arXiv subjects

Fei Zhou

Publications and source records attributed to Fei Zhou.

At least 73 records · Page 4Linked to original sources

arXiv2023

Emergent Infrared Conformal Dynamics in Strongly Interacting Quantum Gases

Conformal dynamics can appear in quantum gases when the interactions are fine tuned to be scale symmetric. One well-known example of such a system is a three-dimensional Fermi gas at a Feshbach resonance. In this letter, we illustrate how conformal dynamics can also emerge in the infrared limit in one-dimensional harmonically trapped Fermi gases, even though the system may not have exactly scale symmetric interactions. The conformal dynamics are induced by strong renormalization effects due to the nearby infrared stable scale invariant interaction. When the system approaches the infrared limit, or when the external harmonic trapping frequency $ω_f \rightarrow 0$, the dynamics are characterized by a unique vanishingly small dissipation rate, $Γ\propto ω_f$, rather than a constant as in generic interacting systems. We also examine the work done in a two-quench protocol, $W$, and the average power $\mathcal{P}$. In one dimension, the average power, $\mathcal{P} \propto ω_f$, becomes vanishingly small in the infrared limit, a signature of emergent conformal dynamics.

cond-mat.quant-gas↗

arXiv2023

Collaborative Auto-encoding for Blind Image Quality Assessment

Blind image quality assessment (BIQA) is a challenging problem with important real-world applications. Recent efforts attempting to exploit powerful representations by deep neural networks (DNN) are hindered by the lack of subjectively annotated data. This paper presents a novel BIQA method which overcomes this fundamental obstacle. Specifically, we design a pair of collaborative autoencoders (COAE) consisting of a content autoencoder (CAE) and a distortion autoencoder (DAE) that work together to extract content and distortion representations, which are shown to be highly descriptive of image quality. While the CAE follows a standard codec procedure, we introduce the CAE-encoded feature as an extra input to the DAE's decoder for reconstructing distorted images, thus effectively forcing DAE's encoder to extract distortion representations. The self-supervised learning framework allows the COAE including two feature extractors to be trained by almost unlimited amount of data, thus leaving limited samples with annotations to finetune a BIQA model. We will show that the proposed BIQA method achieves state-of-the-art performance and has superior generalization capability over other learning based models. The codes are available at: https://github.com/Macro-Zhou/NRIQA-VISOR/.

cs.CV↗

arXiv2023

Score-based denoising for atomic structure identification

We propose an effective method for removing thermal vibrations that complicate the task of analyzing complex dynamics in atomistic simulation of condensed matter. Our method iteratively subtracts thermal noises or perturbations in atomic positions using a denoising score function trained on synthetically noised but otherwise perfect crystal lattices. The resulting denoised structures clearly reveal underlying crystal order while retaining disorder associated with crystal defects. Purely geometric, agnostic to interatomic potentials, and trained without inputs from explicit simulations, our denoiser can be applied to simulation data generated from vastly different interatomic interactions. The denoiser is shown to improve existing classification methods such as common neighbor analysis and polyhedral template matching, reaching perfect classification accuracy on a recent benchmark dataset of thermally perturbed structures up to the melting point. Demonstrated here in a wide variety of atomistic simulation contexts, the denoiser is general, robust, and readily extendable to delineate order from disorder in structurally and chemically complex materials.

cond-mat.mtrl-sci↗

arXiv2023

Accelerating discrete dislocation dynamics simulations with graph neural networks

Discrete dislocation dynamics (DDD) is a widely employed computational method to study plasticity at the mesoscale that connects the motion of dislocation lines to the macroscopic response of crystalline materials. However, the computational cost of DDD simulations remains a bottleneck that limits its range of applicability. Here, we introduce a new DDD-GNN framework in which the expensive time-integration of dislocation motion is entirely substituted by a graph neural network (GNN) model trained on DDD trajectories. As a first application, we demonstrate the feasibility and potential of our method on a simple yet relevant model of a dislocation line gliding through an array of obstacles. We show that the DDD-GNN model is stable and reproduces very well unseen ground-truth DDD simulation responses for a range of straining rates and obstacle densities, without the need to explicitly compute nodal forces or dislocation mobilities during time-integration. Our approach opens new promising avenues to accelerate DDD simulations and to incorporate more complex dislocation motion behaviors.

