arXiv subjects

Bin Jiang

Publications and source records attributed to Bin Jiang.

At least 37 records · Page 2Linked to original sources

arXiv2026

Stability and Robustness of Tensor-Coupled Flow-Conservation Dynamical Systems on Hypergraphs

This paper develops an entropy-based stability and robustness framework for nonlinear hypergraph dynamics with conservation and flow balance. We consider generator-form systems on the simplex whose state-dependent transition rates capture higher-order (tensor) interactions among nodes. Under a tensor generalized detailed-balance (TGDB) condition, we show that the system admits a unique equilibrium and an entropy Lyapunov function ensuring global asymptotic stability. The Jacobian restricted to the tangent subspace of the simplex is Hurwitz, and its spectral gap determines the exponential convergence rate. Building on this structure, we derive first-order sensitivity bounds of the equilibrium under perturbations of the coupling tensor and establish a local input-to-state stability (ISS) estimate with respect to external inputs. The results reveal a quantitative link between the spectral gap and the system's robustness margin: larger spectral gaps imply smaller equilibrium shifts and faster recovery under structural or parametric perturbations. Numerical experiments on tensor-coupled flow models confirm the theoretical predictions and illustrate how the proposed entropy-dissipating framework unifies stability and robustness analysis for conservative higher-order network systems.

eess.SY↗

arXiv2026

EEG-Based Brain-LLM Interface for Human Preference Aligned Generation

Large language models (LLMs) are becoming an increasingly important component of human--computer interaction, enabling users to coordinate a wide range of intelligent agents through natural language. While language-based interfaces are powerful and flexible, they implicitly assume that users can reliably produce explicit linguistic input, an assumption that may not hold for users with speech or motor impairments, e.g., Amyotrophic Lateral Sclerosis (ALS). In this work, we investigate whether neural signals can be used as an alternative input to LLMs, particularly to support those socially marginalized or underserved users. We build a simple brain-LLM interface, which uses EEG signals to guide image generation models at test time. Specifically, we first train a classifier to estimate user satisfaction from EEG signals. Its predictions are then incorporated into a test-time scaling (TTS) framework that dynamically adapts model inference using neural feedback collected during user evaluation. The experiments show that EEG can predict user satisfaction, suggesting that neural activity carries information on real-time preference inference. These findings provide a first step toward integrating neural feedback into adaptive language-model inference, and hopefully open up new possibilities for future research on adaptive LLM interaction.

eess.SP↗

arXiv2025

Angular dependence of third-order law in anisotropic MHD turbulence

In solar wind turbulence, the energy transfer/dissipation rate is typically estimated using MHD third-order structure functions calculated using spacecraft observations. However, the inherent anisotropy of solar wind turbulence leads to significant variations in structure functions along different observational directions, thereby affecting the accuracy of energy-dissipation rate estimation. An unresolved issue is how to optimise the selection of observation angles under limited directional sampling to improve estimation precision. We conduct a series of MHD turbulence simulations with different mean magnetic field strengths, $ B_0 $. Our analysis of the third-order structure functions reveals that the global energy dissipation rate estimated around a polar angle of $ θ= 60^\circ$ agrees reasonably with the exact one for $ 0 \le B_0/b_{rms} \le 5 $, where $b_{rms}$ denotes the root-mean-square magnetic field fluctuation. The speciality of $60^\circ$ polar angle can be understood by the Mean Value Theorem of Integrals, since the spherical integral of the polar-angle component ($\widetilde{T_θ}$) of the divergence of Yaglom flux is zero, and $\widetilde{T_θ}$ changes sign around 60$^\circ$. Existing theory on the energy flux vector as a function of the polar angle is assessed, and supports the speciality of $60^\circ$ polar angle. The angular dependence of the third-order structure functions is further assessed with virtual spacecraft data analysis. The present results can be applied to measure the turbulent dissipation rates of energy in the solar wind, which are of potential importance to other areas in which turbulence takes place, such as laboratory plasmas and astrophysics.

