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Qing Zhang

Publications and source records attributed to Qing Zhang.

At least 145 records · Page 8Linked to original sources
arXiv2021

MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction

Human motion prediction is a challenging task due to the stochasticity and aperiodicity of future poses. Recently, graph convolutional network has been proven to be very effective to learn dynamic relations among pose joints, which is helpful for pose prediction. On the other hand, one can abstract a human pose recursively to obtain a set of poses at multiple scales. With the increase of the abstraction level, the motion of the pose becomes more stable, which benefits pose prediction too. In this paper, we propose a novel Multi-Scale Residual Graph Convolution Network (MSR-GCN) for human pose prediction task in the manner of end-to-end. The GCNs are used to extract features from fine to coarse scale and then from coarse to fine scale. The extracted features at each scale are then combined and decoded to obtain the residuals between the input and target poses. Intermediate supervisions are imposed on all the predicted poses, which enforces the network to learn more representative features. Our proposed approach is evaluated on two standard benchmark datasets, i.e., the Human3.6M dataset and the CMU Mocap dataset. Experimental results demonstrate that our method outperforms the state-of-the-art approaches. Code and pre-trained models are available at https://github.com/Droliven/MSRGCN.

cs.CV↗
arXiv2021

A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction

In this paper, we propose $\text{HF}^2$-VAD, a Hybrid framework that integrates Flow reconstruction and Frame prediction seamlessly to handle Video Anomaly Detection. Firstly, we design the network of ML-MemAE-SC (Multi-Level Memory modules in an Autoencoder with Skip Connections) to memorize normal patterns for optical flow reconstruction so that abnormal events can be sensitively identified with larger flow reconstruction errors. More importantly, conditioned on the reconstructed flows, we then employ a Conditional Variational Autoencoder (CVAE), which captures the high correlation between video frame and optical flow, to predict the next frame given several previous frames. By CVAE, the quality of flow reconstruction essentially influences that of frame prediction. Therefore, poorly reconstructed optical flows of abnormal events further deteriorate the quality of the final predicted future frame, making the anomalies more detectable. Experimental results demonstrate the effectiveness of the proposed method. Code is available at \href{https://github.com/LiUzHiAn/hf2vad}{https://github.com/LiUzHiAn/hf2vad}.

cs.CV↗
arXiv2021

From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data

Single image dehazing is a challenging task, for which the domain shift between synthetic training data and real-world testing images usually leads to degradation of existing methods. To address this issue, we propose a novel image dehazing framework collaborating with unlabeled real data. First, we develop a disentangled image dehazing network (DID-Net), which disentangles the feature representations into three component maps, i.e. the latent haze-free image, the transmission map, and the global atmospheric light estimate, respecting the physical model of a haze process. Our DID-Net predicts the three component maps by progressively integrating features across scales, and refines each map by passing an independent refinement network. Then a disentangled-consistency mean-teacher network (DMT-Net) is employed to collaborate unlabeled real data for boosting single image dehazing. Specifically, we encourage the coarse predictions and refinements of each disentangled component to be consistent between the student and teacher networks by using a consistency loss on unlabeled real data. We make comparison with 13 state-of-the-art dehazing methods on a new collected dataset (Haze4K) and two widely-used dehazing datasets (i.e., SOTS and HazeRD), as well as on real-world hazy images. Experimental results demonstrate that our method has obvious quantitative and qualitative improvements over the existing methods.

cs.CV↗
arXiv2021

NTIRE 2021 Challenge on Perceptual Image Quality Assessment

This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021. As a new type of image processing technology, perceptual image processing algorithms based on Generative Adversarial Networks (GAN) have produced images with more realistic textures. These output images have completely different characteristics from traditional distortions, thus pose a new challenge for IQA methods to evaluate their visual quality. In comparison with previous IQA challenges, the training and testing datasets in this challenge include the outputs of perceptual image processing algorithms and the corresponding subjective scores. Thus they can be used to develop and evaluate IQA methods on GAN-based distortions. The challenge has 270 registered participants in total. In the final testing stage, 13 participating teams submitted their models and fact sheets. Almost all of them have achieved much better results than existing IQA methods, while the winning method can demonstrate state-of-the-art performance.

eess.IV↗
arXiv2021

Modular categories with transitive Galois actions

In this paper, we study modular categories whose Galois group actions on their simple objects are transitive. We show that such modular categories admit unique factorization into prime transitive factors. The representations of $SL_2(\mathbb{Z})$ associated with transitive modular categories are proven to be minimal and irreducible. Together with the Verlinde formula, we characterize prime transitive modular categories as the Galois conjugates of the adjoint subcategory of the quantum group modular category $\mathcal{C}(\mathfrak{sl}_2,p-2)$ for some prime $p > 3$. As a consequence, we completely classify transitive modular categories. Transitivity of super-modular categories can be similarly defined. A unique factorization of any transitive super-modular category into s-simple transitive factors is obtained, and the split transitive super-modular categories are completely classified.

