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Zhiming Wang

Publications and source records attributed to Zhiming Wang.

At least 37 records · Page 2Linked to original sources
arXiv2025

Nonlinear Chiroptical Effect in Refractory Plasmonic Molybdenum Metasurface: Chiral Saturated Absorption and Chiral Reverse Saturable Absorption

The absorptive chiral metasurface shows evidence of giant nonlinear chiroptical effects, including chiral third harmonic generation, chiral saturated absorption, and chiral reverse saturable absorption. We finally demonstrate proof of concept circular polarized light limiter based on the refractory chiral Mo metasurface and paves the way for nonlinear chiral sensing, nonlinear hot electron generation, and XOR gate of the all-optical full-adder, showcasing the potential of refractory chiral Mo metasurfaces in overcoming the limitations of traditional chiral plasmonic materials.

physics.optics↗
arXiv2025

TdAttenMix: Top-Down Attention Guided Mixup

CutMix is a data augmentation strategy that cuts and pastes image patches to mixup training data. Existing methods pick either random or salient areas which are often inconsistent to labels, thus misguiding the training model. By our knowledge, we integrate human gaze to guide cutmix for the first time. Since human attention is driven by both high-level recognition and low-level clues, we propose a controllable Top-down Attention Guided Module to obtain a general artificial attention which balances top-down and bottom-up attention. The proposed TdATttenMix then picks the patches and adjust the label mixing ratio that focuses on regions relevant to the current label. Experimental results demonstrate that our TdAttenMix outperforms existing state-of-the-art mixup methods across eight different benchmarks. Additionally, we introduce a new metric based on the human gaze and use this metric to investigate the issue of image-label inconsistency. Project page: \url{https://github.com/morning12138/TdAttenMix}

cs.CV↗
arXiv2025

Generalizable Audio Deepfake Detection via Latent Space Refinement and Augmentation

Advances in speech synthesis technologies, like text-to-speech (TTS) and voice conversion (VC), have made detecting deepfake speech increasingly challenging. Spoofing countermeasures often struggle to generalize effectively, particularly when faced with unseen attacks. To address this, we propose a novel strategy that integrates Latent Space Refinement (LSR) and Latent Space Augmentation (LSA) to improve the generalization of deepfake detection systems. LSR introduces multiple learnable prototypes for the spoof class, refining the latent space to better capture the intricate variations within spoofed data. LSA further diversifies spoofed data representations by applying augmentation techniques directly in the latent space, enabling the model to learn a broader range of spoofing patterns. We evaluated our approach on four representative datasets, i.e. ASVspoof 2019 LA, ASVspoof 2021 LA and DF, and In-The-Wild. The results show that LSR and LSA perform well individually, and their integration achieves competitive results, matching or surpassing current state-of-the-art methods.

eess.AS↗
arXiv2025

Optical rotation of ferrofluid on a horizontal substrate by Marangoni and thermomagnetic forces

Light-actuation enables non-contact, precise, and flexible manipulation of liquids in microfluidics and liquid robots. However, it has long been a challenge to achieve optical rotation of liquid on a horizontal substrate because of the weak force of light. Here, we report, for the first time, the laser-induced rotation of macroscopic liquid placed on a horizontal substrate above cylindrical magnets. The investigated ferrofluid exhibits multiple spikes due to the Rosensweig phenomenon on the surface with different strengths of external magnetic field and rotates in a controllable direction by adjusting the laser beam position around ferrofluid spikes. This rotation results from the laser-induced displacement between the ferrofluid gravitational center, magnetic field center, Marangoni force, and non-uniform thermomagnetic force. Movement tracking of the spikes, thermal magnetization, and thermal imaging of ferrofluid were performed to elucidate the mechanism. Additionally, the influence of laser power, laser irradiation position, and magnetic field intensity on laser-induced rotation of ferrofluid are systematically studied. This work demonstrates a novel horizontal rotation of liquid using light and provides a deep understanding of the underlying mechanism and a flexible strategy for manipulating liquid for various optofluidic applications.

physics.optics↗
arXiv2025

Anti-counterfeiting tags with camouflaged QR codes on nanocavities, using polymer-dispersed-liquid-crystals

