arXiv subjects

Song Zhang

Publications and source records attributed to Song Zhang.

At least 55 records · Page 3Linked to original sources

arXiv2024

Effects of the $α$-cluster structure and the intrinsic momentum component of nuclei on the longitudinal asymmetry in relativistic heavy-ion collisions

The longitudinal asymmetry in relativistic heavy ion collisions arises from the fluctuation in the number of nucleons involved. This asymmetry causes a rapidity shift in the center of mass of the participating zone. Both the rapidity shift and the longitudinal asymmetry have been found to be significant at the top CERN Large Hadron Collider (LHC) energy for collisions of identical nuclei, and the longitudinal asymmetry is important for reconstructing the colliding vertex and correcting the rapidity shift. However, much discussion of the longitudinal asymmetry has treated the initial condition as a nonzero momentum contributed only by the number of participants, i.e., the asymmetry depends only on the number of participating nucleons. So we naturally raise a physical problem, can other initial conditions, such as two typical initial conditions for nuclei, geometric configuration, and momentum distribution, provide effects on the longitudinal asymmetry? Therefore, in this work we consider other effects on the longitudinal asymmetry other than the fluctuation in the number of participants, e.g., the α clustering structure as well as the intrinsic momentum distribution in the target and projectile nuclei for the collisions in the framework of a multiphase transport (AMPT) model. By introducing systems with different α-clustering structure and intrinsic momentum distribution, we calculated the ratio of the rapidity distributions of different systems and extracted expansion coefficients to analyze the difference contributed by these factors. ...

hep-ph↗

arXiv2023

A Multi-Qubit Quantum Gate Using the Zeno Effect

The Zeno effect, in which repeated observation freezes the dynamics of a quantum system, stands as an iconic oddity of quantum mechanics. When a measurement is unable to distinguish between states in a subspace, the dynamics within that subspace can be profoundly altered, leading to non-trivial behavior. Here we show that such a measurement can turn a non-interacting system with only single-qubit control into a two- or multi-qubit entangling gate, which we call a Zeno gate. The gate works by imparting a geometric phase on the system, conditioned on it lying within a particular nonlocal subspace. We derive simple closed-form expressions for the gate fidelity under a number of non-idealities and show that the gate is viable for implementation in circuit and cavity QED systems. More specifically, we illustrate the functioning of the gate via dispersive readout in both the Markovian and non-Markovian readout regimes, and derive conditions for longitudinal readout to ideally realize the gate.

quant-ph↗

arXiv2023

Impact of inherent energy barrier on spin-orbit torques in magnetic-metal/semimetal heterojunctions

Spintronic devices are based on heterojunctions of two materials with different magnetic and electronic properties. Although an energy barrier is naturally formed even at the interface of metallic heterojunctions, its impact on spin transport has been overlooked. Here, using diffusive spin Hall currents, we provide evidence that the inherent energy barrier governs the spin transport even in metallic systems. We find a sizable field-like torque, much larger than the damping-like counterpart, in Ni$_{81}$Fe$_{19}$/Bi$_{0.1}$Sb$_{0.9}$ bilayers. This is a distinct signature of barrier-mediated spin-orbit torques, which is consistent with our theory that predicts a strong modification of the spin mixing conductance induced by the energy barrier. Our results suggest that the spin mixing conductance and the corresponding spin-orbit torques are strongly altered by minimizing the work function difference in the heterostructure. These findings provide a new mechanism to control spin transport and spin torque phenomena by interfacial engineering of metallic heterostructures.

cond-mat.mtrl-sci↗

arXiv2023

ARAI-MVSNet: A multi-view stereo depth estimation network with adaptive depth range and depth interval

