arXiv subjects

Zhen Tao

Publications and source records attributed to Zhen Tao.

At least 37 records · Page 2Linked to original sources

arXiv2025

The Phase-Space Way To Electronic Structure Theory and Subsequently Chemical Dynamics

Phase-space electronic structure theory offers up a new and powerful approach for tackling problems with coupled nuclear-electronic dynamics in a fashion that goes beyond Born-Oppenheimer (BO) theory. Whereas BO theory stipulates that we consider electronic states parameterized by nuclear position $X$ only, i.e. molecular orbitals are functions of nuclear positions but not nuclear velocities, phase-space (PS) theory allows for electronic states to be parameterized by both nuclear position X and nuclear momentum $P$. As a result, within a phase-space approach, one can directly recover many new features, including electronic momentum and vibrational circular dichroism spectra. Moreover, phase-space electronic structure theory is exact for the hydrogen atom and, for a set of model problems, the method can even improve upon vibrational energies relative to BO theory. Perhaps most importantly, the phase-space approach offers up a very new perspective on spin physics, stipulating that molecules and materials with degenerate or nearly degenerate ground states (due to spin degeneracy) display broken-symmetry ground states in their phase-space potential energy surfaces. This last feature opens up very new possibilities for exploring spin chemistry (including the Einstein-de Haas effect and chiral induced spin selectivity) within the context of more established electronic structure theory. At the end of the day, in order to tackle electronic dynamical phenomena, especially subtle problems in magnetic chemistry, it will be essential for the electronic structure community to pivot towards diagonalizing $\hat H_{PS}(X, P)$ rather than $\hat H_{BO}(X)$.

physics.chem-ph↗

arXiv2025

Conical Intersections and Electronic Momentum As Viewed From Phase Space Electronic Structure Theory

We investigate the structure of a prototypical two-state conical intersection (BeH$_2$) using a phase space electronic Hamiltonian $\hat{H}_{PS}(\bR,\bP)$ that goes beyond the Born-Oppenheimer framework. By parameterizing the electronic Schr{ö}dinger equation by both nuclear position ($\bR$) and momentum ($\bP$), we solve for quantum electronic states in a moving frame that can break time reversal symmetry and, as a result, the branching plane of the conical intersection within a phase space framework now has dimension three (rather than dimension two as found within the standard Born-Oppenheimer framework). Moreover, we note that, if one fixes a geometry in real space that lies in the conical intersection seam and scans over the corresponding momentum space, one finds a double well (with minima at $\pm \bP_{min} \ne 0$), indicating that the stationary electronic states of the phase space electronic Hamiltonian carry electronic momentum -- a feature that cannot be captured by a Born-Oppenheimer electronic state. Interestingly, for $BeH_2$, this electronic momenta (as calculated with full configuration interaction) agrees with what is predicted by approximate complex restricted Hartree-Fock calculations, indicating a physical interpretation of complex instabilities in modern electronic structure calculations. Altogether, this study suggests that we have still have a lot to learn about conical intersections when it comes to electronic momentum, and highlights the urgent need for more experiments to probe what photochemical observables can and/or cannot be captured by standard electronic structure that isolates conical intersections within the Born-Oppenheimer framework.

physics.chem-ph↗

arXiv2025

CAT-LLM: Style-enhanced Large Language Models with Text Style Definition for Chinese Article-style Transfer

