arXiv subjects

Xin Zhang

Publications and source records attributed to Xin Zhang.

At least 595 records · Page 33Linked to original sources

arXiv2024

The Velocity Aberration Effect of the CSST Main Survey Camera

In this study, we conducted simulations to find the geometric aberrations expected for images taken by the Main Survey Camera (MSC) of the Chinese Space Station Telescope (CSST) due to its motion. As anticipated by previous work, our findings indicate that the geometric distortion of light impacts the focal plane's apparent scale, with a more pronounced influence as the size of the focal plane increases. Our models suggest that the effect consistently influences the pixel scale in both the vertical and parallel directions. The apparent scale variation follows a sinusoidal distribution throughout one orbit period. Simulations reveal that the effect is particularly pronounced in the center of the Galaxy and gradually diminishes along the direction of ecliptic latitude. At low ecliptic latitudes, the total aberration leads to about 0.94 pixels offset (a 20-minute exposure) and 0.26 pixels offset (a 300-second exposure) at the edge of the field of view, respectively. Appropriate processings for the geometric effect during the CSST pre- and post-observation phases are presented.

astro-ph.IM↗

arXiv2024

Study of the effects of external imaginary electric field and chiral chemical potential on quark matter

The behavior of quark matter with both external electric field and chiral chemical potential is theoretically and experimentally interesting to consider. In this paper, the case of simultaneous presence of imaginary electric field and chiral chemical potential is investigated using the lattice QCD approach with $N_f=1+1$ dynamical staggered fermions. We find that overall both the imaginary electric field and the chiral chemical potential can exacerbate chiral symmetry breaking, which is consistent with theoretical predictions. However we also find a non-monotonic behavior of chiral condensation at specific electric field strengths and chiral chemical potentials. In addition to this, we find that the behavior of Polyakov loop in the complex plane is not significantly affected by chiral chemical potential in the region of the parameters consider in this paper.

hep-lat↗

arXiv2024

PromptKD: Unsupervised Prompt Distillation for Vision-Language Models

Prompt learning has emerged as a valuable technique in enhancing vision-language models (VLMs) such as CLIP for downstream tasks in specific domains. Existing work mainly focuses on designing various learning forms of prompts, neglecting the potential of prompts as effective distillers for learning from larger teacher models. In this paper, we introduce an unsupervised domain prompt distillation framework, which aims to transfer the knowledge of a larger teacher model to a lightweight target model through prompt-driven imitation using unlabeled domain images. Specifically, our framework consists of two distinct stages. In the initial stage, we pre-train a large CLIP teacher model using domain (few-shot) labels. After pre-training, we leverage the unique decoupled-modality characteristics of CLIP by pre-computing and storing the text features as class vectors only once through the teacher text encoder. In the subsequent stage, the stored class vectors are shared across teacher and student image encoders for calculating the predicted logits. Further, we align the logits of both the teacher and student models via KL divergence, encouraging the student image encoder to generate similar probability distributions to the teacher through the learnable prompts. The proposed prompt distillation process eliminates the reliance on labeled data, enabling the algorithm to leverage a vast amount of unlabeled images within the domain. Finally, the well-trained student image encoders and pre-stored text features (class vectors) are utilized for inference. To our best knowledge, we are the first to (1) perform unsupervised domain-specific prompt-driven knowledge distillation for CLIP, and (2) establish a practical pre-storing mechanism of text features as shared class vectors between teacher and student. Extensive experiments on 11 datasets demonstrate the effectiveness of our method.

cs.CV↗

arXiv2024

The oscillating and vibrating cylinder: a benchmark to study low Mach number aeroacoustics

Hydrodynamic and acoustic scales separate as the Mach number decreases, making the modelling of aeroacoustic phenomena singular in this flow regime. The benchmark of the flow developing around an oscillating and vibrating cylinder is one of the scarce configuration that is fully analytically tractable, and is thus precious in the validation of new theories or solvers. This work carefully derives the complete incompressible flow solution for this case, extending the axisymmetric results of the literature to more complex cylinder motion. High-order finite element method solution of the incompressible Navier-Stokes equations provide a reference to validate the analytical formulae derived here. Both analytical and numerical investigations agree on an independence of the configuration to the Reynolds number. Up to the critical Reynolds number, found to lie above $10^4$, the hydrodynamic solution of this configuration is solely governed by the Stokes number. The exact expression of the sound radiated in the far field is computed by accounting for the cylinder scattering by means of tailored Green's functions.

