arXiv subjects

Jiaqi Lin

Publications and source records attributed to Jiaqi Lin.

At least 19 recordsLinked to original sources

arXiv2026

Empirical Near-Infrared Spectral Templates from SPHEREx: Disentangling AGN and Host-Galaxy Emission

SPHEREx conducts the first all-sky near-infrared spectral survey over 0.75-5.0 $\mu$m, spanning the transition from host-galaxy starlight to AGN-heated torus emission and enabling spectroscopic identification of active galactic nuclei (AGNs). We develop a data-driven framework combining weighted non-negative matrix factorization with forward modeling to learn empirical rest-frame templates from 2,777 normal galaxies and 1,064 AGNs in the North Ecliptic Pole (NEP) field. The resulting 14-component dictionary contains seven galaxy templates spanning stellar-population continua and nebular emission and seven AGN templates capturing emission lines and warm and hot torus continua, providing an additive, operational decomposition. Inferred near-infrared host fractions correlate with independent SDSS spectral and HSC imaging decompositions (at Pearson $r=0.68$ and $r=0.59$), comparable to the SDSS-HSC correlation ($r=0.66$) and supporting the reliability of the AGN-host separation. In the NEP deep field, the same templates recover redshifts with $|z_{\rm fit}-z_{\rm spec}| < 0.01$ for 93.0% of galaxies and 93.5% of AGNs and distinguish galaxies, narrow-line AGNs, and broad-line AGNs with 81.3% accuracy. At all-sky depth, classification retains 72.9% accuracy. The near-infrared coefficients also flag questionable DESI spectral classifications and redshifts, showing how SPHEREx complements optical spectroscopy. Learned directly from survey spectrophotometry and extending beyond the $K$ band, this empirical dictionary fills a long-standing gap in near-to-mid-infrared templates and provides a data-driven foundation for population-scale studies of AGN activity and its connection to host galaxies across the SPHEREx all-sky survey.

astro-ph.GA↗

arXiv2026

Comparative analysis of missing data imputation methods for CSST survey: Impact on photometric redshift estimation performance

Improving the accuracy of photometric redshifts (photo-$z$) is essential for reliable statistical studies of cosmology and galaxy evolution. However, missing photometric bands are a common observational challenge that can significantly degrade photo-$z$ estimation accuracy. In this work, we present a systematic evaluation of data imputation methods aimed at improving photo-$z$ performance. We benchmark a range of representative machine learning (ML) and deep learning (DL) architectures, identifying k-nearest neighbors (KNN) and the attention-based SAITS model as the leading performers. These models are then applied to China Space Station Survey Telescope (CSST) mock data to assess their performance under realistic observational conditions. Our results show that KNN yields the highest accuracy under idealized missing completely at random (MCAR) conditions with complete training sets, whereas robustness tests reveal that SAITS significantly outperforms KNN when training data is incomplete or when applied to realistic mixed-mechanism scenarios. We find that domain consistency between training and testing missingness patterns is a prerequisite for optimal performance, highlighting the risks of domain shift in supervised regression tasks. Furthermore, our analysis demonstrates that while general imputation models are highly effective for MCAR and missing at random (MAR) data, they are detrimental when applied to missing not at random (MNAR) data arising from flux limits, as statistical models fail to capture the physical information inherent in these non-detections. Consequently, we advocate for more sophisticated architectures capable of disentangling stochastic missingness from physical non-detections to address these distinct mechanisms individually.

astro-ph.GA↗

arXiv2026

Zoom In, Reason Out: Efficient Far-field Anomaly Detection in Expressway Surveillance Videos via Focused VLM Reasoning Guided by Bayesian Inference

