arXiv subjects

Mingwei He

Publications and source records attributed to Mingwei He.

7 recordsLinked to original sources

arXiv2026

Molecule-dependent Abundance Behavior of Oxygen-bearing Complex Organics in High-Mass Star-Forming Regions: A Uniform 50-source Survey

We present a uniform IRAM-30\,m survey analysis of four oxygen-bearing complex organic molecules (COMs), methanol (CH$_3$OH), acetaldehyde (CH$_3$CHO), methyl formate (CH$_3$OCHO), and dimethyl ether (CH$_3$OCH$_3$), toward 50 high-mass star-forming regions (HMSFRs) associated with 6.7\,GHz methanol masers. Column densities were derived through a homogeneous rotation-diagram approach, with CH$_3$CN used as a proxy excitation-temperature reference when needed. In CH$_3$OH-normalized abundance-ratio space, CH$_3$OCHO/CH$_3$OH and CH$_3$OCH$_3$/CH$_3$OH show the strongest pairwise correlation, whereas the correlations involving CH$_3$CHO are weaker. No clear monotonic trends are found with Galactocentric distance or beam-averaged H$_2$ column density. Comparison with previous observations places the CH$_3$OCHO--CH$_3$OCH$_3$ behavior within the range of earlier abundance-ratio measurements, while CH$_3$CHO shows larger inter-study variation. A representative warm-up chemical model is used only for qualitative comparison with the observed abundance ranges, which are most closely matched during the decline from the post-desorption abundance peaks in the model. These results provide homogeneous beam-averaged abundance-ratio constraints for common O-bearing COMs in high-mass star-forming regions and show that their source-to-source behavior is molecule-dependent rather than fully described by a single common abundance pattern.

astro-ph.GA↗

arXiv2026

Making Reconstruction FID Predictive of Diffusion Generation FID

It is well known that the reconstruction FID (rFID) of a VAE is poorly correlated with the generation FID (gFID) of a latent diffusion model. We propose interpolated FID (iFID), a simple variant of rFID that exhibits a strong correlation with gFID. Specifically, for each dataset element, we retrieve its nearest neighbor in latent space, interpolate between their latent representations, decode the interpolated latent, and compute the FID between the decoded samples and the original dataset. We provide an intuitive explanation for why iFID correlates well with gFID, and why reconstruction metrics can be negatively correlated with gFID, by connecting iFID to recent results on diffusion generalization and hallucination. Theoretically, we show that iFID evaluates decoded interpolations aligned with the ridge set around which diffusion samples concentrate, thereby measuring a quantity closely related to diffusion sample quality. Empirically, iFID is the first metric shown to strongly correlate with diffusion gFID across diverse VAEs, achieving Pearson and Spearman correlations of approximately $0.85$. The project page is available at https://tongdaxu.github.io/pages/ifid.html.

cs.CV↗

arXiv2025

Versatile Recompression-Aware Perceptual Image Super-Resolution

Perceptual image super-resolution (SR) methods restore degraded images and produce sharp outputs. In practice, those outputs are usually recompressed for storage and transmission. Ignoring recompression is suboptimal as the downstream codec might add additional artifacts to restored images. However, jointly optimizing SR and recompression is challenging, as the codecs are not differentiable and vary in configuration. In this paper, we present \textbf{Versatile Recompression-Aware Perceptual Super-Resolution (VRPSR)}, which makes existing perceptual SR aware of versatile compression. First, we formulate compression as conditional text-to-image generation and utilize a pre-trained diffusion model to build a generalizable codec simulator. Next, we propose a set of training techniques tailored for perceptual SR, including optimizing the simulator using perceptual targets and adopting slightly compressed images as the training target. Empirically, our VRPSR achieves 10% - 40% bitrate savings based on Real-ESRGAN and S3Diff under H.264/H.265/H.266 single-picture (intra) compression. Besides, our VRPSR facilitates joint optimization of SR and the post-processing model after recompression.

cs.CV↗

arXiv2025

The ALMA-QUARKS survey: Extensive detection of acetamide in multiple high-mass star-forming regions

Acetamide (CH$_{3}$CONH$_{2}$), a key interstellar amide and a methyl derivative of formamide (NH$_{2}$CHO), has been sparsely detected, limiting insights into its prebiotic relevance. We present the first systematic survey for acetamide toward 52 hot molecular cores using ALMA Band 6 data. Acetamide has been detected in 10 cores, markedly expanding the inventory of known emitters. The derived column densities of acetamide range from $(2.5\pm0.9)\times10^{14}$ to $(1.5\pm0.6)\times10^{16}$ cm$^{-2}$, compared to formamide's $(1.1\pm0.1)\times10^{15}$ to $(6.9\pm0.4)\times10^{16}$ cm$^{-2}$. The nearly constant abundance ratios (~3-9) and strong abundance correlation between the two amides across sources suggest a chemically linked formation pathway, likely on grain surfaces. The presence of peptide-like molecules in these regions implies that complex organic species can survive star formation processes, offering a potential pathway toward prebiotic chemistry. These findings constrain the dominant grain surface formation routes of acetamide, confirm its broader prevalence in highmass star-forming regions, and underscore the importance of targeted amide surveys in tracing the chemical evolution toward prebiotic complexity.

