arXiv subjects

Qi Zeng

Publications and source records attributed to Qi Zeng.

At least 19 recordsLinked to original sources

arXiv2026

The TNG50-SKIRT Atlas: Spatially resolved synthetic galaxies from the ultraviolet to the submillimetre (DR2)

We present the second data release (DR2) of the TNG50-SKIRT Atlas (TSA), a library of synthetic, spatially resolved galaxy observables. The atlas is constructed by post-processing a stellar-mass-complete ($10^{9.8}~{\text{M}}_\odot < M_\star < 10^{12}~{\text{M}}_\odot$) sample of 1154 $z=0$ galaxies from the TNG50 cosmological hydrodynamical simulation with the Monte Carlo radiative transfer code SKIRT. Compared to the first release, TSA DR2 extends the wavelength coverage from the ultraviolet to the submillimetre, including dust emission, and incorporates updated stellar population models together with an improved treatment of dust-enshrouded star-forming regions. The atlas provides spatially resolved spectral energy distributions, broadband images, and physical property maps for multiple viewing orientations, as well as a catalogue of integrated properties enabling direct comparison with unresolved observations. We validate the data products through extensive quality control, including an assessment of Monte Carlo noise, and demonstrate their internal consistency using diagnostic relations between luminosities and star formation rates. TSA DR2 provides a versatile resource for studies of dust attenuation and emission, star formation tracers, galaxy morphology, and multi-wavelength scaling relations across spatial scales. The atlas and associated data products are publicly released and are intended to support a wide range of observationally oriented studies of galaxy evolution.

astro-ph.GA↗

arXiv2026

NavThinker: Action-Conditioned World Models for Coupled Prediction and Planning in Social Navigation

Social navigation requires robots to act safely in dynamic human environments. Effective behavior demands thinking ahead: reasoning about how the scene and pedestrians evolve under different robot actions rather than reacting to current observations alone. This creates a coupled prediction-planning challenge, where robot actions and human motion mutually influence each other. To address this challenge, we propose NavThinker, a future-aware framework that couples an action-conditioned world model with on-policy reinforcement learning. The world model operates in the Depth Anything V2 patch feature space and performs autoregressive prediction of future scene geometry and human motion; multi-head decoders then produce future depth maps and human trajectories, yielding a future-aware state aligned with traversability and interaction risk. Crucially, we train the policy with DD-PPO while injecting world-model think-ahead signals via: (i) action-conditioned future features fused into the current observation embedding and (ii) social reward shaping from predicted human trajectories. Experiments on single- and multi-robot Social-HM3D show state-of-the-art navigation success, with zero-shot transfer to Social-MP3D and real-world deployment on a Unitree Go2, validating generalization and practical applicability. Webpage: https://hutslib.github.io/NavThinker.

cs.RO↗

arXiv2026

Atlas-Assisted Segment Anything Model for Fetal Brain MRI (FeTal-SAM)

This paper presents FeTal-SAM, a novel adaptation of the Segment Anything Model (SAM) tailored for fetal brain MRI segmentation. Traditional deep learning methods often require large annotated datasets for a fixed set of labels, making them inflexible when clinical or research needs change. By integrating atlas-based prompts and foundation-model principles, FeTal-SAM addresses two key limitations in fetal brain MRI segmentation: (1) the need to retrain models for varying label definitions, and (2) the lack of insight into whether segmentations are driven by genuine image contrast or by learned spatial priors. We leverage multi-atlas registration to generate spatially aligned label templates that serve as dense prompts, alongside a bounding-box prompt, for SAM's segmentation decoder. This strategy enables binary segmentation on a per-structure basis, which is subsequently fused to reconstruct the full 3D segmentation volumes. Evaluations on two datasets, the dHCP dataset and an in-house dataset demonstrate FeTal-SAM's robust performance across gestational ages. Notably, it achieves Dice scores comparable to state-of-the-art baselines which were trained for each dataset and label definition for well-contrasted structures like cortical plate and cerebellum, while maintaining the flexibility to segment any user-specified anatomy. Although slightly lower accuracy is observed for subtle, low-contrast structures (e.g., hippocampus, amygdala), our results highlight FeTal-SAM's potential to serve as a general-purpose segmentation model without exhaustive retraining. This method thus constitutes a promising step toward clinically adaptable fetal brain MRI analysis tools.

