arXiv subjects

Lei Zeng

Publications and source records attributed to Lei Zeng.

At least 19 recordsLinked to original sources

arXiv2026

Multiscale Modelling of Ferroelectrics using a Physics-Informed Neural Network Driven by Molecular Dynamics Data: Parameter Identification and Field Reconstruction

In multiscale modeling of ferroelectrics, combining atomistic simulation with continuum-scale phase-field models (PFM) remains a fundamental challenge. A key difficulty lies in faithfully capturing discrete atomic-level information within a continuum modeling framework, while accurately representing material behavior at the mesoscale. In this paper, a Physics-Informed Neural Network (PINN) driven by molecular dynamics (MD) data is used. The loss function of the network consists of a supervised term that fits the discrete spatial polarization distributions obtained from MD simulations of systems containing domain walls, and a physics-based term that incorporates the residuals of partial differential equations (PDEs) of steady-state PFM. To ensure stable and balanced training among the different loss components, adaptive gradient normalization (GradNorm) is used to dynamically adjust the task weights. By minimizing the total loss, the model not only reconstructs the polarization field along with the associated strain, stress, and energy landscape at the continuum scale, but also identifies critical physical parameters of the phase-field model, including the characteristic energy density, characteristic length factor, gradient energy anisotropy factor, and Landau polynomial coefficients. By using the PINN-predicted physical parameters in COMSOL Multiphysics to solve the corresponding PDEs within a finite element framework, we demonstrate that these parameters enable accurate reproduction of the ferroelectric domain structure and the associated material response, including stress/strain distributions and energy landscape. This framework provides an effective methodology for establishing multiscale connections between atomistic and continuum descriptions, and holds the potential to infer underlying physical properties directly from polarization distributions for a wide range of materials.

cond-mat.mtrl-sci↗

arXiv2026

A 3D SAM-Based Progressive Prompting Framework for Multi-Task Segmentation of Radiotherapy-induced Normal Tissue Injuries in Limited-Data Settings

Radiotherapy-induced normal tissue injury is a clinically important complication, and accurate segmentation of injury regions from medical images could facilitate disease assessment, treatment planning, and longitudinal monitoring. However, automatic segmentation of these lesions remains largely unexplored because of limited voxel-level annotations and substantial heterogeneity across injury types, lesion size, and imaging modality. To address this gap, we curate a dedicated head-and-neck radiotherapy-induced normal tissue injury dataset covering three manifestations: osteoradionecrosis (ORN), cerebral edema (CE), and cerebral radiation necrosis (CRN). We further propose a 3D SAM-based progressive prompting framework for multi-task segmentation in limited-data settings. The framework progressively incorporates three complementary prompts: text prompts for task-aware adaptation, dose-guided box prompts for coarse localization, and click prompts for iterative refinement. A small-target focus loss is introduced to improve local prediction and boundary delineation for small and sparse lesions. Experiments on ORN, CE, and CRN demonstrate that the proposed method achieves reliable segmentation performance across diverse injury types and outperforms state-of-the-art methods.

cs.CV↗

arXiv2026

Causal machine learning reveals age-dependent radiation dose effects on mandibular osteoradionecrosis

Distinguishing causal relationships from statistical correlations remains a fundamental challenge in clinical research, limiting the translation of observational findings into interventional treatment guidelines. Here we apply causal machine learning to establish causal effects of radiation dose parameters on mandibular osteoradionecrosis (ORN) in 931 head and neck cancer patients treated with volumetric-modulated arc therapy. Using generalized random forests, we demonstrate that all examined dosimetric factors exhibit significant positive causal effects on ORN development (average treatment effects: 0.092-0.141). Integration with explainable machine learning reveals substantial treatment effect heterogeneity, with patients aged 50-60 years showing the strongest causal dose-response relationships (conditional average treatment effects up to 0.229), while patients over 70 years demonstrate minimal effects. These results suggest that age-stratified treatment optimization and personalized treatment planning for the dosimetric factors could reduce ORN risk. Our findings demonstrate that causal inference methods can transform clinical retrospective radiotherapy data into personalized treatment recommendations, providing a methodological framework applicable to toxicity prediction across oncology and other clinical domains where treatment decisions depend on complex dose-response relationships.

