arXiv subjects

Yu Zhai

Publications and source records attributed to Yu Zhai.

10 recordsLinked to original sources

arXiv2026

Geometric Phase Effect in Thermodynamic Properties and in the Imaginary-Time Multi-Electronic-State Path Integral Formulation

The geometric phase (GP) is a fundamental quantum effect arising from conical intersections (CIs), with profound consequences for vibronic energy levels. Standard imaginary-time path integral molecular dynamics (PIMD) based on the Born-Oppenheimer approximation does not account for the GP, potentially leading to significant errors in low-temperature thermodynamic properties. In this Perspective, we demonstrate that the multi-electronic-state path integral (MES-PI) formulation in imaginary time (developed in J. Chem. Phys. 2018, 148, 102319) naturally captures the GP effect through the electronic trace of the product of statistically weighted overlap matrices between successive imaginary-time slices. This crucial capability was already implicit in the benchmark MES-PIMD simulations in that foundational work. To isolate this topological effect from other nonadiabatic effects, we introduce a geometric signature matrix (for the CI) and a winding-number-induced phase factor, constructing an ad hoc GP-excluded MES-PI method. Comparing this ad hoc baseline against the rigorous MES-PI approach allows us to unambiguously quantify the impact of the GP on thermodynamic properties. While simpler approximations exist when only the ground electronic-state is considered, MES-PIMD is the most general and accurate approach applicable to real complex systems where the location and topology of CI seams are often not known a priori.

physics.chem-ph↗

arXiv2026

MedPriv-Bench: Benchmarking the Privacy-Utility Trade-off of Large Language Models in Medical Open-Ended Question Answering

Recent advances in Retrieval-Augmented Generation enable LLMs to ground outputs in clinical evidence, but connections to external databases create the risk of contextual leakage, where unique combinations of medical details enable patient re-identification without explicit identifiers. Existing healthcare benchmarks emphasize accuracy while overlooking this risk. To fill this gap, we present MedPriv-Bench, the first benchmark for jointly evaluating privacy preservation and clinical utility in medical open-ended question answering. Our framework utilizes a multi-agent, human-in-the-loop pipeline to synthesize sensitive medical contexts and clinically relevant queries that create realistic privacy pressure. We also establish an automated evaluation protocol using a fine-tuned RoBERTa-NLI model, which achieved an instance-level F1 score of 75.3%, sensitivity of 90.7%, and an average inference time of 0.056 s per sample against human annotations. Across nine LLMs and three privacy-preserving methods, we observed a pervasive privacy-utility trade-off. Relative to unprotected Med42-v2-8B (utility 3.87/5; leakage 72.8%), supervised fine-tuning improved utility to 4.25 and reduced leakage to 38.9%, whereas local differential privacy reduced leakage to 20.5% but lowered utility to 3.03. These results demonstrate the need for domain-specific benchmarks to validate medical AI systems in privacy-sensitive settings.

cs.CL↗

arXiv2025

ExplainableGuard: Interpretable Adversarial Defense for Large Language Models Using Chain-of-Thought Reasoning

Large Language Models (LLMs) are increasingly vulnerable to adversarial attacks that can subtly manipulate their outputs. While various defense mechanisms have been proposed, many operate as black boxes, lacking transparency in their decision-making. This paper introduces ExplainableGuard, an interpretable adversarial defense framework leveraging the chain-of-thought (CoT) reasoning capabilities of DeepSeek-Reasoner. Our approach not only detects and neutralizes adversarial perturbations in text but also provides step-by-step explanations for each defense action. We demonstrate how tailored CoT prompts guide the LLM to perform a multi-faceted analysis (character, word, structural, and semantic) and generate a purified output along with a human-readable justification. Preliminary results on the GLUE Benchmark and IMDB Movie Reviews dataset show promising defense efficacy. Additionally, a human evaluation study reveals that ExplainableGuard's explanations outperform ablated variants in clarity, specificity, and actionability, with a 72.5% deployability-trust rating, underscoring its potential for more trustworthy LLM deployments.

cs.CR↗

arXiv2025

Disentangling Cation-Polyanion Coupling in Solid Electrolytes: Which Anion Motion Dominates Cation Transport?

Lithium and sodium solid electrolytes feature polyanion frameworks and highly mobile cations. Understanding and quantifying the impact of polyanion dynamics on cations will help us to unravel the complex role that anion play in superionic conductors. However, no experimental or computational method can directly extract this information, as polyanion dynamics are always coupled with other factors that affect ion mobility. Here, we present the pioneering study that combines constraint algorithm and machine-learning molecular dynamics to quantitatively reveal the effects of polyanion translation, rotation, and vibration on cation mobility across a diverse material class. Ultralong-time, large-scale machine-learning molecular dynamics simulations with selective constraints on each anion motion mode unequivocally yield results at near room and elevated temperatures. In sharp contrast to the previous understanding that facile anion rotation primarily facilitates cation transport, the strong coupling between anion translation and vibration with cation diffusion has been unraveled for the first time; we find that translation, rotation, and vibration can each directly drive superionicity, with one typically dominant in each class of materials. Anion rotation dominates cation transport when the rotation frequency matches the cation hopping frequency, whereas anion translation prevails at higher and vibration at lower rotation frequencies. The impact of anion dynamics on cation diffusion becomes more prominent at lower temperatures.

