arXiv subjects

Zhenyu Yang

Publications and source records attributed to Zhenyu Yang.

At least 91 records · Page 5Linked to original sources

arXiv2022

Complicated Background Suppression of ViSAR Image For Moving Target Shadow Detection

The existing Video Synthetic Aperture Radar (ViSAR) moving target shadow detection methods based on deep neural networks mostly generate numerous false alarms and missing detections, because of the foreground-background indistinguishability. To solve this problem, we propose a method to suppress complicated background of ViSAR for moving target detection. In this work, the proposed method is used to suppress background; then, we use several target detection networks to detect the moving target shadows. The experimental result shows that the proposed method can effectively suppress the interference of complicated back-ground information and improve the accuracy of moving target shadow detection in ViSAR. The existing Video Synthetic Aperture Radar (ViSAR) moving target shadow detection methods based on deep neural networks mostly generate numerous false alarms and missing detections, because of the foreground-background indistinguishability. To solve this problem, we propose a method to suppress complicated background of ViSAR for moving target detection. In this work, the proposed method is used to suppress background; then, we use several target detection networks to detect the moving target shadows. The experimental result shows that the proposed method can effectively suppress the interference of complicated back-ground information and improve the accuracy of moving target shadow detection in ViSAR.

eess.SP↗

arXiv2022

Quantification of lung function on CT images based on pulmonary radiomic filtering

Purpose: To develop a radiomics filtering technique for characterizing spatial-encoded regional pulmonary ventilation information on lung CT. Methods: The lung volume was segmented on 46 CT images, and a 3D sliding window kernel was implemented across the lung volume to capture the spatial-encoded image information. Fifty-three radiomic features were extracted within the kernel, resulting in a 4th order tensor object. As such, each voxel coordinate of the original lung was represented as a 53-dimensional feature vector, such that radiomic features could be viewed as feature maps within the lungs. To test the technique as a potential pulmonary ventilation biomarker, the radiomic feature maps were compared to paired functional images (Galligas PET or DTPA-SPECT) based on Spearman correlation (r) analysis. Results: The radiomic feature map GLRLM-based Run-Length Non-Uniformity and GLCOM-based Sum Average are found to be highly correlated with the functional imaging. The achieved r (median [range]) for the two features are 0.46 [0.05, 0.67] and 0.45 [0.21, 0.65] across 46 patients and 2 functional imaging modalities, respectively. Conclusions: The results provide evidence that local regions of sparsely encoded heterogeneous lung parenchyma on CT are associated with diminished radiotracer uptake and measured lung ventilation defects on PET/SPECT imaging. These findings demonstrate the potential of radiomics to serve as a complementary tool to the current lung quantification techniques and provide hypothesis-generating data for future studies.

physics.med-ph↗

arXiv2022

Phase-Only Holographic Assisted Planar Printing for Massively Multiplexed Optical Display and Encryption

Multiplexed planar printings, made of single or few layer micro and nano optical platforms, are essential for high capacity display, information storage and encryption. Although having been developed rapidly, the demonstrated channels are still limited and also lack instantaneity. Here, holograms and printings, always regarded as two independent information coding domains with totally different principles, are combined together through our proposed angle multiplexing framework, leading to multiplexed printings with hundreds of channels. Based on such approach, we experimentally encode respectively 25 gray scale printings into 25 angles and even 8 gray scale videos into 8 angles with a phase-only spatial light modulator. As a bridge between printings and holograms, our method allows to generate printings combining various holographic methods. Beneficial from this, we demonstrate a gradient metasurface based 324 channel printing which multiplexes angles, polarizations and wavelengths simultaneously. Our work paves the way to flexibly angle-dependent printing display and massively multiplexed encryption systems.

physics.optics↗

arXiv2022

Silicon Metasurface Embedded Fabry-Perot Cavity Enables High Quality Transmission Structural Color

