arXiv subjects

Shuo Zhang

Publications and source records attributed to Shuo Zhang.

At least 145 records · Page 8Linked to original sources

arXiv2025

Research on the recommendation framework of foreign enterprises from the perspective of multidimensional proximity

As global economic integration progresses, foreign-funded enterprises play an increasingly crucial role in fostering local economic growth and enhancing industrial development. However, there are not many researches to deal with this aspect in recent years. This study utilizes the multidimensional proximity theory to thoroughly examine the criteria for selecting high-quality foreign-funded companies that are likely to invest in and establish factories in accordance with local conditions during the investment attraction process.First, this study leverages databases such as Wind and Osiris, along with government policy documents, to investigate foreign-funded enterprises and establish a high-quality database. Second, using a two-step method, enterprises aligned with local industrial strategies are identified. Third, a detailed analysis is conducted on key metrics, including industry revenue, concentration (measured by the Herfindahl-Hirschman Index), and geographical distance (calculated using the Haversine formula). Finally, a multi-criteria decision analysis ranks the top five companies as the most suitable candidates for local investment, with the methodology validated through a case study in a district of Beijing.The example results show that the established framework helps local governments identify high-quality foreign-funded enterprises.

stat.AP↗

arXiv2025

Exploring MAXI J1744-294: IXPE insights into a Newly Discovered X-ray Transient

We present the first IXPE spectro-polarimetric observation of the black hole candidate MAXI J1744$-$294, a transient X-ray source discovered during a bright 2025 outburst in the Galactic center region. During the $\sim$150 ks observation, the source was found in the soft state, and its spectrum was well described by an absorbed multicolor disk with a minor high-energy tail. No significant polarization was detected, and we derived a 3$σ$ upper limit on the polarization degree of $1.3\%$ in the 2--8 keV energy band. This result is consistent with previous findings for soft-state black hole binaries observed at low to intermediate inclination angles. By comparing the polarization degree upper limit with theoretical predictions for standard accretion disk emission, we constrain the disk inclination to $i \lesssim 38^\circ$--$71^\circ$, depending on the black hole spin and the disk atmosphere albedo.

astro-ph.HE↗

arXiv2025

Benchmark Granularity and Model Robustness for Image-Text Retrieval

Image-Text Retrieval (ITR) systems are central to multimodal information access, with Vision-Language Models (VLMs) showing strong performance on standard benchmarks. However, these benchmarks predominantly rely on coarse-grained annotations, limiting their ability to reveal how models perform under real-world conditions, where query granularity varies. Motivated by this gap, we examine how dataset granularity and query perturbations affect retrieval performance and robustness across four architecturally diverse VLMs (ALIGN, AltCLIP, CLIP, and GroupViT). Using both standard benchmarks (MS-COCO, Flickr30k) and their fine-grained variants, we show that richer captions consistently enhance retrieval, especially in text-to-image tasks, where we observe an average improvement of 16.23%, compared to 6.44% in image-to-text. To assess robustness, we introduce a taxonomy of perturbations and conduct extensive experiments, revealing that while perturbations typically degrade performance, they can also unexpectedly improve retrieval, exposing nuanced model behaviors. Notably, word order emerges as a critical factor -- contradicting prior assumptions of model insensitivity to it. Our results highlight variation in model robustness and a dataset-dependent relationship between caption granularity and perturbation sensitivity and emphasize the necessity of evaluating models on datasets of varying granularity.

cs.CV↗

arXiv2025

Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition

Multi-task learning (MTL) enables the efficient transfer of extra knowledge acquired from other tasks. The high correlation between multimodal sentiment analysis (MSA) and multimodal emotion recognition (MER) supports their joint training. However, existing methods primarily employ hard parameter sharing, ignoring parameter conflicts caused by complex task correlations. In this paper, we present a novel MTL method for MSA and MER, termed Multimodal Mixture of Low-Rank Experts (MMoLRE). MMoLRE utilizes shared and task-specific experts to distinctly model common and unique task characteristics, thereby avoiding parameter conflicts. Additionally, inspired by low-rank structures in the Mixture of Experts (MoE) framework, we design low-rank expert networks to reduce parameter and computational overhead as the number of experts increases. Extensive experiments on the CMU-MOSI and CMU-MOSEI benchmarks demonstrate that MMoLRE achieves state-of-the-art performance on the MSA task and competitive results on the MER task.

