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Xiaowei Zhang

Xiaowei Zhang appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
cond-mat.mtrl-sciMachine LearningMethodologycond-mat.mes-hallComputer Visionphysics.ins-detphysics.opticsArtificial Intelligenceastro-ph.SRComputation and LanguageEmerging Technologieshep-exInformation Retrievalmath.OCmath.STphysics.app-phphysics.class-phq-fin.PMStatistics Theory

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Published work

20 published item(s)

preprint2026arXiv

Beyond GSD-as-Token: Continuous Scale Conditioning for Remote Sensing VLMs

Remote sensing vision-language models (RS-VLMs) face a fundamental mismatch with natural-image counterparts: the same geographic object exhibits radically different visual evidence across ground sampling distances (GSDs) spanning multiple orders of magnitude. Yet existing RS-VLMs often discard GSD or inject it as a discrete text token, forcing a single static parameter set to absorb the entire scale spectrum. We introduce ScaleEarth, a parameter-efficient fine-tuning framework built on Qwen3-VL that treats GSD as a continuous conditioning variable governing the model's computation path. At its core, CS-HLoRA (Continuous Scale-Conditioned Hyper-LoRA) modulates the LoRA low-rank subspace through a GSD-driven gate, enabling the model to dynamically route computation by physical scale. To remove reliance on sensor metadata at deployment, we pair CS-HLoRA with SSE-U, a lightweight heteroscedastic sub-head that predicts GSD and its uncertainty from visual features. To provide matching supervision, we construct GeoScale-VQA, a 1.5M-sample scale-layered RS-VQA corpus whose question-answer generation is conditioned on the same physical scalar that drives CS-HLoRA, forming a closed method-data loop. Trained with QLoRA on an 8B backbone, ScaleEarth achieves state-of-the-art results on remote-sensing benchmarks covering diverse Earth-system tasks, including XLRS-Bench and OmniEarth-Bench.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

SPARKLE: A Nonparametric Approach for Online Decision-Making with High-Dimensional Covariates

Personalized services are central to today's digital economy, and their sequential decisions are often modeled as contextual bandits. Modern applications pose two main challenges: high-dimensional covariates and the need for nonparametric models to capture complex reward-covariate relationships. We propose SPARKLE, a novel contextual bandit algorithm based on a sparse additive reward model that addresses both challenges through (i) a doubly penalized estimator for nonparametric reward estimation and (ii) an epoch-based design with adaptive screening to balance exploration and exploitation. We prove a sublinear regret bound that grows only logarithmically in the covariate dimensionality; to our knowledge, this is the first such result for nonparametric contextual bandits with high-dimensional covariates. We also derive an information-theoretic lower bound, and the gap to the upper bound vanishes as the reward smoothness increases. Extensive experiments on synthetic data and real data from video recommendation and personalized medicine show strong performance in high-dimensional settings.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Uncertainty-Adjusted Sorting for Asset Pricing with Machine Learning

Machine learning is central to empirical asset pricing, but portfolio construction still relies on point predictions and largely ignores asset-specific estimation uncertainty. We propose a simple change: sort assets using uncertainty-adjusted prediction bounds instead of point predictions alone. Across a broad set of ML models and a U.S. equity panel, this approach improves portfolio performance relative to point-prediction sorting. These gains persist even when bounds are built from partial or misspecified uncertainty information. They arise mainly from reduced volatility and are strongest for flexible machine learning models. Identification and robustness exercises show that these improvements are driven by asset-level rather than time or aggregate predictive uncertainty.

0 citations0 reviewsNo code linkedOpen access
preprint2024arXiv

A First-Principle Approach to X-ray Active Optics: Design and Verification

This paper presents the first-principle design approach for X-ray active optics, using the simulation-modulation cycle in place of the measurement-modulation feedback loops used in traditional active optics. Hence, the new active optics have the potential to outperform the accuracy of surface-shape metrology instruments. We apply an X-ray mirror with localized thermal elastic deformation to validate the idea. Both the finite element simulations and surface shape measurements have demonstrated that the active optics modulation accuracy limit can be achieved at the atomic layer level. It is believed that the implementation of the first-principle design strategy has the capacity to revolutionize both the manufacturing processes of X-ray mirrors and the beamline engineering of synchrotron radiation.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Ab initio calculations of spin-nonconserving exciton-phonon scattering in monolayer transition metal dichalcogenides

