arXiv subjects

Yongtao Liu

Publications and source records attributed to Yongtao Liu.

72 records · Page 4Linked to original sources

arXiv2022

Exploiting dynamic nonlinearity in upconversion nanoparticles for super-resolution imaging

Single-beam super-resolution microscopy, also known as superlinear microscopy, exploits the nonlinear response of fluorescent probes in confocal microscopy. The technique requires no complex purpose-built system, light field modulation, or beam shaping. Here, we present a strategy to enhance spatial resolution of superlinear microscopy by modulating excitation intensity during image acquisition. This modulation induces dynamic optical nonlinearity in upconversion nanoparticles (UCNPs), resulting in variations of higher spatial-frequency information in the obtained images. The high-order information can be extracted with a proposed weighted finite difference imaging algorithm from raw fluorescence images, to generate an image with a higher resolution than superlinear microscopy images. We apply this approach to resolve two adjacent nanoparticles within a diffraction-limited area, improving the resolution to 130 nm. This work suggests a new scope for developing dynamic nonlinear fluorescent probes in super-resolution nanoscopy.

physics.optics↗

arXiv2022

RGBT Salient Object Detection: A Large-scale Dataset and Benchmark

Salient object detection in complex scenes and environments is a challenging research topic. Most works focus on RGB-based salient object detection, which limits its performance of real-life applications when confronted with adverse conditions such as dark environments and complex backgrounds. Taking advantage of RGB and thermal infrared images becomes a new research direction for detecting salient object in complex scenes recently, as thermal infrared spectrum imaging provides the complementary information and has been applied to many computer vision tasks. However, current research for RGBT salient object detection is limited by the lack of a large-scale dataset and comprehensive benchmark. This work contributes such a RGBT image dataset named VT5000, including 5000 spatially aligned RGBT image pairs with ground truth annotations. VT5000 has 11 challenges collected in different scenes and environments for exploring the robustness of algorithms. With this dataset, we propose a powerful baseline approach, which extracts multi-level features within each modality and aggregates these features of all modalities with the attention mechanism, for accurate RGBT salient object detection. Extensive experiments show that the proposed baseline approach outperforms the state-of-the-art methods on VT5000 dataset and other two public datasets. In addition, we carry out a comprehensive analysis of different algorithms of RGBT salient object detection on VT5000 dataset, and then make several valuable conclusions and provide some potential research directions for RGBT salient object detection.

cs.CV↗

arXiv2022

Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries

Machine learning is rapidly becoming an integral part of experimental physical discovery via automated and high-throughput synthesis, and active experiments in scattering and electron/probe microscopy. This, in turn, necessitates the development of active learning methods capable of exploring relevant parameter spaces with the smallest number of steps. Here we introduce an active learning approach based on co-navigation of the hypothesis and experimental spaces. This is realized by combining the structured Gaussian Processes containing probabilistic models of the possible system's behaviors (hypotheses) with reinforcement learning policy refinement (discovery). This approach closely resembles classical human-driven physical discovery, when several alternative hypotheses realized via models with adjustable parameters are tested during an experiment. We demonstrate this approach for exploring concentration-induced phase transitions in combinatorial libraries of Sm-doped BiFeO3 using Piezoresponse Force Microscopy, but it is straightforward to extend it to higher-dimensional parameter spaces and more complex physical problems once the experimental workflow and hypothesis-generation are available.

cond-mat.mtrl-sci↗

arXiv2022

Active learning in open experimental environments: selecting the right information channel(s) based on predictability in deep kernel learning

Active learning methods are rapidly becoming the integral component of automated experiment workflows in imaging, materials synthesis, and computation. The distinctive aspect of many experimental scenarios is the presence of multiple information channels, including both the intrinsic modalities of the measurement system and the exogenous environment and noise signals. One of the key tasks in experimental studies is hence establishing which of these channels is predictive of the behaviors of interest. Here we explore the problem of discovery of the optimal predictive channel for structure-property relationships (in microscopy) using deep kernel learning for modality selection in an active experiment setting. We further pose that this approach can be directly applicable to similar active learning tasks in automated synthesis and the discovery of quantitative structure-activity relations in molecular systems.

