arXiv subjects

Guodong Li

Publications and source records attributed to Guodong Li.

At least 37 records · Page 2Linked to original sources

arXiv2025

Investigating the Impacts of AGN Activities on Dwarf Galaxies with FAST HI Observations

We present the results of Hi line observations towards 26 Active Galactic Nuclei (AGN)-hosting and one star-forming dwarf galaxies (Mstar < 10^9.5 Msun) with the 19-beam spectral line receiver of FAST at 1.4 GHz. Our FAST observed targets are combined with other AGN-hosting dwarf galaxies covered in the ALFALFA footprint to form a more comprehensive sample. Utilizing the information from optical surveys, we further divide them into isolated and accompanied subsamples by their vicinity of nearby massive galaxies. We compare the Hi gas abundance and star-forming rate (SFR) between the subsamples to assess the role of internal and external processes that may regulate the gas content in dwarf galaxies. As a result, we find that AGN are more commonly identified in accompanied dwarf galaxies than in their isolated counterparts. Meanwhile, AGN-hosting dwarf galaxies have slightly but significant lower Hi mass fraction relatively to the non-AGN control sample in accompanied dwarf galaxies. On the other hand, we find a decreasing SFR in AGN-hosting dwarf galaxies towards denser environments, as well as an extremely low incidence of quenched isolated dwarfs within both AGN and non-AGN subsamples. These results indicate that although these AGN could potentially regulate the gas reservoir of dwarf galaxies, environmental effects are likely the dominant quenching mechanism in the low-mass universe.

astro-ph.GA↗

arXiv2025

Improving time series estimation and prediction via transfer learning

There are many time series in the literature with high dimension yet limited sample sizes, such as macroeconomic variables, and it is almost impossible to obtain efficient estimation and accurate prediction by using the corresponding datasets themselves. This paper fills the gap by introducing a novel representation-based transfer learning framework for vector autoregressive models, and information from related source datasets with rich observations can be leveraged to enhance estimation efficiency through representation learning. A two-stage regularized estimation procedure is proposed with well established non-asymptotic properties, and algorithms with alternating updates are suggested to search for the estimates. Our transfer learning framework can handle time series with varying sample sizes and asynchronous starting and/or ending time points, thereby offering remarkable flexibility in integrating information from diverse datasets. Simulation experiments are conducted to evaluate the finite-sample performance of the proposed methodology, and its usefulness is demonstrated by an empirical analysis on 20 macroeconomic variables from Japan and another nine countries.

stat.ME↗

arXiv2025

High-dimensional low-rank matrix regression with unknown latent structures

We study low-rank matrix regression in settings where matrix-valued predictors and scalar responses are observed across multiple individuals. Rather than assuming a fully homogeneous coefficient matrices across individuals, we accommodate shared low-dimensional structure alongside individual-specific deviations. To this end, we introduce a tensor-structured homogeneity pursuit framework, wherein each coefficient matrix is represented as a product of shared low-rank subspaces and individualized low-rank loadings. We propose a scalable estimation procedure based on scaled gradient descent, and establish non-asymptotic bounds demonstrating that the proposed estimator attains improved convergence rates by leveraging shared information while preserving individual-specific signals. The framework is further extended to incorporate scaled hard thresholding for recovering sparse latent structures, with theoretical guarantees in both linear and generalized linear model settings. Our approach provides a principled middle ground between fully pooled and fully separate analyses, achieving strong theoretical performance, computational tractability, and interpretability in high-dimensional multi-individual matrix regression problems.

stat.ME↗

arXiv2025

Probing the Physics of Dusty Outflows through Complex Organic Molecules in the Early Universe

