arXiv subjects

Min Du

Publications and source records attributed to Min Du.

At least 37 records · Page 2Linked to original sources

arXiv2024

The evolutionary pathways of disk galaxies with different sizes

From the IllustrisTNG-50 simulation, a sample of 836 central disk galaxies with tiny stellar halos is chosen to study the inherent evolution of galaxies driven by nature. These galaxies are classified as compact, normal, or extended by referencing their locations on the mass-size ($M_\star-R_{\rm 1/2}$) diagram. This research demonstrates the distinctive evolutionary pathways of galaxies with different sizes in IllustrisTNG simulations, primarily driven by nature. It is confirmed that disk galaxies inherit the angular momentum of their parent dark matter halos. More compact galaxies form earlier within halos possessing lower specific angular momentum through heightened star formation during the early phase at redshifts above 2. During the later phase, the size of extended galaxies experiences more pronounced growth by accreting gas with high angular momentum. Additionally, we reveal that many key characteristics of galaxies are linked to their mass and size: (1) compact galaxies tend to exhibit higher metal content, proportional to the potential well $\frac{M_\star}{R_{\rm 1/2}}$, (2) compact galaxies host more massive bulges and black holes, and higher central concentration. Furthermore, our analysis indicates that galaxies of all types continue to actively engage in star formation, with no evident signs of quenching attributed to their varying sizes and angular momenta.

astro-ph.GA↗

arXiv2024

Contrastive Credibility Propagation for Reliable Semi-Supervised Learning

Producing labels for unlabeled data is error-prone, making semi-supervised learning (SSL) troublesome. Often, little is known about when and why an algorithm fails to outperform a supervised baseline. Using benchmark datasets, we craft five common real-world SSL data scenarios: few-label, open-set, noisy-label, and class distribution imbalance/misalignment in the labeled and unlabeled sets. We propose a novel algorithm called Contrastive Credibility Propagation (CCP) for deep SSL via iterative transductive pseudo-label refinement. CCP unifies semi-supervised learning and noisy label learning for the goal of reliably outperforming a supervised baseline in any data scenario. Compared to prior methods which focus on a subset of scenarios, CCP uniquely outperforms the supervised baseline in all scenarios, supporting practitioners when the qualities of labeled or unlabeled data are unknown.

cs.LG↗

arXiv2024

The Physical Origin of the Mass-Size Relation and Its Scatter of Disk Galaxies

Utilizing a kinematic decomposition of simulated galaxies, we focus on galaxies with tiny kinematically inferred stellar halos, indicative of weak external influences. We investigate the intricate interplay between internal (natural) and external (nurture) processes in shaping the scaling relationships of specific angular momentum ($j_\star$), stellar mass ($M_\star$), and size of disk galaxies within the IllustrisTNG simulation. The correlation among mass, size, and angular momentum of galaxies is examined by comparing simulations with observations and the theoretical predictions of the exponential hypothesis. Galaxies with tiny stellar halos exhibit a large scatter in the $j_\star$-$M_\star$ relation, which suggests that it is inherently present in their initial conditions. The analysis reveals that the disks of these galaxies adhere to the exponential hypothesis, resulting in a tight fiducial $j_\star$-$M_\star$-scale length (size) relation that is qualitatively consistent with observations. The inherent scatter in $j_\star$ provides a robust explanation for the mass-size relation and its substantial variability. Notably, galaxies that are moderately influenced by external processes closely adhere to a scaling relation akin to that of galaxies with tiny stellar halos. This result underscores the dominant role of internal processes in shaping the overall $j_\star$-$M_\star$ and mass-size relation, with external effects playing a relatively minor role in disk galaxies. Furthermore, the correlation between galaxy size and the virial radius of the dark matter halo exists but fails to provide strong evidence of the connection between galaxies and their parent dark matter halos.

astro-ph.GA↗

arXiv2023

Temporal-Spatial Entropy Balancing for Causal Continuous Treatment-Effect Estimation

