arXiv subjects

Qian Zhao

Publications and source records attributed to Qian Zhao.

At least 91 records · Page 5Linked to original sources

arXiv2023

Generation of $γ$ photons with extremely large orbital angular momenta

Vortex $γ$ photons, which carry large intrinsic orbital angular momenta (OAM), have significant applications in nuclear, atomic, hadron, particle and astro-physics, but their production remains unclear. In this work, we investigate the generation of such photons from nonlinear Compton scattering of circularly polarized monochromatic lasers on vortex electrons. We develop a quantum radiation theory for ultrarelativistic vortex electrons in lasers by using the harmonics expansion and spin eigenfunctions, which allows us to explore the kinematical characteristics, angular momentum transfer mechanisms, and formation conditions of vortex $γ$ photons. The multiphoton absorption of electrons enables the vortex $γ$ photons, with fixed polarizations and energies, to exist in mixed states comprised of multiple harmonics. Each harmonic represents a vortex eigenmode and has transverse momentum broadening due to transverse momenta of the vortex electrons. The large topological charges associated with vortex electrons offer the possibility for $γ$ photons to carry adjustable OAM quantum numbers from tens to thousands of units, even at moderate laser intensities. $γ$ photons with large OAM and transverse coherence length can assist in influencing quantum selection rules and extracting phase of the scattering amplitude in scattering processes.

hep-ph↗

arXiv2023

Long-tail Augmented Graph Contrastive Learning for Recommendation

Graph Convolutional Networks (GCNs) has demonstrated promising results for recommender systems, as they can effectively leverage high-order relationship. However, these methods usually encounter data sparsity issue in real-world scenarios. To address this issue, GCN-based recommendation methods employ contrastive learning to introduce self-supervised signals. Despite their effectiveness, these methods lack consideration of the significant degree disparity between head and tail nodes. This can lead to non-uniform representation distribution, which is a crucial factor for the performance of contrastive learning methods. To tackle the above issue, we propose a novel Long-tail Augmented Graph Contrastive Learning (LAGCL) method for recommendation. Specifically, we introduce a learnable long-tail augmentation approach to enhance tail nodes by supplementing predicted neighbor information, and generate contrastive views based on the resulting augmented graph. To make the data augmentation schema learnable, we design an auto drop module to generate pseudo-tail nodes from head nodes and a knowledge transfer module to reconstruct the head nodes from pseudo-tail nodes. Additionally, we employ generative adversarial networks to ensure that the distribution of the generated tail/head nodes matches that of the original tail/head nodes. Extensive experiments conducted on three benchmark datasets demonstrate the significant improvement in performance of our model over the state-of-the-arts. Further analyses demonstrate the uniformity of learned representations and the superiority of LAGCL on long-tail performance. Code is publicly available at https://github.com/im0qianqian/LAGCL

cs.IR↗

arXiv2023

Variational Denoising Network: Toward Blind Noise Modeling and Removal

Blind image denoising is an important yet very challenging problem in computer vision due to the complicated acquisition process of real images. In this work we propose a new variational inference method, which integrates both noise estimation and image denoising into a unique Bayesian framework, for blind image denoising. Specifically, an approximate posterior, parameterized by deep neural networks, is presented by taking the intrinsic clean image and noise variances as latent variables conditioned on the input noisy image. This posterior provides explicit parametric forms for all its involved hyper-parameters, and thus can be easily implemented for blind image denoising with automatic noise estimation for the test noisy image. On one hand, as other data-driven deep learning methods, our method, namely variational denoising network (VDN), can perform denoising efficiently due to its explicit form of posterior expression. On the other hand, VDN inherits the advantages of traditional model-driven approaches, especially the good generalization capability of generative models. VDN has good interpretability and can be flexibly utilized to estimate and remove complicated non-i.i.d. noise collected in real scenarios. Comprehensive experiments are performed to substantiate the superiority of our method in blind image denoising.

cs.CV↗

arXiv2023

Single channel based interference-free and self-powered human-machine interactive interface using eigenfrequency-dominant mechanism

