arXiv subjects

Yuxin He

Publications and source records attributed to Yuxin He.

At least 37 records · Page 2Linked to original sources

arXiv2025

Cloud-cloud collision and star formation in G013.313+0.193

We study the G013.313+0.193 G013.313 region, a complex environment characterized by molecular cloud interactions indicative of cloud-cloud collision (CCC). Observations of the NH3(1,1) and (2,2) inversion transitions were obtained using the Nanshan 26 m radio telescope, while HCO+ (1-0), 12CO, 13CO, and C18O(1-0) transitions from the Purple Mountain Observatory Delingha 14 m telescope. Archival data are also included. We identified key observational signatures of CCC, including complementary spatial distributions, U-shaped structures, bridge features, and V-shaped velocity distributions. The position-velocity diagrams (P-V) reveal clear indications of gas interaction between two velocity components, suggesting an ongoing collision at an estimated angle of approximately 45 degree to the line of sight. The estimated collision timescale is 0.35-1.03 Myr, aligned with the inferred ages of young stellar objects (YSOs) in the region, supporting the hypothesis of collision-induced star formation. Hub-filament system (HFS) are identified in the compressed gas region, where filaments converge toward a dense hub, suggesting the CCC as a potential driver of HFS formation and massive star formation. The high column density suggests favorable conditions for the formation of massive stars. Although alternative kinematic drivers such as longitudinal collapse and shear motion are considered, CCC remains the most plausible explanation for the observed features. Our findings contribute to our understanding of the mechanisms of cloud dynamics and massive star formation in turbulent molecular environments.

astro-ph.GA↗

arXiv2025

TheaterGen: Character Management with LLM for Consistent Multi-turn Image Generation

Recent advances in diffusion models can generate high-quality and stunning images from text. However, multi-turn image generation, which is of high demand in real-world scenarios, still faces challenges in maintaining semantic consistency between images and texts, as well as contextual consistency of the same subject across multiple interactive turns. To address this issue, we introduce TheaterGen, a training-free framework that integrates large language models (LLMs) and text-to-image (T2I) models to provide the capability of multi-turn image generation. Within this framework, LLMs, acting as a "Screenwriter", engage in multi-turn interaction, generating and managing a standardized prompt book that encompasses prompts and layout designs for each character in the target image. Based on these, Theatergen generate a list of character images and extract guidance information, akin to the "Rehearsal". Subsequently, through incorporating the prompt book and guidance information into the reverse denoising process of T2I diffusion models, Theatergen generate the final image, as conducting the "Final Performance". With the effective management of prompt books and character images, TheaterGen significantly improves semantic and contextual consistency in synthesized images. Furthermore, we introduce a dedicated benchmark, CMIGBench (Consistent Multi-turn Image Generation Benchmark) with 8000 multi-turn instructions. Different from previous multi-turn benchmarks, CMIGBench does not define characters in advance. Both the tasks of story generation and multi-turn editing are included on CMIGBench for comprehensive evaluation. Extensive experimental results show that TheaterGen outperforms state-of-the-art methods significantly. It raises the performance bar of the cutting-edge Mini DALLE 3 model by 21% in average character-character similarity and 19% in average text-image similarity.

cs.CV↗

arXiv2025

Evidence for Core-Core Collision in Barnard 68

The prestellar core Barnard 68 (B68) is a prototypical source to study the initial conditions and chemical processes of star formation. A previous numerical simulation suggested the southeastern bullet is impacting on the main body of B68. In order to obtain more observational evidence, mapping observations of the ground state SO ($1_0-0_1$) emission line at 30 GHz were made with the Effelsberg 100 m telescope. Based on the velocity field and channel maps derived from SO, three velocity components were clearly detected. The velocity field of the main body indicates rotation and is well fitted by a solid-body rotation model. The measured radial velocity difference between the bullet and the main core is about 0.4 km s$^{-1}$, which is almost equal to the velocity obtained by the previous numerical simulation. Therefore, the bullet is most likely impacting onto the rotating main body of B68. A 1D spherical non-LTE Monte-Carlo radiation transfer RATRAN code is performed to derive the radial abundance profile of SO by analyzing the observed velocity-integrated intensity. SO is depleted inside a 60$^{\prime\prime}$ (0.02 pc) radius from the core. The abundance stays constant at 2.0$\times$10$^{-9}$ for radii larger than 60$^{\prime\prime}$ from the center of the main core. The abundance is enhanced at the interface of the bullet and the main core indicating that shock waves were produced by the collision between the bullet and the main core. In conclusion, based on the kinematical and chemical analysis, our observational results support the previously proposed core-core collision scenario in B68.