math.QA↗
arXiv2021

Atomic-scale imaging of CH3NH3PbI3 structure and its decomposition pathway

Understanding the atomic structure and structural instability of organic-inorganic hybrid perovskites is the key to appreciate their remarkable photoelectric properties and failure mechanism. Here, using low-dose imaging technique by direct-detection electron-counting camera in transmission electron microscope, we investigate the atomic structure and decomposition pathway of CH3NH3PbI3 (MAPbI3) at the atomic scale. We successfully image the atomic structure of perovskite in real space under ultra-low electron dose condition, and observe a two-step decomposition process, i.e. initial loss of MA followed by the collapse of perovskite structure into 6H-PbI2 with their critical threshold dose also determined. Interestingly, an intermediate phase (MA0.5PbI3) with locally ordered vacancies can robustly exist before perovskite collapses, enlightening strategies for prevention and recovery of perovskite structure during degradation. Associated with structure evolution, the bandgap gradually increases from ~1.6 eV to ~2.1 eV, and it is found that both C-N and N-H bonds can be destroyed under irradiation, releasing NH3 and leaving hydrocarbons. These findings enhance our understanding of the photoelectric properties and failure mechanism of MAPbI3, providing potential strategy into material optimization.

physics.app-ph↗
arXiv2021

Single-shot quantitative polarization imaging of complex birefringent structure dynamics

Polarization light microscopes are powerful tools for probing molecular order and orientation in birefringent materials. While a multitude of polarization light microscopy techniques are often used to access steady-state properties of birefringent samples, quantitative measurements of the molecular orientation dynamics on the millisecond time scale have remained a challenge. We propose polarized shearing interference microscopy (PSIM), a single-shot quantitative polarization imaging method, for extracting the retardance and orientation angle of the laser beam transmitting through optically anisotropic specimens with complex structures. The measurement accuracy and imaging performances of PSIM are validated by imaging a rotating wave plate and a bovine tendon specimen. We demonstrate that PSIM can quantify the dynamics of a flowing lyotropic chromonic liquid crystal in a microfluidic channel at an imaging speed of 506 frames per second (only limited by the camera frame rate), with a field-of-view of up to $350\times350 μm^2$ and a diffraction-limit spatial resolution of $\sim 2μm$. We envision that PSIM will find a broad range of applications in quantitative material characterization under dynamical conditions.

physics.optics↗
arXiv2021

Robust Weyl Semimetal and Pressure-induced Superconductivity in Quasi-One-Dimensional (NbSe4)2I

A search for the single material system that simultaneously exhibits topological phase and intrinsic superconductivity has been largely limited, although such a system is far more favorable especially for the quantum device applications. Except artificially engineered topological superconductivity in heterostructure systems, another alternative is to have superconductivity arising from the topological materials by pressure or other clean technology. Here, based on first-principles calculations, we first show that quasi-one-dimensional compound (NbSe4)2I represents a rare example of a chiral Weyl semimetal in which the set of symmetry-related Weyl points (WPs) exhibit the same chiral charge at a certain energy. The net chiral charge (NCC) of the below Fermi level EF (or a certain energy) can be tuned by pressure. In addition, a partial disorder induced by pressure accompanied with superconductivity emerges. Although amorphization of the iodine sub-lattice under high pressure, the one-dimensional NbSe4 chains in (NbSe4)2I remain intact and provide a superconducting channel in one dimension. Our combined theoretical and experimental research provide critical insight into a new phase of the one-dimensional system, in which distinctive phase transitions and correlated topological states emerge upon compression.

cond-mat.supr-con↗
arXiv2021

Hybridized hyperbolic surface phonon polaritons at α-MoO3 and polar dielectric interfaces