The recent development of wearable devices is revolutionizing the way of human-machine interaction (HMI). Nowadays, an interactive interface that carries more embedded information is desired to fulfil the increasing demand in era of Internet of Things. However, present approach normally relies on sensor arrays for memory expansion, which inevitably brings the concern of wiring complexity, signal differentiation, power consumption, and miniaturization. Herein, a one-channel based self-powered HMI interface, which uses the eigenfrequency of magnetized micropillar (MMP) as identification mechanism, is reported. When manually vibrated, the inherent recovery of the MMP caused a damped oscillation that generates current signals because of Faraday's Law of induction. The time-to-frequency conversion explores the MMP-related eigenfrequency, which provides a specific solution to allocate diverse commands in an interference-free behavior even with one electric channel. A cylindrical cantilever model was built to regulate the MMP eigenfrequencies via precisely designing the dimensional parameters and material properties. We show that using one device and two electrodes, high-capacity HMI interface can be realized when the MMPs with different eigenfrequencies have been integrated. This study provides the reference value to design the future HMI system especially for situations that require a more intuitive and intelligent communication experience with high-memory demand.

physics.app-ph↗

arXiv2023

Beta-trees: Multivariate histograms with confidence statements

Multivariate histograms are difficult to construct due to the curse of dimensionality. Motivated by $k$-d trees in computer science, we show how to construct an efficient data-adaptive partition of Euclidean space that possesses the following two properties: With high confidence the distribution from which the data are generated is close to uniform on each rectangle of the partition; and despite the data-dependent construction we can give guaranteed finite sample simultaneous confidence intervals for the probabilities (and hence for the average densities) of each rectangle in the partition. This partition will automatically adapt to the sizes of the regions where the distribution is close to uniform. The methodology produces confidence intervals whose widths depend only on the probability content of the rectangles and not on the dimensionality of the space, thus avoiding the curse of dimensionality. Moreover, the widths essentially match the optimal widths in the univariate setting. The simultaneous validity of the confidence intervals allows to use this construction, which we call {\sl Beta-trees}, for various data-analytic purposes. We illustrate this by using Beta-trees for visualizing data and for multivariate mode-hunting.

stat.ME↗

arXiv2023

Enhanced signature of vacuum birefringence in a plasma wakefield

Vacuum birefringence (VB) is a basic phenomenon predicted in quantum electrodynamics (QED). However, due to the smallness of the signal, conventional magnet-based and extremely intense laser-driven detection methods are still very challenging. This is because in the first case the interaction length is large but the field is limited, and vice versa in the second case. We put forward a method to generate and detect VB in a plasma bubble wakefield, which combines both advantages, providing large fields along large interaction lengths. A polarized $γ$-photon beam is considered to probe the wakefield along a propagation distance of millimeters to centimeters in the plasma bubble. We find via plasma particle-in-cell simulations that the VB signal in terms of Stokes parameters can reach about $ 10^{-5}$ ($10^{-3}$-$10^{-2}$) for tens of MeV (GeV) probe photons with moderately intense lasers ($10^{20}$-$10^{21}~\mathrm{W/cm^2}$). The main source of noise from plasma electrons is mitigated, in particular, by a choice of $γ$-photon polarization and by proper modulation of the plasma density. The proposed method represents an attractive alternative for the experimental observation of VB via laser-plasma interaction.

physics.plasm-ph↗

arXiv2023

Simulations of spin/polarization-resolved laser-plasma interactions in the nonlinear QED regime

Strong-field quantum electrodynamics (SF-QED) plays a crucial role in ultraintense laser matter interactions, and demands sophisticated techniques to understand the related physics with new degrees of freedom, including spin angular momentum. To investigate the impact of SF-QED processes, we have introduced spin/polarization-resolved nonlinear Compton scattering, nonlinear Breit-Wheeler and vacuum birefringence processes into our particle-in-cell (PIC) code. In this article, we will provide details of the implementation of these SF-QED modules and share known results that demonstrate exact agreement with existing single particle codes. By coupling normal PIC with spin/polarization-resolved SF-QED processes, we create a new theoretical platform to study strong field physics in currently running or planned petawatt or multi-petawatt laser facilities.

