arXiv subjects

Yongxiong Wang

Publications and source records attributed to Yongxiong Wang.

10 recordsLinked to original sources

arXiv2026

ALOHA IRDCs Molecular Line Follow-up: I. Gas properties and kinematics

Infrared Dark Clouds are ideal sites for investigating the initial conditions of massive star and cluster formation. The A Lei Of the Habitat and Assembly of Infrared Dark Clouds (ALOHA IRDCs), a James Clerk Maxwell Telescope (JCMT) Large Program, has mapped nearby IRDCs with SCUBA-2. Complementary molecular line observations are needed to characterise the physical, kinematic, and chemical properties of the dense gas. We aim to determine the thermal, kinematic, and chemical properties of clumps identified in the ALOHA IRDCs, and to assess their evolutionary status and level of star-forming activity. We performed single-pointing K-band and W-band observations towards 56 ALOHA IRDCs clumps using the Effelsberg 100-m and Yebes 40-m telescopes, respectively. We derived NH3 kinetic temperatures using the hyperfine group ratio (HFGR) method and identified infall and shock signatures from HCO+, H13CO+, SiO, and HNCO profiles. Water masers and NH2D emission were used as complementary tracers of chemical evolution and star formation. The clumps exhibit kinetic temperatures of 15-29 K. We detect NH2D emission towards 18 sources, with NH2D centroid velocities consistent with NH3, indicating both species trace the same dense gas component. More than half of the clumps display blue-asymmetric HCO+ profiles, identifying them as infall candidates. Water masers are detected in 22 sources, with prominent velocity ranges and variability. Broad SiO emission (>~20 km/s) indicates strong shocks, while narrower extents (<~6km/s) likely trace large-scale interactions or low-velocity shocks. The widespread infall signatures, shock tracers, masers, and NH2D emission suggest that relatively quiescent, chemically young material can coexist with dynamically active gas affected by early protostellar feedback, providing insight into the coupled physical and chemical evolution of massive IRDC clumps.

astro-ph.GA↗

arXiv2026

Meta-Black-Box Optimization with Ensemble Surrogate Modeling for Robustness-Accuracy Trade-off within SAEA

Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for expensive black-box optimization problems. However, their reliance on rigid and manually designed components limits their flexibility and generalization across tasks. Meta-black-box optimization (MetaBBO) provides a promising paradigm for adaptively configuring algorithmic components. Nevertheless, existing MetaBBO methods usually control only a single component, and few studies have investigated the unified control of multi-component optimizers such as SAEAs. Moreover, the robustness-accuracy trade-off in surrogate modeling, which is crucial for stable early-stage exploration and accurate late-stage exploitation, has rarely been explicitly considered. To address these issues, we propose AdaE-SAEA, an adaptive ensemble surrogate-assisted evolutionary algorithm for expensive multi-objective optimization. AdaE-SAEA embeds SAEA as the low-level optimizer within the MetaBBO framework and jointly controls the infill criterion and ensemble-based surrogate modeling. Specifically, bagging and boosting are designed as surrogate modeling modules to adaptively balance robustness and accuracy across different search phases, while the meta-policy simultaneously selects the infill criterion to enable adaptive sampling decisions. The meta-policy is trained through reinforcement learning with parallel sampling and centralized training, improving both training efficiency and transferability. Experiments on synthetic and real-world problems demonstrate that AdaE-SAEA outperforms state-of-the-art baselines and MetaBBO-based methods. We further verify the effectiveness of TabPFN as the base surrogate model for ensemble learning. To the best of our knowledge, this is the first work to unify the control of surrogate modeling and infill criteria in SAEAs while explicitly addressing the robustness--accuracy trade-off.

cs.NE↗

arXiv2026

An inverted infall profile for the collapse of the massive star-forming IRDC SDC335.579-0.292

There is increasing evidence for global collapse of clumps over parsec-scales in massive star formation regions. Such collapse may result in characteristic molecular line emission profiles but the spatial variation of such lines has rarely been quantitatively examined. Here we explore the infall properties using the spatially-resolved HCO$^+$ J=1--0 and H$^{13}$CO$^+$ J=1--0 maps of the massive infrared dark cloud (IRDC) SDC335.579-0.292. We compare the observations with the analytical Hill5 model and radiative transfer models. This shows that the best-fit infall velocity towards the cloud centre to be well-constrained to $-0.6$ to $-1.6$ km s$^{-1}$ and the mass infall rate between a few $\times10^{-3}$ and $10^{-2}$ M$_{\odot}$yr$^{-1}$. The comparison also highlights some limitations of the Hill5 method. We demonstrate that the width of optically thin spectral lines, which are usually interpreted as resulting from turbulent motions, are in fact dominated by unresolved, ordered infall motions within the beam. Our results suggest a complex collapse situation where there is a minimum in the infall velocity at $\sim2\times10^{18}$ cm (0.7 pc) with the infall velocity increasing at both smaller and larger radii. The parsec-scale infall with an inverted velocity profile indicates that the accretion in this massive star-forming cloud should have intermediate scales, at which fragmentation or filament formation has to occur before material flows onto the cloud centre.