cs.AI↗

arXiv2025

HGFormer: Topology-Aware Vision Transformer with HyperGraph Learning

The computer vision community has witnessed an extensive exploration of vision transformers in the past two years. Drawing inspiration from traditional schemes, numerous works focus on introducing vision-specific inductive biases. However, the implicit modeling of permutation invariance and fully-connected interaction with individual tokens disrupts the regional context and spatial topology, further hindering higher-order modeling. This deviates from the principle of perceptual organization that emphasizes the local groups and overall topology of visual elements. Thus, we introduce the concept of hypergraph for perceptual exploration. Specifically, we propose a topology-aware vision transformer called HyperGraph Transformer (HGFormer). Firstly, we present a Center Sampling K-Nearest Neighbors (CS-KNN) algorithm for semantic guidance during hypergraph construction. Secondly, we present a topology-aware HyperGraph Attention (HGA) mechanism that integrates hypergraph topology as perceptual indications to guide the aggregation of global and unbiased information during hypergraph messaging. Using HGFormer as visual backbone, we develop an effective and unitive representation, achieving distinct and detailed scene depictions. Empirical experiments show that the proposed HGFormer achieves competitive performance compared to the recent SoTA counterparts on various visual benchmarks. Extensive ablation and visualization studies provide comprehensive explanations of our ideas and contributions.

cs.CV↗

arXiv2025

Constant Approximation of Fréchet Distance in Strongly Subquadratic Time

Let $τ$ and $σ$ be two polygonal curves in $\mathbb{R}^d$ for any fixed $d$. Suppose that $τ$ and $σ$ have $n$ and $m$ vertices, respectively, and $m\le n$. While conditional lower bounds prevent approximating the Fréchet distance between $τ$ and $σ$ within a factor of 3 in strongly subquadratic time, the current best approximation algorithm attains a ratio of $n^c$ in strongly subquadratic time, for some constant $c\in(0,1)$. We present a randomized algorithm with running time $O(nm^{0.99}\log(n/\varepsilon))$ that approximates the Fréchet distance within a factor of $7+\varepsilon$, with a success probability at least $1-1/n^6$. We also adapt our techniques to develop a randomized algorithm that approximates the \emph{discrete} Fréchet distance within a factor of $7+\varepsilon$ in strongly subquadratic time. They are the first algorithms to approximate the Fréchet distance and the discrete Fréchet distance within constant factors in strongly subquadratic time.

cs.CG↗

arXiv2025

OmniEraser: Remove Objects and Their Effects in Images with Paired Video-Frame Data

Inpainting algorithms have achieved remarkable progress in removing objects from images, yet still face two challenges: 1) struggle to handle the object's visual effects such as shadow and reflection; 2) easily generate shape-like artifacts and unintended content. In this paper, we propose Video4Removal, a large-scale dataset comprising over 100,000 high-quality samples with realistic object shadows and reflections. By constructing object-background pairs from video frames with off-the-shelf vision models, the labor costs of data acquisition can be significantly reduced. To avoid generating shape-like artifacts and unintended content, we propose Object-Background Guidance, an elaborated paradigm that takes both the foreground object and background images. It can guide the diffusion process to harness richer contextual information. Based on the above two designs, we present OmniEraser, a novel method that seamlessly removes objects and their visual effects using only object masks as input. Extensive experiments show that OmniEraser significantly outperforms previous methods, particularly in complex in-the-wild scenes. And it also exhibits a strong generalization ability in anime-style images. Datasets, models, and codes will be published.

cs.CV↗

arXiv2025

Detection of molecular clouds in the PeVatron candidate source LHAASO J0341$+$5258 by the Nobeyama 45-m radio telescope

