arXiv subjects

Chao Meng

Publications and source records attributed to Chao Meng.

At least 19 recordsLinked to original sources

arXiv2026

Electro-Optic Active Metasurfaces for High-Speed Photonic Applications

Metasurfaces are artificially engineered ultrathin nanostructured surfaces, capable of flexibly manipulating light-matter interactions on compact platforms, and thereby of great significance for a wide range of applications within modern optics and photonics, including communications, computing, sensing, and quantum technologies. However, the inherently static nature of conventional metasurfaces severely limits their functionalities and thus range of possible applications. Benefiting from integration of the metasurface platform for shaping optical wavefronts with ultrafast electro-optic (EO) materials, active EO metasurfaces have emerged as a frontier research direction targeting advanced photonic devices. This paper systematically reviews the latest progress in this field, featuring a comprehensive comparison of performances and application scenarios of mainstream EO materials such as lithium niobate, barium titanate and organic EO polymers. Modulation mechanisms based on the Pockels and Kerr effects along with the corresponding active metasurface implementations are summarized. Furthermore, improvements in modulation efficiency enabled by advantageously exploiting resonant structural designs and associated phenomena, including Fabry-Perot resonances, Mie resonances, surface plasmon polaritons, quasi-bound states in the continuum, surface lattice resonances, and guided-mode resonances, are presented and summerized in detail. Current challenges related to metasurface design, nanofabrication, performance and heterogeneous integration are also discussed. Finally, future research directions are outlined, highlighting interdisciplinary developments, novel material engineering, and AI-assisted design as key pathways to enable practical use of active EO metasurfaces in modern optics and photonics, including quantum information technologies.

physics.optics↗

arXiv2026

Learning-Based Multi-Criteria Decision Making Model for Sawmill Location Problems

Strategically locating a sawmill is vital for enhancing the efficiency, profitability, and sustainability of timber supply chains. Our study proposes a Learning-Based Multi-Criteria Decision-Making (LB-MCDM) framework that integrates machine learning (ML) with GIS-based spatial location analysis via MCDM. The proposed framework provides a data-driven, unbiased, and replicable approach to assessing site suitability. We demonstrate the utility of the proposed model through a case study in Mississippi (MS). We apply five ML algorithms (Random Forest Classifier, Support Vector Classifier, XGBoost Classifier, Logistic Regression, and K-Nearest Neighbors Classifier) to identify the most suitable sawmill locations in Mississippi. Among these models, the Random Forest Classifier achieved the highest performance. We use the SHAP (SHapley Additive exPlanations) technique to determine the relative importance of each criterion, revealing the Supply-Demand Ratio, a composite feature that reflects local market competition dynamics, as the most influential factor, followed by Road, Rail Line and Urban Area Distance. The validation of suitability maps generated by our LB-MCDM model suggests that 10-11% of the MS landscape is highly suitable for sawmill location.

cs.LG↗

arXiv2025

Demultiplexing through a multimode fiber using chip-scale diffractive neural networks

In today's information age, advanced fiber optic transmission technology is of paramount importance. Multimode fibers (MMFs) using space-division multiplexing (SDM) are promising for improved transmission capacity, connection flexibility, and security of data. However, the complex transmission characteristics of MMFs significantly hinder precise mode demultiplexing. Conventional approaches, including holographic measurements, phase retrieval algorithms, photonic lanterns, and multiplane light conversion, are limited by system complexity, size, and flexibility. In this paper, we demonstrate for the first time a purely optical, chip-scale AI solution for high-mode isolation, speed-of-light demultiplexing of MMF modes using a three-dimensional diffractive neural network (DNN). The DNN is trained with synthetic modal data and fabricated using two-photon nanolithography. It features a compact size of $120{\mu}m \times 120{\mu}m \times 80{\mu}m$ and a diffractive structure size of $1{\mu}m^{2}$ for the neurons at the hidden layers of the network. Experimentally, the DNN demultiplexer achieves a relative demultiplexing accuracy of over 80%. The AI approach of DNN allows for flexible design and overcomes the size and performance limitations of digital-optical demultiplexers. This work paves the way for compact, low-latency optical processors for high-performance demultiplexers and enables scalable, chip-integrated solutions for next-generation fiber optic networks.