astro-ph.GA↗

arXiv2024

Towards Accurate Camouflaged Object Detection with Mixture Convolution and Interactive Fusion

Camouflaged object detection (COD), which aims to identify the objects that conceal themselves into the surroundings, has recently drawn increasing research efforts in the field of computer vision. In practice, the success of deep learning based COD is mainly determined by two key factors, including (i) A significantly large receptive field, which provides rich context information, and (ii) An effective fusion strategy, which aggregates the rich multi-level features for accurate COD. Motivated by these observations, in this paper, we propose a novel deep learning based COD approach, which integrates the large receptive field and effective feature fusion into a unified framework. Specifically, we first extract multi-level features from a backbone network. The resulting features are then fed to the proposed dual-branch mixture convolution modules, each of which utilizes multiple asymmetric convolutional layers and two dilated convolutional layers to extract rich context features from a large receptive field. Finally, we fuse the features using specially-designed multilevel interactive fusion modules, each of which employs an attention mechanism along with feature interaction for effective feature fusion. Our method detects camouflaged objects with an effective fusion strategy, which aggregates the rich context information from a large receptive field. All of these designs meet the requirements of COD well, allowing the accurate detection of camouflaged objects. Extensive experiments on widely-used benchmark datasets demonstrate that our method is capable of accurately detecting camouflaged objects and outperforms the state-of-the-art methods.

cs.CV↗

arXiv2023

A Lightweight Domain Adversarial Neural Network Based on Knowledge Distillation for EEG-based Cross-subject Emotion Recognition

Individual differences of Electroencephalogram (EEG) could cause the domain shift which would significantly degrade the performance of cross-subject strategy. The domain adversarial neural networks (DANN), where the classification loss and domain loss jointly update the parameters of feature extractor, are adopted to deal with the domain shift. However, limited EEG data quantity and strong individual difference are challenges for the DANN with cumbersome feature extractor. In this work, we propose knowledge distillation (KD) based lightweight DANN to enhance cross-subject EEG-based emotion recognition. Specifically, the teacher model with strong context learning ability is utilized to learn complex temporal dynamics and spatial correlations of EEG, and robust lightweight student model is guided by the teacher model to learn more difficult domain-invariant features. In the feature-based KD framework, a transformer-based hierarchical temporalspatial learning model is served as the teacher model. The student model, which is composed of Bi-LSTM units, is a lightweight version of the teacher model. Hence, the student model could be supervised to mimic the robust feature representations of teacher model by leveraging complementary latent temporal features and spatial features. In the DANN-based cross-subject emotion recognition, we combine the obtained student model and a lightweight temporal-spatial feature interaction module as the feature extractor. And the feature aggregation is fed to the emotion classifier and domain classifier for domain-invariant feature learning. To verify the effectiveness of the proposed method, we conduct the subject-independent experiments on the public dataset DEAP with arousal and valence classification. The outstanding performance and t-SNE visualization of latent features verify the advantage and effectiveness of the proposed method.

eess.SP↗

arXiv2022

STILN: A Novel Spatial-Temporal Information Learning Network for EEG-based Emotion Recognition

The spatial correlations and the temporal contexts are indispensable in Electroencephalogram (EEG)-based emotion recognition. However, the learning of complex spatial correlations among several channels is a challenging problem. Besides, the temporal contexts learning is beneficial to emphasize the critical EEG frames because the subjects only reach the prospective emotion during part of stimuli. Hence, we propose a novel Spatial-Temporal Information Learning Network (STILN) to extract the discriminative features by capturing the spatial correlations and temporal contexts. Specifically, the generated 2D power topographic maps capture the dependencies among electrodes, and they are fed to the CNN-based spatial feature extraction network. Furthermore, Convolutional Block Attention Module (CBAM) recalibrates the weights of power topographic maps to emphasize the crucial brain regions and frequency bands. Meanwhile, Batch Normalizations (BNs) and Instance Normalizations (INs) are appropriately combined to relieve the individual differences. In the temporal contexts learning, we adopt the Bidirectional Long Short-Term Memory Network (Bi-LSTM) network to capture the dependencies among the EEG frames. To validate the effectiveness of the proposed method, subject-independent experiments are conducted on the public DEAP dataset. The proposed method has achieved the outstanding performance, and the accuracies of arousal and valence classification have reached 0.6831 and 0.6752 respectively.