Text style transfer plays a vital role in online entertainment and social media. However, existing models struggle to handle the complexity of Chinese long texts, such as rhetoric, structure, and culture, which restricts their broader application. To bridge this gap, we propose a Chinese Article-style Transfer (CAT-LLM) framework, which addresses the challenges of style transfer in complex Chinese long texts. At its core, CAT-LLM features a bespoke pluggable Text Style Definition (TSD) module that integrates machine learning algorithms to analyze and model article styles at both word and sentence levels. This module acts as a bridge, enabling LLMs to better understand and adapt to the complexities of Chinese article styles. Furthermore, it supports the dynamic expansion of internal style trees, enabling the framework to seamlessly incorporate new and diverse style definitions, enhancing adaptability and scalability for future research and applications. Additionally, to facilitate robust evaluation, we created ten parallel datasets using a combination of ChatGPT and various Chinese texts, each corresponding to distinct writing styles, significantly improving the accuracy of the model evaluation and establishing a novel paradigm for text style transfer research. Extensive experimental results demonstrate that CAT-LLM, combined with GPT-3.5-Turbo, achieves state-of-the-art performance, with a transfer accuracy F1 score of 79.36% and a content preservation F1 score of 96.47% on the "Fortress Besieged" dataset. These results highlight CAT-LLM's innovative contributions to style transfer research, including its ability to preserve content integrity while achieving precise and flexible style transfer across diverse Chinese text domains. Building on these contributions, CAT-LLM presents significant potential for advancing Chinese digital media and facilitating automated content creation.

cs.CL↗

arXiv2025

Symmetry Breaking as Predicted by a Phase Space Hamiltonian with a Spin Coriolis Potential

We perform electronic structure calculations for a set of molecules with degenerate spin-dependent ground states ($^3$CH$_2$, $^2$CH$_3^{\bullet}$, $^3$O$_2$) going beyond the Born-Oppenheimer approximation and accounting for nuclear motion. According to a phase space (PS) approach that parametrizes electronic states ($|Φ\rangle$) and electronic energies ($E$) by nuclear position and momentum (i.e., $|Φ(\mathbf{R},\mathbf{P}) \rangle$ and $E(\mathbf{R},\mathbf{P})$), we find that the presence of degenerate spin degrees of freedom leads to broken symmetry ground states. More precisely, rather than a single degenerate minimum at $(\mathbf{R},\mathbf{P}) = (\mathbf{R}_{min}, 0)$, the ground state energy has two minima at $(\mathbf{R},\mathbf{P}) = (\mathbf{R}_{min}',\pm \mathbf{P}_{min})$ (where $\mathbf{R}_{min}'$ is close to $\mathbf{R}_{min}$), dramatically contradicting the notion that the total energy of the system can be written in separable form as $E = \frac{\mathbf{P}^2}{2M} + V_{el}$. Although we find that the broken symmetry solutions have small barriers between them for the small molecules, we hypothesize that the barriers should be macroscopically large for metallic solids, thus offering up a new phase-space potential energy surface for simulating the Einstein-de Haas effect.

physics.chem-ph↗

arXiv2025

Privacy Bills of Materials: A Transparent Privacy Information Inventory for Collaborative Privacy Notice Generation in Mobile App Development

Privacy regulations mandate that developers must provide authentic and comprehensive privacy notices, e.g., privacy policies or labels, to inform users of their apps' privacy practices. However, due to a lack of knowledge of privacy requirements, developers often struggle to create accurate privacy notices, especially for sophisticated mobile apps with complex features and in crowded development teams. To address these challenges, we introduce Privacy Bills of Materials (PriBOM), a systematic software engineering approach that leverages different development team roles to better capture and coordinate mobile app privacy information. PriBOM facilitates transparency-centric privacy documentation and specific privacy notice creation, enabling traceability and trackability of privacy practices. We present a pre-fill of PriBOM based on static analysis and privacy notice analysis techniques. We demonstrate the perceived usefulness of PriBOM through a human evaluation with 150 diverse participants. Our findings suggest that PriBOM could serve as a significant solution for providing privacy support in DevOps for mobile apps.

cs.CR↗

arXiv2025

Learning Accurate, Efficient, and Interpretable MLPs on Multiplex Graphs via Node-wise Multi-View Ensemble Distillation

