arXiv subjects

Qian Zhao

Publications and source records attributed to Qian Zhao.

At least 109 records · Page 6Linked to original sources

arXiv2022

Production of polarized particle beams via ultraintense laser pulses

High-energy spin-polarized electron, positron, and $γ$-photon beams have many significant applications in the study of material properties, nuclear structure, particle physics, and high-energy astrophysics. Thus,efficient production of such polarized beams attracts a broad spectrum of research interests. This is driven mainly by the rapid advancements in ultrashort and ultraintense laser technology. Currently available laser pulses can achieve peak intensities in the range of $10^{22}-10^{23}$ Wcm$^{-2}$, with pulse durations of tens of femtoseconds. The dynamics of particles in laser fields of the available intensities is dominated by quantum electrodynamics (QED) and the interaction mechanisms have reached regimes spanned by nonlinear multiphoton absorbtion (strong-field QED processes). In strong-field QED processes, the scattering cross sections obviously depend on the spin and polarization of the particles, and the spin-dependent photon emission and the radiation-reaction effects can be utilized to produce the polarized particles. An ultraintense laser-driven polarized particle source possesses the advantages of high-brilliance and compactness, which could open the way for the unexplored aspects in a range of researches. In this work, we briefly review the seminal conclusions from the study of the polarization effects in strong-field QED processes, as well as the progress made by recent proposals for production of the polarized particles by laser-beam or laser-plasma interactions.

physics.plasm-ph↗

arXiv2022

Temperature-dependent structure of an intermetallic ErPd$_2$Si$_2$ single crystal: A combined synchrotron and in-house X-ray diffraction study

We have grown intermetallic ErPd$_2$Si$_2$ single crystals employing laser-diodes with the floating-zone method. The temperature-dependent crystallography was determined using synchrotron and in-house X-ray powder diffraction measurements from 20 to 500 K. The diffraction patterns fit well with the tetragonal $I$4/$mmm$ space group (No. 139) with two chemical formulas within one unit cell. Our synchrotron X-ray powder diffraction study shows that the refined lattice constants are $a$ = 4.10320(2) Å, $c$ = 9.88393(5) Å at 298 K and $a$ = 4.11737(2) Å, $c$ = 9.88143(5) Å at 500 K, resulting in the unit-cell volume $V$ = 166.408(1) Å$^3$ (298 K) and 167.517(2) Å$^3$ (500 K). In the whole studied temperature range, we did not find any structural phase transition. Upon cooling, the lattice constants a and c are shortened and elongated, respectively.

cond-mat.mtrl-sci↗

arXiv2022

Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and Kernel

While researches on model-based blind single image super-resolution (SISR) have achieved tremendous successes recently, most of them do not consider the image degradation sufficiently. Firstly, they always assume image noise obeys an independent and identically distributed (i.i.d.) Gaussian or Laplacian distribution, which largely underestimates the complexity of real noise. Secondly, previous commonly-used kernel priors (e.g., normalization, sparsity) are not effective enough to guarantee a rational kernel solution, and thus degenerates the performance of subsequent SISR task. To address the above issues, this paper proposes a model-based blind SISR method under the probabilistic framework, which elaborately models image degradation from the perspectives of noise and blur kernel. Specifically, instead of the traditional i.i.d. noise assumption, a patch-based non-i.i.d. noise model is proposed to tackle the complicated real noise, expecting to increase the degrees of freedom of the model for noise representation. As for the blur kernel, we novelly construct a concise yet effective kernel generator, and plug it into the proposed blind SISR method as an explicit kernel prior (EKP). To solve the proposed model, a theoretically grounded Monte Carlo EM algorithm is specifically designed. Comprehensive experiments demonstrate the superiority of our method over current state-of-the-arts on synthetic and real datasets. The source code is available at https://github.com/zsyOAOA/BSRDM.

cs.CV↗

arXiv2022

An Effective Graph Learning based Approach for Temporal Link Prediction: The First Place of WSDM Cup 2022

Temporal link prediction, as one of the most crucial work in temporal graphs, has attracted lots of attention from the research area. The WSDM Cup 2022 seeks for solutions that predict the existence probabilities of edges within time spans over temporal graph. This paper introduces the solution of AntGraph, which wins the 1st place in the competition. We first analysis the theoretical upper-bound of the performance by removing temporal information, which implies that only structure and attribute information on the graph could achieve great performance. Based on this hypothesis, then we introduce several well-designed features. Finally, experiments conducted on the competition datasets show the superiority of our proposal, which achieved AUC score of 0.666 on dataset A and 0.902 on dataset B, the ablation studies also prove the efficiency of each feature. Code is publicly available at https://github.com/im0qianqian/WSDM2022TGP-AntGraph.

