arXiv subjects

Zhe Fei

Publications and source records attributed to Zhe Fei.

At least 19 recordsLinked to original sources

arXiv2026

Engineering Plasmons in Oxide/Graphene Heterostructures via Interfacial Charge Transfer

Interfacial charge transfer provides an effective route for tailoring the optical and electronic properties of two-dimensional materials. Here, we investigate infrared surface plasmon polaritons in oxide/graphene heterostructures using scattering-type scanning near-field optical microscopy. Ultrathin oxide overlayers deposited by physical vapor deposition enable systematic engineering of graphene plasmons through interfacial charge redistribution. MoOx strongly enhances the plasmonic response, producing a longer plasmon wavelength, stronger fringe contrast, and reduced damping, whereas a subsequently deposited ZnOx overlayer partially reverses these changes. Energy-dependent nano-infrared imaging combined with quantitative modeling reveals an increased graphene carrier density and the resulting modification of the plasmon dispersion. Thickness-dependent measurements show a rapid increase in charge-transfer doping at sub-nanometer MoOx thicknesses, followed by a weaker long-range contribution at larger overlayer thicknesses. Electrostatic gating further modulates the carrier density and produces a nonlinear response consistent with gate-dependent interfacial charge redistribution. In addition, an approximately 3-nm-thick MoOx overlayer stabilizes the plasmonic response for at least seven months under ambient conditions. These results establish oxide/graphene heterostructures as a robust platform compatible with scalable fabrication, providing a pathway toward stable and tunable infrared nanophotonic and optoelectronic devices.

cond-mat.mes-hall↗

arXiv2025

Customizing Open Source LLMs for Quantitative Medication Attribute Extraction across Heterogeneous EHR Systems

Harmonizing medication data across Electronic Health Record (EHR) systems is a persistent barrier to monitoring medications for opioid use disorder (MOUD). In heterogeneous EHR systems, key prescription attributes are scattered across differently formatted fields and freetext notes. We present a practical framework that customizes open source large language models (LLMs), including Llama, Qwen, Gemma, and MedGemma, to extract a unified set of MOUD prescription attributes (prescription date, drug name, duration, total quantity, daily quantity, and refills) from heterogeneous, site specific data and compute a standardized metric of medication coverage, \emph{MOUD days}, per patient. Our pipeline processes records directly in a fixed JSON schema, followed by lightweight normalization and cross-field consistency checks. We evaluate the system on prescription level EHR data from five clinics in a national OUD study (25{,}605 records from 1{,}257 patients), using a previously annotated benchmark of 10{,}369 records (776 patients) as the ground truth. Performance is reported as coverage (share of records with a valid, matchable output) and record-level exact-match accuracy. Larger models perform best overall: Qwen2.5-32B achieves \textbf{93.4\%} coverage with \textbf{93.0\%} exact-match accuracy across clinics, and MedGemma-27B attains \textbf{93.1\%}/\textbf{92.2\%}. A brief error review highlights three common issues and fixes: imputing missing dosage fields using within-drug norms, handling monthly/weekly injectables (e.g., Vivitrol) by setting duration from the documented schedule, and adding unit checks to prevent mass units (e.g., ``250 g'') from being misread as daily counts. By removing brittle, site-specific ETL and supporting local, privacy-preserving deployment, this approach enables consistent cross-site analyses of MOUD exposure, adherence, and retention in real-world settings.

cs.AI↗

arXiv2025

Novel Knockoff Generation and Importance Measures with Heterogeneous Data via Conditional Residuals and Local Gradients

Knockoff variable selection is a powerful framework that creates synthetic knockoff variables to mirror the correlation structure of the observed features, enabling principled control of the false discovery rate in variable selection. However, existing methods often assume homogeneous data types or known distributions, limiting their applicability in real-world settings with heterogeneous, distribution-free data. Moreover, common variable importance measures rely on linear outcome models, hindering their effectiveness for complex relationships. We propose a flexible knockoff generation framework based on conditional residuals that accommodates mixed data types without assuming known distributions. To assess variable importance, we introduce the Mean Absolute Local Derivative (MALD), an interpretable metric compatible with nonlinear outcome functions, including random forests and neural networks. Simulations show that our approach achieves better false discovery rate control and higher power than existing methods. We demonstrate its practical utility on a DNA methylation dataset from mouse tissues, identifying CpG sites linked to aging. Software is available in R (rangerKnockoff) and Python (MALDimportance).