Multi-View Stereo~(MVS) is a fundamental problem in geometric computer vision which aims to reconstruct a scene using multi-view images with known camera parameters. However, the mainstream approaches represent the scene with a fixed all-pixel depth range and equal depth interval partition, which will result in inadequate utilization of depth planes and imprecise depth estimation. In this paper, we present a novel multi-stage coarse-to-fine framework to achieve adaptive all-pixel depth range and depth interval. We predict a coarse depth map in the first stage, then an Adaptive Depth Range Prediction module is proposed in the second stage to zoom in the scene by leveraging the reference image and the obtained depth map in the first stage and predict a more accurate all-pixel depth range for the following stages. In the third and fourth stages, we propose an Adaptive Depth Interval Adjustment module to achieve adaptive variable interval partition for pixel-wise depth range. The depth interval distribution in this module is normalized by Z-score, which can allocate dense depth hypothesis planes around the potential ground truth depth value and vice versa to achieve more accurate depth estimation. Extensive experiments on four widely used benchmark datasets~(DTU, TnT, BlendedMVS, ETH 3D) demonstrate that our model achieves state-of-the-art performance and yields competitive generalization ability. Particularly, our method achieves the highest Acc and Overall on the DTU dataset, while attaining the highest Recall and $F_{1}$-score on the Tanks and Temples intermediate and advanced dataset. Moreover, our method also achieves the lowest $e_{1}$ and $e_{3}$ on the BlendedMVS dataset and the highest Acc and $F_{1}$-score on the ETH 3D dataset, surpassing all listed methods.Project website: https://github.com/zs670980918/ARAI-MVSNet

cs.CV↗

arXiv2023

Pixel-wise rational model for structured light system

This Letter presents a novel structured light system model that effectively considers local lens distortion by pixel-wise rational functions. We leverage the stereo method for initial calibration and then estimate the rational model for each pixel. Our proposed model can achieve high measurement accuracy within and outside the calibration volume, demonstrating its robustness and accuracy.

physics.optics↗

arXiv2023

A Unified HDR Imaging Method with Pixel and Patch Level

Mapping Low Dynamic Range (LDR) images with different exposures to High Dynamic Range (HDR) remains nontrivial and challenging on dynamic scenes due to ghosting caused by object motion or camera jitting. With the success of Deep Neural Networks (DNNs), several DNNs-based methods have been proposed to alleviate ghosting, they cannot generate approving results when motion and saturation occur. To generate visually pleasing HDR images in various cases, we propose a hybrid HDR deghosting network, called HyHDRNet, to learn the complicated relationship between reference and non-reference images. The proposed HyHDRNet consists of a content alignment subnetwork and a Transformer-based fusion subnetwork. Specifically, to effectively avoid ghosting from the source, the content alignment subnetwork uses patch aggregation and ghost attention to integrate similar content from other non-reference images with patch level and suppress undesired components with pixel level. To achieve mutual guidance between patch-level and pixel-level, we leverage a gating module to sufficiently swap useful information both in ghosted and saturated regions. Furthermore, to obtain a high-quality HDR image, the Transformer-based fusion subnetwork uses a Residual Deformable Transformer Block (RDTB) to adaptively merge information for different exposed regions. We examined the proposed method on four widely used public HDR image deghosting datasets. Experiments demonstrate that HyHDRNet outperforms state-of-the-art methods both quantitatively and qualitatively, achieving appealing HDR visualization with unified textures and colors.

cs.CV↗

arXiv2023

SMAE: Few-shot Learning for HDR Deghosting with Saturation-Aware Masked Autoencoders

Generating a high-quality High Dynamic Range (HDR) image from dynamic scenes has recently been extensively studied by exploiting Deep Neural Networks (DNNs). Most DNNs-based methods require a large amount of training data with ground truth, requiring tedious and time-consuming work. Few-shot HDR imaging aims to generate satisfactory images with limited data. However, it is difficult for modern DNNs to avoid overfitting when trained on only a few images. In this work, we propose a novel semi-supervised approach to realize few-shot HDR imaging via two stages of training, called SSHDR. Unlikely previous methods, directly recovering content and removing ghosts simultaneously, which is hard to achieve optimum, we first generate content of saturated regions with a self-supervised mechanism and then address ghosts via an iterative semi-supervised learning framework. Concretely, considering that saturated regions can be regarded as masking Low Dynamic Range (LDR) input regions, we design a Saturated Mask AutoEncoder (SMAE) to learn a robust feature representation and reconstruct a non-saturated HDR image. We also propose an adaptive pseudo-label selection strategy to pick high-quality HDR pseudo-labels in the second stage to avoid the effect of mislabeled samples. Experiments demonstrate that SSHDR outperforms state-of-the-art methods quantitatively and qualitatively within and across different datasets, achieving appealing HDR visualization with few labeled samples.

