arXiv subjects

Mengting Liu

Publications and source records attributed to Mengting Liu.

15 recordsLinked to original sources

arXiv2026

ALOHA IRDCs Molecular Line Follow-up: I. Gas properties and kinematics

Infrared Dark Clouds are ideal sites for investigating the initial conditions of massive star and cluster formation. The A Lei Of the Habitat and Assembly of Infrared Dark Clouds (ALOHA IRDCs), a James Clerk Maxwell Telescope (JCMT) Large Program, has mapped nearby IRDCs with SCUBA-2. Complementary molecular line observations are needed to characterise the physical, kinematic, and chemical properties of the dense gas. We aim to determine the thermal, kinematic, and chemical properties of clumps identified in the ALOHA IRDCs, and to assess their evolutionary status and level of star-forming activity. We performed single-pointing K-band and W-band observations towards 56 ALOHA IRDCs clumps using the Effelsberg 100-m and Yebes 40-m telescopes, respectively. We derived NH3 kinetic temperatures using the hyperfine group ratio (HFGR) method and identified infall and shock signatures from HCO+, H13CO+, SiO, and HNCO profiles. Water masers and NH2D emission were used as complementary tracers of chemical evolution and star formation. The clumps exhibit kinetic temperatures of 15-29 K. We detect NH2D emission towards 18 sources, with NH2D centroid velocities consistent with NH3, indicating both species trace the same dense gas component. More than half of the clumps display blue-asymmetric HCO+ profiles, identifying them as infall candidates. Water masers are detected in 22 sources, with prominent velocity ranges and variability. Broad SiO emission (>~20 km/s) indicates strong shocks, while narrower extents (<~6km/s) likely trace large-scale interactions or low-velocity shocks. The widespread infall signatures, shock tracers, masers, and NH2D emission suggest that relatively quiescent, chemically young material can coexist with dynamically active gas affected by early protostellar feedback, providing insight into the coupled physical and chemical evolution of massive IRDC clumps.

astro-ph.GA↗

arXiv2026

Cross-Subject EEG Emotion Recognition Based on Temporal Asynchronous Alignment Contrastive Learning

With the advancement of science and technology, the importance of emotion research has become increasingly evident. Electroencephalography (EEG)-based emotion recognition has emerged as an active research area in recent years, owing to its objectivity and high temporal resolution. However, most existing methods focus on optimizing encoder structures to enhance feature extraction capabilities, while paying relatively little attention to similarity calculation strategies, particularly overlooking the potential temporal misalignment of responses among different subjects. To address these shortcomings, this paper draws inspiration from the late interaction mechanism of ColBERT in natural language processing (NLP) and proposes a Temporal Asynchronous Alignment-based Contrastive Learning (TA2CL) framework. This method transforms the traditional global "hard alignment" similarity calculation approach into a fine-grained local matching mechanism, enabling the model to adaptively search for and align "locally highly correlated" segments between two EEG signals, thereby effectively mitigating the effects of inter-subject differences and temporal delays. Experimental results demonstrate that the proposed method achieves strong performance across multiple public datasets. Specifically, on the FACED dataset, it achieves an accuracy of 64.5% for the nine-class classification task and 79.5% for the binary classification task, while on the SEED and SEED-V datasets, it achieves accuracies of 86.4% and 70.1%, respectively, validating the method's effectiveness and generalization capability.

cs.HC↗

arXiv2026

M-Gaussian: An Magnetic Gaussian Framework for Efficient Multi-Stack MRI Reconstruction

Magnetic Resonance Imaging (MRI) is a crucial non-invasive imaging modality. In routine clinical practice, multi-stack thick-slice acquisitions are widely used to reduce scan time and motion sensitivity, particularly in challenging scenarios such as fetal brain imaging. However, the resulting severe through-plane anisotropy compromises volumetric analysis and downstream quantitative assessment, necessitating robust reconstruction of isotropic high-resolution volumes. Implicit neural representation methods, while achieving high quality, suffer from computational inefficiency due to complex network structures. We present M-Gaussian, adapting 3D Gaussian Splatting to MRI reconstruction. Our contributions include: (1) Magnetic Gaussian primitives with physics-consistent volumetric rendering, (2) neural residual field for high-frequency detail refinement, and (3) multi-resolution progressive training. Our method achieves an optimal balance between quality and speed. On the FeTA dataset, M-Gaussian achieves 40.31 dB PSNR while being 14 times faster, representing the first successful adaptation of 3D Gaussian Splatting to multi-stack MRI reconstruction.

