arXiv subjects

Qiang Li

Publications and source records attributed to Qiang Li.

At least 361 records · Page 20Linked to original sources

arXiv2022

Instability of the Electroweak Vacuum in Starobinsky Inflation

We study the stability of the electroweak vacuum during and after the Starobinsky inflation, assuming the existence of the non-minimal Higgs coupling to the Ricci scalar. In the Starobinsky inflation, there exists $R^2$ term (with $R$ being the Ricci scalar), which modifies the evolution equation of the Higgs field. We consider the case that the non-minimal coupling is sizable so that the quantum fluctuation of the Higgs field is suppressed and that the Higgs amplitude is settled near the origin during the inflation. In such a case, the Higgs amplitude may be amplified in the preheating epoch after inflation because of the parametric resonance due to the non-minimal coupling. We perform a detailed analysis of the evolution of the Higgs field in the preheating epoch by a numerical lattice simulation and derive an upper bound on the non-minimal coupling constant $ξ$ in order to realize the electroweak vacuum in the present universe. We find that the upper bound on $ξ$ in the Starobinsky inflation model is more stringent than that in conventional inflation models without the $R^2$ term.

hep-ph↗

arXiv2022

Twenty years of quantum contextuality at USTC

Quantum contextuality is one of the most perplexing and peculiar features of quantum mechanics. Concisely, it refers to the observation that the result of a single measurement in quantum mechanics depends on the set of joint measurements actually performed. The study of contextuality has a long history at University of Science and Technology of China (USTC). Here we review the theoretical and experimental advances in this direction achieved at USTC over the last 20 years. We start by introducing the renowned simplest proof of state-independent contextuality. We then present several experimental tests of quantum versus noncontextual theories with photons. Finally, we discuss the investigation on the role of contextuality in general quantum information science and its application in quantum computation.

quant-ph↗

arXiv2022

Topological Antiferromagnetic Semimetal for Spintronics: A Case Study of a Layered Square Net System EuZnSb$_2$

We use the first principles and effective Hamiltonian methods to study the electronic structure and magnetic properties of a recently synthesized layered antiferromagnetic square net topological semimetal EuZnSb$_2$ [1]. The main message of the paper is that effects of small changes in the band structure produced by the magnetic ordering and changes in the orientation of the \Neel vector are amplified in such transport properties as the spin Hall conductivity. We predict that the effects of the broken symmetry introduced by the ordering of the \Neel vector, being very weak in the bulk, are pronounced in the surface electronic dispersion, suggesting that surface probes may be more suited to measure them. The coexistence of the magnetism with many other competing phases make this material interesting and possibly useful for quantum spintronics applications.

cond-mat.mtrl-sci↗

arXiv2022

Testing for pair-density-wave order in La$_{1.875}$Ba$_{0.125}$CuO$_4$

Charge order is commonly believed to compete with superconducting order. An intertwined form of superconducting wave function, known as pair-density-wave (PDW) order, has been proposed; however, there has been no direct evidence, theoretical or experimental, that it forms the ground state of any cuprate superconductor. As a test case, we consider \lbco\ with $x=1/8$, where charge and spin stripe orders within the CuO$_2$ planes compete with three-dimensional superconducting order. We report measurements of the superconducting critical current perpendicular to the planes in the presence of an in-plane magnetic field. The variation of the critical current with orientation of the field is inconsistent with a theoretical prediction specific to the PDW model. It appears, instead, that the orientation dependence of the critical-current density might be determined by a minority phase of $d$-wave superconductivity that is present as a consequence of doped-charge inhomogeneity.

cond-mat.supr-con↗

arXiv2022

Strong decays of excited $ 2^+ $ charmed mesons

The new charmed resonance $ D_2^*(3000) $ was observed by the LHCb Collaboration in $ B $ decays. In this paper, by assigning it as four possible excited states of the $ 2^+ $ family, we use the instantaneous Bethe-Salpeter method to calculate their Okubo-Zweig-Iizuka-allowed two-body strong decays. The results of $ 1^3F_2 $ and $ 3^3P_2 $ states deviate from the present experimental observation while $ 2^3P_2 $ and $ 2^3F_2 $ are in the error range. Our study also reveals that the widths of these states depend strongly on the masses. Due to the large uncertainties and different mass input, variable models get inconsistent conclusions at the moment. The analysis in this work can provide essential assistance for future measurements and investigations.

hep-ph↗

arXiv2022

Accurate Scoliosis Vertebral Landmark Localization on X-ray Images via Shape-constrained Multi-stage Cascaded CNNs