physics.plasm-ph↗

arXiv2023

MultiModal-GPT: A Vision and Language Model for Dialogue with Humans

We present a vision and language model named MultiModal-GPT to conduct multi-round dialogue with humans. MultiModal-GPT can follow various instructions from humans, such as generating a detailed caption, counting the number of interested objects, and answering general questions from users. MultiModal-GPT is parameter-efficiently fine-tuned from OpenFlamingo, with Low-rank Adapter (LoRA) added both in the cross-attention part and the self-attention part of the language model. We first construct instruction templates with vision and language data for multi-modality instruction tuning to make the model understand and follow human instructions. We find the quality of training data is vital for the dialogue performance, where few data containing short answers can lead the model to respond shortly to any instructions. To further enhance the ability to chat with humans of the MultiModal-GPT, we utilize language-only instruction-following data to train the MultiModal-GPT jointly. The joint training of language-only and visual-language instructions with the \emph{same} instruction template effectively improves dialogue performance. Various demos show the ability of continuous dialogue of MultiModal-GPT with humans. Code, dataset, and demo are at https://github.com/open-mmlab/Multimodal-GPT

cs.CV↗

arXiv2023

Angle-dependent pair production in the polarized two-photon Breit-Wheeler process

The advent of laser-driven high-intensity $γ$-photon beams has opened up new opportunities for designing advanced photon-photon colliders. Such colliders have the potential to produce a large yield of linear Breit-Wheeler (LBW) pairs in a single shot, which offers a unique platform for studying the polarized LBW process. In our recent work [Phys. Rev. D 105, L071902(2022)], we investigated the polarization characteristics of LBW pair production in CP $γ$-photon collisions. To fully clarify the polarization effects involving both CP and LP $γ$-photons, here we further investigate the LBW process using the polarized cross section with explicit azimuthal-angle dependence due to the base rotation of photon polarization vectors. We accomplished this by defining a new spin basis for positrons and electrons, which enables us to decouple the transverse and longitudinal spin components of $e^\pm$. By means of analytical calculations and Monte Carlo simulations, we find that the linear polarization of photon can induce the highly angle-dependent pair yield and polarization distributions. The comprehensive knowledge of the polarized LBW process will also open up avenues for investigating the higher-order photon-photon scattering, the laser-driven quantum electrodynamic plasmas and the high-energy astrophysics.

hep-ph↗

arXiv2023

Fourier Series Expansion Based Filter Parametrization for Equivariant Convolutions

It has been shown that equivariant convolution is very helpful for many types of computer vision tasks. Recently, the 2D filter parametrization technique plays an important role when designing equivariant convolutions. However, the current filter parametrization method still has its evident drawbacks, where the most critical one lies in the accuracy problem of filter representation. Against this issue, in this paper we modify the classical Fourier series expansion for 2D filters, and propose a new set of atomic basis functions for filter parametrization. The proposed filter parametrization method not only finely represents 2D filters with zero error when the filter is not rotated, but also substantially alleviates the fence-effect-caused quality degradation when the filter is rotated. Accordingly, we construct a new equivariant convolution method based on the proposed filter parametrization method, named F-Conv. We prove that the equivariance of the proposed F-Conv is exact in the continuous domain, which becomes approximate only after discretization. Extensive experiments show the superiority of the proposed method. Particularly, we adopt rotation equivariant convolution methods to image super-resolution task, and F-Conv evidently outperforms previous filter parametrization based method in this task, reflecting its intrinsic capability of faithfully preserving rotation symmetries in local image features.

cs.CV↗

arXiv2023

Interactive Segmentation as Gaussian Process Classification

Click-based interactive segmentation (IS) aims to extract the target objects under user interaction. For this task, most of the current deep learning (DL)-based methods mainly follow the general pipelines of semantic segmentation. Albeit achieving promising performance, they do not fully and explicitly utilize and propagate the click information, inevitably leading to unsatisfactory segmentation results, even at clicked points. Against this issue, in this paper, we propose to formulate the IS task as a Gaussian process (GP)-based pixel-wise binary classification model on each image. To solve this model, we utilize amortized variational inference to approximate the intractable GP posterior in a data-driven manner and then decouple the approximated GP posterior into double space forms for efficient sampling with linear complexity. Then, we correspondingly construct a GP classification framework, named GPCIS, which is integrated with the deep kernel learning mechanism for more flexibility. The main specificities of the proposed GPCIS lie in: 1) Under the explicit guidance of the derived GP posterior, the information contained in clicks can be finely propagated to the entire image and then boost the segmentation; 2) The accuracy of predictions at clicks has good theoretical support. These merits of GPCIS as well as its good generality and high efficiency are substantiated by comprehensive experiments on several benchmarks, as compared with representative methods both quantitatively and qualitatively.