Counterfeiting poses an evergrowing challenge, driving the need for innovative and sophisticated anti-counterfeiting strategies and technologies. Many solutions focus on tags characterized by optical features that are partially or completely camouflaged to the human eye, thus discouraging scammers. In this paper, a QR code is laser printed on a thin plastic foil previously coated by a specific nanocavity consisting of a metal/insulator/metal/insulator (MIMI) multilayer. This metamaterial possesses unique features in terms of light transmission that are due to the specific design. A thin layer of polymer dispersed liquid crystals, fabricated incorporating specific nematic liquid crystals in a polymer matrix, is able to camouflage the QR code that becomes, then, readable only under specific thermal conditions. Three anti-counterfeiting tags were fabricated, each using a distinct LC with its own nematic-isotropic transition temperature. The peculiar combination of the unique optical properties of nematic liquid crystals and optical nanocavities results in the creation of a novel type of tags showing two different encoding levels. Stress tests including water immersion, bending test, and prolonged heating have been performed ensuring the long-term stability of the tags. The realized two security-level anti-counterfeiting tags are cost-effective, straightforward to manufacture and, thanks to their flexibility, can be easily integrated into packaging and products.

cs.CR↗
arXiv2024

Unveiling the anisotropy of linear and nonlinear charge-spin conversion in Weyl semimetal TaIrTe4

In Weyl semimetals, the nonlinear planar Hall effect (NPHE) and spin-orbit torque (SOT) are prominent manifestations of nonlinear and linear charge-spin conversion, respectively. However, simultaneous investigations of these phenomena within a single material system are scarce, limiting our understanding of their intrinsic connection and underlying mechanisms. Here, we report the first simultaneous observation of NPHE and SOT in a TaIrTe4/Py heterostructure. By employing harmonic Hall measurements and developing a magnetic field-dependent method, we successfully separated the contributions from NPHE, field-like SOT, and damping-like SOT, enabling accurate characterization of both linear and nonlinear charge-spin conversion properties. Our experiments revealed significant anisotropy along the [100] and [010] crystallographic directions of TaIrTe4, with stronger nonlinear responses and field-like SOT along the [100] direction, and larger damping-like SOT along the [010] direction. The distinct directional dependence of these phenomena provides new insights into the interplay between surface and bulk contributions to charge-spin conversion in Weyl semimetals. These findings enhance our understanding of anisotropic charge-spin conversion mechanisms in Weyl semimetals, which may inform future research and development of spintronic devices based on topological materials.

cond-mat.mtrl-sci↗
arXiv2024

Manipulating Momentum-Space and Real-Space Topological States in Metallic Strontium Ruthenate Ultrathin Films

SrRuO3, a 4d transition metal oxide, has gained significant interest due to its topological states in both momentum space (Weyl points) and real space (skyrmions). However, probing topological states in ultrathin SrRuO3 faces challenges such as the metal-insulator transition and questioned existence of skyrmions due to possible superposition of opposite anomalous Hall effect (AHE) signals. To address these issues, we investigate ultrathin SrRuO3/SrIrO3 heterostructures and their AHE and topological Hall effect (THE). Our results reveal metallized ultrathin SrRuO3 down to the monolayer limit with an AHE signal. ARPES measurements confirm the metallic and topological band structure of ultrathin SrRuO3. Furthermore, the AHE sign remains negative over a wide thickness range, where THE is still observed. This observation excludes the two-channel explanation of THE and provides evidence for the existence of skyrmions in ultrathin SrRuO3.

cond-mat.mtrl-sci↗
arXiv2024

CCExpert: Advancing MLLM Capability in Remote Sensing Change Captioning with Difference-Aware Integration and a Foundational Dataset

Remote Sensing Image Change Captioning (RSICC) aims to generate natural language descriptions of surface changes between multi-temporal remote sensing images, detailing the categories, locations, and dynamics of changed objects (e.g., additions or disappearances). Many current methods attempt to leverage the long-sequence understanding and reasoning capabilities of multimodal large language models (MLLMs) for this task. However, without comprehensive data support, these approaches often alter the essential feature transmission pathways of MLLMs, disrupting the intrinsic knowledge within the models and limiting their potential in RSICC. In this paper, we propose a novel model, CCExpert, based on a new, advanced multimodal large model framework. Firstly, we design a difference-aware integration module to capture multi-scale differences between bi-temporal images and incorporate them into the original image context, thereby enhancing the signal-to-noise ratio of differential features. Secondly, we constructed a high-quality, diversified dataset called CC-Foundation, containing 200,000 image pairs and 1.2 million captions, to provide substantial data support for continue pretraining in this domain. Lastly, we employed a three-stage progressive training process to ensure the deep integration of the difference-aware integration module with the pretrained MLLM. CCExpert achieved a notable performance of $S^*_m=81.80$ on the LEVIR-CC benchmark, significantly surpassing previous state-of-the-art methods. The code and part of the dataset will soon be open-sourced at https://github.com/Meize0729/CCExpert.

cs.CV↗
arXiv2024

Cooperatively Modulating Magnetic Anisotropy and Colossal Magnetoresistance via Atomic-Scale Buffer Layers in Highly Strained La0.7Sr0.3MnO3 Films

