arXiv subjects

Xiaotong Guo

Publications and source records attributed to Xiaotong Guo.

At least 37 records · Page 2Linked to original sources

arXiv2025

Observational evidence for a correlation between the magnetic field of jets and star formation rate in host galaxies

Accretion supermassive black holes in the center of active galaxies usually produce ``jets''-collimated bipolar outflows of relativistic particles. Magnetic fields near the black hole event horizon may play a crucial role in the formation of jets/outflows. Both theory and observation indicate that jets/outflows driven by centrally active supermassive black holes (SMBHs) have a feedback effect on the overall properties of the host galaxies. Therefore, the magnetic field is a key ingredient for the formation and evolution of galaxies. Here we report a clear correlation between the magnetic field of jets and star formation rate (SFR) for a large sample of 96 galaxies hosting supermassive black holes, which suggests that the star formation of active galactic nuclei (AGN) host galaxies may be powered by the jets.

astro-ph.HE↗

arXiv2025

The relation between black hole spin and star formation in massive star-forming galaxies

It has always been believed that feedback from active galactic nuclei (AGN) has an important impact on star formation in massive galaxies. Black hole spin is an important physical parameter of AGN. We use a large sample of massive star-forming galaxies to study the effects of AGN on star formation. Our main results are as follows: (i) There are significant correlations between black hole spin and star formation rate, specific star formation rate, and star formation activity parameter for massive star-forming early-type and late-type galaxies, respectively. These results indicate that the spin of supermassive black holes regulates the star formation of massive star-forming early-type and late-type galaxies. (2) The slopes of the relationship between black hole spin and star formation rate, specific star formation rate, and star formation activity parameter for massive star-forming early-type galaxies and late-type galaxies are similar within the error range. These results imply that the mechanism of black hole spin regulating star formation may be similar in massive star-forming early-type and late-type galaxies.

astro-ph.HE↗

arXiv2025

Identifying Compton-thick AGNs in the COSMOS. I. Among X-ray AGNs with Low Photon Counts

Compton-thick active galactic nuclei (CT-AGNs), characterized by a significant absorption with column densities of $\mathrm{N_H}\geqslant 1.5\times 10^{24} \ \mathrm{cm}^{-2}$, emit feeble X-ray radiation and are even undetectable by X-ray instruments, making them difficult to identify. X-ray radiation from AGNs is the predominant source of the cosmic X-ray background (CXB). Based on AGN synthesis models for the CXB, the fraction of CT-AGNs should constitute a substantial portion of AGN population, approximately 30\% or more. The fraction of CT-AGNs discovered in the Cosmological Evolution Survey (COSMOS) is significantly lower than this value. This means that many CT-AGNs may be hidden in AGNs that exhibit low photon counts or that have not been detected by X-ray instruments. This work focuses on identifying CT-AGNs hidden in AGNs with low photon counts. Firstly, we selected 440 AGNs with abundant multiwavelength data as our sample. Secondly, we analyzed multiwavelength data, extracting crucial physical parameters required for the CT-AGN diagnosis. Finally, we used multiwavelength approaches to identify CT-AGNs. We have successfully identified 18 CT-AGNs in our sample. Among the CT-AGNs, four AGNs show discrepant results across different diagnostic methods. We discuss the potential reasons behind these diagnostic discrepancies. We explore the impact of estimating [O~III]$λ~5007$ luminosities based on [O~II]$λ~3727$ luminosities for the CT-AGN diagnosis. We have also found that the properties of host galaxies for CT-AGNs and non-CT-AGNs do not show significant discrepancies.