physics.med-ph↗

arXiv2026

Plasma Confinement State Classification in Fusion Power Plants: Profile Reflectometer and Ensemble Diagnostics

As Fusion Pilot Plants (FPPs) are increasingly viewed as within reach, many engineering challenges remain. Not many diagnostics are expected to be available in a reactor environment. Survivability, maintainability, and limited port space substantially restrict the number of FPP-relevant diagnostics. One remaining challenge is developing tools and devices to extract plasma state information necessary for controlling an FPP from a limited subset of diagnostics. This work is part of an overarching project to address this challenge. The specific diagnostic subset to be used in FPPs is still under debate. We take the approach of developing machine-learning-based tools for different significant plasma state parameters, using already known FPP-viable diagnostics. Previously we developed a plasma confinement mode classifier utilizing the Electron Cyclotron Emission (ECE) diagnostic. Here, we expand on this by developing a Profile Reflectometer (PR) based classifier with 97\% test accuracy, and an ensemble model that combines the ECE and PR models into a single model, achieving 99\% test accuracy.

physics.plasm-ph↗

arXiv2025

Causal Machine Learning Analysis of Empirical Relative Biological Effectiveness (RBE) for Mandible Osteoradionecrosis in Head and Neck Cancer Radiotherapy

Mandible Osteoradionecrosis (ORN) is one of the most severe adverse events (AEs) for head and neck (H&N) cancer radiotherapy. Previous retrospective investigations on real-world data relied on conventional statistical models that primarily elucidate correlation rather than establishing causal relationships. Through the novel causal machine learning, we aim to obtain empirical relative biological effectiveness (RBE) for ORN in H&N cancer patients treated with pencil-beam-scanning proton therapy (PBSPT). 335 patients treated by PBSPT and 931 patients treated by volumetric-modulated arc therapy (VMAT) were included. We use 1:1 case-matching to minimize the imbalance in clinical factors between PBSPT and VMAT. The bias test of standardized mean differences (SMD) was applied on the case-matched patient cohorts. The causal machine learning method, causal forest (CF), was adopted to investigate the causal effects between dosimetric factors and the incidence of ORN. The dose volume constraints (DVCs) for VMAT and PBSPT were derived based on causal effects. RBE values were further empirically derived based on tolerance curves formed from DVCs. 335 VMAT patients were case-matched to 335 PBSPT patients; however, SMD analysis revealed persistent covariate imbalances within each group, indicating residual confounding influence. Using CF modeling, we identified DVCs of mandible ORN and found that PBSPT had lower critical volumes than those of VMAT, leading to empirical RBE exceeding 1.1 in the moderate dose range (1.61 at 40 Gy[RBE=1.1], 1.30 at 50 Gy, and 1.13 at 60 Gy). This study presents a novel application of causal machine learning to evaluate mandible ORN in radiotherapy. The results indicate that proton RBE may significantly exceed 1.1 in the moderate dose range, underscoring the importance of incorporating the variable RBE into PBSPT treatment planning to mitigate the risk of ORN.

physics.med-ph↗

arXiv2024

Simulating Moving Contact Lines in Three-Phase Suspensions Using a Front Tracking Method

Three-phase multiphase flows are found in an extraordinarily large number of applications. Often those involve a liquid phase and a gas phase in addition to a third phase that consists of either liquid drops or solid particles, suspended in the flow. Frequently the third phase is in contact with both the liquid and the gas, resulting in a contact line where all the phases meet. Here, we present an extension of a front tracking method, where the interface between two fluid phases is followed using connected marker points, to simulate the motion of triple contact lines for both three fluids systems and systems containing two fluids and suspended solid particles. We describe two related strategies, one where the contact line is tracked explicitly and one where it is captured implicitly, and show that both approaches achieve comparable accuracy. The second approach is, however, easier to implement, particularly for three-dimensional flows. For both tracked and untracked approaches for solid particles, and for the untracked three fluids case, we use a ``virtual interface,'' where the boundary of a liquid phase is extended into either another fluid or the solid. For three fluids systems the surface tension of the virtual interface is zero, but for systems with solids the surface tension of the virtual interface is the same as that of the physical interface.

