arXiv subjects

Chen Wu

Publications and source records attributed to Chen Wu.

At least 145 records · Page 8Linked to original sources

arXiv2023

SAAN: Similarity-aware attention flow network for change detection with VHR remote sensing images

Change detection (CD) is a fundamental and important task for monitoring the land surface dynamics in the earth observation field. Existing deep learning-based CD methods typically extract bi-temporal image features using a weight-sharing Siamese encoder network and identify change regions using a decoder network. These CD methods, however, still perform far from satisfactorily as we observe that 1) deep encoder layers focus on irrelevant background regions and 2) the models' confidence in the change regions is inconsistent at different decoder stages. The first problem is because deep encoder layers cannot effectively learn from imbalanced change categories using the sole output supervision, while the second problem is attributed to the lack of explicit semantic consistency preservation. To address these issues, we design a novel similarity-aware attention flow network (SAAN). SAAN incorporates a similarity-guided attention flow module with deeply supervised similarity optimization to achieve effective change detection. Specifically, we counter the first issue by explicitly guiding deep encoder layers to discover semantic relations from bi-temporal input images using deeply supervised similarity optimization. The extracted features are optimized to be semantically similar in the unchanged regions and dissimilar in the changing regions. The second drawback can be alleviated by the proposed similarity-guided attention flow module, which incorporates similarity-guided attention modules and attention flow mechanisms to guide the model to focus on discriminative channels and regions. We evaluated the effectiveness and generalization ability of the proposed method by conducting experiments on a wide range of CD tasks. The experimental results demonstrate that our method achieves excellent performance on several CD tasks, with discriminative features and semantic consistency preserved.

cs.CV↗

arXiv2023

T-UNet: Triplet UNet for Change Detection in High-Resolution Remote Sensing Images

Remote sensing image change detection aims to identify the differences between images acquired at different times in the same area. It is widely used in land management, environmental monitoring, disaster assessment and other fields. Currently, most change detection methods are based on Siamese network structure or early fusion structure. Siamese structure focuses on extracting object features at different times but lacks attention to change information, which leads to false alarms and missed detections. Early fusion (EF) structure focuses on extracting features after the fusion of images of different phases but ignores the significance of object features at different times for detecting change details, making it difficult to accurately discern the edges of changed objects. To address these issues and obtain more accurate results, we propose a novel network, Triplet UNet(T-UNet), based on a three-branch encoder, which is capable to simultaneously extract the object features and the change features between the pre- and post-time-phase images through triplet encoder. To effectively interact and fuse the features extracted from the three branches of triplet encoder, we propose a multi-branch spatial-spectral cross-attention module (MBSSCA). In the decoder stage, we introduce the channel attention mechanism (CAM) and spatial attention mechanism (SAM) to fully mine and integrate detailed textures information at the shallow layer and semantic localization information at the deep layer.

cs.CV↗

arXiv2023

DeepCL: Deep Change Feature Learning on Remote Sensing Images in the Metric Space

Change detection (CD) is an important yet challenging task in the Earth observation field for monitoring Earth surface dynamics. The advent of deep learning techniques has recently propelled automatic CD into a technological revolution. Nevertheless, deep learning-based CD methods are still plagued by two primary issues: 1) insufficient temporal relationship modeling and 2) pseudo-change misclassification. To address these issues, we complement the strong temporal modeling ability of metric learning with the prominent fitting ability of segmentation and propose a deep change feature learning (DeepCL) framework for robust and explainable CD. Firstly, we designed a hard sample-aware contrastive loss, which reweights the importance of hard and simple samples. This loss allows for explicit modeling of the temporal correlation between bi-temporal remote sensing images. Furthermore, the modeled temporal relations are utilized as knowledge prior to guide the segmentation process for detecting change regions. The DeepCL framework is thoroughly evaluated both theoretically and experimentally, demonstrating its superior feature discriminability, resilience against pseudo changes, and adaptability to a variety of CD algorithms. Extensive comparative experiments substantiate the quantitative and qualitative superiority of DeepCL over state-of-the-art CD approaches.

