arXiv subjects

Shu Wang

Publications and source records attributed to Shu Wang.

At least 73 records · Page 4Linked to original sources

arXiv2025

A Large Sample of JWST/NIRSpec Brown Dwarfs: New Distant Discoveries

Brown dwarfs are essential probes of stellar and planetary formation, yet their low luminosities pose challenges for detection at large Galactic distances. The James Webb Space Telescope (JWST), with its unprecedented near-infrared sensitivity, enables the discovery and characterization of distant substellar objects, including those in the Milky Way's thick disk and halo. We conducted a systematic search using over 40,000 publicly available JWST/NIRSpec PRISM/CLEAR spectra and identified 68 brown dwarfs through spectral template matching and visual inspection. Among them, 12 are newly identified candidates, including 8 T dwarfs and 4 M/L dwarfs, most at distances exceeding 1 kpc. Remarkably, two sources -- JWST J001418.22-302223.2 and JWST J033240.07-274907.8 -- are found at distances greater than 5 kpc, making them the most distant brown dwarfs within the Milky Way. Spectral fits were performed using a nested sampling Monte Carlo algorithm with three model grids: Sonora Elf Owl, LOWZ, and SAND. The analysis reveals that cloud-free models are unable to reproduce L/T transition spectra, whereas the SAND model provides a more accurate representation of cloud effects in metal-poor environments. With the newly identified distant brown dwarfs, we also investigated the vertical metallicity gradient of brown dwarfs. Overall, the metallicities do not show an evident trend with Galactic height $|Z|$, due to the limited sample size and the uncertainties in metallicity measurements.

astro-ph.SR↗

arXiv2025

How General Are Measures of Choice Consistency? Evidence from Experimental and Scanner Data

Choice consistency with utility maximization, as a key assumption in economics, has been extensively used to evaluate decision quality of individuals and to predict real-world outcomes across different contexts. Here we investigate the generalizability of consistency measures derived from budgetary decisions in the lab-in-the-field experiment and purchasing decisions using supermarket scanner data. In the first study, we observe a lack of correlation between consistency scores derived from risky decisions in the experiment and those from supermarket food purchasing decisions. In the second study, we observe moderate correlations between experimental tasks and low to moderate correlations across purchasing categories and over time periods within the supermarket. Moreover, consistency in the two settings exhibits distinct predictive validity in predicting consumer behavior. These results suggest that choice consistency, as a measure of decision quality, may be better characterized as a multidimensional skill set rather than a single-dimensional ability.

econ.GN↗

arXiv2025

Optimal decay rates to the contact wave for 1-D compressible Navier-Stokes equations

This paper investigates the decay rates of the contact wave in one-dimensional Navier-Stokes equations. We study two cases of perturbations, with and without zero mass condition, i.e., the integration of initial perturbations is zero and non-zero, respectively. For the case without zero mass condition, we obtain the optimal decay rate $(1+t)^{-\frac{1}{2}}$ for the perturbation in $L^\infty$ norm, which provides a positive answer to the conjecture in \cite{HMX}. We applied the anti-derivative method, introducing the diffusion wave to carry the initial excess mass, diagonalizing the integrated system, and estimating the energy of perturbation in the diagonalized system. Precisely, due to the presence of diffusion waves, the decay rates for errors of perturbed system are too poor to get the optimal decay rate. We find the dissipation structural in the diagonalized system, see \cref{ds}. This observation makes us able to fully utilize the fact that the sign of the derivative of the contact wave is invariant and to control the terms with poor decay rates in energy estimates. For the case with zero mass condition, there are also terms with poor decay rates. In this case, note that there is a cancellation in the linearly degenerate field so that the terms with poor decay rates will not appear in the second equation of the diagonalized system. Thanks to this cancellation and a Poincaré type of estimate obtained by a critical inequality introduced by \cite{HLM}, we get the decay rate of $\ln^{\frac{1}{2}} (2+t)$ for $L^2$ norm of anti-derivatives of perturbation and $(1+t)^{-\frac{1}{2}}\ln^{\frac{1}{2}}(2+t)$ for the $L^2$ norm of perturbation itself, the decay rates are optimal, which is consistent with the results obtained by using pointwise estimate in \cite{XZ} for the system with artificial viscosity.

math.AP↗

arXiv2025

ChronoLLM: Customizing Language Models for Physics-Based Simulation Code Generation