Multiplex graphs, with multiple edge types (graph views) among common nodes, provide richer structural semantics and better modeling capabilities. Multiplex Graph Neural Networks (MGNNs), typically comprising view-specific GNNs and a multi-view integration layer, have achieved advanced performance in various downstream tasks. However, their reliance on neighborhood aggregation poses challenges for deployment in latency-sensitive applications. Motivated by recent GNN-to-MLP knowledge distillation frameworks, we propose Multiplex Graph-Free Neural Networks (MGFNN and MGFNN+) to combine MGNNs' superior performance and MLPs' efficient inference via knowledge distillation. MGFNN directly trains student MLPs with node features as input and soft labels from teacher MGNNs as targets. MGFNN+ further employs a low-rank approximation-based reparameterization to learn node-wise coefficients, enabling adaptive knowledge ensemble from each view-specific GNN. This node-wise multi-view ensemble distillation strategy allows student MLPs to learn more informative multiplex semantic knowledge for different nodes. Experiments show that MGFNNs achieve average accuracy improvements of about 10% over vanilla MLPs and perform comparably or even better to teacher MGNNs (accurate); MGFNNs achieve a 35.40$\times$-89.14$\times$ speedup in inference over MGNNs (efficient); MGFNN+ adaptively assigns different coefficients for multi-view ensemble distillation regarding different nodes (interpretable).

cs.LG↗

arXiv2025

Norm Augmented Graph AutoEncoders for Link Prediction

Link Prediction (LP) is a crucial problem in graph-structured data. Graph Neural Networks (GNNs) have gained prominence in LP, with Graph AutoEncoders (GAEs) being a notable representation. However, our empirical findings reveal that GAEs' LP performance suffers heavily from the long-tailed node degree distribution, i.e., low-degree nodes tend to exhibit inferior LP performance compared to high-degree nodes. \emph{What causes this degree-related bias, and how can it be mitigated?} In this study, we demonstrate that the norm of node embeddings learned by GAEs exhibits variation among nodes with different degrees, underscoring its central significance in influencing the final performance of LP. Specifically, embeddings with larger norms tend to guide the decoder towards predicting higher scores for positive links and lower scores for negative links, thereby contributing to superior performance. This observation motivates us to improve GAEs' LP performance on low-degree nodes by increasing their embedding norms, which can be implemented simply yet effectively by introducing additional self-loops into the training objective for low-degree nodes. This norm augmentation strategy can be seamlessly integrated into existing GAE methods with light computational cost. Extensive experiments on various datasets and GAE methods show the superior performance of norm-augmented GAEs.

cs.LG↗

arXiv2024

A Basis-Free Phase Space Electronic Hamiltonian That Recovers Beyond Born-Oppenheimer Electronic Momentum and Current Density

We present a phase-space electronic Hamiltonian $\hat{H}_{PS}$ (parameterized by both nuclear position $\mathbf{X}$ and momentum $\mathbf{P}$) that boosts each electron into the moving frame of the nuclei that are closest in real space -- without presuming the existence of an atomic orbital basis. We show that $(i)$ quantum-classical dynamics along such a Hamiltonian maintains momentum conservation and $(ii)$ diagonalizing such a Hamiltonian can recover the electronic momentum and electronic current density reasonably well. In conjunction with other reports in the literature that such a phase-space approach can also recover vibrational circular dichroism (VCD) spectra, we submit that the present phase-space approach offers a testable and powerful approach to post-Born-Oppenheimer electronic structure theory. Moreover, the approach is inexpensive and can be immediately applied to simulations of chiral induced spin selectivity experiments (where the transfer of angular momentum between nuclei and electrons is considered critical).

physics.chem-ph↗

arXiv2024

Towards Reliable Detection of LLM-Generated Texts: A Comprehensive Evaluation Framework with CUDRT

