arXiv subjects

Shuying Li

Publications and source records attributed to Shuying Li.

11 recordsLinked to original sources

arXiv2026

Influence of effective mass of the relativistic mean field theory on core collapse supernovae and compact objects

We study the influence of the effective mass in the relativistic mean field (RMF) theory on the properties of the central core of collapse-driven supernovae and the formation of compact objects. Influence of the effective mass has been so far studied within the non-relativistic frameworks. In order to clarify the role of the effective mass in the relativistic frameworks, which is different from non-relativistic ones, we adopt the set of equation of state (EOS) tables using the parameterizations TM1e and TM1m, which have different effective masses but with the same saturation properties, in the RMF theory. We show that choices of the effective mass in supernova matter affect both the stiffness of the EOS through pressure and the thermodynamical behavior through temperature under the RMF frameworks. We explore differences in matter evolution with neutrino emissions by performing a set of numerical simulations of the gravitational collapse and bounce of massive stars and the cooling of the proto-neutron stars. The EOS with large effective mass leads to compact proto-neutron stars and early collapse to black holes with high densities and temperatures due to the softness. It leads to high energy neutrinos in long emission from the proto-neutron star cooling and in short burst from the black hole formation.

astro-ph.HE↗

arXiv2026

MDAFNet: Multiscale Differential Edge and Adaptive Frequency Guided Network for Infrared Small Target Detection

Infrared small target detection (IRSTD) plays a crucial role in numerous military and civilian applications. However, existing methods often face the gradual degradation of target edge pixels as the number of network layers increases, and traditional convolution struggles to differentiate between frequency components during feature extraction, leading to low-frequency backgrounds interfering with high-frequency targets and high-frequency noise triggering false detections. To address these limitations, we propose MDAFNet (Multi-scale Differential Edge and Adaptive Frequency Guided Network for Infrared Small Target Detection), which integrates the Multi-Scale Differential Edge (MSDE) module and Dual-Domain Adaptive Feature Enhancement (DAFE) module. The MSDE module, through a multi-scale edge extraction and enhancement mechanism, effectively compensates for the cumulative loss of target edge information during downsampling. The DAFE module combines frequency domain processing mechanisms with simulated frequency decomposition and fusion mechanisms in the spatial domain to effectively improve the network's capability to adaptively enhance high-frequency targets and selectively suppress high-frequency noise. Experimental results on multiple datasets demonstrate the superior detection performance of MDAFNet.

cs.CV↗

arXiv2026

DCCS-Det: Directional Context and Cross-Scale-Aware Detector for Infrared Small Target

Infrared small target detection (IRSTD) is critical for applications like remote sensing and surveillance, which aims to identify small, low-contrast targets against complex backgrounds. However, existing methods often struggle with inadequate joint modeling of local-global features (harming target-background discrimination) or feature redundancy and semantic dilution (degrading target representation quality). To tackle these issues, we propose DCCS-Det (Directional Context and Cross-Scale Aware Detector for Infrared Small Target), a novel detector that incorporates a Dual-stream Saliency Enhancement (DSE) block and a Latent-aware Semantic Extraction and Aggregation (LaSEA) module. The DSE block integrates localized perception with direction-aware context aggregation to help capture long-range spatial dependencies and local details. On this basis, the LaSEA module mitigates feature degradation via cross-scale feature extraction and random pooling sampling strategies, enhancing discriminative features and suppressing noise. Extensive experiments show that DCCS-Det achieves state-of-the-art detection accuracy with competitive efficiency across multiple datasets. Ablation studies further validate the contributions of DSE and LaSEA in improving target perception and feature representation under complex scenarios. \href{https://huggingface.co/InPeerReview/InfraredSmallTargetDetection-IRSTD.DCCS}{DCCS-Det Official Code is Available Here!}

cs.CV↗

arXiv2026

HA2F: Dual-module Collaboration-Guided Hierarchical Adaptive Aggregation Framework for Remote Sensing Change Detection