This contribution is concerned with the following issue: can pretrained large language models (LLMs) be refined and customized to the point where they become virtual assistants helping experts with the effective use of a simulation tool? In this case study, the ``simulation tool'' considered is PyChrono, an open source multi-physics dynamics engine for multibody systems. We present a framework for refining and customizing both open- and closed-source LLMs to harness the power of AI in generating scripts that perform PyChrono virtual experiments. We refine and customize several classes of LLMs through a process that leads to a quantifiable improvement in the quality of the generated PyChrono simulation scripts. These scripts can range from simple single-pendulum simulations to complex virtual experiments involving full vehicles on deformable terrain. While the generated scripts are rarely perfect, they often serve as strong starting points for the user to modify and improve on. Additionally, the LLM can answer specific API questions about the simulator, or recommend modeling approaches. The framework discussed is general and can be applied to lower the entry barrier for simulation tools associated with other application domains.

cs.AI↗

arXiv2025

Portfolio Selection with Costly Information Acquisition

We investigate joint optimization on information acquisition and portfolio selection within a Bayesian adaptive framework. The investor dynamically controls the precision of a private signal and incurs costs while updating her belief about the unobservable asset drift. Controllable information acquisition fails the classical separation principle of stochastic filtering. We adopt functional modeling of control to address the consequential endogeneity issues, then solve our optimization problem through dynamic programming. When the unknown drift follows a Gaussian prior, the HJB equation is often explicitly solvable via the method of characteristics, yielding sufficiently smooth classical solution to establish a verification theorem and confirm the optimality of feedback controls. In such settings, we find that the investor's information acquisition strategy is deterministic and could be decoupled from her trading strategy, indicating a weaker separation property. In some degenerate cases where classical solutions may fail, semi-explicit optimal controls remain attainable by regularizing the information cost.

math.OC↗

arXiv2025

Four-mode quantum sensing and Fisher information in a spin-orbit-coupled Bose gas

Multi-mode squeezing and entanglement are important resources in quantum metrology and sensing. For spin-1/2 Bose-Einstein condensates subject to spin-orbit coupling (SOC), previous studies on spin squeezing have been limited to two-mode systems. In this work, we demonstrate that such a system can naturally construct a four-mode model spanning an $\mathfrak{su}(4)$ algebra with six SU(2) subspaces. Using spin squeezing parameters and quantum Fisher information matrices, we analyze the dynamical evolution of coherent spin states. The results show that, beyond two-mode models, the SOC-induced four-mode couplings give rise to richer entanglement-enhanced sensing approaching the Heisenberg limit across various SU(2) subspaces. Additionally, by tuning a single system parameter (the Raman Rabi frequency), one can selectively control the optimal measurement directions across different subspaces.

cond-mat.quant-gas↗

arXiv2025

The Intermediate-Mass Black Hole Reverberation Mapping Project: First Detection of Mid-Infrared Lags in Prototypical IMBHs in NGC 4395 and POX 52

The search for robust evidence of intermediate-mass black holes (IMBHs) is crucial for understanding black hole seeding process and the formation of supermassive black holes in the early Universe. NGC 4395 and POX 52 are two prototypical IMBH hosts, both exhibiting multi-line evidence of low-mass black hole activity. Here, we report the first detection of mid-infrared (MIR) lags in response to optical variability, with measurements of $3.0^{+2.4}_{-1.9}$ days for NGC 4395 and $35.2^{+14.2}_{-11.7}$ days for POX~52 at $3.4$ $μ$m, respectively, using archival optical data and observations from the Wide-field Infrared Survey Explorer (WISE). This detection provides the first reverberation evidence of low-mass black hole activity in POX 52. The time lags of these two low-mass, low-luminosity active galactic nuclei (AGNs) generally follow the extent of the $R_{\rm dust}-L_{\rm 5100}$ relation found in higher-mass AGNs. Based on an empirical relation between the broad-line region and dusty torus size, we constrain the black hole mass of POX 52 to log($M_{\rm BH}$/$M_\odot$) = 5.5 $\pm$ 0.37 (systemic and statistical errors), confirming its IMBH nature. Furthermore, long-term optical continuum monitoring of POX 52 reveals a mild inter-band lag of $\lesssim$ 1 day. However, no significant intranight variability was detected during its one-night, high-cadence monitoring, which we attribute to the longer duty cycle of fast variability in POX 52 compared to that in NGC 4395.

astro-ph.GA↗

arXiv2025

When Experimental Economics Meets Large Language Models: Evidence-based Tactics

Advancements in large language models (LLMs) have sparked a growing interest in measuring and understanding their behavior through experimental economics. However, there is still a lack of established guidelines for designing economic experiments for LLMs. Inspired by principles from experimental economics with insights from LLM research in artificial intelligence, we outline key considerations in the experimental design and implementation stage, and perform two sets of experiments to assess the impact of these considerations on LLMs' responses. Based on our findings, we discuss seven practical tactics for conducting experiments with LLMs. Our study enhances the design, replicability, and generalizability of LLM experiments, and broadens the scope of experimental economics in the digital age.