While nanoscale color generations have been studied for years, the high performance transmission structural color, simultaneously equipped with large gamut, high resolution, and optical multiplexing abilities, still remains as a hanging issue. Here, a silicon metasurface embedded Fabry-Pérot cavity is demonstrated to address this problem. By changing the planar geometries of metasurface units, the cavities provide transmission colors with 194% sRGB gamut coverage and 141111 DPI resolution, along with more than 300% enhanced angular tolerance. Such high density allows two-dimensional color mixing at diffraction limit scale. Beneficial from the polarization manipulation capacity of the metasurface, arbitrary color arrangements between cyan and red for two orthogonal linear polarizations are also realized. Our proposed cavities can be used in filters, printings, optical storages and many other applications in need of high quality and density colors.

physics.optics↗

arXiv2022

Curriculum-Based Self-Training Makes Better Few-Shot Learners for Data-to-Text Generation

Despite the success of text-to-text pre-trained models in various natural language generation (NLG) tasks, the generation performance is largely restricted by the number of labeled data in downstream tasks, particularly in data-to-text generation tasks. Existing works mostly utilize abundant unlabeled structured data to conduct unsupervised pre-training for task adaption, which fail to model the complex relationship between source structured data and target texts. Thus, we introduce self-training as a better few-shot learner than task-adaptive pre-training, which explicitly captures this relationship via pseudo-labeled data generated by the pre-trained model. To alleviate the side-effect of low-quality pseudo-labeled data during self-training, we propose a novel method called Curriculum-Based Self-Training (CBST) to effectively leverage unlabeled data in a rearranged order determined by the difficulty of text generation. Experimental results show that our method can outperform fine-tuning and task-adaptive pre-training methods, and achieve state-of-the-art performance in the few-shot setting of data-to-text generation.

cs.CL↗

arXiv2022

Semantic-Enhanced Explainable Finetuning for Open-Domain Dialogues

This paper propose to combine pretrained language models with the modular dialogue paradigm for open-domain dialogue modeling. Our method, semantic-enhanced finetuning, instantiates conversation understanding, planning, and response generation as a language model finetuning task. At inference, we disentangle semantic and token variations by specifying sampling methods and constraints for each module separately. For training and evaluation, we present X-Weibo, a Chinese multi-turn open-domain dialogue dataset with automatic annotation for emotions, DAs, and topical words. Experiments show that semantic-enhanced finetuning outperforms strong baselines on non-semantic and semantic metrics, improves the human-evaluated relevance, coherence, and informativeness, and exhibits considerable controllability over semantic variables.

cs.CL↗

arXiv2022

LaMemo: Language Modeling with Look-Ahead Memory

Although Transformers with fully connected self-attentions are powerful to model long-term dependencies, they are struggling to scale to long texts with thousands of words in language modeling. One of the solutions is to equip the model with a recurrence memory. However, existing approaches directly reuse hidden states from the previous segment that encodes contexts in a uni-directional way. As a result, this prohibits the memory to dynamically interact with the current context that provides up-to-date information for token prediction. To remedy this issue, we propose Look-Ahead Memory (LaMemo) that enhances the recurrence memory by incrementally attending to the right-side tokens, and interpolating with the old memory states to maintain long-term information in the history. LaMemo embraces bi-directional attention and segment recurrence with an additional computation overhead only linearly proportional to the memory length. Experiments on widely used language modeling benchmarks demonstrate its superiority over the baselines equipped with different types of memory.

cs.CL↗

arXiv2022

A Neural Ordinary Differential Equation Model for Visualizing Deep Neural Network Behaviors in Multi-Parametric MRI based Glioma Segmentation