physics.flu-dyn↗

arXiv2023

Optical Memory for Arbitrary Perfect Poincaré States in an Atomic Ensemble

Inherent spin angular momentum (SAM) and orbital angular momentum (OAM) which manifest as polarization and spatial degrees of freedom (DOF) of photons, hold a promise of large capability for applications in classical and quantum information processing. To enable these photonic spin and orbital dynamic properties strongly coupled with each other, Poincaré states have been proposed and offer advantages in data multiplexing, information encryption, precision metrology, and quantum memory. However, since the transverse size of Laguerre Gaussian beams strongly depends on their topological charge numbers $\left| l \right|$, it is difficult to store asymmetric Poincaré states due to the significantly different light-matter interaction for distinct spatial modes. Here, we experimentally realize the storage of perfect Poincaré states with arbitrary OAM quanta using the perfect optical vortex, in which 121 arbitrarily-selected perfect Poincaré states have been stored with high fidelity. The reported work has great prospects in optical communication and quantum networks for dramatically increased encoding flexibility of information.

quant-ph↗

arXiv2023

High-dimensional frequency conversion in hot atomic system

One of the major difficulties in realizing a high-dimensional frequency converter for conventional optical vortex (COV) stems from the difference in ring diameter of COV modes with different topological charge numbers l. Here, we implement a high-dimensional frequency convertor for perfect optical vortex (POV) modes with invariant size through the four-wave mixing (FWM) process by utilizing Bessel-Gaussian beams instead of Laguerre-Gaussian beams. The measured conversion efficiency from 1530 nm to 795 nm is independent of l at least in subspace of {-6,...,6}, and the achieved conversion fidelities for two-dimensional (2D) superposed POV states exceed 97%. We further realize the frequency conversion of 3D, 5D and 7D superposition states with fidelities as high as 96.70%, 89.16% and 88.68%, respectively. The reported scheme is implemented in hot atomic vapor, it's also compatible with the cold atomic system and may find applications in high-capacity and long-distance quantum communication.

physics.optics↗

arXiv2023

Highly efficient storage of 25-dimensional photonic qudit in a cold-atom-based quantum memory

Building an efficient quantum memory in high-dimensional Hilbert spaces is one of the fundamental requirements for establishing high-dimensional quantum repeaters, where it offers many advantages over two-dimensional quantum systems, such as a larger information capacity and enhanced noise resilience. To date, there have been no reports about how to achieve an efficient high-dimensional quantum memory. Here, we experimentally realize a quantum memory that is operational in Hilbert spaces of up to 25 dimensions with a storage efficiency of close to 60%. The proposed approach exploits the spatial-mode-independent interaction between atoms and photons which are encoded in transverse size-invariant orbital angular momentum modes. In particular, our memory features uniform storage efficiency and low cross-talk disturbance for 25 individual spatial modes of photons, thus allowing storing arbitrary qudit states programmed from 25 eigenstates within the high-dimensional Hilbert spaces, and eventually contributing to the storage of a 25-dimensional qudit state. These results would have great prospects for the implementation of long-distance high-dimensional quantum networks and quantum information processing.

quant-ph↗

arXiv2022

Detection of infrared light through stimulated four-wave mixing process

Infrared optical measurement has a wide range of applications in industry and science, but infrared light detectors suffer from high costs and inferior performance than visible light detectors. Four-wave mixing (FWM) process allows detection in the infrared range by detecting correlated visible light. We experimentally investigate the stimulated FWM process in a hot $^{85}$Rb atomic vapor cell, in which a weak infrared signal laser at $1530~$nm induces the FWM process and is amplified and converted into a strong FWM light at $780~$nm, the latter can be detected more easily. We find the optimized single- and two-photon detunings by studying the dependence of the frequency of input laser on the generated FWM light. What's more, the power gain increases rapidly as the signal intensity decreases, which is consistent with our theoretical analysis. As a result, the power gain can reach up to 500 at a signal laser power of $0.1~μ$W and the number of detected photons increased by a factor of 250. Finally, we experimentally prove that our amplification process can work in a broad band in the frequency domain by exploring the response rate of our stimulated FWM process.

physics.optics↗

arXiv2022

Experimental realization of quantum non-reciprocity based on cold atomic ensembles