astro-ph.GA↗

arXiv2025

An ALMA Study of Molecular Complexity in the Hot Core G336.99-00.03 MM1

High-mass star formation involves complex processes, with the hot core phase playing a crucial role in chemical enrichment and the formation of complex organic molecules. However, molecular inventories in hot cores remain limited. Using data from the ALMA Three-millimeter Observations of Massive Star-forming regions survey (ATOMS), the molecular composition and evolutionary stages of two distinct millimeter continuum sources in the high-mass star forming region G336.99-00.03 have been characterized. MM1, with 19 distinct molecular species detected, along with 8 isotopologues and several vibrationally/torsionally excited states, has been identified as a hot core. MM2 with only 5 species identified, was defined as a HII region. Isotopic ratios in MM1 were derived, with $^{12}$C/$^{13}$C ranging from 16.0 to 29.2, $^{16}$O/$^{18}$O at 47.7, and $^{32}$S/$^{34}$S at 19.2. Molecular abundances in MM1 show strong agreement with other sources and three-phase warm-up chemical models within an order of magnitude for most species. Formation pathways of key molecules were explored, revealing chemical links and reaction networks. This study provides a detailed molecular inventory of two millimeter continuum sources, shedding light on the chemical diversity and evolutionary processes in high-mass star-forming regions. The derived molecular parameters and isotopic ratios offer benchmarks for astrochemical models, paving the way for further investigation into the formation and evolution of complex organic molecules during the hot core phase.

astro-ph.GA↗

arXiv2025

Diffuse Optical Ptychography

Various imaging techniques have significantly enhanced our ability to visualize objects embedded within complex media such as biological tissues, fog, atmosphere, or various turbid media. Optical imaging, in particular, offers multiple advantages, including non-invasive capabilities, absence of ionizing radiation, and high contrast for many biological tissues. However, optical imaging through substantially thick scattering media remains challenging due to extensive photon diffusion, significantly restricting reconstruction quality and achievable resolution. To address these limitations, we introduce Diffuse Optical Ptychography (DOP), a novel imaging method inspired by ptychography technique, which exploits additional spatial information gained from multiple overlapping illumination patterns. The primary technical innovation of DOP lies in its effective use of overlapping yet minimally correlated illuminations, significantly enhancing reconstruction accuracy and image quality. Compared to existing optical imaging methods through thick diffusive media, DOP achieves superior resolution (down to 1 mm) and reliably reconstructs both binary and grayscale objects embedded within media thicker than 100 transport mean free paths. Importantly, DOP demonstrates robust reconstruction performance both with accurately calibrated diffusion properties and even without prior calibration. Furthermore, the experimental setup for DOP remains straightforward, utilizing only a conventional camera and scanning illumination spots. Our demonstrations underscore the broad potential impact of DOP in applications ranging from medical diagnostics to non-destructive testing, thus opening promising avenues for high-resolution imaging in highly scattering environments.

physics.optics↗

arXiv2021

DAIL: Dataset-Aware and Invariant Learning for Face Recognition

To achieve good performance in face recognition, a large scale training dataset is usually required. A simple yet effective way to improve recognition performance is to use a dataset as large as possible by combining multiple datasets in the training. However, it is problematic and troublesome to naively combine different datasets due to two major issues. First, the same person can possibly appear in different datasets, leading to an identity overlapping issue between different datasets. Naively treating the same person as different classes in different datasets during training will affect back-propagation and generate non-representative embeddings. On the other hand, manually cleaning labels may take formidable human efforts, especially when there are millions of images and thousands of identities. Second, different datasets are collected in different situations and thus will lead to different domain distributions. Naively combining datasets will make it difficult to learn domain invariant embeddings across different datasets. In this paper, we propose DAIL: Dataset-Aware and Invariant Learning to resolve the above-mentioned issues. To solve the first issue of identity overlapping, we propose a dataset-aware loss for multi-dataset training by reducing the penalty when the same person appears in multiple datasets. This can be readily achieved with a modified softmax loss with a dataset-aware term. To solve the second issue, domain adaptation with gradient reversal layers is employed for dataset invariant learning. The proposed approach not only achieves state-of-the-art results on several commonly used face recognition validation sets, including LFW, CFP-FP, and AgeDB-30, but also shows great benefit for practical use.

cs.CV↗