Remote sensing change detection (RSCD) aims to identify the spatio-temporal changes of land cover, providing critical support for multi-disciplinary applications (e.g., environmental monitoring, disaster assessment, and climate change studies). Existing methods focus either on extracting features from localized patches, or pursue processing entire images holistically, which leads to the cross temporal feature matching deviation and exhibiting sensitivity to radiometric and geometric noise. Following the above issues, we propose a dual-module collaboration guided hierarchical adaptive aggregation framework, namely HA2F, which consists of dynamic hierarchical feature calibration module (DHFCM) and noise-adaptive feature refinement module (NAFRM). The former dynamically fuses adjacent-level features through perceptual feature selection, suppressing irrelevant discrepancies to address multi-temporal feature alignment deviations. The NAFRM utilizes the dual feature selection mechanism to highlight the change sensitive regions and generate spatial masks, suppressing the interference of irrelevant regions or shadows. Extensive experiments verify the effectiveness of the proposed HA2F, which achieves state-of-the-art performance on LEVIR-CD, WHU-CD, and SYSU-CD datasets, surpassing existing comparative methods in terms of both precision metrics and computational efficiency. In addition, ablation experiments show that DHFCM and NAFRM are effective. \href{https://huggingface.co/InPeerReview/RemoteSensingChangeDetection-RSCD.HA2F}{HA2F Official Code is Available Here!}

cs.CV↗

arXiv2025

Domain-Specific Foundation Model Improves AI-Based Analysis of Neuropathology

Foundation models have transformed computational pathology by providing generalizable representations from large-scale histology datasets. However, existing models are predominantly trained on surgical pathology data, which is enriched for non-nervous tissue and overrepresents neoplastic, inflammatory, metabolic, and other non-neurological diseases. Neuropathology represents a markedly different domain of histopathology, characterized by unique cell types (neurons, glia, etc.), distinct cytoarchitecture, and disease-specific pathological features including neurofibrillary tangles, amyloid plaques, Lewy bodies, and pattern-specific neurodegeneration. This domain mismatch may limit the ability of general-purpose foundation models to capture the morphological patterns critical for interpreting neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and cerebellar ataxias. To address this gap, we developed NeuroFM, a foundation model trained specifically on whole-slide images of brain tissue spanning diverse neurodegenerative pathologies. NeuroFM demonstrates superior performance compared to general-purpose models across multiple neuropathology-specific downstream tasks, including mixed dementia disease classification, hippocampal region segmentation, and neurodegenerative ataxia identification encompassing cerebellar essential tremor and spinocerebellar ataxia subtypes. This work establishes that domain-specialized foundation models trained on brain tissue can better capture neuropathology-specific features than models trained on general surgical pathology datasets. By tailoring foundation models to the unique morphological landscape of neurodegenerative diseases, NeuroFM enables more accurate and reliable AI-based analysis for brain disease diagnosis and research, setting a precedent for domain-specific model development in specialized areas of digital pathology.

cs.CV↗

arXiv2025

Nuclear Matter and Finite Nuclei: Relativistic Thomas-Fermi Approximation Versus Relativistic Mean-Field Approach

The Thomas-Fermi approximation is a powerful method that has been widely used to describe atomic structures, finite nuclei, and nonuniform matter in supernovae and neutron-star crusts. Nonuniform nuclear matter at subnuclear density is assumed to be composed of a lattice of heavy nuclei surrounded by dripped nucleons, and the Wigner-Seitz cell is commonly introduced to simplify the calculations. The self-consistent Thomas--Fermi approximation can be employed to study both a nucleus surrounded by nucleon gas in the Wigner-Seitz cell and an isolated nucleus in the nuclide chart. A detailed comparison is made between the self-consistent Thomas-Fermi approximation and the relativistic mean-field approach for the description of finite nuclei, based on the same nuclear interaction. These results are then examined using experimental data from the corresponding nuclei.

nucl-th↗

arXiv2024

Influence of effective nucleon mass on equation of state for supernova simulations and neutron stars

We investigate the influence of the effective nucleon mass on the equation of state (EOS), which is constructed for simulations of core-collapse supernovae and binary neutron star mergers, within the relativistic mean-field (RMF) framework. The study introduces a new RMF parameter set, TM1m, which is a modification of the TM1e model with an adjusted effective mass, maintaining the saturation properties of nuclear matter. The TM1m model, with a larger effective mass ratio ($M^{\ast}/M \sim 0.8$) compared to the TM1e model ($M^{\ast}/M \sim 0.63$), is employed to construct a new EOS table, EOS5. This EOS table is designed to offer insights into the influence of the effective nucleon mass on the EOS within a relativistic framework, particularly above the saturation density. The results of EOS5 are compared with those obtained from other models, including both relativistic and nonrelativistic approaches. The properties of cold neutron stars, calculated using the TM1m model, are compatible with the existence of a $2\ M_\odot$ pulsar and the latest constraints on the tidal deformability and radii of a canonical $1.4\ M_\odot$ neutron star, derived from astrophysical observations.

