arXiv subjects

Liqiang Zhu

Publications and source records attributed to Liqiang Zhu.

16 recordsLinked to original sources

arXiv2026

Transport Properties of QGP within a Bayesian Holographic QCD Model

Using a holographic QCD model augmented by Bayesian inference, we calculate key transport coefficients of the quark-gluon plasma (QGP)$\text{-}$including the drag force, jet quenching parameter, heavy quark diffusion coefficient, and shear and bulk viscosities$\text{-}$at finite temperature and chemical potential. Posterior parameter distributions at the 68\% and 95\% confidence levels (CL), as well as the maximum a posteriori (MAP) estimates, are employed to quantify uncertainties. Our findings indicate that the diffusion coefficient within the Bayesian credible regions aligns with lattice QCD results for $T \sim 1.2T_c$ to $2T_c$, and is consistent with ALICE experimental measurements near $T_c$. The jet quenching parameter obtained from the Bayesian analysis agrees with RHIC and LHC data, while viscosity coefficients show compatibility with existing literature. These results demonstrate the efficacy of a Bayesian holographic approach in elucidating the nonperturbative transport properties of QCD matter.

hep-ph↗

arXiv2025

Bayesian inference of the magnetic field and chemical potential on holographic jet quenching in heavy-ion collisions

Jet quenching is studied in a background magnetic field and a finite baryon chemical potential. The production of energetic partons is calculated using the next-to-leading order (NLO) perturbative Quantum Chromodynamics (pQCD) parton model, while the parton energy loss formula is obtained from the AdS/CFT correspondence incorporating the magnetic field and baryon chemical potential effects. Using Bayesian inference, we systemically compare the theoretical calculations with experimental data for the nuclear modification factor $R_{AA}$ of the large transverse momentum hadrons in different-centrality nucleus-nucleus collisions at 0.2, 2.76 and 5.02 TeV, respectively. The form of the holographic energy loss leads to a strong negative correlation between the magnetic field and the chemical potential after the calibration, from which we discussed the sensitivity of jet quenching phenomena to magnetic field and baryon chemical potential.

hep-ph↗

arXiv2025

Thermodynamics of Heavy Quarkonium in a Bayesian Holographic QCD model

Leveraging high-precision lattice QCD data on the equation of state and baryon number susceptibility at vanishing chemical potential, we construct a Bayesian holographic QCD model and systematically analyze the thermodynamic properties of heavy quarkonium in QCD matter under varying temperatures and chemical potentials. We compute the quark-antiquark interquark distance, potential energy, entropy, binding energy, and internal energy. We present detailed posterior distribution results of the thermodynamic quantities of heavy quarkonium, including maximum a posteriori (MAP) value estimates and 95\% confidence levels (CL). Through numerical simulations and theoretical analysis, we find that increasing temperature and chemical potential decrease the quark distance, thereby facilitating the dissociation of heavy quarkonium and leading to suppressed potential energy. The increase in temperature and chemical potential also raise the entropy and entropy force, further accelerating the dissociation of heavy quarkonium. The calculated results of binding energy indicate that higher temperature and chemical potential enhance the tendency of heavy quarkonium to dissociate into free quarks. Internal energy also increases with rising temperature and chemical potential. These findings provide significant theoretical insights into the properties of strongly interacting matter under extreme conditions and lay a solid foundation for the interpretation and validation of future experimental data. Finally, we also present the results for the free energy, entropy, and internal energy of single quark.

hep-ph↗

arXiv2025

Bayesian Inference of the Critical Endpoint in 2+1-Flavor System from Holographic QCD