cs.CV↗

arXiv2023

Learning to Backdoor Federated Learning

In a federated learning (FL) system, malicious participants can easily embed backdoors into the aggregated model while maintaining the model's performance on the main task. To this end, various defenses, including training stage aggregation-based defenses and post-training mitigation defenses, have been proposed recently. While these defenses obtain reasonable performance against existing backdoor attacks, which are mainly heuristics based, we show that they are insufficient in the face of more advanced attacks. In particular, we propose a general reinforcement learning-based backdoor attack framework where the attacker first trains a (non-myopic) attack policy using a simulator built upon its local data and common knowledge on the FL system, which is then applied during actual FL training. Our attack framework is both adaptive and flexible and achieves strong attack performance and durability even under state-of-the-art defenses.

cs.LG↗

arXiv2023

Impacts of symmetry energy slope on the oscillation frequencies of neutron stars with short-range correlation and admixed dark matter

Oscillation modes of compact stars, in general, can serve as a fingerprint in determining the equation of state (EOS) of dense matter. In this study, we examine the impact of symmetry energy slope ($L$) on the oscillation frequencies of neutron stars (NSs) with nucleon-nucleon short range correlation (SRC) and admixed dark matter (DM) for the first time within the relativistic mean-field theory. By adjusting the $L$, we revise the EOS and coupling parameters in light of the SRC and DM effects, and construct the new sets. The results reveal that NSs containing SRC and DM inside are more likely to satisfy the observational constraints, and we find that smaller $L$ exhibits larger fundamental non-radial and radial frequencies, and that the effect on Large Separation (LG) is also mainly concentrated in the low-mass region. Moreover, we update the linear relationship between the non-radial frequency and mean density, and we further give empirical relations between non-radial and radial frequencies and tidal deformability at different $L$ for 1.4$M_{\odot}$ and 2$M_{\odot}$. These findings will enable us to more effectively confine the NS EOSs, in turn, also provide a strategy to place constraints on the $L$.

nucl-th↗

arXiv2023

An Investigation of Face Mask Use with Busking Videos on YouTube during COVID-19: a Case Study in South Korea

Wearing face mask is an effective measure to reduce the risk of COVID-19 infections and control its transmission, thus its usage survey is important for better policy decision to mitigate the epidemic spread. Current existing worldwide surveys are mostly self-reported, whose accuracies are hard to guaranteed, and may exaggerate the percentage of face mask wearing. Therefore, we collected busking videos with a large amount on YouTube from December 2019 to December 2020, mainly from South Korea, and reported an objective investigation of face mask use in the crowds outdoor. It is found that the face mask wearing rate has an obvious positive correlation with effective reproductive number (Rt) in the South Korea, which indicates that the people in South Korea kept sensitive to the COVID-19 epidemic. The face mask wearing rate in South Korea is higher than some other countries, and two rate droppings in June and September also corresponds to the temporary remission in 2020. This study shows significant potentials to utilize public big video data to make an accurate worldwide survey of face mask use with the support of deep learning technology.

cs.CY↗

arXiv2023

GlobalMind: Global Multi-head Interactive Self-attention Network for Hyperspectral Change Detection

