arXiv subjects

Yuxin Lin

Publications and source records attributed to Yuxin Lin.

At least 37 records · Page 2Linked to original sources

arXiv2025

VLM-Assisted Continual learning for Visual Question Answering in Self-Driving

In this paper, we propose a novel approach for solving the Visual Question Answering (VQA) task in autonomous driving by integrating Vision-Language Models (VLMs) with continual learning. In autonomous driving, VQA plays a vital role in enabling the system to understand and reason about its surroundings. However, traditional models often struggle with catastrophic forgetting when sequentially exposed to new driving tasks, such as perception, prediction, and planning, each requiring different forms of knowledge. To address this challenge, we present a novel continual learning framework that combines VLMs with selective memory replay and knowledge distillation, reinforced by task-specific projection layer regularization. The knowledge distillation allows a previously trained model to act as a "teacher" to guide the model through subsequent tasks, minimizing forgetting. Meanwhile, task-specific projection layers calculate the loss based on the divergence of feature representations, ensuring continuity in learning and reducing the shift between tasks. Evaluated on the DriveLM dataset, our framework shows substantial performance improvements, with gains ranging from 20.11% to 35.16% across various metrics. These results highlight the effectiveness of combining continual learning with VLMs in enhancing the resilience and reliability of VQA systems in autonomous driving. We will release our source code.

cs.CV↗

arXiv2025

RoboDriveVLM: A Novel Benchmark and Baseline towards Robust Vision-Language Models for Autonomous Driving

Current Vision-Language Model (VLM)-based end-to-end autonomous driving systems often leverage large language models to generate driving decisions directly based on their understanding of the current scene. However, such systems introduce multiple risks in real-world driving scenarios. To evaluate whether VLMs are truly viable for autonomous driving, we introduce RoboDriveBench, the first robustness benchmark focused on end-to-end trajectory prediction tasks. This benchmark systematically evaluates two critical categories of real-world challenges for VLM-based end-to-end autonomous driving systems through 11 simulated scenarios encompassing various corruption types, including 6 scenarios of sensor corruption caused by environmental variations, along with 5 cases of prompt corruption resulting from human intervention and data transmission failures. Each corruption type includes 250 unique driving scenarios and 5,689 frames, resulting in 64,559 total trajectory prediction cases per evaluation. To overcome these real-world challenges, we propose a novel VLM-based autonomous driving framework called RoboDriveVLM, which enhances robustness by mapping more multimodal data-e.g., lidar and radar-into a unified latent space. Furthermore, we introduce a new Test-Time Adaptation (TTA) method based on cross-modal knowledge distillation to improve the robustness of VLM-based autonomous driving systems. Through extensive experiments, our work highlights the limitations of current VLM-based end-to-end autonomous driving systems and provides a more reliable solution for real-world deployment. Source code and datasets will be released.

cs.AI↗

arXiv2025

Tails of Gravity: Persistence of Star Formation in the CMZ Environment

We characterize star-forming gas in six molecular clouds (Sgr B1-off, Sgr B2, Sgr C, the 20 km s$^{-1}$ and 50 km s$^{-1}$ molecular clouds, and the Brick) in the Galactic central molecular zone (CMZ), and compare their star-forming activities with those in molecular clouds outside the CMZ. Using multi-band continuum observations taken from ${\it Planck}$, ${\it Herschel}$, JCMT/SCUBA-2, and CSO/SHARC2, we derived 8.5" resolution column density maps for the CMZ clouds and evaluated the column density probability distribution functions (N-PDFs). With the archival Atacama Large Millimeter/submillimeter Array (ALMA) 1.3 mm dust continuum data, we further evaluated the mass of the most massive cores ($M_{\rm core}^{\rm ma x}$). We find that the N-PDFs of four of the selected CMZ clouds are well described by a piecewise log-normal + power-law function, while the N-PDFs of the remaining two can be approximated by log-normal functions. In the first four targets, the masses in the power-law component ($M_{\rm gas}^{\rm bound}$), $M_{\rm core}^{\rm max}$, and star formation rate (SFR) are correlated. These correlations are very similar to those derived from low-mass clouds in the Solar neighborhood and massive star-forming regions on the Galactic disk. These findings lead to our key hypotheses: (1) In the extreme environment of the CMZ, the power-law component in the N-PDF also represents self-gravitationally bound gas structures, and (2) evolution and star-forming activities of self-gravitationally bound gas structures may be self-regulated, insensitive to the exterior environment on $\gtrsim$5-10 pc scales.