In analog to counterparts widely used in electronic circuits, all optical non-reciprocal devices are basic building blocks for both classical and quantum optical information processing. Approaching the fundamental limit of such devices, where the propagation of a single photon exhibits a good non-reciprocal characteristic, requires an asymmetric strong coupling between a single photon and a matter. Unfortunately it has been not realized yet. Here, we propose and experimentally realize a quantum non-reciprocity device with low optical losses and a high isolation of larger than 14 dB based on the cold atoms. Besides, the non-reciprocal transmission of a quantum qubit and non-reciprocal quantum storage of a true single photon are also realized. All results achieved would be very promising in building up quantum non-reciprocal devices for quantum networks.

quant-ph↗

arXiv2022

Long-lived storage of orbital angular momentum quantum states

Quantum memories are indispensible for establishing a long-distance quantum network. High-dimensional quantum memories enable a higher channel capacity compared to a quantum memory working in a two-dimensional space, and have a lower requirement for storage lifetime in the field of quantum coomunication. The photonic transverse spatial modes such as Laguerra-Gaussian modes orbital angular momentum (OAM) are ideal candidates for encoding high-dimensional information, because it can form an infinite-dimensional Hilbert space. Although the faithful storage of an OAM qubit or qutrit has been realized in pioneering works, the longest storage lifetime for the former is only in the order of a few microseconds, and hundreds of nano-seconds for the latter. Here we implement a quantum memory for OAM qubits and qutrits using a cold atomic ensemble, the experimental results clearly show that our memory can still beat the classical limit after a storage time of $400μs$,which is two orders of magnitude higher than the previous work. The retrieval efficiency at this time equals to $44\%$ of the value when the storage time is set to be $10μs$. Our work is very promising for establishing a high dimensional quantum network.

quant-ph↗

arXiv2021

A modified divide-and-conquer based machine learning method for predicting creep life of superalloys

Recently Liu et al. (Acta Mater., 2020) proposed a new divide-and-conquer based machine learning method for predicting creep life of superalloys. The idea is enlightening though, the prediction accuracy and intelligence remain to be improved. In the present work, we modify the method by adding a dimensionality reduction algorithm before the clustering step and introducing a grid search algorithm to the regression model selection step. As a consequence, the clustering result becomes much more desirable and the accuracy of predicted creep life is dramatically improved. The root-mean-square error, mean-absolute-percentage error and relevant coefficient of the original method are 0.2341, 0.0595 and 0.9121, while those of the modified method are 0.0285, 0.0196, and 0.9806, respectively. Moreover, the ad-hoc determination of hyperparameters in the original method is replaced by automated determination of hyperparameters in the modified method, which considerably improves the intelligence of the method.

cond-mat.mtrl-sci↗

arXiv2021

Synchronized resistance to inhomogeneous magnetic field-induced dephasing of an image stored in a cold atomic ensemble

Long-lived storage of arbitrary transverse multimodes is important for establishing a high-channel-capacity quantum network. Most of the pioneering works focused on atomic diffusion as the dominant impact on the retrieved pattern in an atom-based memory. In this work, we demonstrate that the unsynchronized Larmor precession of atoms in the inhomogeneous magnetic field dominates the distortion of the pattern stored in a cold-atom-based memory. We find that this distortion effect can be eliminated by applying a strong uniform polarization magnetic field. By preparing atoms in magnetically insensitive states, the destructive interference between different spin-wave components is diminished, and the stored localized patterns are synchronized further in a single spin-wave component; then, an obvious enhancement in preserving patterns for a long time is obtained. The reported results are very promising for studying transverse multimode decoherence in storage and high-dimensional quantum networks in the future.

quant-ph↗

arXiv2020

Experimental demonstration of Cavity-Free Optical Isolators and Optical Circulators

Cavity-free optical nonreciprocity components, which have an inherent strong asymmetric interaction between the forward- and backward-propagation direction of the probe field, are key to produce such as optical isolators and circulators. According to the proposal presented by Xia et al., [Phys. Rev. Lett. 121, 203602 (2018)], we experimentally build a device that uses cross-Kerr nonlinearity to achieve a cavity-free optical isolator and circulator. Its nonreciprocal behavior arises from the thermal motion of N-type configuration atoms, which induces a strong chiral cross-Kerr nonlinear response for the weak probe beam. We obtain a two-port optical isolator for up to 20 dB of isolation ratio in a specially designed Sagnac interferometer. The distinct propagation directions of the weak probe field determine its cross-phase shift and transmission, by which we demonstrate the accessibility of a four-port optical circulator.