Purpose: To develop a neural ordinary differential equation (ODE) model for visualizing deep neural network (DNN) behavior during multi-parametric MRI (mp-MRI) based glioma segmentation as a method to enhance deep learning explainability. Methods: By hypothesizing that deep feature extraction can be modeled as a spatiotemporally continuous process, we designed a novel deep learning model, neural ODE, in which deep feature extraction was governed by an ODE without explicit expression. The dynamics of 1) MR images after interactions with DNN and 2) segmentation formation can be visualized after solving ODE. An accumulative contribution curve (ACC) was designed to quantitatively evaluate the utilization of each MRI by DNN towards the final segmentation results. The proposed neural ODE model was demonstrated using 369 glioma patients with a 4-modality mp-MRI protocol: T1, contrast-enhanced T1 (T1-Ce), T2, and FLAIR. Three neural ODE models were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor (WT). The key MR modalities with significant utilization by DNN were identified based on ACC analysis. Segmentation results by DNN using only the key MR modalities were compared to the ones using all 4 MR modalities. Results: All neural ODE models successfully illustrated image dynamics as expected. ACC analysis identified T1-Ce as the only key modality in ET and TC segmentations, while both FLAIR and T2 were key modalities in WT segmentation. Compared to the U-Net results using all 4 MR modalities, Dice coefficient of ET (0.784->0.775), TC (0.760->0.758), and WT (0.841->0.837) using the key modalities only had minimal differences without significance. Conclusion: The neural ODE model offers a new tool for optimizing the deep learning model inputs with enhanced explainability. The presented methodology can be generalized to other medical image-related deep learning applications.

q-bio.QM↗

arXiv2022

A Deep Learning Model with Radiomics Analysis Integration for Glioblastoma Post-Resection Survival Prediction

Purpose: To develop a novel deep-learning model that integrates radiomics analysis in a multi-dimensional feature fusion workflow for glioblastoma (GBM) post-resection survival prediction. Methods: A cohort of 235 GBM patients with complete surgical resection was divided into short-term/long-term survival groups with 1-yr survival time threshold. Each patient received a pre-surgery multi-parametric MRI exam, and three tumor subregions were segmented by neuroradiologists. The developed model comprises three data source branches: in the 1st radiomics branch, 456 radiomics features (RF) were from each patient; in the 2nd deep learning branch, an encoding neural network architecture was trained for survival group prediction using each single MR modality, and high-dimensional parameters of the last two network layers were extracted as deep features (DF). The extracted radiomics features and deep features were processed by a feature selection procedure to reduce dimension size of each feature space. In the 3rd branch, non-image-based patient-specific clinical features (PSCF) were collected. Finally, data sources from all three branches were fused as an integrated input for a supporting vector machine (SVM) execution for survival group prediction. Different strategies of model design, including 1) 2D/3D-based image analysis, and 2) different data source combinations in SVM input design, were investigated in comparison studies. Results: The model achieved 0.638 prediction accuracy when using PSCF only, which was higher than the results using RF or DF only in both 2D and 3D analysis. The joint use of RF/PSCF improved accuracy results to 0.681 in 3D analysis. The most accurate models in 2D/3D analysis reached the highest accuracy 0.745 with different combinations of RF/DF/ PSCF, and the corresponding ROC AUC results were 0.69(2D) and 0.71(3D), respectively.

physics.med-ph↗

arXiv2022

High fill factor confocal compound eyes fabricated by direct laser writing for better imaging quality

We fabricate two kinds of 100% fill factor compound eye structures using direct laser writing, including conventional compound eyes (CVCEs) with the same focal length of each microlens unit, and specially designed confocal compound eyes (CFCEs). For CFCEs, the focal length of each microlens unit is determined by its position and is equal to the distance between the microlens unit and the image sensor. In this letter, the optical properties of CVCEs and CFCEs are tested and compared. It is found that compared with CVCEs, CFCEs can improve the focusing efficiency by about 7%, enlarge the imaging area by about 25%, and have better imaging quality at the edge of the field of view.

