arXiv subjects

Yixiao Liu

Publications and source records attributed to Yixiao Liu.

15 recordsLinked to original sources

arXiv2026

A Dusty Quenching-candidate AGN host at z=5.7: massive quiescent galaxies may quench already during the dust-obscured phase

We present a spectro-photometric analysis of RUBIES-EGS-9809, a broad-line AGN host at z=5.7 with a Balmer break and a dominant point source. The source is dust reddened ($A_V>$3 mag), shows hot- and cold-dust emission, and is X-ray Compton thick, with X-ray-to-bolometric ratio consistent with luminous AGN. The large $A_V$ agrees with strong NaI absorption (EW=17$\pm 2 \AA$), possibly from a neutral-gas outflow (3 $\sigma$), while broad [O III] traces an ionized outflow, suggesting AGN feedback may be affecting the host. We estimate a stellar mass log(M*/M$_\odot$)=10.7$\pm$0.3, Lbol$\sim4\times10^{46}$ erg s$^{-1}$ , and black-hole mass log(M$_{BH}/M_\odot)\sim8.1$ (subject to large systematics). The resolved Balmer break (0.1 arcsec, 0.6 kpc) is consistent with a stellar origin, implying the galaxy is already old in stars while still heavily obscured. This shows that massive galaxies can host evolved stellar populations during their compact, dust-obscured phase, consistent with formation in an earlier dusty starburst. SED modelling suggests a declining SFR, but low-resolution spectroscopy alone cannot constrain the recent SFR, so we treat the source as a strong quenching candidate rather than a secure post-starburst system. Because quenching follows the decline in SFR, star-formation-driven outflows are disfavoured, leaving AGN feedback as a plausible driver. We speculate that such rapid feedback ($\sim$100 Myr timescale) may precipitate quenching while the post-quenching phase stays dust-enshrouded, concealing the UV-bright quenched phase that would otherwise be easily detected, if dust free. By the time dust clears, the UV-luminous stars may have already dwindled, leaving a classic dust-free quiescent galaxy. This 'dusty path' to quiescence may explain the lack of intermediate-age quenched progenitors (ages 50-100 Myr) linking dusty starbursts to massive quiescent systems at z=3-7.

astro-ph.GA↗

arXiv2026

MedGame: Storytelling Gamification Empowered by Large Language Models for Medical Education

Large Language Models (LLMs) show promise for medical education, but most existing systems focus on localized interactions such as question answering or single-turn feedback, rather than organizing an entire clinical case into a decision-centered learning trajectory. We introduce \textit{MedGame}, a framework that transforms static clinical cases into structured, executable storytelling games. MedGame uses a dual-engine design: a Medical Narrative Designer synthesizes case-grounded clinical storylines with states and decision nodes, while a Story Director converts them into dependency-aware multimodal orchestration plans rendered by our released interactive platform. We construct MedGame Bench, a 5,000-case benchmark and evaluation protocol for Medical Narrative Generation and Story Direction. Experiments show that task-specific fine-tuning substantially improves open-source LLMs on MedGame Bench and narrows the gap with commercial models. A pilot student study further shows that learners perceive MedGame as more engaging and useful than text-only alternatives.

cs.CL↗

arXiv2026

Gravitationally Lensed View of DSFG-1 in PLCK G165.7+67.0: Strong Dust Emission and Spatially Resolved Stellar Population Analysis with JWST and SMA

