arXiv subjects

Liang Li

Publications and source records attributed to Liang Li.

At least 325 records · Page 18Linked to original sources

arXiv2020

Metamagnetic transitions and anomalous magnetoresistance in EuAg$_4$As$_2$ single crystal

In this paper, the magnetic and transport properties were systematically studied for EuAg$_4$As$_2$ single crystals, crystallizing in a centrosymmetric trigonal CaCu$_4$P$_2$ type structure. It was confirmed that two magnetic transitions occur at $\textit{T}$$_{N1}$ = 10 K and $\textit{T}$$_{N2}$ = 15 K, respectively. With the increasing field, the two transitions are noticeably driven to lower temperature. At low temperatures, applying a magnetic field in the $\textit{ab}$ plane induces two successive metamagnetic transitions. For both $\textit{H}$ $\parallel$ $\textit{ab}$ and $\textit{H}$ $\parallel$ $\textit{c}$, EuAg$_4$As$_2$ shows a positive, unexpected large magnetoresistance (up to 202\%) at low fields below 10 K, and a large negative magnetoresistance (up to -78\%) at high fields/intermediate temperatures. Such anomalous field dependence of magnetoresistance may have potential application in the future magnetic sensors. Finally, the magnetic phase diagrams of EuAg$_{4}$As$_{2}$ were constructed for both $\textit{H}$ $\parallel$ $\textit{ab}$ and $\textit{H}$ $\parallel$ $\textit{c}$.

cond-mat.mtrl-sci↗

arXiv2020

Atomic origin for hydrogenation promoted bulk oxygen vacancies removal in vanadium dioxide

Oxygen vacancies (VO), a common type of point defects in metal oxides materials, play important roles on the physical and chemical properties. To obtain stoichiometric oxide crystal, the pre-existing VO is always removed via careful post-annealing treatment at high temperature in air or oxygen atmosphere. However, the annealing conditions is difficult to control and the removal of VO in bulk phase is restrained due to high energy barrier of VO migration. Here, we selected VO2 crystal film as the model system and developed an alternative annealing treatment aided by controllable hydrogen doping, which can realizes effective removal of VO defects in VO2-δ crystal at lower temperature. This finding is attributed to the hydrogenation accelerated oxygen vacancies recovery in VO2-δ crystal. Theoretical calculations revealed that the H-doping induced electrons are prone to accumulate around the oxygen defects in VO2-δ film, which facilitates the diffusion of VO and thus makes it easier to be removed. The methodology is expected to be applied to other metal oxides for oxygen-related point defects control.

cond-mat.mtrl-sci↗

arXiv2020

Absolutely convergent fixed-point fast sweeping WENO methods for steady state of hyperbolic conservation laws

Fixed-point iterative sweeping methods were developed in the literature to efficiently solve steady state solutions of Hamilton-Jacobi equations and hyperbolic conservation laws. Similar as other fast sweeping schemes, the key components of this class of methods are the Gauss-Seidel iterations and alternating sweeping strategy to achieve fast convergence rate. Furthermore, good properties of fixed-point iterative sweeping methods include that they have explicit forms and do not involve inverse operation of nonlinear local systems, and they can be applied to general hyperbolic equations using any monotone numerical fluxes and high order approximations easily. In [L. Wu, Y.-T. Zhang, S. Zhang and C.-W. Shu, Commun. Comput. Phys., 20 (2016)], a fifth order fixed-point sweeping WENO scheme was designed and it was shown that the scheme converges much faster than the total variation diminishing (TVD) Runge-Kutta approach by stability improvement of high order schemes with a forward Euler time-marching. An open problem is that for some benchmark numerical examples, the iteration residue of the fixed-point sweeping WENO scheme hangs at a truncation error level instead of settling down to machine zero. This issue makes it difficult to determine the convergence criterion for the iteration and challenging to apply the method to complex problems. To solve this issue, in this paper we apply the multi-resolution WENO scheme developed in [J. Zhu and C.-W. Shu, J. Comput. Phys., 375 (2018)] to the fifth order fixed-point sweeping WENO scheme and obtain an absolutely convergent fixed-point fast sweeping method for steady state of hyperbolic conservation laws, i.e., the residue of the fast sweeping iterations converges to machine zero / round off errors for all benchmark problems tested.