nucl-th↗

arXiv2022

On the evolutions of induction zone structure in wedge-stabilized oblique detonation with water mist flows

Two-dimensional wedge-stabilized oblique detonations in stoichiometric and fuel-lean H2/O2/Ar mixtures with water mists are studied with Eulerian-Lagrangian method. The effects of water droplet mass flow rate on flow and chemical structures in the induction zone, as well as physical / chemical roles of water vapor, are investigated. The results show that the oblique detonation wave (ODW) can stand in a range of water mass flow rates for both stoichiometric and fuel-lean mixtures. With increased droplet mass flow rate, the deflagration front in the induction zone is distorted and becomes zigzagged, but the transition mode from oblique shock wave (OSW) to ODW does not change. Moreover, the initiation and transition locations monotonically increase, and the OSW and ODW angles decrease, due to droplet evaporation and water vapor dilution in the induction region. For fuel-lean mixtures, the sensitivity of characteristic locations to the droplet loading variations is mild, which signifies better intrinsic stability and resilience to the oncoming water droplets. The chemical explosiveness of the gaseous mixture between the lead shock and reaction front is studied with the chemical explosive method analysis. The smooth transition is caused by the highly enhanced reactivity of the gas immediately behind the curved shock, intensified by the compression waves. Nonetheless, the abrupt transition results from the intersection between the beforehand generated detonation wave in the induction zone and OSW. Besides, the degree to which the gas chemical reactivity in the induction zone for fuel-lean mixtures is reduced by evaporating droplets is generally lower than that for stoichiometric gas. Also, physical and chemical effects of water vapor from liquid droplets result in significant differences in ODW initiation and morphology.

physics.flu-dyn↗

arXiv2018

Hypergraph p-Laplacian Regularization for Remote Sensing Image Recognition

It is of great importance to preserve locality and similarity information in semi-supervised learning (SSL) based applications. Graph based SSL and manifold regularization based SSL including Laplacian regularization (LapR) and Hypergraph Laplacian regularization (HLapR) are representative SSL methods and have achieved prominent performance by exploiting the relationship of sample distribution. However, it is still a great challenge to exactly explore and exploit the local structure of the data distribution. In this paper, we present an effect and effective approximation algorithm of Hypergraph p-Laplacian and then propose Hypergraph p-Laplacian regularization (HpLapR) to preserve the geometry of the probability distribution. In particular, p-Laplacian is a nonlinear generalization of the standard graph Laplacian and Hypergraph is a generalization of a standard graph. Therefore, the proposed HpLapR provides more potential to exploiting the local structure preserving. We apply HpLapR to logistic regression and conduct the implementations for remote sensing image recognition. We compare the proposed HpLapR to several popular manifold regularization based SSL methods including LapR, HLapR and HpLapR on UC-Merced dataset. The experimental results demonstrate the superiority of the proposed HpLapR.

cs.CV↗

arXiv2018

ILGNet: Inception Modules with Connected Local and Global Features for Efficient Image Aesthetic Quality Classification using Domain Adaptation

In this paper, we address a challenging problem of aesthetic image classification, which is to label an input image as high or low aesthetic quality. We take both the local and global features of images into consideration. A novel deep convolutional neural network named ILGNet is proposed, which combines both the Inception modules and an connected layer of both Local and Global features. The ILGnet is based on GoogLeNet. Thus, it is easy to use a pre-trained GoogLeNet for large-scale image classification problem and fine tune our connected layers on an large scale database of aesthetic related images: AVA, i.e. \emph{domain adaptation}. The experiments reveal that our model achieves the state of the arts in AVA database. Both the training and testing speeds of our model are higher than those of the original GoogLeNet.

cs.CV↗

arXiv2013

Hybrid Group Decoding for Scalable Video over MIMO-OFDM Downlink Systems

We propose a scalable video broadcasting scheme over MIMO-OFDM systems. The scalable video source layers are channel encoded and modulated into independent signal streams, which are then transmitted from the allocated antennas in certain time-frequency blocks. Each receiver employs the successive group decoder to decode the signal streams of interest by treating other signal streams as interference. The transmitter performs adaptive coding and modulation, and transmission antenna and subcarrier allocation, based on the rate feedback from the receivers. We also propose a hybrid receiver that switches between the successive group decoder and the MMSE decoder depending on the rate. Extensive simulations are provided to demonstrate the performance gain of the proposed group-decoding-based scalable video broadcasting scheme over the one based on the conventional MMSE decoding.

cs.IT↗