arXiv subjects

Yuxuan Dai

Publications and source records attributed to Yuxuan Dai.

4 recordsLinked to original sources

arXiv2025

PinChecker: Identifying Unsound Safe Abstractions of Rust Pinning APIs

The pinning APIs of Rust language guarantee memory location stability for self-referential and asynchronous constructs, as long as used according to the pinning API contract. Rust ensures violations of such contract are impossible in regular safe code, but not in unsafe code where unsafe pinning APIs can be used. Library authors can encapsulate arbitrary unsafe code within regular library functions. These can be freely called in higher-level code without explicit warnings. Therefore, it is crucial to analyze library functions to rule out pinning API contract violations. Unfortunately, such testing relies on manual analysis by library authors, which is ineffective. Our goal is to develop a methodology that, given a library, attempts to construct programs that intentionally breach the pinning API contract by chaining library function calls, thereby verifying their soundness. We introduce RPIL, a novel intermediate representation that models functions' critical behaviors pertaining to pinning APIs. We implement PinChecker, a synthesis-driven violation detection tool guided by RPIL, which automatically synthesizes bug-revealing programs. Our experiments on 13 popular Rust libraries from crates.io found 2 confirmed bugs.

cs.SE↗

arXiv2024

On the non-universality of heavy quark hadronization in elementary high-energy collisions

It has been traditionally hypothesized that the heavy quark (charm, $c$ and bottom, $b$) fragmentation is universal across different collision systems, based on the notion that hadronization as a soft process should occur at the characteristic non-perturbative QCD scale, $Λ_{QCD}$. However, this universality hypothesis has recently been challenged by the observation that the $c$- and $b$-baryon production relative to their meson counterparts in minimum bias proton-proton ($pp$) collisions at the LHC energies is significantly enhanced as compared to the electron-positron ($e^+e^-$) collisions. The conception of non-universality is unambiguously reinforced by the latest measurement of the charged-particle multiplicity dependence of the $b$-baryon-to-meson yield ratio, $Λ_b/B$, by the LHCb experiment in $\sqrt{s}=13$\,TeV $pp$ collisions at the LHC, evolving continuously from the saturation value in minimum bias $pp$ collisions toward the small value in $e^+e^-$ collisions as the system size gradually reduces. We address the multiplicity dependence of $b$-baryon production in the canonical statistical hadronization model with input $b$-hadron spectrum augmented with many hitherto unobserved states from quark model predictions. We demonstrate that the decreasing trend of the $Λ_b/B$ toward low multiplicities can be quantitatively understood from the canonical suppression on the yield of $Λ_b$, as caused by the requirement of strict conservation of baryon number in sufficiently small systems. We have therefore proposed a plausible scenario for understanding the origin of the non-universality of heavy quark fragmentation in elementary collisions.

hep-ph↗

arXiv2022

Pre-Training on Dynamic Graph Neural Networks

The pre-training on the graph neural network model can learn the general features of large-scale networks or networks of the same type by self-supervised methods, which allows the model to work even when node labels are missing. However, the existing pre-training methods do not take network evolution into consideration. This paper proposes a pre-training method on dynamic graph neural networks (PT-DGNN), which uses dynamic attributed graph generation tasks to simultaneously learn the structure, semantics, and evolution features of the graph. The method includes two steps: 1) dynamic sub-graph sampling, and 2) pre-training with dynamic attributed graph generation task. Comparative experiments on three realistic dynamic network datasets show that the proposed method achieves the best results on the link prediction fine-tuning task.

cs.LG↗

arXiv2021

Transferring Reinforcement Learning for DC-DC Buck Converter Control via Duty Ratio Mapping: From Simulation to Implementation

Reinforcement learning (RL) control approach with application into power electronics systems has become an emerging topic whilst the sim-to-real issue remains a challenging problem as very few results can be referred to in the literature. Indeed, due to the inevitable mismatch between simulation models and real-life systems, offline trained RL control strategies may sustain unexpected hurdles in practical implementation during transferring procedure. As the main contribution of this paper, a transferring methodology via a delicately designed duty ratio mapping (DRM) is proposed for a DC-DC buck converter. Then, a detailed sim-to-real process is presented to enable the implementation of a model-free deep reinforcement learning (DRL) controller. The feasibility and effectiveness of the proposed methodology are demonstrated by comparative experimental studies.

eess.SY↗