Expressway video anomaly detection is important for traffic safety, but remains challenging across diverse scenes, particularly for far-field vehicles with subtle abnormal motion. Vision-Language Models (VLMs) provide strong semantic reasoning capabilities, yet processing full frames can dilute evidence from distant targets and introduce substantial computational overhead. To address these challenges, we propose VIBES, an asynchronous framework that uses Bayesian inference to guide focused VLM reasoning. Specifically, an online kinematics-guided Bayesian inference module continuously estimates a context-dependent normal-motion distribution from vehicle trajectories and updates its probabilistic boundaries. Deviations from these boundaries produce asynchronous triggers that localize candidate anomalies in time and space. Instead of processing continuous full-frame video, the VLM reasons only over selected frames and localized visual regions associated with the triggers, reducing irrelevant visual content and unnecessary inference. Extensive experiments show that VIBES improves far-field anomaly detection and semantic interpretation while achieving real-time processing efficiency across diverse expressway conditions.

cs.CV↗

arXiv2026

Suppression of the tendency toward antiferromagnetic order in the Dirac semimetal SrIrO$_3$

The entangled charge and spin dynamics in strongly electron correlated system has been a fruitful playground for exploring new physical phenomena. Here with resonant inelastic X-ray scattering we studied the spin dynamics of SrIrO$_3$, a half-filled paramagnetic semimetal hosting highly itinerant Dirac Fermions due to its topological band structure. Our results show that its magnetic excitations share much similarity to the ordered compounds upon Sn substitution in exchange strength and AFM instability, while the system maintains spin non-ordered. Further, the non-ordered pristine SrIrO$_3$ hosts even longer lifetime magnetic excitations near the AFM zone center comparing to the Sn substituted ordered compounds, contrary to general expectation. These observations indicate an interesting connection between band topology and electron correlation in SrIrO$_3$.

cond-mat.str-el↗

arXiv2026

A Robust Geometric Distortion Solution for Main Survey Camera of CSST

The advancement in sensitivity and field of view of next-generation wide-field survey telescopes requires astrometric measurements with high precision, even in the presence of significant geometric distortions. To address this challenge, we develop a Weighted Polynomial Distortion Correction in 2-Phase (WPDC-2P) method. This approach enhances stellar cross-matching, incorporates distance-based weighting into the traditional polynomial fitting, and employs a look-up table to absorb the remaining distortion residuals. Validated on simulated data from the Main Survey Camera of the \emph{Chinese Space Station Survey Telescope} (CSST), incorporating geometric distortions up to approximately $200$ pixels, the method achieves astrometric standard deviation ranging from 0.013 to 0.107 pixels (0.03 pixels for the $g$-1 detector) across all 18 detectors. Under extreme crowding conditions (e.g., globular cluster NGC 2298), the astrometric precision for the $g$-1 detector reaches 0.05-pixel level within the central region ($r_d < 4000$), despite a centroiding precision of $\sim$0.04 pixels. When applied to the Beijing-Arizona Sky Survey data, for which the standard pipeline delivers an astrometric uncertainty of $\sim$20 mas, our method reduces the positional scatter to $ \sigma_{\Delta\alpha}=5.494$ mas (0.01 pixels) and $ \sigma_{\Delta\delta}=9.981$ mas (0.02 pixels) using only a weighted 3rd-order polynomial correction. The method has been integrated into the CSST data processing pipeline and is prepared for further refinement using on-orbit calibration data.

astro-ph.IM↗

arXiv2026

Bridging Object Detection and Segmentation with Polygon Detection Transformers

Box detection and mask segmentation are two dominant paradigms for foreground representation: boxes are efficient but too coarse for object shapes, while masks are accurate but over-modeled for compact geometry. To bridge this gap, we present a Polygon Detection Transformer (Poly-DETR) built upon Polar Representation, where object queries regress a starting point and its fixed number of radial distances to directly construct the contour-approximating polygon. This formulation can be integrated into most DETR-like detectors by linear extension, since box is a degenerate case of Polar Representation with four rays. Furthermore, we propose two simple but necessary designs, Polar Deformable Attention and Position-Aware Training Scheme, to align feature sampling and polygon supervision. As a DETR-oriented advancement of Polar Representation, Poly-DETR outperforms existing polar-based methods by 4.7 mAP on MS COCO. Moreover, we explore the application regimes of polygon detection in geometry-driven domains, including remote sensing, medical imaging, and autonomous driving. In particular, Poly-DETR shows stronger scalability than its mask-based counterpart in high-resolution scenarios. Additional experiments show that, owing to its Transformer structure, Poly-DETR can be naturally extended to recent DETR variants equipped with foundation-model priors.

