arXiv subjects

Shuai Feng

Publications and source records attributed to Shuai Feng.

At least 37 records · Page 2Linked to original sources

arXiv2025

The Velocity Map Asymmetry of Ionized Gas in MaNGA II. Correlation between Velocity Map Morphology, Star Formation, and Metallicity in Regular Disk Galaxies

The morphology of ionized gas velocity maps provides a direct probe of the internal gas kinematics of galaxies. Using integral field spectroscopy from SDSS-IV MaNGA, we analyze a sample of 528 low-inclination, regular disk galaxies to investigate the correlations between velocity map morphology, star formation rate, and gas-phase metallicity. We quantify velocity map morphology using harmonic expansion and adopt two complementary diagnostics: the global kinematic asymmetry, which traces non-axisymmetric perturbations, and the first-order term ratio, which captures axisymmetric radial motions. We find that galaxies with higher kinematic asymmetry are more likely to deviate from the scaling relations, typically lying either above or below the star formation main sequence and systematically below the mass-metallicity relation. In contrast, the first-order term ratio shows only a correlation with gas-phase metallicity in the low-mass range and no significant dependence on star formation rate. Moreover, galaxies below the mass-metallicity relation generally exhibit higher HI gas fractions. These results suggest that external gas accretion is the primary driver of the observed phenomena: inflowing metal-poor gas increases velocity map asymmetry in disk galaxies, dilutes the metallicity, and triggers enhanced star formation. Feedback-driven outflows, bar- and spiral-driven inflows, and galaxy mergers may also contribute, but likely play a secondary role.

astro-ph.GA↗

arXiv2025

Long-Tailed Out-of-Distribution Detection with Refined Separate Class Learning

Out-of-distribution (OOD) detection is crucial for deploying robust machine learning models. However, when training data follows a long-tailed distribution, the model's ability to accurately detect OOD samples is significantly compromised, due to the confusion between OOD samples and head/tail classes. To distinguish OOD samples from both head and tail classes, the separate class learning (SCL) approach has emerged as a promising solution, which separately conduct head-specific and tail-specific class learning. To this end, we examine the limitations of existing works of SCL and reveal that the OOD detection performance is notably influenced by the use of static scaling temperature value and the presence of uninformative outliers. To mitigate these limitations, we propose a novel approach termed Refined Separate Class Learning (RSCL), which leverages dynamic class-wise temperature adjustment to modulate the temperature parameter for each in-distribution class and informative outlier mining to identify diverse types of outliers based on their affinity with head and tail classes. Extensive experiments demonstrate that RSCL achieves superior OOD detection performance while improving the classification accuracy on in-distribution data.

cs.LG↗

arXiv2025

RealMirror: A Comprehensive, Open-Source Vision-Language-Action Platform for Embodied AI

The emerging field of Vision-Language-Action (VLA) for humanoid robots faces several fundamental challenges, including the high cost of data acquisition, the lack of a standardized benchmark, and the significant gap between simulation and the real world. To overcome these obstacles, we propose RealMirror, a comprehensive, open-source embodied AI VLA platform. RealMirror builds an efficient, low-cost data collection, model training, and inference system that enables end-to-end VLA research without requiring a real robot. To facilitate model evolution and fair comparison, we also introduce a dedicated VLA benchmark for humanoid robots, featuring multiple scenarios, extensive trajectories, and various VLA models. Furthermore, by integrating generative models and 3D Gaussian Splatting to reconstruct realistic environments and robot models, we successfully demonstrate zero-shot Sim2Real transfer, where models trained exclusively on simulation data can perform tasks on a real robot seamlessly, without any fine-tuning. In conclusion, with the unification of these critical components, RealMirror provides a robust framework that significantly accelerates the development of VLA models for humanoid robots. Project page: https://terminators2025.github.io/RealMirror.github.io

cs.RO↗

arXiv2025

Dependence of halo properties on central-satellite magnitude gaps through weak lensing measurements