In the field of intracity freight transportation, changes in order volume are significantly influenced by temporal and spatial factors. When building subsidy and pricing strategies, predicting the causal effects of these strategies on order volume is crucial. In the process of calculating causal effects, confounding variables can have an impact. Traditional methods to control confounding variables handle data from a holistic perspective, which cannot ensure the precision of causal effects in specific temporal and spatial dimensions. However, temporal and spatial dimensions are extremely critical in the logistics field, and this limitation may directly affect the precision of subsidy and pricing strategies. To address these issues, this study proposes a technique based on flexible temporal-spatial grid partitioning. Furthermore, based on the flexible grid partitioning technique, we further propose a continuous entropy balancing method in the temporal-spatial domain, which named TS-EBCT (Temporal-Spatial Entropy Balancing for Causal Continue Treatments). The method proposed in this paper has been tested on two simulation datasets and two real datasets, all of which have achieved excellent performance. In fact, after applying the TS-EBCT method to the intracity freight transportation field, the prediction accuracy of the causal effect has been significantly improved. It brings good business benefits to the company's subsidy and pricing strategies.

stat.ME↗

arXiv2023

The interplay between accretion, galaxy downsizing and the formation of box/peanut bulges in TNG50

From the TNG50 cosmological simulation we build a sample of 191 well-resolved barred galaxies with a stellar mass $\log M_\star > 10$ at $z=0$. We search for box/peanut bulges (BPs) in this sample, finding them in 55 per cent of cases. We compute $f_\mathrm{BP}$, the BP probability for barred galaxies as a function of $M_\star$, and find that this rises to a plateau, as found in observations of nearby galaxies. The transition mass where $f_\mathrm{BP}$ reaches half the plateau value is $\log M_\star = 10.14$, consistent with the observational value within measurement errors. We show that this transition in $f_\mathrm{BP}$ can be attributed to the youth of the bars at low $M_\star$, which is a consequence of downsizing of galaxies. Young bars, being generally shorter and weaker, have not yet had time to form BPs. At high mass, while we find a plateau, the value is at $\sim 60$ per cent, whereas observations saturate at 100 per cent. We attribute this difference to excessive heating in TNG50, due to merger activity and to numerical resolution effects. BPs in TNG50 tend to occur in galaxies with more quiescent merger histories. As a result, the main driver of whether a bar hosts a BP in TNG50 is not the galaxy mass, but how long and strong the bar is. Separating the BP sample into those that have visibly buckled and those that have not, we find that fully half of BP galaxies show clear signs of buckling, despite the excessive heating and limited vertical resolution of TNG50.

astro-ph.GA↗

arXiv2023

Pseudo Label-Guided Data Fusion and Output Consistency for Semi-Supervised Medical Image Segmentation

Supervised learning algorithms based on Convolutional Neural Networks have become the benchmark for medical image segmentation tasks, but their effectiveness heavily relies on a large amount of labeled data. However, annotating medical image datasets is a laborious and time-consuming process. Inspired by semi-supervised algorithms that use both labeled and unlabeled data for training, we propose the PLGDF framework, which builds upon the mean teacher network for segmenting medical images with less annotation. We propose a novel pseudo-label utilization scheme, which combines labeled and unlabeled data to augment the dataset effectively. Additionally, we enforce the consistency between different scales in the decoder module of the segmentation network and propose a loss function suitable for evaluating the consistency. Moreover, we incorporate a sharpening operation on the predicted results, further enhancing the accuracy of the segmentation. Extensive experiments on three publicly available datasets demonstrate that the PLGDF framework can largely improve performance by incorporating the unlabeled data. Meanwhile, our framework yields superior performance compared to six state-of-the-art semi-supervised learning methods. The codes of this study are available at https://github.com/ortonwang/PLGDF.

eess.IV↗

arXiv2023

A Skew-Sensitive Evaluation Framework for Imbalanced Data Classification

Class distribution skews in imbalanced datasets may lead to models with prediction bias towards majority classes, making fair assessment of classifiers a challenging task. Metrics such as Balanced Accuracy are commonly used to evaluate a classifier's prediction performance under such scenarios. However, these metrics fall short when classes vary in importance. In this paper, we propose a simple and general-purpose evaluation framework for imbalanced data classification that is sensitive to arbitrary skews in class cardinalities and importances. Experiments with several state-of-the-art classifiers tested on real-world datasets from three different domains show the effectiveness of our framework - not only in evaluating and ranking classifiers, but also training them.

cs.LG↗

arXiv2023

How Nested Bars Enhance, Modulate, and are Destroyed by Gas Inflows

Gas flows in the presence of two independently-rotating nested bars remain not fully understood, which is likely to play an important role in fueling the central black hole. We use high-resolution hydrodynamical simulations with detailed models of subgrid physics to study this problem. Our results show that the inner bar in double-barred galaxies can help drive gas flow from the nuclear ring to the center. In contrast, gas inflow usually stalls at the nuclear ring in single-barred galaxies. The inner bar causes a quasi-periodic inflow with a frequency determined by the difference between the two bar pattern speeds. We find that the star formation rate is higher in the model with two bars than in that with one bar. The inner bar in our model gradually weakens and dissolves due to gas inflow over a few billion years. Star formation produces metal-rich/$α$-poor stars which slows the weakening of the inner bar, but does not halt its eventual decay. We also present a qualitative comparison of the gas morphology and kinematics in our simulations with those of observed double-barred galaxies.