cond-mat.mtrl-sci↗

arXiv2023

Microwave electrometry with Rydberg atoms in a vapor cell using microwave amplitude modulation

We have theoretically and experimentally studied the dispersive signal of the Rydberg atomic electromagnetically induced transparency (EIT) - Autler-Townes (AT) splitting spectra obtained using amplitude modulation of the microwave (MW) field. In addition to the two zero-crossing points, the dispersion signal has two positive maxima with an interval defined as the shoulder interval of the dispersion signal $Δf_{\text{sho}}$. The relationship of MW field strength $E_{\text{MW}}$ and $Δf_{\text{sho}}$ are studied at the MW frequencies of 31.6 GHz, 22.1 GHz, and 9.2 GHz respectively. The results show that $Δf_{\text{sho}}$ can be used to character the much weaker $E_{\text{MW}}$ than the interval of two zero-crossing points $Δf_{\text{zeros}}$ and the traditional EIT-AT splitting interval $Δf_{\text{m}}$, the minimum $E_{\text{MW}}$ measured by $Δf_{\text{sho}}$ is about 30 times smaller than that by $Δf_{\text{m}}$. As an example, the minimum $E_{\text{MW}}$ at 9.2 GHz that can be characterized by $Δf_{\text{sho}}$ is 0.056 mV/cm, which is the minimum value characterized by frequency interval using vapour cell without adding any auxiliary fields. The proposed method can improve the weak limit and sensitivity of $E_{\text{MW}}$ measured by spectral frequency interval, which is important in the direct measurement of weak $E_{\text{MW}}$.

physics.atom-ph↗

arXiv2023

Emergent $U(1)$ Symmetries and $τ$-$σ$ Duality in Gapless Superfluids or Superconductors

A superfluid spontaneously breaks the usual $U(1)$ symmetry because of condensation. In this article, we illustrate six classes of emergent $U(1)$ symmetries naturally appear in infrared limits in a broad class of gapless topological superfluids (that either belong to a stable phase or are quantum critical). In gapless states we have considered, emergent $U(1)$ symmetry groups are embedded in an $Spin(4)=SU(2) \otimes SU(2)$ group that are algebraically isomorphic to an $SO(4)$ group. All $U(1)$ charges associated with symmetries are further invariant under an $SU(2)$ spin group or an equivalent of it but always break pre-existing higher space-time Lorentz symmetry of $SO(3,1)$ group. Emergent $U(1)$ symmetries can be further spontaneously broken only if interactions are strong enough and resultant strong coupling states become fully gapped. However if states remain gapless, emergent $U(1)$ symmetries are always present, despite that these states may exhibit much lower space-time symmetries compared to their weakly interacting gapless Lorentz symmetric counter parts. In the limit of our interests, we have identified all possible gapless real fermions with or without Lorentz symmetries and find that they all display emergent $U(1)$ symmetries in the infrared limit.We argue emergent $U(1)$ symmetries in infrared are intrinsic in a broad class of interacting gapless superfluid or superconducting states and are typically well defined in high dimensions where there are infrared stable fixed points dictating emergent properties.

cond-mat.str-el↗

arXiv2023

Far-Away-From-Equilibrium Quantum Critical Conformal Dynamics: Reversibility, Thermalization, and Hydrodynamics

Generic far-away-from-equilibrium many-body dynamics involve entropy production, and hence are thermodynamically irreversible. Near quantum critical points, an emergent conformal symmetry can impose strong constraints on entropy production rates, and in some cases completely forbid entropy production, which usually occurs for systems that deviate from quantum critical points. In this article, we illustrate how the vanishing entropy production near a quantum critical point results in reversible far-away-from-equilibrium dynamics at finite temperatures that are otherwise irreversible. Away from the quantum critical point, the quantum dynamics are damped, and our analysis directly relates the thermalization time scale to the hydrodynamic viscosity near quantum critical points with dynamical critical exponent $z=2$. We demonstrate how both controllable reversible and irreversible dynamics can be potentially studied in cold gas experiments using Feshbach resonances.

