arXiv subjects

Hui Deng

Publications and source records attributed to Hui Deng.

At least 55 records · Page 3Linked to original sources

arXiv2023

Dimensionality crossover to 2D vestigial nematicity from 3D zigzag antiferromagnetism in an XY-type honeycomb van der Waals magnet

Fluctuations and disorder effects are substantially enhanced in reduced dimensionalities. While they are mostly considered as the foe for long-range orders, fluctuations and disorders can also stimulate the emergence of novel phases of matter, for example, vestigial orders. Taking 2D magnetism as a platform, existing efforts have been focused on maintaining 2D long-range magnetic orders by suppressing fluctuations, whereas the other side, exploiting fluctuations for realizing new 2D magnetic phases, remains as an uncharted territory. Here, using a combination of NV spin relaxometry, optical spectroscopy, and Monte Carlo simulations, we report, in an XY-type honeycomb magnet NiPS3, the phase transition from the zigzag AFM order in 3D bulk to a new Z3 vestigial Potts-nematicity in 2D few layers. Spin fluctuations are shown to significantly enhance over the GHz-THz range as the layer number of NiPS3 reduces, using the NV spin relaxometry and the optical Raman quasi-elastic scattering. As a result, the Raman signatures of the zigzag AFM for bulk NiPS3, a zone-folded phonon at ~30cm-1 from the broken translational symmetry (PBTS) and a degeneracy lift of two phonons at ~180cm-1 for the broken 3-fold rotational symmetry (PBRS), evolve into the disappearance of PBTS and the survival of PBRS in few-layer NiPS3, with a critical thickness of ~10nm. The optical linear dichroism microscopy images all three nematic domain states in a single few-layer NiPS3 flake. The large-scale Monte Carlo simulations for bilayer NiPS3 model confirms the absence of long-range zigzag AFM order but the formation of the Z3 vestigial Potts-nematic phase, corroborating with the experimental finding. Our results demonstrate the positivity of strong fluctuations in creating new phases of matter after destroying more conventional ones, and offer an unprecedented pathway for developing novel 2D phases.

cond-mat.mtrl-sci↗

arXiv2023

"Extraordinary" Phase Transition Revealed in a van der Waals Antiferromagnet

While the surface-bulk correspondence has been ubiquitously shown in topological phases, the relationship between surface and bulk in Landau-like phases is much less explored. Theoretical investigations since 1970s for semi-infinite systems have predicted the possibility of the surface order emerging at a higher temperature than the bulk, clearly illustrating a counterintuitive situation and greatly enriching phase transitions. But experimental realizations of this prediction remain missing. Here, we demonstrate the higher-temperature surface and lower-temperature bulk phase transitions in CrSBr, a van der Waals (vdW) layered antiferromagnet. We leverage the surface sensitivity of electric dipole second harmonic generation (SHG) to resolve surface magnetism, the bulk nature of electric quadrupole SHG to probe bulk spin correlations, and their interference to capture the two magnetic domain states. Our density functional theory calculations show the suppression of ferromagnetic-antiferromagnetic competition at the surface responsible for this enhanced surface magnetism. Our results not only show unexpected, richer phase transitions in vdW magnets, but also provide viable ways to enhance magnetism in their 2D form.

cond-mat.mtrl-sci↗

arXiv2023

Polariton-Induced Unconventional Superconductivity and Emergent SU(2) Symmetry in Moire flat bands

We propose a polariton-moiré coupled system to realize an exact solvable model of strongly correlated superconductors. The polariton condensate induces intervalley attraction between moiré electrons in quasi-flat topological bands, leading to emergent SU(2) symmetry and exactly solutions of the many-body ground state. This system expands the condensate-induced superconductivity to the strong-correlation regime, enabling superconductivity at elevated temperatures and allowing the study of non-Fermi liquid states with a solvable, predictive model. It is promising as a versatile platform for emulating unconventional superconductivity and other strongly correlated phenomena in complex, correlated materials.