cs.CV↗

arXiv2023

The Asymptotic Distribution of the MLE in High-dimensional Logistic Models: Arbitrary Covariance

We study the distribution of the maximum likelihood estimate (MLE) in high-dimensional logistic models, extending the recent results from Sur (2019) to the case where the Gaussian covariates may have an arbitrary covariance structure. We prove that in the limit of large problems holding the ratio between the number $p$ of covariates and the sample size $n$ constant, every finite list of MLE coordinates follows a multivariate normal distribution. Concretely, the $j$th coordinate $\hat β_j$ of the MLE is asymptotically normally distributed with mean $α_\star β_j$ and standard deviation $σ_\star/τ_j$; here, $β_j$ is the value of the true regression coefficient, and $τ_j$ the standard deviation of the $j$th predictor conditional on all the others. The numerical parameters $α_\star > 1$ and $σ_\star$ only depend upon the problem dimensionality $p/n$ and the overall signal strength, and can be accurately estimated. Our results imply that the MLE's magnitude is biased upwards and that the MLE's standard deviation is greater than that predicted by classical theory. We present a series of experiments on simulated and real data showing excellent agreement with the theory.

math.ST↗

arXiv2022

RCDNet: An Interpretable Rain Convolutional Dictionary Network for Single Image Deraining

As a common weather, rain streaks adversely degrade the image quality. Hence, removing rains from an image has become an important issue in the field. To handle such an ill-posed single image deraining task, in this paper, we specifically build a novel deep architecture, called rain convolutional dictionary network (RCDNet), which embeds the intrinsic priors of rain streaks and has clear interpretability. In specific, we first establish a RCD model for representing rain streaks and utilize the proximal gradient descent technique to design an iterative algorithm only containing simple operators for solving the model. By unfolding it, we then build the RCDNet in which every network module has clear physical meanings and corresponds to each operation involved in the algorithm. This good interpretability greatly facilitates an easy visualization and analysis on what happens inside the network and why it works well in inference process. Moreover, taking into account the domain gap issue in real scenarios, we further design a novel dynamic RCDNet, where the rain kernels can be dynamically inferred corresponding to input rainy images and then help shrink the space for rain layer estimation with few rain maps so as to ensure a fine generalization performance in the inconsistent scenarios of rain types between training and testing data. By end-to-end training such an interpretable network, all involved rain kernels and proximal operators can be automatically extracted, faithfully characterizing the features of both rain and clean background layers, and thus naturally lead to better deraining performance. Comprehensive experiments substantiate the superiority of our method, especially on its well generality to diverse testing scenarios and good interpretability for all its modules. Code is available in \emph{\url{https://github.com/hongwang01/DRCDNet}}.

eess.IV↗

arXiv2022

Investigation of the particle-particle interaction effects in the cosmic Zevatron based on cyclotron auto-resonance by particle-in-cell simulations

Cyclotron autoresonance acceleration has been recently advanced as a potential mechanism for accelerating nuclei to ZeV energies (1 ZeV = $10^{21}$ eV). All results have been based on single- and many-particle calculations employing analytic solutions to the relativistic equations of motion in the combined magnetic and radiation fields, excluding effects related to the particle-particle interactions. Here, results from many-particle calculations and Particle-In-Cell (PIC) simulations, are presented which lend support to the single-particle investigations. Each single-particle result is found to lie well within one standard deviation about the ensemble average obtained from the corresponding many-particle calculation. The PIC simulations show that, even for number densities far exceeding those employed in the non-interacting case, the energy gain drops markedly due to the particle-particle interactions, over the first $\sim 8~ mm$ of the acceleration length. Together with the substantial attenuation, this finding supports the conclusion that the particle-particle interaction effects can be negligibly small over acceleration lengths of typically many kilometers.