astro-ph.GA↗

arXiv2025

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study

Urban prediction tasks, such as forecasting traffic flow, temperature, and crime rates, are crucial for efficient urban planning and management. However, existing Spatiotemporal Graph Neural Networks (ST-GNNs) often rely solely on accuracy, overlooking spatial and demographic disparities in their predictions. This oversight can lead to imbalanced resource allocation and exacerbate existing inequities in urban areas. This study introduces a Residual-Aware Attention (RAA) Block and an equality-enhancing loss function to address these disparities. By adapting the adjacency matrix during training and incorporating spatial disparity metrics, our approach aims to reduce local segregation of residuals and errors. We applied our methodology to urban prediction tasks in Chicago, utilizing a travel demand dataset as an example. Our model achieved a 48% significant improvement in fairness metrics with only a 9% increase in error metrics. Spatial analysis of residual distributions revealed that models with RAA Blocks produced more equitable prediction results, particularly by reducing errors clustered in central regions. Attention maps demonstrated the model's ability to dynamically adjust focus, leading to more balanced predictions. Case studies of various community areas in Chicago further illustrated the effectiveness of our approach in addressing spatial and demographic disparities, supporting more balanced and equitable urban planning and policy-making.

cs.LG↗

arXiv2025

ITINERA: Integrating Spatial Optimization with Large Language Models for Open-domain Urban Itinerary Planning

Citywalk, a recently popular form of urban travel, requires genuine personalization and understanding of fine-grained requests compared to traditional itinerary planning. In this paper, we introduce the novel task of Open-domain Urban Itinerary Planning (OUIP), which generates personalized urban itineraries from user requests in natural language. We then present ITINERA, an OUIP system that integrates spatial optimization with large language models to provide customized urban itineraries based on user needs. This involves decomposing user requests, selecting candidate points of interest (POIs), ordering the POIs based on cluster-aware spatial optimization, and generating the itinerary. Experiments on real-world datasets and the performance of the deployed system demonstrate our system's capacity to deliver personalized and spatially coherent itineraries compared to current solutions. Source codes of ITINERA are available at https://github.com/YihongT/ITINERA.

cs.AI↗

arXiv2025

Self-adaptive vision-language model for 3D segmentation of pulmonary artery and vein

Accurate segmentation of pulmonary structures iscrucial in clinical diagnosis, disease study, and treatment planning. Significant progress has been made in deep learning-based segmentation techniques, but most require much labeled data for training. Consequently, developing precise segmentation methods that demand fewer labeled datasets is paramount in medical image analysis. The emergence of pre-trained vision-language foundation models, such as CLIP, recently opened the door for universal computer vision tasks. Exploiting the generalization ability of these pre-trained foundation models on downstream tasks, such as segmentation, leads to unexpected performance with a relatively small amount of labeled data. However, exploring these models for pulmonary artery-vein segmentation is still limited. This paper proposes a novel framework called Language-guided self-adaptive Cross-Attention Fusion Framework. Our method adopts pre-trained CLIP as a strong feature extractor for generating the segmentation of 3D CT scans, while adaptively aggregating the cross-modality of text and image representations. We propose a s pecially designed adapter module to fine-tune pre-trained CLIP with a self-adaptive learning strategy to effectively fuse the two modalities of embeddings. We extensively validate our method on a local dataset, which is the largest pulmonary artery-vein CT dataset to date and consists of 718 labeled data in total. The experiments show that our method outperformed other state-of-the-art methods by a large margin. Our data and code will be made publicly available upon acceptance.

cs.CV↗

arXiv2024

An efficient unsupervised classification model for galaxy morphology: Voting clustering based on coding from ConvNeXt large model

