arXiv subjects

Sicen Guo

Publications and source records attributed to Sicen Guo.

8 recordsLinked to original sources

arXiv2026

Quiescent fractions in high-redshift galaxy groups reflect their hot-or-cold state of gas accretion

Cold accretion and quenching are closely related aspects of galaxy evolution, as sustained gas supply is required to maintain star formation. High-redshift galaxy groups therefore provide a valuable laboratory for testing how the thermal state of accreting gas relates to the emergence of quiescence. We measure quiescent fractions in a sample of 16 spectroscopically confirmed galaxy groups at $1.6<z<3.6$, spanning halo masses from $10^{12.8},{\rm M_\odot}$ to $10^{13.9},{\rm M_\odot}$, by fitting the SEDs of candidate member galaxies selected from the COSMOS2020 catalog and using a membership-probability approach to estimate group quiescent fractions. We compare these quiescent fractions to the expected cold or hot accretion state of each halo and find evidence for a correlation: quiescent fractions reach about 50 percent in groups in the hot-accretion regime and are consistent with zero in groups in the cold-accretion regime. In mature hot-accreting groups, massive quiescent galaxies are preferentially found in the inner regions ($R<0.5R_{\rm vir}$), with a 4.4-sigma excess relative to the outskirts. Most groups lack a clearly established brightest group galaxy and instead show small stellar-mass gaps, typically $M_{*,1}/M_{*,2}<3$, indicating that they remain in an active assembly phase rather than being dynamically evolved systems. Consistently, the stellar-mass excess of the dominant galaxy, measured relative to the SHMR expectation, does not predict the group quiescent fraction. Taken together, our results support a picture in which the cold-to-hot transition in gas accretion contributes to the onset of quiescence, possibly through inside-out starvation associated with filament disruption in shock-heated intra-group gas, and suggest that environment plays a greater role than internal processes in shaping the quiescent galaxy population in these structures.

astro-ph.GA↗

arXiv2025

Ly-alpha emission reveals two satellite halos around massive groups at z ~ 3: the puzzling case of a quiescent central galaxy

We present the discovery and characterisation of two Ly$\alpha$ nebulae (LANs), RO-1001-Sat and RO-0959-Sat, as satellite structures of two giant LANs at $z=2.920$ and 3.092. They are found neighbouring two out of four known giant LANs at $z\sim3$ in our MUSE follow-up observations, reinforcing the idea that Ly$\alpha$ emission can be used to trace massive dark matter halos at high-$z$. This high occurrence of massive satellite halos agrees with simulations. With sizes of $\simeq80\times160$ and $80\times100~\mathrm{pkpc}^2$, the two nebulae are both $\sim$300pkpc from the main LANs. The Ly$\alpha$ emission is only shifted by $\simeq100-300$ km s$^{-1}$ between each of the two pairs, suggesting connections via large-scale structure. RO-1001-Sat and RO-0959-Sat are estimated to have log$(M_\mathrm{h}/M_\odot)\simeq13.2\pm0.3$ and $12.8\pm0.3$, putting them potentially close to the regime of cold-mode accretion. The central brightest galaxies in the two halos are morphologically distinct despite having similar stellar mass $\sim10^{11}M_\odot$, one being an elliptical quiescent galaxy in RO-1001-Sat and the other being a dusty star-forming spiral in RO-0959-Sat. Intriguingly, the quiescent galaxy aligns well with the peak of the LAN as well as the potential well of the host halo, making it the first clear-cut case where the cold gas ought to be accreting onto the galaxy but with no observable star formation, either due to morphological quenching or, more likely, radio-mode feedback from an active galactic nucleus. Finally, we show a tentative detection of a Ly$\alpha$ filament connecting RO-1001 and RO-1001-Sat. This work shows how panoramic MUSE (and in the future, BlueMUSE) observations of massive halo seeds can be used to efficiently search for additional halos, unveiling their large-scale structure and enabling the study of Ly$\alpha$-selected galaxy groups.

astro-ph.GA↗

arXiv2025

Noema formIng Cluster survEy (NICE): A Census of Star Formation and Cold Gas Properties in Massive protoclusters at 1.5<z<4

Massive protoclusters at z~1.5-4, the peak of the cosmic star formation history, are key to understanding the formation mechanisms of massive galaxies in today's clusters. However, studies of protoclusters at these high redshifts remain limited, primarily due to small sample sizes and heterogeneous selection criteria. In this work, we conduct a systematic investigation of the star formation and cold gas properties of member galaxies of eight massive protoclusters in the COSMOS field, using the statistical and homogeneously selected sample from the Noema formIng Cluster survEy (NICE). Our analysis reveals a steep increase in the star formation rates per halo mass ($\Sigma_{\rm SFR} /M_{\rm halo}$) with redshifts in these intensively star-forming protoclusters, reaching values one to two orders of magnitude higher than those observed in the field at z>2. We further show that, instead of an enhancement of starbursts, this increase is largely driven by the concentration of massive and gas-rich star-forming galaxies in the protocluster cores. The member galaxies still generally follow the same star formation main sequence as in the field, with a moderate enhancement at the low mass end. Notably, the most massive protocluster galaxies ($M_\star$>8$\times$10$^{10}$M$_\odot$) exhibit higher $f_{\rm gas}$ and $\tau_{\rm gas}$ than their field counterparts, while remaining on the star forming main sequence. These gas-rich, massive, and star-forming galaxies are predominantly concentrated in the protocluster cores and are likely progenitors of massive ellipticals in the center of today's clusters. These results suggest that the formation of massive galaxies in such environments is sustained by substantial gas reservoirs, which support persistent star formation and drive early mass assembly in forming cluster cores.

