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Cheng Liu

Publications and source records attributed to Cheng Liu.

At least 127 records · Page 7Linked to original sources
arXiv2023

Dark-Mode Theorems for Quantum Networks

We propose and prove two theorems for determining the number of dark modes in linear two-component quantum networks composed of two types of bosonic modes. This is achieved by diagonalizing the two sub-networks of the same type of modes, mapping the networks to either a standard or a thick arrowhead matrix, and analyzing the linear dependence and independence between the column vectors associated with degenerate normal modes in the coupling matrix. We confirm the two theorems by checking the simultaneous ground-state cooling of the mechanical modes in linearized optomechanical networks. These results also work for linear fermionic networks and other networks described by quadratic coupled-mode Hamiltonian. The present method can be extended to study the dark-state effect in driven atom systems and to construct large decoherence-free subspaces for processing quantum information. This work will initiate the studies on dynamical, transport, and statistical properties of linear networks with decoupled subspaces.

quant-ph↗
arXiv2023

High-resolution power equipment recognition based on improved self-attention

The current trend of automating inspections at substations has sparked a surge in interest in the field of transformer image recognition. However, due to restrictions in the number of parameters in existing models, high-resolution images can't be directly applied, leaving significant room for enhancing recognition accuracy. Addressing this challenge, the paper introduces a novel improvement on deep self-attention networks tailored for this issue. The proposed model comprises four key components: a foundational network, a region proposal network, a module for extracting and segmenting target areas, and a final prediction network. The innovative approach of this paper differentiates itself by decoupling the processes of part localization and recognition, initially using low-resolution images for localization followed by high-resolution images for recognition. Moreover, the deep self-attention network's prediction mechanism uniquely incorporates the semantic context of images, resulting in substantially improved recognition performance. Comparative experiments validate that this method outperforms the two other prevalent target recognition models, offering a groundbreaking perspective for automating electrical equipment inspections.

cs.CV↗
arXiv2023

Quantum phase transition of the Jaynes-Cummings model

Herein, we propose an experimentally feasible scheme to show the quantum phase transition of the Jaynes-Cummings (JC) model by modulating the transition frequency of a two-level system in a quantum Rabi model with strong coupling. By tuning the modulation frequency and amplitude, the ratio of the effective coupling strength of the rotating terms to the effective cavity (atomic transition) frequency can enter the deep-strong coupling regime, while the counter-rotating terms can be neglected. Thus, a deep-strong JC model is obtained. The ratio of the coupling strength to resonance frequencies in the deep-strong JC model is two orders of magnitude larger than the corresponding ratio in the original quantum Rabi model. Our scheme can be employed in atom-cavity resonance and off-resonance cases, and it is valid over a broad range. The nonzero average cavity photons of the ground state indicate the emergence of a quantum phase transition. Further, we demonstrate the dependence of the phase diagram on the atom-cavity detuning and modulation parameters. All the parameters used in our scheme are within the reach of current experimental technology. Our scheme provides a new mechanism for investigating the critical phenomena of finite-sized systems without requiring classical field limits, thereby opening a door for studying fundamental quantum phenomena occurring in the ultrastrong and even deep-strong coupling regimes.

quant-ph↗
arXiv2023

Constraints on Hairy Kerr black hole with quasi-periodic oscillations

The Hairy Kerr black hole is a novel black hole solution that depicts a rotating space-time encompassed by an axisymmetric fluid. It has significant observational importance and is an excellent candidate for an astrophysical black hole. Our study investigates the impact of the hairy charge on the quasi-periodic oscillations (QPOs) of X-ray binaries in the Hairy Kerr black hole (HKBH) space-time. The relativistic precession model is employed to compute the three principal frequencies of the accretion disk encircling the HKBH. We compare our outcomes with the observations of five X-ray binaries and employ a Markov chain Monte Carlo (MCMC) simulation for restricting the hairy charge parameters. There is no substantial evidence for the existence of hairy charge in the HKBH space-time. Therefore, we are placing observational constraints on the deformation parameters with $0<α<0.07697$ and hairy charge values ranging from $0.27182<l_0/M<2.0$.

astro-ph.HE↗
arXiv2023

High pressure behaviour of the magnetic van der Waals molecular framework Ni(NCS)$_2$