econ.GN↗

arXiv2025

A Detailed Analysis of the Milky Way Warp Based on Classical Cepheids

Classical Cepheids (CCs) are important probes for the large-scale warp structure of the Milky Way. Using Gaia DR3 CCs, we establish an optimal time-dependent warp model, where the warp height increases with radius following a power-law, the line of nodes (LONs) exhibit linear twisting with radius, following a leading spiral pattern, and the LONs undergo prograde evolution over time. Structurally, we identify significant warp features in the $5-9$ kpc region of the Galactic disk, where the warp model performs better than the flat model. Beyond 15 kpc, the model with the second Fourier term does not fit the observations well, whereas the model with twisted LONs better matches the data. Kinematically, we derived expressions for the vertical velocities using direct differentiation and then calculated the precession rates for each CC. Our results intuitively indicate a nearly uniform and low warp precession rate of $ω= 4.86 \pm (0.88)_{stat} \pm (2.14)_{sys}$ km s$^{-1}$ kpc$^{-1}$ beyond 12.5 kpc, in agreement with classical kinematic estimates. Based on these findings, we propose a simple yet comprehensive time-dependent warp model, $Z_{w}(t) = 0.00019R^{3.08}\sin(ϕ- (3.87R-41.79 + 4.86t))$, which provides a unified framework for describing both the geometric and kinematic evolution of the Galactic warp. We analyzed the impact of the adopted solar vertical velocity on the inferred warp precession rate and confirmed the reliability of the measured precession rate. In addition, we found that extinction treatment affects the warp amplitude in the inner disk, while its influence on the outer disk warp structure and the precession rate is negligible.

astro-ph.GA↗

arXiv2025

A search of periodic variable stars in the LMC by JWST photometry

Based on high-resolution near-infrared photometric data from the James Webb Space Telescope (JWST) targeting the Large Magellanic Cloud (LMC), this study attempts to evaluate the feasibility and sensitivity limits of variable star detection in crowded stellar fields. Through light curve analysis, we identified a total of 304 periodic variable stars, including 71 EW-type eclipsing binaries, 7 EA-type eclipsing binaries, 177 rotational variables, 38 $δ$ Scuti (DSCT) stars, and 12 RR Lyrae stars. Period--luminosity relations (PLRs) were derived for EW-type eclipsing binaries, DSCT stars, and RR Lyrae stars. The PLRs for EW-type and RR Lyrae stars are in good agreement with previous studies, while the PLR zero point for DSCT stars appears systematically fainter by approximately 0.15--0.30 mag. Our PLRs exhibit low dispersion and are minimally affected by crowding. We analyzed the capability of JWST archival data to detect low-amplitude variables and found that only stars with amplitudes greater than approximately 0.05 mag can be reliably detected. Through simulations, we quantified how increasing the number of photometric epochs improves the detectability of low-amplitude, low signal-to-noise ratio variables. Despite current limitations in observational cadence, JWST demonstrates unique advantages in detecting short-period eclipsing binaries, rotational variables, and high-amplitude pulsators. Its exceptional spatial resolution enables high-precision PLR calibrations, offering new opportunities for future studies in variable star astrophysics and extragalactic distance measurements.

astro-ph.SR↗

arXiv2025

Distances of Supernova Remnants Associated with Neutron Stars in the Galaxy

Accurate distance measurements to supernova remnants (SNRs) are essential for determining their physical parameters, such as size, age, explosion energy, and for constraining the properties of associated neutron stars (NSs). We present an extinction--distance method that combines precise Gaia DR3 photometry, parallax, and stellar parameters from the SHBoost catalog to homogeneously construct extinction--distance profiles for 44 NS-associated Galactic SNRs. Applying a statistical model, we identify clear extinction jumps along each sightline, corresponding to probable SNR distances. We classify the results into three reliability levels (A, B, and C), primarily based on comparisons with previously reported kinematic distances, supplemented by independent estimates from other methods. Our results show that the majority of reliable distances (17 Level A and 8 Level B) are located within 5 kpc, predominantly in the Local Arm. This study presents an independent and effective method for determining distances to SNRs, particularly for those with small angular sizes or located in the second and third Galactic quadrants. Although the current method is limited to within 5 kpc due to the precision constraints of Gaia parallax and photometry, the upcoming Gaia DR4 release, combined with complementary infrared data, will extend its applicability to more distant and heavily obscured SNRs, and help resolve kinematic distance ambiguities.