astro-ph.HE↗

arXiv2022

KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution

Although current deep learning-based methods have gained promising performance in the blind single image super-resolution (SISR) task, most of them mainly focus on heuristically constructing diverse network architectures and put less emphasis on the explicit embedding of the physical generation mechanism between blur kernels and high-resolution (HR) images. To alleviate this issue, we propose a model-driven deep neural network, called KXNet, for blind SISR. Specifically, to solve the classical SISR model, we propose a simple-yet-effective iterative algorithm. Then by unfolding the involved iterative steps into the corresponding network module, we naturally construct the KXNet. The main specificity of the proposed KXNet is that the entire learning process is fully and explicitly integrated with the inherent physical mechanism underlying this SISR task. Thus, the learned blur kernel has clear physical patterns and the mutually iterative process between blur kernel and HR image can soundly guide the KXNet to be evolved in the right direction. Extensive experiments on synthetic and real data finely demonstrate the superior accuracy and generality of our method beyond the current representative state-of-the-art blind SISR methods. Code is available at: https://github.com/jiahong-fu/KXNet.

cs.CV↗

arXiv2022

An Adaptively Resized Parametric Bootstrap for Inference in High-dimensional Generalized Linear Models

Accurate statistical inference in logistic regression models remains a critical challenge when the ratio between the number of parameters and sample size is not negligible. This is because approximations based on either classical asymptotic theory or bootstrap calculations are grossly off the mark. This paper introduces a resized bootstrap method to infer model parameters in arbitrary dimensions. As in the parametric bootstrap, we resample observations from a distribution, which depends on an estimated regression coefficient sequence. The novelty is that this estimate is actually far from the maximum likelihood estimate (MLE). This estimate is informed by recent theory studying properties of the MLE in high dimensions, and is obtained by appropriately shrinking the MLE towards the origin. We demonstrate that the resized bootstrap method yields valid confidence intervals in both simulated and real data examples. Our methods extend to other high-dimensional generalized linear models.

stat.ME↗

arXiv2022

Evidencing non-Bloch dynamics in temporal topolectrical circuits

One of the core concepts from the non-Hermitian skin effect is the extended complex wavevectors (CW) in the generalized Brillouin zone (GBZ), while the origin of CW remains elusive, and further experimental demonstration of GBZ is still lacking. We show that the bulk states of an open quantum system dynamically governed by the Lindblad master equation exhibit non-Bloch evolution which results in CW. Experimentally, we present temporal topolectrical circuits to serve as simulators for the dynamics of an open system. By reconstructing the correspondence between the bulk states of an open system and circuit voltage modes through gauge scale potentials in the circuit, the non-Bloch evolution is demonstrated. Facilitated by the simulators and proper approach to characterize the non-Bloch band proposed here, the GBZ is confirmed. Our work may advance the investigation of the dissipative topological modes and provide a versatile platform for exploring the unique evolution and topology for both closed and open systems.

cond-mat.mtrl-sci↗

arXiv2022

Observing the nodal-line conversion determined by the relative homotopy

Directly identifying the non-Abelian nodal-line semimetals (NASM) is quite challenging because nodal-line semimetals typically do not possess topologically protected boundary modes. Here, by reconstructing the correspondence between the bulk states of Hermitian systems and circuit voltage modes through gauge scale potential, the temporal topolectrical circuits (TTC) for evidencing NASM are proposed. Following the logical progress of discovering NASM, we start by demonstrating the relative homotopy group of two-band models using TTC, which can faithfully determine the conversion rules between the nodes in and out of the non-local-symmetry invariant subspace. Next, we show that those rules dramatically change with the consideration of the additional band, historically leading to the arising of the NASM. Also, we demonstrate the unique non-Abelian constrained nodal configuration -- earring nodal lines. Our results established NASM for further investigating topological line degeneracies, and proposed TTC will be a versatile platform for exploring nodal-line semimetals.

cond-mat.mes-hall↗