arXiv subjects

Yu Zhang

Publications and source records attributed to Yu Zhang.

At least 685 records · Page 38Linked to original sources

arXiv2025

Searching for long-lived light neutralinos and axionlike particles at the SHiNESS experiment

Recently Soleti et al. [JHEP03(2024)148] proposed a new experiment called SHiNESS at the upcoming European Spallation Source (ESS) facility, making use of the 2-GeV proton beam there impinging on a fixed target, in order to search for hidden sterile neutrinos that could lie in different mass ranges and arise with distinct signatures. Such signatures include excesses in electron-positron pairs that may originate from displaced decays of long-lived particles (LLPs). At the ESS, the dominant sources of such LLPs are decays at rest of $π^+$ mesons and $μ^+$ leptons. We choose to investigate theoretical scenarios of long-lived light neutralinos in the R-parity-violating supersymmetry and long-lived weak-violating electrophilic axionlike particles, as these LLPs can be produced from decays of $π^+$ and $μ^+$. Since the $π^+$'s and $μ^+$'s decay at rest at the ESS, we compute the spectra of the therefrom produced LLPs, and thus estimate the expected sensitivity reach of SHiNESS to the LLPs in these two scenarios. Our calculation shows that in multiple relevant benchmark scenarios, SHiNESS can probe large parameter regions of these models beyond the existing bounds, in just a couple of years of data-collection time.

hep-ph↗

arXiv2025

3DGS$^2$: Near Second-order Converging 3D Gaussian Splatting

3D Gaussian Splatting (3DGS) has emerged as a mainstream solution for novel view synthesis and 3D reconstruction. By explicitly encoding a 3D scene using a collection of Gaussian kernels, 3DGS achieves high-quality rendering with superior efficiency. As a learning-based approach, 3DGS training has been dealt with the standard stochastic gradient descent (SGD) method, which offers at most linear convergence. Consequently, training often requires tens of minutes, even with GPU acceleration. This paper introduces a (near) second-order convergent training algorithm for 3DGS, leveraging its unique properties. Our approach is inspired by two key observations. First, the attributes of a Gaussian kernel contribute independently to the image-space loss, which endorses isolated and local optimization algorithms. We exploit this by splitting the optimization at the level of individual kernel attributes, analytically constructing small-size Newton systems for each parameter group, and efficiently solving these systems on GPU threads. This achieves Newton-like convergence per training image without relying on the global Hessian. Second, kernels exhibit sparse and structured coupling across input images. This property allows us to effectively utilize spatial information to mitigate overshoot during stochastic training. Our method converges an order faster than standard GPU-based 3DGS training, requiring over $10\times$ fewer iterations while maintaining or surpassing the quality of the compared with the SGD-based 3DGS reconstructions.

cs.CV↗

arXiv2025

Searching for long-lived particles from stopped pions and muons at the CiADS-BDE

The CiADS-BDE is a beam-dump experiment recently proposed for searching for light, long-lived particles (LLPs) at China initiative Accelerator Driven System. Primarily thanks to the large numbers of protons on target at the experiment, it has been shown to be sensitive to large, unique regions of the parameter space of dark photon, with a small detector volume of $\mathcal{O}(0.01\text{--}1)$ m$^3$. Here, we explore the search prospect of the CiADS-BDE for a series of new-physic models predicting LLPs that could emanate from decays at rest of charged pions and muons at the facility, namely, heavy neutral leptons, axionlike particles, and light binos in the R-parity-violating supersymmetry. For these benchmark models, we find that the CiADS-BDE can also probe vast parameter regions beyond the existing bounds.

hep-ph↗

arXiv2025

Exploring the Feasibility of Deep Learning Models for Long-term Disease Prediction: A Case Study for Wheat Yellow Rust in England

Wheat yellow rust, caused by the fungus Puccinia striiformis, is a critical disease affecting wheat crops across Britain, leading to significant yield losses and economic consequences. Given the rapid environmental changes and the evolving virulence of pathogens, there is a growing need for innovative approaches to predict and manage such diseases over the long term. This study explores the feasibility of using deep learning models to predict outbreaks of wheat yellow rust in British fields, offering a proactive approach to disease management. We construct a yellow rust dataset with historial weather information and disease indicator acrossing multiple regions in England. We employ two poweful deep learning models, including fully connected neural networks and long short-term memory to develop predictive models capable of recognizing patterns and predicting future disease outbreaks.The models are trained and validated in a randomly sliced datasets. The performance of these models with different predictive time steps are evaluated based on their accuracy, precision, recall, and F1-score. Preliminary results indicate that deep learning models can effectively capture the complex interactions between multiple factors influencing disease dynamics, demonstrating a promising capacity to forecast wheat yellow rust with considerable accuracy. Specifically, the fully-connected neural network achieved 83.65% accuracy in a disease prediction task with 6 month predictive time step setup. These findings highlight the potential of deep learning to transform disease management strategies, enabling earlier and more precise interventions. Our study provides a methodological framework for employing deep learning in agricultural settings but also opens avenues for future research to enhance the robustness and applicability of predictive models in combating crop diseases globally.