Two-dimensional materials offer a unique range of magnetic, electronic and mechanical properties which can be controlled by external stimuli. Pressure is a particularly important stimulus, as it can be achieved readily and can produce large responses, especially in low-dimensional materials. In this paper we explore the pressure-dependence of the structural and magnetic properties of a two-dimensional van der Waals (vdW) molecular framework antiferromagnet with ferromagnetic layers, Ni(NCS)$_2$, up to 8.4 kbar. Through a combination of X-ray and neutron diffraction analysis, we find that Ni(NCS)$_2$ is significantly more compressible than comparable vdW metal halides, and its response is anisotropic not only out of the plane, but also within the layers. Using bulk magnetisation and neutron diffraction data, we show that the ambient layered antiferromagnetic phase is maintained up to the largest investigated pressure, but with an enhanced Néel temperature, $T_\mathrm{N}$, ($ΔT_\mathrm{N} / T_\mathrm{N} = +19$ %) and a large pressure sensitivity ($Q = \frac{1}{T_\mathrm{N}} \frac{\mathrm{d}T_\mathrm{N}}{\mathrm{d}P} = +2.3$ % kbar$^{-1}$), one of the larger values of magnetic pressure responsiveness for a vdW material. Density functional theory calculations suggest that this is due to increasing three-dimensionality. These results provide some of the first insights into the pressure response of molecular framework vdW magnets and suggest investigation of other molecular framework vdW magnets might uncover contenders for future pressure-switchable devices.

cond-mat.mtrl-sci↗
arXiv2023

DeepBurning-MixQ: An Open Source Mixed-Precision Neural Network Accelerator Design Framework for FPGAs

Mixed-precision neural networks (MPNNs) that enable the use of just enough data width for a deep learning task promise significant advantages of both inference accuracy and computing overhead. FPGAs with fine-grained reconfiguration capability can adapt the processing with distinct data width and models, and hence, can theoretically unleash the potential of MPNNs. Nevertheless, commodity DPUs on FPGAs mostly emphasize generality and have limited support for MPNNs especially the ones with lower data width. In addition, primitive DSPs in FPGAs usually have much larger data width than that is required by MPNNs and haven't been sufficiently co-explored with MPNNs yet. To this end, we propose an open source MPNN accelerator design framework specifically tailored for FPGAs. In this framework, we have a systematic DSP-packing algorithm to pack multiple lower data width MACs in a single primitive DSP and enable efficient implementation of MPNNs. Meanwhile, we take DSP packing efficiency into consideration with MPNN quantization within a unified neural network architecture search (NAS) framework such that it can be aware of the DSP overhead during quantization and optimize the MPNN performance and accuracy concurrently. Finally, we have the optimized MPNN fine-tuned to a fully pipelined neural network accelerator template based on HLS and make best use of available resources for higher performance. Our experiments reveal the resulting accelerators produced by the proposed framework can achieve overwhelming advantages in terms of performance, resource utilization, and inference accuracy for MPNNs when compared with both handcrafted counterparts and prior hardware-aware neural network accelerators on FPGAs.

cs.AR↗
arXiv2023

Exploring Winograd Convolution for Cost-effective Neural Network Fault Tolerance

Winograd is generally utilized to optimize convolution performance and computational efficiency because of the reduced multiplication operations, but the reliability issues brought by winograd are usually overlooked. In this work, we observe the great potential of winograd convolution in improving neural network (NN) fault tolerance. Based on the observation, we evaluate winograd convolution fault tolerance comprehensively from different granularities ranging from models, layers, and operation types for the first time. Then, we explore the use of inherent fault tolerance of winograd convolution for cost-effective NN protection against soft errors. Specifically, we mainly investigate how winograd convolution can be effectively incorporated with classical fault-tolerant design approaches including triple modular redundancy (TMR), fault-aware retraining, and constrained activation functions. According to our experiments, winograd convolution can reduce the fault-tolerant design overhead by 55.77\% on average without any accuracy loss compared to standard convolution, and further reduce the computing overhead by 17.24\% when the inherent fault tolerance of winograd convolution is considered. When it is applied on fault-tolerant neural networks enhanced with fault-aware retraining and constrained activation functions, the resulting model accuracy generally shows significant improvement in presence of various faults.