In this work, we update the unsupervised machine learning (UML) step by proposing an algorithm based on ConvNeXt large model coding to improve the efficiency of unlabeled galaxy morphology classifications. The method can be summarized into three key aspects as follows: (1) a convolutional autoencoder is used for image denoising and reconstruction and the rotational invariance of the model is improved by polar coordinate extension; (2) utilizing a pre-trained convolutional neural network (CNN) named ConvNeXt for encoding the image data. The features were further compressed via a principal component analysis (PCA) dimensionality reduction; (3) adopting a bagging-based multi-model voting classification algorithm to enhance robustness. We applied this model to I-band images of a galaxy sample with $I_{\rm mag}< 25$ in the COSMOS field. Compared to the original unsupervised method, the number of clustering groups required by the new method is reduced from 100 to 20. Finally, we managed to classify about 53\% galaxies, significantly improving the classification efficiency. To verify the validity of the morphological classification, we selected massive galaxies with $M(*)>10^{10}(M(sun))$ for morphological parameter tests. The corresponding rules between the classification results and the physical properties of galaxies on multiple parameter surfaces are consistent with the existing evolution model. Our method has demonstrated the feasibility of using large model encoding to classify galaxy morphology, which not only improves the efficiency of galaxy morphology classification, but also saves time and manpower. Furthermore, in comparison to the original UML model, the enhanced classification performance is more evident in qualitative analysis and has successfully surpassed a greater number of parameter tests.

astro-ph.GA↗

arXiv2024

Multi-Layer Perceptron for Predicting Galaxy Parameters (MLP-GaP): stellar masses and star formation rates

The large-scale imaging survey will produce massive photometric data in multi-bands for billions of galaxies. Defining strategies to quickly and efficiently extract useful physical information from this data is mandatory. Among the stellar population parameters for galaxies, their stellar masses and star formation rates (SFRs) are the most fundamental. We develop a novel tool, \textit{Multi-Layer Perceptron for Predicting Galaxy Parameters} (MLP-GaP), that uses a machine-learning (ML) algorithm to accurately and efficiently derive the stellar masses and SFRs from multi-band catalogs. We first adopt a mock dataset generated by the \textit{Code Investigating GALaxy Emission} (CIGALE) for training and testing datasets. Subsequently, we used a multi-layer perceptron model to build MLP-GaP and effectively trained it with the training dataset. The results of the test performed on the mock dataset show that MLP-GaP can accurately predict the reference values. Besides MLP-GaP has a significantly faster processing speed than CIGALE. To demonstrate the science-readiness of the MLP-GaP, we also apply it to a real data sample and compare the stellar masses and SFRs with CIGALE. Overall, the predicted values of MLP-GaP show a very good consistency with the estimated values derived from SED fitting. Therefore, the capability of MLP-GaP to rapidly and accurately predict stellar masses and SFRs makes it particularly well-suited for analyzing huge amounts of galaxies in the era of large sky surveys.

astro-ph.GA↗

arXiv2024

Jets, accretion and spin in supermassive black holes

The theoretical model suggests that relativistic jets of AGN rely on the black hole spin and/or accretion. We study the relationship between jet, accretion, and spin using supermassive black hole samples with reliable spin of black holes. Our results are as follows: (1) There is a weak correlation between radio luminosity and the spin of black hole for our sample, which may imply that the jet of the supermassive black hole in our sample depends on the other physical parameters besides black hole spins, such as accretion disk luminosity. (2) The jet power of a supermassive black hole can be explained by the hybrid model with magnetic field of corona. (3) There is a significant correlation between radio-loudness and black hole spin for our sample. These sources with high radio-loudness tend to have high black hole spins. These results provide observational evidence that the black hole spin may explain the bimodal phenomena of radio-loud and radio-quiet AGN.

astro-ph.HE↗

arXiv2024

Journey-Based Transit Equity Analysis: A Case Study in the Greater Boston Area

In this paper, a new methodology, journey-based equity analysis, is presented for measuring the equity of transit convenience between income groups. Two data sources are combined in the proposed transit equity analysis: on-board ridership surveys and passenger origin-destination data. The spatial unit of our proposed transit equity analysis is census blocks, which are relatively stable over time and allows an exploration of the data that is granular enough to make conclusions about the service convenience various communities are facing. A case study in the Greater Boston area using real data from the Massachusetts Bay Transportation Authority (MBTA) bus network demonstrates a significant difference in transit service convenience, measured by number of transfers per unit distance, transfer wait time and travel time per unit distance, between low-income riders and high income riders. Implications of analysis results to transit agencies are also discussed.