cs.LG↗

arXiv2025

CvhSlicer 2.0: Immersive and Interactive Visualization of Chinese Visible Human Data in XR Environments

The study of human anatomy through advanced visualization techniques is crucial for medical research and education. In this work, we introduce CvhSlicer 2.0, an innovative XR system designed for immersive and interactive visualization of the Chinese Visible Human (CVH) dataset. Particularly, our proposed system operates entirely on a commercial XR headset, offering a range of visualization and interaction tools for dynamic 2D and 3D data exploration. By conducting comprehensive evaluations, our CvhSlicer 2.0 demonstrates strong capabilities in visualizing anatomical data, enhancing user engagement and improving educational effectiveness. A demo video is available at https://youtu.be/CfR72S_0N-4

cs.HC↗

arXiv2025

Acoustic Emission Sensor Network Optimization Based on Grid Loop Search and Particle Swarm Source Location

The layout of acoustic emission sensors plays a critical role in non-destructive structural testing. This study proposes a grid-based optimization method focused on multi-source location results, in contrast to traditional sensor layout optimization methods that construct a correlation matrix based on sensor layout and one source location. Based on the seismic source travel-time theory, the proposed method establishes a location objective function based on minimum travel-time differences, which is solved through the particle swarm optimization (PSO) algorithm. Furthermore, based on location accuracy across various configurations, the method systematically evaluates potential optimal sensor locations through grid search. Synthetic tests and laboratory pencil-lead break (PLB) experiments are conducted to compare the effectiveness of PSO, genetic algorithm, and simulated annealing, with the following conclusions: (1) In synthetic tests, the proposed method achieved an average location error of 1.78 mm, outperforming that based on the traditional layout, genetic algorithm (GA), and simulated annealing (SA). (2) For different noise cases, the location accuracy separately improved by 24.89% (σ=0.5μs), 12.59% (σ=2μs), and 15.06% (σ=5μs) compared with the traditional layout. (3) For the PLB experiments, the optimized layout achieved an average location error of 9.37 mm, which improved the location accuracy by 59.15% compared with the Traditional layout.

physics.geo-ph↗

arXiv2025

Elliptic Harnack inequality and its applications on Finsler metric measure spaces

In this paper, we study the elliptic Harnack inequality and its applications on forward complete Finsler metric measure spaces under the conditions that the weighted Ricci curvature ${\rm Ric}_{\infty}$ has non-positive lower bound and the distortion $τ$ is of linear growth, $|τ|\leq ar+b$, where $a,b$ are some non-negative constants, $r=d(x_0,x)$ is the distance function for some point $x_{0} \in M$. We obtain an elliptic $p$-Harnack inequality for positive harmonic functions from a local uniform Poincaré inequality and a mean value inequality. As applications of the Harnack inequality, we derive the Hölder continuity estimate and a Liouville theorem for positive harmonic functions. Furthermore, we establish a gradient estimate for positive harmonic functions.

math.DG↗

arXiv2025

Parallelizing Linear Transformers with the Delta Rule over Sequence Length

Transformers with linear attention (i.e., linear transformers) and state-space models have recently been suggested as a viable linear-time alternative to transformers with softmax attention. However, these models still underperform transformers especially on tasks that require in-context retrieval. While more expressive variants of linear transformers which replace the additive update in linear transformers with the delta rule (DeltaNet) have been found to be more effective at associative recall, existing algorithms for training such models do not parallelize over sequence length and are thus inefficient to train on modern hardware. This work describes a hardware-efficient algorithm for training linear transformers with the delta rule, which exploits a memory-efficient representation for computing products of Householder matrices. This algorithm allows us to scale up DeltaNet to standard language modeling settings. We train a 1.3B model for 100B tokens and find that it outperforms recent linear-time baselines such as Mamba and GLA in terms of perplexity and zero-shot performance on downstream tasks. We also experiment with two hybrid models which combine DeltaNet layers with (1) sliding-window attention layers every other layer or (2) two global attention layers, and find that these hybrids outperform strong transformer baselines.

