arXiv subjects

Zhong Liu

Publications and source records attributed to Zhong Liu.

At least 55 records · Page 3Linked to original sources

arXiv2023

Can the Parker Solar Probe Detect a CME-flare Current Sheet?

A current sheet (CS) is the central structure in the disrupting magnetic configuration during solar eruptions. More than 90\% of the free magnetic energy (the difference between the energy in the non-potential magnetic field and that in the potential one) stored in the coronal magnetic field beforehand is converted into heating and kinetic energy of the plasma, as well as accelerating charged particles, by magnetic reconnection occurring in the CS. However, the detailed physical properties and fine structures of the CS are still unknown since there is no relevant information obtained via in situ detections. The Parker Solar Probe (PSP) may provide us such information should it traverse a CS in the eruption. The perihelion of PSP's final orbit is located at about 10 solar radii from the center of the Sun, so it can observe the CS at a very close distance, or even traverses the CS, which provides us a unique opportunity to look into fine properties and structures of the CS, helping reveal the detailed physics of large-scale reconnection that was impossible before. We evaluate the probability that PSP can traverse a CS, and examine the orbit of a PSP-like spacecraft that has the highest probability to traverse a CS.

astro-ph.SR↗

arXiv2023

Self-Supervised Monocular Depth Estimation with Self-Reference Distillation and Disparity Offset Refinement

Monocular depth estimation plays a fundamental role in computer vision. Due to the costly acquisition of depth ground truth, self-supervised methods that leverage adjacent frames to establish a supervisory signal have emerged as the most promising paradigms. In this work, we propose two novel ideas to improve self-supervised monocular depth estimation: 1) self-reference distillation and 2) disparity offset refinement. Specifically, we use a parameter-optimized model as the teacher updated as the training epochs to provide additional supervision during the training process. The teacher model has the same structure as the student model, with weights inherited from the historical student model. In addition, a multiview check is introduced to filter out the outliers produced by the teacher model. Furthermore, we leverage the contextual consistency between high-scale and low-scale features to obtain multiscale disparity offsets, which are used to refine the disparity output incrementally by aligning disparity information at different scales. The experimental results on the KITTI and Make3D datasets show that our method outperforms previous state-of-the-art competitors.

cs.CV↗

arXiv2023

Reply to: Deep reinforced learning heuristic tested on spin-glass ground states: The larger picture

We wish to thank Stefan Boettcher for prompting us to further check and highlight the accuracy and scaling of our results. Here we provide a comprehensive response to the Comment written by him. We argue that the Comment did not account for the fairness of the comparison between different methods in searching for the spin-glass ground states. We demonstrate that, with a reasonably larger number of initial spin configurations, our results agree with the asymptotic scaling form assumed by finite-size corrections.

cond-mat.dis-nn↗

arXiv2023

URCDC-Depth: Uncertainty Rectified Cross-Distillation with CutFlip for Monocular Depth Estimation

This work aims to estimate a high-quality depth map from a single RGB image. Due to the lack of depth clues, making full use of the long-range correlation and the local information is critical for accurate depth estimation. Towards this end, we introduce an uncertainty rectified cross-distillation between Transformer and convolutional neural network (CNN) to learn a unified depth estimator. Specifically, we use the depth estimates from the Transformer branch and the CNN branch as pseudo labels to teach each other. Meanwhile, we model the pixel-wise depth uncertainty to rectify the loss weights of noisy pseudo labels. To avoid the large capacity gap induced by the strong Transformer branch deteriorating the cross-distillation, we transfer the feature maps from Transformer to CNN and design coupling units to assist the weak CNN branch to leverage the transferred features. Furthermore, we propose a surprisingly simple yet highly effective data augmentation technique CutFlip, which enforces the model to exploit more valuable clues apart from the vertical image position for depth inference. Extensive experiments demonstrate that our model, termed~\textbf{URCDC-Depth}, exceeds previous state-of-the-art methods on the KITTI, NYU-Depth-v2 and SUN RGB-D datasets, even with no additional computational burden at inference time. The source code is publicly available at \url{https://github.com/ShuweiShao/URCDC-Depth}.