Galaxy-scale outflows are of critical importance for galaxy formation and evolution. Dust grains are the main sites for the formation of molecules needed for star formation but are also important for the acceleration of outflows that can remove the gas reservoir critical for stellar mass growth. Using the MIRI medium-resolution integral field spectrograph aboard the James Webb Space Telescope (JWST), we detect the 3.28 $μ$m aromatic and the 3.4 $μ$m aliphatic hydrocarbon dust features in absorption in a redshift 4.601 hot dust-obscured galaxy, blue-shifted by $Δ$V=$-5250^{+276}_{-339}$ kms$^{-1}$ from the systemic redshift of the galaxy. The extremely high velocity of the dust indicates that the wind was accelerated by radiation pressure from the central quasar. These results pave a novel way for probing the physics of dusty outflows in active galaxies at early cosmic time.

astro-ph.GA↗

arXiv2025

Robust Gradient Descent Estimation for Tensor Models under Heavy-Tailed Distributions

Low-rank tensor models are widely used in statistics. However, most existing methods rely heavily on the assumption that data follows a sub-Gaussian distribution. To address the challenges associated with heavy-tailed distributions encountered in real-world applications, we propose a novel robust estimation procedure based on truncated gradient descent for general low-rank tensor models. We establish the computational convergence of the proposed method and derive optimal statistical rates under heavy-tailed distributional settings of both covariates and noise for various low-rank models. Notably, the statistical error rates are governed by a local moment condition, which captures the distributional properties of tensor variables projected onto certain low-dimensional local regions. Furthermore, we present numerical results to demonstrate the effectiveness of our method.

stat.ME↗

arXiv2025

SocialTrack: Multi-Object Tracking in Complex Urban Traffic Scenes Inspired by Social Behavior

As a key research direction in the field of multi-object tracking (MOT), UAV-based multi-object tracking has significant application value in the analysis and understanding of urban intelligent transportation systems. However, in complex UAV perspectives, challenges such as small target scale variations, occlusions, nonlinear crossing motions, and motion blur severely hinder the stability of multi-object tracking. To address these challenges, this paper proposes a novel multi-object tracking framework, SocialTrack, aimed at enhancing the tracking accuracy and robustness of small targets in complex urban traffic environments. The specialized small-target detector enhances the detection performance by employing a multi-scale feature enhancement mechanism. The Velocity Adaptive Cubature Kalman Filter (VACKF) improves the accuracy of trajectory prediction by incorporating a velocity dynamic modeling mechanism. The Group Motion Compensation Strategy (GMCS) models social group motion priors to provide stable state update references for low-quality tracks, significantly improving the target association accuracy in complex dynamic environments. Furthermore, the Spatio-Temporal Memory Prediction (STMP) leverages historical trajectory information to predict the future state of low-quality tracks, effectively mitigating identity switching issues. Extensive experiments on the UAVDT and MOT17 datasets demonstrate that SocialTrack outperforms existing state-of-the-art (SOTA) methods across several key metrics. Significant improvements in MOTA and IDF1, among other core performance indicators, highlight its superior robustness and adaptability. Additionally, SocialTrack is highly modular and compatible, allowing for seamless integration with existing trackers to further enhance performance.

cs.CV↗

arXiv2025

Investigating Little Red Dots with UV Excess: Are They the High-Redshift Siblings of Blue Hot DOGs?

Little Red Dots (LRDs), newly identified compact and dusty galaxies with an unexpectedly high number density observed by JWST, have an unusual "V-shaped" rest-frame UV to near-infrared spectral energy distribution (SED). A group of hyper-luminous, obscured quasars with excess blue emission, called Blue-excess Hot Dust-Obscured Galaxies (BHDs), also exhibit qualitatively similar SEDs to those of LRDs. They represent a rare population of galaxies hosting supermassive black holes (SMBHs) accreting near the Eddington limit at redshifts z \sim 1--4. In this study, we compare their multi-wavelength SEDs to investigate whether LRDs, or a subset of them, could be high-redshift analogs of BHDs. Our analysis reveals that despite their similar "V-shape" SEDs, LRDs appear to be a different population than BHDs. The "V-shape" of BHDs appear at longer wavelengths compared to LRDs due to different selection strategies, suggesting LRDs have much less dust attenuation than typical BHDs. The bluer colors in the rest-frame infrared (continuum) emission of LRDs suggest the absence of hot dust heated by AGN accretion activities. We also argue that the blue excess in LRDs is unlikely from AGN scattered light. The compact morphologies and lower X-ray detection frequencies of LRDs suggest a distinct formation pathway from BHDs -- which are thought to be powered by super-Eddington accretion onto central SMBHs following major galaxy mergers.