astro-ph.GA↗

arXiv2025

Deep Andromeda JCMT-SCUBA2 Observations. The Submillimeter Maps and Giant Molecular Clouds

We have carried out unprecedentedly deep, nearly confusion-limited JCMT-SCUBA2 mapping observations on the nearest spiral galaxy, M31 (Andromeda). The 850 $μ$m image with a $\sim$50 pc resolution yields a comprehensive catalog of 383 giant molecular clouds (GMCs) that are associated with the spiral arms. In addition, it unveiled a population of 189 compact inter-arm GMCs in M31, which are mostly unresolved or marginally resolved. The masses of all these GMCs are in the range of 2$\times$10$^4$ -- 6$\times$10$^6$ $M_{\odot}$; the sizes are in the range of 30--130 pc. They follow a mass-size correlation, $M$ $\propto$ $R_{c}$$^{2.5}$. The inter-arm GMCs are systematically less massive, more diffuse, colder, and have lower star-forming efficiency (SFE) than on-arm GMCs. Moreover, within individual spatially resolved on-arm and off-arm M31 GMCs, the SFE is considerably lower than the SFE in molecular clouds in main sequence and green valley galaxies. Follow-up investigations on M31 GMCs may provide clues for how star formation may be quenched in galactic environments. Finally, we reconstrained the dust opacity spectral index $β$ in the M31 galaxy by combining our new JCMT observations with archival Herschel and Planck data and found that the radial variation of $β$ may not be as large as was proposed by previous studies.

astro-ph.GA↗

arXiv2025

Nascent chemical complexity in prestellar core IRAS 16293E: complex organics and deuterated methanol

Prestellar cores represent early sites of low-mass ($M$ $\leq$ few M$_\odot$) star and planet formation and provide insight into initial chemical conditions of complex organic molecules (COMs). Deuterated COMs trace the degree of molecular inheritance and/or reprocessing, as high deuteration in protostellar systems suggests COMs forming during the prestellar stage when deuteration is enhanced. Within the L1689N molecular cloud, the prestellar core IRAS 16293E sits $90^{"}$ eastward of the chemically-rich IRAS 16293-2422 A and B protostellar system. A unique view of star formation inside a common natal cloud, IRAS 16293A, B, and E all show some of the highest levels of deuteration in the ISM, with a number of D/H ratios $10^{5}$ times higher than Solar. We investigate for the first time the deuteration levels of the simplest COM, methanol (CH$_3$OH), in IRAS 16293E. Using the Arizona Radio Observatory (ARO) 12 m telescope, we target favorable transitions of CH$_2$DOH, CHD$_2$OH, $^{13}$CH$_3$OH, and several higher complexity COMs (including acetaldehyde, CH$_3$CHO, methyl formate, HCOOCH$_3$, and dimethyl ether, CH$_3$OCH$_3$) in the 3 mm band. Follow-up observations with the Yebes 40 m telescope provided additional transitions in the 7 mm (Q-band). We report the first detections of these COMs and deuterated methanol in prestellar core IRAS 16293E and use our observations to calculate excitation temperatures, column densities, and relative abundance ratios. Striking similarities are found between relative molecular ratios and D/H values when comparing IRAS 16293E to the A and B protostars, as well as to a heterogeneous sample of other prestellar cores, protostars, and the comet 67P/Churyumov-Gerasimenko. Our results support the idea that there is a limited amount of chemical reprocessing of COMs when prestellar cores collapse and heat-up during the protostellar phase.

astro-ph.GA↗

arXiv2025

HairShifter: Consistent and High-Fidelity Video Hair Transfer via Anchor-Guided Animation

Hair transfer is increasingly valuable across domains such as social media, gaming, advertising, and entertainment. While significant progress has been made in single-image hair transfer, video-based hair transfer remains challenging due to the need for temporal consistency, spatial fidelity, and dynamic adaptability. In this work, we propose HairShifter, a novel "Anchor Frame + Animation" framework that unifies high-quality image hair transfer with smooth and coherent video animation. At its core, HairShifter integrates a Image Hair Transfer (IHT) module for precise per-frame transformation and a Multi-Scale Gated SPADE Decoder to ensure seamless spatial blending and temporal coherence. Our method maintains hairstyle fidelity across frames while preserving non-hair regions. Extensive experiments demonstrate that HairShifter achieves state-of-the-art performance in video hairstyle transfer, combining superior visual quality, temporal consistency, and scalability. The code will be publicly available. We believe this work will open new avenues for video-based hairstyle transfer and establish a robust baseline in this field.