Vertebral landmark localization is a crucial step for variant spine-related clinical applications, which requires detecting the corner points of 17 vertebrae. However, the neighbor landmarks often disturb each other for the homogeneous appearance of vertebrae, which makes vertebral landmark localization extremely difficult. In this paper, we propose multi-stage cascaded convolutional neural networks (CNNs) to split the single task into two sequential steps, i.e., center point localization to roughly locate 17 center points of vertebrae, and corner point localization to find 4 corner points for each vertebra without distracted by others. Landmarks in each step are located gradually from a set of initialized points by regressing offsets via cascaded CNNs. Principal Component Analysis (PCA) is employed to preserve a shape constraint in offset regression to resist the mutual attraction of vertebrae. We evaluate our method on the AASCE dataset that consists of 609 tight spinal anterior-posterior X-ray images and each image contains 17 vertebrae composed of the thoracic and lumbar spine for spinal shape characterization. Experimental results demonstrate our superior performance of vertebral landmark localization over other state-of-the-arts with the relative error decreasing from 3.2e-3 to 7.2e-4.

cs.CV↗

arXiv2022

Muon Collider Physics Summary

The perspective of designing muon colliders with high energy and luminosity, which is being investigated by the International Muon Collider Collaboration, has triggered a growing interest in their physics reach. We present a concise summary of the muon colliders potential to explore new physics, leveraging on the unique possibility of combining high available energy with very precise measurements.

hep-ph↗

arXiv2022

The physics case of a 3 TeV muon collider stage

In the path towards a muon collider with center of mass energy of 10 TeV or more, a stage at 3 TeV emerges as an appealing option. Reviewing the physics potential of such muon collider is the main purpose of this document. In order to outline the progression of the physics performances across the stages, a few sensitivity projections for higher energy are also presented. There are many opportunities for probing new physics at a 3 TeV muon collider. Some of them are in common with the extensively documented physics case of the CLIC 3 TeV energy stage, and include measuring the Higgs trilinear coupling and testing the possible composite nature of the Higgs boson and of the top quark at the 20 TeV scale. Other opportunities are unique of a 3 TeV muon collider, and stem from the fact that muons are collided rather than electrons. This is exemplified by studying the potential to explore the microscopic origin of the current $g$-2 and $B$-physics anomalies, which are both related with muons.

hep-ph↗

arXiv2022

A Comparative Study of Feature Expansion Unit for 3D Point Cloud Upsampling

Recently, deep learning methods have shown great success in 3D point cloud upsampling. Among these methods, many feature expansion units were proposed to complete point expansion at the end. In this paper, we compare various feature expansion units by both theoretical analysis and quantitative experiments. We show that most of the existing feature expansion units process each point feature independently, while ignoring the feature interaction among different points. Further, inspired by upsampling module of image super-resolution and recent success of dynamic graph CNN on point clouds, we propose a novel feature expansion units named ProEdgeShuffle. Experiments show that our proposed method can achieve considerable improvement over previous feature expansion units.

cs.CV↗

arXiv2022

Microwave excitations and hysteretic magnetization dynamics of stripe domain films

FeNi films with the stripe domain (SD) pattern are prepared by electrodeposition and sputtering methods. The magnetic domain, static magnetic parameters, and quality factor, as well as dynamic properties of the two films, are respectively performed. The results show the magnetizations of the film were dependent on the direction of SD, and the rotation of the SD is lagging behind the magnetization reversal. The microwave properties of the SD emerge dynamic hysteresis before the saturation magnetic field. These microwave properties are selectively excited with acoustic mode, optical mode, and spin-wave mode. The frequency and intensity of different resonance modes of stripe domain are determined by the local magnetization. The magnetization variations and the rotation of SD of different modes are further illuminated by the micromagnetic simulation. The magnetic anisotropy and the resonance intensity of permeability of different modes were finally described by the modified resonance equations.

cond-mat.mtrl-sci↗

arXiv2022

The mass spectrum and wave functions of the $B_c$ system

The spectrum and relativistic wave functions of $B_c$ system are investigated via solving the complete Salpeter equation. Emphases are put on the study of the partial waves of each $J^P$ state. Our study shows that there are three categories of $J^P$ states. The first category contains $0^-$ and $0^+$ states, which are ${}^1S_0$ dominant state with a small amount of $P$ wave and ${}^3P_0$ dominant state with a small amount of $S$ wave, respectively. The second category includes the natural parity states, such as $1^-$, $2^+$, $3^-$, etc. Taking the $1^-$ state as an example, we study it in two cases. One is the ${}^3S_1$ dominant state with a small amount of $P$ and $D$ waves, and the other is the ${}^3D_1$ dominant state but contains a large amount of $S$ and $P$ wave components. The third category includes the unnatural parity states, such as $1^+$, $2^-$, $3^+$, etc. For the $1^+$ spectrum, the states are grouped into pairs with different radial quantum numbers. Each pair contains two ${}^1P_1-{}^3P_1$ mixing states, and the corresponding mixing angles are calculated by using the relativistic wave functions.