We present a detailed stellar population analysis of the strongly lensed dusty star-forming galaxy (DSFG) PLCK G165.7+67.0 DSFG-1 at $z = 2.236$, combining JWST NIRCam imaging with new Submillimeter Array (SMA) observations. This source is multiply imaged into two lensed components: image 1a, with a moderate magnification factor of $\mu \sim 5$, and image 1bc, with an extreme magnification factor of $\mu \sim 40$. The new SMA observations detect significant dust continuum emission at 225GHz and 273GHz, with combined flux densities of $S_{\rm cont}=(1.19\pm0.38)$ mJy in image 1a and $S_{\rm cont}=(10.02\pm0.85)$ mJy in image 1bc, indicating active star formation at sub-kpc scale. Based on the integrated SED modeling, DSFG-1 exhibits a lensing amplification-corrected stellar mass of $M_{\star} = (1.2 \pm 0.4) \times 10^{10} M_{\odot}$, and a star-formation rate (SFR) of $(103 \pm 14) M_{\odot}\,\mathrm{yr^{-1}}$, similar to previous $H\alpha$-based results, placing it four times above the star-forming main sequence at this redshift. Its location on the size-mass plane and its morphological properties suggest that the system occupies a transitional phase between star-forming late-type galaxies and compact early-type systems. Together with its elevated star-formation activity, this is consistent with a rapidly evolving galaxy observed during Cosmic Noon. We further investigate the spatially resolved stellar population properties, and found significant spatial variations in stellar age and dust attenuation. These results point to a non-uniform star-formation history and highlight the complex interplay between dust geometry, stellar growth, and gravitational lensing, consistent with a merger scenario.

astro-ph.GA↗

arXiv2026

A Steep-Extinction Quasi-stellar Object at z=4.6: JWST Evidence for Abundant Small Dust Grains

The rapid accumulation of massive dust reservoirs in the early Universe remains a major challenge in astrophysics. While core-collapse supernovae can inject large dust grains ($a \gtrsim 0.1\,\mu{\rm m}$) on short timescales, explaining the total dust budgets in the early Universe likely requires efficient grain growth in the interstellar medium (ISM). Such growth depends critically on an abundant population of small grains, which maximize the surface area available for accretion and may be generated by rapid dust-processing or dust-formation channels. Here, we report the discovery of a QSO, UDS-27023, at $z=4.556\pm0.003$, identified using JWST/NIRSpec spectroscopy. By quantitatively comparing the spectra to QSO composite templates, we find that UDS-27023 displays an exceptionally steep far-UV extinction curve ($A_{1500}/A_V \approx 8$) but notably lacks the 2175 A bump ($A_\mathrm{bump}/A_V<0.34$ at $3\sigma$), indicating a dominance of small silicate dust grains. We interpret this phenomenology as evidence for active small-grain production and processing in the QSO environment. Mechanical shattering of pre-existing large grains by QSO-driven shocks and outflows provides one natural pathway, while in situ condensation of silicate grains inside dense QSO-driven winds may offer an additional route. Such a population of steep-extinction QSOs (SEQs) may therefore reveal a short-lived phase in which luminous active galactic nuclei generate, process, and redistribute small grains, potentially facilitating rapid ISM grain growth and enriching the circumgalactic medium.

astro-ph.GA↗

arXiv2026

Generalized Recognition of Basic Surgical Actions Enables Skill Assessment and Vision-Language-Model-based Surgical Planning

Artificial intelligence, imaging, and large language models have the potential to transform surgical practice, training, and automation. Understanding and modeling of basic surgical actions (BSA), the fundamental unit of operation in any surgery, is important to drive the evolution of this field. In this paper, we present a BSA dataset comprising 10 basic actions across 6 surgical specialties with over 11,000 video clips, which is the largest to date. Based on the BSA dataset, we developed a new foundation model that conducts general-purpose recognition of basic actions. Our approach demonstrates robust cross-specialist performance in experiments validated on datasets from different procedural types and various body parts. Furthermore, we demonstrate downstream applications enabled by the BAS foundation model through surgical skill assessment in prostatectomy using domain-specific knowledge, and action planning in cholecystectomy and nephrectomy using large vision-language models. Multinational surgeons' evaluation of the language model's output of the action planning explainable texts demonstrated clinical relevance. These findings indicate that basic surgical actions can be robustly recognized across scenarios, and an accurate BSA understanding model can essentially facilitate complex applications and speed up the realization of surgical superintelligence.