cs.LG↗

arXiv2022

Physical discovery in representation learning via conditioning on prior knowledge: applications for ferroelectric domain dynamics

Recent advances in electron, scanning probe, optical, and chemical imaging and spectroscopy yield bespoke data sets containing the information of structure and functionality of complex systems. In many cases, the resulting data sets are underpinned by low-dimensional simple representations encoding the factors of variability within the data. The representation learning methods seek to discover these factors of variability, ideally further connecting them with relevant physical mechanisms. However, generally the task of identifying the latent variables corresponding to actual physical mechanisms is extremely complex. Here, we explore an approach based on conditioning the data on the known (continuous) physical parameters, and systematically compare it with the previously introduced approach based on the invariant variational autoencoders. The conditional variational autoencoders (cVAE) approach does not rely on the existence of the invariant transforms, and hence allows for much greater flexibility and applicability. Interestingly, cVAE allows for limited extrapolation outside of the original domain of the conditional variable. However, this extrapolation is limited compared to the cases when true physical mechanisms are known, and the physical factor of variability can be disentangled in full. We further show that introducing the known conditioning results in the simplification of the latent distribution if the conditioning vector is correlated with the factor of variability in the data, thus allowing to separate relevant physical factors. We initially demonstrate this approach using 1D and 2D examples on a synthetic dataset and then extend it to the analysis of experimental data on ferroelectric domain dynamics visualized via Piezoresponse Force Microscopy.

cond-mat.mtrl-sci↗

arXiv2021

Experimental discovery of structure-property relationships in ferroelectric materials via active learning

Emergent functionalities of structural and topological defects in ferroelectric materials underpin an extremely broad spectrum of applications ranging from domain wall electronics to high dielectric and electromechanical responses. Many of these have been discovered and quantified via local scanning probe microscopy methods. However, the search for these functionalities has until now been based by either trial and error or using auxiliary information such as topography or domain wall structure to identify potential objects of interest based on the intuition of operator or preexisting hypotheses, with subsequent manual exploration. Here, we report the development and implementation of a machine learning framework that actively discovers relationships between local domain structure and polarization switching characteristics in ferroelectric materials encoded in the hysteresis loop. The latter and descriptors such as nucleation bias, coercive bias, hysteresis loop area, or more complex functionals of hysteresis loop shape and corresponding uncertainties are used to guide the discovery via automated piezoresponse force microscopy (PFM) and spectroscopy experiments. As such, this approach combines the power of machine learning methods to learn the correlative relationships between high dimensional data, and human-based physics insights encoded in the acquisition function. For ferroelectric, this automated workflow demonstrates that the discovery path and sampling points of on-field and off-field hysteresis loops are largely different, indicating the on-field and off-field hysteresis loops are dominated by different mechanisms. The proposed approach is universal and can be applied to a broad range of modern imaging and spectroscopy methods ranging from other scanning probe microscopy modalities to electron microscopy and chemical imaging.

cond-mat.mtrl-sci↗

arXiv2021

Exploring leakage in dielectric films via automated experiment in scanning probe microscopy

Electronic conduction pathways in dielectric thin films are explored using automated experiments in scanning probe microscopy (SPM). Here, we use large field of view scanning to identify the position of localized conductive spots and develop a SPM workflow to probe their dynamic behavior at higher spatial resolution as a function of time, voltage, and scanning process in an automated fashion. Using this approach, we observe the variable behaviors of the conductive spots in a 20 nm thick ferroelectric Hf0.54Zr0.48O2 film, where conductive spots disappear and reappear during continuous scanning. There are also new conductive spots that appear during scanning. The automated workflow is universal and can be integrated into a wide range of microscopy techniques, including SPM, electron microscopy, optical microscopy, and chemical imaging.

cond-mat.mtrl-sci↗

arXiv2021

Exploring causal physical mechanisms via non-gaussian linear models and deep kernel learning: applications for ferroelectric domain structures