cond-mat.str-el↗

arXiv2023

Unsupervised 3D Pose Estimation with Non-Rigid Structure-from-Motion Modeling

Most of the previous 3D human pose estimation work relied on the powerful memory capability of the network to obtain suitable 2D-3D mappings from the training data. Few works have studied the modeling of human posture deformation in motion. In this paper, we propose a new modeling method for human pose deformations and design an accompanying diffusion-based motion prior. Inspired by the field of non-rigid structure-from-motion, we divide the task of reconstructing 3D human skeletons in motion into the estimation of a 3D reference skeleton, and a frame-by-frame skeleton deformation. A mixed spatial-temporal NRSfMformer is used to simultaneously estimate the 3D reference skeleton and the skeleton deformation of each frame from 2D observations sequence, and then sum them to obtain the pose of each frame. Subsequently, a loss term based on the diffusion model is used to ensure that the pipeline learns the correct prior motion knowledge. Finally, we have evaluated our proposed method on mainstream datasets and obtained superior results outperforming the state-of-the-art.

cs.CV↗

arXiv2023

Vicinity Vision Transformer

Vision transformers have shown great success on numerous computer vision tasks. However, its central component, softmax attention, prohibits vision transformers from scaling up to high-resolution images, due to both the computational complexity and memory footprint being quadratic. Although linear attention was introduced in natural language processing (NLP) tasks to mitigate a similar issue, directly applying existing linear attention to vision transformers may not lead to satisfactory results. We investigate this problem and find that computer vision tasks focus more on local information compared with NLP tasks. Based on this observation, we present a Vicinity Attention that introduces a locality bias to vision transformers with linear complexity. Specifically, for each image patch, we adjust its attention weight based on its 2D Manhattan distance measured by its neighbouring patches. In this case, the neighbouring patches will receive stronger attention than far-away patches. Moreover, since our Vicinity Attention requires the token length to be much larger than the feature dimension to show its efficiency advantages, we further propose a new Vicinity Vision Transformer (VVT) structure to reduce the feature dimension without degenerating the accuracy. We perform extensive experiments on the CIFAR100, ImageNet1K, and ADE20K datasets to validate the effectiveness of our method. Our method has a slower growth rate of GFlops than previous transformer-based and convolution-based networks when the input resolution increases. In particular, our approach achieves state-of-the-art image classification accuracy with 50% fewer parameters than previous methods.

cs.CV↗

arXiv2023

Linearized Relative Positional Encoding

Relative positional encoding is widely used in vanilla and linear transformers to represent positional information. However, existing encoding methods of a vanilla transformer are not always directly applicable to a linear transformer, because the latter requires a decomposition of the query and key representations into separate kernel functions. Nevertheless, principles for designing encoding methods suitable for linear transformers remain understudied. In this work, we put together a variety of existing linear relative positional encoding approaches under a canonical form and further propose a family of linear relative positional encoding algorithms via unitary transformation. Our formulation leads to a principled framework that can be used to develop new relative positional encoding methods that preserve linear space-time complexity. Equipped with different models, the proposed linearized relative positional encoding (LRPE) family derives effective encoding for various applications. Experiments show that compared with existing methods, LRPE achieves state-of-the-art performance in language modeling, text classification, and image classification. Meanwhile, it emphasizes a general paradigm for designing broadly more relative positional encoding methods that are applicable to linear transformers. The code is available at https://github.com/OpenNLPLab/Lrpe.

cs.CL↗

arXiv2023

Logical Magic State Preparation with Fidelity Beyond the Distillation Threshold on a Superconducting Quantum Processor

Fault-tolerant quantum computing based on surface code has emerged as an attractive candidate for practical large-scale quantum computers to achieve robust noise resistance. To achieve universality, magic states preparation is a commonly approach for introducing non-Clifford gates. Here, we present a hardware-efficient and scalable protocol for arbitrary logical state preparation for the rotated surface code, and further experimentally implement it on the \textit{Zuchongzhi} 2.1 superconducting quantum processor. An average of \hhl{$0.8983 \pm 0.0002$} logical fidelity at different logical states with distance-three is achieved, \hhl{taking into account both state preparation and measurement errors.} In particular, \hhl{the magic states $|A^{π/4}\rangle_L$, $|H\rangle_L$, and $|T\rangle_L$ are prepared non-destructively with logical fidelities of $0.8771 \pm 0.0009 $, $0.9090 \pm 0.0009 $, and $0.8890 \pm 0.0010$, respectively, which are higher than the state distillation protocol threshold, 0.859 (for H-type magic state) and 0.827 (for T -type magic state).} Our work provides a viable and efficient avenue for generating high-fidelity raw logical magic states, which is essential for realizing non-Clifford logical gates in the surface code.