cs.CV↗

arXiv2026

From Pets to Robots: MojiKit as a Data-Informed Toolkit for Affective HRI Design

Designing affective behaviors for animal-inspired social robots often relies on intuition and personal experience, leading to fragmented outcomes. To provide more systematic guidance, we first coded and analyzed human-pet interaction videos, validated insights through literature and interviews, and created structured reference cards that map the design space of pet-inspired affective interactions. Building on this, we developed MojiKit, a toolkit combining reference cards, a zoomorphic robot prototype (MomoBot), and a behavior control studio. We evaluated MojiKit in co-creation workshops with 18 participants, finding that MojiKit helped them design 35 affective interaction patterns beyond their own pet experiences, while the code-free studio lowered the technical barrier and enhanced creative agency. Our contributions include the data-informed structured resource for pet-inspired affective HRI design, an integrated toolkit that bridges reference materials with hands-on prototyping, and empirical evidence showing how MojiKit empowers users to systematically create richer, more diverse affective robot behaviors.

cs.HC↗

arXiv2026

A Physics-Guided Neural Framework for Rheology Measurement from Dynamical Laser Speckles

Critical breakthroughs in the area of biomedicine and materials science increasingly depend on rapid, non-contact methods for viscoelastic characterization. Laser Speckle Rheology (LSR) is positioned to meet this demand, effectively circumventing the speed and invasiveness bottlenecks inherent to traditional mechanical rheometer. However, its application in turbid fluids is severely constrained by multiple scattering, where standard physical inversions rely heavily on precise, sample-specific optical transport parameters that are difficult to measure in situ. To overcome this barrier, we propose a physics-guided deep learning framework that infers a Maxwell relaxation spectrum from the intensity autocorrelation g2(t) and speckle-intensity histogram statistics. The resulting spectrum is then propagated through a Maxwell forward model to predict G'and G'' under physics-consistency constraints. Quantitatively, the framework achieves RMSElog as low as 0.009 against reference and generalizes to previously unseen scattering conditions, preserving physically plausible frequency dependence and G'- G'' phase behavior. It reduces reliance on optical transport parameters that are hard to determine in situ and returns an interpretable generalized Maxwell relaxation spectrum, improving the practicality of LSR in turbid media.

physics.optics↗

arXiv2025

Differential Distance Correlation and Its Applications

In this paper, we propose a novel Euclidean-distance-based coefficient, named differential distance correlation, to measure the strength of dependence between a random variable $ Y \in \mathbb{R} $ and a random vector $ \boldsymbol{X} \in \mathbb{R}^p $. The coefficient has a concise expression and is invariant to arbitrary orthogonal transformations of the random vector. Moreover, the coefficient is a strongly consistent estimator of a simple and interpretable dependent measure, which is 0 if and only if $ \boldsymbol{X} $ and $ Y $ are independent and equal to 1 if and only if $ Y $ determines $ \boldsymbol{X} $ almost surely. An alternative approach is also proposed to address the limitation that the coefficient is non-robust to outliers. Furthermore, the coefficient exhibits asymptotic normality with a simple variance under the independent hypothesis, facilitating fast and accurate estimation of $ p $-value for testing independence. Three simulation experiments show that the proposed coefficient is more computationally efficient for independence testing and more effective in detecting oscillatory relationships than several competing methods. We also apply our method to analyze a real data example.

stat.ME↗

arXiv2025

LG-CD: Enhancing Language-Guided Change Detection through SAM2 Adaptation

Remote Sensing Change Detection (RSCD) typically identifies changes in land cover or surface conditions by analyzing multi-temporal images. Currently, most deep learning-based methods primarily focus on learning unimodal visual information, while neglecting the rich semantic information provided by multimodal data such as text. To address this limitation, we propose a novel Language-Guided Change Detection model (LG-CD). This model leverages natural language prompts to direct the network's attention to regions of interest, significantly improving the accuracy and robustness of change detection. Specifically, LG-CD utilizes a visual foundational model (SAM2) as a feature extractor to capture multi-scale pyramid features from high-resolution to low-resolution across bi-temporal remote sensing images. Subsequently, multi-layer adapters are employed to fine-tune the model for downstream tasks, ensuring its effectiveness in remote sensing change detection. Additionally, we design a Text Fusion Attention Module (TFAM) to align visual and textual information, enabling the model to focus on target change regions using text prompts. Finally, a Vision-Semantic Fusion Decoder (V-SFD) is implemented, which deeply integrates visual and semantic information through a cross-attention mechanism to produce highly accurate change detection masks. Our experiments on three datasets (LEVIR-CD, WHU-CD, and SYSU-CD) demonstrate that LG-CD consistently outperforms state-of-the-art change detection methods. Furthermore, our approach provides new insights into achieving generalized change detection by leveraging multimodal information.