We report a new CO observation survey of LHAASO J0341$+$5258 using the Nobeyama Radio Observatory (NRO) 45-m telescope. LHAASO J0341$+$5258 is one of the unidentified ultra-high-energy (UHE; E $>$100 TeV) gamma-ray sources detected by LHAASO. Our CO observations were conducted in February and March 2024, with a total observation time of 36 hours, covering the LHAASO source ($\sim$0.3-0.5 degrees in radius) and its surrounding area (1$\times$1.5 degrees). Within the LHAASO source extent, we identified five compact ($<$ 2 pc) molecular clouds at nearby distances ($<$ 1-4 kpc). These clouds can serve as proton-proton collision targets, producing hadronic gamma rays via neutral pion decays. Based on the hydrogen densities (700-5000 cm$^{-3}$) estimated from our CO observations and archived HI data from the DRAO survey, we derived the total proton energy of $W_p$ (E $>$ 1 TeV) $\sim$ 10$^{45}$ erg to account for the gamma-ray flux. One of the molecular clouds appears to be likely associated with an asymptotic giant branch (AGB) star with an extended CO tail, which may indicate some particle acceleration activities. However, the estimated maximum particle energy below 100 TeV makes the AGB-like star unlikely to be a PeVatron site. We conclude that the UHE emission observed in LHAASO J0341$+$5258 could be due to hadronic interactions between the newly discovered molecular clouds and TeV-PeV protons originating from a distant SNR or due to leptonic emission from a pulsar wind nebula candidate, which is reported in our companion X-ray observation paper (DiKerby et al. 2025).

astro-ph.HE↗

arXiv2025

PromptMID: Modal Invariant Descriptors Based on Diffusion and Vision Foundation Models for Optical-SAR Image Matching

The ideal goal of image matching is to achieve stable and efficient performance in unseen domains. However, many existing learning-based optical-SAR image matching methods, despite their effectiveness in specific scenarios, exhibit limited generalization and struggle to adapt to practical applications. Repeatedly training or fine-tuning matching models to address domain differences is not only not elegant enough but also introduces additional computational overhead and data production costs. In recent years, general foundation models have shown great potential for enhancing generalization. However, the disparity in visual domains between natural and remote sensing images poses challenges for their direct application. Therefore, effectively leveraging foundation models to improve the generalization of optical-SAR image matching remains challenge. To address the above challenges, we propose PromptMID, a novel approach that constructs modality-invariant descriptors using text prompts based on land use classification as priors information for optical and SAR image matching. PromptMID extracts multi-scale modality-invariant features by leveraging pre-trained diffusion models and visual foundation models (VFMs), while specially designed feature aggregation modules effectively fuse features across different granularities. Extensive experiments on optical-SAR image datasets from four diverse regions demonstrate that PromptMID outperforms state-of-the-art matching methods, achieving superior results in both seen and unseen domains and exhibiting strong cross-domain generalization capabilities. The source code will be made publicly available https://github.com/HanNieWHU/PromptMID.

cs.CV↗

arXiv2025

Discovery of a Pulsar Wind Nebula Candidate Associated with the Galactic PeVatron 1LHAASO J0343+5254u

The astronomical origin of the most energetic galactic cosmic rays and gamma rays is still uncertain. X-ray followup of candidate "PeVatrons", systems producing cosmic rays with energies exceeding 1 PeV, can constrain their spatial origin, identify likely counterparts, and test particle emission models. Using 120 ks of XMM-Newton observations, we report the discovery of a candidate pulsar wind nebula, a possible counterpart for the LHAASO PeVatron J0343+5254u. This extended source has a power law X-ray spectrum with spectral index of 1.9 - softer at greater distance from the center - and asymmetric spatial extension out to 2'. We conduct leptonic modeling of the X-ray and gamma ray radiation from this complex system, showing that a fully leptonic model with elevated IR photon fields can explain the multiwavelength emission from this source, similar to other VHE pulsar wind nebulae; excess gamma ray emissivity not explained by a leptonic model may be due to hadronic interactions in nearby molecular cloud regions, which might also produce detectable astroparticle flux.

astro-ph.HE↗

arXiv2025

MTVHunter: Smart Contracts Vulnerability Detection Based on Multi-Teacher Knowledge Translation