cs.CV↗

arXiv2023

Time Encoding Sampling of Bandpass Signals

This paper investigates the problem of sampling and reconstructing bandpass signals using time encoding machine(TEM). It is shown that the sampling in principle is equivalent to periodic non-uniform sampling (PNS). Then the TEM parameters can be set according to the signal bandwidth and amplitude instead of upper-edge frequency and amplitude as in the case of bandlimited/lowpass signals. For a bandpass signal of a single information band, it can be perfectly reconstructed if the TEM parameters are such that the difference between any consecutive values of the time sequence in each channel is bounded by the inverse of the signal bandwidth. A reconstruction method incorporating the interpolation functions of PNS is proposed. Numerical experiments validate the feasibility and effectiveness of the proposed TEM scheme.

cs.IT↗

arXiv2022

Real-time Local Feature with Global Visual Information Enhancement

Local feature provides compact and invariant image representation for various visual tasks. Current deep learning-based local feature algorithms always utilize convolution neural network (CNN) architecture with limited receptive field. Besides, even with high-performance GPU devices, the computational efficiency of local features cannot be satisfactory. In this paper, we tackle such problems by proposing a CNN-based local feature algorithm. The proposed method introduces a global enhancement module to fuse global visual clues in a light-weight network, and then optimizes the network by novel deep reinforcement learning scheme from the perspective of local feature matching task. Experiments on the public benchmarks demonstrate that the proposal can achieve considerable robustness against visual interference and meanwhile run in real time.

cs.CV↗

arXiv2022

Production of proton-rich nuclei in the vicinity of 100Sn via multinucleon transfer reactions

The production of new proton-rich nuclei in the vicinity of 100Sn is investigated via multinucleon transfer reactions within the framework of microscopic time-dependent Hartree-Fock (TDHF) and statistical model GEMINI++. The TDHF+GEMINI method has demonstrated the reliable description in the multinucleon transfer dynamics and the agreement between theoretical results and experimental data is quite satisfactory in the observed transfer reactions. We reveal the production cross sections of proton-rich nuclei in 100Sn region via multinucleon transfer reactions to be several orders of magnitude higher than those measured via fusion-evaporation and projectile fragmentation experiments. About 19 new proton-rich isotopes with cross sections of larger than 1 nb are predicted to be produced in multinucleon transfer reaction of 58Ni with 112Sn. The reaction mechanisms are discussed to lead the experimental production of these previously unreported nuclei. Multinucleon transfer reactions provide a fascinating possibility to reach the proton drip-line in 100Sn region and beyond.

nucl-th↗

arXiv2022

Systematic shell-model study on spectroscopic properties in the south region of $^{208}$Pb

We aim to study the properties of nuclei in the south region of $^{208}$Pb systematically, including the binding and excitation energies and electromagnetic properties, in order to predict unknown properties of these nuclei, such as isomerism, utilizing a theoretical model which describes the experimentally known properties precisely. We also address whether the $N=126$ shell closure is robust or not when the proton number decreases from $^{208}$Pb. We performed large-scale shell-model calculations with a new Hamiltonian suggested in the present work. The model space is taken as the five proton orbits within $50<Z\leqslant82$ and the thirteen neutron orbits within $82<N\leqslant184$. And one-particle one-hole excitation is allowed across the $N=126$ gap. The Hamiltonian is constructed by combining the existing Hamiltonians, KHHE (with adjustment of its proton-proton part) and KHPE, and the monopole based universal interaction. The shell-model results well reproduce the experimentally observed binding energies and spectroscopic properties, such as isomerism, core excitation, and electromagnetic properties. Some possible isomeric states in neutron-rich Pb, Tl, and Hg isotopes are predicted with transition energies and half-lives. We also examine the effective charges and the quenching of the $g$ factors suitable for this region by systematic comparisons between observed and calculated electromagnetic properties. A new Hamiltonian is constructed for nuclei in the south region of $^{208}$Pb, mainly including Pb, Tl, Hg, Au, Pt, Ir, Os, Re, and W isotopes around $N=126$, and provides them reasonable descriptions on nuclear properties including binding energies, excitation energies and electromagnetic properties through comprehensive and systematic studies.