Surface phonon polaritons (SPhPs) in polar dielectrics offer new opportunities for infrared nanophotonics due to sub-diffraction confinement with low optical losses. Though the polaritonic field confinement can be significantly improved by modifying the dielectric environment, it is challenging to break the fundamental limits in photon confinement and propagation behavior of SPhP modes. In particular, as SPhPs inherently propagate isotropically in these bulk polar dielectrics, how to collectively realize ultra-large field confinement, in-plane hyperbolicity and unidirectional propagation remains elusive. Here, we report an approach to solve the aforementioned issues of bulk polar dielectric's SPhPs at one go by constructing a heterostructural interface between biaxial van der Waals material (e.g., MoO3) and bulk polar dielectric (e.g., SiC, AlN, and GaN). Due to anisotropy-oriented mode couplings at the interface, the hybridized SPhPs with a large confinement factor (>100) show in-plane hyperbolicity that has been switched to the orthogonal direction as compared to that in natural MoO3. More interestingly, this proof of concept allows steerable, angle-dependent and unidirectional polariton excitation by suspending MoO3 on patterned SiC air cavities. Our finding exemplifies a generalizable framework to manipulate the flow of nano-light and engineer unusual polaritonic responses in many other hybrid systems consisting of van der Waals materials and bulk polar dielectrics.

physics.optics↗
arXiv2021

Toward the endoscopic classification of unipotent representations of $p$-adic $G_2$

We begin this paper by reviewing the Langlands correspondence for unipotent representations of the exceptional group of type $G_2$ over a $p$-adic field $F$ and present it in an explicit form. Then we compute all ABV-packets, as defined in [CFM+21] following ideas from Vogan's 1993 paper The local Langlands Conjecture, and prove that these packets satisfy properties derived from the expectation that they are generalized A-packets. We attach distributions to ABV-packets for $G_2$ and its endoscopic groups and study a geometric endoscopic transfer of these distributions. This paper builds on earlier work by the same authors.

math.RT↗
arXiv2021

Growth morphology and symmetry selection of interfacial instabilities in anisotropic environments

The displacement of a fluid by another less viscous one in a quasi-two dimensional geometry typically leads to complex fingering patterns. In an isotropic system, dense-branching growth arises, which is characterized by repeated tip-splitting of evolving fingers. When anisotropy is present in the interfacial dynamics, the growth morphology changes to dendritic growth characterized by regular structures. We introduce anisotropy by engraving a six-fold symmetric lattice of channels on a Hele-Shaw cell. We show that the morphology transition in miscible fluids depends not only on the previously reported degree of anisotropy set by the lattice topography, but also on the viscosity ratio between the two fluids. Remarkably, the viscosity ratio and the degree of anisotropy also govern the global features of the dendritic patterns, inducing a systematic change from six-fold towards twelve-fold symmetric dendrites. Varying either control parameter provides a new method to tune the symmetry of complex patterns, which may also have relevance for analogous phenomena of gradient-driven interfacial dynamics, such as directional solidification or electrodeposition.

physics.flu-dyn↗
arXiv2020

Automatic sampling and training method for wood-leaf classification based on tree terrestrial point cloud

Terrestrial laser scanning technology provides an efficient and accuracy solution for acquiring three-dimensional information of plants. The leaf-wood classification of plant point cloud data is a fundamental step for some forestry and biological research. An automatic sampling and training method for classification was proposed based on tree point cloud data. The plane fitting method was used for selecting leaf sample points and wood sample points automatically, then two local features were calculated for training and classification by using support vector machine (SVM) algorithm. The point cloud data of ten trees were tested by using the proposed method and a manual selection method. The average correct classification rate and kappa coefficient are 0.9305 and 0.7904, respectively. The results show that the proposed method had better efficiency and accuracy comparing to the manual selection method.

cs.CV↗
arXiv2020

PanelPRO: A R package for multi-syndrome, multi-gene risk modeling for individuals with a family history of cancer

Identifying individuals who are at high risk of cancer due to inherited germline mutations is critical for effective implementation of personalized prevention strategies. Most existing models to identify these individuals focus on specific syndromes by including family and personal history for a small number of cancers. Recent evidence from multi-gene panel testing has shown that many syndromes once thought to be distinct are overlapping, motivating the development of models that incorporate family history information on several cancers and predict mutations for more comprehensive panels of genes. Once such class of models are Mendelian risk prediction models, which use family history information and Mendelian laws of inheritance to estimate the probability of carrying genetic mutations, as well as future risk of developing associated cancers. To flexibly model the complexity of many cancer-mutation associations, we present a new software tool called PanelPRO, a R package that extends the previously developed BayesMendel R package to user-selected lists of susceptibility genes and associated cancers. The model identifies individuals at an increased risk of carrying cancer susceptibility gene mutations and predicts future risk of developing hereditary cancers associated with those genes. Additional functionalities adjust for prophylactic interventions, known genetic testing results, and risk modifiers such as race and ancestry. The package comes with a customizable database with default parameter values estimated from published studies. The PanelPRO package is open-source and provides a fast and flexible back-end for multi-gene, multi-cancer risk modeling with pedigree data. The software enables the identification of high-risk individuals, which will have an impact on personalized prevention strategies for cancer and individualized decision making about genetic testing.