cs.SI↗

arXiv2022

Diagnosing Batch Normalization in Class Incremental Learning

Extensive researches have applied deep neural networks (DNNs) in class incremental learning (Class-IL). As building blocks of DNNs, batch normalization (BN) standardizes intermediate feature maps and has been widely validated to improve training stability and convergence. However, we claim that the direct use of standard BN in Class-IL models is harmful to both the representation learning and the classifier training, thus exacerbating catastrophic forgetting. In this paper we investigate the influence of BN on Class-IL models by illustrating such BN dilemma. We further propose BN Tricks to address the issue by training a better feature extractor while eliminating classification bias. Without inviting extra hyperparameters, we apply BN Tricks to three baseline rehearsal-based methods, ER, DER++ and iCaRL. Through comprehensive experiments conducted on benchmark datasets of Seq-CIFAR-10, Seq-CIFAR-100 and Seq-Tiny-ImageNet, we show that BN Tricks can bring significant performance gains to all adopted baselines, revealing its potential generality along this line of research.

cs.LG↗

arXiv2022

Low-light Image Enhancement by Retinex Based Algorithm Unrolling and Adjustment

Motivated by their recent advances, deep learning techniques have been widely applied to low-light image enhancement (LIE) problem. Among which, Retinex theory based ones, mostly following a decomposition-adjustment pipeline, have taken an important place due to its physical interpretation and promising performance. However, current investigations on Retinex based deep learning are still not sufficient, ignoring many useful experiences from traditional methods. Besides, the adjustment step is either performed with simple image processing techniques, or by complicated networks, both of which are unsatisfactory in practice. To address these issues, we propose a new deep learning framework for the LIE problem. The proposed framework contains a decomposition network inspired by algorithm unrolling, and adjustment networks considering both global brightness and local brightness sensitivity. By virtue of algorithm unrolling, both implicit priors learned from data and explicit priors borrowed from traditional methods can be embedded in the network, facilitate to better decomposition. Meanwhile, the consideration of global and local brightness can guide designing simple yet effective network modules for adjustment. Besides, to avoid manually parameter tuning, we also propose a self-supervised fine-tuning strategy, which can always guarantee a promising performance. Experiments on a series of typical LIE datasets demonstrated the effectiveness of the proposed method, both quantitatively and visually, as compared with existing methods.

cs.CV↗

arXiv2022

Generation of arbitrarily polarized muon pairs via polarized $e^-e^+$ collision

Generation of arbitrarily spin polarized muon pairs is investigated via polarized $e^-e^+$ collision. We calculate the fully spin-resolved cross section ${\rm d}σ_{e^-e^+\rightarrow μ^-μ^+}$ and utilize the Monte Carlo method of binary collision to describe the production and polarization processes of muon pairs. We find that, due to the dependence of mixed helicities on the scattering angle, arbitrarily polarized muon pairs with both of the longitudinal and transverse spin components can be produced. The collision of tightly collimated electron and positron beams with highly longitudinal polarization and nC charge can generate about $40\%$ muon pairs with longitudinal polarization and about $60\%$ muon pairs with transverse polarization. The compact high-flux $e^-e^+\rightarrowμ^-μ^+$ muon source could be implemented through the next-generation laser-plasma linear collider, and would be essential to facilitate the investigation of fundamental physics and the measurement technology in broad areas.

hep-ph↗

arXiv2021

Progress in Water-Based Metamaterial Absorber: A Review

Increasing attention on microwave ultra-broadband metamaterial absorbers has been paid due to their promising applications. While most microwave ultra-broadband metamaterial absorbers developed so far are based on metallic resonant structures, dispersive dielectric water-based metamaterial opens a simpler and more versatile route for the construction of polarization- and angle- insensitive ultra-broadband absorption. Here, we review the recent progresses of water-based metamaterial absorber by providing an illustration of the mechanisms to realize ultra-broadband, tunable and multi-functional absorption. We also address the further development direction and some potential novel applications.

