arXiv subjects

Xiaolong Zhang

Publications and source records attributed to Xiaolong Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Spatiotemporal programming via asymmetric dielectric engineering for nonvolatile 2D optoelectronics

Ambipolar two dimensional (2D) semiconductors integrated with floating-gate architectures offer a promising platform for nonvolatile, reconfigurable electronics. However, the switching between p-n and n-p junction polarities has conventionally required complex multi-gate designs, hindering the scalability and integration density. Here, we demonstrate a spatiotemporal programming strategy using a dual-floating-gate architecture with a symmetry broken tunneling dielectric. An asymmetric dielectric stack creates distinct tunneling thresholds for two floating gates, enabling a single input gate to encode spatial doping profiles in the 2D channel via defined voltage pulse sequences. We achieve on demand, nonvolatile, and reversible switching between p-n and n-p configurations with excellent retention and endurance. The reconfigurable homojunction serves as a multifunctional platform for logic encoding, rectification, photodetection, and in sensor computing. This work establishes a design paradigm that replaces spatial input complexity with spatiotemporal programming, paving the way for high-density, multifunctional intelligent hardware.

cond-mat.mes-hall↗

arXiv2026

High-speed and high-gain graphene photovoltaic phototransistor gated by a van der Waals heterojunction

Two-dimensional (2D) material-based phototransistors offer a unique combination of optical sensing, signal amplification, and logic operation within a single device, yet fundamentally suffering from an inherent gain-speed trade-off. Here, we demonstrate a 2D photovoltaic phototransistor that overcomes this limitation using a MoS2/PtSe2 heterojunction to gate a graphene channel. The ultrafast photovoltaic effect in the heterojunction enables charge separation, yielding ultrahigh photoconductive gain (up to 10^8) in graphene channel via interfacial gating. Besides, the response time (below the instrumental resolution of 550 ns) is governed by carrier transit in graphene channel, enabling simultaneous high speed and high gain. Moreover, broadband photodetection from visible to near-infrared is enabled by the optical properties of the MoS2/PtSe2 heterojunction, with the detectivity exceeding 10^11 Jones. These results establish a new paradigm for high-performance 2D phototransistors by harnessing photovoltaic and photogating effects to overcome the classical gain-speed trade-off.

cond-mat.mtrl-sci↗

arXiv2026

Histopathology Image Normalization via Latent Manifold Compaction

Batch effects arising from technical variations in histopathology staining protocols, scanners, and acquisition pipelines pose a persistent challenge for computational pathology, hindering cross-batch generalization and limiting reliable deployment of models across clinical sites. In this work, we introduce Latent Manifold Compaction (LMC), an unsupervised representation learning framework that performs image harmonization by learning batch-invariant embeddings from a single source dataset through explicit compaction of stain-induced latent manifolds. This allows LMC to generalize to target domain data unseen during training. Evaluated on three challenging public and in-house benchmarks, LMC substantially reduces batch-induced separations across multiple datasets and consistently outperforms state-of-the-art normalization methods in downstream cross-batch classification and detection tasks, enabling superior generalization.

cs.LG↗

arXiv2026

Cross-Fusion Distance: A Novel Metric for Measuring Fusion and Separability Between Data Groups in Representation Space

Quantifying degrees of fusion and separability between data groups in representation space is a fundamental problem in representation learning, particularly under domain shift. A meaningful metric should capture fusion-altering factors like geometric displacement between representation groups, whose variations change the extent of fusion, while remaining invariant to fusion-preserving factors such as global scaling and sampling-induced layout changes, whose variations do not. Existing distributional distance metrics conflate these factors, leading to measures that are not informative of the true extent of fusion between data groups. We introduce Cross-Fusion Distance (CFD), a principled measure that isolates fusion-altering geometry while remaining robust to fusion-preserving variations, with linear computational complexity. We characterize the invariance and sensitivity properties of CFD theoretically and validate them in controlled synthetic experiments. For practical utility on real-world datasets with domain shift, CFD aligns more closely with downstream generalization degradation than commonly used alternatives. Overall, CFD provides a theoretically grounded and interpretable distance measure for representation learning.

cs.LG↗

arXiv2026

Complex Cognition: A New Theoretical Foundation for the Design and Evaluation of Visual Analytics Systems