cs.CV↗

arXiv2026

Unveiling Implicit Advantage Symmetry: Why GRPO Struggles with Exploration and Difficulty Adaptation

Reinforcement Learning with Verifiable Rewards (RLVR), particularly GRPO, has become the standard for eliciting LLM reasoning. However, its efficiency in exploration and difficulty adaptation remains an open challenge. In this work, we argue that these bottlenecks stem from an implicit advantage symmetry inherent in Group Relative Advantage Estimation (GRAE). This symmetry induces two critical limitations: (i) at the group level, strict symmetry in weights between correct and incorrect trajectories leaves unsampled action logits unchanged, thereby hindering exploration of novel correct solution. (ii) at the sample level, the algorithm implicitly prioritizes medium-difficulty samples, remaining agnostic to the non-stationary demands of difficulty focus. Through controlled experiments, we reveal that this symmetric property is sub-optimal, yielding two pivotal insights: (i) asymmetrically suppressing the advantages of correct trajectories encourages essential exploration. (ii) learning efficiency is maximized by a curriculum-like transition-prioritizing simpler samples initially before gradually shifting to complex ones. Motivated by these findings, we propose Asymmetric GRAE (A-GRAE), which dynamically modulates exploration incentives and sample-difficulty focus. Experiments across seven benchmarks demonstrate that A-GRAE consistently improves GRPO and its variants across both LLMs and MLLMs.

cs.LG↗

arXiv2026

Boosting Overlapping Organoid Instance Segmentation Using Pseudo-Label Unmixing and Synthesis-Assisted Learning

Organoids, sophisticated in vitro models of human tissues, are crucial for medical research due to their ability to simulate organ functions and assess drug responses accurately. Accurate organoid instance segmentation is critical for quantifying their dynamic behaviors, yet remains profoundly limited by high-quality annotated datasets and pervasive overlap in microscopy imaging. While semi-supervised learning (SSL) offers a solution to alleviate reliance on scarce labeled data, conventional SSL frameworks suffer from biases induced by noisy pseudo-labels, particularly in overlapping regions. Synthesis-assisted SSL (SA-SSL) has been proposed for mitigating training biases in semi-supervised semantic segmentation. We present the first adaptation of SA-SSL to organoid instance segmentation and reveal that SA-SSL struggles to disentangle intertwined organoids, often misrepresenting overlapping instances as a single entity. To overcome this, we propose Pseudo-Label Unmixing (PLU), which identifies erroneous pseudo-labels for overlapping instances and then regenerates organoid labels through instance decomposition. For image synthesis, we apply a contour-based approach to synthesize organoid instances efficiently, particularly for overlapping cases. Instance-level augmentations (IA) on pseudo-labels before image synthesis further enhances the effect of synthetic data (SD). Rigorous experiments on two organoid datasets demonstrate our method's effectiveness, achieving performance comparable to fully supervised models using only 10% labeled data, and state-of-the-art results. Ablation studies validate the contributions of PLU, contour-based synthesis, and augmentation-aware training. By addressing overlap at both pseudo-label and synthesis levels, our work advances scalable, label-efficient organoid analysis, unlocking new potential for high-throughput applications in precision medicine.

cs.CV↗

arXiv2025

QSMnet-INR: Single-Orientation Quantitative Susceptibility Mapping via Implicit Neural Representation in k-Space

Quantitative Susceptibility Mapping (QSM) quantifies tissue magnetic susceptibility from magnetic-resonance phase data and plays a crucial role in brain microstructure imaging, iron-deposition assessment, and neurological-disease research. However, single-orientation QSM inversion remains highly ill-posed because the dipole kernel exhibits a cone-null region in the Fourier domain, leading to streaking artifacts and structural loss. To overcome this limitation, we propose QSMnet-INR, a deep, physics-informed framework that integrates an Implicit Neural Representation (INR) into the k-space domain. The INR module continuously models multi-directional dipole responses and explicitly completes the cone-null region, while a frequency-domain residual-weighted Dipole Loss enforces physical consistency. The overall network combines a 3D U-Net-based QSMnet backbone with the INR module through alternating optimization for end-to-end joint training. Experiments on the 2016 QSM Reconstruction Challenge, a multi-orientation GRE dataset, and both in-house and public single-orientation clinical data demonstrate that QSMnet-INR consistently outperforms conventional and recent deep-learning approaches across multiple quantitative metrics. The proposed framework shows notable advantages in structural recovery within cone-null regions and in artifact suppression. Ablation studies further confirm the complementary contributions of the INR module and Dipole Loss to detail preservation and physical stability. Overall, QSMnet-INR effectively alleviates the ill-posedness of single-orientation QSM without requiring multi-orientation acquisition, achieving high accuracy, robustness, and strong cross-scenario generalization-highlighting its potential for clinical translation.