eess.SP↗

arXiv2022

Temporal-spatial Representation Learning Transformer for EEG-based Emotion Recognition

Both the temporal dynamics and spatial correlations of Electroencephalogram (EEG), which contain discriminative emotion information, are essential for the emotion recognition. However, some redundant information within the EEG signals would degrade the performance. Specifically,the subjects reach prospective intense emotions for only a fraction of the stimulus duration. Besides, it is a challenge to extract discriminative features from the complex spatial correlations among a number of electrodes. To deal with the problems, we propose a transformer-based model to robustly capture temporal dynamics and spatial correlations of EEG. Especially, temporal feature extractors which share the weight among all the EEG channels are designed to adaptively extract dynamic context information from raw signals. Furthermore, multi-head self-attention mechanism within the transformers could adaptively localize the vital EEG fragments and emphasize the essential brain regions which contribute to the performance. To verify the effectiveness of the proposed method, we conduct the experiments on two public datasets, DEAP and MAHNOBHCI. The results demonstrate that the proposed method achieves outstanding performance on arousal and valence classification.

eess.SP↗

arXiv2022

ASD: Towards Attribute Spatial Decomposition for Prior-Free Facial Attribute Recognition

Representing the spatial properties of facial attributes is a vital challenge for facial attribute recognition (FAR). Recent advances have achieved the reliable performances for FAR, benefiting from the description of spatial properties via extra prior information. However, the extra prior information might not be always available, resulting in the restricted application scenario of the prior-based methods. Meanwhile, the spatial ambiguity of facial attributes caused by inherent spatial diversities of facial parts is ignored. To address these issues, we propose a prior-free method for attribute spatial decomposition (ASD), mitigating the spatial ambiguity of facial attributes without any extra prior information. Specifically, assignment-embedding module (AEM) is proposed to enable the procedure of ASD, which consists of two operations: attribute-to-location assignment and location-to-attribute embedding. The attribute-to-location assignment first decomposes the feature map based on latent factors, assigning the magnitude of attribute components on each spatial location. Then, the assigned attribute components from all locations to represent the global-level attribute embeddings. Furthermore, correlation matrix minimization (CMM) is introduced to enlarge the discriminability of attribute embeddings. Experimental results demonstrate the superiority of ASD compared with state-of-the-art prior-based methods, while the reliable performance of ASD for the case of limited training data is further validated.

cs.CV↗

arXiv2022

Towards Trustworthy Multi-label Sewer Defect Classification via Evidential Deep Learning

An automatic vision-based sewer inspection plays a key role of sewage system in a modern city. Recent advances focus on utilizing deep learning model to realize the sewer inspection system, benefiting from the capability of data-driven feature representation. However, the inherent uncertainty of sewer defects is ignored, resulting in the missed detection of serious unknown sewer defect categories. In this paper, we propose a trustworthy multi-label sewer defect classification (TMSDC) method, which can quantify the uncertainty of sewer defect prediction via evidential deep learning. Meanwhile, a novel expert base rate assignment (EBRA) is proposed to introduce the expert knowledge for describing reliable evidences in practical situations. Experimental results demonstrate the effectiveness of TMSDC and the superior capability of uncertainty estimation is achieved on the latest public benchmark.

cs.CV↗

arXiv2021

Studying Infall in Infrared Dark Clouds with Multiple HCO+ Transitions

We investigate the infall properties in a sample of 11 infrared dark clouds (IRDCs) showing blue-asymmetry signatures in HCO$^{+}$ J=1--0 line profiles. We used JCMT to conduct mapping observations in HCO$^{+}$ J=4--3 as well as single-pointing observations in HCO$^{+}$ J =3--2, towards 23 clumps in these IRDCs. We applied the HILL model to fit these observations and derived infall velocities in the range of 0.5-2.7 km s$^{-1}$, with a median value of 1.0 km s$^{-1}$, and obtained mass accretion rates of 0.5-14$\times$10$^{-3}$ Msun yr$^{-1}$. These values are comparable to those found in massive star forming clumps in later evolutionary stages. These IRDC clumps are more likely to form star clusters. HCO$^{+}$ J =3--2 and HCO$^{+}$ J =1--0 were shown to trace infall signatures well in these IRDCs with comparable inferred properties. HCO$^{+}$ J=4--3, on the other hand, exhibits infall signatures only in a few very massive clumps, due to smaller opacties. No obvious correlation for these clumps was found between infall velocity and the NH3/CCS ratio.

astro-ph.GA↗