Current research on visual analytics systems largely follows the research paradigm of interactive system design in the field of Human-Computer Interaction (HCI), and includes key methodologies including design requirement development based on user needs, interactive system design, and system evaluation. However, most studies under this paradigm have a contradiction: there is a significant mismatch between the research methods developed for simple cognitive behaviors (e.g., color perception, the perception of spatial relationship among interactive artifacts) and research goals targeting for complex analytical behaviors (e.g., reasoning, problem-solving, decision-making). This mismatch may hurt the theoretical contributions of research studies, in particularly the internal validity of a designed system and the external validity of design methods. To address this challenge, this paper argues for a need to go beyond traditional HCI theoretical foundations and proposes to adopt complex cognition theories to build new theoretical foundations. Specifically, this paper analyzes how current design and evaluation methods in research on visual analytics systems constrain the internal and external validity of research, discusses the connections between complex cognition theories and visual analytics tasks, and explores how problem-solving theories from complex cognition can guide research on visual analytics systems.

cs.HC↗

arXiv2025

A Deep Learning System for Rapid and Accurate Warning of Acute Aortic Syndrome on Non-contrast CT in China

The accurate and timely diagnosis of acute aortic syndromes (AAS) in patients presenting with acute chest pain remains a clinical challenge. Aortic CT angiography (CTA) is the imaging protocol of choice in patients with suspected AAS. However, due to economic and workflow constraints in China, the majority of suspected patients initially undergo non-contrast CT as the initial imaging testing, and CTA is reserved for those at higher risk. In this work, we present an artificial intelligence-based warning system, iAorta, using non-contrast CT for AAS identification in China, which demonstrates remarkably high accuracy and provides clinicians with interpretable warnings. iAorta was evaluated through a comprehensive step-wise study. In the multi-center retrospective study (n = 20,750), iAorta achieved a mean area under the receiver operating curve (AUC) of 0.958 (95% CI 0.950-0.967). In the large-scale real-world study (n = 137,525), iAorta demonstrated consistently high performance across various non-contrast CT protocols, achieving a sensitivity of 0.913-0.942 and a specificity of 0.991-0.993. In the prospective comparative study (n = 13,846), iAorta demonstrated the capability to significantly shorten the time to correct diagnostic pathway. For the prospective pilot deployment that we conducted, iAorta correctly identified 21 out of 22 patients with AAS among 15,584 consecutive patients presenting with acute chest pain and under non-contrast CT protocol in the emergency department (ED) and enabled the average diagnostic time of these 21 AAS positive patients to be 102.1 (75-133) mins. Last, the iAorta can help avoid delayed or missed diagnosis of AAS in settings where non-contrast CT remains the unavoidable the initial or only imaging test in resource-constrained regions and in patients who cannot or did not receive intravenous contrast.

eess.IV↗

arXiv2025

Optimal Rates for Ergodic SDEs Driven by Multiplicative $α$-Stable Processes in Wasserstein-1 distance

This paper establishes the quantitative stability of invariant measures $μ_α$ for $\mathbb{R}^d$-valued ergodic stochastic differential equations driven by rotationally invariant multiplicative $α$-stable processes with $α\in(1,2]$. Under structural assumptions on the coefficients with a fixed parameter vector $\bmθ$, we derive optimal convergence rates in the Wasserstein-$1$ ($\cW_{1}$) distance between the invariant measures introduced above, namely, \item[(i)] For any interval $[α_0, \vartheta_0] \subset (1,2)$, there exists $C_1 = C(α_0, \vartheta_0,\bmθ,d) > 0$ such that \cW_{1}(μ_α, μ_\vartheta) \leq C_1 |α- \vartheta|, \quad \forall α, \vartheta \in [α_0, \vartheta_0]. \item[(ii)] For any $α_0\in (1,2)$, there exists $C_2 = C(α_0, \bmθ) > 0$ such that \begin{align*} \cW_{1}(μ_α, μ_2) \leq C_2\, d(2 - α), \quad \forall α\in [α_0, 2). The optimality of these rates is rigorously verified by explicit calculations for the Ornstein-Uhlenbeck systems in \cite{Deng2023Optimal}. It is worth emphasizing that \cite{Deng2023Optimal} addressed only case (ii) under additive noise, whereas our analysis establishes results for both cases (i) and (ii) under multiplicative $α$-stable noise, employing fundamentally different analytical methods.