astro-ph.GA↗

arXiv2025

RoboAct-CLIP: Video-Driven Pre-training of Atomic Action Understanding for Robotics

Visual Language Models (VLMs) have emerged as pivotal tools for robotic systems, enabling cross-task generalization, dynamic environmental interaction, and long-horizon planning through multimodal perception and semantic reasoning. However, existing open-source VLMs predominantly trained for generic vision-language alignment tasks fail to model temporally correlated action semantics that are crucial for robotic manipulation effectively. While current image-based fine-tuning methods partially adapt VLMs to robotic applications, they fundamentally disregard temporal evolution patterns in video sequences and suffer from visual feature entanglement between robotic agents, manipulated objects, and environmental contexts, thereby limiting semantic decoupling capability for atomic actions and compromising model generalizability.To overcome these challenges, this work presents RoboAct-CLIP with dual technical contributions: 1) A dataset reconstruction framework that performs semantic-constrained action unit segmentation and re-annotation on open-source robotic videos, constructing purified training sets containing singular atomic actions (e.g., "grasp"); 2) A temporal-decoupling fine-tuning strategy based on Contrastive Language-Image Pretraining (CLIP) architecture, which disentangles temporal action features across video frames from object-centric characteristics to achieve hierarchical representation learning of robotic atomic actions.Experimental results in simulated environments demonstrate that the RoboAct-CLIP pretrained model achieves a 12% higher success rate than baseline VLMs, along with superior generalization in multi-object manipulation tasks.

cs.RO↗

arXiv2025

The Impact of Expanding HII Regions on Filament G37:Curved Magnetic Field and Multiple Direction Material Flows

Filament G37 exhibits a distinctive "caterpillar" shape, characterized by two semicircular structures within its 40\,pc-long body, providing an ideal target to investigate the formation and evolution of filaments. By analyzing multiple observational data, such as CO spectral line, the H$α$\,RRL, and multi-wavelength continuum, we find that the expanding H\,{\scriptsize II} regions surrounding filament G37 exert pressure on the structure of the filament body, which kinetic process present as the gas flows in multiple directions along its skeleton. The curved magnetic field structure of filament G37 derived by employing the Velocity Gradient Technique with CO is found to be parallel to the filament body and keeps against the pressure from expanded H\,{\scriptsize II} regions. The multi-directional flows in the filament G37 could cause the accumulation and subsequent collapse of gas, resulting in the formation of massive clumps. The curved structure and star formation observed in filament G37 are likely a result of the filament body being squeezed by the expanding H\,{\scriptsize II} region. This physical process occurs over a timescale of approximately 5\,Myr. The filament G37 provides a potential candidate for end-dominated collapse.

astro-ph.GA↗

arXiv2024

A Prompt Refinement-based Large Language Model for Metro Passenger Flow Forecasting under Delay Conditions