physics.space-ph↗

arXiv2025

Real-Time Coupled Electron-Nuclear Dynamics of Chemical Bond Formation: Hydrogen Scattering from a Semiconductor Surface

A first-principles coupled electron-nuclear dynamics simulation based on real-time, time-dependent density functional theory and Ehrenfest dynamics quantitatively repro-duces bimodal translational energy loss and angular distributions observed in experiment for hydrogen atom scattering from Ge(111)-c(2*8). The theory elucidates a site-selective mechanism of electronically nonadiabatic energy transfer associated with the formation of different Ge-H bonds. When a hydrogen atom approaches a Ge rest-atom, it is strongly accelerated toward the potential minimum forming a transient Ge-H bond and then re-flected by the repulsive wall. This transient bond formation triggers an ultrafast electron transfer event from the rest-atom to an adjacent Ge-adatom, involving several crossings between valence and conduction bands of the substrate. Electronic equilibration is impos-sible within such a short time (Born-Oppenheimer failure) allowing the H-atom kinetic energy to be converted to inter-band electronic excitation of the substrate. H-atom colli-sions at other Ge atoms also form a transient bond but exhibit no electronic excitation, resulting in distinctly less efficient energy loss in scattered H-atoms. The nucle-ar-to-electronic energy transfer observed in this system reflects the electronic dynamics of covalent bond formation at a semiconductor surface, a mechanism that is quite distinct from previously identified nonadiabatic energy transfer mechanisms at metal surfaces mediated by electronic friction or transient negative ions.

physics.chem-ph↗

arXiv2025

COPE: Chain-Of-Thought Prediction Engine for Open-Source Large Language Model Based Stroke Outcome Prediction from Clinical Notes

Predicting outcomes in acute ischemic stroke (AIS) guides clinical decision-making, patient counseling, and resource allocation. Clinical notes contain rich contextual information, but their unstructured nature limits their use in traditional predictive models. We developed and evaluated the Chain-of-Thought (CoT) Outcome Prediction Engine (COPE), a reasoning-enhanced large language model framework, for predicting 90-day functional outcomes after AIS from unstructured clinical notes. This study included 464 AIS patients with discharge summaries and 90-day modified Rankin Scale (mRS) scores. COPE uses a two-step CoT framework based on sequential open-source LLaMA-3-8B models: the first generates clinical reasoning, and the second outputs an mRS prediction. We compared COPE with GPT-4.1, ClinicalBERT, a structured variable-based machine learning model (Clinical ML), and a single-step LLM without CoT. Performance was evaluated using mean absolute error (MAE), accuracy within +/-1 mRS point, and exact accuracy. COPE achieved an MAE of 1.01 (95% CI 0.92-1.11), +/-1 accuracy of 74.4% (69.9, 78.8%), and exact accuracy of 32.8% (28.0, 37.6%), comparable to GPT-4.1 and superior to ClinicalBERT [MAE 1.24 (1.13-1.36)], Clinical ML [1.28 (1.18-1.39)], and the single-step LLM [1.20 (1.09-1.33)]. Subgroup analyses showed consistent performance across sex and age, with slightly higher error among older patients, those undergoing thrombectomy, and those with longer summaries. These findings demonstrate that COPE, a lightweight, interpretable, and privacy-preserving open-source framework, provides an accurate and practical solution for outcome prediction from unstructured clinical text.

cs.AI↗

arXiv2025

Global and local observability of hypergraphs

This paper studies observability for non-uniform hypergraphs with inputs and outputs. To capture higher-order interactions, we define a canonical non-homogeneous dynamical system with nonlinear outputs on hypergraphs. We then construct algebraic necessary and sufficient conditions based on polynomial ideals and varieties for global observability at an initial state of hypergraphs. An example is given to illustrate the proposed criteria for observability. Further, necessary and sufficient conditions for local observability are derived based on rank conditions of observability matrices, which provide a framework to study local observability for non-uniform hypergraphs. Finally, the similarity of observability for hypergraphs is proposed using similarity of tensors, which reveals the relation of observability between two hypergraphs and helps to check the observability intuitively.