astro-ph.GA↗

arXiv2025

LIX: Implicitly Infusing Spatial Geometric Prior Knowledge into Visual Semantic Segmentation for Autonomous Driving

Despite the impressive performance achieved by data-fusion networks with duplex encoders for visual semantic segmentation, they become ineffective when spatial geometric data are not available. Implicitly infusing the spatial geometric prior knowledge acquired by a data-fusion teacher network into a single-modal student network is a practical, albeit less explored research avenue. This article delves into this topic and resorts to knowledge distillation approaches to address this problem. We introduce the Learning to Infuse ''X'' (LIX) framework, with novel contributions in both logit distillation and feature distillation aspects. We present a mathematical proof that underscores the limitation of using a single, fixed weight in decoupled knowledge distillation and introduce a logit-wise dynamic weight controller as a solution to this issue. Furthermore, we develop an adaptively-recalibrated feature distillation algorithm, including two novel techniques: feature recalibration via kernel regression and in-depth feature consistency quantification via centered kernel alignment. Extensive experiments conducted with intermediate-fusion and late-fusion networks across various public datasets provide both quantitative and qualitative evaluations, demonstrating the superior performance of our LIX framework when compared to other state-of-the-art approaches.

cs.CV↗

arXiv2025

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing

Recent vision foundation models (VFMs), typically based on Vision Transformer (ViT), have significantly advanced numerous computer vision tasks. Despite their success in tasks focused solely on RGB images, the potential of VFMs in RGB-depth driving scene parsing remains largely under-explored. In this article, we take one step toward this emerging research area by investigating a feasible technique to fully exploit VFMs for generalizable RGB-depth driving scene parsing. Specifically, we explore the inherent characteristics of RGB and depth data, thereby presenting a Heterogeneous Feature Integration Transformer (HFIT). This network enables the efficient extraction and integration of comprehensive heterogeneous features without re-training ViTs. Relative depth prediction results from VFMs, used as inputs to the HFIT side adapter, overcome the limitations of the dependence on depth maps. Our proposed HFIT demonstrates superior performance compared to all other traditional single-modal and data-fusion scene parsing networks, pre-trained VFMs, and ViT adapters on the Cityscapes and KITTI Semantics datasets. We believe this novel strategy paves the way for future innovations in VFM-based data-fusion techniques for driving scene parsing. Our source code is publicly available at https://mias.group/HFIT.

cs.CV↗

arXiv2024

ODFormer: Semantic Fundus Image Segmentation Using Transformer for Optic Nerve Head Detection

Optic nerve head (ONH) detection has been a crucial area of study in ophthalmology for years. However, the significant discrepancy between fundus image datasets, each generated using a single type of fundus camera, poses challenges to the generalizability of ONH detection approaches developed based on semantic segmentation networks. Despite the numerous recent advancements in general-purpose semantic segmentation methods using convolutional neural networks (CNNs) and Transformers, there is currently a lack of benchmarks for these state-of-the-art (SoTA) networks specifically trained for ONH detection. Therefore, in this article, we make contributions from three key aspects: network design, the publication of a dataset, and the establishment of a comprehensive benchmark. Our newly developed ONH detection network, referred to as ODFormer, is based upon the Swin Transformer architecture and incorporates two novel components: a multi-scale context aggregator and a lightweight bidirectional feature recalibrator. Our published large-scale dataset, known as TongjiU-DROD, provides multi-resolution fundus images for each participant, captured using two distinct types of cameras. Our established benchmark involves three datasets: DRIONS-DB, DRISHTI-GS1, and TongjiU-DROD, created by researchers from different countries and containing fundus images captured from participants of diverse races and ages. Extensive experimental results demonstrate that our proposed ODFormer outperforms other state-of-the-art (SoTA) networks in terms of performance and generalizability. Our dataset and source code are publicly available at mias.group/ODFormer.

eess.IV↗

arXiv2024

UDTIRI: An Online Open-Source Intelligent Road Inspection Benchmark Suite

In the nascent domain of urban digital twins (UDT), the prospects for leveraging cutting-edge deep learning techniques are vast and compelling. Particularly within the specialized area of intelligent road inspection (IRI), a noticeable gap exists, underscored by the current dearth of dedicated research efforts and the lack of large-scale well-annotated datasets. To foster advancements in this burgeoning field, we have launched an online open-source benchmark suite, referred to as UDTIRI. Along with this article, we introduce the road pothole detection task, the first online competition published within this benchmark suite. This task provides a well-annotated dataset, comprising 1,000 RGB images and their pixel/instance-level ground-truth annotations, captured in diverse real-world scenarios under different illumination and weather conditions. Our benchmark provides a systematic and thorough evaluation of state-of-the-art object detection, semantic segmentation, and instance segmentation networks, developed based on either convolutional neural networks or Transformers. We anticipate that our benchmark will serve as a catalyst for the integration of advanced UDT techniques into IRI. By providing algorithms with a more comprehensive understanding of diverse road conditions, we seek to unlock their untapped potential and foster innovation in this critical domain.

cs.CV↗

arXiv2022

Computer-Aided Road Inspection: Systems and Algorithms

Road damage is an inconvenience and a safety hazard, severely affecting vehicle condition, driving comfort, and traffic safety. The traditional manual visual road inspection process is pricey, dangerous, exhausting, and cumbersome. Also, manual road inspection results are qualitative and subjective, as they depend entirely on the inspector's personal experience. Therefore, there is an ever-increasing need for automated road inspection systems. This chapter first compares the five most common road damage types. Then, 2-D/3-D road imaging systems are discussed. Finally, state-of-the-art machine vision and intelligence-based road damage detection algorithms are introduced.

cs.CV↗