arXiv subjects

Yifei Yan

Publications and source records attributed to Yifei Yan.

8 recordsLinked to original sources

arXiv2026

Characterization of ultrathin nickel films deposited by thermal laser evaporation

Thermal laser evaporation is a physical vapor deposition technique of increasing interest because of its ability to evaporate essentially any solid element, even the most refractory such as W. However, many films deposited by this method, especially non-epitaxial films, remain to be characterized; further, key system components such as the laser delivery system have not been described in detail. Here, we present the evaporation and characterization of ultrathin Ni films deposited with a home-built thermal laser evaporation system. The system employs a continuous-wave 1 kW fiber laser (1070 nm) focused to sub-millimeter diameter onto a Ni target rod mounted inside an ultrahigh-vacuum chamber. The laser heats the target to a temperature high enough to produce vapor for film deposition; for Ni, this temperature is around the melting point of 1725 K. We report the characterization of the surface roughness, composition, and room-temperature electrical properties of the films along with the design of the major components of our system. This work advances the growing consensus regarding the potential of thermal laser evaporation for thin film deposition and epitaxy and provides the necessary design information to facilitate broader adoption of the technique.

cond-mat.mtrl-sci↗

arXiv2026

A Rapid Deployment Pipeline for Autonomous Humanoid Grasping Based on Foundation Models

Deploying a humanoid robot to manipulate a new object has traditionally required one to two days of effort: data collection, manual annotation, 3D model acquisition, and model training. This paper presents an end-to-end rapid deployment pipeline that integrates three foundation-model components to shorten the onboarding cycle for a new object to approximately 30 minutes: (i) Roboflow-based automatic annotation to assist in training a YOLOv8 object detector; (ii) 3D reconstruction based on Meta SAM 3D, which eliminates the need for a dedicated laser scanner; and (iii) zero-shot 6-DoF pose tracking based on FoundationPose, using the SAM~3D-generated mesh directly as the template. The estimated pose drives a Unity-based inverse kinematics planner, whose joint commands are streamed via UDP to a Unitree~G1 humanoid and executed through the Unitree SDK. We demonstrate detection accuracy of mAP@0.5 = 0.995, pose tracking precision of $\sigma < 1.05$ mm, and successful grasping on a real robot at five positions within the workspace. We further verify the generality of the pipeline on an automobile-window glue-application task. The results show that combining foundation models for perception with everyday imaging devices (e.g., smartphones) can substantially lower the deployment barrier for humanoid manipulation tasks.

cs.RO↗

arXiv2026

Tree Learning: A Multi-Skill Continual Learning Framework for Humanoid Robots

As reinforcement learning for humanoid robots evolves from single-task to multi-skill paradigms, efficiently expanding new skills while avoiding catastrophic forgetting has become a key challenge in embodied intelligence. Existing approaches either rely on complex topology adjustments in Mixture-of-Experts (MoE) models or require training extremely large-scale models, making lightweight deployment difficult. To address this, we propose Tree Learning, a multi-skill continual learning framework for humanoid robots. The framework adopts a root-branch hierarchical parameter inheritance mechanism, providing motion priors for branch skills through parameter reuse to fundamentally prevent catastrophic forgetting. A multi-modal feedforward adaptation mechanism combining phase modulation and interpolation is designed to support both periodic and aperiodic motions. A task-level reward shaping strategy is also proposed to accelerate skill convergence. Unity-based simulation experiments show that, in contrast to simultaneous multi-task training, Tree Learning achieves higher rewards across various representative locomotion skills while maintaining a 100% skill retention rate, enabling seamless multi-skill switching and real-time interactive control. We further validate the performance and generalization capability of Tree Learning on two distinct Unity-simulated tasks: a Super Mario-inspired interactive scenario and autonomous navigation in a classical Chinese garden environment.

cs.RO↗

arXiv2026

Multi-Agent Causal Reasoning for Suicide Ideation Detection Through Online Conversations

Suicide remains a pressing global public health concern. While social media platforms offer opportunities for early risk detection through online conversation trees, existing approaches face two major limitations: (1) They rely on predefined rules (e.g., quotes or relies) to log conversations that capture only a narrow spectrum of user interactions, and (2) They overlook hidden influences such as user conformity and suicide copycat behavior, which can significantly affect suicidal expression and propagation in online communities. To address these limitations, we propose a Multi-Agent Causal Reasoning (MACR) framework that collaboratively employs a Reasoning Agent to scale user interactions and a Bias-aware Decision-Making Agent to mitigate harmful biases arising from hidden influences. The Reasoning Agent integrates cognitive appraisal theory to generate counterfactual user reactions to posts, thereby scaling user interactions. It analyses these reactions through structured dimensions, i.e., cognitive, emotional, and behavioral patterns, with a dedicated sub-agent responsible for each dimension. The Bias-aware Decision-Making Agent mitigates hidden biases through a front-door adjustment strategy, leveraging the counterfactual user reactions produced by the Reasoning Agent. Through the collaboration of reasoning and bias-aware decision making, the proposed MACR framework not only alleviates hidden biases, but also enriches contextual information of user interactions with counterfactual knowledge. Extensive experiments on real-world conversational datasets demonstrate the effectiveness and robustness of MACR in identifying suicide risk.