cs.CV↗

arXiv2025

Explicit Temporal-Semantic Modeling for Dense Video Captioning via Context-Aware Cross-Modal Interaction

Dense video captioning jointly localizes and captions salient events in untrimmed videos. Recent methods primarily focus on leveraging additional prior knowledge and advanced multi-task architectures to achieve competitive performance. However, these pipelines rely on implicit modeling that uses frame-level or fragmented video features, failing to capture the temporal coherence across event sequences and comprehensive semantics within visual contexts. To address this, we propose an explicit temporal-semantic modeling framework called Context-Aware Cross-Modal Interaction (CACMI), which leverages both latent temporal characteristics within videos and linguistic semantics from text corpus. Specifically, our model consists of two core components: Cross-modal Frame Aggregation aggregates relevant frames to extract temporally coherent, event-aligned textual features through cross-modal retrieval; and Context-aware Feature Enhancement utilizes query-guided attention to integrate visual dynamics with pseudo-event semantics. Extensive experiments on the ActivityNet Captions and YouCook2 datasets demonstrate that CACMI achieves the state-of-the-art performance on dense video captioning task.

cs.CV↗

arXiv2025

DMTrack: Deformable State-Space Modeling for UAV Multi-Object Tracking with Kalman Fusion and Uncertainty-Aware Association

Multi-object tracking (MOT) from unmanned aerial vehicles (UAVs) presents unique challenges due to unpredictable object motion, frequent occlusions, and limited appearance cues inherent to aerial viewpoints. These issues are further exacerbated by abrupt UAV movements, leading to unreliable trajectory estimation and identity switches. Conventional motion models, such as Kalman filters or static sequence encoders, often fall short in capturing both linear and non-linear dynamics under such conditions. To tackle these limitations, we propose DMTrack, a deformable motion tracking framework tailored for UAV-based MOT. Our DMTrack introduces three key components: DeformMamba, a deformable state-space predictor that dynamically aggregates historical motion states for adaptive trajectory modeling; MotionGate, a lightweight gating module that fuses Kalman and Mamba predictions based on motion context and uncertainty; and an uncertainty-aware association strategy that enhances identity preservation by aligning motion trends with prediction confidence. Extensive experiments on the VisDrone-MOT and UAVDT benchmarks demonstrate that our DMTrack achieves state-of-the-art performance in identity consistency and tracking accuracy, particularly under high-speed and non-linear motion. Importantly, our method operates without appearance models and maintains competitive efficiency, highlighting its practicality for robust UAV-based tracking.

eess.SY↗

arXiv2025

A novel method of half-life determination for highly charged ions based on isochronous mass spectrometry

The lifetime of the isomeric state in fully stripped 94Ru44+ ions has been measured using isochronous mass spectrometry (IMS) at the experimental Cooler Storage Ring (CSRe) of the Heavy Ion Research Facility in Lanzhou (HIRFL). Previously, the isomeric lifetime was determined by analyzing the decay time points of individual decay events. In this paper, we present a novel approach to determine the isomeric lifetime based on the survival time of ions obtained from IMS. The survival lifetimes of the ground and isomeric states of 94Ru44+ were measured to be s and s in the laboratory, respectively. Given that the ground state of 94Ru44+ has a natural lifetime of approximately 75 min, its survival lifetime in the experimental setup was predominantly determined by the beam-loss lifetime, including interactions with residual gas in the storage ring and carbon foil of the detector. In contrast, the survival lifetime of 94mRu44+ was governed by its intrinsic nuclear lifetime and additional beam-loss effects. The nuclear decay lifetime of 94mRu44+ was extracted through differential survival lifetime analysis between ground and isomeric states, under the assumption that the beam-loss lifetimes for both quantum systems are identical. Using this novel methodology, the lifetime measured in the laboratory frame was s. After relativistic time-dilation corrections, the corresponding rest-frame half-life was calculated to be s. This result demonstrates excellent consistency with previous experimental results, validating the reliability of the new method. This method is suitable for determining half-lives of highly charged ions in the range of several tens of microseconds to milliseconds using IMS.