cs.CV↗

arXiv2026

Local Analogs of Little Red Dots: Optical Variability and Evidence for an Active Galatic Nucleus Origin

Little red dots (LRDs) draw extensive attention because of their unique observational characteristics and apparent overabundance in the early Universe, raising new insights into early black hole formation and growth. Early studies show that LRDs exhibit weak variability in broad-band photometry and emission-line fluxes, suggesting a preference for super-Eddington accretion or disfavouring an AGN origin. However, the cadence of the current data, and therefore, the resulting light curves for LRDs, is limited, preventing us from placing strong constraints on their variability. Based on Zwicky Transient Facility (ZTF) light curves with a baseline of $\sim6$ years, we here study the optical variability of seven previously reported local analogs of LRDs at $z \sim 0.3$, offering an insight into LRDs from a low-redshift sample. Three out of seven local analogs show excess variances on all three bands of their light curves, and two of them can be fitted with the damping random walk model, supporting their AGN origins for the variability. The remaining sources show weak variance in at least one band, but no detectable variability at the current sensitivity level, exhibiting $\rm SF_\infty$ upper limits consistent with estimates from high-redshift (high-$z$) LRDs. Their non-detection of variability is likely due to the large photometric uncertainty. As an implication, by simulating long baseline light curves with the variability amplitude of local analogs and adopting JWST observation cadence, we investigate the limitation of the variability amplitude estimate for LRDs. Our mock observations imply that the current constraints on LRDs' variability are probably underestimated. This underestimation might be induced by the short temporal baseline of observations, as well as the intrinsic scatter of the empirical $M_{\rm BH}-\tau$ relation.

astro-ph.GA↗

arXiv2025

Discovery of a Luminosity-dependent Continuum Lag in NGC 4151 from Photometric and Spectroscopic Continuum Reverberation Mapping

Accretion onto supermassive black holes (SMBHs) powers active galactic nuclei (AGNs) and drives feedback that shapes galaxy evolution. Constraining AGN accretion disk structure is therefore essential for understanding black hole growth and feedback processes. However, direct constraints on disk size remain rare -- particularly from long-term, multi-season spectroscopic reverberation mapping (RM), which is critical for isolating the intrinsic disk response from the broad-line region (BLR). We present results from an intensive multi-wavelength RM campaign of NGC 4151 during its brightest state in nearly two decades. This represents the third high-cadence monitoring over the past decade, capturing accretion states spanning the transitional regime between thin and thick disks, making NGC 4151 the only AGN with continuum RM observations across such a wide range in accretion states. Combining spectroscopy from the Lijiang 2.4 m telescope with coordinated Swift UV/X-ray monitoring, we measure inter-band continuum lags from UV to optical. The wavelength-dependent lags follow a tight $\tau \propto \lambda^{4/3}$ relation, consistent with reprocessing in a thin disk, but exceed theoretical predictions by a factor of 6.6. Our lag spectrum reveals clear excesses near the Balmer and possibly Paschen jumps, confirming diffuse continuum (DC) contamination from the BLR. By comparing the three campaigns, we discover a non-monotonic lag-luminosity trend ($>3\sigma$), which cannot be explained by DC emission alone. We propose the lags reflect combined disk and BLR contributions, and present the first evidence that the DC component follows an intrinsic Baldwin effect. These results offer new insights into SMBH mass measurements and theoretical models of AGN inner structure.

astro-ph.GA↗

arXiv2025

StochEP: Stochastic Equilibrium Propagation for Spiking Convergent Recurrent Neural Networks