The increasing prevalence of large language models (LLMs) has significantly advanced text generation, but the human-like quality of LLM outputs presents major challenges in reliably distinguishing between human-authored and LLM-generated texts. Existing detection benchmarks are constrained by their reliance on static datasets, scenario-specific tasks (e.g., question answering and text refinement), and a primary focus on English, overlooking the diverse linguistic and operational subtleties of LLMs. To address these gaps, we propose CUDRT, a comprehensive evaluation framework and bilingual benchmark in Chinese and English, categorizing LLM activities into five key operations: Create, Update, Delete, Rewrite, and Translate. CUDRT provides extensive datasets tailored to each operation, featuring outputs from state-of-the-art LLMs to assess the reliability of LLM-generated text detectors. This framework supports scalable, reproducible experiments and enables in-depth analysis of how operational diversity, multilingual training sets, and LLM architectures influence detection performance. Our extensive experiments demonstrate the framework's capacity to optimize detection systems, providing critical insights to enhance reliability, cross-linguistic adaptability, and detection accuracy. By advancing robust methodologies for identifying LLM-generated texts, this work contributes to the development of intelligent systems capable of meeting real-world multilingual detection challenges. Source code and dataset are available at GitHub.

cs.CL↗

arXiv2024

A Phase-Space Electronic Hamiltonian for Molecules in a Static Magnetic Field I: Conservation of Total Pseudomomentum and Angular Momentum

We develop a phase-space electronic structure theory of molecules in magnetic fields. For a system of electrons in a magnetic field with vector potential $\bf{A}(\hat{\bf{r}})$, the usual Born-Oppenheimer Hamiltonian is the sum of the nuclear kinetic energy and the electronic Hamiltonian, $\frac{(\bf{P} - q\bf{A}(\bf{X}) )^2}{2M} + \hat{H}_{e}(\bf{X})$ (where $q$ is a nuclear charge). To include the effects of coupled nuclear-electron motion in the presence of magnetic field, we propose that the proper phase-space electronic structure Hamiltonian will be of the form $\frac{(\bf{P} - q^{\textit{eff}}\bf{A}(\bf{X}) - e\hat{\bfΓ})^2}{2M} + \hat{H}_{e}(\bf{X})$. Here, $q^{\textit{eff}}$ represents the {\em screened} nuclear charges and the $\hat{\bfΓ}$ term captures the local pseudomomentum of the electrons. This form reproduces exactly the energy levels for a hydrogen atom in a magnetic field; moreover, single-surface dynamics along the eigenstates is guaranteed to conserve both the total pseudomomentum as well as the total angular momentum in the direction of the magnetic field. This Hamiltonian form can be immediately implemented within modern electronic structure packages (where the electronic orbitals will now depend on nuclear position ($\bf{X}$) and nuclear momentum ($\bf{P}$)). One can expect to find novel beyond Born-Oppenheimer magnetic field effects for strong enough fields and/or nonadiabatic systems.

physics.chem-ph↗

arXiv2024

A Phase-Space Electronic Hamiltonian for Molecules in a Static Magnetic Field II: Quantum Chemistry Calculations with Gauge Invariant Atomic Orbitals

In a companion paper, we have developed a phase-space electronic structure theory of molecules in magnetic fields, whereby the electronic energy levels arise from diagonalizing a phase-space Hamiltonian $\hat H_{PS}(\bf{X},\bfΠ)$ that depends parametrically on nuclear position and momentum. The resulting eigenvalues are translationally invariant; moreover, if the magnetic field is in the $z-$direction, then the eigenvalues are also invariant to rotations around the $z-$direction. However, like all Hamiltonians in a magnetic field, the theory has a gauge degree of freedom (corresponding to the position of the magnetic origin in the vector potential), and requires either $(i)$ formally, a complete set of electronic states or $(ii)$ in practice, gauge invariant atomic orbitals (GIAOs) in order to realize such translational and rotational invariance. Here we describe how to implement a phase-space electronic Hamiltonian using GIAOs within a practical electronic structure package (in our case, Q-Chem). We further show that novel phenomena can be observed with finite $\bf{B}-$fields, including minimum energy structures with $\bfΠ_{min} \ne 0$, indicating non-zero electronic motion in the ground-state.

physics.chem-ph↗

arXiv2024

Unveiling Large Language Models Generated Texts: A Multi-Level Fine-Grained Detection Framework