math.NA↗

arXiv2020

Strong field induced electron density tide in high harmonic generation from solids

In strong laser fields, the electron density in solids can show up tidal motions caused by the laser ``pulling'' on the electrons, which forms an electron density tide (EDT). However, the strong field processes in solids are always explained by the single-active-electron (SAE) model and the fluctuation of background electrons is neglected previously. Here, we demonstrate the strong field induced EDT effect and propose a model for revealing its role in high harmoinc generation (HHG) from solids. We show that the EDT effect induces an additional polarization current beyond the SAE approximation. It gives a new mechanism for HHG and leads to new anisotropic structures, which are experimentally observed with MgO. Our experiment indicates that the EDT effect becomes more obvious with increasing the laser intensity. Our work establishes the bridge between the HHG and the microscopic EDT in solids induced by the strong laser field, which paves the way to probe the ultrafast electron dynamics.

physics.optics↗

arXiv2020

Thermal Components in Gamma-ray Bursts. II. Constraining the Hybrid Jet Model

In explaining the physical origin of the jet composition of gamma-ray bursts (GRBs), a more general picture, i.e. the hybrid jet model (which introduced another magnetization parameter $σ_{0}$ on the basis of the traditional fireball model), has been well studied in Gao \& Zhang. However, it still has not yet been applied to a large GRB sample. Here, we first employ the "top-down" approach of Gao \& Zhang to diagnose the photosphere properties at the central engine to see how the hybrid model can account for the observed data as well, through applying a {\it Fermi} GRB sample (eight bursts) with the detected photosphere component, as presented in Li (our Paper I). We infer all physical parameters of a hybrid problem with three typical values of the radius of the jet base ($r_{0}$ = 10$^{7}$, 10$^{8}$, and 10$^{9}$ cm). We find that the dimensionless entropy for all the bursts shows $η\gg$ 1 while the derived (1+$σ_{0}$) for five bursts (GRB 081224, GRB 110721A, GRB 090719, GRB 100707, and GRB 100724) is larger than unity, indicating that in addition to a hot fireball component, another cold Poynting-flux component may also play an important role. Our analysis also shows that in a few time bins for all $r_{0}$ in GRB 081224 and GRB 110721A, the magnetization parameter at $\sim$ 10$^{15}$cm (1+$σ_{\rm r15}$) is greater than unity, which implies that internal-collision-induced magnetic reconnection and turbulence may be the mechanism to power the nonthermal emission, rather than internal shocks. We conclude that the majority of bursts (probably all) can be well explained by the hybrid jet problem.

astro-ph.HE↗

arXiv2020

Delay-Aware Model-Based Reinforcement Learning for Continuous Control

Action delays degrade the performance of reinforcement learning in many real-world systems. This paper proposes a formal definition of delay-aware Markov Decision Process and proves it can be transformed into standard MDP with augmented states using the Markov reward process. We develop a delay-aware model-based reinforcement learning framework that can incorporate the multi-step delay into the learned system models without learning effort. Experiments with the Gym and MuJoCo platforms show that the proposed delay-aware model-based algorithm is more efficient in training and transferable between systems with various durations of delay compared with off-policy model-free reinforcement learning methods. Codes available at: https://github.com/baimingc/dambrl.

cs.LG↗

arXiv2020

Secret Sharing based Secure Regressions with Applications

Nowadays, the utilization of the ever expanding amount of data has made a huge impact on web technologies while also causing various types of security concerns. On one hand, potential gains are highly anticipated if different organizations could somehow collaboratively share their data for technological improvements. On the other hand, data security concerns may arise for both data holders and data providers due to commercial or sociological concerns. To make a balance between technical improvements and security limitations, we implement secure and scalable protocols for multiple data holders to train linear regression and logistic regression models. We build our protocols based on the secret sharing scheme, which is scalable and efficient in applications. Moreover, our proposed paradigm can be generalized to any secure multiparty training scenarios where only matrix summation and matrix multiplications are used. We demonstrate our approach by experiments which shows the scalability and efficiency of our proposed protocols, and finally present its real-world applications.