astro-ph.GA↗

arXiv2025

Raising the Bar: Investigating the Values of Large Language Models via Generative Evolving Testing

Warning: Contains harmful model outputs. Despite significant advancements, the propensity of Large Language Models (LLMs) to generate harmful and unethical content poses critical challenges. Measuring value alignment of LLMs becomes crucial for their regulation and responsible deployment. Although numerous benchmarks have been constructed to assess social bias, toxicity, and ethical issues in LLMs, those static benchmarks suffer from evaluation chronoeffect, in which, as models rapidly evolve, existing benchmarks may leak into training data or become saturated, overestimating ever-developing LLMs. To tackle this problem, we propose GETA, a novel generative evolving testing approach based on adaptive testing methods in measurement theory. Unlike traditional adaptive testing methods that rely on a static test item pool, GETA probes the underlying moral boundaries of LLMs by dynamically generating test items tailored to model capability. GETA co-evolves with LLMs by learning a joint distribution of item difficulty and model value conformity, thus effectively addressing evaluation chronoeffect. We evaluated various popular LLMs with GETA and demonstrated that 1) GETA can dynamically create difficulty-tailored test items and 2) GETA's evaluation results are more consistent with models' performance on unseen OOD and i.i.d. items, laying the groundwork for future evaluation paradigms.

cs.CL↗

arXiv2025

Latent Thought Models with Variational Bayes Inference-Time Computation

We propose a novel class of language models, Latent Thought Models (LTMs), which incorporate explicit latent thought vectors that follow an explicit prior model in latent space. These latent thought vectors guide the autoregressive generation of ground tokens through a Transformer decoder. Training employs a dual-rate optimization process within the classical variational Bayes framework: fast learning of local variational parameters for the posterior distribution of latent vectors (inference-time computation), and slow learning of global decoder parameters. Empirical studies reveal that LTMs possess additional scaling dimensions beyond traditional Large Language Models (LLMs), such as the number of iterations in inference-time computation and number of latent thought vectors. Higher sample efficiency can be achieved by increasing training compute per token, with further gains possible by trading model size for more inference steps. Designed based on these scaling properties, LTMs demonstrate superior sample and parameter efficiency compared to autoregressive models and discrete diffusion models. They significantly outperform these counterparts in validation perplexity and zero-shot language modeling tasks. Additionally, LTMs exhibit emergent few-shot in-context reasoning capabilities that scale with model size, and achieve competitive performance in conditional and unconditional text generation.

cs.CL↗

arXiv2025

The Samples and Binary Fractions of Red Supergiant in M31 and M33 by the HST Observations

The binarity of red supergiants (RSGs) influences their evolution and the fate of supernovae. We investigate the binary fraction of RSGs in the Andromeda Galaxy (M31) and Triangulum Galaxy (M33) using photometry from the Hubble Space Telescope (HST), which offers high spatial resolution to resolve more RSGs. A preliminary step involves identifying a reliable and complete RSG sample using the F110W $-$ F160W versus F160W diagram, yielding 2,612 RSGs from the Panchromatic Hubble Andromeda Treasury (PHAT) survey of M31 3,294 RSGs from the Panchromatic Hubble Andromeda Treasury: Triangulum Extended Region (PHATTER) survey of M33. These samples suggest total RSG populations in M31 and M33 of 6,563 and 7,572, respectively. These estimates significantly exceed previous ones from the ground-based observations, an increase attributed to the superior spatial resolution of the HST. The stellar parameters of these RSGs, including effective temperature ($T_{\mathrm{eff}}$), radius ($R$), and luminosity ($L$), are derived by fitting their spectral energy distribution (SED) across optical and near-infrared bands. Binary candidates are identified by detecting ultraviolet (UV) excesses in their SEDs compared to the single-star RSG model prediction. The binary fraction is determined to be 33.4% $\pm$ 0.9% for M31 and 30.9% $\pm$ 0.8% for M33. For more luminous RSGs with log $L/L_{\odot} > 4.0$, the binary fraction decreases to 31.6% $\pm$ 1.9% in M31 and increases to 34.7% $\pm$ 1.8% in M33, respectively. These results are in good agreement with predictions from the BPASS binary evolution model.