cs.CV↗

arXiv2025

Rethinking Multilingual Vision-Language Translation: Dataset, Evaluation, and Adaptation

Vision-Language Translation (VLT) is a challenging task that requires accurately recognizing multilingual text embedded in images and translating it into the target language with the support of visual context. While recent Large Vision-Language Models (LVLMs) have demonstrated strong multilingual and visual understanding capabilities, there is a lack of systematic evaluation and understanding of their performance on VLT. In this work, we present a comprehensive study of VLT from three key perspectives: data quality, model architecture, and evaluation metrics. (1) We identify critical limitations in existing datasets, particularly in semantic and cultural fidelity, and introduce AibTrans -- a multilingual, parallel, human-verified dataset with OCR-corrected annotations. (2) We benchmark 11 commercial LVLMs/LLMs and 6 state-of-the-art open-source models across end-to-end and cascaded architectures, revealing their OCR dependency and contrasting generation versus reasoning behaviors. (3) We propose Density-Aware Evaluation to address metric reliability issues under varying contextual complexity, introducing the DA Score as a more robust measure of translation quality. Building upon these findings, we establish a new evaluation benchmark for VLT. Notably, we observe that fine-tuning LVLMs on high-resource language pairs degrades cross-lingual performance, and we propose a balanced multilingual fine-tuning strategy that effectively adapts LVLMs to VLT without sacrificing their generalization ability.

cs.CV↗

arXiv2025

Chinese Toxic Language Mitigation via Sentiment Polarity Consistent Rewrites

Detoxifying offensive language while preserving the speaker's original intent is a challenging yet critical goal for improving the quality of online interactions. Although large language models (LLMs) show promise in rewriting toxic content, they often default to overly polite rewrites, distorting the emotional tone and communicative intent. This problem is especially acute in Chinese, where toxicity often arises implicitly through emojis, homophones, or discourse context. We present ToxiRewriteCN, the first Chinese detoxification dataset explicitly designed to preserve sentiment polarity. The dataset comprises 1,556 carefully annotated triplets, each containing a toxic sentence, a sentiment-aligned non-toxic rewrite, and labeled toxic spans. It covers five real-world scenarios: standard expressions, emoji-induced and homophonic toxicity, as well as single-turn and multi-turn dialogues. We evaluate 17 LLMs, including commercial and open-source models with variant architectures, across four dimensions: detoxification accuracy, fluency, content preservation, and sentiment polarity. Results show that while commercial and MoE models perform best overall, all models struggle to balance safety with emotional fidelity in more subtle or context-heavy settings such as emoji, homophone, and dialogue-based inputs. We release ToxiRewriteCN to support future research on controllable, sentiment-aware detoxification for Chinese.

cs.CL↗

arXiv2025

Enhancing Trust Management System for Connected Autonomous Vehicles Using Machine Learning Methods: A Survey

Connected Autonomous Vehicles (CAVs) operate in dynamic, open, and multi-domain networks, rendering them vulnerable to various threats. Trust Management Systems (TMS) systematically organize essential steps in the trust mechanism, identifying malicious nodes against internal threats and external threats, as well as ensuring reliable decision-making for more cooperative tasks. Recent advances in machine learning (ML) offer significant potential to enhance TMS, especially for the strict requirements of CAVs, such as CAV nodes moving at varying speeds, and opportunistic and intermittent network behavior. Those features distinguish ML-based TMS from social networks, static IoT, and Social IoT. This survey proposes a novel three-layer ML-based TMS framework for CAVs in the vehicle-road-cloud integration system, i.e., trust data layer, trust calculation layer and trust incentive layer. A six-dimensional taxonomy of objectives is proposed. Furthermore, the principles of ML methods for each module in each layer are analyzed. Then, recent studies are categorized based on traffic scenarios that are against the proposed objectives. Finally, future directions are suggested, addressing the open issues and meeting the research trend. We maintain an active repository that contains up-to-date literature and open-source projects at https://github.com/octoberzzzzz/ML-based-TMS-CAV-Survey.