Smart contracts, closely intertwined with cryptocurrency transactions, have sparked widespread concerns about considerable financial losses of security issues. To counteract this, a variety of tools have been developed to identify vulnerability in smart contract. However, they fail to overcome two challenges at the same time when faced with smart contract bytecode: (i) strong interference caused by enormous non-relevant instructions; (ii) missing semantics of bytecode due to incomplete data and control flow dependencies. In this paper, we propose a multi-teacher based bytecode vulnerability detection method, namely Multi-Teacher Vulnerability Hunter (MTVHunter), which delivers effective denoising and missing semantic to bytecode under multi-teacher guidance. Specifically, we first propose an instruction denoising teacher to eliminate noise interference by abstract vulnerability pattern and further reflect in contract embeddings. Secondly, we design a novel semantic complementary teacher with neuron distillation, which effectively extracts necessary semantic from source code to replenish the bytecode. Particularly, the proposed neuron distillation accelerate this semantic filling by turning the knowledge transition into a regression task. We conduct experiments on 229,178 real-world smart contracts that concerns four types of common vulnerabilities. Extensive experiments show MTVHunter achieves significantly performance gains over state-of-the-art approaches.

cs.CR↗

arXiv2025

Scrambling Enabled Entropy Accumulation in Open Quantum Systems

In closed quantum many-body systems, initially localized information spreads throughout the system and becomes highly complex. This phenomenon, known as information scrambling, is closely related to entropy growth and quantum thermalization. Recent studies have shown that dissipation in open systems can hinder information scrambling, driving the system into a dissipative phase when the system-bath coupling is strong. However, the signature of this scrambling transition in entropy dynamics remains unexplored. In this work, we unveil a novel phenomenon in open quantum systems, termed entropy accumulation, which occurs exclusively within the scrambling phase. We consider a setup in which a probe is weakly coupled to a system that is already interacting with a bath. We calculate the increase in the second Rényi entropy induced by an external impulse on the system, after tracing out the probe. Despite the system-probe coupling being weak, the entropy continues to increase and eventually saturates at a finite value due to operator growth. In contrast, the entropy increase is limited by the coupling strength in the dissipative phase. The theoretical prediction is derived from both general arguments and an explicit example using generalized Boltzmann equations. Our results offer new insights into the intriguing relationship between entropy dynamics and information scrambling in open quantum systems.

quant-ph↗

arXiv2025

L-PR: Exploiting LiDAR Fiducial Marker for Unordered Low Overlap Multiview Point Cloud Registration

Point cloud registration is a prerequisite for many applications in computer vision and robotics. Most existing methods focus on pairwise registration of two point clouds with high overlap. Although there have been some methods for low overlap cases, they struggle in degraded scenarios. This paper introduces a novel framework dubbed L-PR, designed to register unordered low overlap multiview point clouds leveraging LiDAR fiducial markers. We refer to them as LiDAR fiducial markers, but they are the same as the popular AprilTag and ArUco markers, thin sheets of paper that do not affect the 3D geometry of the environment. We first propose an improved adaptive threshold marker detection method to provide robust detection results when the viewpoints among point clouds change dramatically. Then, we formulate the unordered multiview point cloud registration problem as a maximum a-posteriori (MAP) problem and develop a framework consisting of two levels of graphs to address it. The first-level graph, constructed as a weighted graph, is designed to efficiently and optimally infer initial values of scan poses from the unordered set. The second-level graph is constructed as a factor graph. By globally optimizing the variables on the graph, including scan poses, marker poses, and marker corner positions, we tackle the MAP problem. We conduct both qualitative and quantitative experiments to demonstrate that the proposed method surpasses previous state-of-the-art (SOTA) methods and to showcase that L-PR can serve as a low-cost and efficient tool for 3D asset collection and training data collection. In particular, we collect a new dataset named Livox-3DMatch using L-PR and incorporate it into the training of the SOTA learning-based method, SGHR, which brings evident improvements for SGHR on various benchmarks.

cs.CV↗

arXiv2025

Half a Million M Dwarf Stars Characterized Using Domain-Adapted Spectral Analysis

We present fundamental atmospheric parameters (Teff and log g) and metallicities ([M/H]) for 507,513 M dwarf stars using low-resolution spectra (R~1800) from LAMOST DR10. By employing Cycle-StarNet, an innovative domain adaptation approach, we successfully bridge the gap between theoretical PHOENIX synthetic spectra and observed LAMOST spectra, enabling parameter measurements even for lower signal-to-noise data (S/N>5). The fitting residual analysis shows a reduction from 2.0 times to 1.68 times the flux uncertainty. Comparing with available literature values, we find systematic offsets and precisions of 12$\pm$70 K in Teff, -0.04$\pm$0.17 dex in log g, and -0.06$\pm$0.20 dex in [M/H]. The precision improves for higher quality spectra (S/N>50) to 47 K, 0.12 dex, and 0.14 dex respectively. The metallicity consistency between wide binaries shows a scatter of 0.24 dex, improving to 0.15 dex at S/N>50. We provide a comprehensive catalog including stellar parameters, spectral classifications, activity indicators, and binary/variability flags, establishing a resource for studies of the most numerous stellar population. The complete catalog is available at https://doi.org/10.5281/zenodo.14030249.