physics.ins-det↗

arXiv2025

For-Value: Efficient Forward-Only Data Valuation for finetuning LLMs and VLMs

Data valuation is essential for enhancing the transparency and accountability of large language models (LLMs) and vision-language models (VLMs). However, existing methods typically rely on gradient computations, making them computationally prohibitive for billion-parameter models and precluding batch parallelization. In this work, we introduce For-Value, a forward-only data valuation framework that enables efficient batch-scalable value estimation while maintaining effectiveness. Leveraging the expressive power of pretrained LLMs/VLMs, we theoretically demonstrate that data valuation can be captured by the alignment between the final hidden representations and prediction errors at the last layer. In light of this insight, For-Value computes data value using a simple closed-form expression with a single forward pass, eliminating the need for costly backpropagation and enabling efficient batch calculating at scale. Extensive experiments show that For-Value matches or outperforms gradient-based baselines in detecting influential data and mislabeled data, while achieving significant efficiency improvements.

cs.CL↗

arXiv2025

Global strong solutions to the frame hydrodynamics for biaxial nematic phases

In this article, we consider the frame hydrodynamics of biaxial nematic phases, a coupled system between the evolution of the orthonormal frame and the Navier--Stokes equation, which is derived from a molecular-theory-based dynamical tensor model about two second-order tensors. In two and three dimensions, we establish global well-posedness of strong solutions to the Cauchy problem of frame hydrodynamics for small initial data. The key ingredient of the proof relies on estimates of nonlinear terms with rotational derivatives on $SO(3)$, together with the dissipative structure of the frame hydrodynamics.

math.AP↗

arXiv2025

SDSS-IV MaNGA: Physical Origins of Double-Peaked Narrow Emission-Line Spaxels in Barred Galaxies

The physical origins of double-peaked narrow emission-line spaxels (DPSs) in barred galaxies are explored through the analysis of a sample of 72 barred double-peaked emission-line galaxies (DPGs) extracted from the MaNGA dataset. In this study, we examine two potential scenarios: the gas inflow along the bar and the formation of a bar-induced gaseous nuclear ring. By applying a classical galactic dynamics model, we calculate the radii and rotational velocities of the nuclear rings for all barred DPGs, and compare them with the observed properties of their DPSs. Our analysis reveals a significant correlation between the predicted radii of the nuclear rings and the maximum centric distances of the DPSs, as well as a marginal correlation between the predicted rotational velocities of the nuclear rings and the observed maximum velocity differences of the DPSs. These findings provide strong evidence to support the hypothesis that the DPSs of a barred DPG in MaNGA primarily originate from the convolution of the PSF effect with its bar-induced fast-rotating gaseous nuclear ring.

astro-ph.GA↗

arXiv2025

Non-Parametric Attenuation Curves in Local Star-Forming Galaxies: Geometry Effect, Dust Evolution, and ISS

We introduce a non-parametric approach, the Stellar Population Synthesis with Equivalent Widths (SEW) method, to reconstruct spectrally-resolved attenuation curves for 169,568 star-forming galaxies from the Sloan Digital Sky Survey Data Release 7 (SDSS DR7). Composite attenuation curves, stacked by stellar mass and inclination, reveal systematic trends: a higher stellar mass correlates with steeper slopes (lower $R_V$), while edge-on galaxies exhibit flatter curves due to geometric saturation effects. This flattening occurs because, as optical depth increases along the line of sight, the observed light becomes increasingly dominated by emission from the outer, less obscured layers of the galaxy. Using a simplified radiative transfer treatment based on a uniform dust-star mixture, we find the inclination-dependent slope variations are consistent with geometric effects, whereas the mass-dependent slope steepening indicates evolution in intrinsic dust properties, suggesting feedback-driven grain fragmentation in massive galaxies. Additionally, intermediate-scale structures (ISSs) are tentatively identified in the attenuation curves at approximately 4870, 6370, and 7690 \r{A}. These results illustrate how the interplay among dust-star geometry, grain size evolution, and the galactic environment shapes attenuation curves.