stat.AP↗

arXiv2024

Multi-band analysis of strong gravitationally lensed post-blue nugget candidates from the Kilo-Degree Survey

During the early stages of galaxy evolution, a significant fraction of galaxies undergo a transitional phase between the ``blue nugget" systems, which arise from the compaction of large, active star-forming disks, and the ``red nuggets", which are red and passive compact galaxies. These objects are typically only observable with space telescopes, and detailed studies of their size, mass, and stellar population parameters have been conducted on relatively small samples. Strong gravitational lensing can offer a new opportunity to study them in detail, even with ground-based observations. In this study, we present the first 6 \textit{bona fide} sample of strongly lensed post-blue nugget (pBN) galaxies, which were discovered in the Kilo Degree Survey. By using the lensing-magnified luminosity from optical and near-infrared bands, we have derived robust structural and stellar population properties of the multiple images of the background sources. \rui{The pBN galaxies have very small sizes of $\rm R_{eff}<1.3$ kpc, high mass density inside 1 kpc of $ \rm \log (Σ_1/M_{\odot} \mathrm{kpc}^{-2})>9.3$, and low specific star formation rates of $\log (\mathrm{sSFR/Gyr})\lesssim0$, The size-mass and $Σ_1$-mass relations of this sample are consistent with those of the red nuggets, while their sSFR is close to the lower end of compact star-forming blue nugget systems at the same redshift, suggesting a clear evolutionary link between them.

astro-ph.GA↗

arXiv2024

Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network

Transportation mode share analysis is important to various real-world transportation tasks as it helps researchers understand the travel behaviors and choices of passengers. A typical example is the prediction of communities' travel mode share by accounting for their sociodemographics like age, income, etc., and travel modes' attributes (e.g. travel cost and time). However, there exist only limited efforts in integrating the structure of the urban built environment, e.g., road networks, into the mode share models to capture the impacts of the built environment. This task usually requires manual feature engineering or prior knowledge of the urban design features. In this study, we propose deep hybrid models (DHM), which directly combine road networks and sociodemographic features as inputs for travel mode share analysis. Using graph embedding (GE) techniques, we enhance travel demand models with a more powerful representation of urban structures. In experiments of mode share prediction in Chicago, results demonstrate that DHM can provide valuable spatial insights into the sociodemographic structure, improving the performance of travel demand models in estimating different mode shares at the city level. Specifically, DHM improves the results by more than 20\% while retaining the interpretation power of the choice models, demonstrating its superiority in interpretability, prediction accuracy, and geographical insights.

cs.LG↗

arXiv2024

Design of Transit-Centric Multimodal Urban Mobility System with Autonomous Mobility-on-Demand

This paper addresses the pressing challenge of urban mobility in the context of growing urban populations, changing demand patterns for urban mobility, and emerging technologies like Mobility-on-Demand (MoD) platforms and Autonomous Vehicle (AV). As urban areas swell and demand pattern changes, the integration of Autonomous Mobility-on-Demand (AMoD) systems with existing public transit (PT) networks presents great opportunities to enhancing urban mobility. We propose a novel optimization framework for solving the Transit-Centric Multimodal Urban Mobility with Autonomous Mobility-on-Demand (TCMUM-AMoD) at scale. The system operator (public transit agency) determines the network design and frequency settings of the PT network, fleet sizing and allocations of AMoD system, and the pricing for using the multimodal system with the goal of minimizing passenger disutility. Passengers' mode and route choice behaviors are modeled explicitly using discrete choice models. A first-order approximation algorithm is introduced to solve the problem at scale. Using a case study in Chicago, we showcase the potential to optimize urban mobility across different demand scenarios. To our knowledge, ours is the first paper to jointly optimize transit network design, fleet sizing, and pricing for the multimodal mobility system while considering passengers' mode and route choices.