eess.SY↗

arXiv2025

Massive MIMO-OFDM Channel Acquisition with Multi-group Adjustable Phase Shift Pilots

Massive multiple-input multiple-output - orthogonal frequency division multiplexing (MIMO-OFDM) systems face the challenge of high channel acquisition overhead while providing significant spectral efficiency (SE). Adjustable phase shift pilots (APSPs) are an effective technique to acquire channels with low overhead by exploiting channel sparsity. In this paper, we extend it to multiple groups and propose multi-group adjustable phase shift pilots (MAPSPs) to improve SE further. We first introduce a massive MIMO-OFDM system model and transform the conventional channel model in the space-frequency domain to the angle-delay domain, obtaining a sparse channel matrix. Then, we propose a method of generating MAPSPs through multiple basic sequences and investigate channel estimation processes. By analyzing the components of pilot interference, we elucidate the underlying mechanism by which interference affects MMSE estimation. Building upon this foundation, we demonstrate the benefit of phase scheduling in MAPSP channel estimation and establish the optimal design condition tailored for scheduling. Furthermore, we propose an implementation scheme based on Zadoff-Chu sequences that includes received signal pre-processing and pilot scheduling methods to mitigate pilot interference. Simulation results indicate that the MAPSP method achieves a lower mean square error (MSE) of estimation than APSP and significantly enhances SE in mobility scenarios.

eess.SP↗

arXiv2025

A Universal Deep Learning Force Field for Molecular Dynamic Simulation and Vibrational Spectra Prediction

Accurate and efficient simulation of infrared (IR) and Raman spectra is essential for molecular identification and structural analysis. Traditional quantum chemistry methods based on the harmonic approximation neglect anharmonicity and nuclear quantum effects, while ab initio molecular dynamics (AIMD) remains computationally expensive. Here, we integrate our deep equivariant tensor attention network (DetaNet) with a velocity-Verlet integrator to enable fast and accurate machine learning molecular dynamics (MLMD) simulations for spectral prediction. Trained on the QMe14S dataset containing energies, forces, dipole moments, and polarizabilities for 186,102 small organic molecules, DetaNet yields a universal and transferable force field with high-order tensor prediction capability. Using time-correlation functions derived from MLMD and ring-polymer molecular dynamics (RPMD) trajectories, we computed IR and Raman spectra that accurately reproduce anharmonic and nuclear quantum effects. Benchmark tests on isolated molecules, including polycyclic aromatic hydrocarbons, demonstrate that the DetaNet-based MD approach achieves near-experimental spectral accuracy with speedups up to three orders of magnitude over AIMD. Furthermore, the framework extends seamlessly to molecular and inorganic crystals, molecular aggregates, and biological macromolecules such as polypeptides with minimal fine-tuning. In all systems, DetaNet maintains high accuracy while significantly reducing computational cost. Overall, this work establishes a universal machine learning force field and tensor-aware MLMD framework that enable fast, accurate, and broadly applicable dynamic simulations and IR/Raman spectral predictions across diverse molecular and material systems.

physics.chem-ph↗

arXiv2025

Spec2VolCAMU-Net: A Spectrogram-to-Volume Model for EEG-to-fMRI Reconstruction based on Multi-directional Time-Frequency Convolutional Attention Encoder and Vision-Mamba U-Net