Simultaneous control of magnetic anisotropy and magnetoresistance, especially with atomic scale precision, remains a pivotal challenge for realizing advanced spintronic functionalities. Here we demonstrate cooperative continuous control over both magnetoresistance and magnetic anisotropy in highly strained La0.7Sr0.3MnO3 (LSMO) thin films. By inserting varying perovskite buffer layers, compressively strained LSMO films transition from a ferromagnetic insulator with out-of-plane magnetic anisotropy to a metallic state with in-plane anisotropy. Atomic-scale buffer layer insertion enables remarkably acute, precise control to sharply modulate this magnetic phase transformation. A gigantic 10,000% modulation of the colossal magnetoresistance (CMR) and an exceptionally sharp transition from out-of-plane to in-plane magnetic anisotropy are attained in just a few contiguous layers. These atomic-scale correlations among electronic, magnetic, and structural order parameters yield flexible multifunctional control promising for next-generation oxide spintronics.

cond-mat.mtrl-sci↗
arXiv2024

Hot electrons and electromagnetic effects in the broadband Au, Ag, and Ag-Au nanocrystals: The UV, visible, and NIR plasmons

Energetic and optical properties of plasmonic nanocrystals strongly depend on their sizes, shapes, and composition. Whereas using plasmonic nanoparticles in biotesting has become routine, applications of plasmonics in energy are still early in development. Here, we investigate hot electron (HE) generation and related electromagnetic effects in both mono- and bi-metallic nanorods (NRs) and focus on one promising type of bi-metallic nanocrystals - core-shell Au-Ag nanorods. The spectra of the NRs are broadband, highly tunable with their geometry, and have few plasmon resonances. In this work, we provide a new quantum formalism describing the HE generation in bi-metallic nanostructures. Interestingly, we observe that the HE generation rate at the UV plasmon resonance of Au-Ag NRs appears to be very high. These HEs are highly energetic and suitable for carbon-fuel reactions. Simultaneously, the HE generation at the longitudinal plasmon (L-plasmon) peaks, which can be tuned from the yellow to near-IR, depends on the near-field and electromagnetic Mie effects, limiting the HE efficiencies for the long and large NRs. These properties of the L-plasmon relate to all kinds of NRs (Au, Ag, and Au-Ag). We also consider the generation of the interband d-holes in Au and Ag, since the involvement of the d-band is crucial for the energetic properties of UV plasmons. The proposed formalism is an important development for the description of bi-metallic (or tri-metallic, or more complex) nanostructures, and it paves the way to the efficient application of the plasmonic HEs and hot holes in sensing, nanotechnology, photocatalysis, and electrophotochemistry.

cond-mat.mes-hall↗
arXiv2023

Mie Sensing with Neural Networks: Recognition of Nano-Object Parameters, the Invisibility Point, and Restricted Models

In this work, we use artificial neural networks (ANNs) to recognize the material composition, sizes of nanoparticles and their concentrations in different media with high accuracy, solely from the absorbance spectrum of a macroscopic sample. We construct ANNs operating in the following two schemes. The first scheme is designed to recognize the dimensions and refractive indices of dielectric scatterers in mixed ensembles. The second ANN model simultaneously recognizes the dimensions of gold nanospheres in a mixture and the refractive index of a matrix. A challenge in the first scheme arises at and near the invisibility point, i.e., when the refractive index of nanoparticles is close to that of the medium. Of course, particle recognition in this regime faces fundamental physical limitations. However, such recognition near the invisibility point is possible, and our study reveals its unique properties. Interestingly, the recognition process for the refractive index in the vicinity of the invisibility point shows very small errors. In contrast, the errors for the recognition of the radius grow strongly near this point. Another regime with limited recognition occurs when the extinction spectra are not unique and can correspond to different realizations of nanoparticle mixtures. Regarding multi-particle or polydisperse solutions, the ML-based models should in such cases be rationally restricted to maintain the feasibility of the recognition process. Overall, the recognition schemes proposed and investigated by us can find their applications in the field of sensing.

physics.optics↗
arXiv2023

SRMAE: Masked Image Modeling for Scale-Invariant Deep Representations

Due to the prevalence of scale variance in nature images, we propose to use image scale as a self-supervised signal for Masked Image Modeling (MIM). Our method involves selecting random patches from the input image and downsampling them to a low-resolution format. Our framework utilizes the latest advances in super-resolution (SR) to design the prediction head, which reconstructs the input from low-resolution clues and other patches. After 400 epochs of pre-training, our Super Resolution Masked Autoencoders (SRMAE) get an accuracy of 82.1% on the ImageNet-1K task. Image scale signal also allows our SRMAE to capture scale invariance representation. For the very low resolution (VLR) recognition task, our model achieves the best performance, surpassing DeriveNet by 1.3%. Our method also achieves an accuracy of 74.84% on the task of recognizing low-resolution facial expressions, surpassing the current state-of-the-art FMD by 9.48%.