cs.CV↗

arXiv2025

BASIL: Fast broadband line-rich spectral-cube fitting and image visualization via Bayesian quadrature

Mapping the spatial distributions and abundances of complex organic molecules in hot cores and hot corinos is crucial for understanding the astrochemical pathways and the inheritance of prebiotic material by nascent planetary systems. However, the line-rich spectra from these sources pose significant challenges for robustly fitting molecular parameters due to severe line blending and unidentified lines. We present an efficient framework, Bayesian Active Spectral-cube Inference and Learning (BASIL), for estimating molecular parameter maps for hundreds of molecules based on the local thermodynamic equilibrium (LTE) model, applied to wideband spectral datacubes of line-rich sources. We adopted stochastic variational inference to infer molecular parameters from spectra at individual positions, balancing between fitting accuracy and computational speed. For obtaining parameter maps, instead of querying every location or pixel, we introduced an active learning framework based on Bayesian quadrature and its parallelization. Specifically, we assessed and selected the locations or pixels of spectrum that are most informative for estimating the entire set of parameter maps by training a Gaussian processes model. By greedily selecting locations with maximum information gain, we achieve sublinear convergence. We benchmarked BASIL on a large synthetic datacube and demonstrated that it produces accurate 468 molecular parameter maps from 117 molecules within ~180 hours, orders of magnitude faster than traditional pixel-by-pixel fitting using Markov chain Monte Carlo methods, with visually reliable results emerging in just ~20 hours. Additional training iterations provide progressively more accurate results. This quick visualization meets the demands of big data in modern astronomical surveys.

astro-ph.GA↗

arXiv2025

Predicting Turn-Taking and Backchannel in Human-Machine Conversations Using Linguistic, Acoustic, and Visual Signals

This paper addresses the gap in predicting turn-taking and backchannel actions in human-machine conversations using multi-modal signals (linguistic, acoustic, and visual). To overcome the limitation of existing datasets, we propose an automatic data collection pipeline that allows us to collect and annotate over 210 hours of human conversation videos. From this, we construct a Multi-Modal Face-to-Face (MM-F2F) human conversation dataset, including over 1.5M words and corresponding turn-taking and backchannel annotations from approximately 20M frames. Additionally, we present an end-to-end framework that predicts the probability of turn-taking and backchannel actions from multi-modal signals. The proposed model emphasizes the interrelation between modalities and supports any combination of text, audio, and video inputs, making it adaptable to a variety of realistic scenarios. Our experiments show that our approach achieves state-of-the-art performance on turn-taking and backchannel prediction tasks, achieving a 10% increase in F1-score on turn-taking and a 33% increase on backchannel prediction. Our dataset and code are publicly available online to ease of subsequent research.

cs.CL↗

arXiv2025

On a conjecture of Beelen, Datta and Ghorpade for the number of points of varieties over finite fields

Consider a finite field $\mathbb{F}_q$ and positive integers $d,m,r$ with $1\leq r\leq \binom{m+d}{d}$. Let $S_d(m)$ be the $\mathbb{F}_q$ vector space of all homogeneous polynomials of degree $d$ in $X_0,\dots,X_m$. Let $e_r(d,m)$ be the maximum number of $\mathbb{F}_q$-rational points in the vanishing set of $W$ as $W$ varies through all subspaces of $S_d(m)$ of dimension $r$. Beelen, Datta and Ghorpade had conjectured an exact formula of $e_r(d,m)$ when $q\geq d+1$. We prove that their conjectured formula is true when $q$ is sufficiently large in terms of $m,d,r$. The problem of determining $e_r(d,m)$ is equivalent to the problem of computing the $r^{th}$ generalized hamming weights of projective the Reed Muller code $PRM_q(d,m)$. It is also equivalent to the problem of determining the maximum number of points on sections of Veronese varieties by linear subvarieties of codimension $r$.

math.AG↗

arXiv2025

Why is the Star Formation Rate Proportional to Dense Gas Mass?