Spiking Neural Networks (SNNs) promise energy-efficient, sparse, biologically inspired computation. Training them with Backpropagation Through Time (BPTT) and surrogate gradients achieves strong performance but remains biologically implausible. Equilibrium Propagation (EP) provides a more local and biologically grounded alternative. However, existing EP frameworks, primarily based on deterministic neurons, either require complex mechanisms to handle discontinuities in spiking dynamics or fail to scale beyond simple visual tasks. Inspired by the stochastic nature of biological spiking mechanism and recent hardware trends, we propose a stochastic EP framework that integrates probabilistic spiking neurons into the EP paradigm. This formulation smoothens the optimization landscape, stabilizes training, and enables scalable learning in deep convolutional spiking convergent recurrent neural networks (CRNNs). We provide theoretical guarantees showing that the proposed stochastic EP dynamics approximate deterministic EP under mean-field theory, thereby inheriting its underlying theoretical guarantees. The proposed framework narrows the gap to both BPTT-trained SNNs and EP-trained non-spiking CRNNs in vision benchmarks while preserving locality, highlighting stochastic EP as a promising direction for neuromorphic and on-chip learning.

cs.ET↗

arXiv2025

Joint Analysis of Optical, Near-Infrared And Mid-Infrared Variability of 4 Quasars at Redshift < 1

Amid rapid advances in time-domain astronomy, multi-wavelength (e.g., optical and infrared) time-domain studies of quasars remain scarce. Here we present a systematic analysis of four quasars initially selected by their Ks-band variability amplitudes in the VISTA Variables in the V\'{\i}a L\'actea Survey (VVV/VVVX). For these objects, we obtain complementary optical light curves from Pan-STARRS1 (PS1) and the Zwicky Transient Facility (ZTF), and W1-band light curves from the Wide-field Infrared Survey Explorer (WISE). We perform correlation analysis to study the time lags between different bands, which may be directly related to the size of the dust torus. After correcting for infrared flux contamination from the accretion disk and accounting for the redshift effect, we measure the Ks-optical and W1-optical lags for the targets VVV J1834-2925 and VVV J1845-2426. Using typical sublimation temperatures and reverberation time lags, we obtain a graphite-to-silicate grain size ratio of $\frac{a_C}{a_S}\sim$ 0.4. Through SED fitting, we determine the luminosities of these quasars and find that their dust torus sizes follow the established $R_{dust}-L_{AGN}$ relation reported in previous studies.

astro-ph.GA↗

arXiv2025

Optimizing Long-term Variability of AGN Light Curves. I. A Case Study with ZTF Observations in the EGS Field

Optical variability is a key observational probe for studying the accretion dynamics and central engine physics of Active Galactic Nuclei (AGNs). The quality and completeness of light curves have a direct impact on variability studies, particularly for faint AGNs and high-redshift AGNs. To improve the quality of long-term light curves for AGNs, we bin and stack multi-epoch images balancing the image depths and temporal resolution. As a case study, we apply this method to Zwicky Transient Facility (ZTF) observations in the Extended Groth Strip (EGS) field, where the overlapping region covers an area of about 370 arcmin$^2$ and includes $g$-band and $r$-band data taken from March 2018 to December 2024. The co-added images are approximately 2.0 to 2.5 magnitudes deeper than the ZTF single-epoch images. With co-added images, we construct light curves for 73 AGNs in the EGS field. Compared to the traditional ZTF light curves, our light curves maintain consistent long-term variability trends but with higher photometric precision. Furthermore, this method can help detect AGNs with weak variability which are missed from the traditional ZTF data due to the noisy light curves or below the detection limit in ZTF's single-epoch exposure. Among the 73 AGNs, the majority exhibit a bluer-when-brighter (BWB) trend on long-term timescales, which is consistent with previous studies. This work offers insights for optimizing AGN light curves in both current and upcoming all-sky time-domain surveys.

astro-ph.GA↗

arXiv2025

Scalable Equilibrium Propagation via Intermediate Error Signals for Deep Convolutional CRNNs