physics.flu-dyn↗

arXiv2024

Cooperative colorings of hypergraphs

Given a class $\mathcal{H}$ of $m$ hypergraphs ${H}_1, {H}_2, \ldots, {H}_m$ with the same vertex set $V$, a cooperative coloring of them is a partition $\{I_1, I_2, \ldots, I_m\}$ of $V$ in such a way that each $I_i$ is an independent set in ${H}_i$ for $1\leq i\leq m$. The cooperative chromatic number of a class $\mathcal{H}$ is the smallest number of hypergraphs from $\mathcal{H}$ that always possess a cooperative coloring. For the classes of $k$-uniform tight cycles, $k$-uniform loose cycles, $k$-uniform tight paths, and $k$-uniform loose paths, we find that their cooperative chromatic numbers are all exactly two utilizing a new proved set system partition theorem, which also has its independent interests and offers a broader perspective. For the class of $k$-partite $k$-uniform hypergraphs with sufficient large maximum degree $d$, we prove that its cooperative chromatic number has lower bound $Ω(\log_k d)$ and upper bound $\text{O}\left(\frac{d}{\ln d}\right)^{\frac{1}{k-1}}$.

math.CO↗

arXiv2024

Cosmological perturbations in the energy-momentum squared gravity theory: constraints from gravitational wave standard sirens and redshift space distortions

We investigate the linear cosmological perturbations in the context of the so-called energy-momentum squared gravity (EMSG) theory. Recent researches show that the EMSG theory can reproduce viable background cosmological evolution comparable to $Λ$CDM, while the matter-dominated era exhibits slight distinctions. In this paper, we mainly focus on the power-law EMSG models and derive the equations for the linear cosmological perturbations. We explore the propagation of the gravitational wave (GW) and the growth of matter density perturbation at the first order, and estimate the model parameters from the simulated GW data and the observed redshift space distortion data. Our analysis reveals that the model parameters should be small and positive in $1σ$ confidence interval, which indicates that the theory is in good agreement with the observational data and can be regarded as an alternative for the standard cosmological model.

astro-ph.CO↗

arXiv2024

A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks

In an era defined by the explosive growth of data and rapid technological advancements, Multimodal Large Language Models (MLLMs) stand at the forefront of artificial intelligence (AI) systems. Designed to seamlessly integrate diverse data types-including text, images, videos, audio, and physiological sequences-MLLMs address the complexities of real-world applications far beyond the capabilities of single-modality systems. In this paper, we systematically sort out the applications of MLLM in multimodal tasks such as natural language, vision, and audio. We also provide a comparative analysis of the focus of different MLLMs in the tasks, and provide insights into the shortcomings of current MLLMs, and suggest potential directions for future research. Through these discussions, this paper hopes to provide valuable insights for the further development and application of MLLM.

cs.AI↗

arXiv2024

3D Variational Inference-Based Double-Difference Seismic Tomography Method and Application to the SAFOD Site, California

Seismic tomography is a crucial technique used to image subsurface structures at various scales, accomplished by solving a nonlinear and nonunique inverse problem. It is therefore important to quantify velocity model uncertainties for accurate earthquake locations and geological interpretations. Monte Carlo sampling techniques are usually used for this purpose, but those methods are computationally intensive, especially for large datasets or high-dimensional parameter spaces. In comparison, Bayesian variational inference provides a more efficient alternative by delivering probabilistic solutions through optimization. The method has been proven to be efficient in 2D tomographic problems. In this study, we apply variational inference to solve 3D double-difference (DD) seismic tomographic system using both absolute and differential travel time data. Synthetic tests demonstrate that the new method can produce more accurate velocity models than the original DD tomography method by avoiding regularization constraints, and at the same time provides more reliable uncertainty estimates. Compared to traditional checkerboard resolution tests, the resulting uncertainty estimates measure more accurately the reliability of the solution. We further apply the new method to data recorded by a local dense seismic array around the San Andreas Fault Observatory at Depth (SAFOD) site along the San Andreas Fault (SAF) at Parkfield. Similar to other researches, the obtained velocity models show significant velocity contrasts across the fault. More importantly, the new method produces velocity uncertainties of less than 0.34 km/s for Vp and 0.23 km/s for Vs. We therefore conclude that variational inference provides a powerful and efficient tool for solving 3D seismic tomographic problems and quantifying model uncertainties.

physics.geo-ph↗

arXiv2024

FSSC: Federated Learning of Transformer Neural Networks for Semantic Image Communication