physics.optics↗

arXiv2025

Hybrid SU(1,1) interferometry in optomechanics

In non-degenerate SU(1,1) interferometers, beam splitters are replaced by two-mode squeezers, enabling sub-shot-noise sensitivity without input squeezing and robustness to detection losses by quantum entanglement. We propose a hybrid implementation in optomechanics where one "arm" is a mechanical mode undergoing two consecutive, mode-matched interactions with a traveling optical field (constituting the other arm). Such engineered interactions allow for sub-shot-noise phase detection even in the presence of mechanical thermal noise and optical losses, advancing precision interferometry in hybrid systems.

quant-ph↗

arXiv2025

Dynamic and Continuous Control of Second-Harmonic Chirality through Lithium Niobate Nonlocal Metasurface

Nonlinear chiral light sources are crucial for emerging applications in chiroptics, including ultrafast spin dynamics and quantum state manipulation. However, achieving precise and dynamic control over nonlinear optical chirality with natural materials and metasurfaces, particularly those based on non-centrosymmetric materials such as lithium niobate (LN), is hindered by the complex tensorial nature of the second-order nonlinear susceptibility. Here, we demonstrate a nonlinear nonlocal metasurface, comprising plasmonic nanoantennas atop an x-cut LN thin film, that enables dynamic and continuous control of second-harmonic (SH) chirality. By leveraging two spectrally detuned resonances arising from the excitation of orthogonally propagating guided modes, enabled by lattice anisotropy and LN birefringence, we achieve full-range tuning of SH chirality, from right- to left-handed circular polarization, simply by rotating the polarization of a linearly polarized pump. The SH chirality, quantified by the Stokes parameter S3, is thereby continuously tuned from 0.991 to -0.993 in simulations and from 0.920 to -0.815 in experiments, while maintaining consistently enhanced SH intensity across the entire tuning range. Our approach opens new avenues for developing compact and tunable chiral sources, with potential applications in integrated nonlinear photonics and adaptable quantum technologies.

physics.optics↗

arXiv2025

Membrane phononic integrated circuits

Phononic circuits constructed from high tensile stress membranes offer a range of desirable features such as high acoustic confinement, controllable nonlinearities, low mass, compact footprint, and ease of fabrication. This tutorial presents a systematic approach to modelling and designing phononic integrated circuits on this platform, beginning with acoustic confinement, wave propagation and dispersion, mechanical and actuation nonlinearities, as well as resonator dynamics. By adapting coupled mode theory from optoelectronics to suspended membranes, and validating this theory with several numerical techniques (finite element modelling, finite difference time domain simulations, and the transfer matrix method), we then provide a comprehensive framework to engineer a broad variety of phononic circuit building blocks. As illustrative examples, we describe the implementation of several acoustic circuit elements including resonant and non-resonant variable-ratio power splitters, mode converters, mode (de)multiplexers, and in-line Fabry-Perot cavities based on evanescent tunnel barriers. These building blocks lay the foundation for phononic integrated circuits with applications in sensing, acoustic signal processing, and power-efficient and radiation-hard computing.

physics.app-ph↗

arXiv2025

Integrated angstrom-tunable polarization-resolved solid-state photon sources

The development of high-quality solid-state photon sources is essential to nano optics, quantum photonics, and related fields. A key objective of this research area is to develop tunable photon sources that not only enhance the performance but also offer dynamic functionalities. However, the realization of compact and robust photon sources with precise and wide range tunability remains a long-standing challenge. Moreover, the lack of an effective approach to integrate nanoscale photon sources with dynamic systems has hindered tunability beyond mere spectral adjustments, such as simultaneous polarization control. Here we propose a platform based on quantum emitter (QE) embedded metasurfaces (QEMS) integrated with a microelectromechanical system (MEMS)-positioned microcavity, enabling on-chip multi-degree control of solid-state photon sources. Taking advantages of MEMS-QEMS, we show that typically broadband room-temperature emission from nanodiamonds containing nitrogen-vacancy centres can be narrowed to 3.7 nm and dynamically tuned with angstrom resolution. Furthermore, we design a wavelength-polarization-multiplexed QEMS and demonstrate polarization-resolved control of the MEMS-QEMS emission in a wide wavelength range (650 nm to 700 nm) along with polarization switching at sub-millisecond timescales. We believe that the proposed MEMS-QEMS platform can be adapted for most existing QEs, significantly expanding their room-temperature capabilities and thereby enhancing their potential for advanced photonic applications.