quant-ph↗

arXiv2023

Blind Search of The Solar Neighborhood Galactic Disk within 5kpc: 1,179 new Star clusters found in Gaia DR3

Studying open clusters (OCs) is essential for a comprehensive understanding of the structure and evolution of the Milky Way. Many previous studies have systematically searched for OCs near the solar system within 1.2 kpc or 20 degrees of galactic latitude. However, few studies searched for OCs at higher galactic latitudes and deeper distances. In this study, based on a hybrid unsupervised clustering algorithm (Friends-of-Friends and pyUPMASK) and a binary classification algorithm (Random Forest), we extended the search region (i.e., galactic latitude |b|>=20 degrees) and performed a fine-grained blind search of Galactic clusters in Gaia DR3. After cross-matching, the newly discovered cluster candidates are fitted using isochrone fitting to estimate the main physical parameters (age and metallicity) of these clusters. These cluster candidates were then checked using manual visual inspection. Their statistical properties were compared with previously exposed cluster catalogs as well. In the end, we found 1,179 new clusters with considerable confidence within 5kpc.

astro-ph.GA↗

arXiv2023

Experimental Simulation of Larger Quantum Circuits with Fewer Superconducting Qubits

Although near-term quantum computing devices are still limited by the quantity and quality of qubits in the so-called NISQ era, quantum computational advantage has been experimentally demonstrated. Moreover, hybrid architectures of quantum and classical computing have become the main paradigm for exhibiting NISQ applications, where low-depth quantum circuits are repeatedly applied. In order to further scale up the problem size solvable by the NISQ devices, it is also possible to reduce the number of physical qubits by "cutting" the quantum circuit into different pieces. In this work, we experimentally demonstrated a circuit-cutting method for simulating quantum circuits involving many logical qubits, using only a few physical superconducting qubits. By exploiting the symmetry of linear-cluster states, we can estimate the effectiveness of circuit-cutting for simulating up to 33-qubit linear-cluster states, using at most 4 physical qubits for each subcircuit. Specifically, for the 12-qubit linear-cluster state, we found that the experimental fidelity bound can reach as much as 0.734, which is about 19\% higher than a direct simulation {on the same} 12-qubit superconducting processor. Our results indicate that circuit-cutting represents a feasible approach of simulating quantum circuits using much fewer qubits, while achieving a much higher circuit fidelity.

quant-ph↗

arXiv2023

Quasi-periodic Variations of Coronal Mass Ejections with Different Angular Widths

Coronal mass ejections (CMEs) are energetic expulsions of organized magnetic features from the Sun. The study of CME quasi-periodicity helps establish a possible relationship between CMEs, solar flares, and geomagnetic disturbances. We used the angular width of CMEs as a criterion for classifying the CMEs in the study. Based on 25 years of observational data, we systematically analyzed the quasi-periodic variations corresponding to the CME occurrence rate of different angular widths in the northern and southern hemispheres, using frequency and time-frequency analysis methods. There are various periods for CMEs of different angular widths: 9 months, 1.7 years, and 3.3-4.3 years. Compared with previous studies based on the occurrence rate of CMEs, we obtained the same periods of 1.2(+-0.01) months, 3.1(+-0.04) months, ~6.1(+-0.4) months, 1.2(+-0.1) years, and 2.4(+-0.4) years. We also found additional periods of all CMEs that appear only in one hemisphere or during a specific solar cycle. For example, 7.1(+-0.2) months and 4.1(+-0.2) years in the northern hemisphere, 1(+-0.004) months, 5.9(+-0.2) months, 1(+-0.1) years, 1.4(+-0.1) years, and 2.4(+-0.4) years in the southern hemisphere, 6.1(+-0.4) months in solar cycle 23 (SC23) and 6.1(+-0.4) months, 1.2(+-0.1) years, and 3.7(+-0.2) years in solar cycle 24 (SC24). The analysis shows that quasi-periodic variations of the CMEs are a link among oscillations in coronal magnetic activity, solar flare eruptions, and interplanetary space.