stat.ME↗

arXiv2025

Optimal Investment in Equity and Credit Default Swaps in the Presence of Default

We consider an equity market subject to risk from both unhedgeable shocks and default. The novelty of our work is that to partially offset default risk, investors may dynamically trade in a credit default swap (CDS) market. Assuming investment opportunities are driven by functions of an underlying diffusive factor process, we identify the certainty equivalent for a constant absolute risk aversion investor with a semi-linear partial differential equation (PDE) which has quadratic growth in both the function and gradient coefficients. For general model specifications, we prove existence of a solution to the PDE which is also the certainty equivalent. We show the optimal policy in the CDS market covers not only equity losses upon default (as one would expect), but also losses due to restricted future trading opportunities. We use our results to price default dependent claims though the principal of utility indifference, and we show that provided the underlying equity market is complete absent the possibility of default, the equity-CDS market is complete accounting for default. Lastly, through a numerical application, we show the optimal CDS policies are essentially static (and hence easily implementable) and that investing in CDS dramatically increases investor indirect utility.

q-fin.MF↗

arXiv2024

Self-supervised denoising of visual field data improves detection of glaucoma progression

Perimetric measurements provide insight into a patient's peripheral vision and day-to-day functioning and are the main outcome measure for identifying progression of visual damage from glaucoma. However, visual field data can be noisy, exhibiting high variance, especially with increasing damage. In this study, we demonstrate the utility of self-supervised deep learning in denoising visual field data from over 4000 patients to enhance its signal-to-noise ratio and its ability to detect true glaucoma progression. We deployed both a variational autoencoder (VAE) and a masked autoencoder to determine which self-supervised model best smooths the visual field data while reconstructing salient features that are less noisy and more predictive of worsening disease. Our results indicate that including a categorical p-value at every visual field location improves the smoothing of visual field data. Masked autoencoders led to cleaner denoised data than previous methods, such as variational autoencoders. A 4.7% increase in detection of progressing eyes with pointwise linear regression (PLR) was observed. The masked and variational autoencoders' smoothed data predicted glaucoma progression 2.3 months earlier when p-values were included compared to when they were not. The faster prediction of time to progression (TTP) and the higher percentage progression detected support our hypothesis that masking out visual field elements during training while including p-values at each location would improve the task of detection of visual field progression. Our study has clinically relevant implications regarding masking when training neural networks to denoise visual field data, resulting in earlier and more accurate detection of glaucoma progression. This denoising model can be integrated into future models for visual field analysis to enhance detection of glaucoma progression.

eess.IV↗

arXiv2024

U-learning for Prediction Inference via Combinatory Multi-Subsampling: With Applications to LASSO and Neural Networks

Epigenetic aging clocks play a pivotal role in estimating an individual's biological age through the examination of DNA methylation patterns at numerous CpG (Cytosine-phosphate-Guanine) sites within their genome. However, making valid inferences on predicted epigenetic ages, or more broadly, on predictions derived from high-dimensional inputs, presents challenges. We introduce a novel U-learning approach via combinatory multi-subsampling for making ensemble predictions and constructing confidence intervals for predictions of continuous outcomes when traditional asymptotic methods are not applicable. More specifically, our approach conceptualizes the ensemble estimators within the framework of generalized U-statistics and invokes the H\'ajek projection for deriving the variances of predictions and constructing confidence intervals with valid conditional coverage probabilities. We apply our approach to two commonly used predictive algorithms, Lasso and deep neural networks (DNNs), and illustrate the validity of inferences with extensive numerical studies. We have applied these methods to predict the DNA methylation age (DNAmAge) of patients with various health conditions, aiming to accurately characterize the aging process and potentially guide anti-aging interventions.