cs.LG↗

arXiv2020

Real-world Person Re-Identification via Degradation Invariance Learning

Person re-identification (Re-ID) in real-world scenarios usually suffers from various degradation factors, e.g., low-resolution, weak illumination, blurring and adverse weather. On the one hand, these degradations lead to severe discriminative information loss, which significantly obstructs identity representation learning; on the other hand, the feature mismatch problem caused by low-level visual variations greatly reduces retrieval performance. An intuitive solution to this problem is to utilize low-level image restoration methods to improve the image quality. However, existing restoration methods cannot directly serve to real-world Re-ID due to various limitations, e.g., the requirements of reference samples, domain gap between synthesis and reality, and incompatibility between low-level and high-level methods. In this paper, to solve the above problem, we propose a degradation invariance learning framework for real-world person Re-ID. By introducing a self-supervised disentangled representation learning strategy, our method is able to simultaneously extract identity-related robust features and remove real-world degradations without extra supervision. We use low-resolution images as the main demonstration, and experiments show that our approach is able to achieve state-of-the-art performance on several Re-ID benchmarks. In addition, our framework can be easily extended to other real-world degradation factors, such as weak illumination, with only a few modifications.

cs.CV↗

arXiv2020

State-Relabeling Adversarial Active Learning

Active learning is to design label-efficient algorithms by sampling the most representative samples to be labeled by an oracle. In this paper, we propose a state relabeling adversarial active learning model (SRAAL), that leverages both the annotation and the labeled/unlabeled state information for deriving the most informative unlabeled samples. The SRAAL consists of a representation generator and a state discriminator. The generator uses the complementary annotation information with traditional reconstruction information to generate the unified representation of samples, which embeds the semantic into the whole data representation. Then, we design an online uncertainty indicator in the discriminator, which endues unlabeled samples with different importance. As a result, we can select the most informative samples based on the discriminator's predicted state. We also design an algorithm to initialize the labeled pool, which makes subsequent sampling more efficient. The experiments conducted on various datasets show that our model outperforms the previous state-of-art active learning methods and our initially sampling algorithm achieves better performance.

cs.CV↗

arXiv2020

Parsing-based View-aware Embedding Network for Vehicle Re-Identification

Vehicle Re-Identification is to find images of the same vehicle from various views in the cross-camera scenario. The main challenges of this task are the large intra-instance distance caused by different views and the subtle inter-instance discrepancy caused by similar vehicles. In this paper, we propose a parsing-based view-aware embedding network (PVEN) to achieve the view-aware feature alignment and enhancement for vehicle ReID. First, we introduce a parsing network to parse a vehicle into four different views, and then align the features by mask average pooling. Such alignment provides a fine-grained representation of the vehicle. Second, in order to enhance the view-aware features, we design a common-visible attention to focus on the common visible views, which not only shortens the distance among intra-instances, but also enlarges the discrepancy of inter-instances. The PVEN helps capture the stable discriminative information of vehicle under different views. The experiments conducted on three datasets show that our model outperforms state-of-the-art methods by a large margin.

cs.CV↗

arXiv2020

Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations

The learning of the deep networks largely relies on the data with human-annotated labels. In some label insufficient situations, the performance degrades on the decision boundary with high data density. A common solution is to directly minimize the Shannon Entropy, but the side effect caused by entropy minimization, i.e., reduction of the prediction diversity, is mostly ignored. To address this issue, we reinvestigate the structure of classification output matrix of a randomly selected data batch. We find by theoretical analysis that the prediction discriminability and diversity could be separately measured by the Frobenius-norm and rank of the batch output matrix. Besides, the nuclear-norm is an upperbound of the Frobenius-norm, and a convex approximation of the matrix rank. Accordingly, to improve both discriminability and diversity, we propose Batch Nuclear-norm Maximization (BNM) on the output matrix. BNM could boost the learning under typical label insufficient learning scenarios, such as semi-supervised learning, domain adaptation and open domain recognition. On these tasks, extensive experimental results show that BNM outperforms competitors and works well with existing well-known methods. The code is available at https://github.com/cuishuhao/BNM.