One of the most profound empirical laws of star formation is the Gao-Solomon relation, a linear correlation between the star formation rate (SFR) and the dense molecular gas mass. It is puzzling how the complicated physics in star-formation results in this surprisingly simple proportionality. Using archival Herschel and Atacama Large Millimeter/submillimeter Array Observations, we derived the masses of the most massive cores ($M^{\rm max}_{\rm core}$) and masses of the gravitationally bound gas ($ M_{\rm gas}^{\rm bound}$) in the parent molecular clouds for a sample of low-mass and high-mass star-forming regions. We discovered a significant correlation $\log(M^{\rm max}_{\rm core}/M_{\odot}) = 0.506 \log(M_{\rm gas}^{\rm bound}/M_{\odot})-0.32$. Our discovered $M^{\rm max}_{\rm core}$-$M_{\rm gas}^{\rm bound}$ correlation can be approximately converted to the Gao-Solomon relation if there is (1) a constant 30% efficiency of converting $M^{\rm max}_{\rm core}$ to the mass of the most massive star ($m^{\rm max}_{\rm star}$), and (2) if SFR and $m^{\rm max}_{\rm star}$ are tightly related through $\log({\rm SFR}/(M_{\odot} {\rm yr}^{-1})) = 2.04 \log(m^{\rm max}_{\rm star}/M_{\odot})-5.80$. Intriguingly, both requirements have been suggested by previous theoretical studies (c.f. Yan et al. 2017). Based on this result, we hypothesize that the Gao-Solomon relation is a consequence of combining the following three non-trivial relations (i) SFR vs. $m^{\rm max}_{\rm star}$, (ii) $m^{\rm max}_{\rm star}$ vs. $M^{\rm max}_{\rm core}$, and (iii) $M^{\rm max}_{\rm core}$ vs. $M_{\rm gas}^{\rm bound}$. This finding may open a new possibility to understand the Gao-Solomon relation in an analytic sense.

astro-ph.GA↗

arXiv2025

Gravitationally Bound Gas Determines Star Formation in the Galaxy

Stars form from molecular gas under complex conditions influenced by multiple competing physical mechanisms, such as gravity, turbulence, and magnetic fields. However, accurately identifying the fraction of gas actively involved in star formation remains challenging. Using dust continuum observations from the Herschel Space Observatory, we derived column density maps and their associated probability distribution functions (N-PDFs). Assuming the power-law component in the N-PDFs corresponds to gravitationally bound (and thus star-forming) gas, we analyzed a diverse sample of molecular clouds spanning a wide range of mass and turbulence conditions. This sample included 21 molecular clouds from the solar neighborhood ($d<$500 pc) and 16 high-mass star-forming molecular clouds. For these two groups, we employed the counts of young stellar objects (YSOs) and mid-/far-infrared luminosities as proxies for star formation rates (SFR), respectively. Both groups revealed a tight linear correlation between the mass of gravitationally bound gas and the SFR, suggesting a universally constant star formation efficiency in the gravitationally bound gas phase. The star-forming gas mass derived from threshold column densities ($N_{\mbox {threshold}}$) varies from cloud to cloud and is widely distributed over the range of $\sim$1--17$\times$10$^{21}$ cm$^{-2}$ based on N-PDF analysis. But in solar neighborhood clouds, it is in rough consistency with the traditional approach using $A_{\rm V}$ $\ge$ 8 mag. In contrast, in high turbulent regions (e.g., the Central Molecular Zone) where the classical approach fails, the gravitationally bound gas mass and SFR still follow the same correlation as other high-mass star-forming regions in the Milky Way. Our findings also strongly support the interpretation that gas in the power-law component of the N-PDF is undergoing self-gravitational collapse to form stars.

astro-ph.GA↗

arXiv2025

Wavelet-based Global-Local Interaction Network with Cross-Attention for Multi-View Diabetic Retinopathy Detection

Multi-view diabetic retinopathy (DR) detection has recently emerged as a promising method to address the issue of incomplete lesions faced by single-view DR. However, it is still challenging due to the variable sizes and scattered locations of lesions. Furthermore, existing multi-view DR methods typically merge multiple views without considering the correlations and redundancies of lesion information across them. Therefore, we propose a novel method to overcome the challenges of difficult lesion information learning and inadequate multi-view fusion. Specifically, we introduce a two-branch network to obtain both local lesion features and their global dependencies. The high-frequency component of the wavelet transform is used to exploit lesion edge information, which is then enhanced by global semantic to facilitate difficult lesion learning. Additionally, we present a cross-view fusion module to improve multi-view fusion and reduce redundancy. Experimental results on large public datasets demonstrate the effectiveness of our method. The code is open sourced on https://github.com/HuYongting/WGLIN.