physics.optics↗

arXiv2025

Learning-Based Multi-Criteria Decision Model for Site Selection Problems

Strategically locating sawmills is critical for the efficiency, profitability, and sustainability of timber supply chains, yet it involves a series of complex decision-making affected by various factors, such as proximity to resources and markets, proximity to roads and rail lines, distance from the urban area, slope, labor market, and existing sawmill data. Although conventional Multi-Criteria Decision-Making (MCDM) approaches utilize these factors while locating facilities, they are susceptible to bias since they rely heavily on expert opinions to determine the relative factor weights. Machine learning (ML) models provide an objective, data-driven alternative for site selection that derives these weights directly from the patterns in large datasets without requiring subjective weighting. Additionally, ML models autonomously identify critical features, eliminating the need for subjective feature selection. In this study, we propose integrated ML and MCDM methods and showcase the utility of this integrated model to improve sawmill location decisions via a case study in Mississippi. This integrated model is flexible and applicable to site selection problems across various industries.

cs.LG↗

arXiv2025

Ignite Forecasting with SPARK: An Efficient Generative Framework for Refining LLMs in Temporal Knowledge Graph Forecasting

Temporal Knowledge Graph (TKG) forecasting is crucial for predicting future events using historical data. With the surge of Large Language Models (LLMs), recent studies have begun exploring their integration into TKG forecasting and achieved some success. However, they still face limitations such as limited input length, inefficient output generation, and resource-intensive refinement, which undermine their performance and practical applicability. To address these limitations, we introduce SPARK, a Sequence-level Proxy-Adapting framework for Refining LLMs in TKG forecasting. Inspired by inference-time algorithms adopted in controlling generation, SPARK offers a cost-effective, plug-and-play solution through two key innovations: (1) Beam Sequence-Level Generation, which reframes TKG forecasting as a top-K sequence-level generation task, using beam search for efficiently generating next-entity distribution in a single forward pass. (2) TKG Adapter for Refinement, which employs traditional TKG models as trainable proxy adapters to leverage global graph information and refine LLM outputs, overcoming both the input length and the resource-intensive fine-tuning problems. Experiments across diverse datasets validate SPARK's forecasting performance, robust generalization capabilities, and high efficiency. We release source codes at https://github.com/yin-gz/SPARK.

cs.LG↗

arXiv2025

Metasurface Polarimeter for Structural Imaging and Tissue Diagnostics

Histopathology, the study and diagnosis of disease through analysis of tissue samples, is an indispensable part of modern medicine. However, the practice is time consuming and labor intensive, compelling efforts to improve the process and develop new approaches. One perspective technique involves mapping changes in the polarization state of light scattered by the tissue, but the conventional implementation requires bulky polarization optics and is slow. We report the design, fabrication and characterization of a compact metasurface polarimeter operating at 640 nm enabling simultaneous determination of Stokes parameters and degree of polarization with $\pm$2% accuracy. To validate its use for histopathology we map polarization state changes in a tissue phantom mimicking a biopsy with a cancerous inclusion, comparing it to a commercial polarimeter. The results indicate a great potential and suggest several improvements with which we believe metasurface polarimeter based devices will be ready for practical histopathology application in clinical environment.

physics.optics↗

arXiv2024

Enhancement of mechanical squeezing via feedback control

We explore the generation of nonclassical mechanical states by combining continuous position measurement and feedback control. We find that feedback-induced spring softening can greatly enhance position squeezing. Conversely, even with a pure position measurement, we find that spring hardening can enable momentum squeezing. Beyond enhanced squeezing, we show that feedback also mitigates degradation introduced by background mechanical modes. Together, this significantly lowers the barrier to measurement-based preparation of nonclassical mechanical states at room temperature.