The magnitude gap between the central and satellite galaxies encodes information about the mass accretion history of a dark matter halo, and serves as a useful observational probe for the mass distribution in a halo. In this work, we perform the first weak lensing test of the connections between the magnitude gap and the halo profile. We measure the halo profiles of isolated central galaxies (ICGs) selected primarily from the SDSS Main Galaxy Sample. Halo mass and concentration are inferred by fitting stacked lensing profiles in bins of central luminosity, $L_\mathrm{c}$, and the central-satellite magnitude gap, $L_\mathrm{gap}$. We detect dependence on the magnitude gap in both halo properties. The dependence is the strongest in the ICG luminosity range of $10^{10.3}<L_\mathrm{c}[h^{-2}L_\odot]\leq 10^{10.7}$, where halos with smaller gaps have higher masses and lower concentrations. When $10^{10.7} <L_c[h^{-2}L_\odot] \leq 10^{11.1}$, however, no significant gap dependence is detected. In the range of $10^{9.9}<L_\mathrm{c}[h^{-2}L_\odot] \leq 10^{10.3}$, a disordering of the gap dependence is marginally observable. We compare the observational results with predictions by two lightcone catalogs built from the Illustris TNG300 and the Millennium simulations. The gap dependence in the two mock samples show overall consistency with observations, but neither matches them in all $L_\mathrm{c}$ bins to a quantitative level. We also compare the significance of the gap dependence on halo mass and concentration and find that our measurement prefers gap dependence in both parameters, while the halo mass dependence is preferred over the concentration if only one of the two dependencies is allowed.

astro-ph.CO↗

arXiv2025

Misaligned external gas acquisition boosts central black hole activities

One important question in active galactic nucleus (AGN) is how gas is brought down to the galaxy center. Both internal secular evolution (torque induced by non-axisymmetric galactic structures such as bars) and external processes (e.g. mergers or interactions) are expected to redistribute the angular momentum (AM) and transport gas inward. However, it is still under debate whether these processes can significantly affect AGN activities. Here we for the first time report that AGN fraction increases with the difference of kinematic position angles ($ΔPA\equiv|PA_{\mathrm{gas}}-PA_{\mathrm{star}}|$) between ionized gas ($PA_{\mathrm{gas}}$) and stellar disks ($PA_{\mathrm{star}}$) in blue and green galaxies, meanwhile this fraction remains roughly constant for red galaxies. Also the high luminosity AGN fraction increases with $ΔPA$ while the low luminosity AGN fraction is independent with $ΔPA$. These observational results support a scenario in which the interaction between accreted and pre-existing gas provides the AM loss mechanism, thereby the gas inflow fuels the central BH activities, and the AM loss efficiency is positively correlated with the $ΔPA$.

astro-ph.GA↗

arXiv2025

Whispers of Many Shores: Cultural Alignment through Collaborative Cultural Expertise

The integration of large language models (LLMs) into global applications necessitates effective cultural alignment for meaningful and culturally-sensitive interactions. Current LLMs often lack the nuanced understanding required for diverse cultural contexts, and adapting them typically involves costly full fine-tuning. To address this, we introduce a novel soft prompt fine-tuning framework that enables efficient and modular cultural alignment. Our method utilizes vectorized prompt tuning to dynamically route queries to a committee of culturally specialized 'expert' LLM configurations, created by optimizing soft prompt embeddings without altering the base model's parameters. Extensive experiments demonstrate that our framework significantly enhances cultural sensitivity and adaptability, improving alignment scores from 0.208 to 0.820, offering a robust solution for culturally-aware LLM deployment. This research paves the way for subsequent investigations into enhanced cultural coverage and dynamic expert adaptation, crucial for realizing autonomous AI with deeply nuanced understanding in a globally interconnected world.

cs.AI↗

arXiv2025

Asymptotic stabilization under homomorphic encryption: A re-encryption free method

In this paper, we propose methods to encrypted a pre-given dynamic controller with homomorphic encryption, without re-encrypting the control inputs. We first present a preliminary result showing that the coefficients in a pre-given dynamic controller can be scaled up into integers by the zooming-in factor in dynamic quantization, without utilizing re-encryption. However, a sufficiently small zooming-in factor may not always exist because it requires that the convergence speed of the pre-given closed-loop system should be sufficiently fast. Then, as the main result, we design a new controller approximating the pre-given dynamic controller, in which the zooming-in factor is decoupled from the convergence rate of the pre-given closed-loop system. Therefore, there always exist a (sufficiently small) zooming-in factor of dynamic quantization scaling up all the controller's coefficients to integers, and a finite modulus preventing overflow in cryptosystems. The process is asymptotically stable and the quantizer is not saturated.