physics.chem-ph↗

arXiv2024

Asymptotics of the confluent hypergeometric process with a varying external potential in the super-exponential region

In this paper, we investigate a determinantal point process on the interval $(-s,s)$, associated with the confluent hypergeometric kernel. Let $\mathcal{K}^{(\alpha,\beta)}_s$ denote the trace class integral operator acting on $L^2(-s, s)$ with the confluent hypergeometric kernel. Our focus is on deriving the asymptotics of the Fredholm determinant $\det(I-\gamma \mathcal{K}^{(\alpha,\beta)}_s)$ as $s \to +\infty$, while simultaneously $\gamma \to 1^-$ in a super-exponential region. In this regime of double scaling limit, our asymptotic result also gives us asymptotics of the eigenvalues $\lambda^{(\alpha, \beta)}_k(s)$ of the integral operator $\mathcal{K}^{(\alpha,\beta)}_s$ as $s \to +\infty$. Based on the integrable structure of the confluent hypergeometric kernel, we derive our asymptotic results by applying the Deift-Zhou nonlinear steepest descent method to analyze the related Riemann-Hilbert problem.

math.PR↗

arXiv2022

BALF: Simple and Efficient Blur Aware Local Feature Detector

Local feature detection is a key ingredient of many image processing and computer vision applications, such as visual odometry and localization. Most existing algorithms focus on feature detection from a sharp image. They would thus have degraded performance once the image is blurred, which could happen easily under low-lighting conditions. To address this issue, we propose a simple yet both efficient and effective keypoint detection method that is able to accurately localize the salient keypoints in a blurred image. Our method takes advantages of a novel multi-layer perceptron (MLP) based architecture that significantly improve the detection repeatability for a blurred image. The network is also light-weight and able to run in real-time, which enables its deployment for time-constrained applications. Extensive experimental results demonstrate that our detector is able to improve the detection repeatability with blurred images, while keeping comparable performance as existing state-of-the-art detectors for sharp images.

cs.CV↗

arXiv2022

Peng: A program for transport properties of low-density binary gas mixtures

The fundamental properties of molecules bridge experiment and theory. Transport properties (diffusion, thermal diffusion, thermal conductivity and viscosity) of binary mixtures are measurable in experiments, and well-defined in theory, but difficult to compute with high accuracy. In addition to high-accuracy inter-molecular potential energy curves (PECs), a reliable and high-order solution program that compute the properties based on the PECs is required. In this work, we present a computer program called Peng that performs the collision integration numerically, and solves the Boltzmann equation in Chapman-Enskog fashion. The program has been devised to perform both parts of the solution procedure to arbitrary order, so that no hard-coded limitation will prevent a user from computing at higher precision, except the amount of RAM and the required computational time. Peng is well-designed in an Object-Oriented Programming (OOP) fashion, which make the program clear and easy to modify. In addition to the end-user oriented program, Peng is also compiled as a dynamic shared library that may readily be extended and embedded in users' programs.

physics.chem-ph↗

arXiv2022

Asymptotics of the deformed Fredholm determinant of the confluent hypergeometric kernel

In this paper, we consider the deformed Fredholm determinant of the confluent hypergeometric kernel. This determinant represents the gap probability of the corresponding determinantal point process where each particle is removed independently with probability $1- \gamma$, $0 \leq \gamma <1$. We derive asymptotics of the deformed Fredholm determinant when the gap interval tends to infinity, up to and including the constant term. As an application of our results, we establish a central limit theorem for the eigenvalue counting function and a global rigidity upper bound for its maximum deviation.

math-ph↗

arXiv2020

Watch the Unobserved: A Simple Approach to Parallelizing Monte Carlo Tree Search

Monte Carlo Tree Search (MCTS) algorithms have achieved great success on many challenging benchmarks (e.g., Computer Go). However, they generally require a large number of rollouts, making their applications costly. Furthermore, it is also extremely challenging to parallelize MCTS due to its inherent sequential nature: each rollout heavily relies on the statistics (e.g., node visitation counts) estimated from previous simulations to achieve an effective exploration-exploitation tradeoff. In spite of these difficulties, we develop an algorithm, WU-UCT, to effectively parallelize MCTS, which achieves linear speedup and exhibits only limited performance loss with an increasing number of workers. The key idea in WU-UCT is a set of statistics that we introduce to track the number of on-going yet incomplete simulation queries (named as unobserved samples). These statistics are used to modify the UCT tree policy in the selection steps in a principled manner to retain effective exploration-exploitation tradeoff when we parallelize the most time-consuming expansion and simulation steps. Experiments on a proprietary benchmark and the Atari Game benchmark demonstrate the linear speedup and the superior performance of WU-UCT comparing to existing techniques.

cs.LG↗

arXiv2006

No invariant line fields on Cantor Julia sets

In this paper, we prove that a rational map with a Cantor Julia set carries no invariant line fields on its Julia set. It follows that a structurally stable rational map with a Cantor Julia set is hyperbolic.

math.DS↗