Equilibrium Propagation (EP) is a biologically inspired local learning rule first proposed for convergent recurrent neural networks (CRNNs), in which synaptic updates depend only on neuron states from two distinct phases. EP estimates gradients that closely align with those computed by Backpropagation Through Time (BPTT) while significantly reducing computational demands, positioning it as a potential candidate for on-chip training in neuromorphic architectures. However, prior studies on EP have been constrained to shallow architectures, as deeper networks suffer from the vanishing gradient problem, leading to convergence difficulties in both energy minimization and gradient computation. To alleviate the vanishing gradient problem in deep EP networks, we propose a novel EP framework that incorporates layer-wise learning signals to provide auxiliary supervision, which enhances the convergence of neuron dynamics. This is the first work to integrate knowledge distillation and local error signals into EP, enabling the training of significantly deeper architectures. Our proposed approach achieves state-of-the-art performance on the CIFAR-10 and CIFAR-100 datasets, showcasing its scalability on deep VGG architectures. These results represent a significant advancement in the scalability of EP, suggesting that intermediate learning signals can extend the practical applicability of EP to deeper architectures.

cs.LG↗

arXiv2025

Quantifying and Alleviating Co-Adaptation in Sparse-View 3D Gaussian Splatting

3D Gaussian Splatting (3DGS) has demonstrated impressive performance in novel view synthesis under dense-view settings. However, in sparse-view scenarios, despite the realistic renderings in training views, 3DGS occasionally manifests appearance artifacts in novel views. This paper investigates the appearance artifacts in sparse-view 3DGS and uncovers a core limitation of current approaches: the optimized Gaussians are overly-entangled with one another to aggressively fit the training views, which leads to a neglect of the real appearance distribution of the underlying scene and results in appearance artifacts in novel views. The analysis is based on a proposed metric, termed Co-Adaptation Score (CA), which quantifies the entanglement among Gaussians, i.e., co-adaptation, by computing the pixel-wise variance across multiple renderings of the same viewpoint, with different random subsets of Gaussians. The analysis reveals that the degree of co-adaptation is naturally alleviated as the number of training views increases. Based on the analysis, we propose two lightweight strategies to explicitly mitigate the co-adaptation in sparse-view 3DGS: (1) random gaussian dropout; (2) multiplicative noise injection to the opacity. Both strategies are designed to be plug-and-play, and their effectiveness is validated across various methods and benchmarks. We hope that our insights into the co-adaptation effect will inspire the community to achieve a more comprehensive understanding of sparse-view 3DGS.

cs.CV↗

arXiv2025

Hybrid Mesh-Gaussian Representation for Efficient Indoor Scene Reconstruction

3D Gaussian splatting (3DGS) has demonstrated exceptional performance in image-based 3D reconstruction and real-time rendering. However, regions with complex textures require numerous Gaussians to capture significant color variations accurately, leading to inefficiencies in rendering speed. To address this challenge, we introduce a hybrid representation for indoor scenes that combines 3DGS with textured meshes. Our approach uses textured meshes to handle texture-rich flat areas, while retaining Gaussians to model intricate geometries. The proposed method begins by pruning and refining the extracted mesh to eliminate geometrically complex regions. We then employ a joint optimization for 3DGS and mesh, incorporating a warm-up strategy and transmittance-aware supervision to balance their contributions seamlessly.Extensive experiments demonstrate that the hybrid representation maintains comparable rendering quality and achieves superior frames per second FPS with fewer Gaussian primitives.

cs.CV↗

arXiv2025

On the Adversarial Robustness of Spiking Neural Networks Trained by Local Learning

Recent research has shown the vulnerability of Spiking Neural Networks (SNNs) under adversarial examples that are nearly indistinguishable from clean data in the context of frame-based and event-based information. The majority of these studies are constrained in generating adversarial examples using Backpropagation Through Time (BPTT), a gradient-based method which lacks biological plausibility. In contrast, local learning methods, which relax many of BPTT's constraints, remain under-explored in the context of adversarial attacks. To address this problem, we examine adversarial robustness in SNNs through the framework of four types of training algorithms. We provide an in-depth analysis of the ineffectiveness of gradient-based adversarial attacks to generate adversarial instances in this scenario. To overcome these limitations, we introduce a hybrid adversarial attack paradigm that leverages the transferability of adversarial instances. The proposed hybrid approach demonstrates superior performance, outperforming existing adversarial attack methods. Furthermore, the generalizability of the method is assessed under multi-step adversarial attacks, adversarial attacks in black-box FGSM scenarios, and within the non-spiking domain.