High-resolution functional magnetic resonance imaging (fMRI) is essential for mapping human brain activity; however, it remains costly and logistically challenging. If comparable volumes could be generated directly from widely available scalp electroencephalography (EEG), advanced neuroimaging would become significantly more accessible. Existing EEG-to-fMRI generators rely on plain Convolutional Neural Networks (CNNs) that fail to capture cross-channel time-frequency cues or on heavy transformer/Generative Adversarial Network (GAN) decoders that strain memory and stability. To address these limitations, we propose Spec2VolCAMU-Net, a lightweight architecture featuring a Multi-directional Time-Frequency Convolutional Attention Encoder for rich feature extraction and a Vision-Mamba U-Net decoder that uses linear-time state-space blocks for efficient long-range spatial modelling. We frame the goal of this work as establishing a new state of the art in the spatial fidelity of single-volume reconstruction, a foundational prerequisite for the ultimate aim of generating temporally coherent fMRI time series. Trained end-to-end with a hybrid SSI-MSE loss, Spec2VolCAMU-Net achieves state-of-the-art fidelity on three public benchmarks, recording Structural Similarity Index (SSIM) of 0.693 on NODDI, 0.725 on Oddball and 0.788 on CN-EPFL, representing improvements of 14.5%, 14.9%, and 16.9% respectively over previous best SSIM scores. Furthermore, it achieves competitive Signal-to-Noise Ratio (PSNR) scores, particularly excelling on the CN-EPFL dataset with a 4.6% improvement over the previous best PSNR, thus striking a better balance in reconstruction quality. The proposed model is lightweight and efficient, making it suitable for real-time applications in clinical and research settings. The code is available at https://github.com/hdy6438/Spec2VolCAMU-Net.

eess.IV↗

arXiv2025

Generative Modeling for Adversarial Lane-Change Scenarios

Decision-making in long-tail scenarios is pivotal to autonomous-driving development, and realistic and challenging simulations play a crucial role in testing safety-critical situations. However, existing open-source datasets lack systematic coverage of long-tail scenes, and lane-change maneuvers being emblematic, rendering such data exceedingly scarce. To bridge this gap, we introduce a data mining framework that exhaustively analyzes two widely used datasets, NGSIM and INTERACTION, to identify sequences marked by hazardous behavior, thereby replenishing these neglected scenarios. Using Generative Adversarial Imitation Learning (GAIL) enhanced with Proximal Policy Optimization (PPO), and enriched by vehicular-environment interaction analytics, our method iteratively refines and parameterizes newly generated trajectories. Distinguished by a rationally adversarial and sensitivity-aware perspective, the approach optimizes the creation of challenging scenes. Experiments show that, compared to unfiltered data and baseline models, our method produces behaviors that are simultaneously both adversarial and natural, judged by collision frequency, acceleration profiles, and lane-change dynamics, offering constructive insights to amplifying long-tailed lane-change instances in datasets and advancing decision-making training.

cs.RO↗

arXiv2025

Machine learning accelerated finite-field simulations for electrochemical interfaces

Electrochemical interfaces are of fundamental importance in electrocatalysis, batteries, and metal corrosion. Finite-field methods are one of most reliable approaches for modeling electrochemical interfaces in complete cells under realistic constant-potential conditions. However, previous finite-field studies have been limited to either expensive ab initio molecular dynamics or less accurate classical descriptions of electrodes and electrolytes. To overcome these limitations, we present a machine learning-based finite-field approach that combines two neural network models: one predicts atomic forces under applied electric fields, while the other describes the corresponding charge response. Both models are trained entirely on first-principles data without employing any classical approximations. As a proof-of-concept demonstration in a prototypical Au(100)/NaCl(aq) system, this approach not only dramatically accelerates fully first-principles finite-field simulations but also successfully extrapolates to cell potentials beyond the training range while accurately predicting key electrochemical properties. Interestingly, we reveal a turnover of both density and orientation distributions of interfacial water molecules at the anode, arising from competing interactions between the positively charged anode and adsorbed Cl$^-$ ions with water molecules as the applied potential increases. This novel computational scheme shows great promise in efficient first-principles modelling of large-scale electrochemical interfaces under potential control.

physics.chem-ph↗

arXiv2025

Efficient Parallelization of Message Passing Neural Network Potentials for Large-scale Molecular Dynamics