astro-ph.SR↗

arXiv2022

Quantum Neuronal Sensing of Quantum Many-Body States on a 61-Qubit Programmable Superconducting Processor

Classifying many-body quantum states with distinct properties and phases of matter is one of the most fundamental tasks in quantum many-body physics. However, due to the exponential complexity that emerges from the enormous numbers of interacting particles, classifying large-scale quantum states has been extremely challenging for classical approaches. Here, we propose a new approach called quantum neuronal sensing. Utilizing a 61 qubit superconducting quantum processor, we show that our scheme can efficiently classify two different types of many-body phenomena: namely the ergodic and localized phases of matter. Our quantum neuronal sensing process allows us to extract the necessary information coming from the statistical characteristics of the eigenspectrum to distinguish these phases of matter by measuring only one qubit. Our work demonstrates the feasibility and scalability of quantum neuronal sensing for near-term quantum processors and opens new avenues for exploring quantum many-body phenomena in larger-scale systems.

quant-ph↗

arXiv2022

Linear Video Transformer with Feature Fixation

Vision Transformers have achieved impressive performance in video classification, while suffering from the quadratic complexity caused by the Softmax attention mechanism. Some studies alleviate the computational costs by reducing the number of tokens in attention calculation, but the complexity is still quadratic. Another promising way is to replace Softmax attention with linear attention, which owns linear complexity but presents a clear performance drop. We find that such a drop in linear attention results from the lack of attention concentration on critical features. Therefore, we propose a feature fixation module to reweight the feature importance of the query and key before computing linear attention. Specifically, we regard the query, key, and value as various latent representations of the input token, and learn the feature fixation ratio by aggregating Query-Key-Value information. This is beneficial for measuring the feature importance comprehensively. Furthermore, we enhance the feature fixation by neighborhood association, which leverages additional guidance from spatial and temporal neighbouring tokens. The proposed method significantly improves the linear attention baseline and achieves state-of-the-art performance among linear video Transformers on three popular video classification benchmarks. With fewer parameters and higher efficiency, our performance is even comparable to some Softmax-based quadratic Transformers.

cs.CV↗

arXiv2022

Grating-based microcavity with independent control of resonance energy and linewidth for non-Hermitian polariton system

Exciton-polaritons have become an emerging platform for implementing non-Hermitian physics. The implementation commonly requires control of both the real and imaginary parts of the eigenmodes of the system. We present an experimental method to achieve this purpose using microcavities with sub-wavelength gratings as reflectors. The reflectivity and reflection phase of the grating can be changed by its geometric parameters and they determine the energy and linewidth of the polariton modes. We demonstrate that this method allows a wide range of possible polariton energy and linewidth, suitable for implementing non-Hermitian polariton systems with coupled modes.

physics.optics↗

arXiv2022

A Study on Non-coplanar Baseline Effects for Mingantu Spectral Radioheliograph

As a dedicated solar radioheliograph, the MingantU SpEctral RadioHeliograph (MUSER) has a maximum baseline of more than 3000 meters and a frequency range of 400 MHz -- 15 GHz. According to the classical radio interferometry theory, the non-coplanar baseline effect (i.e., w-term effect) would be considered and calibrated for such a radio instrument. However, little previous literature made the qualitative or quantitative analyses on w-term effects of solar radioheliograph in-depth. This study proposes a complete quantitative analysis of w-term effects for the MUSER. After a brief introduction of the MUSER, we systematically investigate the baseline variations over a year and analyze the corresponding variations of w-term. We further studied the effects of the w-term in the imaging for the specified extended source, i.e., the Sun. We discussed the possible effects of the w-term, such as image distortion and so on. The simulated results show that the w-term is an essential and unavoidable issue for solar radio imaging with high spatial resolution.