cs.AI↗

arXiv2025

Measuring Feature-Label Dependence Using Projection Correlation Statistic

Detecting dependence between variables is a crucial issue in statistical science. In this paper, we propose a novel metric, named label projection correlation, to measure the dependence between numerical and categorical variables. The proposed correlation does not require any conditions on the numerical variable, and is equal to zero if and only if the two variables are independent. Moreover, when the numerical variable is one-dimensional, we demonstrate that the computational cost of the correlation estimation can be reduced from $\mathrm{O}(n^3)$ to $\mathrm{O}(n \log n)$, where $ n $ is the sample size. Furthermore, if the one-dimensional variable is continuous, the metric can be simplified to a concise rank-based expression. The asymptotic theorem of the estimation is also established. Two simulated experiments are presented to demonstrate the effectiveness of our proposed correlation in feature selection. Furthermore, our approach is applied to feature selection in drivers' facial images and cancer mass-spectrometric data.

stat.ME↗

arXiv2024

A Close Look at Ly$α$ Emitters with JWST/NIRCam at $z\approx3.1$

We study 10 spectroscopically confirmed Ly$α$ emitters (LAEs) at $z\approx3.1$ in the UDS field, covered by JWST/NIRCam in the PRIMER program. All LAEs are detected in all NIRCam bands from F090W to F444W, corresponding to restframe 2200Å--1.2$\mathrm{μm}$. Based on morphological analysis of the F200W images, three out of the 10 targets are resolved into pair-like systems with separations of $<0.9''$, and another three show asymmetric structures. We then construct the spectral energy distributions (SEDs) of these LAEs. All sources, including the pairs, show similar SED shapes, with a prominent flux excess in the F200W band, corresponding to extremely strong [O III]+H$β$ emission lines (${\rm EW_{rest}}=740$--$6500\,$Å). The median effective radii, stellar mass, and UV slope of our sample are 0.36$\,$kpc, $3.8\times10^7\,M_\odot$, and --2.48, respectively. The average burst age, estimated by stellar mass over star formation rate, is $<40\,$Myr. These measurements reveal an intriguing starbursting dwarf galaxy population lying off the extrapolations of the $z \sim 3$ scaling relations to the low-mass end: $\sim 0.7$ dex above the star-forming main sequence, $\sim 0.35$ dex below the mass--size relation, and bluer in the UV slope than typical high-z galaxies at similar UV luminosities. We speculate that these numbers may require a larger main sequence scatter or tail in the dwarf galaxy regime towards the starburst outliers.

astro-ph.GA↗

arXiv2022

WayFAST: Navigation with Predictive Traversability in the Field

We present a self-supervised approach for learning to predict traversable paths for wheeled mobile robots that require good traction to navigate. Our algorithm, termed WayFAST (Waypoint Free Autonomous Systems for Traversability), uses RGB and depth data, along with navigation experience, to autonomously generate traversable paths in outdoor unstructured environments. Our key inspiration is that traction can be estimated for rolling robots using kinodynamic models. Using traction estimates provided by an online receding horizon estimator, we are able to train a traversability prediction neural network in a self-supervised manner, without requiring heuristics utilized by previous methods. We demonstrate the effectiveness of WayFAST through extensive field testing in varying environments, ranging from sandy dry beaches to forest canopies and snow covered grass fields. Our results clearly demonstrate that WayFAST can learn to avoid geometric obstacles as well as untraversable terrain, such as snow, which would be difficult to avoid with sensors that provide only geometric data, such as LiDAR. Furthermore, we show that our training pipeline based on online traction estimates is more data-efficient than other heuristic-based methods.

cs.RO↗