astro-ph.GA↗

arXiv2025

Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge

Accurate fetal brain tissue segmentation and biometric analysis are essential for studying brain development in utero. The FeTA Challenge 2024 advanced automated fetal brain MRI analysis by introducing biometry prediction as a new task alongside tissue segmentation. For the first time, our diverse multi-centric test set included data from a new low-field (0.55T) MRI dataset. Evaluation metrics were also expanded to include the topology-specific Euler characteristic difference (ED). Sixteen teams submitted segmentation methods, most of which performed consistently across both high- and low-field scans. However, longitudinal trends indicate that segmentation accuracy may be reaching a plateau, with results now approaching inter-rater variability. The ED metric uncovered topological differences that were missed by conventional metrics, while the low-field dataset achieved the highest segmentation scores, highlighting the potential of affordable imaging systems when paired with high-quality reconstruction. Seven teams participated in the biometry task, but most methods failed to outperform a simple baseline that predicted measurements based solely on gestational age, underscoring the challenge of extracting reliable biometric estimates from image data alone. Domain shift analysis identified image quality as the most significant factor affecting model generalization, with super-resolution pipelines also playing a substantial role. Other factors, such as gestational age, pathology, and acquisition site, had smaller, though still measurable, effects. Overall, FeTA 2024 offers a comprehensive benchmark for multi-class segmentation and biometry estimation in fetal brain MRI, underscoring the need for data-centric approaches, improved topological evaluation, and greater dataset diversity to enable clinically robust and generalizable AI tools.

cs.CV↗

arXiv2025

SemICP: Semantic Non-Rigid Point Cloud Registration with Elastic Energy Regularization

Purpose: Accurate point cloud registration is essential in computer-aided interventions (CAI) to align multi-modal medical images for intraoperative guidance. Classical methods, such as Iterative Closest Point (ICP), remain attractive for their explainability and minimal training requirements, but typically ignore anatomical semantics and biomechanical properties during regularization. Methods: We present Semantic ICP (SemICP), a novel non-rigid point cloud registration framework that combines semantically informed point matching with deformation regularization. Semantic labels are used to improve correspondence matching by constraining correspondences to be anatomically consistent. A novel control-point deformation representation with linear-elastic energy regularization is introduced to encourage biomechanically plausible deformations. SemICP was evaluated on four datasets on US-CT, MR-CT, MR-MR and MR-US registration against established baselines. It was also tested with labels from AI-based segmentation in a fully automatic segmentation-registration pipeline. Results: Across all datasets, SemICP achieves lower Hausdorff distance, mean surface distance, and target registration error than competing methods. The fully automatic registration pipeline was shown to be effective for US-MR registration and to improve the alignment of expert-annotated structures. Conclusion: SemICP improves deformable point cloud registration accuracy and robustness by combining semantic correspondence constraints and linear energy regularization. Combined with AI-based segmentation, SemICP provides an effective pipeline for multi-modal registration in CAI.

cs.CV↗

arXiv2025

FetDTIAlign: A Deep Learning Framework for Affine and Deformable Registration of Fetal Brain dMRI

Diffusion MRI (dMRI) provides unique insights into fetal brain microstructure in utero. Longitudinal and cross-sectional fetal dMRI studies can reveal crucial neurodevelopmental changes but require precise spatial alignment across scans and subjects. This is challenging due to low data quality, rapid brain development, and limited anatomical landmarks. Existing registration methods, designed for high-quality adult data, struggle with these complexities. To address this, we introduce FetDTIAlign, a deep learning approach for fetal brain dMRI registration, enabling accurate affine and deformable alignment. FetDTIAlign features a dual-encoder architecture and iterative feature-based inference, reducing the impact of noise and low resolution. It optimizes network configurations and domain-specific features at each registration stage, enhancing both robustness and accuracy. We validated FetDTIAlign on data from 23 to 36 weeks gestation, covering 60 white matter tracts. It consistently outperformed two classical optimization-based methods and a deep learning pipeline, achieving superior anatomical correspondence. Further validation on external data from the Developing Human Connectome Project confirmed its generalizability across acquisition protocols. Our results demonstrate the feasibility of deep learning for fetal brain dMRI registration, providing a more accurate and reliable alternative to classical techniques. By enabling precise cross-subject and tract-specific analyses, FetDTIAlign supports new discoveries in early brain development.