We present a Bayesian holographic model constructed by integrating the equation of state and baryon number susceptibility at zero chemical potential from lattice QCD. The model incorporates error estimates derived from lattice data. With this model, we systematically investigate the thermodynamic properties of the 2+1-flavor QCD system. Using Bayesian Inference, we perform precise calibration of the model parameters and determined the critical endpoint (CEP) position under the maximum a posterior (MAP) estimation to be $(T^{c},μ_{B}^{c})=(0.0859\;\mathrm{GeV},0.742\;\mathrm{GeV})$. Additionally, we predict the CEP positions within 68\% and 95\% confidence levels, yielding $(T^{c},\; μ_{B}^{c})_{68\%}$=$(0.0820\text{--}0.0889, 0.71\text{--}0.77)\;\mathrm{GeV}$ and $(T^{c},\; μ_{B}^{c})_{95\%}$=$(0.0816\text{--}0.0898,\; 0.71\text{--}0.79)\;\mathrm{GeV}$, respectively. Moreover, to validate the reliability and predictive power of our approach, we conduct a comprehensive comparison between our predictions and potential CEP locations proposed by other theoretical models. This work not only establishes a novel Bayesian framework for holographic modeling but also provides valuable insights and theoretical support for exploring phase transitions in strongly-interacting matter under extreme conditions.

hep-ph↗

arXiv2023

When SAM Meets Sonar Images

Segment Anything Model (SAM) has revolutionized the way of segmentation. However, SAM's performance may decline when applied to tasks involving domains that differ from natural images. Nonetheless, by employing fine-tuning techniques, SAM exhibits promising capabilities in specific domains, such as medicine and planetary science. Notably, there is a lack of research on the application of SAM to sonar imaging. In this paper, we aim to address this gap by conducting a comprehensive investigation of SAM's performance on sonar images. Specifically, we evaluate SAM using various settings on sonar images. Additionally, we fine-tune SAM using effective methods both with prompts and for semantic segmentation, thereby expanding its applicability to tasks requiring automated segmentation. Experimental results demonstrate a significant improvement in the performance of the fine-tuned SAM.

cs.CV↗

arXiv2023

IDMS: Instance Depth for Multi-scale Monocular 3D Object Detection

Due to the lack of depth information of images and poor detection accuracy in monocular 3D object detection, we proposed the instance depth for multi-scale monocular 3D object detection method. Firstly, to enhance the model's processing ability for different scale targets, a multi-scale perception module based on dilated convolution is designed, and the depth features containing multi-scale information are re-refined from both spatial and channel directions considering the inconsistency between feature maps of different scales. Firstly, we designed a multi-scale perception module based on dilated convolution to enhance the model's processing ability for different scale targets. The depth features containing multi-scale information are re-refined from spatial and channel directions considering the inconsistency between feature maps of different scales. Secondly, so as to make the model obtain better 3D perception, this paper proposed to use the instance depth information as an auxiliary learning task to enhance the spatial depth feature of the 3D target and use the sparse instance depth to supervise the auxiliary task. Finally, by verifying the proposed algorithm on the KITTI test set and evaluation set, the experimental results show that compared with the baseline method, the proposed method improves by 5.27\% in AP40 in the car category, effectively improving the detection performance of the monocular 3D object detection algorithm.

cs.CV↗

arXiv2022

A Lightweight Reconstruction Network for Surface Defect Inspection

Currently, most deep learning methods cannot solve the problem of scarcity of industrial product defect samples and significant differences in characteristics. This paper proposes an unsupervised defect detection algorithm based on a reconstruction network, which is realized using only a large number of easily obtained defect-free sample data. The network includes two parts: image reconstruction and surface defect area detection. The reconstruction network is designed through a fully convolutional autoencoder with a lightweight structure. Only a small number of normal samples are used for training so that the reconstruction network can be A defect-free reconstructed image is generated. A function combining structural loss and $\mathit{L}1$ loss is proposed as the loss function of the reconstruction network to solve the problem of poor detection of irregular texture surface defects. Further, the residual of the reconstructed image and the image to be tested is used as the possible region of the defect, and conventional image operations can realize the location of the fault. The unsupervised defect detection algorithm of the proposed reconstruction network is used on multiple defect image sample sets. Compared with other similar algorithms, the results show that the unsupervised defect detection algorithm of the reconstructed network has strong robustness and accuracy.

