiv2021

Cellular Network Radio Propagation Modeling with Deep Convolutional Neural Networks

Radio propagation modeling and prediction is fundamental for modern cellular network planning and optimization. Conventional radio propagation models fall into two categories. Empirical models, based on coarse statistics, are simple and computationally efficient, but are inaccurate due to oversimplification. Deterministic models, such as ray tracing based on physical laws of wave propagation, are more accurate and site specific. But they have higher computational complexity and are inflexible to utilize site information other than traditional global information system (GIS) maps. In this article we present a novel method to model radio propagation using deep convolutional neural networks and report significantly improved performance compared to conventional models. We also lay down the framework for data-driven modeling of radio propagation and enable future research to utilize rich and unconventional information of the site, e.g. satellite photos, to provide more accurate and flexible models.

cs.IT↗

arXiv2021

Logarithmic bounds for the diameters of some Cayley graphs

Let $S\subset\text{GL}_n(\mathbb Z)$ be a finite symmetric set. We show that if the Zariski closure of $Γ=\langle S\rangle$ is a product of $\text{SL}_d$ or a special affine linear group, then the diameter of the Cayley graph $\text{Cay}(Γ/Γ(q),π_q(S))$ is $O(\log q)$, where $q$ is an arbitrary positive integer, $π_q:Γ\to Γ/Γ(q)$ is the canonical projection induced by the reduction modulo $q$, and the implied constant depends only on $S$.

math.GR↗