Accurate short-term forecasts of passenger flow in metro systems under delay conditions are crucial for emergency response and service recovery, which pose significant challenges and are currently under-researched. Due to the rare occurrence of delay events, the limited sample size under delay condictions make it difficult for conventional models to effectively capture the complex impacts of delays on passenger flow, resulting in low forecasting accuracy. Recognizing the strengths of large language models (LLMs) in few-shot learning due to their powerful pre-training, contextual understanding, ability to perform zero-shot and few-shot reasoning, to address the issues that effectively generalize and adapt with minimal data, we propose a passenger flow forecasting framework under delay conditions that synthesizes an LLM with carefully designed prompt engineering. By Refining prompt design, we enable the LLM to understand delay event information and the pattern from historical passenger flow data, thus overcoming the challenges of passenger flow forecasting under delay conditions. The propmpt engineering in the framework consists of two main stages: systematic prompt generation and prompt refinement. In the prompt generation stage, multi-source data is transformed into descriptive texts understandable by the LLM and stored. In the prompt refinement stage, we employ the multidimensional Chain of Thought (CoT) method to refine the prompts. We verify the proposed framework by conducting experiments using real-world datasets specifically targeting passenger flow forecasting under delay conditions of Shenzhen metro in China. The experimental results demonstrate that the proposed model performs particularly well in forecasting passenger flow under delay conditions.

cs.AI↗

arXiv2024

DelayPTC-LLM: Metro Passenger Travel Choice Prediction under Train Delays with Large Language Models

Train delays can propagate rapidly throughout the Urban Rail Transit (URT) network under networked operation conditions, posing significant challenges to operational departments. Accurately predicting passenger travel choices under train delays can provide interpretable insights into the redistribution of passenger flow, offering crucial decision support for emergency response and service recovery. However, the diversity of travel choices due to passenger heterogeneity and the sparsity of delay events leads to issues of data sparsity and sample imbalance in the travel choices dataset under metro delays. It is challenging to model this problem using traditional machine learning approaches, which typically rely on large, balanced datasets. Given the strengths of large language models (LLMs) in text processing, understanding, and their capabilities in small-sample and even zero-shot learning, this paper proposes a novel Passenger Travel Choice prediction framework under metro delays with the Large Language Model (DelayPTC-LLM). The well-designed prompting engineering is developed to guide the LLM in making and rationalizing predictions about travel choices, taking into account passenger heterogeneity and features of the delay events. Utilizing real-world data from Shenzhen Metro, including Automated Fare Collection (AFC) data and detailed delay logs, a comparative analysis of DelayPTC-LLM with traditional prediction models demonstrates the superior capability of LLMs in handling complex, sparse datasets commonly encountered under disruption of transportation systems. The results validate the advantages of DelayPTC-LLM in terms of predictive accuracy and its potential to provide actionable insights for big traffic data.

cs.LG↗

arXiv2024

D2Vformer: A Flexible Time Series Prediction Model Based on Time Position Embedding

Time position embeddings capture the positional information of time steps, often serving as auxiliary inputs to enhance the predictive capabilities of time series models. However, existing models exhibit limitations in capturing intricate time positional information and effectively utilizing these embeddings. To address these limitations, this paper proposes a novel model called D2Vformer. Unlike typical prediction methods that rely on RNNs or Transformers, this approach can directly handle scenarios where the predicted sequence is not adjacent to the input sequence or where its length dynamically changes. In comparison to conventional methods, D2Vformer undoubtedly saves a significant amount of training resources. In D2Vformer, the Date2Vec module uses the timestamp information and feature sequences to generate time position embeddings. Afterward, D2Vformer introduces a new fusion block that utilizes an attention mechanism to explore the similarity in time positions between the embeddings of the input sequence and the predicted sequence, thereby generating predictions based on this similarity. Through extensive experiments on six datasets, we demonstrate that Date2Vec outperforms other time position embedding methods, and D2Vformer surpasses state-of-the-art methods in both fixed-length and variable-length prediction tasks.