eess.IV↗

arXiv2025

Exploring Federated Pruning for Large Language Models

LLM pruning has emerged as a promising technology for compressing LLMs, enabling their deployment on resource-limited devices. However, current methodologies typically require access to public calibration samples, which can be challenging to obtain in privacy-sensitive domains. To address this issue, we introduce FedPrLLM, a comprehensive federated pruning framework designed for the privacy-preserving compression of LLMs. In FedPrLLM, each client only needs to calculate a pruning mask matrix based on its local calibration data and share it with the server to prune the global model. This approach allows for collaborative pruning of the global model with the knowledge of each client while maintaining local data privacy. Additionally, we conduct extensive experiments to explore various possibilities within the FedPrLLM framework, including different comparison groups, pruning strategies, and the decision to scale weights. Our extensive evaluation reveals that one-shot pruning with layer comparison and no weight scaling is the optimal choice within the FedPrLLM framework. We hope our work will help guide future efforts in pruning LLMs in privacy-sensitive fields. Our code is available at https://github.com/Pengxin-Guo/FedPrLLM.

cs.LG↗

arXiv2025

Tiny-Scale Properties within the Interstellar Medium towards PSR J1644$-$4559: I. Observational Evidence of Turbulence-induced Tiny Scale Atomic Structures

We investigated HI absorption toward a single pulsar, PSR J1644$-$4559, and its variability over timescales from days to years, using Murriyang, CSIRO's Parkes Radio Telescope. Our 19 epochs of spectral observations, spanning 1.2 years with intervals as short as 1 day, provide the most comprehensive cadence coverage for monitoring HI absorption to date. We identified two significant detections of tiny-scale atomic structure (TSAS) with spatial scales ranging from a lower limit of $\sim$11 au to an upper limit of 165 au, both exhibiting integrated signal-to-noise ratios exceeding 5.0. We find a relationship between linear size and optical depth variation in the cold neutral medium (CNM) component hosting the TSAS, described by a power-law relationship, $\Delta\tau_{\rm int} = \Delta\tau_0 (\Delta L)^{(\alpha-2)/2}$, with $\alpha = 4.1 \pm 0.4$. This is the first observational evidence explicitly connecting TSAS to turbulence in CNM. This power-law index is significantly steeper than previously reported values for the CNM, where $\alpha$ ranges from 2.3 to 2.9, but similar to those observed in the warm ionized gas. Additionally, we observe no significant variation in $\alpha$ across the entire range of spatial scales traced in our study, indicating that turbulence may be cascading down and dissipating at smaller scales. While there is no precise proper motion measurement for this pulsar, our estimates for the turbulence dissipation in the CNM place the lower and upper limits at less than 0.03 au and 0.4 au, respectively.

astro-ph.GA↗

arXiv2025

First Search for Pulsed CH Maser Emission Stimulated by a Pulsar

We present the first search for pulsed CH maser emission potentially stimulated by PSR J1644$-$4559, conducted using the ultra-wide-bandwidth low-frequency receiver on Murriyang, CSIRO's Parkes Radio Telescope. Observations targeted three CH $\Lambda$-doublet transitions at 3264, 3335, and 3349 MHz, with a variability timescale of 78 ms. We detected ten CH emission features at 3335 and 3349 MHz, and seven features at 3264 MHz, during both pulsar-ON and pulsar-OFF phases. The observed velocities align with the OH emission and absorption reported by a previous study, suggesting a close spatial association between CH and OH molecules. The derived column densities for CH clouds within the Parkes beam range from $0.05$ to $9.8 \times 10^{13}$ cm$^{-2}$, indicating that these clouds are likely in diffuse and translucent states. Upper limits for CH column densities within the pulsar beam ranged from $0.3$ to $9.8 \times 10^{13}$ cm$^{-2}$. Comparison of these column densities suggests that CH clouds may exhibit clumpiness and substructure. No significant stimulated emission feature was detected in the optical depth spectra. Additionally, as part of our search for pulsed stimulated emission, we investigated the potential CH absorption of the pulsar signal and found none, in agreement with astrophysical expectations. The upper limits for the potential maser amplification factors towards PSR J1644$-$4559 at 3264, 3335, and 3349 MHz are 1.014, 1.009, and 1.009, respectively. This study demonstrates the feasibility of detecting pulsed CH maser emission in the interstellar medium stimulated by pulsar photons.