cs.LG↗

arXiv2025

Decoupling Appearance Variations with 3D Consistent Features in Gaussian Splatting

Gaussian Splatting has emerged as a prominent 3D representation in novel view synthesis, but it still suffers from appearance variations, which are caused by various factors, such as modern camera ISPs, different time of day, weather conditions, and local light changes. These variations can lead to floaters and color distortions in the rendered images/videos. Recent appearance modeling approaches in Gaussian Splatting are either tightly coupled with the rendering process, hindering real-time rendering, or they only account for mild global variations, performing poorly in scenes with local light changes. In this paper, we propose DAVIGS, a method that decouples appearance variations in a plug-and-play and efficient manner. By transforming the rendering results at the image level instead of the Gaussian level, our approach can model appearance variations with minimal optimization time and memory overhead. Furthermore, our method gathers appearance-related information in 3D space to transform the rendered images, thus building 3D consistency across views implicitly. We validate our method on several appearance-variant scenes, and demonstrate that it achieves state-of-the-art rendering quality with minimal training time and memory usage, without compromising rendering speeds. Additionally, it provides performance improvements for different Gaussian Splatting baselines in a plug-and-play manner.

cs.CV↗

arXiv2024

Rethinking Spatio-Temporal Transformer for Traffic Prediction:Multi-level Multi-view Augmented Learning Framework

Traffic prediction is a challenging spatio-temporal forecasting problem that involves highly complex spatio-temporal correlations. This paper proposes a Multi-level Multi-view Augmented Spatio-temporal Transformer (LVSTformer) for traffic prediction. The model aims to capture spatial dependencies from three different levels: local geographic, global semantic, and pivotal nodes, along with long- and short-term temporal dependencies. Specifically, we design three spatial augmented views to delve into the spatial information from the perspectives of local, global, and pivotal nodes. By combining three spatial augmented views with three parallel spatial self-attention mechanisms, the model can comprehensively captures spatial dependencies at different levels. We design a gated temporal self-attention mechanism to effectively capture long- and short-term temporal dependencies. Furthermore, a spatio-temporal context broadcasting module is introduced between two spatio-temporal layers to ensure a well-distributed allocation of attention scores, alleviating overfitting and information loss, and enhancing the generalization ability and robustness of the model. A comprehensive set of experiments is conducted on six well-known traffic benchmarks, the experimental results demonstrate that LVSTformer achieves state-of-the-art performance compared to competing baselines, with the maximum improvement reaching up to 4.32%.

cs.LG↗

arXiv2024

MirrorGaussian: Reflecting 3D Gaussians for Reconstructing Mirror Reflections

3D Gaussian Splatting showcases notable advancements in photo-realistic and real-time novel view synthesis. However, it faces challenges in modeling mirror reflections, which exhibit substantial appearance variations from different viewpoints. To tackle this problem, we present MirrorGaussian, the first method for mirror scene reconstruction with real-time rendering based on 3D Gaussian Splatting. The key insight is grounded on the mirror symmetry between the real-world space and the virtual mirror space. We introduce an intuitive dual-rendering strategy that enables differentiable rasterization of both the real-world 3D Gaussians and the mirrored counterpart obtained by reflecting the former about the mirror plane. All 3D Gaussians are jointly optimized with the mirror plane in an end-to-end framework. MirrorGaussian achieves high-quality and real-time rendering in scenes with mirrors, empowering scene editing like adding new mirrors and objects. Comprehensive experiments on multiple datasets demonstrate that our approach significantly outperforms existing methods, achieving state-of-the-art results. Project page: https://mirror-gaussian.github.io/.

cs.CV↗