arXiv subjects

Min Dai

Publications and source records attributed to Min Dai.

At least 19 recordsLinked to original sources

arXiv2026

JWST Reveals a Galaxy-Wide Association of Red Supergiants with OB Stars in NGC 5584

The spatial relationship between young and evolved massive-star tracers offers a means of probing recent star formation across galactic disks. We combine JWST/NIRCam, $Swift$/UVOT UVW2, and GALEX FUV/NUV images to investigate the spatial association between red-supergiant (RSG) candidates and UV-selected star-forming complexes (SFCs) in the spiral galaxy NGC 5584. We identify 5,310 RSG candidates using colour--magnitude criteria and define 106 UV-selected SFCs from the UVOT/UVW2 morphology, with their FUV and NUV luminosities measured independently using the GALEX images. We find that 82% of the UV-selected SFCs overlap significant RSG overdensities, indicating a galaxy-wide statistical association between UV-bright SFCs and the evolved massive-star population. Among the 93 SFCs with reliable JWST coverage, the UV-$\beta$-corrected FUV and NUV luminosities correlate tightly with the JWST/NIRCam luminosities, with Pearson correlation coefficients of $r=0.962$--$0.971$. MIST isochrone comparisons for 43 RSG-rich SFCs indicate a characteristic age of $\log(\mathrm{age/yr})\simeq 7.16$. These results show that RSG overdensities and UV-selected SFCs trace related but distinct phases of recent massive-star formation. Their spatial correspondence is statistical rather than one-to-one and likely reflects differences in stellar age, dust extinction, spatial resolution, aperture coverage, and local star-formation history.

astro-ph.SR↗

arXiv2026

Merton's Problem with Recursive Perturbed Utility

The classical Merton investment problem predicts deterministic, state-dependent portfolio rules; however, laboratory and field evidence suggests that individuals often prefer randomized decisions leading to stochastic and noisy choices. Fudenberg et al. (2015) develop the additive perturbed utility theory to explain the preference for randomization in the static setting, which, however, becomes ill-posed or intractable in the dynamic setting. We introduce the recursive perturbed utility (RPU), a special stochastic differential utility that incorporates an entropy-based preference for randomization into a recursive aggregator. RPU endogenizes the intertemporal trade-off between utilities from randomization and bequest via a discounting term dependent on past accumulated randomization, thereby avoiding excessive randomization and yielding a well-posed problem. In a general Markovian incomplete market with CRRA preferences, we prove that the RPU-optimal portfolio policy (in terms of the risk exposure ratio) is Gaussian and can be expressed in closed form, independent of wealth. Its variance is inversely proportional to risk aversion and stock volatility, while its mean is based on the solution to a partial differential equation. Moreover, the mean is the sum of a myopic term and an intertemporal hedging term (against market incompleteness) that intertwines with policy randomization. Finally, we carry out an asymptotic expansion in terms of the perturbed utility weight to show that the optimal mean policy deviates from the classical Merton policy at first order, while the associated relative wealth loss is of a higher order, quantifying the financial cost of the preference for randomization.

q-fin.MF↗

arXiv2026

Walk the PLANC: Physics-Guided RL for Agile Humanoid Locomotion on Constrained Footholds

Bipedal humanoid robots must precisely coordinate balance, timing, and contact decisions when locomoting on constrained footholds such as stepping stones, beams, and planks -- even minor errors can lead to catastrophic failure. Classical optimization and control pipelines handle these constraints well but depend on highly accurate mathematical representations of terrain geometry, making them prone to error when perception is noisy or incomplete. Meanwhile, reinforcement learning has shown strong resilience to disturbances and modeling errors, yet end-to-end policies rarely discover the precise foothold placement and step sequencing required for discontinuous terrain. These contrasting limitations motivate approaches that guide learning with physics-based structure rather than relying purely on reward shaping. In this work, we introduce a locomotion framework in which a reduced-order stepping planner supplies dynamically consistent motion targets that steer the RL training process via Control Lyapunov Function (CLF) rewards. This combination of structured footstep planning and data-driven adaptation produces accurate, agile, and hardware-validated stepping-stone locomotion on a humanoid robot, substantially improving reliability compared to conventional model-free reinforcement-learning baselines.

cs.RO↗

arXiv2025

RoMoCo: Robotic Motion Control Toolbox for Reduced-Order Model-Based Locomotion on Bipedal and Humanoid Robots

We present RoMoCo, an open-source C++ toolbox for the synthesis and evaluation of reduced-order model-based planners and whole-body controllers for bipedal and humanoid robots. RoMoCo's modular architecture unifies state-of-the-art planners and whole-body locomotion controllers under a consistent API, enabling rapid prototyping and reproducible benchmarking. By leveraging reduced-order models for platform-agnostic gait generation, RoMoCo enables flexible controller design across diverse robots. We demonstrate its versatility and performance through extensive simulations on the Cassie, Unitree H1, and G1 robots, and validate its real-world efficacy with hardware experiments on the Cassie and G1 humanoids.