We investigate the spin-nonconserving relaxation channel of excitons by their couplings with phonons in two-dimensional transition metal dichalcogenides using $\textit{ab initio}$ approaches. Combining $\text{GW}$-Bethe-Salpeter equation method and density functional perturbation theory, we calculate the electron-phonon and exciton-phonon coupling matrix elements for the spin-flip scattering in monolayer WSe$_{\text{2}}$, and further analyze the microscopic mechanisms influencing these scattering strengths. We find that phonons could produce effective in-plane magnetic fields which flip spin of excitons, giving rise to relaxation channels complimentary to the spin-conserving relaxation. Finally, we calculate temperature-dependent spin-flip exciton-phonon relaxation times. Our method and analysis can be generalized to study other two-dimensional materials and would stimulate experimental measurements of spin-flip exciton relaxation dynamics.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Knowledge Gradient for Selection with Covariates: Consistency and Computation

Knowledge gradient is a design principle for developing Bayesian sequential sampling policies to solve optimization problems. In this paper we consider the ranking and selection problem in the presence of covariates, where the best alternative is not universal but depends on the covariates. In this context, we prove that under minimal assumptions, the sampling policy based on knowledge gradient is consistent, in the sense that following the policy the best alternative as a function of the covariates will be identified almost surely as the number of samples grows. We also propose a stochastic gradient ascent algorithm for computing the sampling policy and demonstrate its performance via numerical experiments.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Pre-training Tasks for User Intent Detection and Embedding Retrieval in E-commerce Search

BERT-style models pre-trained on the general corpus (e.g., Wikipedia) and fine-tuned on specific task corpus, have recently emerged as breakthrough techniques in many NLP tasks: question answering, text classification, sequence labeling and so on. However, this technique may not always work, especially for two scenarios: a corpus that contains very different text from the general corpus Wikipedia, or a task that learns embedding spacial distribution for a specific purpose (e.g., approximate nearest neighbor search). In this paper, to tackle the above two scenarios that we have encountered in an industrial e-commerce search system, we propose customized and novel pre-training tasks for two critical modules: user intent detection and semantic embedding retrieval. The customized pre-trained models after fine-tuning, being less than 10% of BERT-base's size in order to be feasible for cost-efficient CPU serving, significantly improve the other baseline models: 1) no pre-training model and 2) fine-tuned model from the official pre-trained BERT using general corpus, on both offline datasets and online system. We have open sourced our datasets for the sake of reproducibility and future works.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Sample and Computationally Efficient Stochastic Kriging in High Dimensions

Stochastic kriging has been widely employed for simulation metamodeling to predict the response surface of complex simulation models. However, its use is limited to cases where the design space is low-dimensional because, in general, the sample complexity (i.e., the number of design points required for stochastic kriging to produce an accurate prediction) grows exponentially in the dimensionality of the design space. The large sample size results in both a prohibitive sample cost for running the simulation model and a severe computational challenge due to the need to invert large covariance matrices. Based on tensor Markov kernels and sparse grid experimental designs, we develop a novel methodology that dramatically alleviates the curse of dimensionality. We show that the sample complexity of the proposed methodology grows only slightly in the dimensionality, even under model misspecification. We also develop fast algorithms that compute stochastic kriging in its exact form without any approximation schemes. We demonstrate via extensive numerical experiments that our methodology can handle problems with a design space of more than 10,000 dimensions, improving both prediction accuracy and computational efficiency by orders of magnitude relative to typical alternative methods in practice.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Engineering of Atomic-Scale Flexoelectricity at Grain Boundaries

Flexoelectricity is a type of ubiquitous and prominent electromechanical coupling, pertaining to the response of electrical polarization to mechanical strain gradients while not restricted to the symmetry of materials. However, large elastic deformation in most solids is usually difficult to achieve and the strain gradient at minuscule is challenging to control. Here we exploit the exotic structural inhomogeneity of grain boundary to achieve a huge strain gradient (~ 1.2 nm-1) within 3 ~ 4 unit-cells, and thus obtain atomic-scale flexoelectric polarization up to ~ 38 μC/cm2 at a 24 LaAlO3 grain boundary. The nanoscale flexoelectricity also modifies the electrical activity of grain boundaries. Moreover, we prove that it is a general and feasible way to form large strain gradients at atomic scale by altering the misorientation angles of grain boundaries in different dielectric materials. Thus, engineering of grain boundaries provides an effective pathway to achieve tunable flexoelectricity and broadens the electromechanical functionalities of non-piezoelectric materials.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Ranking and Selection with Covariates for Personalized Decision Making