physics.app-ph↗

arXiv2021

Insights into the structural symmetry of single-crystal YCrO$_3$ from synchrotron X-ray diffraction

We report on the crystallographic information such as lattice parameters, atomic positions, bond lengths and angles, and local crystalline distortion size and mode of single-crystal YCrO$_3$ compound by a high-resolution synchrotron X-ray diffraction study. The data was collected at 120 K (below $T_\textrm{N} \sim$ 141.5 K), 300 K (within [$T_\textrm{N}$, $T_\textrm{C}$]), and 500 K (above $T_\textrm{C} \sim$ 473 K). Taking advantages of high intensity and brilliance of synchrotron X-rays, we are able to refine collected patterns with the noncentrosymmetric monoclinic structural model ($P12_11$, No. 4) that was proposed previously but detailed structural parameters have not determined yet. Meanwhile, we calculated patterns with the centrosymmetric orthorhombic space group (\emph{Pmnb}, No. 62) for a controlled study. Lattice constants \emph{a}, \emph{b}, and \emph{c} as well as unit-cell volume almost increase linearly upon warming. We observed more dispersive distributions of bond length and angle and local distortion strength with the $P12_11$ space group. This indicates that (i) The local distortion mode of Cr2O$_6$ at 120 K correlates the formation of the canted antiferromagnetic order by Cr1-Cr2 spin interactions mainly through intermediate of O3 and O4 ions. (ii) The strain-balanced Cr1-O3(O4) and Cr2-O5(O5) bonds as well as the local distortion modes of Cr1O$_6$ and Cr2O$_6$ octohedra at 300 K may be a microscopic origin of the previously-reported dielectric anomaly. Our study demonstrates that local crystalline distortion is a key factor for the formation of ferroelectric order and provides a complete set of crystallography for a full understanding of the interesting magnetic and quasi-ferroelectric properties of YCrO$_3$ compound.

cond-mat.mtrl-sci↗

arXiv2021

Signatures of linear Breit-Wheeler pair production in polarized $γγ$ collisions

Laser-driven brilliant controllable polarized $γ$-photon sources open the way for designing compact $γγ$ collider, which enable the large yield of linear Breit-Wheeler (LBW) pairs in a single shot and thus provide an opportunity for the investigation of polarized LBW process. In this work we investigate the polarization characteristics of LBW pair production via our developed spin-resolved binary collision simulation method. Polarization of $γ$-photons modifies the kinematics of scattering particles and induces the correlated energy-angle shift of LBW pairs, and the latter's polarization characteristic depends on the helicity configures of scattering particles. Our method confirms that the polarized $γγ$ collider with an asymmetric setup can be performed with currently achievable laser facilities to produce abundant polarized LBW pairs, fulfilling the detection power of polarimetries. The precise knowledge of polarized LBW process is in favor of the calibration and monitor of polarized $γγ$ collider, and could enhance the opacity of $γ$-photons in high-energy astrophysical objects to exacerbate the inconsistency between some observations and standard models.

hep-ph↗

arXiv2021

Brilliant circularly polarized $γ$-ray sources via single-shot laser plasma interaction

Circularly polarized (CP) $γ$-ray sources are versatile for broad applications in nuclear physics, high-energy physics and astrophysics. The laser-plasma based particle accelerators provide accessibility for much higher flux $γ$-ray sources than conventional approaches, in which, however, the circular polarization properties of emitted $γ$-photons are used to be neglected. In this letter, we show that brilliant CP $γ$-ray beams can be generated via the combination of laser plasma wakefield acceleration and plasma mirror techniques. In weakly nonlinear Compton scattering scheme with moderate laser intensities, the helicity of the driving laser can be transferred to the emitted $γ$-photons, and their average polarization degree can reach about $\sim 37\%$ ($21\%$) with a peak brilliance of $\gtrsim 10^{21}~$photons/(s $\cdot$ mm$^2 \cdot$ mrad$^2 \cdot$ 0.1% BW) around 1~MeV (100~MeV). Moreover, our proposed method is easily feasible and robust with respect to the laser and plasma parameters.