In this paper, we address the problem of image semantic communication in a multi-user deployment scenario and propose a federated learning (FL) strategy for a Swin Transformer-based semantic communication system (FSSC). Firstly, we demonstrate that the adoption of a Swin Transformer for joint source-channel coding (JSCC) effectively extracts semantic information in the communication system. Next, the FL framework is introduced to collaboratively learn a global model by aggregating local model parameters, rather than directly sharing clients' data. This approach enhances user privacy protection and reduces the workload on the server or mobile edge. Simulation evaluations indicate that our method outperforms the typical JSCC algorithm and traditional separate-based communication algorithms. Particularly after integrating local semantics, the global aggregation model has further increased the Peak Signal-to-Noise Ratio (PSNR) by more than 2dB, thoroughly proving the effectiveness of our algorithm.

cs.AI↗

arXiv2024

CSST Strong Lensing Preparation: Forecasting the galaxy-galaxy strong lensing population for the China Space Station Telescope

Galaxy-galaxy strong gravitational lensing (GGSL) is a powerful probe for the formation and evolution of galaxies and cosmology, while the sample size of GGSLs leads to considerable uncertainties and potential bias. The China Space Station Telescope (CSST, to be launched in late 2026) will conduct observations across 17,500 square degrees of the sky, capturing images in the $ugriz$ bands with a spatial resolution comparable to that of the Hubble Space Telescope. We ran a set of Monte Carlo simulations to predict that the CSST's wide-field survey will observe $\sim$160,000 galaxy-galaxy strong lenses over its lifespan, increasing the number of existing galaxy-galaxy strong lens samples by three orders of magnitude. This is comparable to the capabilities of the $\it Euclid$ telescope but with the added benefit of additional color information. Specifically, the CSST can detect strong lenses with Einstein radii about $0.64\pm0.42^{"}$, corresponding to the velocity dispersions of $217.19 \pm 50.55 \, \text{km/s}$. These lenses exhibit a median magnification of $\sim$5. The apparent magnitude of the unlensed sources in the g-band is $25.87 \pm 1.19$. The signal-to-noise ratio of the lensed images covers a range of $\sim 20$ to $\sim 1000$, allowing us to determine the Einstein radius with an accuracy ranging from $\sim 1 \%$ to $\sim 0.1 \%$, ignoring various modeling systematics. Our estimates indicate that CSST can observe rare systems like double source-plane and spiral galaxy lenses. The above selection functions of the CSST strong lensing observation help optimize the strategy of finding and modeling GGSLs.

astro-ph.GA↗

arXiv2024

Revising the Problem of Partial Labels from the Perspective of CNNs' Robustness

Convolutional neural networks (CNNs) have gained increasing popularity and versatility in recent decades, finding applications in diverse domains. These remarkable achievements are greatly attributed to the support of extensive datasets with precise labels. However, annotating image datasets is intricate and complex, particularly in the case of multi-label datasets. Hence, the concept of partial-label setting has been proposed to reduce annotation costs, and numerous corresponding solutions have been introduced. The evaluation methods for these existing solutions have been primarily based on accuracy. That is, their performance is assessed by their predictive accuracy on the test set. However, we insist that such an evaluation is insufficient and one-sided. On one hand, since the quality of the test set has not been evaluated, the assessment results are unreliable. On the other hand, the partial-label problem may also be raised by undergoing adversarial attacks. Therefore, incorporating robustness into the evaluation system is crucial. For this purpose, we first propose two attack models to generate multiple partial-label datasets with varying degrees of label missing rates. Subsequently, we introduce a lightweight partial-label solution using pseudo-labeling techniques and a designed loss function. Then, we employ D-Score to analyze both the proposed and existing methods to determine whether they can enhance robustness while improving accuracy. Extensive experimental results demonstrate that while certain methods may improve accuracy, the enhancement in robustness is not significant, and in some cases, it even diminishes.

cs.CV↗

arXiv2024

Prospects for weighing neutrinos in interacting dark energy models using joint observations of gravitational waves and $γ$-ray bursts