We consider a problem of ranking and selection via simulation in the context of personalized decision making, where the best alternative is not universal but varies as a function of some observable covariates. The goal of ranking and selection with covariates (R&S-C) is to use simulation samples to obtain a selection policy that specifies the best alternative with certain statistical guarantee for subsequent individuals upon observing their covariates. A linear model is proposed to capture the relationship between the mean performance of an alternative and the covariates. Under the indifference-zone formulation, we develop two-stage procedures for both homoscedastic and heteroscedastic simulation errors, respectively, and prove their statistical validity in terms of average probability of correct selection. We also generalize the well-known slippage configuration, and prove that the generalized slippage configuration is the least favorable configuration for our procedures. Extensive numerical experiments are conducted to investigate the performance of the proposed procedures, the experimental design issue, and the robustness to the linearity assumption. Finally, we demonstrate the usefulness of R&S-C via a case study of selecting the best treatment regimen in the prevention of esophageal cancer. We find that by leveraging disease-related personal information, R&S-C can substantially improve patients' expected quality-adjusted life years by providing patient-specific treatment regimen.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

The Unusual Eruption of the Extragalactic Classical Nova M31N 2017-09a

M31N 2017-09a is a classical nova and was observed for some 160 days following its initial eruption, during which time it underwent a number of bright secondary outbursts. The light-curve is characterized by continual variation with excursions of at least 0.5 magnitudes on a daily time-scale. The lower envelope of the eruption suggests that a single power-law can describe the decline rate. The eruption is relatively long with $t_2 = 111$, and $t_3 = 153$ days.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Acoustic Multifunctional Logic Gates and Amplifier based on Passive Parity-Time Symmetry

Acoustic analogue computation and signal processing are of great significance, however, it's challenging to realize the acoustic computing devices because of their limitations of single function and complex structure. In this paper, an acoustic multifunctional device, which can gate or amplify acoustic waves without resorting to altering the frequency and structure using a passive acoustic parity-time (PT)-symmetric metamaterial, is realized theoretically and experimentally. The metamaterial is constructed by five lossless-loss periodically distributed media which are modulated to achieve the passive PT symmetry. At the coherent perfect absorber (CPA)-emitter point in the broken PT-symmetric phase, the logic gates (AND, OR, XOR and NOT) and small signal amplifier are realized in a single system by adjusting the phase and amplitude differences between two incoming beams, respectively. This work provides a new route for the connection between the PT symmetry and the acoustic metamaterial, which has great potential applications in acoustic modulation and acoustic multifunctional device.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Rule-Guided Compositional Representation Learning on Knowledge Graphs

Representation learning on a knowledge graph (KG) is to embed entities and relations of a KG into low-dimensional continuous vector spaces. Early KG embedding methods only pay attention to structured information encoded in triples, which would cause limited performance due to the structure sparseness of KGs. Some recent attempts consider paths information to expand the structure of KGs but lack explainability in the process of obtaining the path representations. In this paper, we propose a novel Rule and Path-based Joint Embedding (RPJE) scheme, which takes full advantage of the explainability and accuracy of logic rules, the generalization of KG embedding as well as the supplementary semantic structure of paths. Specifically, logic rules of different lengths (the number of relations in rule body) in the form of Horn clauses are first mined from the KG and elaborately encoded for representation learning. Then, the rules of length 2 are applied to compose paths accurately while the rules of length 1 are explicitly employed to create semantic associations among relations and constrain relation embeddings. Besides, the confidence level of each rule is also considered in optimization to guarantee the availability of applying the rule to representation learning. Extensive experimental results illustrate that RPJE outperforms other state-of-the-art baselines on KG completion task, which also demonstrate the superiority of utilizing logic rules as well as paths for improving the accuracy and explainability of representation learning.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Laser-heating-based active optics for synchrotron radiation applications

Active optics has attracted considerable interest from researchers in synchrotron radiation facilities, because of its capacity for x-ray wavefront correction. Here, we report a novel and efficient technique for correcting or modulating a mirror surface profile based on laser-heating-induced thermal expansion. An experimental study of the characteristics of the surface thermal deformation response indicates that the power of a milliwatt laser yields a bump height as low as sub-nanometer scale, and that variation of the spot size modulates the response function width effectively. In addition, the capacity of the laser-heating technique for free-form surface modulation is demonstrated via a surface correction experiment. The developed method is a promising new approach towards effective x-ray active optics coupled with at-wavelength metrology techniques.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Transduction on Directed Graphs via Absorbing Random Walks

In this paper we consider the problem of graph-based transductive classification, and we are particularly interested in the directed graph scenario which is a natural form for many real world applications. Different from existing research efforts that either only deal with undirected graphs or circumvent directionality by means of symmetrization, we propose a novel random walk approach on directed graphs using absorbing Markov chains, which can be regarded as maximizing the accumulated expected number of visits from the unlabeled transient states. Our algorithm is simple, easy to implement, and works with large-scale graphs. In particular, it is capable of preserving the graph structure even when the input graph is sparse and changes over time, as well as retaining weak signals presented in the directed edges. We present its intimate connections to a number of existing methods, including graph kernels, graph Laplacian based methods, and interestingly, spanning forest of graphs. Its computational complexity and the generalization error are also studied. Empirically our algorithm is systematically evaluated on a wide range of applications, where it has shown to perform competitively comparing to a suite of state-of-the-art methods.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Adsorption site determination of a molecular monolayer via inelastic tunneling