math.PR↗

arXiv2025

Fiber to the Room: Key Technologies, Challenges, and Prospects

Fiber to the Room (FTTR) is a next-generation access network designed to deliver high bandwidth, low latency, and room-level optical coverage. This paper presents a comprehensive analysis of the FTTR system architecture and protocol stack, focusing on three key technical aspects: centralized scheduling and control, integrated management and maintenance, and green energy-saving mechanisms. A simplified FTTR architecture based on the convergence of the medium access control (MAC) and physical (PHY) layers is introduced to enhance coordination and scheduling efficiency. An extended remote management scheme, based on the optical network unit management and control interface (OMCI), is described to enable unified control across main fiber units (MFUs) and sub-fiber units (SFUs). Furthermore, a service-aware energy-saving framework is discussed for dynamic power optimization. The paper also explores the integration of artificial intelligence (AI) and passive sensing into FTTR systems to support intelligent scheduling, energy management, and environment-aware optimization. These insights provide technical guidance for the scalable deployment and future evolution of FTTR networks.

cs.NI↗

arXiv2025

A multi-detector neutral helium atom microscope

Scanning helium microscopy (SHeM) is an emerging technique that uses a beam of neutral atoms to image and analyse surfaces. The low energies ($\sim$64 meV) and completely non-destructive nature of the probe particles provide exceptional sensitivity for studying delicate samples and thin devices, including 2D materials. To date, around five such instruments have been constructed and are described in the literature. All represent the first attempts at SHeM construction in different laboratories, and use a single detection device. Here, we describe our second generation microscope, which is the first to offer multi-detector capabilities. The new instrument builds on recent research into SHeM optimisation and incorporates many improved design features over our previous instrument. We present measurements that highlight some of the unique capabilities the instrument provides, including 3D surface profiling, alternative imaging modes, and simultaneous acquisition of images from a mixed species beam.

physics.ins-det↗

arXiv2024

$W_{\bf d}$-convergence rate of EM schemes for invariant measures of supercritical stable SDEs

By establishing the regularity estimates for nonlocal Stein/Poisson equations under $γ$-order Hölder and dissipative conditions on the coefficients, we derive the $W_{\bf d}$-convergence rate for the Euler-Maruyama schemes applied to the invariant measure of SDEs driven by multiplicative $α$-stable noises with $α\in (\frac{1}{2}, 2)$, where $W_{\bf d}$ denotes the Wasserstein metric with ${\bf d}(x,y)=|x-y|^γ\wedge 1$ and $γ\in ((1-α)_+, 1]$.

math.PR↗

arXiv2024

Concurrent operando neutron imaging and diffraction analysis revealing spatial lithiation phase evolution in an ultra-thick graphite electrode

Energy efficient, safe and reliable Li-ion batteries (LIBs) are required for a wide range of applications. Charging capabilities of thick electrodes still holding their stored high-energy is a most desirable characteristic in future advanced LIBs. The introduction of ultra-thick graphite anode meets limitations in internal electrode transport properties, leading to Li-ion gradients with detrimental consequences for battery cell performance and lifetime. Yet, there is a lack of experimental tools capable of providing a complete view of local processes and evolving gradients within such thick electrodes. Here, we introduce a multi-modal operando measurement approach, enabling quantitative spatio-temporal observations of Li concentrations and intercalation phases in ultra-thick, graphite electrodes. Neutron imaging and diffraction concurrently provide correlated information from the macroscopic scale of the cell and electrode down to the crystallographic scale portraying the intercalation and deintercalation processes. In particular, the evolving formation of the solid electrolyte interphase (SEI), observation of gradients in total lithium content, as well as in the formation of ordered LixC6 phases and trapped lithium have been mapped throughout the first charge-discharge cycle of the cell. Different lithiation stages co-exist during charging and discharging of an ultra-thick composite graphite-based electrode; delayed lithiation and delithiation processes are observed at the central region of the electrode, while the SEI formation, potential plating and dead lithium are predominantly found closer to the interface with the separator. The study furthermore emphasizes the potential of the method to study Li ion diffusion and the kinetics of lithiation phase formation in advanced ultra-thick electrodes.

cond-mat.mtrl-sci↗

arXiv2024

Low regularity estimates of the Lie-Totter time-splitting Fourier spectral method for the logarithmic Schrödinger equation