cs.CV↗

arXiv2023

Noisy intermediate-scale quantum computers

Quantum computers have made extraordinary progress over the past decade, and significant milestones have been achieved along the path of pursuing universal fault-tolerant quantum computers. Quantum advantage, the tipping point heralding the quantum era, has been accomplished along with several waves of breakthroughs. Quantum hardware has become more integrated and architectural compared to its toddler days. The controlling precision of various physical systems is pushed beyond the fault-tolerant threshold. Meanwhile, quantum computation research has established a new norm by embracing industrialization and commercialization. The joint power of governments, private investors, and tech companies has significantly shaped a new vibrant environment that accelerates the development of this field, now at the beginning of the noisy intermediate-scale quantum era. Here, we first discuss the progress achieved in the field of quantum computation by reviewing the most important algorithms and advances in the most promising technical routes, and then summarizing the next-stage challenges. Furthermore, we illustrate our confidence that solid foundations have been built for the fault-tolerant quantum computer and our optimism that the emergence of quantum killer applications essential for human society shall happen in the future.

quant-ph↗

arXiv2023

Collective flows of protons and deuterons in Au + Au collisions at $E_{beam}$ = 1.23$A$ GeV by the IQMD model

Collective flows of protons and deuterons for Au + Au collisions at beam energy $E_{beam}$ = 1.23 $A$ GeV were simulated by an Isospin dependent Quantum Molecular Dynamics (IQMD) model. Two coalescence models, namely naive coalescence and dynamical coalescence models, for the formation of deuterons are compared. After reasonable match of rapidity spectra of protons and deuterons to the High Acceptance DiElectron Spectrometer (HADES) data is reached, we apply an event-plane method to calculate the first four-order collective flow coefficients as well as the ratios of $ / ^2$ and $ / $, and observe the number of constituent nucleon scaling among protons and deuterons. In addition, the dependence of $ε_n$ and $v_n$ as well as the ratio $ $/$ε_n$ on the centrality is obtained. Lastly, we further investigate the Pearson coefficients $corr(v_n,v_m)$ between the first four harmonic flows for protons and deuterons as a function of rapidity and centrality.

nucl-th↗

arXiv2023

Simulations of momentum correlation functions of light (anti)nuclei in relativistic heavy-ion collisions at $\sqrt{s_{NN}}$ = 39 GeV

Momentum correlation functions of light (anti)nuclei formed by the coalescence mechanism of (anti)nucleons are calculated for several central heavy-ion collision systems, namely $_{5}^{10}\textrm{B}+_{5}^{10}\textrm{B}$, $_{8}^{16}\textrm{O}+_{8}^{16}\textrm{O}$, $_{20}^{40}\textrm{Ca}+_{20}^{40}\textrm{Ca}$ as well as $_{79}^{197}\textrm{Au}+_{79}^{197}\textrm{Au}$ in different centralities at center of mass energy $\sqrt{s_{NN}}$ = 39 GeV within the framework of A Multi-Phase Transport (AMPT) model complemented by the Lednick$\acute{y}$ and Lyuboshitz analytical method. Momentum correlation functions for identical or nonidentical light (anti)nuclei are constructed and analyzed for the above collision systems. The Au + Au results demonstrate that emission of light (anti)nuclei occurs from a source with smaller space extent in more peripheral collisions. The effect of system-size on the momentum correlation functions of identical or nonidentical light (anti)nuclei is also explored by several collision system in central collisions. The results indicate that the emission source-size of light (anti)nuclei pairs deduced from their momentum correlation functions and system-size is self-consistent. Momentum correlation functions of nonidentical light nuclei pairs gated on velocity are applied to infer the average emission sequence of them. The results illustrate that protons are emitted in average on a similar time scale with neutrons but earlier than deuterons or tritons in the small relative momentum region. In addition, larger interval of the average emission order among them is exhibited for smaller collision systems or at more peripheral collisions.