hep-ph↗

arXiv2022

Magnetic freeze-out and anomalous Hall effect in ZrTe$_5$

The ultra-quantum limit is achieved when a magnetic field confines an electron gas in its lowest spin-polarised Landau level. Here we show that in this limit, electron doped ZrTe$_5$ shows a metal-insulator transition followed by a sign change of the Hall and Seebeck effects at low temperature. We attribute this transition to a magnetic freeze-out of charge carriers on the ionised impurities. The reduction of the charge carrier density gives way to an anomalous Hall response of the spin-polarised electrons. This behaviour, at odds with the usual magnetic freeze-out scenario, occurs in this Dirac metal because of its tiny Fermi energy, extremely narrow band gap and a large $g$-factor. We discuss the different possible sources (intrinsic or extrinsic) for this anomalous Hall contribution.

cond-mat.mtrl-sci↗

arXiv2022

Joint Progressive and Coarse-to-fine Registration of Brain MRI via Deformation Field Integration and Non-Rigid Feature Fusion

Registration of brain MRI images requires to solve a deformation field, which is extremely difficult in aligning intricate brain tissues, e.g., subcortical nuclei, etc. Existing efforts resort to decomposing the target deformation field into intermediate sub-fields with either tiny motions, i.e., progressive registration stage by stage, or lower resolutions, i.e., coarse-to-fine estimation of the full-size deformation field. In this paper, we argue that those efforts are not mutually exclusive, and propose a unified framework for robust brain MRI registration in both progressive and coarse-to-fine manners simultaneously. Specifically, building on a dual-encoder U-Net, the fixed-moving MRI pair is encoded and decoded into multi-scale deformation sub-fields from coarse to fine. Each decoding block contains two proposed novel modules: i) in Deformation Field Integration (DFI), a single integrated sub-field is calculated, warping by which is equivalent to warping progressively by sub-fields from all previous decoding blocks, and ii) in Non-rigid Feature Fusion (NFF), features of the fixed-moving pair are aligned by DFI-integrated sub-field, and then fused to predict a finer sub-field. Leveraging both DFI and NFF, the target deformation field is factorized into multi-scale sub-fields, where the coarser fields alleviate the estimate of a finer one and the finer field learns to make up those misalignments insolvable by previous coarser ones. The extensive and comprehensive experimental results on both private and public datasets demonstrate a superior registration performance of brain MRI images over progressive registration only and coarse-to-fine estimation only, with an increase by at most 8% in the average Dice.

eess.IV↗

arXiv2022

Dual-Stage Approach Toward Hyperspectral Image Super-Resolution

Hyperspectral image produces high spectral resolution at the sacrifice of spatial resolution. Without reducing the spectral resolution, improving the resolution in the spatial domain is a very challenging problem. Motivated by the discovery that hyperspectral image exhibits high similarity between adjacent bands in a large spectral range, in this paper, we explore a new structure for hyperspectral image super-resolution (DualSR), leading to a dual-stage design, i.e., coarse stage and fine stage. In coarse stage, five bands with high similarity in a certain spectral range are divided into three groups, and the current band is guided to study the potential knowledge. Under the action of alternative spectral fusion mechanism, the coarse SR image is super-resolved in band-by-band. In order to build model from a global perspective, an enhanced back-projection method via spectral angle constraint is developed in fine stage to learn the content of spatial-spectral consistency, dramatically improving the performance gain. Extensive experiments demonstrate the effectiveness of the proposed coarse stage and fine stage. Besides, our network produces state-of-the-art results against existing works in terms of spatial reconstruction and spectral fidelity.

eess.IV↗

arXiv2022

Unimodal-Concentrated Loss: Fully Adaptive Label Distribution Learning for Ordinal Regression