eess.SY↗

arXiv2025

Quantized distributed Nash equilibrium seeking under DoS attacks

This paper studies distributed Nash equilibrium (NE) seeking under Denial-of-Service (DoS) attacks and quantization. The players can only exchange information with their own direct neighbors. The transmitted information is subject to quantization and packet losses induced by malicious DoS attacks. We propose a quantized distributed NE seeking strategy based on the approach of dynamic quantized consensus. To solve the quantizer saturation problem caused by DoS attacks, the quantization mechanism is equipped to have zooming-in and holding capabilities, in which the holding capability is consistent with the results in quantized consensus under DoS. A sufficient condition on the number of quantizer levels is provided, under which the quantizers are free from saturation under DoS attacks. The proposed distributed quantized NE seeking strategy is shown to have the so-called maximum resilience to DoS attacks. Namely, if the bound characterizing the maximum resilience is violated, an attacker can deny all the transmissions and hence distributed NE seeking is impossible.

eess.SY↗

arXiv2025

Optical Flares Detected on a Contact Binary: The First Photometric and Spectroscopic Analysis of a Long-period Low Mass Ratio Contact Binary HAT 307-0007476

This paper presents the photometric and spectroscopic analysis of a long-period totally eclipsing contact binary (HAT 307-0007476) for the first time. This system is a low mass ratio ($q\sim0.114$) and medium contact binary ($f\sim37.1\%$). Two flare events were detected in multiple bands observations in December 2022. The interval between the two flare events is 4 days. The average duration of these two flares is about 2289s. Both the two flares achieve the energy levels of superflares. The excess emission of the H$_α$ line in the LAMOST spectra of this object was analyzed, indicating its chromospheric activity. The $O-C$ diagram showed a long-term orbital period increase, which is due to the mass transfer between the two component stars. We conclude that HAT 307-0007476 is currently in a stable region based on both $J_{spin}/J_{orb}$ and the comparison between the instability parameters and its current values.

astro-ph.SR↗

arXiv2024

MSA-3D: Metallicity Gradients in Galaxies at $z\sim1$ with JWST/NIRSpec Slit-stepping Spectroscopy

The radial gradient of gas-phase metallicity is a powerful probe of the chemical and structural evolution of star-forming galaxies, closely tied to disk formation and gas kinematics in the early universe. We present spatially resolved chemical and dynamical properties for a sample of 25 galaxies at $0.5 \lesssim z \lesssim 1.7$ from the \msasd survey. These innovative observations provide 3D spectroscopy of galaxies at a spatial resolution approaching JWST's diffraction limit and a high spectral resolution of $R\simeq2700$. The metallicity gradients measured in our galaxy sample range from $-$0.03 to 0.02 dex~kpc$^{-1}$. Most galaxies exhibit negative or flat radial gradients, indicating lower metallicity in the outskirts or uniform metallicity throughout the entire galaxy. We confirm a tight relationship between stellar mass and metallicity gradient at $z\sim1$ with small intrinsic scatter of 0.02 dex~kpc$^{-1}$. Our results indicate that metallicity gradients become increasingly negative as stellar mass increases, likely because the more massive galaxies tend to be more ``disky". This relationship is consistent with the predictions from cosmological hydrodynamic zoom-in simulations with strong stellar feedback. This work presents the effort to harness the multiplexing capability of JWST NIRSpec/MSA in slit-stepping mode to map the chemical and kinematic profiles of high-redshift galaxies in large samples and at high spatial and spectral resolution.

astro-ph.GA↗

arXiv2024

The first exploration of the correlations between \textit{WISE} 12 \micron\ and CO emission in early-type galaxies