quant-ph↗

arXiv2023

Scattering holography designed metasurfaces for channeling single-photon emission

Channelling single-photon emission in multiple well-defined directions and simultaneously controlling its polarization characteristics is highly desirable for numerous quantum technology applications. We show that this can be achieved by using quantum emitters (QEs) nonradiatively coupled to surface plasmon polaritons (SPPs), which are scattered into outgoing free-propagating waves by appropriately designed metasurfaces. The QE-coupled metasurface design is based on the scattering holography approach with radially diverging SPPs as reference waves. Using holographic metasurfaces fabricated around nanodiamonds with single Ge vacancy centers, we experimentally demonstrate on-chip integrated efficient generation of two well-collimated single-photon beams propagating along different 15-degree off-normal directions with orthogonal linear polarizations.

physics.optics↗

arXiv2021

Measurement-based preparation of non-Markovian and multimode mechanical states

Nanomechanical resonators are a key tool for future quantum technologies such as quantum force sensors and interfaces, and for studies of macroscopic quantum physics. The ability to prepare room temperature non-classical states is a major outstanding challenge. Here, we explore the use of measurement-based state conditioning to achieve this. We demonstrate conditional cooling of a nanomechanical resonator that has non-Markovian decoherence, and show theoretically that the non-Markovianity makes quantum squeezing significantly easier to achieve. We further show that collective measurement of multiple resonator modes improves the quality of state preparation. This allows us to achieve collective thermomechanical squeezing, in experiments that go beyond the validity of the rotating-wave approximation. Our modelling shows that non-Markovianity and multimode conditioning can both enable room temperature quantum squeezing with existing technology. Together, our results pave the way towards realising room temperature quantum nanomechanical devices and towards their application in quantum technology and fundamental science.

quant-ph↗

arXiv2021

A Latent Survival Analysis Enabled Simulation Platform For Nursing Home Staffing Strategy Evaluation

Nursing homes are critical facilities for caring frail older adults with round-the-clock formal care and personal assistance. To ensure quality care for nursing home residents, adequate staffing level is of great importance. Current nursing home staffing practice is mainly based on experience and regulation. The objective of this paper is to investigate the viability of experience-based and regulation-based strategies, as well as alternative staffing strategies to minimize labor costs subject to heterogeneous service demand of nursing home residents under various scenarios of census. We propose a data-driven analysis framework to model heterogeneous service demand of nursing home residents and further identify appropriate staffing strategies by combing survival model and computer simulation techniques as well as domain knowledge. Specifically, in the analysis, we develop an agent-based simulation tool consisting of four main modules, namely individual length of stay predictor, individual daily staff time generator, facility level staffing strategy evaluator, and graphical user interface. We use real nursing home data to validate the proposed model, and demonstrate that the identified staffing strategy significantly reduces the total labor cost of certified nursing assistants compared to the benchmark strategies. Additionally, the proposed length of stay predictive model that considers multiple discharge dispositions exhibits superior accuracy and offers better staffing decisions than those without the consideration. Further, we construct different census scenarios of nursing home residents to demonstrate the capability of the proposed framework in helping adjust staffing decisions of nursing home administrators in various realistic settings.

stat.AP↗

arXiv2021

Dynamic MEMS-based optical metasurfaces

Optical metasurfaces (OMSs) have shown unprecedented capabilities for versatile wavefront manipulations at the subwavelength scale, thus opening fascinating perspectives for next generation ultracompact optical devices and systems. However, to date, most well-established OMSs are static, featuring well-defined optical responses determined by OMS configurations set during their fabrication. Dynamic OMS configurations investigated so far by using controlled constituent materials or geometrical parameters often exhibit specific limitations and reduced reconfigurability. Here, by combining a thin-film piezoelectric micro-electro-mechanical system (MEMS) with a gap-surface plasmon based OMS, we develop an electrically driven dynamic MEMS-OMS platform that offers controllable phase and amplitude modulation of the reflected light by finely actuating the MEMS mirror. Using this platform, we demonstrate MEMS-OMS components for polarization-independent beam steering and two-dimensional focusing with high modulation efficiencies (~ 50%), broadband operation (~ 20% near the operating wavelength of 800 nm) and fast responses (< 0.4 ms). The developed MEMS-OMS platform offers flexible solutions for realizing complex dynamic 2D wavefront manipulations that could be used in reconfigurable and adaptive optical networks and systems.