cs.SI↗

arXiv2024

$\mathrm{F^2Depth}$: Self-supervised Indoor Monocular Depth Estimation via Optical Flow Consistency and Feature Map Synthesis

Self-supervised monocular depth estimation methods have been increasingly given much attention due to the benefit of not requiring large, labelled datasets. Such self-supervised methods require high-quality salient features and consequently suffer from severe performance drop for indoor scenes, where low-textured regions dominant in the scenes are almost indiscriminative. To address the issue, we propose a self-supervised indoor monocular depth estimation framework called $\mathrm{F^2Depth}$. A self-supervised optical flow estimation network is introduced to supervise depth learning. To improve optical flow estimation performance in low-textured areas, only some patches of points with more discriminative features are adopted for finetuning based on our well-designed patch-based photometric loss. The finetuned optical flow estimation network generates high-accuracy optical flow as a supervisory signal for depth estimation. Correspondingly, an optical flow consistency loss is designed. Multi-scale feature maps produced by finetuned optical flow estimation network perform warping to compute feature map synthesis loss as another supervisory signal for depth learning. Experimental results on the NYU Depth V2 dataset demonstrate the effectiveness of the framework and our proposed losses. To evaluate the generalization ability of our $\mathrm{F^2Depth}$, we collect a Campus Indoor depth dataset composed of approximately 1500 points selected from 99 images in 18 scenes. Zero-shot generalization experiments on 7-Scenes dataset and Campus Indoor achieve $δ_1$ accuracy of 75.8% and 76.0% respectively. The accuracy results show that our model can generalize well to monocular images captured in unknown indoor scenes.

cs.CV↗

arXiv2024

Precise Hybrid-Actuation Robotic Fiber for Enhanced Cervical Disease Treatment

Treatment for high-grade precancerous cervical lesions and early-stage cancers, mainly affecting women of reproductive age, often involves fertility-sparing treatment methods. Commonly used local treatments for cervical precancers have shown the risk of leaving a positive cancer margin and engendering subsequent complications according to the precision and depth of excision. An intra-operative device that allows the careful excision of the disease while conserving healthy cervical tissue would potentially enhance such treatment. In this study, we developed a polymer-based robotic fiber measuring 150 mm in length and 1.7 mm in diameter, fabricated using a highly scalable fiber drawing technique. This robotic fiber utilizes a hybrid actuation mechanism, combining electrothermal and tendon-driven actuation mechanisms, thus enabling a maximum motion range of 46 mm from its origin with a sub-100 μm motion precision. We also developed control algorithms for the actuation methods of this robotic fiber, including predefined path control and telemanipulation, enabling coarse positioning of the fiber tip to the target area followed by a precise scan. The combination of a surgical laser fiber with the robotic fiber allows for high-precision surgical ablation. Additionally, we conducted experiments using a cervical phantom that demonstrated the robotic fiber's ability to access and perform high-precision scans, highlighting its potential for cervical disease treatments and improvement of oncological outcomes.

cs.RO↗

arXiv2023

Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System

The rapid growth of the ride-hailing industry has revolutionized urban transportation worldwide. Despite its benefits, equity concerns arise as underserved communities face limited accessibility to affordable ride-hailing services. A key issue in this context is the vehicle rebalancing problem, where idle vehicles are moved to areas with anticipated demand. Without equitable approaches in demand forecasting and rebalancing strategies, these practices can further deepen existing inequities. In the realm of ride-hailing, three main facets of fairness are recognized: algorithmic fairness, fairness to drivers, and fairness to riders. This paper focuses on enhancing both algorithmic and rider fairness through a novel vehicle rebalancing method. We introduce an approach that combines a Socio-Aware Spatial-Temporal Graph Convolutional Network (SA-STGCN) for refined demand prediction and a fairness-integrated Matching-Integrated Vehicle Rebalancing (MIVR) model for subsequent vehicle rebalancing. Our methodology is designed to reduce prediction discrepancies and ensure equitable service provision across diverse regions. The effectiveness of our system is evaluated using simulations based on real-world ride-hailing data. The results suggest that our proposed method enhances both accuracy and fairness in forecasting ride-hailing demand, ultimately resulting in more equitable vehicle rebalancing in subsequent operations. Specifically, the algorithm developed in this study effectively reduces the standard deviation and average customer wait times by 6.48% and 0.49%, respectively. This achievement signifies a beneficial outcome for ride-hailing platforms, striking a balance between operational efficiency and fairness.