astro-ph.SR↗

arXiv2025

ChainMarks: Securing DNN Watermark with Cryptographic Chain

With the widespread deployment of deep neural network (DNN) models, dynamic watermarking techniques are being used to protect the intellectual property of model owners. However, recent studies have shown that existing watermarking schemes are vulnerable to watermark removal and ambiguity attacks. Besides, the vague criteria for determining watermark presence further increase the likelihood of such attacks. In this paper, we propose a secure DNN watermarking scheme named ChainMarks, which generates secure and robust watermarks by introducing a cryptographic chain into the trigger inputs and utilizes a two-phase Monte Carlo method for determining watermark presence. First, ChainMarks generates trigger inputs as a watermark dataset by repeatedly applying a hash function over a secret key, where the target labels associated with trigger inputs are generated from the digital signature of model owner. Then, the watermarked model is produced by training a DNN over both the original and watermark datasets. To verify watermarks, we compare the predicted labels of trigger inputs with the target labels and determine ownership with a more accurate decision threshold that considers the classification probability of specific models. Experimental results show that ChainMarks exhibits higher levels of robustness and security compared to state-of-the-art watermarking schemes. With a better marginal utility, ChainMarks provides a higher probability guarantee of watermark presence in DNN models with the same level of watermark accuracy.

cs.CR↗

arXiv2025

Semi-Supervised State-Space Model with Dynamic Stacking Filter for Real-World Video Deraining

Significant progress has been made in video restoration under rainy conditions over the past decade, largely propelled by advancements in deep learning. Nevertheless, existing methods that depend on paired data struggle to generalize effectively to real-world scenarios, primarily due to the disparity between synthetic and authentic rain effects. To address these limitations, we propose a dual-branch spatio-temporal state-space model to enhance rain streak removal in video sequences. Specifically, we design spatial and temporal state-space model layers to extract spatial features and incorporate temporal dependencies across frames, respectively. To improve multi-frame feature fusion, we derive a dynamic stacking filter, which adaptively approximates statistical filters for superior pixel-wise feature refinement. Moreover, we develop a median stacking loss to enable semi-supervised learning by generating pseudo-clean patches based on the sparsity prior of rain. To further explore the capacity of deraining models in supporting other vision-based tasks in rainy environments, we introduce a novel real-world benchmark focused on object detection and tracking in rainy conditions. Our method is extensively evaluated across multiple benchmarks containing numerous synthetic and real-world rainy videos, consistently demonstrating its superiority in quantitative metrics, visual quality, efficiency, and its utility for downstream tasks.

cs.CV↗

arXiv2025

Seoul National University AGN Monitoring Project. V. Velocity-resolved H-beta Reverberation Mapping and Evidence of Kinematics Evolution

We present velocity-resolved reverberation lags of H-beta for 20 active galactic nuclei (AGNs) from the Seoul National University AGN Monitoring Project. We detect unambiguous velocity-resolved structures in 12 AGNs, among which eight objects exhibit symmetric structures, two objects show inflow-like characteristics, and two objects display outflow-like signatures. For two AGNs, we successfully measure the velocity-resolved lags in different years, revealing evidence of evolving broad-line region (BLR) kinematics. By combining our sample with the literature velocity-resolved lags, we find that the symmetric velocity-resolved lags are the most common (40%) type among this sample. The frequency of inflow kinematics is also notable (20%), while outflow kinematics are less common (11%). Our sample significantly expands the previous velocity-resolved reverberation mapping sample in the high-luminosity regime, enabling us to constrain BLR kinematics across a large dynamic range of luminosity.

astro-ph.GA↗

arXiv2025

R^3-VQA: "Read the Room" by Video Social Reasoning

"Read the room" is a significant social reasoning capability in human daily life. Humans can infer others' mental states from subtle social cues. Previous social reasoning tasks and datasets lack complexity (e.g., simple scenes, basic interactions, incomplete mental state variables, single-step reasoning, etc.) and fall far short of the challenges present in real-life social interactions. In this paper, we contribute a valuable, high-quality, and comprehensive video dataset named R^3-VQA with precise and fine-grained annotations of social events and mental states (i.e., belief, intent, desire, and emotion) as well as corresponding social causal chains in complex social scenarios. Moreover, we include human-annotated and model-generated QAs. Our task R^3-VQA includes three aspects: Social Event Understanding, Mental State Estimation, and Social Causal Reasoning. As a benchmark, we comprehensively evaluate the social reasoning capabilities and consistencies of current state-of-the-art large vision-language models (LVLMs). Comprehensive experiments show that (i) LVLMs are still far from human-level consistent social reasoning in complex social scenarios; (ii) Theory of Mind (ToM) prompting can help LVLMs perform better on social reasoning tasks. We provide some of our dataset and codes in supplementary material and will release our full dataset and codes upon acceptance.

cs.CV↗