cs.LG↗

arXiv2024

Kinematics and star formation of hub-filament systems in W49A

W49A is a prominent giant molecular cloud (GMC) that exhibits strong star formation activities, yet its structural and kinematic properties remain uncertain. Our study aims to investigate the large-scale structure and kinematics of W49A, and elucidate the role of filaments and hub-filament systems (HFSs) in its star formation activity. We utilized continuum data from Herschel and the James Clerk Maxwell Telescope (JCMT) as well as the molecular lines 12CO (3-2), 13CO (3-2), and C18O (3-2) to identify filaments and HFS structures within W49A. Further analysis focused on the physical properties, kinematics, and mass transport within these structures. Additionally, recombination line emission from the H I/OH/Recombination (THOR) line survey was employed to trace the central H II region and ionized gas. Our findings reveal that W49A comprises one blue-shifted (B-S) HFS and one red-shifted (R-S) HFS, each with multiple filaments and dense hubs. Notably, significant velocity gradients were detected along these filaments, indicative of material transport toward the hubs. High mass accretion rates along the filaments facilitate the formation of massive stars in the HFSs. Furthermore, the presence of V-shaped structures around clumps in position-velocity diagrams suggests ongoing gravitational collapse and local star formation within the filaments. Our results indicate that W49A consists of one R-S HFS and one B-S HFS, and that the material transport from filaments to the hub promotes the formation of massive stars in the hub. These findings underscore the significance of HFSs in shaping the star formation history of W49A.

astro-ph.GA↗

arXiv2024

Infall Motions in the Hot Core Associated with Hypercompact HII Region G345.0061+01.794 B

We report high angular resolution observations, made with the Atacama Large Millimeter Array in band 6, of high excitation molecular lines of $\rm CH_3CN$ and $\rm SO_2$ and of the H29$α$ radio recombination line towards the G345.0061+01.794 B HC H II region, in order to investigate the physical and kinematical characteristics of its surroundings. Emission was detected in all observed components of the J=14$\rightarrow$13 rotational ladder of $\rm CH_3CN$ and in the $30_{4,26}-30_{3,27}$ and $32_{4,28}-32_{3,29}$ lines of $\rm SO_2$. The peak of the velocity integrated molecular emission is located $\sim$0$\,.\!\!^{\prime\prime}$4 northwest of the peak of the continuum emission. The first-order moment images and channel maps show a velocity gradient, of 1.1 km s$^{-1}$ arcsec$^{-1}$, across the source, and a distinctive spot of blueshifted emission towards the peak of the zero-order moment. The rotational temperature is found to decrease from 252$\pm24$ Kelvin at the peak position to 166$\pm16$ Kelvin at its edge, indicating that our molecular observations are probing a hot molecular core that is internally excited. The emission in the H29$α$ line arises from a region of 0$\,.\!\!^{\prime\prime}$65 in size, where its peak coincides with that of the dust continuum. We model the kinematical characteristics of the "central blue spot" feature as due to infalling motions, suggesting a central mass of 172.8$\pm8.8 M_{\odot}$. Our observations indicate that this HC H II region is surrounded by a compact structure of hot molecular gas, which is rotating and infalling toward a central mass, that is most likely confining the ionized region. The observed scenario is reminiscent of a "butterfly pattern" with an approximately edge-on torus and ionized gas roughly parallel to its rotation axis.

astro-ph.GA↗

arXiv2024

On second eigenvalues of closed hyperbolic surfaces for large genus

In this article, we study the second eigenvalues of closed hyperbolic surfaces for large genus. We show that for every closed hyperbolic surface $X_g$ of genus $g$ $(g\geq 3)$, up to uniform positive constants multiplications, the second eigenvalue $λ_2(X_g)$ of $X_g$ is greater than $\frac{\mathcal{L}_2(X_g)}{g^2}$ and less than $\mathcal{L}_2(X_g)$; moreover these two bounds are optimal as $g\to \infty$. Here $\mathcal{L}_2(X_g)$ is the shortest length of simple closed multi-geodesics separating $X_g$ into three components. Furthermore, we also investigate the quantity $\frac{λ_2(X_g)}{\mathcal{L}_2(X_g)}$ for random hyperbolic surfaces of large genus. We show that as $g\to \infty$, a generic hyperbolic surface $X_g$ has $\frac{λ_2(X_g)}{\mathcal{L}_2(X_g)}$ uniformly comparable to $\frac{1}{\ln(g)}$.