stat.ML↗

arXiv2023

Imaging Anisotropic Waveguide Exciton Polaritons in Tin Sulfide

In recent years, novel materials supporting in-plane anisotropic polaritons have attracted a lot of research interest due to their capability of shaping nanoscale field distributions and controlling nanophotonic energy flows. Here we report a nano-optical imaging study of waveguide exciton polaritons (EPs) in tin sulfide (SnS) in the near-infrared (IR) region using the scattering-type scanning near-field optical microscopy (s-SNOM). With s-SNOM, we mapped in real space the propagative EPs in SnS, which show sensitive dependence on the excitation energy and sample thickness. Moreover, we found that both the polariton wavelength and propagation length are anisotropic in the sample plane. In particular, in a narrow spectral range from 1.32 to 1.44 eV, the EPs demonstrate quasi-one-dimensional propagation, which is rarely seen in natural polaritonic materials. Further analysis indicates that the observed polariton anisotropy is originated from the different optical bandgaps and exciton binding energies along the two principal crystal axes of SnS.

physics.optics↗

arXiv2023

Tailored plasmons in pentacene/graphene heterostructures with interlayer electron transfer

Van der Waals (vdW) heterostructures, which are produced by the precise assemblies of varieties of two-dimensional (2D) materials, have demonstrated many novel properties and functionalities. Here we report a nano-plasmonic study of vdW heterostructures that were produced by depositing ordered molecular layers of pentacene on top of graphene. We find through nano-infrared (IR) imaging that surface plasmons formed due to the collective oscillations of Dirac fermions in graphene are highly sensitive to the adjacent pentacene layers. In particular, the plasmon wavelength declines systematically but nonlinearly with increasing pentacene thickness. Further analysis and density functional theory (DFT) calculations indicate that the observed peculiar thickness dependence is mainly due to the tunneling-type electron transfer from pentacene to graphene. Our work unveils a new method for tailoring graphene plasmons and deepens our understanding of the intriguing nano-optical phenomena due to interlayer couplings in novel vdW heterostructures.

cond-mat.mes-hall↗

arXiv2023

Tip-and plasmon-enhanced infrared nanoscopy for ultrasensitive molecular characterizations

We propose a novel method for ultra-sensitive infrared (IR) vibrational spectroscopy of molecules with nanoscale footprints by combining the tip enhancement of the scattering-type scanning near-field optical microscope (s-SNOM) and the plasmon enhancement of the breathing-mode (BM) plasmon resonances of graphene nanodisks (GNDs). To demonstrate that, we developed a quantitative model that is capable of computing accurately the s-SNOM signals of nanoscale samples. With our modeling, we show that the s-SNOM tip can effectively excite gate-tunable BM plasmonic resonances in GNDs with strong field enhancement and sensitive dependence on the size of GND. Moreover, we demonstrate that the intense electric field of tip-excited plasmonic BMs can strongly enhance the IR vibrational modes of molecules. As a result, IR vibrational signatures of individual molecular particles with sizes down to 1-2 nm can be readily observable by s-SNOM. Our study sheds light on future ultra-sensitive IR biosensing that takes advantage of both the tip and plasmon enhancement.

physics.optics↗

arXiv2023

Imaging Stacking-Dependent Surface Plasmon Polaritons in Trilayer Graphene

We report a nano-infrared (IR) imaging study of trilayer graphene (TLG) with both ABA (Bernal) and ABC (rhombohedral) stacking orders using the scattering-type scanning near-field optical microscope (s-SNOM). With s-SNOM operating in the mid-IR region, we mapped in real space the surface plasmon polaritons (SPPs) of ABA-TLG and ABC-TLG, which are tunable with electrical gating. Through quantitative modeling of the plasmonic imaging data, we found that the plasmon wavelength of ABA-TLG is significantly larger than that of ABC-TLG, resulting in a sizable impedance mismatch and hence a strong plasmon reflection at the ABA/ABC lateral junction. Further analysis indicates that the different plasmonic responses of the two types of TLG are directly linked to their electronic structures and carrier properties. Our work uncovers the physics behind the stacking-dependent plasmonic responses of TLG and sheds light on future applications of TLG and the ABA/ABC junctions in IR plasmonics and planar nano-optics.