cs.CV↗

arXiv2022

Pedestrian Spatio-Temporal Information Fusion For Video Anomaly Detection

Aiming at the problem that the current video anomaly detection cannot fully use the temporal information and ignore the diversity of normal behavior, an anomaly detection method is proposed to integrate the spatiotemporal information of pedestrians. Based on the convolutional autoencoder, the input frame is compressed and restored through the encoder and decoder. Anomaly detection is realized according to the difference between the output frame and the true value. In order to strengthen the characteristic information connection between continuous video frames, the residual temporal shift module and the residual channel attention module are introduced to improve the modeling ability of the network on temporal information and channel information, respectively. Due to the excessive generalization of convolutional neural networks, in the memory enhancement modules, the hopping connections of each codec layer are added to limit autoencoders' ability to represent abnormal frames too vigorously and improve the anomaly detection accuracy of the network. In addition, the objective function is modified by a feature discretization loss, which effectively distinguishes different normal behavior patterns. The experimental results on the CUHK Avenue and ShanghaiTech datasets show that the proposed method is superior to the current mainstream video anomaly detection methods while meeting the real-time requirements.

cs.CV↗

arXiv2022

Data Augmentation Vision Transformer for Fine-grained Image Classification

Recently, the vision transformer (ViT) has made breakthroughs in image recognition. Its self-attention mechanism (MSA) can extract discriminative labeling information of different pixel blocks to improve image classification accuracy. However, the classification marks in their deep layers tend to ignore local features between layers. In addition, the embedding layer will be fixed-size pixel blocks. Input network Inevitably introduces additional image noise. To this end, we study a data augmentation vision transformer (DAVT) based on data augmentation and proposes a data augmentation method for attention cropping, which uses attention weights as the guide to crop images and improve the ability of the network to learn critical features. Secondly, we also propose a hierarchical attention selection (HAS) method, which improves the ability of discriminative markers between levels of learning by filtering and fusing labels between levels. Experimental results show that the accuracy of this method on the two general datasets, CUB-200-2011, and Stanford Dogs, is better than the existing mainstream methods, and its accuracy is 1.4\% and 1.6\% higher than the original ViT, respectively

cs.CV↗

arXiv2022

Efficient Unsupervised Video Object Segmentation Network Based on Motion Guidance

Due to the problem of performance constraints of unsupervised video object detection, its large-scale application is limited. In response to this pain point, we propose another excellent method to solve this problematic point. By incorporating motion characterization in unsupervised video object detection, detection accuracy is improved while reducing the computational amount of the network. The whole network structure consists of dual-stream network, motion guidance module, and multi-scale progressive fusion module. The appearance and motion representations of the detection target are obtained through a dual-stream network. Then, the semantic features of the motion representation are obtained through the local attention mechanism in the motion guidance module to obtain the high-level semantic features of the appearance representation. The multi-scale progressive fusion module then fuses the features of different deep semantic features in the dual-stream network further to improve the detection effect of the overall network. We have conducted numerous experiments on the three datasets of DAVIS 16, FBMS, and ViSal. The verification results show that the proposed method achieves superior accuracy and performance and proves the superiority and robustness of the algorithm.

cs.CV↗

arXiv2018

Reliable Identification of Redundant Kernels for Convolutional Neural Network Compression

To compress deep convolutional neural networks (CNNs) with large memory footprint and long inference time, this paper proposes a novel pruning criterion using layer-wised Ln-norm of feature maps. Different from existing pruning criteria, which are mainly based on L1-norm of convolution kernels, the proposed method utilizes Ln-norm of output feature maps after non-linear activations, where n is a variable, increasing from 1 at the first convolution layer to inf at the last convolution layer. With the ability of accurately identifying unimportant convolution kernels, the proposed method achieves a good balance between model size and inference accuracy. The experiments on ImageNet and the successful application in railway surveillance system show that the proposed method outperforms existing kernel-norm-based methods and is generally applicable to any deep neural network with convolution operations.