We present the analysis of a comprehensive sample of 352 early-type galaxies using public data, to investigate the correlations between CO luminosities and mid-infrared luminosities observed by \textit{Wide-field Infrared Survey Explorer} (\textit{WISE}). We find strong correlations between both CO (1-0) and CO (2-1) luminosities and 12 \micron\ luminosity, boasting a correlation coefficient greater than 0.9 and an intrinsic scatter smaller than 0.1 dex. The consistent slopes observed for the relationships of CO (1-0) and CO (2-1) suggest that the line ratio R21 lacks correlation with mid-infrared emission in early-type galaxies, which is significantly different from star-forming galaxies. Moreover, the slopes of $L_{\rm CO (1-0)}$--$L_{\mbox{12\micron}}$ and $L_{\rm CO (2-1)}$--$L_{\mbox{12\micron}}$ relations in early-type galaxies are steeper than those observed in star-forming galaxies. Given the absence of correlation with color, morphology or sSFR, the correlation between deviations and the molecular gas mass surface density could be eliminated by correcting the possible 12 \micron\ emission from old stars or adopting a systematically different $α_{\rm CO}$. The latter, on average, is equivalent to adding an constant CO brightness density, specifically ${2.8{_{-0.6}}\!\!\!\!\!\!\!\!\!^{+0.8}}~[\mathrm{K~km~s^{-1}}]$ and ${4.4{_{-1.4}}\!\!\!\!\!\!\!\!\!^{+2.2}}~[\mathrm{K~km~s^{-1}}]$ for CO (1-0) and (2-1) respectively. These explorations will serve as useful tools for estimating the molecular gas content in gas-poor galaxies and understanding associated quenching processes.

astro-ph.GA↗

arXiv2024

Asymptotic tracking control of dynamic reference over homomorphically encrypted data with finite modulus

This paper considers a tracking control problem, in which the dynamic controller is encrypted with an additively homomorphic encryption scheme and the output of a process tracks a dynamic reference asymptotically. Our paper is motivated by the following problem: When dealing with both asymptotic tracking and dynamic reference, we find that the control input is generally subject to overflow issues under a finite modulus, though the dynamic controller consists of only integer coefficients. First, we provide a new controller design method such that the coefficients of the tracking controller can be transformed into integers leveraging the zooming-in factor of dynamic quantization. By the Cayley-Hamilton theorem, we represent the control input as linear combination of the previous control inputs. Leveraging the property above, we design an algorithm on the actuator side such that it can restore the control input from the lower bits under a finite modulus. A lower bound of the modulus is also provided. As an extension of the first result, we further solve the problem of unbounded internal state taking place in the actuator. In particular, the actuator can restore the correct control input under the same modulus. A simulation example is provided to verify the control schemes proposed in our paper.

eess.SY↗

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$ α$-[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 $Δv$ between their two components: the inner low-$Δv$, inner high-$Δv$ 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-$Δv$ DPSs, AGN-hosting DPGs and inner high-$Δv$ DPSs, as well as tidal DPGs and outer DPSs.

astro-ph.GA↗

arXiv2024

SeMOPO: Learning High-quality Model and Policy from Low-quality Offline Visual Datasets

Model-based offline reinforcement Learning (RL) is a promising approach that leverages existing data effectively in many real-world applications, especially those involving high-dimensional inputs like images and videos. To alleviate the distribution shift issue in offline RL, existing model-based methods heavily rely on the uncertainty of learned dynamics. However, the model uncertainty estimation becomes significantly biased when observations contain complex distractors with non-trivial dynamics. To address this challenge, we propose a new approach - \emph{Separated Model-based Offline Policy Optimization} (SeMOPO) - decomposing latent states into endogenous and exogenous parts via conservative sampling and estimating model uncertainty on the endogenous states only. We provide a theoretical guarantee of model uncertainty and performance bound of SeMOPO. To assess the efficacy, we construct the Low-Quality Vision Deep Data-Driven Datasets for RL (LQV-D4RL), where the data are collected by non-expert policy and the observations include moving distractors. Experimental results show that our method substantially outperforms all baseline methods, and further analytical experiments validate the critical designs in our method. The project website is \href{https://sites.google.com/view/semopo}{https://sites.google.com/view/semopo}.

cs.CV↗

arXiv2024

AD3: Implicit Action is the Key for World Models to Distinguish the Diverse Visual Distractors