cs.LG↗

arXiv2025

Text-Guided Coarse-to-Fine Fusion Network for Robust Remote Sensing Visual Question Answering

Remote Sensing Visual Question Answering (RSVQA) has gained significant research interest. However, current RSVQA methods are limited by the imaging mechanisms of optical sensors, particularly under challenging conditions such as cloud-covered and low-light scenarios. Given the all-time and all-weather imaging capabilities of Synthetic Aperture Radar (SAR), it is crucial to investigate the integration of optical-SAR images to improve RSVQA performance. In this work, we propose a Text-guided Coarse-to-Fine Fusion Network (TGFNet), which leverages the semantic relationships between question text and multi-source images to guide the network toward complementary fusion at the feature level. Specifically, we develop a Text-guided Coarse-to-Fine Attention Refinement (CFAR) module to focus on key areas related to the question in complex remote sensing images. This module progressively directs attention from broad areas to finer details through key region routing, enhancing the model's ability to focus on relevant regions. Furthermore, we propose an Adaptive Multi-Expert Fusion (AMEF) module that dynamically integrates different experts, enabling the adaptive fusion of optical and SAR features. In addition, we create the first large-scale benchmark dataset for evaluating optical-SAR RSVQA methods, comprising 6,008 well-aligned optical-SAR image pairs and 1,036,694 well-labeled question-answer pairs across 16 diverse question types, including complex relational reasoning questions. Extensive experiments on the proposed dataset demonstrate that our TGFNet effectively integrates complementary information between optical and SAR images, significantly improving the model's performance in challenging scenarios. The dataset is available at: https://github.com/mmic-lcl/. Index Terms: Remote Sensing Visual Question Answering, Multi-source Data Fusion, Multimodal, Remote Sensing, OPT-SAR.

cs.CV↗

arXiv2025

Science objectives of the Einstein Probe mission

The Einstein Probe (EP) is an interdisciplinary mission of time-domain and X-ray astronomy. Equipped with a wide-field lobster-eye X-ray focusing imager, EP will discover cosmic X-ray transients and monitor the X-ray variability of known sources in 0.5-4 keV, at a combination of detecting sensitivity and cadence that is not accessible to the previous and current wide-field monitoring missions. EP can perform quick characterisation of transients or outbursts with a Wolter-I X-ray telescope onboard. In this paper, the science objectives of the Einstein Probe mission are presented. EP is expected to enlarge the sample of previously known or predicted but rare types of transients with a wide range of timescales. Among them, fast extragalactic transients will be surveyed systematically in soft X-rays, which include γ-ray bursts and their variants, supernova shock breakouts, and the predicted X-ray transients associated with binary neutron star mergers. EP will detect X-ray tidal disruption events and outbursts from active galactic nuclei, possibly at an early phase of the flares for some. EP will monitor the variability and outbursts of X-rays from white dwarfs, neutron stars and black holes in our and neighbouring galaxies at flux levels fainter than those detectable by the current instruments, and is expected to discover new objects. A large sample of stellar X-ray flares will also be detected and characterised. In the era of multi-messenger astronomy, EP has the potential of detecting the possible X-ray counterparts of gravitational wave events, neutrino sources, and ultra-high energy γ-ray and cosmic ray sources. EP is expected to help advance the studies of extreme objects/phenomena and their underlying physical processes revealed in the dynamic X-ray universe, as well as studies in other areas of X-ray astronomy.

astro-ph.HE↗

arXiv2025

Quark number susceptibility and conserved charge fluctuation for (2+1)-flavor QCD with Möbius domain wall fermions

We present quark number susceptibilities and conserved charge fluctuations for (2+1)-flavor QCD using Möbius Domain Wall fermions with a pion mass of $135~\rm{MeV}$. Our results are compared with hadron resonance gas models below the QCD transition temperature and with $\mathcal{O}(g^2)$ perturbation theory at high temperatures. Additionally, we compare our findings with results from staggered fermion discretizations. Furthermore, we also present results of leading order Kurtosis of electric charge and strangeness fluctuations.