High spectral resolution imagery of the Earth's surface enables users to monitor changes over time in fine-grained scale, playing an increasingly important role in agriculture, defense, and emergency response. However, most current algorithms are still confined to describing local features and fail to incorporate a global perspective, which limits their ability to capture interactions between global features, thus usually resulting in incomplete change regions. In this paper, we propose a Global Multi-head INteractive self-attention change Detection network (GlobalMind) to explore the implicit correlation between different surface objects and variant land cover transformations, acquiring a comprehensive understanding of the data and accurate change detection result. Firstly, a simple but effective Global Axial Segmentation (GAS) strategy is designed to expand the self-attention computation along the row space or column space of hyperspectral images, allowing the global connection with high efficiency. Secondly, with GAS, the global spatial multi-head interactive self-attention (Global-M) module is crafted to mine the abundant spatial-spectral feature involving potential correlations between the ground objects from the entire rich and complex hyperspectral space. Moreover, to acquire the accurate and complete cross-temporal changes, we devise a global temporal interactive multi-head self-attention (GlobalD) module which incorporates the relevance and variation of bi-temporal spatial-spectral features, deriving the integrate potential same kind of changes in the local and global range with the combination of GAS. We perform extensive experiments on five mostly used hyperspectral datasets, and our method outperforms the state-of-the-art algorithms with high accuracy and efficiency.

cs.CV↗

arXiv2023

Theory of the microwave impedance microscopy of Chern insulators

Microwave impedance microscopy (MIM) has been utilized to directly visualize topological edge states in many quantum materials, from quantum Hall systems to topological insulators, across the GHz regime. While the microwave response for conventional metals and insulators can be accurately quantified using simple lumped-element circuits, the applicability of these classical models to more exotic quantum systems remains limited. In this work, we present a general theoretical framework of the MIM response of arbitrary quantum materials within linear response theory. As a special case, we model the microwave response of topological edge states in a Chern insulator and predict an enhanced MIM response at the crystal boundaries due to collective edge magnetoplasmon (EMP) excitations. The resonance frequency of these plasmonic modes should depend quantitatively on the topological invariant of the Chern insulator state and on the sample's circumference, which highlights their non-local, topological nature. To benchmark our analytical predictions, we experimentally probe the MIM response of quantum anomalous Hall edge states in a Cr-doped (Bi,Sb)2Te3 topological insulator and perform numerical simulations using a classical formulation of the EMP modes based on this realistic tip-sample geometry, both of which yield results consistent with our theoretical picture. We also show how the technique of MIM can be used to quantitatively extract the topological invariant of a Chern insulator, disentangle the signatures of topological versus trivial edge states, and shed light on the microscopic nature of dissipation along the crystal boundaries.

cond-mat.mes-hall↗

arXiv2023

Shadows and quasinormal modes of a charged non-commutative black hole by different methods

In this paper, we calculated the quasinormal modes (QNMs) of a charged non-commutative black hole in scalar, electromagnetic and gravitational fields by three methods. We gave the influence of non-commutative parameter $θ$ and charge $Q$ on QNMs in different fields. Thereafter, we calculated the shadow radius of the black hole and provided the valid range of $θ$ and $Q$ using the constraints on the shadow radius of $\text{M87}^{\ast}$ and $\text{Sgr A}^{\ast}$ from the Event Horizon Telescope (EHT). In addition, we estimated the ``relative deviation'' of the shadow radius ($δ_{R_{s}}$) between non-commutative spacetime and commutative spacetime. We found that the maximum values of $δ_{R_{s}}$ decreases with the increase of charge $Q$. In other words, the non-commutativity of spacetime becomes harder to distinguish as the charge of the black hole increases.

gr-qc↗

arXiv2023

Kaon-meson condensation and $Δ$ resonance in hyperonic stellar matter within a relativistic mean-field model

We study the equation of state of dense baryon matter within the relativistic mean-field model, and we include $Δ$(1232) isobars into IUFSU model with hyperons and consider the possibility of kaon meson condensation. We find that it is necessary to consider the $Δ$ resonance state inside the massive neutron star. The critical density of Kaon mesons and hyperons is shifted to a higher density region, in this respect an early appearance of $Δ$ resonances is crucial to guarantee the stability of the branch of hyperonized star with the difference of the coupling parameter $x_{σΔ}$ constrained based on the QCD rules in nuclear matter. The $Δ$ resonance produces a softer equation of state in the low density region, which makes the tidal deformability and radius consistent with the observation of GW170817. As the addition of new degrees of freedom will lead to a softening of the equation of state, the $σ$-cut scheme, which states the decrease of neutron star mass can be lowered if one assumes a limited decrease of the $σ$-meson strength at ${ρ_B}$($ρ_B > ρ_0$), finally we get a maximum mass neutron star with $Δ$ resonance heavier than 2$M_{\odot}$.