nucl-th↗

arXiv2022

Continuously Doping Bi 2 Sr 2 CaCu 2 O 8+δ into Electron-Doped Superconductor by CaH 2 Annealing Method

As a typical hole-doped cuprate superconductor, Bi 2 Sr 2 CaCu 2 O 8+δ (Bi2212) carrier doping is mostly determined by its oxygen content. Traditional doping methods can regulate its doping level within the range of hole doping. Here we report the first application of CaH 2 annealing method in regulating the doping level of Bi2212. By continuously controlling the anneal time, a series of differently doped samples can be obtained. The combined experimental results of x-ray diffraction, scanning transmission electron microscopy, resistance and Hall measurements demonstrate that the CaH 2 induced topochemical reaction can effectively change the oxygen content of Bi2212 within a very wide range, even switching from hole doping to electron doping. We also found evidence of a low-T c superconducting phase in the electron doping side.

cond-mat.supr-con↗

arXiv2022

Multi-ship cooperative air defense model based on queuing theory

The study of the multi-ship air defense model is of great significance in the simulation and evaluation of the actual combat process, the demonstration of air defense tactics, and the improvement of the security of important targets. The traditional multi-ship air defense model does not consider the coordination between ships, and the model assumptions are often too simple to effectively describe the capabilities of the multi-ship cooperative air defense system in realistic combat scenarios. In response to the above problems, this paper proposes a multi-ship cooperative air defense model, which effectively integrates the attack and defense parameters of both sides such as missile launch rate, missile flight speed, missile launch direction, ship interception rate, ship interception range, and the number of ship interception fire units. Then, the cooperative interception capability among ships is modeled by the method of task assignment. Based on the queuing theory, this paper strictly deduces the penetration probability of the cooperative air defense system, and provides an analytical calculation model for the analysis and design of the cooperative air defense system. Finally, through simulation experiments in typical scenarios, this paper studies and compares the air defense capabilities of the system in two different modes with and without coordination, and verifies the superiority of the multi-ship cooperative air defense model in reducing the probability of missile penetration; Further, the ability changes of the defense system under different parameters such as missile speed, speed, angle, ship interception rate, range, and number of fire units are studied, and the weak points of the defense formation, defense range settings, and interception settings are obtained.

eess.SY↗

arXiv2022

A formation mechanism for the large plumes in the prominence

To understand the formation mechanism of large plumes in solar prominences, we investigate the formation process of two such phenomena. We studied the dynamic and thermal properties of two large plumes using observations from New Vacuum Solar Telescope, the Solar Dynamic Observatory, and the Solar Terrestrial Relations Observatory-Ahead. We find that two large plumes observed with high-resolution data are quite different from previously studied small-scale plumes. They are born at the top of a prominence bubble with a large projected area of 10-20 Mm^2 . Before the occurrence of each large plume, the bubble expands and takes on a quasi-semicircular appearance. Meanwhile, the emission intensity of extreme-ultra-violet (EUV) bands increases in the bubble. A small-scale filament is found to erupt in the bubble during the second large plume. At the point at which the height of the bubble is comparable with half the width of the bubble, the bubble becomes unstable and generates the plumes. During the formation of plumes, two side edges of the top of the bubble, which are dominated by opposite Doppler signals, approach each other. The large plume then emerges and keeps rising up with a constant speed of about 13-15 km/s. These two large plumes have temperatures of 1.3 x 10^6 Kelvin and densities of 2.0 x 10^9 cm^-3, two orders hotter and one order less dense than the typical prominence. We also find that the bubble is a hot, low-density volume instead of a void region beneath the cold and dense prominence. Therefore, we conclude that these two large plumes are the result of the breakup of the prominence bubble triggered by an enhancement of thermal pressure; they separate from the bubble, most likely by magnetic reconnection.

astro-ph.SR↗

arXiv2022

Space-Time Adaptive Processing Using Random Matrix Theory Under Limited Training Samples