physics.optics↗

arXiv2023

Real-space imaging of the tailored plasmons in twisted bilayer graphene

We report a systematic plasmonic study of twisted bilayer graphene (TBLG) - two graphene layers stacked with a twist angle. Through real-space nanoimaging of TBLG single crystals with a wide distribution of twist angles, we find that TBLG supports confined infrared plasmons that are sensitively dependent on the twist angle. At small twist angles, TBLG has a plasmon wavelength comparable to that of single-layer graphene. At larger twist angles, the plasmon wavelength of TBLG increases significantly with apparently lower damping. Further analysis and modeling indicate that the observed twist-angle dependence of TBLG plasmons in the Dirac linear regime is mainly due to the Fermi-velocity renormalization, a direct consequence of interlayer electronic coupling. Our work unveils the tailored plasmonic characteristics of TBLG and deepens our understanding of the intriguing nano-optical physics in novel van der Waals coupled two-dimensional materials.

cond-mat.mes-hall↗

arXiv2022

Unusual flat and extended morphology of intercalated Cu under MoS2

A general method was developed to intercalate metals under layered materials through a controlled density of sputtered defects. The method has been already applied to study a range of metals intercalated under graphite and different types of morphologies were realized. In the current work, we extend the method to the study of intercalation under MoS2 noting that work on this system is rather limited. We use Cu as the prototype metal for comparison with Cu intercalation under graphite. Although the growth conditions needed for intercalation under graphite and MoS2 are similar, the type of intercalated phases is very different. Each Cu island which nucleates on top of MoS2 during Cu deposition provides material that is transferred below MoS2, through sputtered defects under the island base; this transfer results in a uniform intercalated Cu "carpet" morphology that extends over the mesoscale. On the contrary, Cu intercalation under graphite results in well separated, compact islands formed by monomer detachment from small Cu islands on top and transfer below through defects far from the islands. The structural techniques (scanning electron microscopy and atomic force microscopy) and spectroscopic techniques (x-ray photoelectron spectroscopy and energy dispersive spectroscopy) are used for the characterization of the intercalated Cu layer.

cond-mat.mtrl-sci↗

arXiv2022

Regulating stochastic clocks

Stochastic clocks represent a class of time change methods for incorporating trading activity into continuous-time financial models, with the ability to deal with typical asymmetrical and tail risks in financial returns. In this paper we propose a significant improvement of stochastic clocks for the same objective but without decreasing the number of trades or changing the trading intensity. Our methodology targets any L\'{e}vy subordinator, or more generally any process of nonnegative independent increments, and is based on various choices of regulating kernels motivated from repeated averaging. By way of a hyperparameter linked to the degree of regulation, arbitrarily large skewness and excess kurtosis of returns can be easily achieved. Generic-time Laplace transforms, characterizing triplets, and cumulants of the regulated clocks and subsequent mixed models are analyzed, serving purposes ranging from statistical estimation and option price calibration to simulation techniques. Under specified jump--diffusion processes and tempered stable processes, a robust moment-based estimation procedure with profile likelihood is developed and a comprehensive empirical study involving S\&P500 and Bitcoin daily returns is conducted to demonstrate a series of desirable effects of the proposed methods.

q-fin.ST↗

arXiv2021

An Overview of Healthcare Data Analytics With Applications to the COVID-19 Pandemic

In the era of big data, standard analysis tools may be inadequate for making inference and there is a growing need for more efficient and innovative ways to collect, process, analyze and interpret the massive and complex data. We provide an overview of challenges in big data problems and describe how innovative analytical methods, machine learning tools and metaheuristics can tackle general healthcare problems with a focus on the current pandemic. In particular, we give applications of modern digital technology, statistical methods, data platforms and data integration systems to improve diagnosis and treatment of diseases in clinical research and novel epidemiologic tools to tackle infection source problems, such as finding Patient Zero in the spread of epidemics. We make the case that analyzing and interpreting big data is a very challenging task that requires a multi-disciplinary effort to continuously create more effective methodologies and powerful tools to transfer data information into knowledge that enables informed decision making.