physics.plasm-ph↗

arXiv2021

Temperature-dependent structure and magnetization of YCrO$_3$ compound

We have grown a YCrO$_3$ single crystal by the floating-zone method and studied its temperature-dependent crystalline structure and magnetization by X-ray powder diffraction and PPMS DynaCool measurements. All diffraction patterns were well indexed by an orthorhombic structure with space group of $Pbnm$ (No. 62). From 36 to 300 K, no structural phase transition occurs in the pulverized YCrO$_3$ single crystal. The antiferromagnetic phase transition temperature was determined as $T_\textrm{N} =$ 141.58(5) K by the magnetization versus temperature measurements. We found weak ferromagnetic behavior in the magnetic hysteresis loops below $T_\textrm{N}$. Especially, we demonstrated that the antiferromagnetism and weak ferromagnetism appear simutaniously upon cooling. The lattice parameters ($a$, $b$, $c$, and $V$) deviate downward from the Gr$\ddot{\textrm{u}}$neisen law, displaying an anisotropic magnetostriction effect. We extracted temperature variation of the local distortion parameter $Δ$. Compared to the $Δ$ value of Cr ions, Y, O1, and O2 ions show one order of magnitude larger $Δ$ values indicative of much stronger local lattice distortions. Moreover, the calculated bond valence states of Y and O2 ions have obvious subduction charges.

cond-mat.str-el↗

arXiv2021

A Deep Variational Bayesian Framework for Blind Image Deblurring

Blind image deblurring is an important yet very challenging problem in low-level vision. Traditional optimization based methods generally formulate this task as a maximum-a-posteriori estimation or variational inference problem, whose performance highly relies on the handcraft priors for both the latent image and the blur kernel. In contrast, recent deep learning methods generally learn, from a large collection of training images, deep neural networks (DNNs) directly mapping the blurry image to the clean one or to the blur kernel, paying less attention to the physical degradation process of the blurry image. In this paper, we present a deep variational Bayesian framework for blind image deblurring. Under this framework, the posterior of the latent clean image and blur kernel can be jointly estimated in an amortized inference fashion with DNNs, and the involved inference DNNs can be trained by fully considering the physical blur model, together with the supervision of data driven priors for the clean image and blur kernel, which is naturally led to by the evidence lower bound objective. Comprehensive experiments are conducted to substantiate the effectiveness of the proposed framework. The results show that it can not only achieve a promising performance with relatively simple networks, but also enhance the performance of existing DNNs for deblurring.

eess.IV↗

arXiv2021

MLR-SNet: Transferable LR Schedules for Heterogeneous Tasks

The learning rate (LR) is one of the most important hyper-parameters in stochastic gradient descent (SGD) algorithm for training deep neural networks (DNN). However, current hand-designed LR schedules need to manually pre-specify a fixed form, which limits their ability to adapt practical non-convex optimization problems due to the significant diversification of training dynamics. Meanwhile, it always needs to search proper LR schedules from scratch for new tasks, which, however, are often largely different with task variations, like data modalities, network architectures, or training data capacities. To address this learning-rate-schedule setting issues, we propose to parameterize LR schedules with an explicit mapping formulation, called \textit{MLR-SNet}. The learnable parameterized structure brings more flexibility for MLR-SNet to learn a proper LR schedule to comply with the training dynamics of DNN. Image and text classification benchmark experiments substantiate the capability of our method for achieving proper LR schedules. Moreover, the explicit parameterized structure makes the meta-learned LR schedules capable of being transferable and plug-and-play, which can be easily generalized to new heterogeneous tasks. We transfer our meta-learned MLR-SNet to query tasks like different training epochs, network architectures, data modalities, dataset sizes from the training ones, and achieve comparable or even better performance compared with hand-designed LR schedules specifically designed for the query tasks. The robustness of MLR-SNet is also substantiated when the training data are biased with corrupted noise. We further prove the convergence of the SGD algorithm equipped with LR schedule produced by our MLR-Net, with the convergence rate comparable to the best-known ones of the algorithm for solving the problem.

cs.LG↗

arXiv2021

Generation of quasi-monoenergetic proton beams via quantum radiative compression

Dense high-energy monoenergetic proton beams are vital for wide applications, thus modern laser-plasma-based ion acceleration methods are aiming to obtain high-energy proton beams with energy spread as low as possible. In this work, we put forward a quantum radiative compression method to post-compress a highly accelerated proton beam and convert it to a dense quasi-monoenergetic one. We find that when the relativistic plasma produced by radiation pressure acceleration collides head-on with an ultraintense laser beam, large-amplitude plasma oscillations are excited due to quantum radiation-reaction and the ponderomotive force, which induce compression of the phase space of protons located in its acceleration phase with negative gradient. Our three-dimensional spin-resolved QED particle-in-cell simulations show that hollow-structure proton beams with a peak energy $\sim$ GeV, relative energy spread of few percents and number $N_p\sim10^{10}$ (or $N_p\sim 10^9$ with a $1\%$ energy spread) can be produced in near future laser facilities, which may fulfill the requirements of important applications, such as, for radiography of ultra-thick dense materials, or as injectors of hadron colliders.