We have combined scanning tunneling microscopy with inelastic electron tunneling spectroscopy (IETS) and density functional theory (DFT) to study a tetracyanoethylene monolayer on Ag(100). Images show that the molecules arrange in locally ordered patterns with three non-equivalent, but undeterminable, adsorption sites. While scanning tunneling spectroscopy only shows subtle variations of the local electronic structure at the three different positions, we find that vibrational modes are very sensitive to the local atomic environment. IETS detects sizeable mode frequency shifts of the molecules located at the three topographically detected sites, which permits us to determine the molecular adsorption sites through identification with DFT calculations.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Experimentally Engineering the Edge Termination of Graphene Nanoribbons

The edges of graphene nanoribbons (GNRs) have attracted much interest due to their potentially strong influence on GNR electronic and magnetic properties. Here we report the ability to engineer the microscopic edge termination of high quality GNRs via hydrogen plasma etching. Using a combination of high-resolution scanning tunneling microscopy and first-principles calculations, we have determined the exact atomic structure of plasma-etched GNR edges and established the chemical nature of terminating functional groups for zigzag, armchair and chiral edge orientations. We find that the edges of hydrogen-plasma-etched GNRs are generally flat, free of structural reconstructions and are terminated by hydrogen atoms with no rehybridization of the outermost carbon edge atoms. Both zigzag and chiral edges show the presence of edge states.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Polarized neutron reflectometry study of Fe16N2 with Giant Saturation Magnetization prepared by N Inter-diffusion in Annealed Fe-N Thin Films

We report a synthesis route to grow iron nitride thin films with giant saturation magnetization (Ms) through an N inter-diffusion process. By post annealing Fe/Fe-N structured films grown on GaAs(001) substrates, nitrogen diffuses from the over-doped amorphous-like Fe-N layer into strained crystalline Fe layer and facilitates the development of metastable Fe16N2 phase. As explored by polarized neutron reflectometry, the depth-dependent Ms profile can be well described by a model with the presence of a giant Ms up to 2360 emu/cm3 at near-substrate interface, corresponding to the strained regions of these annealed films. This is much larger than the currently known limit (Fe65Co35 with Ms \sim 1900 emu/cm3). The present synthesis method can be used to develop writer materials for future magnetic recording application.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Spatially Resolving Spin-split Edge States of Chiral Graphene Nanoribbons

A central question in the field of graphene-related research is how graphene behaves when it is patterned at the nanometer scale with different edge geometries. Perhaps the most fundamental shape relevant to this question is the graphene nanoribbon (GNR), a narrow strip of graphene that can have different chirality depending on the angle at which it is cut. Such GNRs have been predicted to exhibit a wide range of behaviour (depending on their chirality and width) that includes tunable energy gaps and the presence of unique one-dimensional (1D) edge states with unusual magnetic structure. Most GNRs explored experimentally up to now have been characterized via electrical conductivity, leaving the critical relationship between electronic structure and local atomic geometry unclear (especially at edges). Here we present a sub-nm-resolved scanning tunnelling microscopy (STM) and spectroscopy (STS) study of GNRs that allows us to examine how GNR electronic structure depends on the chirality of atomically well-defined GNR edges. The GNRs used here were chemically synthesized via carbon nanotube (CNT) unzipping methods that allow flexible variation of GNR width, length, chirality, and substrate. Our STS measurements reveal the presence of 1D GNR edge states whose spatial characteristics closely match theoretical expectations for GNR's of similar width and chirality. We observe width-dependent splitting in the GNR edge state energy bands, providing compelling evidence of their magnetic nature. These results confirm the novel electronic behaviour predicted for GNRs with atomically clean edges, and thus open the door to a whole new area of applications exploiting the unique magnetoelectronic properties of chiral GNRs.

0 citations0 reviewsNo code linkedOpen access
preprint2009arXiv

Tuning Molecule-Mediated Spin Coupling in Bottom-Up Fabricated Vanadium-TCNE Nanostructures

We have fabricated hybrid magnetic complexes from V atoms and tetracyanoethylene (TCNE) ligands via atomic manipulation with a cryogenic scanning tunneling microscope. Using tunneling spectroscopy we observe spin-polarized molecular orbitals as well as Kondo behavior. For complexes having two V atoms, the Kondo behavior can be quenched for different molecular arrangements, even as the spin-polarized orbitals remain unchanged. This is explained by variable spin-spin (i.e., V-V) ferromagnetic coupling through a single TCNE molecule, as supported by density functional calculations.

0 citations0 reviewsNo code linkedOpen access