math.GT↗

arXiv2024

Magnetic Field of Molecular Gas Measured with the Velocity Gradient Technique II: Curved Magnetic Field in kpc-Scale Bubble of NGC\,628

We report the detection of the ordered alignment between the magnetic field and kpc-scale bubbles in the nearby spiral galaxy, NGC\,628. Applying the Velocity Gradient Technique (VGT) on CO spectroscopic data from the ALMA-PHANGS, the magnetic field of NGC\,628 is measured at the scale of 191\,pc ($\sim$ 4\,$''$). The large-scale magnetic field is oriented parallel to the spiral arms and curves around the galactic bubble structures in the mid-infrared emission observed by the James Webb Space Telescope (JWST). Twenty-one bubble structures have been identified at the edges of spiral arms with scales over 300\,pc, which includes two kpc-scale structures. These bubbles are caused by supernova remnants and prolonged star formation and are similar to the outflow chimneys found in neutral hydrogen in galactic disks. At the edge of the bubbles, the shocks traced by the OIII emission present a curved magnetic field that parallels the bubble's shell. The magnetic field follows the bubble expansion and binds the gas in the shell to trigger further star formation. By analyzing the larger sample of 1694 bubbles, we found a distinct radial-size distribution of bubbles in NGC\,628 indicating the star formation history in the galaxy.

astro-ph.GA↗

arXiv2023

Explorers at #SMM4H 2023: Enhancing BERT for Health Applications through Knowledge and Model Fusion

An increasing number of individuals are willing to post states and opinions in social media, which has become a valuable data resource for studying human health. Furthermore, social media has been a crucial research point for healthcare now. This paper outlines the methods in our participation in the #SMM4H 2023 Shared Tasks, including data preprocessing, continual pre-training and fine-tuned optimization strategies. Especially for the Named Entity Recognition (NER) task, we utilize the model architecture named W2NER that effectively enhances the model generalization ability. Our method achieved first place in the Task 3. This paper has been peer-reviewed and accepted for presentation at the #SMM4H 2023 Workshop.

cs.CL↗

arXiv2023

Magnetic Fields in Giant Filaments Probed by the Velocity Gradient Technique: Regular Magnetic Field interrupted by Magnetization Gaps

We study the magnetic field structures in six giant filaments associated with the spiral arms of the Milky Way by applying the Velocity Gradient technique (VGT) to the 13CO spectroscopic data from GRS, Fugin, and SEDIGSM surveys. Compared to dust polarized emission, the VGT allows us to separate the foreground and background using the velocity information, from which the orientation of the magnetic field can be reliably determined. We find that in most cases, the magnetic fields stay aligned with the filament bodies, which are parallel to the disk midplane. Among these, G29, G47, and G51 exhibit smooth magnetic fields, and G24, G339, and G349 exhibit discontinuities. The fact that most filaments have magnetic fields that stay aligned with the Galactic disk midplane suggests that Galactic shear can be responsible for shaping the filaments. The fact that the magnetic field can stay regular at the resolution of our analysis (<= 10 pc) where the turbulence crossing time is short compared to the shear time suggests that turbulent motion can not effectively disrupt the regular orientation of the magnetic field. The discontinuities found in some filaments can be caused by processes including filament reassembly, gravitational collapse, and stellar feedback.

astro-ph.GA↗

arXiv2023

Large Steklov eigenvalues on hyperbolic surfaces

In this paper, we first construct a sequence of hyperbolic surfaces with connected geodesic boundary such that the first normalized Steklov eigenvalue $\tildeσ_1$ tends to infinity. We then prove that as $g\rightarrow \infty$, a generic $Σ\in \mathcal{M}_{g,n}(L_g)$ satisfies $\tildeσ_1(Σ)>C\cdot \|L_g\|_1$ where $C$ is a positive universal constant. Here $\mathcal{M}_{g,n}(L_g)$ is the moduli space of hyperbolic surfaces of genus $g$ and $n$ boundary components of length $L_g=(L_g^1,\cdots, L_g^n)$ endowed with the Weil-Petersson metric where $\|L_g\|_1\rightarrow\infty$ satisfies certain conditions.