astro-ph.GA↗

arXiv2025

Three-Dimensional Continuous Multi-Walled Carbon Nanotubes Network-Toughened Diamond Composite

Enhancing the fracture toughness of diamond while preserving its hardness is a significant challenge. Traditional toughening strategies have primarily focused on modulating the internal microstructural units of diamonds, including adjustments to stacking sequences, faults, nanotwinning, and the incorporation of amorphous phases, collectively referred to as intrinsic toughening. Here, we introduce an extrinsic toughening strategy to develop an unparalleled tough diamond composite with complex and abundant sp2-sp3 bonding interfaces, by incorporating highly dispersed multi-walled carbon nanotubes (MWCNTs) into the gaps of diamond grains to create a three-dimensional (3D) continuous MWCTNs network-toughen heterogeneous structure. The resultant composite exhibits a hardness of approximately 91.6 GPa and a fracture toughness of roughly 36.4 MPa.m1/2, which is six times higher than that of synthetic diamond and even surpasses that of tungsten alloys, surpassing the benefits achievable through intrinsic toughening alone. The remarkable toughening behavior can be attributed to the formation of numerous mixed sp2-sp3 bonding interactions at the 3D continuous network MWCNTs/diamond interfaces, which facilitate efficient energy dissipation. Our 3D continuous network heterogeneous structure design provides an effective approach for enhancing the fracture toughness of superhard materials, offering a new paradigm for the advanced composite ceramics.

cond-mat.other↗

arXiv2025

An Efficient and Interpretable Autoregressive Model for High-Dimensional Tensor-Valued Time Series

In autoregressive modeling for tensor-valued time series, Tucker decomposition, when applied to the coefficient tensor, provides a clear interpretation of supervised factor modeling but loses its efficiency rapidly with increasing tensor order. Conversely, canonical polyadic (CP) decomposition maintains efficiency but lacks a precise statistical interpretation. To attain both interpretability and powerful dimension reduction, this paper proposes a novel approach under the supervised factor modeling paradigm, which first uses CP decomposition to extract response and covariate features separately and then regresses response features on covariate ones. This leads to a new CP-based low-rank structure for the coefficient tensor. Furthermore, to address heterogeneous signals or potential model misspecifications arising from stringent low-rank assumptions, a low-rank plus sparse model is introduced by incorporating an additional sparse coefficient tensor. Nonasymptotic properties are established for the ordinary least squares estimators, and an alternating least squares algorithm is introduced for optimization. Theoretical properties of the proposed methodology are validated by simulation studies, and its enhanced prediction performance and interpretability are demonstrated by the El Ni$\tilde{\text{n}}$o-Southern Oscillation example.

stat.ME↗

arXiv2025

Seed1.5-VL Technical Report

We present Seed1.5-VL, a vision-language foundation model designed to advance general-purpose multimodal understanding and reasoning. Seed1.5-VL is composed with a 532M-parameter vision encoder and a Mixture-of-Experts (MoE) LLM of 20B active parameters. Despite its relatively compact architecture, it delivers strong performance across a wide spectrum of public VLM benchmarks and internal evaluation suites, achieving the state-of-the-art performance on 38 out of 60 public benchmarks. Moreover, in agent-centric tasks such as GUI control and gameplay, Seed1.5-VL outperforms leading multimodal systems, including OpenAI CUA and Claude 3.7. Beyond visual and video understanding, it also demonstrates strong reasoning abilities, making it particularly effective for multimodal reasoning challenges such as visual puzzles. We believe these capabilities will empower broader applications across diverse tasks. In this report, we mainly provide a comprehensive review of our experiences in building Seed1.5-VL across model design, data construction, and training at various stages, hoping that this report can inspire further research. Seed1.5-VL is now accessible at https://www.volcengine.com/ (Volcano Engine Model ID: doubao-1-5-thinking-vision-pro-250428)

cs.CV↗

arXiv2025

Powerful nuclear outflows and circumgalactic medium shocks driven by the most luminous known obscured quasar in the Universe