cs.LG↗

arXiv2023

Toward a stellar population catalog in the Kilo Degree Survey: the impact of stellar recipes on stellar masses and star formation rates

The Kilo Degree Survey (KiDS) is currently the only sky survey providing optical ($ugri$) plus near-infrared (NIR, $ZYHJK_S$) seeing matched photometry over an area larger than 1000 $\rm deg^2$. This is obtained by incorporating the NIR data from the VISTA Kilo Degree Infrared Galaxy (VIKING) survey, covering the same KiDS footprint. As such, the KiDS multi-wavelength photometry represents a unique dataset to test the ability of stellar population models to return robust photometric stellar mass ($M_*$) and star-formation rate (SFR) estimates. Here we use a spectroscopic sample of galaxies for which we possess $u g r i Z Y J H K_s$ ``gaussianized'' magnitudes from KiDS data release 4. We fit the spectral energy distribution from the 9-band photometry using: 1) three different popular libraries of stellar {population} templates, 2) single burst, simple and delayed exponential star-formation history models, and 3) a wide range of priors on age and metallicity. As template fitting codes we use two popular softwares: LePhare and CIGALE. We investigate the variance of the stellar masses and the star-formation rates from the different combinations of templates, star formation recipes and codes to assess the stability of these estimates and define some ``robust'' median quantities to be included in the upcoming KiDS data releases. As a science validation test, we derive the mass function, the star formation rate function, and the SFR-$M_*$ relation for a low-redshift ($z<0.5$) sample of galaxies, that result in excellent agreement with previous literature data. The final catalog, containing $\sim290\,000$ galaxies with redshift $0.01<z<0.9$, is made publicly available.

astro-ph.GA↗

arXiv2023

The comparison of optical variability of broad-line Seyfert 1 and narrow-line Seyfert 1 galaxies from the view of Pan-STARRS

By means of the data sets of the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS), we investigate the relationship between the variability amplitude and luminosity at 5100 Å, black hole mass, Eddington ratio, $ R_{\rm Fe \, II}$ ( the ratio of the flux of Fe II line within 4435-4685 Å~to the broad proportion of $\rm Hβ$ line) as well as $ R_{5007}$ (the ratio of the flux [O III] line to the total $\rm Hβ$ line) of the broad line Seyfert 1 (BLS1) and narrow line Seyfert 1 (NLS1) galaxies sample in g,r,i,z and y bands, respectively. We also analyze the similarities and differences of the variability characteristics between the BLS1 galaxies and NLS1 galaxies. The results are listed as follows. (1). The cumulative probability distribution of the variability amplitude shows that NLS1 galaxies are lower than that in BLS1 galaxies. (2). We analyze the dependence of the variability amplitude with the luminosity at 5100 Å, black hole mass, Eddington ratio, $ R_{\rm Fe \,II}$ and $ R_{5007}$, respectively. We find significantly negative correlations between the variability amplitude and Eddington ratio, insignificant correlations with the luminosity at 5100 Å. The results also show significantly positive correlations with the black hole mass and $ R_{5007}$, significantly negative correlations with $ R_{\rm Fe\, II}$ which are consistent with Rakshit and Stalin(2017) in low redshift bins (z<0.4) and Ai et al.(2010). (3). The relationship between the variability amplitude and the radio loudness is investigated for 155 BLS1 galaxies and 188 NLS1 galaxies. No significant correlations are found in our results.

astro-ph.HE↗