nucl-th↗

arXiv2023

EMS-Net: Efficient Multi-Temporal Self-Attention For Hyperspectral Change Detection

Hyperspectral change detection plays an essential role of monitoring the dynamic urban development and detecting precise fine object evolution and alteration. In this paper, we have proposed an original Efficient Multi-temporal Self-attention Network (EMS-Net) for hyperspectral change detection. The designed EMS module cuts redundancy of those similar and containing-no-changes feature maps, computing efficient multi-temporal change information for precise binary change map. Besides, to explore the clustering characteristics of the change detection, a novel supervised contrastive loss is provided to enhance the compactness of the unchanged. Experiments implemented on two hyperspectral change detection datasets manifests the out-standing performance and validity of proposed method.

cs.CV↗

arXiv2023

HCGMNET: A Hierarchical Change Guiding Map Network For Change Detection

Very-high-resolution (VHR) remote sensing (RS) image change detection (CD) has been a challenging task for its very rich spatial information and sample imbalance problem. In this paper, we have proposed a hierarchical change guiding map network (HCGMNet) for change detection. The model uses hierarchical convolution operations to extract multiscale features, continuously merges multi-scale features layer by layer to improve the expression of global and local information, and guides the model to gradually refine edge features and comprehensive performance by a change guide module (CGM), which is a self-attention with changing guide map. Extensive experiments on two CD datasets show that the proposed HCGMNet architecture achieves better CD performance than existing state-of-the-art (SOTA) CD methods.

cs.CV↗

arXiv2023

The Big Bang Nucleosynthesis abundances of the light elements using improved thermonuclear reaction rates

Big Bang Nucleosynthesis (BBN) is an important stage of a homogeneous and isotropic expanding universe. The results of calculation of the synthesis of light elements during this epoch can then be compared with the abundances of the light elements. The theoretical calculation of the BBN model depends on the initial conditions of the early universe and reaction cross sections measured by the nuclear physics experiment. Recently, an update of the NACRE (Nuclear Astrophysics Compilation of REactions) database is presented. This improved compilation comprises thermonuclear reaction rates for 34 two-body reactions on light nuclides (fifteen are particle transfer reactions and nineteen are radiative capture reactions). In this work, we calculate the BBN abundances by using these updated thermonuclear reaction rates in the framework of the code \texttt{AlterBBN}. Our results suggest that the new numerical result of the primordial Lithium abundance is 7.1 percent larger than the previous calculation.

nucl-th↗

arXiv2023

Absorption and scattering of a high dimensional noncommutative black hole

In this work, we investigate the scattering of massless plane scalar waves by the high dimensional noncommutative Schwarzschild-Tangherlini black hole. We use the partial wave approach to determine the scattering and absorption cross sections in the incident wavelength range. Our numerical results demonstrate that the bigger the noncommutative parameter, the smaller the maximum value of the related partial absorption cross section, however the tendency is slightly. We also discovered that when the noncommutative parameter is weak, the absorption cross section of the high dimensional black hole oscillates in the low frequency zone. The total absorption cross section oscillates around the geometrical optical limit in the high frequency range, and the scattering characteristics of black holes with various parameters are visibly different. The influence on the differential scattering cross section is particularly pronounced at large angles.

gr-qc↗

arXiv2022

Absorption and scattering of massless scalar wave from Regular Black Holes

In this work, we numerically investigate the scattering and absorption cross section of the massless scalar field from some well-known regular black holes by using the partial wave approach. Our computational results indicate that the larger the parameters, the lower the associated total absorption cross section maxima. When compared to the Schwarzschild black hole, the scattering cross section is enhanced in some regular black hole spacetimes, meanwhile the scattering width is narrow in the forward orientation. Moreover, it is found that the null geodesics of the critical impact parameter and the geometrical optical value in the high frequency regime have similar changing behavior.