eess.IV↗

arXiv2024

Spatio-spectral light modulator of XUV high harmonics

High-order harmonic generation (HHG), characterized by its highly nonlinear nature, often exhibits a complex spatio-temporal profile that poses challenges for practical applications. In this study, we unveil a method for manipulating the spatio-spectral distribution of HHG by guiding the recollision electron trajectory in the spatio-temporal domain using a control field. The resulting far-field high harmonic (HH) radiation inherits the intricate spatio-temporal characteristics of the control field, showcasing diverse features including spatial tilting, spectral shifting, and emission angle deflection. Using the relative delay between the control field and the driving pulse as the primary control parameter, we achieve precise tailoring of the high harmonics in the spatio-spectral domain. This controllability in HH benefits ultrafast metrology and imaging applications in the extreme ultraviolet (XUV) regime.

physics.optics↗

arXiv2024

Double-peaked Narrow Emission-Line Galaxies in SDSS-IV MaNGA

Narrow emission lines in a galaxy's spectrum that show double peaks indicate the presence of distinct gas components with different velocities, and its physical origin remains uncertain. This study uses galaxies from the final MaNGA data release to detect double-peaked narrow emission-line spaxels (DPSs) by examining the double Gaussian profiles of the H$ \alpha $-[N \uppercase\expandafter{\romannumeral2}] doublets across all MaNGA spaxels. A total of 5,420 DPSs associated with 304 double-peaked narrow emission-line galaxies (DPGs) are identified, each DPG containing a minimum of 5 DPSs and being free from overlap with other galaxies. We find that DPSs can be categorized into three groups according to their central distance $r/R_e$ and the velocity difference $\Delta v$ between their two components: the inner low-$\Delta v$, inner high-$\Delta v$ and outer DPSs. By incorporating the physical characteristics of the DPGs into their DPSs, we demonstrate for the first time the existence of statistical correlations between barred DPGs and inner low-$\Delta v$ DPSs, AGN-hosting DPGs and inner high-$\Delta v$ DPSs, as well as tidal DPGs and outer DPSs.

astro-ph.GA↗

arXiv2024

GAD-Generative Learning for HD Map-Free Autonomous Driving

Deep-learning-based techniques have been widely adopted for autonomous driving software stacks for mass production in recent years, focusing primarily on perception modules, with some work extending this method to prediction modules. However, the downstream planning and control modules are still designed with hefty handcrafted rules, dominated by optimization-based methods such as quadratic programming or model predictive control. This results in a performance bottleneck for autonomous driving systems in that corner cases simply cannot be solved by enumerating hand-crafted rules. We present a deep-learning-based approach that brings prediction, decision, and planning modules together with the attempt to overcome the rule-based methods' deficiency in real-world applications of autonomous driving, especially for urban scenes. The DNN model we proposed is solely trained with 10 hours of human driver data, and it supports all mass-production ADAS features available on the market to date. This method is deployed onto a Jiyue test car with no modification to its factory-ready sensor set and compute platform. the feasibility, usability, and commercial potential are demonstrated in this article.

cs.RO↗

arXiv2024

Rigorous uniaxial limit of the Qian--Sheng inertial Q-tensor hydrodynamics for liquid crystals

This article is concerned with the rigorous connections between the inertial Qian--Sheng model and the Ericksen--Leslie model for the liquid crystal flow, under a more general condition of coefficients. More specifically, in the framework of Hilbert expansions, we show that: (i) when the elastic coefficients tend to zero (also called the uniaxial limit), the smooth solution to the inertial Qian--Sheng model converges to that to the full inertial Ericksen--Leslie model; (ii) when the elastic coefficients and the inertial coefficient tend to zero simultaneously, the smooth solution to the inertial Qian--Sheng model converges to that to the noninertial Ericksen--Leslie model.

math.AP↗

arXiv2023

Two-Step Offline Preference-Based Reinforcement Learning with Constrained Actions

Preference-based reinforcement learning (PBRL) in the offline setting has succeeded greatly in industrial applications such as chatbots. A two-step learning framework where one applies a reinforcement learning step after a reward modeling step has been widely adopted for the problem. However, such a method faces challenges from the risk of reward hacking and the complexity of reinforcement learning. To overcome the challenge, our insight is that both challenges come from the state-actions not supported in the dataset. Such state-actions are unreliable and increase the complexity of the reinforcement learning problem at the second step. Based on the insight, we develop a novel two-step learning method called PRC: preference-based reinforcement learning with constrained actions. The high-level idea is to limit the reinforcement learning agent to optimize over a constrained action space that excludes the out-of-distribution state-actions. We empirically verify that our method has high learning efficiency on various datasets in robotic control environments.

cs.LG↗