Space-time adaptive processing (STAP) is one of the most effective approaches to suppressing ground clutters in airborne radar systems. It basically takes two forms, i.e., full-dimension STAP (FD-STAP) and reduced-dimension STAP (RD-STAP). When the numbers of clutter training samples are less than two times their respective system degrees-of-freedom (DOF), the performances of both FD-STAP and RD-STAP degrade severely due to inaccurate clutter estimation. To enhance STAP performance under the limited training samples, this paper develops a STAP theory with random matrix theory (RMT). By minimizing the output clutter-plus-noise power, the estimate of the inversion of clutter plus noise covariance matrix (CNCM) can be obtained through optimally manipulating its eigenvalues, and thus producing the optimal STAP weight vector. Two STAP algorithms, FD-STAP using RMT (RMT-FD-STAP) and RD-STAP using RMT (RMT-RD-STAP), are proposed. It is found that both RMT-FD-STAP and RMT-RD-STAP greatly outperform other-related STAP algorithms when the numbers of training samples are larger than their respective clutter DOFs, which are much less than the corresponding system DOFs. Theoretical analyses and simulation demonstrate the effectiveness and the performance advantages of the proposed STAP algorithms.

cs.IT↗

arXiv2022

Attentional Feature Refinement and Alignment Network for Aircraft Detection in SAR Imagery

Aircraft detection in Synthetic Aperture Radar (SAR) imagery is a challenging task in SAR Automatic Target Recognition (SAR ATR) areas due to aircraft's extremely discrete appearance, obvious intraclass variation, small size and serious background's interference. In this paper, a single-shot detector namely Attentional Feature Refinement and Alignment Network (AFRAN) is proposed for detecting aircraft in SAR images with competitive accuracy and speed. Specifically, three significant components including Attention Feature Fusion Module (AFFM), Deformable Lateral Connection Module (DLCM) and Anchor-guided Detection Module (ADM), are carefully designed in our method for refining and aligning informative characteristics of aircraft. To represent characteristics of aircraft with less interference, low-level textural and high-level semantic features of aircraft are fused and refined in AFFM throughly. The alignment between aircraft's discrete back-scatting points and convolutional sampling spots is promoted in DLCM. Eventually, the locations of aircraft are predicted precisely in ADM based on aligned features revised by refined anchors. To evaluate the performance of our method, a self-built SAR aircraft sliced dataset and a large scene SAR image are collected. Extensive quantitative and qualitative experiments with detailed analysis illustrate the effectiveness of the three proposed components. Furthermore, the topmost detection accuracy and competitive speed are achieved by our method compared with other domain-specific,e.g., DAPN, PADN, and general CNN-based methods,e.g., FPN, Cascade R-CNN, SSD, RefineDet and RPDet.

cs.CV↗

arXiv2021

DSRGAN: Detail Prior-Assisted Perceptual Single Image Super-Resolution via Generative Adversarial Networks

The generative adversarial network (GAN) is successfully applied to study the perceptual single image superresolution (SISR). However, the GAN often tends to generate images with high frequency details being inconsistent with the real ones. Inspired by conventional detail enhancement algorithms, we propose a novel prior knowledge, the detail prior, to assist the GAN in alleviating this problem and restoring more realistic details. The proposed method, named DSRGAN, includes a well designed detail extraction algorithm to capture the most important high frequency information from images. Then, two discriminators are utilized for supervision on image-domain and detail-domain restorations, respectively. The DSRGAN merges the restored detail into the final output via a detail enhancement manner. The special design of DSRGAN takes advantages from both the model-based conventional algorithm and the data-driven deep learning network. Experimental results demonstrate that the DSRGAN outperforms the state-of-the-art SISR methods on perceptual metrics and achieves comparable results in terms of fidelity metrics simultaneously. Following the DSRGAN, it is feasible to incorporate other conventional image processing algorithms into a deep learning network to form a model-based deep SISR.