astro-ph.GA↗

arXiv2024

SurfGNN: A robust surface-based prediction model with interpretability for coactivation maps of spatial and cortical features

Current brain surface-based prediction models often overlook the variability of regional attributes at the cortical feature level. While graph neural networks (GNNs) excel at capturing regional differences, they encounter challenges when dealing with complex, high-density graph structures. In this work, we consider the cortical surface mesh as a sparse graph and propose an interpretable prediction model-Surface Graph Neural Network (SurfGNN). SurfGNN employs topology-sampling learning (TSL) and region-specific learning (RSL) structures to manage individual cortical features at both lower and higher scales of the surface mesh, effectively tackling the challenges posed by the overly abundant mesh nodes and addressing the issue of heterogeneity in cortical regions. Building on this, a novel score-weighted fusion (SWF) method is implemented to merge nodal representations associated with each cortical feature for prediction. We apply our model to a neonatal brain age prediction task using a dataset of harmonized MR images from 481 subjects (503 scans). SurfGNN outperforms all existing state-of-the-art methods, demonstrating an improvement of at least 9.0% and achieving a mean absolute error (MAE) of 0.827+0.056 in postmenstrual weeks. Furthermore, it generates feature-level activation maps, indicating its capability to identify robust regional variations in different morphometric contributions for prediction.

q-bio.NC↗

arXiv2023

Functional Graph Contrastive Learning of Hyperscanning EEG Reveals Emotional Contagion Evoked by Stereotype-Based Stressors

This study delves into the intricacies of emotional contagion and its impact on performance within dyadic interactions. Specifically, it focuses on the context of stereotype-based stress (SBS) during collaborative problem-solving tasks among female pairs. Through an exploration of emotional contagion, this study seeks to unveil its underlying mechanisms and effects. Leveraging EEG-based hyperscanning technology, we introduced an innovative approach known as the functional Graph Contrastive Learning (fGCL), which extracts subject-invariant representations of neural activity patterns from feedback trials. These representations are further subjected to analysis using the Dynamic Graph Classification (DGC) model, aimed at dissecting the process of emotional contagion along three independent temporal stages. The results underscore the substantial role of emotional contagion in shaping the trajectories of participants' performance during collaborative tasks in the presence of SBS conditions. Overall, our research contributes invaluable insights into the neural underpinnings of emotional contagion, thereby enriching our comprehension of the complexities underlying social interactions and emotional dynamics.

eess.SP↗

arXiv2021

Tiny-scale Structure Discovered toward PSR B1557$-$50

Optical depth variations in the Galactic neutral interstellar medium (ISM) with spatial scales from hundreds to thousands of astronomical units have been observed through HI absorption against pulsars and continuum sources, while extremely small structures with spatial scales of tens of astronomical units remain largely unexplored. The nature and formation of such tiny-scale atomic structures (TSAS) need to be better understood. We report a tentative detection of TSAS with a signal-to-noise ratio of 3.2 toward PSR B1557$-$50 in the second epoch of two Parkes sessions just 0.36 yr apart, which are the closest-spaced spectral observations toward this pulsar. One absorption component showing marginal variations has been identified. Based on the pulsar's proper motion of 14 mas $\rm yr^{-1}$ and the component's kinematic distance of 3.3 kpc, the corresponding characteristic spatial scale is 17 au, which is among the smallest sizes of known TSAS. Assuming a similar line-of-sight (LOS) depth, the tentative TSAS cloud detected here is overdense and overpressured relative to the cold neutral medium (CNM), and can radiatively cool fast enough to be in thermal equilibrium with the ambient environment. We find that turbulence is not sufficient to confine the overpressured TSAS. We explore the LOS elongation that would be required for the tentative TSAS to be at the canonical CNM pressure, and find that it is $\sim5000$ -- much larger than filaments observed in the ISM. We see some evidence of line width and temperature variations in the CNM components observed at the two epochs, as predicted by models of TSAS-like cloud formation colliding warm neutral medium flows.