Rapid emergence of the multimodal imaging in scanning probe, electron, and optical microscopies have brought forth the challenge of understanding the information contained in these complex data sets, targeting both the intrinsic correlations between different channels and further exploring the underpinning causal physical mechanisms. Here, we develop such analysis framework for the Piezoresponse Force Microscopy. We argue that under certain conditions, we can bootstrap experimental observations with the prior knowledge of materials structure to get information on certain non-observed properties, and demonstrate linear causal analysis for PFM observables. We further demonstrate that this approach can be extended to complex descriptors using the deep kernel learning (DKL) model. In this DKL analysis, we use the prior information on domain structure within the image to predict the physical properties. This analysis demonstrates the correlative relationships between morphology, piezoresponse, elastic property, etc. at nanoscale. The prediction of morphology and other physical parameters illustrates a mutual interaction between surface condition and physical properties in ferroelectric materials. This analysis is universal and can be extended to explore the correlative relationships of other multi-channel datasets.

cond-mat.mtrl-sci↗

arXiv2021

Photoinduced iodide repulsion and halides-demixing in layered perovskites

Mixing halides in metal halide perovskites (MHPs) is an effective approach to adjust MHPs bandgap for applications in tandem solar cells. However, mixed-halide (MH-) MHPs undergo light-induced-phase-segregation (LIPS) under continuous illumination. Therefore, understanding the mechanism of LIPS is necessary for developing stable MH-MHPs. In this work, we investigated LIPS in layered (L) MHPs and discovered a critical role of spacer cations in LIPS. Through probing chemical changes of LIPS, we unveil light-induced-iodide-repulsion and the formation of Br-rich-phase in illuminated regions during LIPS. This discovery also gives insight into LIPS process in three dimensional (3D) MHPs. By further investigating LIPS in 3D MHPs, we reveal that LIPS induces not only the formation of Br-rich and I-rich domains but also an overall change of halide distribution along the film thickness direction, which can affect the electronic energy alignment and consequently MHPs devices performance. Moreover, LIPS is more significant in the bulk due to larger population of photogenerated charge carriers. Overall, this study reveals the chemical mechanism of LIPS in MHPs and its potential effect on device performance, offering insight into understanding LIPS mechanism and improving the stability of MHPs.

physics.app-ph↗

arXiv2021

Semi-supervised learning of images with strong rotational disorder: assembling nanoparticle libraries

The proliferation of optical, electron, and scanning probe microscopies gives rise to large volumes of imaging data of objects as diversified as cells, bacteria, pollen, to nanoparticles and atoms and molecules. In most cases, the experimental data streams contain images having arbitrary rotations and translations within the image. At the same time, for many cases, small amounts of labeled data are available in the form of prior published results, image collections, and catalogs, or even theoretical models. Here we develop an approach that allows generalizing from a small subset of labeled data with a weak orientational disorder to a large unlabeled dataset with a much stronger orientational (and positional) disorder, i.e., it performs a classification of image data given a small number of examples even in the presence of a distribution shift between the labeled and unlabeled parts. This approach is based on the semi-supervised rotationally invariant variational autoencoder (ss-rVAE) model consisting of the encoder-decoder "block" that learns a rotationally (and translationally) invariant continuous latent representation of data and a classifier that encodes data into a finite number of discrete classes. The classifier part of the trained ss-rVAE inherits the rotational (and translational) invariances and can be deployed independently of the other parts of the model. The performance of the ss-rVAE is illustrated using the synthetic data sets with known factors of variation. We further demonstrate its application for experimental data sets of nanoparticles, creating nanoparticle libraries and disentangling the representations defining the physical factors of variation in the data. The code reproducing the results is available at https://github.com/ziatdinovmax/Semi-Supervised-VAE-nanoparticles.

cs.LG↗

arXiv2021

Disentangling ferroelectric wall dynamics and identification of pinning mechanisms via deep learning