cond-mat.quant-gas↗

arXiv2023

Three-Dimensional Low-Density Fermions in Planckian Limit: Entropy and Dynamics

In this Letter, we explore dynamics in a three-dimensional strongly interacting liquid. In quantum liquids discussed below, thermodynamic properties such as pressure and thermal energies are fully characterized by $s(T)$, the entropy density of the liquid (that is also directly proportional to the hydrodynamic viscosity). We obtain a universal fermion spectral function $A(ω, {\bf k})$ that is distinctly specified by $\hbar / T$, a Planckian time scale. These phenomena can emerge in strongly interacting many-body states with a finite fermion density $ρ$ at temperatures $T^*$ where the chemical potential of fermions $μ(ρ, T=T^*)$ approaches zero and can be thought as many-body simulations of certain aspects of Planckian dynamics.

cond-mat.str-el↗

arXiv2023

Long-lived universal resonant Bose gases

Quantum simulations based on near-resonance Bose gases are limited by their short lifetimes due to severe atom losses. In addition to this, the recently predicted thermodynamical instability adds another constraint on accessing the resonant Bose gases. In this article, we offer a potential solution by proposing long-lived resonant Bose gases in both two and three dimensions, where the conventional few-body losses are strongly suppressed. We show that the thermodynamical properties as well as the lifetimes of these strongly interacting systems are universal, and independent of short-range physics.

cond-mat.quant-gas↗

arXiv2022

One-Stage Cascade Refinement Networks for Infrared Small Target Detection

Single-frame InfraRed Small Target (SIRST) detection has been a challenging task due to a lack of inherent characteristics, imprecise bounding box regression, a scarcity of real-world datasets, and sensitive localization evaluation. In this paper, we propose a comprehensive solution to these challenges. First, we find that the existing anchor-free label assignment method is prone to mislabeling small targets as background, leading to their omission by detectors. To overcome this issue, we propose an all-scale pseudo-box-based label assignment scheme that relaxes the constraints on scale and decouples the spatial assignment from the size of the ground-truth target. Second, motivated by the structured prior of feature pyramids, we introduce the one-stage cascade refinement network (OSCAR), which uses the high-level head as soft proposals for the low-level refinement head. This allows OSCAR to process the same target in a cascade coarse-to-fine manner. Finally, we present a new research benchmark for infrared small target detection, consisting of the SIRST-V2 dataset of real-world, high-resolution single-frame targets, the normalized contrast evaluation metric, and the DeepInfrared toolkit for detection. We conduct extensive ablation studies to evaluate the components of OSCAR and compare its performance to state-of-the-art model-driven and data-driven methods on the SIRST-V2 benchmark. Our results demonstrate that a top-down cascade refinement framework can improve the accuracy of infrared small target detection without sacrificing efficiency. The DeepInfrared toolkit, dataset, and trained models are available at https://github.com/YimianDai/open-deepinfrared to advance further research in this field.

cs.CV↗

arXiv2022

Aesthetically Relevant Image Captioning

Image aesthetic quality assessment (AQA) aims to assign numerical aesthetic ratings to images whilst image aesthetic captioning (IAC) aims to generate textual descriptions of the aesthetic aspects of images. In this paper, we study image AQA and IAC together and present a new IAC method termed Aesthetically Relevant Image Captioning (ARIC). Based on the observation that most textual comments of an image are about objects and their interactions rather than aspects of aesthetics, we first introduce the concept of Aesthetic Relevance Score (ARS) of a sentence and have developed a model to automatically label a sentence with its ARS. We then use the ARS to design the ARIC model which includes an ARS weighted IAC loss function and an ARS based diverse aesthetic caption selector (DACS). We present extensive experimental results to show the soundness of the ARS concept and the effectiveness of the ARIC model by demonstrating that texts with higher ARS's can predict the aesthetic ratings more accurately and that the new ARIC model can generate more accurate, aesthetically more relevant and more diverse image captions. Furthermore, a large new research database containing 510K images with over 5 million comments and 350K aesthetic scores, and code for implementing ARIC are available at https://github.com/PengZai/ARIC.