hep-ph↗

arXiv2022

TiG-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning

To achieve accurate and low-cost 3D object detection, existing methods propose to benefit camera-based multi-view detectors with spatial cues provided by the LiDAR modality, e.g., dense depth supervision and bird-eye-view (BEV) feature distillation. However, they directly conduct point-to-point mimicking from LiDAR to camera, which neglects the inner-geometry of foreground targets and suffers from the modal gap between 2D-3D features. In this paper, we propose the learning scheme of Target Inner-Geometry from the LiDAR modality into camera-based BEV detectors for both dense depth and BEV features, termed as TiG-BEV. First, we introduce an inner-depth supervision module to learn the low-level relative depth relations between different foreground pixels. This enables the camera-based detector to better understand the object-wise spatial structures. Second, we design an inner-feature BEV distillation module to imitate the high-level semantics of different keypoints within foreground targets. To further alleviate the BEV feature gap between two modalities, we adopt both inter-channel and inter-keypoint distillation for feature-similarity modeling. With our target inner-geometry distillation, TiG-BEV can effectively boost BEVDepth by +2.3% NDS and +2.4% mAP, along with BEVDet by +9.1% NDS and +10.3% mAP on nuScenes val set. Code will be available at https://github.com/ADLab3Ds/TiG-BEV.

cs.CV↗

arXiv2022

Global constraint on the magnitude of anomalous chiral effects in heavy-ion collisions

When searching for anomalous chiral effects in heavy-ion collisions, one of the most crucial points is the relationship between the signal and the background. In this letter, we present a simulation in a modified blast wave model at LHC energy, which can simultaneously characterize the majority of measurable quantities, in particular, the chiral magnetic effect (CME) and the chiral magnetic wave (CMW) observables. Such a universal description, for the first time, naturally and quantitatively unifies the CME and the CMW studies and brings to light the connection with the local charge conservation (LCC) background. Moreover, a simple phenomenological approach is performed to introduce the signals, aiming at quantifying the maximum allowable strength of the signals within experimental precision. Such a constraint provides a novel perspective to understand the experimental data and sheds new light on the study of anomalous chiral effects as well as charge dependent correlations.

nucl-th↗

arXiv2022

Self-Supervised Intensity-Event Stereo Matching

Event cameras are novel bio-inspired vision sensors that output pixel-level intensity changes in microsecond accuracy with a high dynamic range and low power consumption. Despite these advantages, event cameras cannot be directly applied to computational imaging tasks due to the inability to obtain high-quality intensity and events simultaneously. This paper aims to connect a standalone event camera and a modern intensity camera so that the applications can take advantage of both two sensors. We establish this connection through a multi-modal stereo matching task. We first convert events to a reconstructed image and extend the existing stereo networks to this multi-modality condition. We propose a self-supervised method to train the multi-modal stereo network without using ground truth disparity data. The structure loss calculated on image gradients is used to enable self-supervised learning on such multi-modal data. Exploiting the internal stereo constraint between views with different modalities, we introduce general stereo loss functions, including disparity cross-consistency loss and internal disparity loss, leading to improved performance and robustness compared to existing approaches. The experiments demonstrate the effectiveness of the proposed method, especially the proposed general stereo loss functions, on both synthetic and real datasets. At last, we shed light on employing the aligned events and intensity images in downstream tasks, e.g., video interpolation application.

cs.CV↗

arXiv2022

Specific heat and its high-order moments in relativistic heavy-ion collisions from a multiphase transport model

Energy dependence of specific heat extracted from temperature fluctuation of Au + Au collisions at $\sqrt{s_{NN}}$ = 7.7 GeV to 200 GeV was investigated by using a multiphase transport (AMPT) model. The results were compared with those from other models and some differences at low $\sqrt{s_{NN}}$ were found. To explain the above differences and describe the properties of the hot dense matter at low $\sqrt{s_{NN}}$, a new quantity $C_v^{*}$ was derived for describing specific heat in heavy-ion collisions. It was found that, by using $C_v^{*}$ together with its high order moments (skewness and kurtosis), thermal properties of the hot dense matter can be described and different thermal properties with or without parton process can be clearly distinguished. The proposed observable provides a way to learn the property of QCD matter % phase transition %along with critical point in heavy-ion collisions.

nucl-th↗

arXiv2022

Machine Learning for Continuous Quantum Error Correction on Superconducting Qubits