hep-lat↗

arXiv2025

Types of dynamical behavior in a quasiperiodic mosaic lattice

Quasiperiodic mosaic systems with the quasiperiodic potential being added periodically with a fixed lattice interval have attracted significant attention due to their peculiar spectral properties with exactly known mobility edges, which separate localized from delocalized states. These mobility edges do not vanish even in the region of large quasiperiodic potential strength, although the width of the energy window of extended states decreases with the increase in potential strength and thus becomes very narrow in the limit of strong quasiperiodic disorder. In this paper, we study the dynamics of a quasiperiodic mosaic lattice and unravel its peculiar dynamical properties. By scrutinizing the expansion dynamics of wave packet and the evolution of density distribution, we unveil that the long-time density distribution displays obviously different behaviors at odd and even sites in the region of large quasiperiodic potential strength. Particularly, the timescale of dynamics exhibits an inverse relationship with the quasiperiodic potential strength. To understand these behaviors, we derive an effective Hamiltonian in the large quasiperiodic potential strength region, which is composed of decoupled Hamiltonians defined on the odd and even sites, respectively. While all eigenstates of the effective Hamiltonian defined on even sites are localized, the eigenstates of effective Hamiltonian defined on odd sites include both localized and extended eigenstates. Our results suggest that the effective Hamiltonian can describe the dynamical behaviors well in the large quasiperiodic potential strength region and provides an intuitive framework for understanding the peculiar dynamical behaviors in the quasiperiodic mosaic lattice.

cond-mat.dis-nn↗

arXiv2025

Photoacoustic Iterative Optimization Algorithm with Shape Prior Regularization

Photoacoustic imaging (PAI) suffers from inherent limitations that can degrade the quality of reconstructed results, such as noise, artifacts and incomplete data acquisition caused by sparse sampling or partial array detection. In this study, we proposed a new optimization method for both two-dimensional (2D) and three-dimensional (3D) PAI reconstruction results, called the regularized iteration method with shape prior. The shape prior is a probability matrix derived from the reconstruction results of multiple sets of random partial array signals in a computational imaging system using any reconstruction algorithm, such as Delay-and-Sum (DAS) and Back-Projection (BP). In the probability matrix, high-probability locations indicate high consistency among multiple reconstruction results at those positions, suggesting a high likelihood of representing the true imaging results. In contrast, low-probability locations indicate higher randomness, leaning more towards noise or artifacts. As a shape prior, this probability matrix guides the iteration and regularization of the entire array signal reconstruction results using the original reconstruction algorithm (the same algorithm for processing random partial array signals). The method takes advantage of the property that the similarity of the object to be imitated is higher than that of noise or artifact in the results reconstructed by multiple sets of random partial array signals of the entire imaging system. The probability matrix is taken as a prerequisite for improving the original reconstruction results, and the optimizer is used to further iterate the imaging results to remove noise and artifacts and improve the imaging fidelity. Especially in the case involving sparse view which brings more artifacts, the effect is remarkable. Simulation and real experiments have both demonstrated the superiority of this method.

physics.optics↗

arXiv2024

Zero-Shot Artifact2Artifact: Self-incentive artifact removal for photoacoustic imaging without any data

Photoacoustic imaging (PAI) uniquely combines optical contrast with the penetration depth of ultrasound, making it critical for clinical applications. However, the quality of 3D PAI is often degraded due to reconstruction artifacts caused by the sparse and angle-limited configuration of detector arrays. Existing iterative or deep learning-based methods are either time-consuming or require large training datasets, significantly limiting their practical application. Here, we propose Zero-Shot Artifact2Artifact (ZS-A2A), a zero-shot self-supervised artifact removal method based on a super-lightweight network, which leverages the fact that reconstruction artifacts are sensitive to irregularities caused by data loss. By introducing random perturbations to the acquired PA data, it spontaneously generates subset data, which in turn stimulates the network to learn the artifact patterns in the reconstruction results, thus enabling zero-shot artifact removal. This approach requires neither training data nor prior knowledge of the artifacts, and is capable of artifact removal for 3D PAI. For maximum amplitude projection (MAP) images or slice images in 3D PAI acquired with arbitrarily sparse or angle-limited detector arrays, ZS-A2A employs a self-incentive strategy to complete artifact removal and improves the Contrast-to-Noise Ratio (CNR). We validated ZS-A2A in both simulation study and $ in\ vivo $ animal experiments. Results demonstrate that ZS-A2A achieves state-of-the-art (SOTA) performance compared to existing zero-shot methods, and for the $ in\ vivo $ rat liver, ZS-A2A improves CNR from 17.48 to 43.46 in just 8 seconds. The project for ZS-A2A will be available in the following GitHub repository: https://github.com/JaegerCQ/ZS-A2A.

cs.CV↗

arXiv2024

Dispersive analysis of the isospin breaking in the $X(3872)~\to~J/ψπ^+π^-$ and $X(3872)~\to~J/ψπ^+π^0π^-$ decays