Learning from a label distribution has achieved promising results on ordinal regression tasks such as facial age and head pose estimation wherein, the concept of adaptive label distribution learning (ALDL) has drawn lots of attention recently for its superiority in theory. However, compared with the methods assuming fixed form label distribution, ALDL methods have not achieved better performance. We argue that existing ALDL algorithms do not fully exploit the intrinsic properties of ordinal regression. In this paper, we emphatically summarize that learning an adaptive label distribution on ordinal regression tasks should follow three principles. First, the probability corresponding to the ground-truth should be the highest in label distribution. Second, the probabilities of neighboring labels should decrease with the increase of distance away from the ground-truth, i.e., the distribution is unimodal. Third, the label distribution should vary with samples changing, and even be distinct for different instances with the same label, due to the different levels of difficulty and ambiguity. Under the premise of these principles, we propose a novel loss function for fully adaptive label distribution learning, namely unimodal-concentrated loss. Specifically, the unimodal loss derived from the learning to rank strategy constrains the distribution to be unimodal. Furthermore, the estimation error and the variance of the predicted distribution for a specific sample are integrated into the proposed concentrated loss to make the predicted distribution maximize at the ground-truth and vary according to the predicting uncertainty. Extensive experimental results on typical ordinal regression tasks including age and head pose estimation, show the superiority of our proposed unimodal-concentrated loss compared with existing loss functions.

cs.CV↗

arXiv2022

Weakly Supervised Regional and Temporal Learning for Facial Action Unit Recognition

Automatic facial action unit (AU) recognition is a challenging task due to the scarcity of manual annotations. To alleviate this problem, a large amount of efforts has been dedicated to exploiting various weakly supervised methods which leverage numerous unlabeled data. However, many aspects with regard to some unique properties of AUs, such as the regional and relational characteristics, are not sufficiently explored in previous works. Motivated by this, we take the AU properties into consideration and propose two auxiliary AU related tasks to bridge the gap between limited annotations and the model performance in a self-supervised manner via the unlabeled data. Specifically, to enhance the discrimination of regional features with AU relation embedding, we design a task of RoI inpainting to recover the randomly cropped AU patches. Meanwhile, a single image based optical flow estimation task is proposed to leverage the dynamic change of facial muscles and encode the motion information into the global feature representation. Based on these two self-supervised auxiliary tasks, local features, mutual relation and motion cues of AUs are better captured in the backbone network. Furthermore, by incorporating semi-supervised learning, we propose an end-to-end trainable framework named weakly supervised regional and temporal learning (WSRTL) for AU recognition. Extensive experiments on BP4D and DISFA demonstrate the superiority of our method and new state-of-the-art performances are achieved.

cs.CV↗

arXiv2022

ITTR: Unpaired Image-to-Image Translation with Transformers

Unpaired image-to-image translation is to translate an image from a source domain to a target domain without paired training data. By utilizing CNN in extracting local semantics, various techniques have been developed to improve the translation performance. However, CNN-based generators lack the ability to capture long-range dependency to well exploit global semantics. Recently, Vision Transformers have been widely investigated for recognition tasks. Though appealing, it is inappropriate to simply transfer a recognition-based vision transformer to image-to-image translation due to the generation difficulty and the computation limitation. In this paper, we propose an effective and efficient architecture for unpaired Image-to-Image Translation with Transformers (ITTR). It has two main designs: 1) hybrid perception block (HPB) for token mixing from different receptive fields to utilize global semantics; 2) dual pruned self-attention (DPSA) to sharply reduce the computational complexity. Our ITTR outperforms the state-of-the-arts for unpaired image-to-image translation on six benchmark datasets.

cs.CV↗

arXiv2022

Sub-40nm Nanogratings Self-Organized in PVP-based Polymer Composite Film by Photoexcitation and Two Sequent Splitting under Femtosecond Laser Irradiation

Laser-induced periodic surface structures (LIPSSs) on various materials have been extensively investigated because of their wide applications. The combination of different materials allows for greater freedom in tailoring their functions and achieving responses not possible in a homogeneous material. By utilizing a femtosecond (fs) laser to irradiate the Fe-doped Polyvinyl Pyrrolidone (PVP) composite film, highly regular ultrafine nanogratings (U-nanogratings) with a period as small as 35.0 ($\pm$ 2.0) nm can be self-organized on the surface with extremely high efficiency. The period of the U-nanogratings can be controlled by varying the scanning speed of the laser beam (deposited energy) and the thickness of the composite film. Based on the experimental, theoretical, and simulation results, we propose a two-step formation mechanism: composite film excitation and two sequent grating-splitting. The high photosensitivity and low glass transition temperature of the composite film facilitate the fabrication of the ultrafine nanostructures. The proposed design method for the composite material and fabrication process could not only provide a strategy for obtaining highly regular U-nanogratings, but also offer a platform to explore the interaction physics between ultra-short pulses and matter under extreme conditions.

physics.optics↗