We report integral field spectroscopy observations with the Near-Infrared Spectrograph on board JWST targeting the 60 kpc environment surrounding the most luminous obscured quasar known at $z=4.6$. We detect ionized gas filaments on 40 kpc scales connecting a network of merging galaxies likely to form a cluster. We find regions of low ionization consistent with large-scale shock excitation surrounding the central dust-obscured quasar, out to distances nearly eight times the effective stellar radius of the quasar host galaxy. In the nuclear region, we find an ionized outflow driven by the quasar with velocities reaching 13,000 km s$^{-1}$, one of the fastest discovered to date with an outflow rate of 2000 M$_\odot$ yr$^{-1}$ and a kinetic luminosity of 6$\times10^{46}$ erg s$^{-1}$ resulting in coupling efficiency between the bolometric luminosity of the quasar and the outflow of 5%. The kinetic luminosity of the outflow is sufficient to power the turbulent motion of the gas on galactic and circumgalactic scales and is likely the primary driver of the radiative shocks on interstellar medium and circumgalactic medium scales. This provides compelling evidence supporting long-standing theoretical predictions that powerful quasar outflows are a main driver in regulating the heating and accretion rate of gas onto massive central cluster galaxies.

astro-ph.GA↗

arXiv2025

Multi-Component Ionized Gas Outflows in a Hot Dust-Obscured Galaxy W2026+0716 with Keck/OSIRIS

We present narrowband-filtered integral field unit (IFU) observations of the Hot Dust-Obscured Galaxy (Hot DOG) WISE J202615.27$+$071624.0 (hereafter W2026$+$0716) at redshift $z=2.570$ using Keck/OSIRIS. Our analysis reveals a multi-component ionized gas outflow structure in this heavily obscured AGN host galaxy. Multi-component Gaussian decomposition of the [O III] and H$α$ emission lines uncovers extremely broad and asymmetric profiles, characteristic of AGN-driven outflows. Kinematic mapping shows spatially distinct structures: the [O III] and H$α$ dominated components (with radii of $1.20 \pm 0.56$ kpc) are separated by a projected offset of $\sim 1.1$ kpc and exhibit divergent velocity regimes. The [O III] outflow reaches a velocity of 3210 $\pm$ 50 km s$^{-1}$, while the H$α$ outflow component attains 2310 $\pm$ 840 km s$^{-1}$. Dynamical modeling supports a biconical outflow structure, with [O III] and H$α$ emissions dominating separate cones and significant dust obscuration of the redshifted outflow. Their comparable momentum outflow rates and energy outflow rates suggest a potential physical connection in their driving mechanisms. Spectral energy distribution (SED) analysis reveals anomalous optical/UV excess, attributed to AGN photon scattering by dust or outflowing material, classifying W2026+0716 as a "Blue Hot DOG". The derived outflow timescale ($\sim10^{5}$ yr) aligns with the evolutionary phase of Blue Hot DOGs, suggesting AGN feedback operates persistently during this transitional stage.

astro-ph.GA↗

arXiv2025

A Massive Gas Outflow Outside the Line-of-Sight: Imaging Polarimetry of the Blue Excess Hot Dust Obscured Galaxy W0204-0506