Field-induced domain wall dynamics in ferroelectric materials underpins multiple applications ranging from actuators to information technology devices and necessitates a quantitative description of the associated mechanisms including giant electromechanical couplings, controlled non-linearities, or low coercive voltages. While the advances in dynamic Piezoresponse Force Microscopy measurements over the last two decades have rendered visualization of polarization dynamics relatively straightforward, the associated insights into the local mechanisms have been elusive. Here we explore the domain dynamics in model polycrystalline materials using a workflow combining deep learning-based segmentation of the domain structures with non-linear dimensionality reduction using multilayer rotationally-invariant autoencoders (rVAE). The former allows unambiguous identification and classification of the ferroelectric and ferroelastic domain walls. The rVAE discover the latent representations of the domain wall geometries and their dynamics, thus providing insight into the intrinsic mechanisms of polarization switching, that can further be compared to simple physical models. The rVAE disentangles the factors affecting the pinning efficiency of ferroelectric walls, offering insights into the correlation of ferroelastic wall distribution and ferroelectric wall pinning.

cond-mat.dis-nn↗

arXiv2021

Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy

A shift-invariant variational autoencoder (shift-VAE) is developed as an unsupervised method for the analysis of spectral data in the presence of shifts along the parameter axis, disentangling the physically-relevant shifts from other latent variables. Using synthetic data sets, we show that the shift-VAE latent variables closely match the ground truth parameters. The shift VAE is extended towards the analysis of band-excitation piezoresponse force microscopy (BE-PFM) data, disentangling the resonance frequency shifts from the peak shape parameters in a model-free unsupervised manner. The extensions of this approach towards denoising of data and model-free dimensionality reduction in imaging and spectroscopic data are further demonstrated. This approach is universal and can also be extended to analysis of X-ray diffraction, photoluminescence, Raman spectra, and other data sets.

cond-mat.dis-nn↗

arXiv2021

Disentangling ferroelectric domain wall geometries and pathways in dynamic piezoresponse force microscopy via unsupervised machine learning

Domain switching pathways in ferroelectric materials visualized by dynamic Piezoresponse Force Microscopy (PFM) are explored via variational autoencoder (VAE), which simplifies the elements of the observed domain structure, crucially allowing for rotational invariance, thereby reducing the variability of local polarization distributions to a small number of latent variables. For small sampling window sizes the latent space is degenerate, and variability is observed only in the direction of a single latent variable that can be identified with the presence of domain wall. For larger window sizes, the latent space is 2D, and the disentangled latent variables can be generally interpreted as the degree of switching and complexity of domain structure. Applied to multiple consecutive PFM images acquired while monitoring domain switching, the polarization switching mechanism can thus be visualized in the latent space, providing insight into domain evolution mechanisms and their correlation with the microstructure.

cond-mat.dis-nn↗

arXiv2021

Unsupervised Machine Learning Discovery of Chemical and Physical Transformation Pathways from Imaging Data

We show that unsupervised machine learning can be used to learn physical and chemical transformation pathways from the observational microscopic data, as demonstrated for atomically resolved images in Scanning Transmission Electron Microscopy (STEM) and ferroelectric domain structures in Piezoresponse Force Microscopy (PFM). To enable this analysis in STEM, we assumed the existence of atoms, a discreteness of atomic classes, and the presence of an explicit relationship between the observed STEM contrast and the presence of atomic units. In PFM, we assumed the uniquely-defined relationship between the measured signal and polarization distribution. With only these postulates, we developed a machine learning method leveraging a rotationally-invariant variational autoencoder (rVAE) that can identify the existing structural units observed within a material. The approach encodes the information contained in image sequences using a small number of latent variables, allowing the exploration of chemical and physical transformation pathways via the latent space of the system. The results suggest that the high-veracity imaging data can be used to derive fundamental physical and chemical mechanisms involved, by providing encodings of the observed structures that act as bottom-up equivalents of structural order parameters. The approach also demonstrates the potential of variational (i.e., Bayesian) methods for physical sciences and will stimulate the development of new ways to encode physical constraints in the encoder-decoder architectures, and generative physical laws, topological invariances, and causal relationships in the latent space of VAEs.