Model-based methods have significantly contributed to distinguishing task-irrelevant distractors for visual control. However, prior research has primarily focused on heterogeneous distractors like noisy background videos, leaving homogeneous distractors that closely resemble controllable agents largely unexplored, which poses significant challenges to existing methods. To tackle this problem, we propose Implicit Action Generator (IAG) to learn the implicit actions of visual distractors, and present a new algorithm named implicit Action-informed Diverse visual Distractors Distinguisher (AD3), that leverages the action inferred by IAG to train separated world models. Implicit actions effectively capture the behavior of background distractors, aiding in distinguishing the task-irrelevant components, and the agent can optimize the policy within the task-relevant state space. Our method achieves superior performance on various visual control tasks featuring both heterogeneous and homogeneous distractors. The indispensable role of implicit actions learned by IAG is also empirically validated.

cs.LG↗

arXiv2024

Are High $Σ_1$ Massive Blue Spiral Galaxies Rejuvenated Systems?

Quiescent galaxies generally possess denser cores than star-forming galaxies with similar mass. As a measurement of the core density, the central stellar mass surface density within a radius of 1 kpc ($Σ_1$) was thus suggested to be closely related to galaxy quenching. Massive star-forming galaxies with high $Σ_1$ do not fit into this picture. To understand the origin of such galaxies, we compare the spatially-resolved stellar population and star formation properties of massive ($ > 10^{10.5}{\rm M}_{\odot}$) blue spiral galaxies with high and low $Σ_1$, divided by $Σ_1 = 10^{9.4} M_\odot \, {\rm kpc}^{-2}$, based on the final release of MaNGA IFU data. We find that both high $Σ_1$ and low $Σ_1$ blue spirals show large diversities in stellar population and star formation properties. Despite the diversities, high $Σ_1$ blue spirals are statistically different from the low $Σ_1$ ones. Specifically, the radial profiles of the luminosity-weighted age and Mgb/${\rm \langle Fe \rangle}$ show that high $Σ_1$ blue spirals consist of a larger fraction of galaxies with younger and less $α$-element enhanced centers than their low $Σ_1$ counterparts, $\sim 55\%$ versus $\sim 30\%$. The galaxies with younger centers mostly have higher central specific star formation rates, which still follow the spaxel-based star formation main sequence relation though. Examinations of the H$α$ velocity field and the optical structures suggest that galactic bars or galaxy interactions should be responsible for the rejuvenation of these galaxies. The remaining $\sim 45\% $ of high $Σ_1$ blue spirals are consistent with the inside-out growth scenario.

astro-ph.GA↗

arXiv2024

DIDA: Denoised Imitation Learning based on Domain Adaptation

Imitating skills from low-quality datasets, such as sub-optimal demonstrations and observations with distractors, is common in real-world applications. In this work, we focus on the problem of Learning from Noisy Demonstrations (LND), where the imitator is required to learn from data with noise that often occurs during the processes of data collection or transmission. Previous IL methods improve the robustness of learned policies by injecting an adversarially learned Gaussian noise into pure expert data or utilizing additional ranking information, but they may fail in the LND setting. To alleviate the above problems, we propose Denoised Imitation learning based on Domain Adaptation (DIDA), which designs two discriminators to distinguish the noise level and expertise level of data, facilitating a feature encoder to learn task-related but domain-agnostic representations. Experiment results on MuJoCo demonstrate that DIDA can successfully handle challenging imitation tasks from demonstrations with various types of noise, outperforming most baseline methods.

cs.LG↗

arXiv2024

SENSOR: Imitate Third-Person Expert's Behaviors via Active Sensoring

In many real-world visual Imitation Learning (IL) scenarios, there is a misalignment between the agent's and the expert's perspectives, which might lead to the failure of imitation. Previous methods have generally solved this problem by domain alignment, which incurs extra computation and storage costs, and these methods fail to handle the \textit{hard cases} where the viewpoint gap is too large. To alleviate the above problems, we introduce active sensoring in the visual IL setting and propose a model-based SENSory imitatOR (SENSOR) to automatically change the agent's perspective to match the expert's. SENSOR jointly learns a world model to capture the dynamics of latent states, a sensor policy to control the camera, and a motor policy to control the agent. Experiments on visual locomotion tasks show that SENSOR can efficiently simulate the expert's perspective and strategy, and outperforms most baseline methods.

cs.RO↗