cs.LG↗
arXiv2023

ChatSim: Underwater Simulation with Natural Language Prompting

Robots are becoming an essential part of many operations including marine exploration or environmental monitoring. However, the underwater environment presents many challenges, including high pressure, limited visibility, and harsh conditions that can damage equipment. Real-world experimentation can be expensive and difficult to execute. Therefore, it is essential to simulate the performance of underwater robots in comparable environments to ensure their optimal functionality within practical real-world contexts.OysterSim generates photo-realistic images and segmentation masks of objects in marine environments, providing valuable training data for underwater computer vision applications. By integrating ChatGPT into underwater simulations, users can convey their thoughts effortlessly and intuitively create desired underwater environments without intricate coding. \invis{Moreover, researchers can realize substantial time and cost savings by evaluating their algorithms across diverse underwater conditions in the simulation.} The objective of ChatSim is to integrate Large Language Models (LLM) with a simulation environment~(OysterSim), enabling direct control of the simulated environment via natural language input. This advancement can greatly enhance the capabilities of underwater simulation, with far-reaching benefits for marine exploration and broader scientific research endeavors.

cs.RO↗
arXiv2023

A Deep-Learning Method Using Auto-encoder and Generative Adversarial Network for Anomaly Detection on Ancient Stone Stele Surfaces

Accurate detection of natural deterioration and man-made damage on the surfaces of ancient stele in the first instance is essential for their preventive conservation. Existing methods for cultural heritage preservation are not able to achieve this goal perfectly due to the difficulty of balancing accuracy, efficiency, timeliness, and cost. This paper presents a deep-learning method to automatically detect above mentioned emergencies on ancient stone stele in real time, employing autoencoder (AE) and generative adversarial network (GAN). The proposed method overcomes the limitations of existing methods by requiring no extensive anomaly samples while enabling comprehensive detection of unpredictable anomalies. the method includes stages of monitoring, data acquisition, pre-processing, model structuring, and post-processing. Taking the Longmen Grottoes' stone steles as a case study, an unsupervised learning model based on AE and GAN architectures is proposed and validated with a reconstruction accuracy of 99.74\%. The method's evaluation revealed the proficient detection of seven artificially designed anomalies and demonstrated precision and reliability without false alarms. This research provides novel ideas and possibilities for the application of deep learning in the field of cultural heritage.

cs.CV↗
arXiv2023

Constraints on the rotating self-dual black hole with quasi-periodic oscillations

An impressive feature of loop quantum gravity (LQG) is that it can elegantly resolve both the big bang and black hole singularities. By using the Newman-Janis algorithm, a regular and effective rotating self-dual black hole(SDBH) metric could be constructed, which alters the Kerr geometry with a polymeric function $P$ from the quantum effects of LQG geometry. In this paper, we investigate its impact on the frequency characteristics of the X-ray quasi-periodic oscillations(QPOs) from 5 X-ray binaries and contrast it with the existing results of the orbital, periastron precession and nodal precession frequencies within the relativistic precession model. We apply a Monte Carlo Markov Chain (MCMC) simulation to examine the possible LQG effects on the X-ray QPOs. We found that the best constraint result for the rotating self-dual geometry from LQG came from the QPOs of X-ray binary GRO J1655-40, which establish an upper bound on the polymeric function $P$ less than $6.17\times 10^{-3}$ at 95\% confidence level. This bound leads to a restriction on the polymeric parameter $δ$ of LQG to be 0.67.

gr-qc↗
arXiv2023

Deep Learning Accelerator in Loop Reliability Evaluation for Autonomous Driving

The reliability of deep learning accelerators (DLAs) used in autonomous driving systems has significant impact on the system safety. However, the DLA reliability is usually evaluated with low-level metrics like mean square errors of the output which remains rather different from the high-level metrics like total distance traveled before failure in autonomous driving. As a result, the high-level reliability metrics evaluated at the post-silicon stage may still lead to DLA design revision and result in expensive reliable DLA design iterations targeting at autonomous driving. To address the problem, we proposed a DLA-in-loop reliability evaluation platform to enable system reliability evaluation at the early DLA design stage.