In this paper, we conduct rigorous error analysis of the Lie-Totter time-splitting Fourier spectral scheme for the nonlinear Schrödinger equation with a logarithmic nonlinear term $f(u)=u\ln|u|^2$ (LogSE) and periodic boundary conditions on a $d$-dimensional torus $\mathbb T^d$. Different from existing works based on regularisation of the nonlinear term $ f(u)\approx f^\varepsilon(u)=u\ln (|u| + \varepsilon )^2,$ we directly discretize the LogSE with the understanding $f(0)=0.$ Remarkably, in the time-splitting scheme, the solution flow map of the nonlinear part: $g(u)= u {\rm e}^{-{\rm} i t \ln|u|^{2}}$ has a higher regularity than $f(u)$ (which is not differentiable at $u=0$ but Hölder continuous), where $g(u)$ is Lipschitz continuous and possesses a certain fractional Sobolev regularity with index $0<s<1$. Accordingly, we can derive the $L^2$-error estimate: $O\big((τ^{s/2} + N^{-s})\ln\! N\big)$ of the proposed scheme for the LogSE with low regularity solution $u\in C((0,T]; H^s( \mathbb{T}^d)\cap L^\infty( \mathbb{T}^d)).$ Moreover, we can show that the estimate holds for $s=1$ with more delicate analysis of the nonlinear term and the associated solution flow maps. Furthermore, we provide ample numerical results to demonstrate such a fractional-order convergence for initial data with low regularity. This work is the first one devoted to the analysis of splitting scheme for the LogSE without regularisation in the low regularity setting, as far as we can tell.

math.NA↗

arXiv2023

Physics and modeling of liquid films in pulsating heat pipes

The present study reports a novel physical model for simulating Pulsating Heat Pipes (PHP). Their high heat performance is due to the phase change over thin liquid films. The simulation of physically correct film behavior is thus crucial. The model adopts the one-dimensional approach, which is computationally efficient yet still capable of capturing major physical phenomena. The model assumes a spatially uniform film thickness, whereas both the film thickness and length can vary over time; therefore, we call it the oscillating film thickness model. It is based on the physical analysis of liquid film deposition by the receding menisci of Taylor bubbles and of contact line dynamics. Three key phenomena are addressed: (i) film deposition, (ii) contact line receding due to dewetting acceleration by evaporation, and (iii) mass exchange over films and contact lines. The model is evaluated by simulating the simplest, single-branch PHP, for which detailed experimental data are available. A quantitative agreement is reached. As the model includes the wetting properties, their impact on oscillations is analyzed; a qualitative agreement with the experiment is demonstrated.

physics.flu-dyn↗

arXiv2023

RPN: A Word Vector Level Data Augmentation Algorithm in Deep Learning for Language Understanding

Data augmentation is a widely used technique in machine learning to improve model performance. However, existing data augmentation techniques in natural language understanding (NLU) may not fully capture the complexity of natural language variations, and they can be challenging to apply to large datasets. This paper proposes the Random Position Noise (RPN) algorithm, a novel data augmentation technique that operates at the word vector level. RPN modifies the word embeddings of the original text by introducing noise based on the existing values of selected word vectors, allowing for more fine-grained modifications and better capturing natural language variations. Unlike traditional data augmentation methods, RPN does not require gradients in the computational graph during virtual sample updates, making it simpler to apply to large datasets. Experimental results demonstrate that RPN consistently outperforms existing data augmentation techniques across various NLU tasks, including sentiment analysis, natural language inference, and paraphrase detection. Moreover, RPN performs well in low-resource settings and is applicable to any model featuring a word embeddings layer. The proposed RPN algorithm is a promising approach for enhancing NLU performance and addressing the challenges associated with traditional data augmentation techniques in large-scale NLU tasks. Our experimental results demonstrated that the RPN algorithm achieved state-of-the-art performance in all seven NLU tasks, thereby highlighting its effectiveness and potential for real-world NLU applications.

cs.CL↗

arXiv2023

Error analysis of a first-order IMEX scheme for the logarithmic Schrödinger equation