(Aims) Hot Dust Obscured Galaxies (Hot DOGs) are a population of hyper-luminous, heavily obscured quasars. Although nuclear obscurations close to Compton-thick are typical, a fraction show blue UV spectral energy distributions consistent with unobscured quasar activity, albeit two orders of magnitude fainter than expected from their mid-IR luminosity. The origin of the UV emission in these Blue excess Hot DOGs (BHDs) has been linked to scattered light from the central engine. Here we study the properties of the UV emission in the BHD WISE J020446.13-050640.8 (W0204-0506). (Methods) We use imaging polarization observations in the $R_{\rm Special}$ band obtained with the FORS2 instrument at VLT. We compare these data with radiative transfer simulations to constrain the characteristics of the scattering material. (Results) We find a spatially integrated polarization fraction of $24.7\pm 0.7$%, confirming the scattered-light nature of the UV emission of W0204-0506. The source is spatially resolved in the observations and we find a gradient in polarization fraction and angle that is aligned with the extended morphology of the source found in HST/WFC3 imaging. A dusty, conical polar outflow starting at the AGN sublimation radius with a half-opening angle $\lesssim 50~\rm deg$ viewed at an inclination $\gtrsim 45~\rm deg$ can reproduce the observed polarization fraction if the dust is graphite-rich. We find that the gas mass and outflow velocity are consistent with the range of values found for [OIII] outflows through spectroscopy in other Hot DOGs, though it is unclear whether the outflow is energetic enough to affect the long-term evolution of the host galaxy. Our study highlights the unique potential for polarization imaging to study dusty quasar outflows, providing complementary constraints to those obtained through traditional spectroscopic studies.

astro-ph.GA↗

arXiv2025

Searching for Low-Redshift Hot Dust-Obscured Galaxies

Hot Dust-Obscured Galaxies (Hot DOGs), discovered by the "W1W2 dropout" selection at high redshifts ($z\sim$ 2-4), are a rare population of hyper-luminous obscured quasars. Their number density is comparable to similarly luminous type 1 quasars in the same redshift range, potentially representing a short, yet critical stage in galaxy evolution. The evolution in their number density towards low redshift, however, remains unclear as their selection function is heavily biased against objects at $z\lesssim2$. We combine data from the WISE and Herschel archives to search for Hot DOGs at $z<0.5$ based on their unique spectral energy distributions. We find 68 candidates, and spectroscopic observations confirm that 3 of them are at $z<0.5$. For those 3 we find their black hole accretion is close to the Eddington limit, with lower bolometric luminosities and black hole masses than those of higher-$z$ Hot DOGs. Compared to high-$z$ systems, these low-$z$ systems are closer to the local relation between host galaxy stellar mass and black hole mass but still lie above it, and we discuss several possible scenarios for it. Finally, we also find the surface number density of $z<$0.5 Hot DOGs is $\rm 2.4 \times 10^{-3}$ deg$^{-2}$, about an order of magnitude lower than high-$z$ Hot DOGs but comparable to hyper-luminous unobscured quasars in the same redshift range. These results further support the idea that Hot DOGs may be a transitional phase of galaxy evolution.

astro-ph.GA↗

arXiv2025

The Host Galaxy of FRB 20190520B and Its Unique Ionized Gas Distribution

The properties of host galaxies associated with Fast Radio Bursts (FRBs) provide critical information for inferring the progenitors and radiation mechanisms of these bursts. We report on the host galaxy of the repeating FRB 20190520B, a dwarf galaxy at the spectroscopic redshift $z=0.241$ with a stellar mass of $(6.2 \pm 0.8) \times 10^8 \ M_{\odot}$. The emission line ratios suggest that the ionized gas is powered by star formation. The total H$α$-traced star formation rate (SFR) is $0.70 \pm 0.01 \ {M_{\odot} ~ \rm yr^{-1}}$, and the metallicity is $\rm 12+log_{10} ([O/H]) \geq 7.4 \pm 0.1$. The specific star formation rate (sSFR) is $\rm log \ sSFR/yr^{-1} = -9.0 \pm 0.1$, higher than the upper limit of $-9.4$ observed in nearby dwarf galaxies. The dispersion measure contribution from the host galaxy is estimated to be $\rm DM_{host} \approx 950 \pm 220 \ pc \ cm^{-3}$, based on the H$α$ emission. The FRB and the associated persistent radio source are located at the H$α$ emission peak, offset by $\sim 1.4^{\prime\prime}$ (5.5 kpc) in projection from the stellar continuum. At this position, the lower limit of $\rm \log \ sSFR/yr^{-1}$ is $-8.5 \pm 0.1$, more than three times the galaxy's total sSFR. The H$α$ velocity difference between the stellar continuum and the offset gas is $39.6 \pm 0.4$ km s$^{-1}$, which is sufficient to draw conclusions about the nature of the offset.