Machine learning potentials have achieved great success in accelerating atomistic simulations. Many of them relying on atom-centered local descriptors are natural for parallelization. More recent message passing neural network (MPNN) models have demonstrated their superior accuracy and become increasingly popular. However, efficiently parallelizing MPNN models across multiple nodes remains challenging, limiting their practical applications in large-scale simulations. Here, we propose an efficient parallel algorithm for MPNN models, in which additional data communication is minimized among local atoms only in each MP layer without redundant computation, thus scaling linearly with the layer number. Integrated with our recursively embedded atom neural network model, this algorithm demonstrates excellent strong scaling and weak scaling behaviors in several benchmark systems. This approach enables massive molecular dynamics simulations on MPNN models as fast as on strictly local models for over 100 million atoms, vastly extending the applicability of the MPNN potential to an unprecedented scale. This general parallelization framework can empower various MPNN models to efficiently simulate very large and complex systems.

physics.chem-ph↗

arXiv2025

Distributed Fault-Tolerant Control for Heterogeneous MAS with Prescribed Performance under Communication Failures

This paper presents a novel approach employing prescribed performance control to address the distributed fault-tolerant formation control problem in a heterogeneous UAV-UGV cooperative system under a directed interaction topology and communication link failures. The proposed distributed fault-tolerant control scheme enables UAVs to accurately track a virtual leader's trajectory and achieve the desired formation, while ensuring UGVs converge within the convex hull formed by leader UAVs. By accounting for differences in system parameters and state dimensions between UAVs and UGVs, the method leverages performance functions to guarantee predefined transient and steady-state behavior. Additionally, a variable prescribed performance boundary control strategy with an adaptive learning rate is introduced to tackle actuator saturation, ensuring reliable formation tracking in real-world scenarios. Simulation results demonstrate the effectiveness and robustness of the proposed approach.

eess.SY↗

arXiv2025

Extending ring polymer molecular dynamics rate theory to reactions with non-separable reactants

The ring polymer molecular dynamics (RPMD) rate theory is an efficient and accurate method for estimating rate coefficients of chemical reactions affected by nuclear quantum effects. The commonly used RPMD treatment of gas-phase bimolecular reactions adopts two dividing surfaces, one at the transition state and another in the reactant asymptote, where partition functions of separated reactants can be readily obtained. With some exceptions, however, this strategy is difficult to implement for processes on surfaces or in liquids, because the reactants are often strongly coupled with the extended medium (surface or solvent) and thus non-separable. Under such circumstances, the RPMD rate theory with a single dividing surface (SDS) is better suited. However, most of its implementations adopted Cartesian forms of the reaction coordinate, which, in many cases, are not ideal for describing complex reactions. Here, we present a SDS-based RPMD implementation, which are able to tackle the aforementioned challenges. This approach is demonstrated in four representative reactions, including the gas phase H + H2 exchange reaction, gas phase CH3NC isomerization, H recombinative desorption from Pt(111), and NO desorption from Pd(111). This implementation, which is applicable to both uni- and bi-molecular reactions, offers a unified treatment of gas-phase and surface reaction rate calculations on the same footing.

physics.chem-ph↗

arXiv2025

Beautimeter: Harnessing GPT for Assessing Architectural and Urban Beauty based on the 15 Properties of Living Structure