astro-ph.GA↗

arXiv2019

Pilot HI Survey of Planck Galactic Cold Clumps with FAST

We present a pilot HI survey of 17 Planck Galactic Cold Clumps (PGCCs) with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). HI Narrow Self-Absorption (HINSA) is an effective method to detect cold HI being mixed with molecular hydrogen H$_2$ and improves our understanding of the atomic to molecular transition in the interstellar medium. HINSA was found in 58\% PGCCs that we observed. The column density of HINSA was found to have an intermediate correlation with that of $^{13}$CO, following $\rm log( N(HINSA)) = (0.52\pm 0.26) log(N_{^{13}CO}) + (10 \pm 4.1) $. HI abundance relative to total hydrogen [HI]/[H] has an average value of $4.4\times 10^{-3}$, which is about 2.8 times of the average value of previous HINSA surveys toward molecular clouds. For clouds with total column density N$\rm_H >5 \times 10^{20}$ cm$^{-2}$, an inverse correlation between HINSA abundance and total hydrogen column density is found, confirming the depletion of cold HI gas during molecular gas formation in more massive clouds. Nonthermal line width of $^{13}$CO is about 0-0.5 km s$^{-1}$ larger than that of HINSA. One possible explanation of narrower nonthermal width of HINSA is that HINSA region is smaller than that of $^{13}$CO. Based on an analytic model of H$_2$ formation and H$_2$ dissociation by cosmic ray, we found the cloud ages to be within 10$^{6.7}$-10$^{7.0}$ yr for five sources.

astro-ph.GA↗

arXiv2019

Numerical Simulation and Completeness Survey of Bubbles in the Taurus and Perseus Molecular Clouds

Previous studies have analyzed the energy injection into the interstellar matter due to molecular bubbles. They found that the total kinetic energies of bubbles are comparable to, or even larger than, those of outflows but still less than the gravitational potential and turbulence energies of the hosting clouds. We examined the possibility that previous studies underestimated the energy injection due to being unable to detect dim or incomplete bubbles. We simulated typical molecular bubbles and inserted them into the $^{13}$CO Five College Radio Astronomical Observatory maps of the Taurus and Perseus Molecular Clouds. We determined bubble identification completeness by applying the same procedures to both simulated and real data sets. We proposed a detectability function for both the Taurus and Perseus molecular clouds based on a multivariate approach. In Taurus, bubbles with kinetic energy less than ~$1 \times 10^{44}$ erg are likely to be missed. We found that the total missing kinetic energy in Taurus is less than a couple of $10^{44}$ erg, which only accounts for around 0.2% of the total kinetic energy of identified bubbles. In Perseus, bubbles with kinetic energy less than ~$2 \times 10^{44}$ erg are likely to be missed. We found that the total missing kinetic energy in Perseus is less than $10^{45}$ erg, which only accounts for around 1% of the total kinetic energy of identified bubbles. We thus conclude that previous manual bubble identification routines used in Taurus and Perseus can be considered to be energetically complete. Therefore, we confirm that energy injection from dynamic structures, namely outflows and bubbles, produced by star formation feedback are sufficient to sustain turbulence at a spatial scale from ~0.1 to ~2.8 pc.

astro-ph.GA↗

arXiv2019

Status and perspectives of the CRAFTS extra-galactic HI survey

The Five-hundred-meter Aperture Spherical radio Telescope(FAST) is expected to complete its commissioning in 2019. FAST will soon begin the Commensal Radio Astronomy FasT Survey(CRAFTS), a novel and unprecedented commensal drift scan survey of the entire sky visible from FAST. The goal of CRAFTS is to cover more than 20000 $deg^{2}$ and reach redshift up to about 0.35. We provide empirical measurements of the beam size and sensitivity of FAST across the 1.05 to 1.45 GHz frequency range of the FAST L-band Array of 19-beams(FLAN). Using a simulated HI-galaxy catalogue based on the HI Mass Function(HIMF), we estimate the number of galaxies that CRAFTS may detect. At redshifts below 0.35, over $6\, \times \, 10^{5}$ HI galaxies may be detected. Below the redshift of 0.07, the CRAFTS HIMF will be complete above a mass threshold of $10^{9.5}\,M_{\odot}$. FAST will be able to investigate the environmental and redshift dependence of the HIMF to an unprecedented depth, shedding light onto the missing baryon and missing satellite problems.

astro-ph.GA↗