astro-ph.GA↗

arXiv2025

Spatially resolved dust properties over 50 kpc in a hyperluminous galaxy merger at $z = 4.6$

We present spatially resolved dust-continuum ALMA observations from rest-frame $\sim$60 to $\sim$600 $μ$m (bands 3-10) of the hyperluminous hot dust-obscured galaxy (hot DOG) WISE J224607.6-052634.9 (W2246-0526), at redshift $z=4.6$. W2246-0526 is interacting with at least three companion galaxies, forming a system connected by tidal streams. We model the multiwavelength ALMA observations of the dust continuum using a modified blackbody, from which we derive the dust properties (mass, emissivity index, area of the emitting region, and temperature) in the hot DOG and resolved structures across a region of nearly $\sim$50 kpc. The peak temperature at the location of the hot DOG, $\sim$110 K, is likely the consequence of heating by the central quasar. The dust temperature drops to $\sim$40 K at a radius of $\sim$8 kpc, suggesting that heating by the quasar beyond that distance is nondominant. The dust in the connecting streams between the host and companion galaxies is at temperatures between 30-40 K, typical of starburst galaxies, suggesting it is most likely heated by recent, in-situ star formation. This is the first time dust properties are spatially resolved over several tens of kpc in a galaxy system beyond Cosmic Noon --this is more than six times the scales previously probed in galaxies at those redshifts.

astro-ph.GA↗

arXiv2025

From Layers to States: A State Space Model Perspective to Deep Neural Network Layer Dynamics

The depth of neural networks is a critical factor for their capability, with deeper models often demonstrating superior performance. Motivated by this, significant efforts have been made to enhance layer aggregation - reusing information from previous layers to better extract features at the current layer, to improve the representational power of deep neural networks. However, previous works have primarily addressed this problem from a discrete-state perspective which is not suitable as the number of network layers grows. This paper novelly treats the outputs from layers as states of a continuous process and considers leveraging the state space model (SSM) to design the aggregation of layers in very deep neural networks. Moreover, inspired by its advancements in modeling long sequences, the Selective State Space Models (S6) is employed to design a new module called Selective State Space Model Layer Aggregation (S6LA). This module aims to combine traditional CNN or transformer architectures within a sequential framework, enhancing the representational capabilities of state-of-the-art vision networks. Extensive experiments show that S6LA delivers substantial improvements in both image classification and detection tasks, highlighting the potential of integrating SSMs with contemporary deep learning techniques.

cs.LG↗

arXiv2025

Constructing $(h,d)$ cooperative MSR codes with sub-packetization $(d-k+h)(d-k+1)^{\lceil n/2 \rceil}$

We address the multi-node failure repair challenges for MDS array codes. Presently, two primary models are employed for multi-node repairs: the centralized model where all failed nodes are restored in a singular data center, and the cooperative model where failed nodes acquire data from auxiliary nodes and collaborate amongst themselves for the repair process.This paper focuses on the cooperative model, and we provide explicit constructions of optimal MDS array codes with $d$ helper nodes under this model. The sub-packetization level of our new codes is $(d-k+h)(d-k+1)^{\lceil n/2 \rceil}$ where $h$ is the number of failed nodes, $k$ the number of information nodes and $n$ the code length. This improves upon recent constructions given by Liu \emph{et al.} (IEEE Transactions on Information Theory, Vol. 69, 2023).

cs.IT↗