math.DG↗

arXiv2023

Clump-scale Gas Infall in High-mass Star Formation: a Multi-transition View with JCMT HCN (4--3) Mapping

Gas infall motions play a crucial role in high-mass star formation and are characterized by observable signatures in the form of blue-shifted asymmetric spectral line profiles ("blue profiles"). However, the connection between blue profiles and infall motions is unclear due to complex gas motions at parsec scales. In this study, we present the results of an HCN (4-3) mapping survey conducted with the JCMT, towards 38 massive clumps exhibiting blue profiles in HCO+ (3-2). We extract 34 HCN cores from the 38 observed fields. The core-averaged spectra show various line profiles, indicating that blue-profile HCO+ (3-2) does not guarantee the same in HCN (4-3). Through non-LTE radiation transfer calculations, we attribute the low detection rate of high-$J$ blue profiles to a combination of insufficient HCN (4-3) opacity and intricate gas motion across different density layers. The comparison between the MALT90 and BGPS line surveys highlights the importance of appropriate tracers, high spectral resolution, and column density thresholds when searching for blue profiles. We select 11 reliable infall candidates and adopt the Hill5 model to fit the infall velocity of 0.2-1.9 km/s, corresponding to 5% to 74% of free-fall velocity. Assuming a spherically collapsing model, we estimate the median and mean mass infall rates to be 4.5E-3 and 7.6E-3 Msun/year, respectively. The consistency of the mass infall rates among different transitions suggests a steady accretion process from the clump gas envelope to the inner region.

astro-ph.GA↗

arXiv2023

Gravitational collapse and accretion flows in the hub filament system G323.46-0.08

We studied the hub filament system G323.46-0.08 based on archival molecular line data from the SEDIGISM 13CO survey and infrared data from the GLIMPSE, MIPS, and Hi-GAL surveys. G323.46-0.08 consists of three filaments, F-north, F-west, and F-south, that converge toward the central high_mass clump AGAL 323.459-0.079. F-west and Part 1 of the F-south show clear large-scale velocity gradients 0.28 and 0.44 km s-1 pc-1, respectively. They seem to be channeling materials into AGAL 323.459-0.079. The minimum accretion rate was estimated to be 1216 M Myr-1. A characteristic V-shape appears around AGAL 323.459-0.079 in the PV diagram, which traces the accelerated gas motions under gravitational collapse. This has also been supported by model fitting results. All three filaments are supercritical and they have fragmented into many dense clumps. The seesaw patterns near most dense clumps in the PV diagram suggests that mass accretion also occurs along the filament toward the clumps. Our results show that filamentary accretion flows appear to be an important mechanism for supplying the materials necessary to form the central high-mass clump AGAL 323.459-0.079 and to propel the star forming activity taking place therein.

astro-ph.GA↗

arXiv2023

Revisiting Event Argument Extraction: Can EAE Models Learn Better When Being Aware of Event Co-occurrences?

Event co-occurrences have been proved effective for event extraction (EE) in previous studies, but have not been considered for event argument extraction (EAE) recently. In this paper, we try to fill this gap between EE research and EAE research, by highlighting the question that ``Can EAE models learn better when being aware of event co-occurrences?''. To answer this question, we reformulate EAE as a problem of table generation and extend a SOTA prompt-based EAE model into a non-autoregressive generation framework, called TabEAE, which is able to extract the arguments of multiple events in parallel. Under this framework, we experiment with 3 different training-inference schemes on 4 datasets (ACE05, RAMS, WikiEvents and MLEE) and discover that via training the model to extract all events in parallel, it can better distinguish the semantic boundary of each event and its ability to extract single event gets substantially improved. Experimental results show that our method achieves new state-of-the-art performance on the 4 datasets. Our code is avilable at https://github.com/Stardust-hyx/TabEAE.

cs.CL↗