astro-ph.GA↗

arXiv2023

PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification

In recent years, deep learning has become a breakthrough technique in assisting medical image diagnosis. Supervised learning using convolutional neural networks (CNN) provides state-of-the-art performance and has served as a benchmark for various medical image segmentation and classification. However, supervised learning deeply relies on large-scale annotated data, which is expensive, time-consuming, and even impractical to acquire in medical imaging applications. Active Learning (AL) methods have been widely applied in natural image classification tasks to reduce annotation costs by selecting more valuable examples from the unlabeled data pool. However, their application in medical image segmentation tasks is limited, and there is currently no effective and universal AL-based method specifically designed for 3D medical image segmentation. To address this limitation, we propose an AL-based method that can be simultaneously applied to 2D medical image classification, segmentation, and 3D medical image segmentation tasks. We extensively validated our proposed active learning method on three publicly available and challenging medical image datasets, Kvasir Dataset, COVID-19 Infection Segmentation Dataset, and BraTS2019 Dataset. The experimental results demonstrate that our PCDAL can achieve significantly improved performance with fewer annotations in 2D classification and segmentation and 3D segmentation tasks. The codes of this study are available at https://github.com/ortonwang/PCDAL.

eess.IV↗

arXiv2023

FAST-ASKAP Synergy: Quantifying Coexistent Tidal and Ram Pressure Strippings in the NGC 4636 Group

Combining new HI data from a synergetic survey of ASKAP WALLABY and FAST with the ALFALFA data, we study the effect of ram pressure and tidal interactions in the NGC 4636 group. We develop two parameters to quantify and disentangle these two effects on gas stripping in HI-bearing galaxies: the strength of external forces at the optical-disk edge, and the outside-in extents of HI-disk stripping. We find that gas stripping is widespread in this group, affecting 80% of HI-detected non-merging galaxies, and that 41% are experiencing both types of stripping. Among the galaxies experiencing both effects, the two types of strengths are independent, while two HI-stripping extents moderately anticorrelate with each other. Both strengths are correlated with HI-disk shrinkage. The tidal strength is related to a rather uniform reddening of low-mass galaxies ($M_*<10^9\,\text{M}_\odot$) when tidal stripping is the dominating effect. In contrast, ram pressure is not clearly linked to the color-changing patterns of galaxies in the group. Combining these two stripping extents, we estimate the total stripping extent, and put forward an empirical model that can describe the decrease of HI richness as galaxies fall toward the group center. The stripping timescale we derived decreases with distance to the center, from $\mathord{\sim}1\,\text{Gyr}$ beyond $R_{200}$ to $\mathord{\lesssim}10\,\text{Myr}$ near the center. Gas-depletion happens $\mathord{\sim}3\,\text{Gyr}$ since crossing $2R_{200}$ for HI-rich galaxies, but much quicker for HI-poor ones. Our results quantify in a physically motivated way the details and processes of environmental-effects-driven galaxy evolution, and might assist in analyzing hydrodynamic simulations in an observational way.

astro-ph.GA↗

arXiv2023

The imprint of clump formation at high redshift. II. The chemistry of the bulge

In Paper I we showed that clumps in high-redshift galaxies, having a high star formation rate density (Σ_SFR), produce disks with two tracks in the [Fe/H]-[α/Fe] chemical space, similar to that of the Milky Way's (MW's) thin + thick disks. Here we investigate the effect of clumps on the bulge's chemistry. The chemistry of the MW's bulge is comprised of a single track with two density peaks separated by a trough. We show that the bulge chemistry of an N-body + smoothed particle hydrodynamics clumpy simulation also has a single track. Star formation within the bulge is itself in the high-Σ_SFR clumpy mode, which ensures that the bulge's chemical track follows that of the thick disk at low [Fe/H] and then extends to high [Fe/H], where it peaks. The peak at low metallicity instead is comprised of a mixture of in-situ stars and stars accreted via clumps. As a result, the trough between the peaks occurs at the end of the thick disk track. We find that the high-metallicity peak dominates near the mid-plane and declines in relative importance with height, as in the MW. The bulge is already rapidly rotating by the end of the clump epoch, with higher rotation at low [α/Fe]. Thus clumpy star formation is able to simultaneously explain the chemodynamic trends of the MW's bulge, thin + thick disks and the Splash.