physics.plasm-ph↗

arXiv2021

Semi-Supervised Video Deraining with Dynamical Rain Generator

While deep learning (DL)-based video deraining methods have achieved significant success recently, they still exist two major drawbacks. Firstly, most of them do not sufficiently model the characteristics of rain layers of rainy videos. In fact, the rain layers exhibit strong physical properties (e.g., direction, scale and thickness) in spatial dimension and natural continuities in temporal dimension, and thus can be generally modelled by the spatial-temporal process in statistics. Secondly, current DL-based methods seriously depend on the labeled synthetic training data, whose rain types are always deviated from those in unlabeled real data. Such gap between synthetic and real data sets leads to poor performance when applying them in real scenarios. Against these issues, this paper proposes a new semi-supervised video deraining method, in which a dynamic rain generator is employed to fit the rain layer, expecting to better depict its insightful characteristics. Specifically, such dynamic generator consists of one emission model and one transition model to simultaneously encode the spatially physical structure and temporally continuous changes of rain streaks, respectively, which both are parameterized as deep neural networks (DNNs). Further more, different prior formats are designed for the labeled synthetic and unlabeled real data, so as to fully exploit the common knowledge underlying them. Last but not least, we also design a Monte Carlo EM algorithm to solve this model. Extensive experiments are conducted to verify the superiorities of the proposed semi-supervised deraining model.

cs.CV↗

arXiv2021

Depth Evaluation for Metal Surface Defects by Eddy Current Testing using Deep Residual Convolutional Neural Networks

Eddy current testing (ECT) is an effective technique in the evaluation of the depth of metal surface defects. However, in practice, the evaluation primarily relies on the experience of an operator and is often carried out by manual inspection. In this paper, we address the challenges of automatic depth evaluation of metal surface defects by virtual of state-of-the-art deep learning (DL) techniques. The main contributions are three-fold. Firstly, a highly-integrated portable ECT device is developed, which takes advantage of an advanced field programmable gate array (Zynq-7020 system on chip) and provides fast data acquisition and in-phase/quadrature demodulation. Secondly, a dataset, termed as MDDECT, is constructed using the ECT device by human operators and made openly available. It contains 48,000 scans from 18 defects of different depths and lift-offs. Thirdly, the depth evaluation problem is formulated as a time series classification problem, and various state-of-the-art 1-d residual convolutional neural networks are trained and evaluated on the MDDECT dataset. A 38-layer 1-d ResNeXt achieves an accuracy of 93.58% in discriminating the surface defects in a stainless steel sheet. The depths of the defects vary from 0.3 mm to 2.0 mm in a resolution of 0.1 mm. In addition, results show that the trained ResNeXt1D-38 model is immune to lift-off signals.

cs.LG↗

arXiv2020

From Rain Generation to Rain Removal

For the single image rain removal (SIRR) task, the performance of deep learning (DL)-based methods is mainly affected by the designed deraining models and training datasets. Most of current state-of-the-art focus on constructing powerful deep models to obtain better deraining results. In this paper, to further improve the deraining performance, we novelly attempt to handle the SIRR task from the perspective of training datasets by exploring a more efficient way to synthesize rainy images. Specifically, we build a full Bayesian generative model for rainy image where the rain layer is parameterized as a generator with the input as some latent variables representing the physical structural rain factors, e.g., direction, scale, and thickness. To solve this model, we employ the variational inference framework to approximate the expected statistical distribution of rainy image in a data-driven manner. With the learned generator, we can automatically and sufficiently generate diverse and non-repetitive training pairs so as to efficiently enrich and augment the existing benchmark datasets. User study qualitatively and quantitatively evaluates the realism of generated rainy images. Comprehensive experiments substantiate that the proposed model can faithfully extract the complex rain distribution that not only helps significantly improve the deraining performance of current deep single image derainers, but also largely loosens the requirement of large training sample pre-collection for the SIRR task.

cs.CV↗