physics.optics↗

arXiv2020

A Hybrid Simulation-based Duopoly Game Framework for Analysis of Supply Chain and Marketing Activities

A hybrid simulation-based framework involving system dynamics and agent-based simulation is proposed to address duopoly game considering multiple strategic decision variables and rich payoff, which cannot be addressed by traditional approaches involving closed-form equations. While system dynamics models are used to represent integrated production, logistics, and pricing determination activities of duopoly companies, agent-based simulation is used to mimic enhanced consumer purchasing behavior considering advertisement, promotion effect, and acquaintance recommendation in the consumer social network. The payoff function of the duopoly companies is assumed to be the net profit based on the total revenue and various cost items such as raw material, production, transportation, inventory and backorder. A unique procedure is proposed to solve and analyze the proposed simulation-based game, where the procedural components include strategy refinement, data sampling, gaming solving, and performance evaluation. First, design of experiment and estimated conformational value of information techniques are employed for strategy refinement and data sampling, respectively. Game solving then focuses on pure strategy equilibriums, and performance evaluation addresses game stability, equilibrium strictness, and robustness. A hypothetical case scenario involving soft-drink duopoly on Coke and Pepsi is considered to illustrate and demonstrate the proposed approach. Final results include P-values of statistical tests, confidence intervals, and simulation steady state analysis for different pure equilibriums.

cs.GT↗

arXiv2020

Mechanical squeezing via fast continuous measurement

We revisit quantum state preparation of an oscillator by continuous linear position measurement. Quite general analytical expressions are derived for the conditioned state of the oscillator. Remarkably, we predict that quantum squeezing is possible outside of both the backaction dominated and quantum coherent oscillation regimes, relaxing experimental requirements even compared to ground-state cooling. This provides a new way to generate non-classical states of macroscopic mechanical oscillators, and opens the door to quantum sensing and tests of quantum macroscopicity at room temperature.

quant-ph↗

arXiv2019

Wide-field 3D nanoscopy on chip through large and tunable spatial-frequency-shift effect

Linear super-resolution microscopy via synthesis aperture approach permits fast acquisition because of its wide-field implementations, however, it has been limited in resolution because a missing spatial-frequency band occurs when trying to use a shift magnitude surpassing the cutoff frequency of the detection system beyond a factor of two, which causes ghosting to appear. Here, we propose a method of chip-based 3D nanoscopy through large and tunable spatial-frequency-shift effect, capable of covering full extent of the spatial-frequency component within a wide passband. The missing of spatial-frequency can be effectively solved by developing a spatial-frequency-shift actively tuning approach through wave vector manipulation and operation of optical modes propagating along multiple azimuthal directions on a waveguide chip to interfere. In addition, the method includes a chip-based sectioning capability, which is enabled by saturated absorption of fluorophores. By introducing ultra-large propagation effective refractive index, nanoscale resolution is possible, without sacrificing the temporal resolution and the field-of-view. Imaging on GaP waveguide material demonstrates a lateral resolution of lamda/10, which is 5.4 folds above Abbe diffraction limit, and an axial resolution of lamda/19 using 0.9 NA detection objective. Simulation with an assumed propagation effective refractive index of 10 demonstrates a lateral resolution of lamda/22, in which the huge gap between the directly shifted and the zero-order components is completely filled to ensure the deep-subwavelength resolvability. It means that, a fast wide-field 3D deep-subdiffraction visualization could be realized using a standard microscope by adding a mass-producible and cost-effective spatial-frequency-shift illumination chip.

physics.optics↗