cs.RO↗

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

Robust Push Recovery on Bipedal Robots: Leveraging Multi-Domain Hybrid Systems with Reduced-Order Model Predictive Control

In this paper, we present a novel control framework to achieve robust push recovery on bipedal robots while locomoting. The key contribution is the unification of hybrid system models of locomotion with a reduced-order model predictive controller determining: foot placement, step timing, and ankle control. The proposed reduced-order model is an augmented Linear Inverted Pendulum model with zero moment point coordinates; this is integrated within a model predictive control framework for robust stabilization under external disturbances. By explicitly leveraging the hybrid dynamics of locomotion, our approach significantly improves stability and robustness across varying walking heights, speeds, step durations, and is effective for both flat-footed and more complex multi-domain heel-to-toe walking patterns. The framework is validated with high-fidelity simulation on Cassie, a 3D underactuated robot, showcasing real-time feasibility and substantially improved stability. The results demonstrate the robustness of the proposed method in dynamic environments.

cs.RO↗

arXiv2025

The Binary Fraction of Red Supergiants in the Magellanic Clouds

Red supergiants (RSGs), as the descendants of OB-type stars and the progenitors of supernovae, provide crucial insights into the evolution of massive stars, particularly in binary systems. Previous studies show that the binary fraction of RSGs ($\approx$ 15% - 40%) is significantly lower than that of their predecessors ($\approx$ 50% - 70%). In this work, we investigate the binary fraction of RSGs with the recently selected largest samples of 4695 and 2097 RSGs in the Large Magellanic Cloud (LMC) and Small Magellanic Cloud (SMC), respectively. The binary system with a hot companion (O-, B- and A-type star) is identified by detecting the ultraviolet (UV) excess in the observed spectral energy distribution (SED) ranging from ultraviolet to mid-infrared after subtracting the model SED of RSG since RSGs are very weak in the UV band. It is found that the lower limit of binarity is 30.2% $\pm$ 0.7% and 32.2% $\pm$ 1% in the LMC and SMC, respectively. If the sample is limited to luminous RSGs with log $L/L_{\odot} > 4.0$, the binary fraction becomes 26.6% $\pm$ 1.1% and 26.4% $\pm$ 1.7% in the LMC and SMC, respectively. The derived binary fraction is valid in the range of $\sim$ 2.3 < $\log P / [\text{d}]$ < $\sim$ 8. Our study suggests that roughly one-third of massive stars host a third companion within $\sim$ 30,000 AU. In addition, 15 RSGs are also identified as binary via HST/STIS spectra, and a handful of the binaries identified by the SED fitting are confirmed by their light curve and radial velocity dispersion. The stellar parameters of the companions, i.e. $T_{\mathrm{eff}}$, $R$, $L$ and log $g$, are calculated by model fitting.

astro-ph.SR↗

arXiv2024

Safety-critical Locomotion of Biped Robots in Infeasible Paths: Overcoming Obstacles during Navigation toward Destination

This paper proposes a safety-critical locomotion control framework employed for legged robots exploring through infeasible path in obstacle-rich environments. Our research focus is on achieving safe and robust locomotion where robots confront unavoidable obstacles en route to their designated destination. Through the utilization of outcomes from physical interactions with unknown objects, we establish a hierarchy among the safety-critical conditions avoiding the obstacles. This hierarchy enables the generation of a safe reference trajectory that adeptly mitigates conflicts among safety conditions and reduce the risk while controlling the robot toward its destination without additional motion planning methods. In addition, robust bipedal locomotion is achieved by utilizing the Hybrid Linear Inverted Pendulum model, coupled with a disturbance observer addressing a disturbance from the physical interaction.

cs.RO↗

arXiv2024

Learning to Optimally Stop Diffusion Processes, with Financial Applications

We study optimal stopping for diffusion processes with unknown model primitives within the continuous-time reinforcement learning (RL) framework developed by Wang et al. (2020), and present applications to option pricing and portfolio choice. By penalizing the corresponding variational inequality formulation, we transform the stopping problem into a stochastic optimal control problem with two actions. We then randomize controls into Bernoulli distributions and add an entropy regularizer to encourage exploration. We derive a semi-analytical optimal Bernoulli distribution, based on which we devise RL algorithms using the martingale approach established in Jia and Zhou (2022a). We establish a policy improvement theorem and prove the fast convergence of the resulting policy iterations. We demonstrate the effectiveness of the algorithms in pricing finite-horizon American put options, solving Merton's problem with transaction costs, and scaling to high-dimensional optimal stopping problems. In particular, we show that both the offline and online algorithms achieve high accuracy in learning the value functions and characterizing the associated free boundaries.