physics.optics↗

arXiv2021

A Two-Weight Boundedness Criterion and Its Applications

In this article, the authors establish a general (two-weight) boundedness criterion for a pair of functions, $(F,f)$, on $\mathbb{R}^n$ in the scale of weighted Lebesgue spaces, weighted Lorentz spaces, (Lorentz--)Morrey spaces, and variable Lebesgue spaces. As applications, the authors give a unified approach to prove the (two-weight) boundedness of Calderón--Zygmund operators, Littlewood--Paley $g$-functions, Lusin area functions, Littlewood--Paley $g^\ast_λ$-functions, and fractional integral operators, in the aforementioned function spaces. Moreover, via applying the above (two-weight) boundedness criterion, the authors further obtain the (two-weight) boundedness of Riesz transforms, Littlewood--Paley $g$-functions, and fractional integral operators associated with second-order divergence elliptic operators with complex bounded measurable coefficients on $\mathbb{R}^n$ in the aforementioned function spaces.

math.AP↗

arXiv2021

John--Nirenberg-$Q$ Spaces via Congruent Cubes

To shed some light on the John--Nirenberg space, the authors in this article introduce the John--Nirenberg-$Q$ space via congruent cubes, $JNQ^α_{p,q}(\mathbb{R}^n)$, which when $p=\infty$ and $q=2$ coincides with the space $Q_α(\mathbb{R}^n)$ introduced by Essén et al. in [Indiana Univ. Math. J. 49 (2000), 575-615]. Moreover, the authors show that, for some particular indices, $JNQ^α_{p,q}(\mathbb{R}^n)$ coincides with the congruent John--Nirenberg space, or that the (fractional) Sobolev space is continuously embedded into $JNQ^α_{p,q}(\mathbb{R}^n)$. Furthermore, the authors characterize $JNQ^α_{p,q}(\mathbb{R}^n)$ via mean oscillations, and then use this characterization to study the dyadic counterparts. Also, the authors obtain some properties of composition operators on such spaces. The main novelties of this article are twofold: establishing a general equivalence principle for a kind of `almost increasing' set function introduced in this article, and using the fine geometrical properties of dyadic cubes to properly classify any collection of cubes with pairwise disjoint interiors and equal edge length.

math.CA↗

arXiv2021

A Radiomics-Boosted Deep-Learning Model for COVID-19 and Non-COVID-19 Pneumonia Classification Using Chest X-ray Image

To develop a deep-learning model that integrates radiomics analysis for enhanced performance of COVID-19 and Non-COVID-19 pneumonia detection using chest X-ray image, two deep-learning models were trained based on a pre-trained VGG-16 architecture: in the 1st model, X-ray image was the sole input; in the 2nd model, X-ray image and 2 radiomic feature maps (RFM) selected by the saliency map analysis of the 1st model were stacked as the input. Both models were developed using 812 chest X-ray images with 262/288/262 COVID-19/Non-COVID-19 pneumonia/healthy cases, and 649/163 cases were assigned as training-validation/independent test sets. In 1st model using X-ray as the sole input, the 1) sensitivity, 2) specificity, 3) accuracy, and 4) ROC Area-Under-the-Curve of COVID-19 vs Non-COVID-19 pneumonia detection were 1) 0.90$\pm$0.07 vs 0.78$\pm$0.09, 2) 0.94$\pm$0.04 vs 0.94$\pm$0.04, 3) 0.93$\pm$0.03 vs 0.89$\pm$0.03, and 4) 0.96$\pm$0.02 vs 0.92$\pm$0.04. In the 2nd model, two RFMs, Entropy and Short-Run-Emphasize, were selected with their highest cross-correlations with the saliency maps of the 1st model. The corresponding results demonstrated significant improvements (p<0.05) of COVID-19 vs Non-COVID-19 pneumonia detection: 1) 0.95$\pm$0.04 vs 0.85$\pm$0.04, 2) 0.97$\pm$0.02 vs 0.96$\pm$0.02, 3) 0.97$\pm$0.02 vs 0.93$\pm$0.02, and 4) 0.99$\pm$0.01 vs 0.97$\pm$0.02. The reduced variations suggested a superior robustness of 2nd model design.