eess.IV↗

arXiv2025

Laboratory rotational spectroscopy leads to the first interstellar detection of singly deuterated methyl mercaptan (CH$_{2}$DSH)

We report an extensive rotational spectroscopic analysis of singly deuterated methyl mercaptan (CH$_{2}$DSH) using both millimeter and far-infrared synchrotron spectra to achieve a global torsional analysis of the three lowest torsional substates (e$_{0}$, e$_{1}$, and o$_{1}$) of this non-rigid species. A fit including 3419 millimeter wave transitions along with 43 infrared torsional subband centers was performed with root mean square deviations of 0.233 MHz and 0.270 cm$^{-1}$, respectively, resulting in 68 fit parameters. A spectroscopic catalogue built from this analysis for a temperature of 125 K has led to the first interstellar detection of CH$_{2}$DSH towards the Solar-like protostar IRAS 16293-2422 B. We report the identification of 46 transitions, including eight relatively unblended lines, resulting in a derived column density of (3.0$\pm$0.3)$\times$10$^{14}$ cm$^{-2}$. The column density ratio for HDCS/CH$_{2}$DSH compared to HDCO/CH$_{2}$DOH suggests a difference in the interstellar chemistry between the sulphur and oxygen complex organics, in particular a different link between H$_{2}$CO and CH$_{3}$OH and between H$_{2}$CS and CH$_{3}$SH. This is the first interstellar detection of a deuterated sulphur-bearing COM and therefore an important step into understanding the chemical origin of sulphur-based prebiotics.

astro-ph.GA↗

arXiv2024

3D-Printed Hydraulic Fluidic Logic Circuitry for Soft Robots

The confluence of soft robotics and fluidic logic have sparked innovations in integrated robots with superior flexibility and potential machine intelligence. However, current fluidically driven soft robots suffer from either a large number of input controlling devices, or limited driving power. Here, we propose a hydraulic fluidic logic circuitry for liquid driven soft robots, leveraging 3D printing technologies. The fundamental building blocks of the system are hydraulic normally-on and normally-off logic gates, namely NOT and AND, along with a multi-connected channel structure functioning as OR. Using minimal-input design principles, the XOR gate can be simplified to only two valves, and used to construct a sensor-free error detector. The design principle can also be extended to full adders, as well as amplifiers, which can greatly improve the flow efficiency of the system. Additionally, taking advantage of the incompressible nature of liquid and optimized logic circuitry using the minimal-input design principle, we present a quadruped soft robot integrated with combinational fluidic logic, realizing bidirectional turtle-like locomotion, controlled by only two inputs. The robot is capable of walking under heavy load and performing controllable underwater locomotion. This hydraulic fluidic soft robotic system utilizes a small number of inputs to control multiple distinct outputs, and alters the internal state of the circuit solely based on external inputs, holding significant promises for the development of microfluidics, fluidic logic, and intricate internal systems of untethered soft robots with machine intelligence.

cs.RO↗

arXiv2024

CausalStock: Deep End-to-end Causal Discovery for News-driven Stock Movement Prediction

There are two issues in news-driven multi-stock movement prediction tasks that are not well solved in the existing works. On the one hand, "relation discovery" is a pivotal part when leveraging the price information of other stocks to achieve accurate stock movement prediction. Given that stock relations are often unidirectional, such as the "supplier-consumer" relationship, causal relations are more appropriate to capture the impact between stocks. On the other hand, there is substantial noise existing in the news data leading to extracting effective information with difficulty. With these two issues in mind, we propose a novel framework called CausalStock for news-driven multi-stock movement prediction, which discovers the temporal causal relations between stocks. We design a lag-dependent temporal causal discovery mechanism to model the temporal causal graph distribution. Then a Functional Causal Model is employed to encapsulate the discovered causal relations and predict the stock movements. Additionally, we propose a Denoised News Encoder by taking advantage of the excellent text evaluation ability of large language models (LLMs) to extract useful information from massive news data. The experiment results show that CausalStock outperforms the strong baselines for both news-driven multi-stock movement prediction and multi-stock movement prediction tasks on six real-world datasets collected from the US, China, Japan, and UK markets. Moreover, getting benefit from the causal relations, CausalStock could offer a clear prediction mechanism with good explainability.