We analyze the latest LHCb data on the $π^+π^-$ spectrum in the isospin-violating $X(3872)\to J/ψπ^+π^-$ decay, based on dispersion theory to deal with the $ππ$ final state interactions. Additionally, the isospin breaking effects are properly introduced, allowing for a reliable and accurate extraction of the ratio, $R_X$, between the $X(3872)$ couplings to the $J/ψρ$ and $J/ψω$ channels from the data. We find very good agreement with the LHCb data for the whole range of the $π^+π^-$ invariant mass, and $R_X$ is determined to be {$0.26\pm 0.03$}. Using this value, we make predictions for the $π^+π^0π^-$ mass distribution in the $X(3872)\to J/ψπ^+π^0π^-$ process, which is currently accessible by the BESIII Collaboration, and update a prediction for the pole positions of the isovector partner states of the $X(3872)$, $W_{c1}$, with $I(J^{PC})=1(1^{++})$.

hep-ph↗

arXiv2024

Altermagnetism and Strain Induced Altermagnetic Transition in Cairo Pentagonal Monolayer

Altermagnetism, a recently discovered class of magnetic order characterized by vanishing net magnetization and spin-splitting band structures, has garnered significant research attention. In this work, we introduce a novel two-dimensional system that exhibits $g$-wave altermagnetism and undergoes a strain-induced transition from $g$-wave to $d$-wave altermagnetism. This system can be realized in an unconventional monolayer Cairo pentagonal lattice, for which we present a realistic tight-binding model that incorporates both magnetic and non-magnetic sites. Furthermore, we demonstrate that non-trivial band topology can emerge in this system by breaking the symmetry that protects the spin-polarized nodal points. Finally, \emph{ab initio} calculations on several candidate materials, such as FeS$_2$ and Nb$_2$FeB$_2$, which exhibit symmetry consistent with the proposed tight-binding Hamiltonian, are also presented. These findings open new avenues for exploring spintronic devices based on altermagnetic systems.

cond-mat.str-el↗

arXiv2024

Quantum-Assisted Online Task Offloading and Resource Allocation in MEC-Enabled Satellite-Aerial-Terrestrial Integrated Networks

In the era of Internet of Things (IoT), multi-access edge computing (MEC)-enabled satellite-aerial-terrestrial integrated network (SATIN) has emerged as a promising technology to provide massive IoT devices with seamless and reliable communication and computation services. This paper investigates the cooperation of low Earth orbit (LEO) satellites, high altitude platforms (HAPs), and terrestrial base stations (BSs) to provide relaying and computation services for vastly distributed IoT devices. Considering the uncertainty in dynamic SATIN systems, we formulate a stochastic optimization problem to minimize the time-average expected service delay by jointly optimizing resource allocation and task offloading while satisfying the energy constraints. To solve the formulated problem, we first develop a Lyapunov-based online control algorithm to decompose it into multiple one-slot problems. Since each one-slot problem is a large-scale mixed-integer nonlinear program (MINLP) that is intractable for classical computers, we further propose novel hybrid quantum-classical generalized Benders' decomposition (HQCGBD) algorithms to solve the problem efficiently by leveraging quantum advantages in parallel computing. Numerical results validate the effectiveness of the proposed MEC-enabled SATIN schemes.

cs.NI↗

arXiv2024

ChatTracker: Enhancing Visual Tracking Performance via Chatting with Multimodal Large Language Model

Visual object tracking aims to locate a targeted object in a video sequence based on an initial bounding box. Recently, Vision-Language~(VL) trackers have proposed to utilize additional natural language descriptions to enhance versatility in various applications. However, VL trackers are still inferior to State-of-The-Art (SoTA) visual trackers in terms of tracking performance. We found that this inferiority primarily results from their heavy reliance on manual textual annotations, which include the frequent provision of ambiguous language descriptions. In this paper, we propose ChatTracker to leverage the wealth of world knowledge in the Multimodal Large Language Model (MLLM) to generate high-quality language descriptions and enhance tracking performance. To this end, we propose a novel reflection-based prompt optimization module to iteratively refine the ambiguous and inaccurate descriptions of the target with tracking feedback. To further utilize semantic information produced by MLLM, a simple yet effective VL tracking framework is proposed and can be easily integrated as a plug-and-play module to boost the performance of both VL and visual trackers. Experimental results show that our proposed ChatTracker achieves a performance comparable to existing methods.

cs.CV↗