Large language models (LLMs) have transformed human writing by enhancing grammar correction, content expansion, and stylistic refinement. However, their widespread use raises concerns about authorship, originality, and ethics, even potentially threatening scholarly integrity. Existing detection methods, which mainly rely on single-feature analysis and binary classification, often fail to effectively identify LLM-generated text in academic contexts. To address these challenges, we propose a novel Multi-level Fine-grained Detection (MFD) framework that detects LLM-generated text by integrating low-level structural, high-level semantic, and deep-level linguistic features, while conducting sentence-level evaluations of lexicon, grammar, and syntax for comprehensive analysis. To improve detection of subtle differences in LLM-generated text and enhance robustness against paraphrasing, we apply two mainstream evasion techniques to rewrite the text. These variations, along with original texts, are used to train a text encoder via contrastive learning, extracting high-level semantic features of sentence to boost detection generalization. Furthermore, we leverage advanced LLM to analyze the entire text and extract deep-level linguistic features, enhancing the model's ability to capture complex patterns and nuances while effectively incorporating contextual information. Extensive experiments on public datasets show that the MFD model outperforms existing methods, achieving an MAE of 0.1346 and an accuracy of 88.56%. Our research provides institutions and publishers with an effective mechanism to detect LLM-generated text, mitigating risks of compromised authorship. Educators and editors can use the model's predictions to refine verification and plagiarism prevention protocols, ensuring adherence to standards.

cs.CL↗

arXiv2024

Basis-independent quantum coherence and its distribution under relativistic motion

Recent studies have increasingly focused on the effect of relativistic motion on quantum coherence. Prior research predominantly examined the influence of relative motion on basis-dependent quantum coherence, underscoring its susceptibility to decoherence under accelerated conditions. Yet, the effect of relativistic motion on basis-independent quantum coherence, which is critical for understanding the intrinsic quantum features of a system, remains an interesting open question. This paper addresses this question by examining how total, collective, and localized coherence are affected by acceleration and coupling strength. Our analysis reveals that both total and collective coherence significantly decrease with increasing acceleration and coupling strength, ultimately vanishing at high levels of acceleration. This underscores the profound impact of Unruh thermal noise. Conversely, localized coherence exhibits relative stability, decreasing to zero only under the extreme condition of infinite acceleration. Moreover, we demonstrate that collective, localized, and basis-independent coherence collectively satisfy the triangle inequality. These findings are crucial for enhancing our understanding of quantum information dynamics in environments subjected to high acceleration and offer valuable insights on the behavior of quantum coherence under relativistic conditions.

quant-ph↗

arXiv2024

Can The Mystery of The Born-Oppenheimer Electronic Current Density Be Explained With A Simple Phase Space Electronic Hamiltonian? Yes (And A Lot More Too)

We show that a phase space electronic Hamiltonian $\hat{H}_{PS}(\mathbf{X},\mathbf{P})$, parameterized by both nuclear position $\mathbf{X}$ and momentum $\mathbf{P}$, can recover not just experimental vibrational circular dichroism (VCD) signals, but also a meaningful electronic current density that explains the features of the VCD rotatory strengths. Combined with earlier demonstrations that such Hamiltonians can also recover qualitatively correct electronic momenta with electronic densities that approximately satisfy a continuity equation, the data would suggest that we have isolated a meaningful alternative approach to electronic structure theory, one that entirely avoids Born-Oppenheimer theory and frozen nuclei. While the dynamical implications of such a phase space electronic Hamiltonian are not yet known, we hypothesize that, by offering classical trajectories the conserve the total angular momentum (unlike Born-Oppenheimer theory), this new phase space electronic structure Hamiltonian may well explain some fraction of the chiral-induced spin selectivity effect.

physics.chem-ph↗

arXiv2024

A Solution toward Transparent and Practical AI Regulation: Privacy Nutrition Labels for Open-source Generative AI-based Applications