cs.NE↗

arXiv2015

Proton Conducting Graphene Oxide Coupled Neuron Transistors for Brain-Inspired Cognitive Systems

Neuron is the most important building block in our brain, and information processing in individual neuron involves the transformation of input synaptic spike trains into an appropriate output spike train. Hardware implementation of neuron by individual ionic/electronic hybrid device is of great significance for enhancing our understanding of the brain and solving sensory processing and complex recognition tasks. Here, we provide a proof-of-principle artificial neuron based on a proton conducting graphene oxide (GO) coupled oxide-based electric-double-layer (EDL) transistor with multiple driving inputs and one modulatory input terminal. Paired-pulse facilitation, dendritic integration and orientation tuning were successfully emulated. Additionally, neuronal gain control (arithmetic) in the scheme of rate coding is also experimentally demonstrated. Our results provide a new-concept approach for building brain-inspired cognitive systems.

q-bio.NC↗

arXiv2015

Dendritic Integration Regulation and Neuronal Arithmetic Implemented in a Proton-Coupled Neuron Transistor

Neuron is the most important building block in our brain, and information processing in individual neuron involves the transformation of input synaptic spike trains into an appropriate output spike train. Hardware implementation of neuron by individual ionic/electronic coupled device is of great importance for enhancing our understanding of the brain and solving sensory processing and complex recognition tasks. Here, we provide a proof-of-principle artificial neuron with multiple presynaptic inputs and one modulatory terminal based on a proton-coupled oxide-based electric-double-layer transistor. Regulation of dendritic integration was realized by tuning the voltage applied on the modulatory terminal. Additionally, neuronal gain control (arithmetic) in the scheme of temporal-correlated coding and rate coding are also mimicked. Our results provide a new-concept approach for building brain-inspired neuromorphic systems.

q-bio.NC↗

arXiv2013

Artificial Synaptic Arrays Intercoupled by Nanogranular Proton Conductors for Building Neuromorphic Systems

The highly parallel process in the neuron networks is mediated through a mass of synaptic interconnections. Mimicking single synapse behaviors and highly paralleled neural networks has become more and more fascinating and important. Here, oxide-based artificial synaptic arrays are fabricated on P-doped nanogranular SiO2-based proton conducting films at room temperature. Synaptic plasticity is demonstrated on individual artificial synapse. Most importantly, without any intentional hard-wired connection, such synaptic arrays are intercoupled due to the electric-field induced lateral proton modulation. The natural interconnection is weakly correlative with distance, and is important for neural networks. At last, paralleled summation is also mimicked, which provides a novel approach for building future brain-like computational systems.

cond-mat.mtrl-sci↗

arXiv2012

Junctionless dual-gate electrostatic modulation of self-aligned oxide channels by chitosan-based proton conductors

Dual-gate electrostatic modulation gives an attractive approach for transistors performance improvement, threshold voltage (Vth) and operation mode modulation, which is favorable for chemical sensor and logic applications. Here, a self-aligned junctionless semiconducting oxide channels are dual-gate electrostatic modulated by solution-processed chitosan-based proton conductors on paper substrates. The low-voltage junctionless paper transistors can be effectively tuned from depletion mode to enhancement mode by the second in-plane gate. OR logic gate was experimentally demonstrated on such dual-in-plane gate junctionless transistors. Such dual-gate organic/inorganic hybrid paper transistors are promising for portable paper electronics.

cond-mat.mtrl-sci↗

arXiv2010

A model for semantic integration of business components

Today, reusable components are available in several repositories. These last are certainly conceived for the reusing However, this re-use is not immediate; it requires, in the fact, to pass through some essential conceptual operations, among them in particular, research, integration, adaptation, and composition. We are interested in the present work to the problem of semantic integration of heterogeneous Business Components. This problem is often put in syntactical terms, while the real stake is of semantic order. Our contribution concerns a model proposal for Business components integration as well as resolution method of semantic naming conflicts, met during the integration of Business Components.

cs.SE↗