cond-mat.mtrl-sci↗

arXiv2020

Heterochromatic nonlinear optical responses in upconversion nanoparticles for point spread function engineering

Point spread function (PSF) engineering of the emitter can code higher spatial frequency information of an image to break diffraction limit but suffer from the complexed optical systems. Here we present a robust strategy to simultaneously achieve diverse PSFs from upconversion nanoparticles under a single doughnut-shape scanning beam. By saturating the four-photon state, the high-frequency information can be extracted through the doughnut emission PSF. In contrast, the complementary lower frequency information can be carried out by the Gaussian-like emission PSF, as a result of over-saturated at the two-photon state. With the Fourier domain heterochromatic fusion, we verify the capability of the synthesised PSF to cover both low and high-frequency information, yielding the overall enhanced image quality. We show a spatial resolution of 40 nm, 1/24th of the excitation wavelength. This work suggests a new scope for developing nonlinear multi-colour emitting probes to improve image quality and noise control in nanoscopy.

physics.optics↗

arXiv2019

Quantitative Electromechanical Atomic Force Microscopy

The ability to probe a materials electromechanical functionality on the nanoscale is critical to applications from energy storage and computing to biology and medicine. Voltage modulated atomic force microscopy (VM-AFM) has become a mainstay characterization tool for investigating these materials due to its unprecedented ability to locally probe electromechanically responsive materials with spatial resolution from microns to nanometers. However, with the wide popularity of VM-AFM techniques such as piezoresponse force microscopy (PFM) and electrochemical strain microscopy (ESM) there has been a rise in reports of nanoscale electromechanical functionality, including hysteresis, in materials that should be incapable of exhibiting piezo- or ferroelectricity. Explanations for the origins of unexpected nanoscale phenomena have included new material properties, surface-mediated polarization changes and/or spatially resolved behavior that is not present in bulk measurements. At the same time, it is well known that VM-AFM measurements are susceptible to numerous forms of crosstalk and, despite efforts within the AFM community, a global approach for eliminating this has remained elusive. In this work, we develop a method for easily demonstrating the presence of hysteretic (ie, false ferroelectric) long-range interactions between the sample and cantilever body. This method should be easy to implement in any VM-AFM measurement. We then go on to demonstrate fully quantitative and repeatable nanoelectromechanical characterization using an interferometer. These quantitative measurements are critical for a wide range of devices including mems actuators and sensors, memristor, energy storage and memory.

cond-mat.mes-hall↗

arXiv2018

A nonlinear transform based analog video transmission framework

Soft-cast, a cross-layer design for wireless video transmission, is proposed to solve the drawbacks of digital video transmission: threshold transmission framework achieving the same effect. Specifically, in encoder, we carry out power allocation on the transformed coefficients and encode the coefficients based on the new formulation of power distortion. In decoder, the process of LLSE estimator is also improved. Accompanied with the inverse nonlinear transform, DCT coefficients can be recovered depending on the scaling factors , LLSE estimator coefficients and metadata. Experiment results show that our proposed framework outperforms the Soft-cast in PSNR 1.08 dB and the MSSIM gain reaches to 2.35% when transmitting under the same bandwidth and total power.

cs.MM↗

arXiv2016

A method for designing the variable-channel high-power cavity combiner

Cavity combiners have been put forward for high power combining due to their advantages of larger combining ability, variable input channels and less power loss. For a high power cavity combiner, it is better to keep the power loss ratio in a reasonable range, because large power loss would lead to strict requirements on the cooling system. A combiner with variable input channels is convenient for outputting different power levels according to practical demands. In this paper, a method for designing a variable-channel high-power cavity combiner is proposed, based on the relation between input and output coupling coefficients obtained by analyzing the equivalent circuit of the cavity combiner. This method can put the designed cavity combiner in a matching state and keep its power loss rate in a reasonable range as the number of input channels changes. As an example, a cavity combiner with 500 MHz and variable input channels from 16 to 64 is designed, and the simulation results show that our proposed method is feasible.

physics.acc-ph↗