astro-ph.GA↗

arXiv2023

Baryonic Effects on Lagrangian Clustering and Angular Momentum Reconstruction

Recent studies illustrate the correlation between the angular momenta of cosmic structures and their Lagrangian properties. However, only baryons are observable and it is unclear whether they reliably trace the cosmic angular momenta. We study the Lagrangian mass distribution, spin correlation, and predictability of dark matter, gas, and stellar components of galaxy-halo systems using IllustrisTNG, and show that the primordial segregations between components are typically small. Their protoshapes are also similar in terms of the statistics of moment of inertia tensors. Under the common gravitational potential they are expected to exert the same tidal torque and the strong spin correlations are not destroyed by the nonlinear evolution and complicated baryonic effects, as confirmed by the high-resolution hydrodynamic simulations. We further show that their late-time angular momenta traced by total gas, stars, or the central galaxies, can be reliably reconstructed by the initial perturbations. These results suggest that baryonic angular momenta can potentially be used in reconstructing the parameters and models related to the initial perturbations.

astro-ph.CO↗

arXiv2022

The Origin of the Relation Between Stellar Angular Momentum and Stellar Mass in Nearby Disk-dominated galaxies

The IllustrisTNG simulations reproduce the observed scaling relation between stellar specific angular momentum (sAM) $j_{\rm s}$ and mass $M_{\rm s}$ of central galaxies. We show that the local $j_{\rm s}$-$M_{\rm s}$ relation ${\rm log}\ j_{\rm s} = 0.55 \ {\rm log}\ M_{\rm s} + 2.77$ develops at $z\lesssim 1$ in disk-dominated galaxies. We provide a simple model that describes well such a connection between halos and galaxies. The index 0.55 of the $j_{\rm s}$-$M_{\rm s}$ relation comes from the product of the indices of the $j_{\rm tot}\propto M_{\rm tot}^{0.81}$, $M_{\rm tot}\propto M_{\rm s}^{0.67}$, and $j_{\rm s}\propto j_{\rm tot}$ relations, where $j_{\rm tot}$ and $M_{\rm tot}$ are overall sAM and mass of a halo. A non-negligible deviation from the tidal torque theory, which predicts $j_{\rm tot}\propto M_{\rm tot}^{2/3}$, should be included. This model further suggests that the stellar-to-halo mass ratio of disk galaxies increases monotonically following a nearly power-law function that is consistent with the latest dynamical measurements. Biased collapse, in which galaxies form from the inner and lower sAM portion of their parent halos, has a minor effect at low redshifts. The retention factor of angular momentum reaches $\sim 1$ in disk galaxies with strong rotations, and it correlates inversely with the mass fraction of the spheroidal component, which partially explains the morphological dependence of the $j_{\rm s}$-$M_{\rm s}$ relation.

astro-ph.GA↗

arXiv2021

RobustECD: Enhancement of Network Structure for Robust Community Detection

Community detection, which focuses on clustering vertex interactions, plays a significant role in network analysis. However, it also faces numerous challenges like missing data and adversarial attack. How to further improve the performance and robustness of community detection for real-world networks has raised great concerns. In this paper, we explore robust community detection by enhancing network structure, with two generic algorithms presented: one is named robust community detection via genetic algorithm (RobustECD-GA), in which the modularity and the number of clusters are combined in a fitness function to find the optimal structure enhancement scheme; the other is called robust community detection via similarity ensemble (RobustECD-SE), integrating multiple information of community structures captured by various vertex similarities, which scales well on large-scale networks. Comprehensive experiments on real-world networks demonstrate, by comparing with two traditional enhancement strategies, that the new methods help six representative community detection algorithms achieve more significant performance improvement. Moreover, experiments on the corresponding adversarial networks indicate that the new methods could also optimize the network structure to a certain extent, achieving stronger robustness against adversarial attack. The source code of this paper is released on https://github.com/jjzhou012/RobustECD.