cs.AI↗
arXiv2023

LAMOST J2043+3413 -- a Fast Disk Precession SW Sextans Candidate in Period Gap

We present follow-up photometric observations and time-series analysis of a nova-like, SW Sextans-type, cataclysmic variable (CV) candidate, LAMOST J204305.95+341340.6 (here after J2043+3413), with Gaia G-band magnitude of 15.30 and a distance of 990 pc, which was identified from the LAMOST spectrum. The photometric data were collected with the Tsinghua-NAOC 0.8-m telescope (TNT), TESS, ZTF, and ASAS-SN. The TESS light curve reveals the presence of two prominent periods of 2.587(8) hours and 1.09(5) days, corresponding to the orbital and superorbital (precession) period, respectively. The TNT data obtained in 2020 shows a possible quasi-periodic oscillation of 1426 seconds. The precession period is about three times shorter than that of CVs with similar orbital periods, indicating an unusually fast precessing accretion disk. The ZTF data is found to show a sudden decline of $\sim0.4$ mag on MJD 58979. From the intermittent behavior of the eclipse, we deduce that J2043+3413 is an intermediate inclination system of CV, similar to V795 Her, which is also situated in the period gap.

astro-ph.SR↗
arXiv2023

Properties and Asteroseismological analysis of a new ZZ ceti discovered by TMTS

Tsinghua university-Ma Huateng Telescope for Survey (TMTS) aims to discover rapidly evolving transients by monitoring the northern sky. The TMTS catalog is cross-matched with the white dwarf (WD) catalog of Gaia EDR3, and light curves of more than a thousand WD candidates are obtained so far. Among them, the WD TMTS J23450729+5813146 (hereafter J2345) is one interesting common source. Based on the light curves from the TMTS and follow-up photometric observations, periods of 967.113 s, 973.734 s, 881.525 s, 843.458 s, 806.916 s and 678.273 s are identified. In addition, the TESS observations suggest a 3.39 h period but this can be attributed to the rotation of a comoving M dwarf located within 3". The spectroscopic observation indicates that this WD is DA type with Teff = 11778+/-617K,log g = 8.38+/-0.31,mass=0.84+/-0.20Msun and age=0.704+/-0.377 Gyrs. Asteroseismological analysis reveals a global best-fit solution of Teff =12110+/-10K and mass=0.760+/-0.005Msun,consistent with the spectral fitting results, and Oxygen and Carbon abundances in the core center are 0.73 and 0.27, respectively. The distance derived from the intrinsic luminosity given by asteroseismology is 93 parsec, which is in agreement with the distance of 98 parsec from Gaia DR3. Additionally, kinematic study shows that this WD is likely a thick disk star. The mass of its zero-age main-sequence mass is estimated to be 3.08 Msun and has a main-sequence plus cooling age of roughly 900 Myrs.

astro-ph.SR↗
arXiv2023

Observational tests of quantum extension of Schwarzschild spacetime in loop quantum gravity with stars in the galactic center

In this paper, we use the publicly available observational data of 17 stellar stars orbiting Sgr A* to test the quantum extension of Schwarzschild spacetime in loop quantum gravity (LQG). For our purpose, we transform the geodesical evolution of a massive particle in the quantum-extended Schwarzschild black hole to the perturbed Kepler problem and calculate the effects of LQG on the pericentre advance of the stellar stars. With these effects, one is able to compare them with the publicly available astrometric and spectroscopic data of stellar stars in the galactic center. We perform Monte Carlo Markov Chain (MCMC) simulations to probe the possible LQG effects on the orbit of S-stars. No significant evidence of the quantum-extended Schwarzschild black hole from LQG is found. Among the posterior analyses of 17 S-stars, the result of S2 gives the strongest bound on the LQG parameter $A_λ$, which places an upper bound at 95\% confidence level on $A_λ$ to be $A_λ< 0.302$.

gr-qc↗
arXiv2023

Prospects for detection rate of very-high-energy γ-ray emissions from short γ-ray bursts with the HADAR experiment

The observation of short gamma ray bursts (SGRBs) in the TeV energy range plays an important role in understanding the radiation mechanism and probing new areas of physics such as Lorentz invariance violation. However, no SGRB has been observed in this energy range due to the short duration of SGRBs and the weakness of current experiments. New experiments with new technology are required to detect sub-TeV SGRBs. In this work, we observe the very high energy (VHE) $γ$-ray emissions from SGRBs and calculate the annual detection rate with the High Altitude Detection of Astronomical Radiation HADAR (HADAR) experiment. First, a set of pseudo-SGRB samples is generated and checked using the observations of Fermi-GBM, Fermi-LAT, and SWIFT measurements. The annual detection rate is calculated from these SGRB samples based on the performance of the HADAR instrument. As a result, the HADAR experiment can detect 0.5 SGRB per year if the spectral break-off of $γ$-rays caused by the internal absorption is larger than 100 GeV. For a GRB09010-like GRB in HADAR's view, it should be possible to detect approximately 2000 photons considering the internal absorption. With a time delay assumption due to the Lorentz invariance violation effects, a simulated light curve of GRB090510 has evident energy dependence. We hope that the HADAR experiment can perform the SGRB observations and test our calculations in the future.