cs.CL↗

arXiv2025

Protective Factor-Aware Dynamic Influence Learning for Suicide Risk Prediction on Social Media

Suicide is a critical global health issue that requires urgent attention. Even though prior work has revealed valuable insights into detecting current suicide risk on social media, little attention has been paid to developing models that can predict subsequent suicide risk over time, limiting their ability to capture rapid fluctuations in individuals' mental state transitions. In addition, existing work ignores protective factors that play a crucial role in suicide risk prediction, focusing predominantly on risk factors alone. Protective factors such as social support and coping strategies can mitigate suicide risk by moderating the impact of risk factors. Therefore, this study proposes a novel framework for predicting subsequent suicide risk by jointly learning the dynamic influence of both risk factors and protective factors on users' suicide risk transitions. We propose a novel Protective Factor-Aware Dataset, which is built from 12 years of Reddit posts along with comprehensive annotations of suicide risk and both risk and protective factors. We also introduce a Dynamic Factors Influence Learning approach that captures the varying impact of risk and protective factors on suicide risk transitions, recognizing that suicide risk fluctuates over time according to established psychological theories. Our thorough experiments demonstrate that the proposed model significantly outperforms state-of-the-art models and large language models across three datasets. In addition, the proposed Dynamic Factors Influence Learning provides interpretable weights, helping clinicians better understand suicidal patterns and enabling more targeted intervention strategies.

cs.CL↗

arXiv2025

Bayesian Inference of Geometric Brownian Motion: An Extension with Jumps

This analysis derives the maximum likelihood estimator and applies Bayesian inference to model geometric Brownian motion, incorporating jump diffusion to account for sudden market shifts. The Bayesian approach is implemented using Markov Chain Monte Carlo simulations on S\&P 500 stock data from 2009 to 2014, providing a robust framework for analyzing stock dynamics and forecasting future trends. Exact solutions are obtained for both the standard Geometric Brownian Motion (GBM) model and the GBM model with Poisson jumps. Although both models yield reasonable results and fit the data well, the GBM with Poisson jumps exhibits superior performance, significantly enhancing model fit and capturing more complex market dynamics.

stat.AP↗

arXiv2025

Advances in Bayesian Modeling: Applications and Methods

This paper explores the versatility and depth of Bayesian modeling by presenting a comprehensive range of applications and methods, combining Markov chain Monte Carlo (MCMC) techniques and variational approximations. Covering topics such as hierarchical modeling, spatial modeling, higher-order Markov chains, and Bayesian nonparametrics, the study emphasizes practical implementations across diverse fields, including oceanography, climatology, epidemiology, astronomy, and financial analysis. The aim is to bridge theoretical underpinnings with real-world applications, illustrating the formulation of Bayesian models, elicitation of priors, computational strategies, and posterior and predictive analyses. By leveraging different computational methods, this paper provides insights into model fitting, goodness-of-fit evaluation, and predictive accuracy, addressing computational efficiency and methodological challenges across various datasets and domains.

stat.AP↗

arXiv2017

A New Family of Error Distributions for Bayesian Quantile Regression

We propose a new family of error distributions for model-based quantile regression, which is constructed through a structured mixture of normal distributions. The construction enables fixing specific percentiles of the distribution while, at the same time, allowing for varying mode, skewness and tail behavior. It thus overcomes the severe limitation of the asymmetric Laplace distribution -- the most commonly used error model for parametric quantile regression -- for which the skewness of the error density is fully specified when a particular percentile is fixed. We develop a Bayesian formulation for the proposed quantile regression model, including conditional lasso regularized quantile regression based on a hierarchical Laplace prior for the regression coefficients, and a Tobit quantile regression model. Posterior inference is implemented via Markov Chain Monte Carlo methods. The flexibility of the new model relative to the asymmetric Laplace distribution is studied through relevant model properties, and through a simulation experiment to compare the two error distributions in regularized quantile regression. Moreover, model performance in linear quantile regression, regularized quantile regression, and Tobit quantile regression is illustrated with data examples that have been previously considered in the literature.

stat.ME↗