Cosmological observations can be used to weigh neutrinos, but this method is model-dependent, with results relying on the cosmological model considered. If we consider interactions between dark energy and dark matter, the neutrino mass constraints differ from those derived under the standard model. On the contrary, gravitational wave (GW) standard siren observations can measure absolute cosmological distances, helping to break parameter degeneracies inherent in traditional cosmological observations, thereby improving constraints on neutrino mass. This paper examines the constraints on neutrino mass within interacting dark energy (IDE) models and explores how future GW standard siren observations could enhance these results. For multi-messenger GW observations, we consider the joint observations of binary neutron star mergers by third-generation ground-based GW detectors and short $γ$-ray burst observations by missions similar to the THESEUS satellite project. Using current cosmological observations (CMB+BAO+SN), we obtain an upper limit on the neutrino mass in the IDE models of 0.15 (or 0.16) eV. With the inclusion of GW data, the upper limit on the neutrino mass improves to 0.14 eV. This indicates that in the context of IDE models, the improvement in neutrino mass constraints from GW observations is relatively limited. However, GW observations significantly enhance the constraints on other cosmological parameters, such as matter density parameter, the Hubble constant, and coupling strength between dark energy and dark matter.

astro-ph.CO↗

arXiv2024

Universal control of four singlet-triplet qubits

The coherent control of interacting spins in semiconductor quantum dots is of strong interest for quantum information processing as well as for studying quantum magnetism from the bottom up. Here, we present a $2\times4$ germanium quantum dot array with full and controllable interactions between nearest-neighbor spins. As a demonstration of the level of control, we define four singlet-triplet qubits in this system and show two-axis single-qubit control of each qubit and SWAP-style two-qubit gates between all neighbouring qubit pairs, yielding average single-qubit gate fidelities of 99.49(8)-99.84(1)% and Bell state fidelities of 73(1)-90(1)%. Combining these operations, we experimentally implement a circuit designed to generate and distribute entanglement across the array. A remote Bell state with a fidelity of 75(2)% and concurrence of 22(4)% is achieved. These results highlight the potential of singlet-triplet qubits as a competing platform for quantum computing and indicate that scaling up the control of quantum dot spins in extended bilinear arrays can be feasible.

cond-mat.mes-hall↗

arXiv2024

LDConv: Linear deformable convolution for improving convolutional neural networks

Neural networks based on convolutional operations have achieved remarkable results in the field of deep learning, but there are two inherent flaws in standard convolutional operations. On the one hand, the convolution operation is confined to a local window, so it cannot capture information from other locations, and its sampled shapes is fixed. On the other hand, the size of the convolutional kernel are fixed to k $\times$ k, which is a fixed square shape, and the number of parameters tends to grow squarely with size. Although Deformable Convolution (Deformable Conv) address the problem of fixed sampling of standard convolutions, the number of parameters also tends to grow in a squared manner. In response to the above questions, the Linear Deformable Convolution (LDConv) is explored in this work, which gives the convolution kernel an arbitrary number of parameters and arbitrary sampled shapes to provide richer options for the trade-off between network overhead and performance. In LDConv, a novel coordinate generation algorithm is defined to generate different initial sampled positions for convolutional kernels of arbitrary size. To adapt to changing targets, offsets are introduced to adjust the shape of the samples at each position. LDConv corrects the growth trend of the number of parameters for standard convolution and Deformable Conv to a linear growth. Moreover, it completes the process of efficient feature extraction by irregular convolutional operations and brings more exploration options for convolutional sampled shapes. Object detection experiments on representative datasets COCO2017, VOC 7+12, and VisDrone-DET2021 fully demonstrate the advantages of LDConv. LDConv is a plug-and-play convolutional operation that can replace the convolutional operation to improve network performance. The code for the relevant tasks can be found at https://github.com/CV-ZhangXin/LDConv.

cs.CV↗

arXiv2024

GDDS: A Single Domain Generalized Defect Detection Frame of Open World Scenario using Gather and Distribute Domain-shift Suppression Network

Efficient and intelligent surface defect detection of photovoltaic modules is crucial for improving the quality of photovoltaic modules and ensuring the reliable operation of large-scale infrastructure. However, the scenario characteristics of data distribution deviation make the construction of defect detection models for open world scenarios such as photovoltaic manufacturing and power plant inspections a challenge. Therefore, we propose the Gather and Distribute Domain shift Suppression Network (GDDS). It adopts a single domain generalized method that is completely independent of the test samples to address the problem of distribution shift. Using a one-stage network as the baseline network breaks through the limitations of traditional domain generalization methods that typically use two-stage networks. It not only balances detection accuracy and speed but also simplifies the model deployment and application process. The GDDS includes two modules: DeepSpine Module and Gather and Distribute Module. Specifically, the DeepSpine Module applies a wider range of contextual information and suppresses background style shift by acquiring and concatenating multi-scale features. The Gather and Distribute Module collects and distributes global information to achieve cross layer interactive learning of multi-scale channel features and suppress defect instance shift. Furthermore, the GDDS utilizes normalized Wasserstein distance for similarity measurement, reducing measurement errors caused by bounding box position deviations. We conducted a comprehensive evaluation of GDDS on the EL endogenous shift dataset and Photovoltaic inspection infrared image dataset. The experimental results showed that GDDS can adapt to defect detection in open world scenarios faster and better than other state-of-the-art methods.