stat.AP↗
arXiv2020

The global geometrical property of jet events in high-energy nuclear collisions

We present the first theoretical study of medium modifications of the global geometrical pattern, i.e., transverse sphericity ($S_{\perp}$) distribution of jet events with parton energy loss in relativistic heavy-ion collisions. In our investigation, POWHEG+PYTHIA is employed to make an accurate description of transverse sphericity in the p+p baseline, which combines the next-to-leading order (NLO) pQCD calculations with the matched parton shower (PS). The Linear Boltzmann Transport (LBT) model of the parton energy loss is implemented to simulate the in-medium evolution of jets. We calculate the event normalized transverse sphericity distribution in central Pb+Pb collisions at the LHC, and give its medium modifications. An enhancement of transverse sphericity distribution at small $S_{\perp}$ region but a suppression at large $S_{\perp}$ region are observed in A+A collisions as compared to their p+p references, which indicates that in overall the geometry of jet events in Pb+Pb becomes more pencil-like. We demonstrate that for events with 2 jets in the final-state of heavy-ion collisions, the jet quenching makes the geometry more sphere-like with medium-induced gluon radiation. However, for events with $\ge 3$~jets, parton energy loss in the QCD medium leads to the events more pencil-like due to jet number reduction, where less energetic jets may lose their energies and then fall off the jet selection kinematic cut. These two effects offset each other and in the end result in more jetty events in heavy-ion collisions relative to that in p+p.

hep-ph↗
arXiv2020

Zone-folded longnitude acoustic phonons driving self-trapped state emission in colloidal CdSe nanoplate superlattice

Colloidal cadmium chalcogenide nanoplates are two-dimensional semiconductors that have shown great application prospect for light-emitting technologies. Self-trapped state (STS), a special localized state originated from strong electron-phonon coupling (EPC), has great potential in one-step white light luminance owing to its broadband emission linewidth. However, achieving STS in cadmium chalcogenide nanocrystals is extremely challenging due to their intrinic weak EPC nature. By building hybrid superlattice (SL) structures via self-assembly of colloidal CdSe nanoplates (NPLs), we demonstrated an emergence of zone-folded longnitude acoustic phonons (ZFLAP) differ from monodispersed NPLs, and observed a broadband STS emission in spectra range of 450-600 nm. Through femtosecond transient absorption and impulsive vibrational spectroscopy, we revealed that STS is generated in time scale of ~500 fs and is driven by strong coupling of excitons and ZFLAPs with Huang-Rhys parameter as large as ~22.7. Our findings provide a new avenue for generating and manipulating STS emission by artificially designing and building hybrid periodic structures superior to single material optimization.

cond-mat.mtrl-sci↗
arXiv2020

Deep Filtering

This paper develops a deep learning method for linear and nonlinear filtering. The idea is to start with a nominal dynamic model and generate Monte Carlo sample paths. Then these samples are used to train a deep neutral network. A least square error is used as a loss function for network training. Then the resulting weights are applied to Monte Carlo sampl\ es from an actual dynamic model. The deep filter obtained in such a way compares favorably to the traditional Kalman filter in linear cases and the extended Kalman filter in nonlinear cases. Moreover, a switching model with jumps is studied to show the adaptiveness and power of our deep filtering method. A main advantage of deep filtering is its robustness when the nominal model and actual model differ. Another advantage of deep filtering is that real data can be used directly to train the deep neutral network. Therefore, one does not need to calibrate the model.

math.OC↗
arXiv2020

Enhancing Underexposed Photos using Perceptually Bidirectional Similarity

Although remarkable progress has been made, existing methods for enhancing underexposed photos tend to produce visually unpleasing results due to the existence of visual artifacts (e.g., color distortion, loss of details and uneven exposure). We observed that this is because they fail to ensure the perceptual consistency of visual information between the source underexposed image and its enhanced output. To obtain high-quality results free of these artifacts, we present a novel underexposed photo enhancement approach that is able to maintain the perceptual consistency. We achieve this by proposing an effective criterion, referred to as perceptually bidirectional similarity, which explicitly describes how to ensure the perceptual consistency. Particularly, we adopt the Retinex theory and cast the enhancement problem as a constrained illumination estimation optimization, where we formulate perceptually bidirectional similarity as constraints on illumination and solve for the illumination which can recover the desired artifact-free enhancement results. In addition, we describe a video enhancement framework that adopts the presented illumination estimation for handling underexposed videos. To this end, a probabilistic approach is introduced to propagate illuminations of sampled keyframes to the entire video by tackling a Bayesian Maximum A Posteriori problem. Extensive experiments demonstrate the superiority of our method over the state-of-the-art methods.

cs.CV↗