cs.CV↗

arXiv2022

Super-resolving Compressed Images via Parallel and Series Integration of Artifact Reduction and Resolution Enhancement

In real-world applications, such as sharing photos on social media platforms, images are always not only sub-sampled but also heavily compressed thus often containing various artefacts. Simple methods for enhancing the resolution of such images will exacerbate the artefacts, rendering them visually objectionable. In spite of its high practical values, super-resolving compressed images is not well studied in the literature. In this paper, we propose a novel compressed image super resolution (CISR) framework based on parallel and series integration of artefacts removal and resolution enhancement. Based on a mathematical inference model for estimating a clean low-resolution (LR) image and a clean high-resolution (HR) image from a down-sampled and compressed observation, we have designed a CISR architecture consisting of two deep neural network modules: the artefacts removal module (ARM) and the resolution enhancement module (REM). The ARM and the REM work in parallel with both taking the compressed LR image as their inputs, at the same time they also work in series with the REM taking the output of the ARM as one of its inputs and the ARM taking the output of the REM as its other input. A technique called unfolding is introduced to recursively suppress the compression artefacts and restore the image resolution. A unique feature of our CISR system is that it exploits the parallel and series connections between the ARM and the REM, and recursive optimization to reduce the model's dependency on specific types of degradation thus making it possible to train a single model to super-resolve images compressed by different methods to different qualities. Codes and datasets are available at https://github.com/luohongming/CISR_PSI.git

eess.IV↗

arXiv2022

HDRfeat: A Feature-Rich Network for High Dynamic Range Image Reconstruction

A major challenge for high dynamic range (HDR) image reconstruction from multi-exposed low dynamic range (LDR) images, especially with dynamic scenes, is the extraction and merging of relevant contextual features in order to suppress any ghosting and blurring artifacts from moving objects. To tackle this, in this work we propose a novel network for HDR reconstruction with deep and rich feature extraction layers, including residual attention blocks with sequential channel and spatial attention. For the compression of the rich-features to the HDR domain, a residual feature distillation block (RFDB) based architecture is adopted. In contrast to earlier deep-learning methods for HDR, the above contributions shift focus from merging/compression to feature extraction, the added value of which we demonstrate with ablation experiments. We present qualitative and quantitative comparisons on a public benchmark dataset, showing that our proposed method outperforms the state-of-the-art.

eess.IV↗

arXiv2022

Restoration of User Videos Shared on Social Media

User videos shared on social media platforms usually suffer from degradations caused by unknown proprietary processing procedures, which means that their visual quality is poorer than that of the originals. This paper presents a new general video restoration framework for the restoration of user videos shared on social media platforms. In contrast to most deep learning-based video restoration methods that perform end-to-end mapping, where feature extraction is mostly treated as a black box, in the sense that what role a feature plays is often unknown, our new method, termed Video restOration through adapTive dEgradation Sensing (VOTES), introduces the concept of a degradation feature map (DFM) to explicitly guide the video restoration process. Specifically, for each video frame, we first adaptively estimate its DFM to extract features representing the difficulty of restoring its different regions. We then feed the DFM to a convolutional neural network (CNN) to compute hierarchical degradation features to modulate an end-to-end video restoration backbone network, such that more attention is paid explicitly to potentially more difficult to restore areas, which in turn leads to enhanced restoration performance. We will explain the design rationale of the VOTES framework and present extensive experimental results to show that the new VOTES method outperforms various state-of-the-art techniques both quantitatively and qualitatively. In addition, we contribute a large scale real-world database of user videos shared on different social media platforms. Codes and datasets are available at https://github.com/luohongming/VOTES.git

cs.CV↗

arXiv2022

Detection of DC electric forces with zeptonewton sensitivity by single-ion phonon laser