quant-ph↗

arXiv2019

Category decoding of visual stimuli from human brain activity using a bidirectional recurrent neural network to simulate bidirectional information flows in human visual cortices

Recently, visual encoding and decoding based on functional magnetic resonance imaging (fMRI) have realized many achievements with the rapid development of deep network computation. Despite the hierarchically similar representations of deep network and human vision, visual information flows from primary visual cortices to high visual cortices and vice versa based on the bottom-up and top-down manners, respectively. Inspired by the bidirectional information flows, we proposed a bidirectional recurrent neural network (BRNN)-based method to decode the categories from fMRI data. The forward and backward directions in the BRNN module characterized the bottom-up and top-down manners, respectively. The proposed method regarded the selected voxels of each visual cortex region (V1, V2, V3, V4, and LO) as one node in the sequence fed into the BRNN module and combined the output of the BRNN module to decode the categories with the subsequent fully connected layer. This new method allows the efficient utilization of hierarchical information representations and bidirectional information flows in human visual cortices. Experiment results demonstrated that our method improved the accuracy of three-level category decoding than other methods, which implicitly validated the hierarchical and bidirectional human visual representations. Comparative analysis revealed that the category representations of human visual cortices were hierarchical, distributed, complementary, and correlative.

q-bio.NC↗

arXiv2019

Temporal Wheeler's delayed-Choice Experiment based on Cold Atomic Quantum Memory

Nowadays the most intriguing features of wave particle complementarity of single photon is exemplified by the famous Wheeler's delayed choice experiment in linear optics, nuclear magnetic resonance and integrated photonic device systems. Studying the wave particle behavior in light and matter interaction at single photon level is challenging and interesting, which gives how single photons complement in light and matter interaction. Here, we demonstrate a Wheeler's delayed choice experiment in an interface of light and atomic memory, in which the cold atomic memory makes the heralded single photon divided into a superposition of atomic collective excitation and leaked pulse, thus acting as memory beam splitters. We observe the morphing behavior between particle and wave of a heralded single photon by changing the relative proportion of quantum random number generator, the second memory efficiency, and the relative storage time of two memories. The reported results exhibit the complementarity behavior of single photon under the interface of light atom interaction.

quant-ph↗

arXiv2018

Accurate reconstruction of image stimuli from human fMRI based on the decoding model with capsule network architecture

In neuroscience, all kinds of computation models were designed to answer the open question of how sensory stimuli are encoded by neurons and conversely, how sensory stimuli can be decoded from neuronal activities. Especially, functional Magnetic Resonance Imaging (fMRI) studies have made many great achievements with the rapid development of the deep network computation. However, comparing with the goal of decoding orientation, position and object category from activities in visual cortex, accurate reconstruction of image stimuli from human fMRI is a still challenging work. In this paper, the capsule network (CapsNet) architecture based visual reconstruction (CNAVR) method is developed to reconstruct image stimuli. The capsule means containing a group of neurons to perform the better organization of feature structure and representation, inspired by the structure of cortical mini column including several hundred neurons in primates. The high-level capsule features in the CapsNet includes diverse features of image stimuli such as semantic class, orientation, location and so on. We used these features to bridge between human fMRI and image stimuli. We firstly employed the CapsNet to train the nonlinear mapping from image stimuli to high-level capsule features, and from high-level capsule features to image stimuli again in an end-to-end manner. After estimating the serviceability of each voxel by encoding performance to accomplish the selecting of voxels, we secondly trained the nonlinear mapping from dimension-decreasing fMRI data to high-level capsule features. Finally, we can predict the high-level capsule features with fMRI data, and reconstruct image stimuli with the CapsNet. We evaluated the proposed CNAVR method on the dataset of handwritten digital images, and exceeded about 10% than the accuracy of all existing state-of-the-art methods on the structural similarity index (SSIM).

cs.CV↗