cs.CV↗

arXiv2020

Secure Social Recommendation based on Secret Sharing

Nowadays, privacy preserving machine learning has been drawing much attention in both industry and academy. Meanwhile, recommender systems have been extensively adopted by many commercial platforms (e.g. Amazon) and they are mainly built based on user-item interactions. Besides, social platforms (e.g. Facebook) have rich resources of user social information. It is well known that social information, which is rich on social platforms such as Facebook, are useful to recommender systems. It is anticipated to combine the social information with the user-item ratings to improve the overall recommendation performance. Most existing recommendation models are built based on the assumptions that the social information are available. However, different platforms are usually reluctant to (or cannot) share their data due to certain concerns. In this paper, we first propose a SEcure SOcial RECommendation (SeSoRec) framework which can (1) collaboratively mine knowledge from social platform to improve the recommendation performance of the rating platform, and (2) securely keep the raw data of both platforms. We then propose a Secret Sharing based Matrix Multiplication (SSMM) protocol to optimize SeSoRec and prove its correctness and security theoretically. By applying minibatch gradient descent, SeSoRec has linear time complexities in terms of both computation and communication. The comprehensive experimental results on three real-world datasets demonstrate the effectiveness of our proposed SeSoRec and SSMM.

cs.LG↗

arXiv2020

PrBi: Topology meets quadrupolar degrees of freedom

Novel materials incorporating electronic degrees of freedom other than charge, including spin, orbital or valley \textit{et al} have manifested themselves to be of the great interests and applicable potentials. Recently, the multipolar degrees of freedom have attracted remarkable attention in the electronic correlated effects. In this work, we systematically studied the transport, magnetic and thermodynamic properties of the topological semimetal candidate PrBi in the framework of crystalline electric field theory. Our results demonstrate the $Γ_3$ non-Kramers doublet as the ground state of Pr$^{3+}$ (4$f^2$) ions. This ground state is nonmagnetic but carries a non-zero quadrupolar moment $\langle\hat{O}_2^0\rangle$. A quadrupolar phase transition is inferred below 0.08 K. No obvious quadrupolar Kondo effect can be identified. Ultrahigh-field quantum oscillation measurements confirm PrBi as a semimetal with non-trivial Berry phase and low total carrier density 0.06 /f.u. We discuss the interplay between low carrier density and $4f^2$ quadrupolar moment, and ascribe the weak quadrupolar ordering and Kondo effect to consequences of the low carrier density. PrBi, thus, opens a new window to the physics of topology and strongly correlated effect with quadrupolar degrees of freedom in the low-carrier-density limit, evoking the need for a reexamination of the Nozières exhaustion problem in the context of multi-channel Kondo effect.

cond-mat.str-el↗

arXiv2020

Two CSCS-based iteration methods for solving absolute value equations

Recently, two families of HSS-based iteration methods are constructed for solving the system of absolute value equations (AVEs), which is a class of non-differentiable NP-hard problems. In this study, we establish the Picard-CSCS iteration method and the nonlinear CSCS-like iteration method for AVEs involving the Toeplitz matrix. Then, we analyze the convergence of the Picard-CSCS iteration method for solving AVEs. By using the theory about nonsmooth analysis, we particularly prove the convergence of the nonlinear CSCS-like iterationsolver for AVEs. The advantage of these methods is that they do not require the storage of coefficient matrices at all, and the sub-system of linear equations can be solved efficiently via the fast Fourier transforms (FFTs). Therefore, computational cost and storage can be saved in practical implementations. Numerical examples including numerical solutions of nonlinear fractional diffusion equations are reported to show the effectiveness of the proposed methods in comparison with some existing methods.

math.NA↗

arXiv2019

Thick-Net: Parallel Network Structure for Sequential Modeling

Recurrent neural networks have been widely used in sequence learning tasks. In previous studies, the performance of the model has always been improved by either wider or deeper structures. However, the former becomes more prone to overfitting, while the latter is difficult to optimize. In this paper, we propose a simple new model named Thick-Net, by expanding the network from another dimension: thickness. Multiple parallel values are obtained via more sets of parameters in each hidden state, and the maximum value is selected as the final output among parallel intermediate outputs. Notably, Thick-Net can efficiently avoid overfitting, and is easier to optimize than the vanilla structures due to the large dropout affiliated with it. Our model is evaluated on four sequential tasks including adding problem, permuted sequential MNIST, text classification and language modeling. The results of these tasks demonstrate that our model can not only improve accuracy with faster convergence but also facilitate a better generalization ability.