The rapid development and widespread adoption of Generative Artificial Intelligence-based (GAI) applications have greatly enriched our daily lives, benefiting people by enhancing creativity, personalizing experiences, improving accessibility, and fostering innovation and efficiency across various domains. However, along with the development of GAI applications, concerns have been raised about transparency in their privacy practices. Traditional privacy policies often fail to effectively communicate essential privacy information due to their complexity and length, and open-source community developers often neglect privacy practices even more. Only 12.2% of examined open-source GAI apps provide a privacy policy. To address this, we propose a regulation-driven GAI Privacy Label and introduce Repo2Label, a novel framework for automatically generating these labels based on code repositories. Our user study indicates a common endorsement of the proposed GAI privacy label format. Additionally, Repo2Label achieves a precision of 0.81, recall of 0.88, and F1-score of 0.84 based on the benchmark dataset, significantly outperforming the developer self-declared privacy notices. We also discuss the common regulatory (in)compliance of open-source GAI apps, comparison with other privacy notices, and broader impacts to different stakeholders. Our findings suggest that Repo2Label could serve as a significant tool for bolstering the privacy transparency of GAI apps and make them more practical and responsible.

cs.CR↗

arXiv2024

A Phase Space Approach to Vibrational Circular Dichroism

We show empirically that a phase-space non-Born-Oppenheimer electronic Hamiltonian approach to quantum chemistry (where the electronic Hamiltonian is parameterized by both nuclear position and momentum, (H(R,P)) is both a practical and accurate means to recover vibrational circular dichroism spectra. We further hypothesize that such a phase space approach may lead to very new dynamical physics beyond spectroscopy circular dichroism, with potential implications for understanding chiral induced spin selectivity (CISS), noting that classical phase space approaches conserve the total nuclear plus electronic momentum, whereas classical Born-Oppenheimer approaches do not (they conserve only the nuclear momentum)

physics.chem-ph↗

arXiv2024

Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunities

In recent years, generative artificial intelligence models, represented by Large Language Models (LLMs) and Diffusion Models (DMs), have revolutionized content production methods. These artificial intelligence-generated content (AIGC) have become deeply embedded in various aspects of daily life and work. However, these technologies have also led to the emergence of Fake Artificial Intelligence Generated Content (FAIGC), posing new challenges in distinguishing genuine information. It is crucial to recognize that AIGC technology is akin to a double-edged sword; its potent generative capabilities, while beneficial, also pose risks for the creation and dissemination of FAIGC. In this survey, We propose a new taxonomy that provides a more comprehensive breakdown of the space of FAIGC methods today. Next, we explore the modalities and generative technologies of FAIGC. We introduce FAIGC detection methods and summarize the related benchmark from various perspectives. Finally, we discuss outstanding challenges and promising areas for future research.

cs.CL↗

arXiv2024

{A New Hope}: Contextual Privacy Policies for Mobile Applications and An Approach Toward Automated Generation

Privacy policies have emerged as the predominant approach to conveying privacy notices to mobile application users. In an effort to enhance both readability and user engagement, the concept of contextual privacy policies (CPPs) has been proposed by researchers. The aim of CPPs is to fragment privacy policies into concise snippets, displaying them only within the corresponding contexts within the application's graphical user interfaces (GUIs). In this paper, we first formulate CPP in mobile application scenario, and then present a novel multimodal framework, named SeePrivacy, specifically designed to automatically generate CPPs for mobile applications. This method uniquely integrates vision-based GUI understanding with privacy policy analysis, achieving 0.88 precision and 0.90 recall to detect contexts, as well as 0.98 precision and 0.96 recall in extracting corresponding policy segments. A human evaluation shows that 77% of the extracted privacy policy segments were perceived as well-aligned with the detected contexts. These findings suggest that SeePrivacy could serve as a significant tool for bolstering user interaction with, and understanding of, privacy policies. Furthermore, our solution has the potential to make privacy notices more accessible and inclusive, thus appealing to a broader demographic. A demonstration of our work can be accessed at https://cpp4app.github.io/SeePrivacy/

cs.CR↗