eess.IV↗

arXiv2021

Integrated scalable metalens array for optical addressing

The scalability of optical addressing is one of the key challenges for the large-scale trapped-ion quantum computing (QC). Metasurfaces is a promising solution due to excellent optical performance, integration, and miniaturization. Here, we propose and numerically demonstrate a design of integrated scalable metalens array (ISMA) working in the UV 350nm range for optical addressing. These ISMAs can focus the collimated individual addressing beams array into diffraction-limited spots array, featuring crosstalk below 0.82%, focusing efficiency as high as 36.90%. For various applications, the x-polarized, polarization-insensitive and right-handed circularly polarized ISMA are designed. The design proposed in this paper can also find applications in optical trapping.

physics.optics↗

arXiv2021

The distance between the weights of the neural network is meaningful

In the application of neural networks, we need to select a suitable model based on the problem complexity and the dataset scale. To analyze the network's capacity, quantifying the information learned by the network is necessary. This paper proves that the distance between the neural network weights in different training stages can be used to estimate the information accumulated by the network in the training process directly. The experiment results verify the utility of this method. An application of this method related to the label corruption is shown at the end.

cs.LG↗

arXiv2020

Compact optical polarization-insensitive zoom metalens-doublet

Metasurface-based lenses (metalenses) offer specific conceptual advantages compared to ordinary refractive lenses. For example, it is possible to tune the focal length of a metalens doublet by varying the relative angle between the two metalenses while fixing their distance, leading to an extremely compact zoom lens. An improved polarization-insensitive design based on silicon-nanocylinders on silica substrates is presented. This design is realized and characterized experimentally at 1550 nm wavelength. By varying the relative angle between the metalenses in steps of 10 degrees, tuning of the doublet focal length is demonstrated from -54 mm to -+3 mm to +54 mm. This results in a zoom factor of an imaging system varying between 1 and 18. For positive focal lengths, the doublet focusing efficiency has a minimum of 34% and a maximum of 83%. Experiment and theory are in very good agreement.

physics.optics↗

arXiv2018

Generalized Hartmann-Shack array of dielectric metalens sub-arrays for polarimetric beam profiling

To define and characterize optical systems, obtaining information on the amplitude, phase, and polarization profile of optical beams is of utmost importance. Polarimetry using bulk optics is well established to characterize the polarization state. Recently, metasurfaces and metalenses have successfully been introduced as compact optical components. Here, we take the metasurface concept to the system level by realizing arrays of metalens 2*3 sub-arrays, allowing to determine the polarization profile of an optical beam. We use silicon-based metalenses with a numerical aperture of 0.32 and a mean measured diffraction efficiency in transmission mode of 28% at 1550 nm wavelength. Together with a standard camera recording the array foci, our system is extremely compact and allows for real-time beam diagnostics by inspecting the foci amplitudes. By further analyzing the foci displacements in the spirit of a Hartmann-Shack wavefront sensor, we can simultaneously detect phase-gradient profiles. As application examples, we diagnose the polarization profiles of a radially polarized beam, an azimuthally polarized beam, and of a vortex beam.

physics.optics↗

arXiv2014

Mechanics of nanoscale wrinkling of graphene on a non-developable spherical surface

The configuration of graphene (GE) sheet conforming to the spherical surface substrate is studied through theoretical model and molecular simulations. Two basic configurations are observed: fully conformation and wrinkling. The final configuration of the adsorbed GE results from the competition between two energy terms: the adhesion energy between GE and substrate, the strain energy stored in the GE due to the deformations. Here, we derive theoretical solutions by accounting for two energy terms, and predict the final morphology of GE on the spherical surface (a special kind of nano-developable curved surface) substrate with using the phase diagram. A critical cone angle of the absorbed GE for an arbitrary spherical surface substrate is obtained. Fully conformation of GE is observed when the cone angle of absorbed GE is below the critical value, otherwise wrinkles appear. Molecular simulations are implemented to verify the theoretical model with results agree well with theoretical predictions. Results from our present work can offer a guide for designing new functional graphene electronical devices (such as nanoswithes) and fabricating high quality nanostructured coating (Fig. 13).

cond-mat.mes-hall↗