cs.CV↗
arXiv2023

Universal imprinting of chirality with chiral light by employing plasmonic metastructures

Chirality, either of light or matter, has proved to be very practical in biosensing and nanophotonics. However, the fundamental understanding of its temporal dynamics still needs to be discovered. A realistic setup for this are the so-called metastructures, since they are optically active and are built massively, hence rendering an immediate potential candidate. Here we propose and study the electromagnetic-optical mechanism leading to chiral optical imprinting on metastructures. Induced photothermal responses create anisotropic permittivity modulations, different for left or right circularly polarized light, leading to temporal-dependent chiral imprinting of hot-spots, namely imprinting of chirality. The above effect has not been observed yet, but it is within reach of modern experimental approaches. The proposed nonlinear chiroptical effect is general and should appear in any anisotropic material; however, we need to design a particular geometry for this effect to be strong. These new chiral time-dependent metastructures may lead to a plethora of applications.

physics.optics↗
arXiv2023

Idealizing Tauc Plot for Accurate Bandgap Determination of Semiconductor with UV-Vis: A Case Study for Cubic Boron Arsenide

The Tauc plot method is widely used to determine the bandgap of semiconductors via UV-visible optical spectroscopy due to its simplicity and perceived accuracy. However, the actual Tauc plot often exhibits significant baseline absorption below the expected bandgap, leading to discrepancies in the calculated bandgap depending on whether the linear fit is extrapolated to zero or non-zero baseline. In this study, we show that both extrapolation methods can produce significant errors by simulating Tauc plots with varying levels of baseline absorption. To address this issue, we propose a new method that involves idealizing the absorption spectrum by removing its baseline before constructing the Tauc plot. Experimental verification of this method using a gallium phosphide (GaP) wafer with intentionally introduced baseline absorptions shows promising results. Furthermore, we apply this new method to cubic boron arsenide (c-BAs) and resolve discrepancies in c-BAs bandgap values reported by different groups, obtaining a converging bandgap of 1.835 eV based on both previous and new transmission spectra. The method is applicable to both indirect and direct bandgap semiconductors, regardless of whether the absorption spectrum is measured via transmission or diffuse reflectance, will become essential to obtain accurate values of their bandgaps.

cond-mat.mtrl-sci↗
arXiv2023

Generalized Expectation Maximization Framework for Blind Image Super Resolution

Learning-based methods for blind single image super resolution (SISR) conduct the restoration by a learned mapping between high-resolution (HR) images and their low-resolution (LR) counterparts degraded with arbitrary blur kernels. However, these methods mostly require an independent step to estimate the blur kernel, leading to error accumulation between steps. We propose an end-to-end learning framework for the blind SISR problem, which enables image restoration within a unified Bayesian framework with either full- or semi-supervision. The proposed method, namely SREMN, integrates learning techniques into the generalized expectation-maximization (GEM) algorithm and infers HR images from the maximum likelihood estimation (MLE). Extensive experiments show the superiority of the proposed method with comparison to existing work and novelty in semi-supervised learning.

cs.CV↗
arXiv2022

Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report

The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20-50 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper.

cs.CV↗
arXiv2022

Theory and Experiments of Pressure-Tunable Broadband Light Emission from Self-Trapped Excitons in Metal Halide Crystals

Hydrostatic pressure has been commonly applied to tune broadband light emissions from self-trapped excitons (STE) in perovskites for producing white light and study of basic electron-phonon interactions. However, a general theory is still lacking to understand pressure-driven evolution of STE emissions. In this work we first identify a theoretical model that predicts the effect of hydrostatic pressure on STE emission spectrum, we then report the observation of extremely broadband photoluminescence emission and its wide pressure spectral tuning in 2D indirect bandgap CsPb2Br5 crystals. An excellent agreement is found between the theory and experiment on the peculiar experimental observation of STE emission with a nearly constant spectral bandwidth but linearly increasing energy with pressure below 2 GPa. Further analysis by the theory and experiment under higher pressure reveals that two types of STE are involved and respond differently to external pressure. We subsequently survey published STE emissions and discovered that most of them show a spectral blue-shift under pressure, as predicted by the theory. The identification of an appropriate theoretical model and its application to STE emission through the coordinate configuration diagram paves the way for engineering the STE emission and basic understanding of electron-phonon interaction.

cond-mat.mtrl-sci↗