astro-ph.SR↗

arXiv2025

Fifteen Years of M31* X-ray Variability and Flares

We append an additional fifteen years (2009-2024) to the Chandra X-ray light curve of M31*, the supermassive black hole at the center of M31, the Andromeda galaxy. Extending and expanding on the work in Li et al. 2011, we show that M31* has remained in an elevated X-ray state from 2006 through at least 2016 (when regular Chandra monitoring ceased) and likely through 2024, with the most recent observations still showing an elevated X-ray flux. We identify one moderate flare in 2013 where the other nuclear X-ray sources are in low-flux states, making that flare a valuable target for followup with multiwavelength and multimessenger archival data. We extract a mostly uncontaminated spectrum for M31* from this observation, showing that its X-ray properties are similar to those observed at Sgr A* in its quiescent state by Baganoff et al. 2003. Furthermore, we find no substantial change in the source's hardness ratio in the 2006 and 2013 flares compared to the post-2006 elevated state, suggesting the these flares are increases in the regular X-ray emission mechanisms instead of entirely new emission modes. Our extended light curve for M31* provides valuable context for multimessenger or multiwavelength observations of nearby supermassive black holes.

astro-ph.HE↗

arXiv2025

First mid-infrared detection and modeling of a flare from Sgr A*

The time-variable emission from the accretion flow of Sgr A*, the supermassive black hole at the Galactic Center, has long been examined in the radio-to-mm, near-infrared (NIR), and X-ray regimes of the electromagnetic spectrum. However, until now, sensitivity and angular resolution have been insufficient in the crucial mid-infrared (MIR) regime. The MIRI instrument on JWST has changed that, and we report the first MIR detection of Sgr A*. The detection was during a flare that lasted about 40 minutes, a duration similar to NIR and X-ray flares, and the source's spectral index steepened as the flare ended. The steepening suggests synchrotron cooling is an important process for Sgr A*'s variability and implies magnetic field strengths $\sim$40--70 Gauss in the emission zone. Observations at $1.3~\mathrm{mm}$ with the Submillimeter Array revealed a counterpart flare lagging the MIR flare by $\approx$10 minutes. The observations can be self-consistently explained as synchrotron radiation from a single population of gradually cooling high-energy electrons accelerated through (a combination of) magnetic reconnection and/or magnetized turbulence.

astro-ph.HE↗

arXiv2025

Channel Modeling and Rate Analysis of Optical Inter-Satellite Link (OISL)

Optical inter-satellite links (OISLs) improve connectivity between satellites in space. They offer advantages such as high-throughput data transfer and reduced size, weight, and power requirements compared to traditional radio frequency transmission. However, the channel model and communication performance for long-distance inter-satellite laser transmission still require in-depth study. In this paper, we first develop a channel model for OISL communication within non-terrestrial networks (NTN) by accounting for pointing errors caused by satellite jitter and tracking noise. We derive the distributions of the channel state arising from these pointing errors and calculate their average value. Additionally, we determine the average achievable data rate for OISL communication in NTN and design a cooperative OISL system, highlighting a trade-off between concentrating beam energy and balancing misalignment. We calculate the minimum number of satellites required in cooperative OISLs to achieve a targeted data transmission size while adhering to latency constraints. This involves exploring the balance between the increased data rate of each link and the cumulative latency across all links. Finally, simulation results validate the effectiveness of the proposed analytical model and provide insights into the optimal number of satellites needed for cooperative OISLs and the optimal laser frequency to use.

eess.SP↗

arXiv2024

Construction of Toda flow via Sato-Segal-Wilson theory

A Toda flow is constructed on a space of bounded initial data through Sato-Segal-Wilson theory. The flow is described by the Weyl functions of the underlying Jacobi operators. This is a continuation of the previous work on the KdV flow.

math.SP↗