gr-qc↗

arXiv2022

Grafting Pre-trained Models for Multimodal Headline Generation

Multimodal headline utilizes both video frames and transcripts to generate the natural language title of the videos. Due to a lack of large-scale, manually annotated data, the task of annotating grounded headlines for video is labor intensive and impractical. Previous researches on pre-trained language models and video-language models have achieved significant progress in related downstream tasks. However, none of them can be directly applied to multimodal headline architecture where we need both multimodal encoder and sentence decoder. A major challenge in simply gluing language model and video-language model is the modality balance, which is aimed at combining visual-language complementary abilities. In this paper, we propose a novel approach to graft the video encoder from the pre-trained video-language model on the generative pre-trained language model. We also present a consensus fusion mechanism for the integration of different components, via inter/intra modality relation. Empirically, experiments show that the grafted model achieves strong results on a brand-new dataset collected from real-world applications.

cs.CV↗

arXiv2022

Comparison of TIDI Line of Sight Winds with ICON-MIGHTI Measurements

The Thermosphere-Ionosphere-Mesosphere Energetics and Dynamics (TIMED) satellite has been making observations of the mesosphere and lower thermosphere (MLT) region for two decades. The TIMED Doppler Interferometer (TIDI) measures the neutral winds using four orthogonal telescopes. In this study, the line of sight (LOS) winds from individual telescopes are compared to the measurements from the Ionospheric Connection Explorer's (ICON's) Michelson Interferometer for Global High-resolution Thermospheric Imaging (MIGHTI) instrument from 90-100 km altitude during 2020. With the MIGHTI vector winds projected onto the LOS direction of each TIDI telescope, coincidences of the two datasets are found. The four telescopes perform differently and the performance depends on the satellite configuration and local solar zenith angle. Measurements from the coldside telescopes, Telescope 1 (Tel1) and Telescope 2 (Tel2), are better correlated with the MIGHTI winds in general with Tel2 having higher correlation coefficients across all conditions. The performance of Tel1 is comparable to that of Tel2 during backward flight while showing systematic errors larger than the average wind speeds during forward flight. The warmside LOS winds from Telescope 3 (Tel3) and Telescope 4 (Tel4) vary widely in magnitude, especially on the nightside. Compared with MIGHTI winds, the Tel4 measurements have the weakest correlation, while the Tel3 performance is comparable to that of the coldside telescopes during the ascending phase but deteriorates during the descending phase. Based on the TIDI/MIGHTI comparisons, figures of merit are generated to quantify the quality of measurements from individual telescopes in different configurations.

physics.space-ph↗

arXiv2022

Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation Learning

Unsupervised multimodal change detection is a practical and challenging topic that can play an important role in time-sensitive emergency applications. To address the challenge that multimodal remote sensing images cannot be directly compared due to their modal heterogeneity, we take advantage of two types of modality-independent structural relationships in multimodal images. In particular, we present a structural relationship graph representation learning framework for measuring the similarity of the two structural relationships. Firstly, structural graphs are generated from preprocessed multimodal image pairs by means of an object-based image analysis approach. Then, a structural relationship graph convolutional autoencoder (SR-GCAE) is proposed to learn robust and representative features from graphs. Two loss functions aiming at reconstructing vertex information and edge information are presented to make the learned representations applicable for structural relationship similarity measurement. Subsequently, the similarity levels of two structural relationships are calculated from learned graph representations and two difference images are generated based on the similarity levels. After obtaining the difference images, an adaptive fusion strategy is presented to fuse the two difference images. Finally, a morphological filtering-based postprocessing approach is employed to refine the detection results. Experimental results on five datasets with different modal combinations demonstrate the effectiveness of the proposed method.

cs.CV↗