math.OC↗

arXiv2024

Data-driven Option Pricing

We propose an innovative data-driven option pricing methodology that relies exclusively on the dataset of historical underlying asset prices. While the dataset is rooted in the objective world, option prices are commonly expressed as discounted expectations of their terminal payoffs in a risk-neutral world. Bridging this gap motivates us to identify a pricing kernel process, transforming option pricing into evaluating expectations in the objective world. We recover the pricing kernel by solving a utility maximization problem, and evaluate the expectations in terms of a functional optimization problem. Leveraging the deep learning technique, we design data-driven algorithms to solve both optimization problems over the dataset. Numerical experiments are presented to demonstrate the efficiency of our methodology.

q-fin.PR↗

arXiv2023

Data-Driven Merton's Strategies via Policy Randomization

We study Merton's expected utility maximization problem in an incomplete market, characterized by a factor process in addition to the stock price process, where all the model primitives are unknown. The agent under consideration is a price taker who has access only to the stock and factor value processes and the instantaneous volatility. We propose an auxiliary problem in which the agent can invoke policy randomization according to a specific class of Gaussian distributions, and prove that the mean of its optimal Gaussian policy solves the original Merton problem. With randomized policies, we are in the realm of continuous-time reinforcement learning (RL) recently developed in Wang et al. (2020) and Jia and Zhou (2022a, 2022b, 2023), enabling us to solve the auxiliary problem in a data-driven way without having to estimate the model primitives. Specifically, we establish a policy improvement theorem based on which we design both online and offline actor-critic RL algorithms for learning Merton's strategies. A key insight from this study is that RL in general and policy randomization in particular are useful beyond the purpose for exploration -- they can be employed as a technical tool to solve a problem that cannot be otherwise solved by mere deterministic policies. At last, we carry out both simulation and empirical studies in a stochastic volatility environment to demonstrate the decisive outperformance of the devised RL algorithms in comparison to the conventional model-based, plug-in method.

q-fin.PM↗

arXiv2023

Multi-Domain Walking with Reduced-Order Models of Locomotion

Drawing inspiration from human multi-domain walking, this work presents a novel reduced-order model based framework for realizing multi-domain robotic walking. At the core of our approach is the viewpoint that human walking can be represented by a hybrid dynamical system, with continuous phases that are fully-actuated, under-actuated, and over-actuated and discrete changes in actuation type occurring with changes in contact. Leveraging this perspective, we synthesize a multi-domain linear inverted pendulum (MLIP) model of locomotion. Utilizing the step-to-step dynamics of the MLIP model, we successfully demonstrate multi-domain walking behaviors on the bipedal robot Cassie -- a high degree of freedom 3D bipedal robot. Thus, we show the ability to bridge the gap between multi-domain reduced order models and full-order multi-contact locomotion. Additionally, our results showcase the ability of the proposed method to achieve versatile speed-tracking performance and robust push recovery behaviors.

cs.RO↗

arXiv2023

A Fast Fourier Convolutional Deep Neural Network For Accurate and Explainable Discrimination Of Wheat Yellow Rust And Nitrogen Deficiency From Sentinel-2 Time-Series Data

Accurate and timely detection of plant stress is essential for yield protection, allowing better-targeted intervention strategies. Recent advances in remote sensing and deep learning have shown great potential for rapid non-invasive detection of plant stress in a fully automated and reproducible manner. However, the existing models always face several challenges: 1) computational inefficiency and the misclassifications between the different stresses with similar symptoms; and 2) the poor interpretability of the host-stress interaction. In this work, we propose a novel fast Fourier Convolutional Neural Network (FFDNN) for accurate and explainable detection of two plant stresses with similar symptoms (i.e. Wheat Yellow Rust And Nitrogen Deficiency). Specifically, unlike the existing CNN models, the main components of the proposed model include: 1) a fast Fourier convolutional block, a newly fast Fourier transformation kernel as the basic perception unit, to substitute the traditional convolutional kernel to capture both local and global responses to plant stress in various time-scale and improve computing efficiency with reduced learning parameters in Fourier domain; 2) Capsule Feature Encoder to encapsulate the extracted features into a series of vector features to represent part-to-whole relationship with the hierarchical structure of the host-stress interactions of the specific stress. In addition, in order to alleviate over-fitting, a photochemical vegetation indices-based filter is placed as pre-processing operator to remove the non-photochemical noises from the input Sentinel-2 time series.