The logarithmic Schrödinger equation (LogSE) has a logarithmic nonlinearity $f(u)=u\ln |u|^2$ that is not differentiable at $u=0.$ Compared with its counterpart with a regular nonlinear term, it possesses richer and unusual dynamics, though the low regularity of the nonlinearity brings about significant challenges in both analysis and computation. Among very limited numerical studies, the semi-implicit regularized method via regularising $f(u)$ as $ u^{\varepsilon}\ln ({\varepsilon}+ |u^{\varepsilon}|)^2$ to overcome the blowup of $\ln |u|^2$ at $u=0$ has been investigated recently in literature. With the understanding of $f(0)=0,$ we analyze the non-regularized first-order Implicit-Explicit (IMEX) scheme for the LogSE. We introduce some new tools for the error analysis that include the characterization of the Hölder continuity of the logarithmic term, and a nonlinear Grönwall's inequality. We provide ample numerical results to demonstrate the expected convergence. We position this work as the first one to study the direct linearized scheme for the LogSE as far as we can tell.

math.NA↗

arXiv2023

Microlayer in nucleate boiling seen as Landau-Levich film with dewetting and evaporation

Both experimental and theoretical studies on the microscale and fast physical phenomena occurring during the growth of vapor bubbles in nucleate pool boiling are reported. The focus is on the liquid film of micrometric thickness (``microlayer'') that can form between the heater and the liquid-vapor interface of a bubble on the millisecond time scale. The microlayer strongly affects the macroscale heat transfer and is thus important to be understood. It is shown that the microlayer can be seen as the Landau-Levich film deposited by the bubble foot edge during its receding when the bubble grows. The microlayer profile measured with white-light interferometry, the temperature distribution over the heater, and the bubble shape were observed with synchronized high-speed cameras. The microlayer consists of two regions: a ridge near the contact line followed by a longer and flatter part. The ridge could not be measured because of the intrinsic limitation of interferometry, which is analyzed. The simulations show that the ridge grows over time due to collection of liquid at contact line receding, the theoretical dynamics of which agrees with the experiment. The flatter part of the microlayer is bumped and its physical origin is explained.

physics.flu-dyn↗

arXiv2023

Time-averaged approach to the dewetting problem at evaporation

Dewetting of liquid films on solid surfaces in the presence of evaporation is a common phenomenon and has been studied by many researchers. The previous numerical approach has revealed that evaporation accelerates the dewetting speed of the triple contact line and established correlations between the dewetting speed and the surface wettability and superheating. However, such a numerical calculation is time- and resource-consuming. ,We examine dewetting physics and propose a time-averaged approach based on the multiscale theory. The new approach averages the dewetting process over time and consists of only several algebraic equations, making the problem easier to solve. It can produce time-averaged values of essential quantities, such as the dewetting speed and contact angle as a function of superheating, which agrees with the previous numerical results. This simple approach is valuable for many applications, such as modeling pulsating heat pipes and describing the microlayer dynamics under growing vapor bubbles in nucleate boiling.

cond-mat.soft↗

arXiv2022

Lagrangian model for passive scalar gradients in turbulence

The equation for the fluid velocity gradient along a Lagrangian trajectory immediately follows from the Navier-Stokes equation. However, such an equation involves two terms that cannot be determined from the velocity gradient along the chosen Lagrangian path: the pressure Hessian and the viscous Laplacian. A recent model handles these unclosed terms using a multi-level version of the recent deformation of Gaussian fields (RDGF) closure (Johnson \& Meneveau, Phys.~Rev.~Fluids, 2017). This model is in remarkable agreement with DNS data and works for arbitrary Taylor Reynolds numbers $\Rey_λ$. Inspired by this, we develop a Lagrangian model for passive scalar gradients in isotropic turbulence. The equation for passive scalar gradients also involves an unclosed term in the Lagrangian frame, namely the scalar gradient diffusion term, which we model using the RDGF approach. However, comparisons of the statistics obtained from this model with direct numerical simulation (DNS) data reveal substantial errors due to erroneously large fluctuations generated by the model. We address this defect by incorporating into the closure approximation information regarding the scalar gradient production along the local trajectory history of the particle. This modified model makes predictions for the scalar gradients, their production rates, and alignments with the strain-rate eigenvectors that are in very good agreement with DNS data. However, while the model yields valid predictions up to around $\Rey_λ\approx 500$, beyond this, the model breaks down.

physics.flu-dyn↗