cs.SI↗

arXiv2021

The Evolutionary Pathways of Disk-, Bulge-, and Halo-dominated Galaxies

To break the degeneracy among galactic stellar components, we extract kinematic structures using the framework described in Du et al. (2019, 2020). For example, the concept of stellar halos is generalized to weakly-rotating structures that are composed of loosely bound stars, which can hence be associated to both disk and elliptical type morphologies. By applying this method to central galaxies with stellar mass $10^{10-11.5}\ M_\odot$ from the TNG50 simulation, we identify three broadly-defined types of galaxies: ones dominated by disk, by bulge, or by stellar halo structures. We then use the simulation to infer the underlying connection between the growth of structures and physical processes over cosmic time. Tracing galaxies back in time, we recognize three fundamental regimes: an early phase of evolution ($z\gtrsim2$), and internal and external (mainly mergers) processes that act at later times. We find that disk- and bulge-dominated galaxies are not significantly affected by mergers since $z\sim2$; the difference in their present-day structures originates from two distinct evolutionary pathways, extended vs. compact, that are likely determined by their parent dark matter halos; i.e., nature. On the other hand, slow rotator elliptical galaxies are typically halo-dominated, forming by external processes (e.g. mergers) in the later phase, i.e., nurture. This picture challenges the general idea that elliptical galaxies are the same objects as classical bulges. In observations, both bulge- and halo-dominated galaxies are likely to be classified as early-type galaxies with compact morphology and quiescent star formation. However, here we find them to have very different evolutionary histories.

astro-ph.GA↗

arXiv2021

Spin Mode Reconstruction in Lagrangian Space

Galaxy angular momentum directions (spins) are observable, well described by the Lagrangian tidal torque theory, and proposed to probe the primordial universe. They trace the spins of dark matter halos, and are indicators of protohalos properties in Lagrangian space. We define a Lagrangian spin parameter and tidal twist parameters and quantify their influence on the spin conservation and predictability in the spin mode reconstruction in $N$-body simulations. We conclude that protohalos in a more tidal twisting environments are preferentially more rotation-supported, and more likely to conserve their spin direction through the cosmic evolution. These tidal environments and spin magnitudes are predictable by a density reconstruction in Lagrangian space, and such predictions can improve the correlation between galaxy spins and the initial conditions in the study of constraining the primordial universe by spin mode reconstruction.

astro-ph.CO↗

arXiv2020

Learning Heatmap-Style Jigsaw Puzzles Provides Good Pretraining for 2D Human Pose Estimation

The target of 2D human pose estimation is to locate the keypoints of body parts from input 2D images. State-of-the-art methods for pose estimation usually construct pixel-wise heatmaps from keypoints as labels for learning convolution neural networks, which are usually initialized randomly or using classification models on ImageNet as their backbones. We note that 2D pose estimation task is highly dependent on the contextual relationship between image patches, thus we introduce a self-supervised method for pretraining 2D pose estimation networks. Specifically, we propose Heatmap-Style Jigsaw Puzzles (HSJP) problem as our pretext-task, whose target is to learn the location of each patch from an image composed of shuffled patches. During our pretraining process, we only use images of person instances in MS-COCO, rather than introducing extra and much larger ImageNet dataset. A heatmap-style label for patch location is designed and our learning process is in a non-contrastive way. The weights learned by HSJP pretext task are utilised as backbones of 2D human pose estimator, which are then finetuned on MS-COCO human keypoints dataset. With two popular and strong 2D human pose estimators, HRNet and SimpleBaseline, we evaluate mAP score on both MS-COCO validation and test-dev datasets. Our experiments show that downstream pose estimators with our self-supervised pretraining obtain much better performance than those trained from scratch, and are comparable to those using ImageNet classification models as their initial backbones.

cs.CV↗

arXiv2020

Learning Enhanced Resolution-wise features for Human Pose Estimation

Recently, multi-resolution networks (such as Hourglass, CPN, HRNet, etc.) have achieved significant performance on pose estimation by combining feature maps of various resolutions. In this paper, we propose a Resolution-wise Attention Module (RAM) and Gradual Pyramid Refinement (GPR), to learn enhanced resolution-wise feature maps for precise pose estimation. Specifically, RAM learns a group of weights to represent the different importance of feature maps across resolutions, and the GPR gradually merges every two feature maps from low to high resolutions to regress final human keypoint heatmaps. With the enhanced resolution-wise features learnt by CNN, we obtain more accurate human keypoint locations. The efficacies of our proposed methods are demonstrated on MS-COCO dataset, achieving state-of-the-art performance with average precision of 77.7 on COCO val2017 set and 77.0 on test-dev2017 set without using extra human keypoint training dataset.

cs.CV↗