Continuous quantum error correction has been found to have certain advantages over discrete quantum error correction, such as a reduction in hardware resources and the elimination of error mechanisms introduced by having entangling gates and ancilla qubits. We propose a machine learning algorithm for continuous quantum error correction that is based on the use of a recurrent neural network to identify bit-flip errors from continuous noisy syndrome measurements. The algorithm is designed to operate on measurement signals deviating from the ideal behavior in which the mean value corresponds to a code syndrome value and the measurement has white noise. We analyze continuous measurements taken from a superconducting architecture using three transmon qubits to identify three significant practical examples of non-ideal behavior, namely auto-correlation at temporal short lags, transient syndrome dynamics after each bit-flip, and drift in the steady-state syndrome values over the course of many experiments. Based on these real-world imperfections, we generate synthetic measurement signals from which to train the recurrent neural network, and then test its proficiency when implementing active error correction, comparing this with a traditional double threshold scheme and a discrete Bayesian classifier. The results show that our machine learning protocol is able to outperform the double threshold protocol across all tests, achieving a final state fidelity comparable to the discrete Bayesian classifier.

quant-ph↗

arXiv2022

A Polyphone BERT for Polyphone Disambiguation in Mandarin Chinese

Grapheme-to-phoneme (G2P) conversion is an indispensable part of the Chinese Mandarin text-to-speech (TTS) system, and the core of G2P conversion is to solve the problem of polyphone disambiguation, which is to pick up the correct pronunciation for several candidates for a Chinese polyphonic character. In this paper, we propose a Chinese polyphone BERT model to predict the pronunciations of Chinese polyphonic characters. Firstly, we create 741 new Chinese monophonic characters from 354 source Chinese polyphonic characters by pronunciation. Then we get a Chinese polyphone BERT by extending a pre-trained Chinese BERT with 741 new Chinese monophonic characters and adding a corresponding embedding layer for new tokens, which is initialized by the embeddings of source Chinese polyphonic characters. In this way, we can turn the polyphone disambiguation task into a pre-training task of the Chinese polyphone BERT. Experimental results demonstrate the effectiveness of the proposed model, and the polyphone BERT model obtain 2% (from 92.1% to 94.1%) improvement of average accuracy compared with the BERT-based classifier model, which is the prior state-of-the-art in polyphone disambiguation.

eess.AS↗

arXiv2022

Impact of nuclear structure on the CME background in $^{96}_{44}$Ru + $^{96}_{44}$Ru and $^{96}_{40}$Zr + $^{96}_{40}$Zr collisions at $\sqrt{s_{NN}}$ = 7.7 $\sim$ 200 GeV from a multiphase transport model

Impacts of nuclear structure on multiplicity ($N_{ch}$) and anisotropic flows ($v_{2}$ and $v_{3}$) in the isobaric collisions of $^{96}_{44}$Ru + $^{96}_{44}$Ru and $^{96}_{40}$Zr + $^{96}_{40}$Zr at $\sqrt{s_{NN}}$ = 7.7, 27, 62.4 and 200 GeV are investigated by using the string melting version of A MultiPhase Transport (AMPT) model. In comparison with the experimental data released recently by the STAR collaboration, it is found that the impact of quadrupole deformation $β_{2}$ on the $v_{2}$ difference is mainly manifested in the most central collisions, while the octupole deformation $β_{3}$ is in the near-central collisions, and the neutron skin effect dominates in the mid-central collisions. Viewing from the energy dependence, these effects are magnified at lower energies.

nucl-th↗

arXiv2022

Influence of nuclear structure in relativistic heavy-ion collisions

Many probes are proposed to determine the quark-gluon plasma and explore its properties in ultra-relativistic heavy-ion collisions. Some of them are related to initial states of the collisions, such as collective flow, Hanbury-Brown-Twiss (HBT) correlation, chiral magnetic effects and so on. The initial states can come from geometry overlap of the colliding nuclei, fluctuations or nuclear structure with the intrinsic geometry asymmetry. The initial geometry asymmetry can transfer to the final momentum distribution in the aspect of hydrodynamics during the evolution of the fireball. Different from traditional methods for nuclear structure study, the ultra-relativistic heavy-ion collisions could provide a potential platform to investigate nuclear structures with the help of the final-state observables after the fireball expansion. This chapter first presents a brief introduction of the initial states in relativistic heavy-ion collisions, and then delivers a mini-review for the nuclear structure effects on experimental observables in the relativistic energy domain.

nucl-th↗