eess.IV↗

arXiv2021

PTR-PPO: Proximal Policy Optimization with Prioritized Trajectory Replay

On-policy deep reinforcement learning algorithms have low data utilization and require significant experience for policy improvement. This paper proposes a proximal policy optimization algorithm with prioritized trajectory replay (PTR-PPO) that combines on-policy and off-policy methods to improve sampling efficiency by prioritizing the replay of trajectories generated by old policies. We first design three trajectory priorities based on the characteristics of trajectories: the first two being max and mean trajectory priorities based on one-step empirical generalized advantage estimation (GAE) values and the last being reward trajectory priorities based on normalized undiscounted cumulative reward. Then, we incorporate the prioritized trajectory replay into the PPO algorithm, propose a truncated importance weight method to overcome the high variance caused by large importance weights under multistep experience, and design a policy improvement loss function for PPO under off-policy conditions. We evaluate the performance of PTR-PPO in a set of Atari discrete control tasks, achieving state-of-the-art performance. In addition, by analyzing the heatmap of priority changes at various locations in the priority memory during training, we find that memory size and rollout length can have a significant impact on the distribution of trajectory priorities and, hence, on the performance of the algorithm.

cs.LG↗

arXiv2021

Finding spin glass ground states through deep reinforcement learning

Spin glasses are disordered magnets with random interactions that are, generally, in conflict with each other. Finding the ground states of spin glasses is not only essential for the understanding of the nature of disordered magnetic and other physical systems, but also useful to solve a broad array of hard combinatorial optimization problems across multiple disciplines. Despite decades-long efforts, an algorithm with both high accuracy and high efficiency is still lacking. Here we introduce DIRAC - a deep reinforcement learning framework, which can be trained purely on small-scale spin glass instances and then applied to arbitrarily large ones. DIRAC displays better scalability than other methods and can be leveraged to enhance any thermal annealing method. Extensive calculations on 2D, 3D and 4D Edwards-Anderson spin glass instances demonstrate the superior performance of DIRAC over existing methods. As many hard combinatorial optimization problems have Ising spin glass formulations, our results suggest a promising tool in solving these hard problems. Moreover, the presented algorithm will help us better understand the nature of the low-temperature spin-glass phase, which is a fundamental challenge in statistical physics.

cond-mat.dis-nn↗

arXiv2021

Discriminative and Semantic Feature Selection for Place Recognition towards Dynamic Environments

Features play an important role in various visual tasks, especially in visual place recognition applied in perceptual changing environments. In this paper, we address the challenges of place recognition due to dynamics and confusable patterns by proposing a discriminative and semantic feature selection network, dubbed as DSFeat. Supervised by both semantic information and attention mechanism, we can estimate pixel-wise stability of features, indicating the probability of a static and stable region from which features are extracted, and then select features that are insensitive to dynamic interference and distinguishable to be correctly matched. The designed feature selection model is evaluated in place recognition and SLAM system in several public datasets with varying appearances and viewpoints. Experimental results conclude that the effectiveness of the proposed method. It should be noticed that our proposal can be readily pluggable into any feature-based SLAM system.

cs.CV↗

arXiv2020

On the Mutual Interference between Spaceborne SARs: Modeling, Characterization, and Mitigation

Since by the International Telecommunications Union (ITU) regulatory the radio spectrum available to spaceborne synthetic aperture radar (SAR) is restricted to certain limited frequency intervals, there are many different spaceborne SAR systems sharing common frequency bands. Due to this reason, it is reported that two spaceborne SARs at orbit cross positions can potentially cause severe mutual interference. Specifically, the transmitting signal of a SAR, typically linear frequency modulated (LFM), can be directly received by the side or back lobes of another SAR's antenna, causing radiometric artefacts in the focused image. This paper tries to model and characterize the artefacts, and study efficient methods for mitigating them. To this end, we formulate an analytical model for describing the artefact, which reveals that the mutual interference can introduce a two-dimensional LFM radiometric artefact in image domain with a limited spatial extent. We show that the artefact is low-rank based on range-azimuth decoupling analysis and two-dimensional high-order Taylor expansion. Based on the low rank model, we show that two methods, i.e., principle component analysis and its robust variant, can be adopted to efficiently mitigate the artefact via processing in image domain. The former method has the advantage of fast processing speed, for example, a sub-swath of Sentinel-1 interferometric wide swath image can be processed within 70 seconds via block-wise operation, whereas the latter provides improved accuracy for sparse point-like scatterers. Experiment results demonstrate that the radiometric artefacts caused by mutual interference in Sentinel-1 level-1 images can be efficiently mitigated via the proposed methods.

eess.SP↗