astro-ph.HE↗
arXiv2023

Deterministic generation of multi-photon bundles in a quantum Rabi model

Multi-photon bundle states are crucial for a broad range of applications such as quantum metrology, quantum lithography, quantum communication, and quantum biology. Here we propose a scheme that generates multi-photon bundles via virtual excitations in a quantum Rabi model. Our approach utilizes a Ξ-type three-level atom, where the upper two levels are coupled to a cavity field to form a quantum Rabi model with ultrastrong coupling, and the transition between the lower two levels is driven by two sequences of Gaussian pulses. We show that the driving pulses induce deterministic emission of multiple photons from the eigenstates of the quantum Rabi model via the stimulated Raman adiabatic passage technique, and hence can create bundles of multiple photons on-demand in the cavity output field. We calculate the generalized second-order correlation functions of the output photons, which reveal that the emitted photons form antibunched multi-photon bundles.

quant-ph↗
arXiv2023

Physics Driven Deep Retinex Fusion for Adaptive Infrared and Visible Image Fusion

Convolutional neural networks have turned into an illustrious tool for image fusion and super-resolution. However, their excellent performance cannot work without large fixed-paired datasets; and additionally, these high-demanded ground truth data always cannot be obtained easily in fusion tasks. In this study, we show that, the structures of generative networks capture a great deal of image feature priors, and then these priors are sufficient to reconstruct high-quality fused super-resolution result using only low-resolution inputs. By this way, we propose a novel self-supervised dataset-free method for adaptive infrared (IR) and visible (VIS) image super-resolution fusion named Deep Retinex Fusion (DRF). The key idea of DRF is first generating component priors which are disentangled from physical model using our designed generative networks ZipperNet, LightingNet and AdjustingNet, then combining these priors which captured by networks via adaptive fusion loss functions based on Retinex theory, and finally reconstructing the super-resolution fusion results. Furthermore, in order to verify the effectiveness of our reported DRF, both qualitative and quantitative experiments via comparing with other state-of-the-art methods are performed using different test sets. These results prove that, comparing with large datasets trained methods, DRF which works without any dataset achieves the best super-resolution fusion performance; and more importantly, DRF can adaptively balance IR and VIS information and has good noise immunity. DRF codes are open source available at https://github.com/GuYuanjie/Deep-Retinex-fusion.

cs.CV↗
arXiv2023

Comparative study of magnetocaloric properties for Gd$^{3+}$ compounds with different frustrated lattice geometries

As materials with suppressed ordering temperatures and enhanced ground state entropies, frustrated magnetic oxides are ideal candidates for cryogenic magnetocaloric refrigeration. While previous materials design has focused on tuning the magnetic moments, their interactions, and density of moments on the lattice, there has been relatively little attention to frustrated lattices. Prior theoretical work has shown that the magnetocaloric cooling rate at the saturation field is proportional to a macroscopic number of soft mode excitations that arise due to the classical ground state degeneracy. The number of these modes is directly determined by the geometry of the frustrating lattice. For corner-sharing geometries, the pyrochlore has 50\% more modes than the garnet and kagome lattices, whereas the edge-sharing \emph{fcc} has only a subextensive number of soft modes. Here, we study the role of soft modes in the magnetocaloric effect of four large-spin Gd$^{3+}$ ($L=0$, $J=S=7/2$) Heisenberg antiferromagnets on a kagome, garnet, pyrochlore, and \emph{fcc} lattice. By comparing measurements of the magnetic entropy change $ΔS_m$ of these materials at fields up to $9$~T with predictions using mean-field theory and Monte Carlo simulations, we are able to understand the relative importance of spin correlations and quantization effects. We observe that tuning the value of the nearest neighbor coupling has a more dominant contribution to the magnetocaloric entropy change in the liquid-He cooling regime ($2$-$20$~K), rather than tuning the number of soft mode excitations. Our results inform future materials design in terms of dimensionality, degree of magnetic frustration, and lattice geometry.

cond-mat.str-el↗