cs.LG↗

arXiv2024

Collecting Larg-Scale Robotic Datasets on a High-Speed Mobile Platform

Mobile robotics datasets are essential for research on robotics, for example for research on Simultaneous Localization and Mapping (SLAM). Therefore the ShanghaiTech Mapping Robot was constructed, that features a multitude high-performance sensors and a 16-node cluster to collect all this data. That robot is based on a Clearpath Husky mobile base with a maximum speed of 1 meter per second. This is fine for indoor datasets, but to collect large-scale outdoor datasets a faster platform is needed. This system paper introduces our high-speed mobile platform for data collection. The mapping robot is secured on the rear-steered flatbed car with maximum field of view. Additionally two encoders collect odometry data from two of the car wheels and an external sensor plate houses a downlooking RGB and event camera. With this setup a dataset of more than 10km in the underground parking garage and the outside of our campus was collected and is published with this paper.

cs.RO↗

arXiv2024

INvestigations of massive Filaments ANd sTar formation (INFANT). I. Core Identification and Core Mass Function

Filamentary structures are ubiquitously found in high-mass star-forming clouds. To investigate the relationship between filaments and star formation, we carry out the INFANT (INvestigations of massive Filaments ANd sTar formation) survey, a multi-scale, multi-wavelength survey of massive filamentary clouds with ALMA band 3/band 6 and VLA K band. In this first paper, we present the ALMA band 6 continuum observations toward a sample of 8 high-mass star forming filaments. We covered each target with approximately rectangular mosaic field of view with two 12-m array configurations, achieving an angular resolution of $\sim$0.6" (2700 AU at 4.5 kpc) and a continuum rms of $\sim$0.1 mJy/beam ($\sim$0.06 Msun in gas mass assuming 15 K). We identify cores using the getsf and astrodendro and find the former is more robust in terms of both identification and measuring flux densities. We identify in total 183 dense cores (15--36 cores in each cloud) and classify their star formation states via outflow and warm gas tracers. The protostellar cores are statistically more massive than the prestellar cores, possibly indicating further accretion onto cores after formation of protostars. For the high-mass end ($M_\text{core}$ $>$ 1.5 Msun) of the core mass function (CMF) we derive a power-law index of $-$1.15 $\pm$ 0.12 for the whole sample, and $-$1.70 $\pm$ 0.25 for the prestellar population. We also find a steepening trend in CMF with cloud evolution ($-$0.89 $\pm$ 0.15 for the young group v.s. $-$1.44 $\pm$ 0.25 for the evolved group) and discuss its implication for cluster formation.

astro-ph.GA↗

arXiv2024

Dark Dragon Breaks Magnetic Chain: Dynamical Substructures of IRDC G28.34 Form in Supported Environments

We have comprehensively studied the multi-scale physical properties of the infrared dark cloud (IRDC) G28.34 (the Dragon cloud) with dust polarization and molecular line data from Planck, FCRAO-14m, JCMT, and ALMA. We find that the averaged magnetic fields of clumps tend to be either parallel with or perpendicular to the cloud-scale magnetic fields, while the cores in clump MM4 tend to have magnetic fields aligned with the clump fields. Implementing the relative orientation analysis (for magnetic fields, column density gradients, and local gravity), Velocity Gradient Technique (VGT), and modified Davis-Chandrasekhar-Fermi (DCF) analysis, we find that: G28.34 is located in a trans-to-sub-Alfvénic environment ($\mathcal{M}_{A}=0.74$ within $r=15$ pc); the magnetic field is effectively resisting gravitational collapse in large-scale diffuse gas, but is distorted by gravity within the cloud and affected by star formation activities in high-density regions; and the normalized mass-to-flux ratio tends to increase with increasing density and decreasing radius. Considering the thermal, turbulent, and magnetic supports, we find that the environmental gas of G28.34 is in a super-virial (supported) state, the infrared dark clumps may be in a near-equilibrium state, and core MM4-core4 is in a sub-virial (gravity-dominant) state. In summary, we suggest that magnetic fields dominate gravity and turbulence in the cloud environment at large scales, resulting in relatively slow cloud formation and evolution processes. Within the cloud, gravity could overwhelm magnetic fields and turbulence, allowing local dynamical star formation to happen.

astro-ph.GA↗