cs.LG↗

arXiv2021

Inference for High Dimensional Censored Quantile Regression

With the availability of high dimensional genetic biomarkers, it is of interest to identify heterogeneous effects of these predictors on patients' survival, along with proper statistical inference. Censored quantile regression has emerged as a powerful tool for detecting heterogeneous effects of covariates on survival outcomes. To our knowledge, there is little work available to draw inference on the effects of high dimensional predictors for censored quantile regression. This paper proposes a novel procedure to draw inference on all predictors within the framework of global censored quantile regression, which investigates covariate-response associations over an interval of quantile levels, instead of a few discrete values. The proposed estimator combines a sequence of low dimensional model estimates that are based on multi-sample splittings and variable selection. We show that, under some regularity conditions, the estimator is consistent and asymptotically follows a Gaussian process indexed by the quantile level. Simulation studies indicate that our procedure can properly quantify the uncertainty of the estimates in high dimensional settings. We apply our method to analyze the heterogeneous effects of SNPs residing in lung cancer pathways on patients' survival, using the Boston Lung Cancer Survival Cohort, a cancer epidemiology study on the molecular mechanism of lung cancer.

stat.ME↗

arXiv2021

Estimation and Inference for High Dimensional Generalized Linear Models: A Splitting and Smoothing Approach

The focus of modern biomedical studies has gradually shifted to explanation and estimation of joint effects of high dimensional predictors on disease risks. Quantifying uncertainty in these estimates may provide valuable insight into prevention strategies or treatment decisions for both patients and physicians. High dimensional inference, including confidence intervals and hypothesis testing, has sparked much interest. While much work has been done in the linear regression setting, there is lack of literature on inference for high dimensional generalized linear models. We propose a novel and computationally feasible method, which accommodates a variety of outcome types, including normal, binomial, and Poisson data. We use a "splitting and smoothing" approach, which splits samples into two parts, performs variable selection using one part and conducts partial regression with the other part. Averaging the estimates over multiple random splits, we obtain the smoothed estimates, which are numerically stable. We show that the estimates are consistent, asymptotically normal, and construct confidence intervals with proper coverage probabilities for all predictors. We examine the finite sample performance of our method by comparing it with the existing methods and applying it to analyze a lung cancer cohort study.

stat.ME↗

arXiv2017

Relative efficiency of polariton emission in two-dimensional materials

We investigated emission and propagation of polaritons in a two dimensional van der Waals material hexagonal boron nitride (hBN). Our specific emphasis in this work is on hyperbolic phonon polariton emission that we investigated by means of scattering-type scanning near-field optical microscopy. Real-space nano-images detail how the polaritons are launched in several common arrangements including: light scattering by the edges of the crystal, metallic nanostructures deposited on the surface of hBN crystals, as well as random defects and impurities. Notably, the scanned tip of the near-field microscope is itself an efficient polariton launcher. Our analysis reveals that the scanning tips are superior to other types of emitters we have investigated. Furthermore, the study of polariton emission and emission efficiency may provide insights for development of polaritonic devices and for fundamental studies of collective modes in other van der Waals materials.

cond-mat.mes-hall↗

arXiv2016

Tunable plasmonic reflection by bound 1D electron states in a 2D Dirac metal

We show that surface plasmons of a two-dimensional Dirac metal such as graphene can be reflected by line-like perturbations hosting one-dimensional electron states. The reflection originates from a strong enhancement of the local optical conductivity caused by optical transitions involving these bound states. We propose that the bound states can be systematically created, controlled, and liquidated by an ultranarrow electrostatic gate. Using infrared nanoimaging, we obtain experimental evidence for the locally enhanced conductivity of graphene induced by a carbon nanotube gate, which supports this theoretical concept.

cond-mat.mes-hall↗