cs.CV↗

arXiv2024

Forecasting Supernova Observations with the CSST: I. Photometric Samples

The China Space Station Telescope (CSST, also known as Xuntian) is a serviceable two-meter-aperture wide-field telescope operating in the same orbit as the China Space Station. The CSST plans to survey a sky area of 17,500 deg$^2$ of the medium-to-high Galactic latitude to a depth of 25-26 AB mag in at least 6 photometric bands over 255-1000 nm. Within such a large sky area, slitless spectra will also be taken over the same wavelength range as the imaging survey. Even though the CSST survey is not dedicated to time-domain studies, it would still detect a large number of transients, such as supernovae (SNe). In this paper, we simulate photometric SN observations based on a strawman survey plan using the Sncosmo package. During its 10-year survey, the CSST is expected to observe about 5 million SNe of various types. With quality cuts, we obtain a "gold" sample that comprises roughly 7,400 SNe Ia, 2,200 SNe Ibc, and 6,500 SNe II candidates with correctly classified percentages reaching 91%, 63%, and 93% (formally defined as classification precision), respectively. The same survey can also trigger alerts for the detection of about 15,500 SNe Ia (precision 61%) and 2,100 SNe II (precision 49%) candidates at least two days before the light maxima. Moreover, the near-ultraviolet observations of the CSST will be able to catch hundreds of shock-cooling events serendipitously every year. These results demonstrate that the CSST can make a potentially significant contribution to SN studies.

astro-ph.HE↗

arXiv2024

Dark photon constraints from a 7.139 GHz cavity haloscope experiment

The dark photon is a promising candidate for the dark matter which comprises most of the matter in our visible Universe. Via kinetic mixing with the Standard Model it can also be resonantly converted to photons in an electromagnetic cavity, offering novel experimental possibilities for the discovery and study of dark matter. We report the results of a pathfinder dark photon dark matter cavity search experiment performed at Hunan Normal University and the Institute of Physics, Chinese Academy of Sciences, representing the first stage of the APEX (Axion and dark Photon EXperiment) program. Finding no statistically significant excess, we place an upper limit on the kinetic mixing parameter $|χ|<3.7\times 10^{-13}$ around $m_A\simeq 29.5$ $μ$eV at 90% confidence level. This result exceeds other constraints on dark photon dark matter in this frequency range by roughly an order of magnitude.

hep-ex↗

arXiv2024

Detection-Triggered Recursive Impact Mitigation against Secondary False Data Injection Attacks in Microgrids

The cybersecurity of microgrid has received widespread attentions due to the frequently reported attack accidents against distributed energy resource (DER) manufactures. Numerous impact mitigation schemes have been proposed to reduce or eliminate the impacts of false data injection attacks (FDIAs). Nevertheless, the existing methods either requires at least one neighboring trustworthy agent or may bring in unacceptable cost burdens. This paper aims to propose a detection-triggered recursive impact mitigation scheme that can timely and precisely counter the secondary FDIAs (SFDIAs) against the communication links among DERs. Once triggering attack alarms, the power line current readings will be utilised to observe the voltage bias injections through the physical interconnections among DERs, based on which the current bias injections can be recursively reconstructed from the residuals generated by unknown input observers (UIOs). The attack impacts are eliminated by subtracting the reconstructed bias from the incoming compromised data. The proposed mitigation method can work even in the worst case where all communication links are under SFDIAs and only require extra current sensors. The bias reconstruction performance under initial errors and system noises is theoretically analysed and the reconstruction error is proved to be bounded regardless of the electrical parameters. To avoid deploying current sensors on all power lines, a cost-effective deployment strategy is presented to secure a spanning tree set of communication links that can guarantee the secondary control performance. Extensive validation studies are conducted in MATLAB/SIMULINK and hardware-in-the-loop (HIL) testbeds to validate the proposed method's effectiveness against single/multiple and continuous/discontinuous SFDIAs.

eess.SY↗