cs.CV↗

arXiv2023

Multi-task Meta Label Correction for Time Series Prediction

Time series classification faces two unavoidable problems. One is partial feature information and the other is poor label quality, which may affect model performance. To address the above issues, we create a label correction method to time series data with meta-learning under a multi-task framework. There are three main contributions. First, we train the label correction model with a two-branch neural network in the outer loop. While in the model-agnostic inner loop, we use pre-existing classification models in a multi-task way and jointly update the meta-knowledge so as to help us achieve adaptive labeling on complex time series. Second, we devise new data visualization methods for both image patterns of the historical data and data in the prediction horizon. Finally, we test our method with various financial datasets, including XOM, S\&P500, and SZ50. Results show that our method is more effective and accurate than some existing label correction techniques.

cs.LG↗

arXiv2022

Data-driven Adaptation for Robust Bipedal Locomotion with Step-to-Step Dynamics

This paper presents an online framework for synthesizing agile locomotion for bipedal robots that adapts to unknown environments, modeling errors, and external disturbances. To this end, we leverage step-to-step (S2S) dynamics which has proven effective in realizing dynamic walking on underactuated robots -- assuming known dynamics and environments. This paper considers the case of uncertain models and environments and presents a data-driven representation of the S2S dynamics that can be learned via an adaptive control approach that is both data-efficient and easy to implement. The learned S2S controller generates desired discrete foot placement, which is then realized on the full-order dynamics of the bipedal robot by tracking desired outputs synthesized from the given foot placement. The benefits of the proposed approach are twofold. First, it improves the ability of the robot to walk at a given desired velocity when compared to the non-adaptive baseline controller. Second, the data-driven approach enables stable and agile locomotion under the effect of various unknown disturbances: additional unmodeled payload, large robot model errors, external disturbance forces, biased velocity estimation, and sloped terrains. This is demonstrated through in-depth evaluation with a high-fidelity simulation of the bipedal robot Cassie subject to the aforementioned disturbances.

cs.RO↗

arXiv2022

Primitive Graph Learning for Unified Vector Mapping

Large-scale vector mapping is important for transportation, city planning, and survey and census. We propose GraphMapper, a unified framework for end-to-end vector map extraction from satellite images. Our key idea is a novel unified representation of shapes of different topologies named "primitive graph", which is a set of shape primitives and their pairwise relationship matrix. Then, we convert vector shape prediction, regularization, and topology reconstruction into a unique primitive graph learning problem. Specifically, GraphMapper is a generic primitive graph learning network based on global shape context modelling through multi-head-attention. An embedding space sorting method is developed for accurate primitive relationship modelling. We empirically demonstrate the effectiveness of GraphMapper on two challenging mapping tasks, building footprint regularization and road network topology reconstruction. Our model outperforms state-of-the-art methods in both tasks on public benchmarks. All code will be publicly available.

cs.CV↗

arXiv2022

Learning effective dynamics from data-driven stochastic systems

Multiscale stochastic dynamical systems have been widely adopted to a variety of scientific and engineering problems due to their capability of depicting complex phenomena in many real world applications. This work is devoted to investigating the effective dynamics for slow-fast stochastic dynamical systems. Given observation data on a short-term period satisfying some unknown slow-fast stochastic systems, we propose a novel algorithm including a neural network called Auto-SDE to learn invariant slow manifold. Our approach captures the evolutionary nature of a series of time-dependent autoencoder neural networks with the loss constructed from a discretized stochastic differential equation. Our algorithm is also validated to be accurate, stable and effective through numerical experiments under various evaluation metrics.

stat.ML↗

arXiv2022

Physical Properties of 29 sdB+dM Eclipsing Binaries in Zwicky Transient Facility

The development of large-scale time-domain surveys provides an opportunity to study the physical properties as well as the evolutionary scenario of B-type subdwarfs (sdB) and M-type dwarfs (dM). Here, we obtained 33 sdB+dM eclipsing binaries based on the Zwicky Transient Facility (ZTF) light curves and $Gaia$ early data release 3 (EDR3) parallaxes. By using the PHOEBE code for light curve analysis, we obtain probability distributions for parameters of 29 sdB+dM. $R_1$, $R_2$, and $i$ are well determined, and the average uncertainty of mass ratio $q$ is 0.08. Our parameters are in good agreement with previous works if a typical mass of sdB is assumed. Based on parameters of 29 sdB+dM, we find that both the mass ratio $q$ and the companion's radius $R_2$ decrease with the shortening of the orbital period. For the three sdB+dMs with orbital periods less than 0.075 days, their companions are all brown dwarfs. The masses and radii of the companions satisfy the mass--radius relation for low-mass stars and brown dwarfs. Companions with radii between $0.12R_\odot$ and $0.15R_\odot$ seem to be missing in the observations. As more short-period sdB+dM eclipsing binaries are discovered and classified in the future with ZTF and $Gaia$, we will have more information to constrain the evolutionary ending of sdB+dM.

astro-ph.SR↗