Detecting extremely small forces helps exploring new physics quantitatively. Here we demonstrate that the phonon laser made of a single trapped $^{40}$Ca$^{+}$ ion behaves as an exquisite sensor for small force measurement. We report our successful detection of small electric forces regarding the DC trapping potential with sensitivity of 2.41$\pm$0.49 zN/$\sqrt{\rm Hz}$, with the ion only under Doppler cooling, based on the injection-locking of the oscillation phase of the phonon laser in addition to the classical squeezing applied to suppress the measurement uncertainty. We anticipate that such a single-ion sensor would reach a much better force detection sensitivity in the future once the trapping system is further improved and the fluorescence collection efficiency is further enhanced.

physics.atom-ph↗

arXiv2022

An Interaction Bulk-Boundary Relation and its Applications Towards Symmetry Breaking and Beyond

In this article, we propose a simple but general scaling relation between interactions in a gapped bulk topological matter and gapless interacting surface states. We explicitly illustrate such a generic bulk-boundary relation (BBR) for a few specific interactions in a topological quantum matter, where we can perform dimensional reduction of a microscopic bulk theory to project out interacting surfaces. We have examined renormalization effects of the gapped bulk fermions on the interacting topological surface fermions. As simple applications, we utilize effective interacting quantum fields implied by BBR to explore feasibility of routes to various fascinating emergent phenomena on surfaces including emergent Majorana fermions induced by spontaneous symmetry breaking. We obtain sufficient conditions for these interacting surface phenomena to take place. We have also found that for given bulk electron-phonon interactions and when Ω_{D} \geq m, the phonon-mediated interactions on surface are strongest if the bulk Debye frequency Ω_{D} matches m, the mass gap of the topological matter

cond-mat.str-el↗

arXiv2022

Portable ground stations for space-to-ground quantum key distribution

Quantum key distribution (QKD) uses the fundamental principles of quantum mechanics to share unconditionally secure keys between distant users. Previous works based on the quantum science satellite "Micius" have initially demonstrated the feasibility of a global QKD network. However, the practical applications of space-based QKD still face many technical problems, such as the huge size and weight of ground stations required to receive quantum signals. Here, we report space-to-ground QKD demonstrations based on portable receiving ground stations. The weight of the portable ground station is less than 100 kg, the space required is less than 1 m$^{3}$ and the installation time requires no more than 12 hours, all of the weight, required space and deployment time are about two orders of magnitude lower than those for the previous systems. Moreover, the equipment is easy to handle and can be placed on the roof of buildings in a metropolis. Secure keys have been successfully generated from the "Micius" satellite to these portable ground stations at six different places in China, and an average final secure key length is around 50 kb can be obtained during one passage. Our results pave the way for, and greatly accelerate the practical application of, space-based QKD.

quant-ph↗

arXiv2021

Topological Quantum Critical Points in Strong Coupling limits: Global Symmetries and Strongly Interacting Majorana Fermions

In this article, we discuss strong coupling limits of topological quantum critical points (TQCPs) where quantum phase transitions between two topological distinct superconducting states take place. We illustrate that while superconducting phases on both sides of TQCPs spontaneously break same symmetries, universality classes of critical states can be identified only when global symmetries in topological states are further specified. In dimensions $d=2,3$, we find that continuous $(d+1)$th order transitions at weakly interacting TQCPs that were pointed out previously in the presence of emergent Lorentz symmetry can be terminated by strongly interacting fixed points of majorana fields. For $2d$ time reversal symmetry breaking TQCPs, termination points are supersymmetric with ${\mathcal N}=4N_f={1}$ (where $N_f$ is the number of four-component Dirac fermions and ${\mathcal N}$ is the number of two-component real fermions) beyond which transitions are discontinuous first order ones. For $2d$ time reversal symmetric TQCPs without other global symmetries, termination points of $(d+1)$th order continuous transition lines are generically conformal invariant without supersymmetry. Beyond these strong coupling fixed points, there are first-order discontinuous transitions as far as the protecting symmetry is not spontaneously broken but no direct transitions if the protecting symmetry is spontaneously broken in the presence of strong interactions. In $3d$, strong coupling termination points can be further effectively represented by new emergent gapless real bosons weakly coupled with free gapless majorana fermions. However, in $1d$, time reversal symmetric $(d+1)$th continuous transition lines of TQCPs are terminated by simple free majorana fermion fixed points.

cond-mat.str-el↗