astro-ph.IM↗

arXiv2022

WS-Snapshot: An effective algorithm for wide-field and large-scale imaging

The Square Kilometre Array (SKA) is the largest radio interferometer under construction in the world. The high accuracy, wide-field and large size imaging significantly challenge the construction of the Science Data Processor (SDP) of SKA. We propose a hybrid imaging method based on improved W-Stacking and snapshots. The w range is reduced by fitting the snapshot $uv$ plane, thus effectively enhancing the performance of the improved W-Stacking algorithm. We present a detailed implementation of WS-Snapshot. With full-scale SKA1-LOW simulations, we present the imaging performance and imaging quality results for different parameter cases. The results show that the WS-Snapshot method enables more efficient distributed processing and significantly reduces the computational time overhead within an acceptable accuracy range, which would be crucial for subsequent SKA science studies.

astro-ph.IM↗

arXiv2022

$Λ_b \to Λ_c$ Form Factors from QCD Light-Cone Sum Rules

In this work, we calculate the transition form factors of $Λ_b$ decaying into $Λ_c$ within the framework of light-cone sum rules with the distribution amplitudes (DAs) of $Λ_b$-baryon. In the hadronic representation of the correlation function, we have isolated both the $Λ_c$ and the $Λ_c^*$ states so that the $Λ_b \rightarrow Λ_c$ form factors can be obtained without ambiguity. We investigate the P-type and A-type current to interpolate the light baryons for a comparison since the interpolation current for the baryon state is not unique. We also employ three parametrization models for DAs of $Λ_b $ in the numerical calculation. We present the numerical predictions on the $Λ_b \rightarrow Λ_c$ form factors and the branching fractions, the averaged forward-backward asymmetry , the averaged final hadron polarization and the averaged lepton polarization of the $Λ_b \to Λ_c \ellμ$ decays, as well as the ratio of branching ratios $R_{Λ_c}$, and the predicted $R_{Λ_c}$ can be consistent with the LHCb data.

hep-ph↗

arXiv2022

The Temporal and Spatial Behaviors of CME Occurrence Rate at Different Latitudes

The statistical study of the Coronal Mass Ejections (CMEs) is a hot topic in solar physics. To further reveal the temporal and spatial behaviors of the CMEs at different latitudes and heights, we analyzed the correlation and phase relationships between the occurrence rate of CMEs, the Coronal Brightness Index (CBI), and the 10.7-cm solar radio flux (F10.7). We found that the occurrence rate of the CMEs correlates with CBI relatively stronger at high latitudes (>=60) than at low latitudes (<=50). At low latitudes, the occurrence rate of the CMEs correlates relatively weaker with CBI than F10.7. There is a relatively stronger correlation relationship between CMEs, F10.7, and CBI during Solar Cycle 24(SC24) than Solar Cycle 23 (SC23). During SC23, the high-latitude CME occurrence rate lags behind F10.7 by three months, and during SC24, the low-latitude CME occurrence rate leads to the low-latitude CBI by one month. The correlation coefficient values turn out to be larger when the very faint CMEsare removed from the samples of the CDAW catalog. Based on our results, we may speculate that the source regions of the high/low-latitude CMEs may vary in height, and the process of magnetic energy accumulation and dissipation is from the lower to the upper atmosphere of the Sun. The temporal offsets between different indicators could help us better understand the physical processes responsible for the solar-terrestrial interactions.

astro-ph.SR↗

arXiv2022

RFI Identification Based On Deep-Learning]{A Robust RFI Identification For Radio Interferometry based on a Convolutional Neural Network

The rapid development of new generation radio interferometers such as the Square Kilometer Array (SKA) has opened up unprecedented opportunities for astronomical research. However, anthropogenic Radio Frequency Interference (RFI) from communication technologies and other human activities severely affects the fidelity of observational data. It also significantly reduces the sensitivity of the telescopes. We proposed a robust Convolutional Neural Network (CNN) model to identify RFI based on machine learning methods. We overlaid RFI on the simulation data of SKA1-LOW to construct three visibility function datasets. One dataset was used for modeling, and the other two were used for validating the model's usability. The experimental results show that the Area Under the Curve (AUC) reaches 0.93, with satisfactory accuracy and precision. We then further investigated the effectiveness of the model by identifying the RFI in the actual observational data from LOFAR and MeerKAT. The results show that the model performs well. The overall effectiveness is comparable to AOFlagger software and provides an improvement over existing methods in some instances.

astro-ph.IM↗