cs.LG↗

arXiv2019

Low rank tensor completion with sparse regularization in a transformed domain

Tensor completion is a challenging problem with various applications. Many related models based on the low-rank prior of the tensor have been proposed. However, the low-rank prior may not be enough to recover the original tensor from the observed incomplete tensor. In this paper, we prose a tensor completion method by exploiting both the low-rank and sparse prior of tensor. Specifically, the tensor completion task can be formulated as a low-rank minimization problem with a sparse regularizer. The low-rank property is depicted by the tensor truncated nuclear norm based on tensor singular value decomposition (T-SVD) which is a better approximation of tensor tubal rank than tensor nuclear norm. While the sparse regularizer is imposed by a $\ell_{1}$-norm in a discrete cosine transformation (DCT) domain, which can better employ the local sparse property of completed data. To solve the optimization problem, we employ an alternating direction method of multipliers (ADMM) in which we only need to solve several subproblems which have closed-form solutions. Substantial experiments on real world images and videos show that the proposed method has better performances than the existing state-of-the-art methods.

math.NA↗

arXiv2019

"Double-tracking" Characteristic of the Spectral Evolution of GRB 131231A: Synchrotron Origin?

The characteristics of the spectral evolution of the prompt emission of gamma-ray bursts (GRBs), which are closely related to the radiation mechanism (synchrotron or photosphere), are still an unsolved subject. Here, by performing the detailed time-resolved spectral fitting of GRB 131231A, which has a very bright and well-defined single pulse, some interesting spectral evolution features have been found. (i) Both the low-energy spectral index $α$ and the peak energy $E_{\rm p}$ exhibit the "flux-tracking" pattern ("double-tracking" characteristics). (ii) The parameter relations, i.e., $F$ (the energy flux)-$α$, $F$-$E_{\rm p}$, and $E_{\rm p}$-$α$, along with the analogous Yonetoku $E_{\rm p}$-$L_{γ,\rm iso}$ relation for the different time-resolved spectra, show strong monotonous (positive) correlations, both in the rising and the decaying phases. (iii) The values of $α$ do not exceed the synchrotron limit ($α$= -2/3) in all slices across the pulse, favoring the synchrotron origin. We argue that the one-zone synchrotron emission model with the emitter streaming away at a large distance from the central engine can explain all of these special spectral evolution characteristics.

astro-ph.HE↗

arXiv2019

Thermal Components in Gamma-ray Bursts. I. How Do They Affect Non-Thermal Spectral Parameters?

The spectral components of the prompt emission of gamma-ray bursts (GRBs) mainly consist of two possible origins: synchrotron (nonthermal) and photosphere (thermal). The typical spectral properties of GRBs can be modeled by a dominant nonthermal component (a Band-like function or cutoff power law), while some of them have an additional thermal component (a Planck-like function). In this paper, we investigate the effects of thermal components on the nonthermal spectral parameters. We focus on eight {\it Fermi} Gamma-ray Burst Monitor bursts of which the spectra deviate from a Band-only function, and the thermal components are significant. We sort them into thermal-subdominant Group I (e.g. GRB 110721A) and thermal-dominant Group II (e.g., GRB 090902B). Several interesting results are found assuming the spectral component is totally attributed to the nonthermal component: (i) the low-energy photon index $α$ becomes harder; (ii) the peak energy $E_{\rm c}$ is significantly smaller and lies between the peak temperature of blackbody component and the peak energy of the cutoff power law + blackbody (CPL+BB) model; (iii) total flux $F$, is generally the same; (iv) the changes ($Δα$ and $ΔE_{\rm c}$) are positively correlated with the ratio between the thermal flux and total flux; and (v) parameter relations ($F-α$, $F-E_{\rm c}$ and $E_{\rm c}$-$α$) also changed prominently. The GRBs in both groups show the same results. Our analysis indicates that the thermal component is important, and it significantly affects the spectral parameters and the consequential physical interpretations.

astro-ph.HE↗