Beautimeter is a new tool powered by generative pre-trained transformer (GPT) technology, designed to evaluate architectural and urban beauty. Rooted in Christopher Alexander's theory of centers, this work builds on the idea that all environments possess, to varying degrees, an innate sense of life. Alexander identified 15 fundamental properties, such as levels of scale and thick boundaries, that characterize living structure, which Beautimeter uses as a basis for its analysis. By integrating GPT's advanced natural language processing capabilities, Beautimeter assesses the extent to which a structure embodies these 15 properties, enabling a nuanced evaluation of architectural and urban aesthetics. Using ChatGPT, the tool helps users generate insights into the perceived beauty and coherence of spaces. We conducted a series of case studies, evaluating images of architectural and urban environments, as well as carpets, paintings, and other artifacts. The results demonstrate Beautimeter's effectiveness in analyzing aesthetic qualities across diverse contexts. Our findings suggest that by leveraging GPT technology, Beautimeter offers architects, urban planners, and designers a powerful tool to create spaces that resonate deeply with people. This paper also explores the implications of such technology for architecture and urban design, highlighting its potential to enhance both the design process and the assessment of built environments. Keywords: Living structure, structural beauty, Christopher Alexander, AI in Design, human centered design

physics.soc-ph↗

arXiv2025

First-Principles Understanding of Vibrational Energy Transfer in Molecule-Surface Scattering: Both Adiabatic and Nonadiabatic Channels Matter

Energy transfer during molecular collisions on metal surfaces plays a pivotal role in a host of critical interfacial processes. Despite significant efforts, our understanding of relevant energy transfer mechanisms, even in an extensively-studied benchmark like NO scattering from Au(111), remains far from complete. To fully disentangle different energy transfer channels, we develop a first-principles nonadiabatic dynamical model that incorporates explicitly all degrees of freedom and the interfacial electron transfer. Our simulations reproduce, for the first time, most experimental observations on vibrational relaxation and excitation of NO molecules under varying initial conditions and clearly elaborate the respective adiabatic and nonadiabatic contributions. This model identifies the direct adiabatic vibration-to-translation coupling as the dominant role of translation, while excluding significant translation-to-electron nonadiabatic coupling. Furthermore, the observed steric effect varying with the initial vibrational state is understood by the change of orientational dependence of the metal-to-molecule electron transfer. These new insights highlight the importance of treating both adiabatic and nonadiabatic energy transfer pathways on an equal footing, offering significant implications for modeling energy transfer processes in more complex systems, such as plasmonic photocatalysis.

physics.chem-ph↗

arXiv2025

The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials

Recent years have witnessed the fast development of machine learning potentials (MLPs) and their widespread applications in chemistry, physics, and material science. By fitting discrete ab initio data faithfully to continuous and symmetry-preserving mathematical forms, MLPs have enabled accurate and efficient atomistic simulations in a large scale from first principles. In this review, we provide an overview of the evolution of MLPs in the past two decades and focus on the state-of-the-art MLPs proposed in the last a few years for molecules, reactions, and materials. We discuss some representative applications of MLPs and the trend of developing universal potentials across a variety of systems. Finally, we outline a list of open challenges and opportunities in the development and applications of MLPs.

physics.chem-ph↗

arXiv2025

First Principles Reactive Flux Theory for Surface Reactions: Multiple Channels and Recrossing Dynamics

Heterogenous reactions typically consist of multiple elementary steps and their rate coefficients are of fundamental importance in elucidating the mechanisms and micro-kinetics of these processes. Transition-state theory (TST) for calculating surface reaction rate coefficients often relies solely on the harmonic approximation of adsorbent vibrations and neglects recrossing dynamics. Here, we combine, for the first time, an efficient metadynamics enhanced sampling method with a more general reactive flux approach to calculate rate coefficients of surface reactions of any order and/or with multiple reaction coordinates, overcoming these limitations of TST. We apply this approach to a textbook surface reaction, CO oxidation on Pt(111), for which rate constants have been precisely measured, using a full-dimensional neural network potential energy surface constructed from first-principles data. An accurate multi-dimensional free-energy surface is obtained by incorporating three collective variables, yielding rate coefficients for both CO oxidation and the competing CO desorption that are in good agreement with experimental data. Interestingly, our results reveal significant dynamic recrossing in both channels, which however arises from distinct physical mechanisms. This approach represents an accurate and general framework for calculating rate coefficients of elementary surface processes from first-principles, which is vital for developing predictive kinetic models for heterogenous catalysis.

physics.chem-ph↗