arXiv subjects

Ping Yu

Publications and source records attributed to Ping Yu.

54 records · Page 3Linked to original sources

arXiv2021

Electrically-controlled digital metasurface device for light projection displays

Light projection displays play an increasingly important role in our modern life. Core projection systems including liquid crystal displays and digital micromirror devices can impose spatial light modulation and actively shape light waves. Recently, the advent of metasurfaces has revolutionized design concepts in nanophotonics, enabling a new family of optical elements with exceptional degrees of freedom. Here, we demonstrate a light projection display technology based on optical metasurfaces, called digital metasurface device (DMSD). Each metasurface pixel in a DMSD is electrically reconfigurable with well-controlled programmability and addressability. The DMSD can not only continuously modulate the intensity of light with high contrast, but also shape the wavefront of light generated by each metasurface pixel and dynamically switch between arbitrary holographic patterns. Our approach will pave an avenue towards the development of customized light projection devices. It will also drive the field of dynamic optical metasurfaces with fresh momentum and inspiring concepts.

physics.optics↗

arXiv2021

A Reusable Metasurface Template

Metasurfaces have revolutionized the design concepts for opticalcomponents, fostering an excitingfield offlat optics. Thanks to theflat and ultrathinnature, metasurfaces possess unique advantages over conventional optical components,such as lightweight, high compatibility, among others. To comply with potential appli-cations, further research endeavors need to be exerted for advancing the reusability andpracticality of metasurfaces. In this work, we demonstrate a reusable template approachto achieve optical multifunctionality with metasurfaces, utilizing both the geometric andpropagation phases to shape light waves. In principle, such a metasurface template can beemployed infinite times to enable a large variety of optical functions. As proof-of-conceptexperiments, we demonstrate metalensing, holography, and vortex beam shaping. Ourwork will leverage the high scalability aspects of metasurface devices for practical applications.

physics.optics↗

arXiv2021

Dynamic Janus Metasurfaces in the Visible Spectral Region

Janus monolayers have long been captivated as a popular notion for breaking in-plane and out-of-plane structural symmetry. Originated from chemistry and materials science, the concept of Janus functions have been recently extended to ultrathin metasurfaces by arranging meta-atoms asymmetrically with respect to the propagation or polarization direction of the incident light. However, such metasurfaces are intrinsically static and the information they carry can be straightforwardly decrypted by scanning the incident light directions and polarization states once the devices are fabricated. In this Letter, we present a dynamic Janus metasurface scheme in the visible spectral region. In each super unit cell, three plasmonic pixels are categorized into two sets. One set contains a magnesium nanorod and a gold nanorod that are orthogonally oriented with respect to each other, working as counter pixels. The other set only contains a magnesium nanorod. The effective pixels on the Janus metasurface can be reversibly regulated by hydrogenation/dehydrogenation of the magnesium nanorods. Such dynamic controllability at visible frequencies allows for flat optical elements with novel functionalities including beam steering, bifocal lensing, holographic encryption, and dual optical function switching.

physics.optics↗

arXiv2021

Generation of Switchable Singular Beams with Dynamic Metasurfaces

Singular beams have attracted great attention due to their optical properties and broad applications from light manipulation to optical communications. However, there has been a lack of practical schemes with which to achieve switchable singular beams with sub-wavelength resolution using ultrathin and flat optical devices. In this work, we demonstrate the generation of switchable vector and vortex beams utilizing dynamic metasurfaces at visible frequencies. The dynamic functionality of the metasurface pixels is enabled by the utilization of magnesium nanorods, which possess plasmonic reconfigurability upon hydrogenation and dehydrogenation. We show that switchable vector beams of different polarization states and switchable vortex beams of different topological charges can be implemented through simple hydrogenation and dehydrogenation of the same metasurfaces. Furthermore, we demonstrate a two-cascade metasurface scheme for holographic pattern switching, taking inspiration from orbital angular momentum-shift keying. Our work provides an additional degree of freedom to develop high-security optical elements for anti-counterfeiting applications.

physics.optics↗

arXiv2021

Improve Variational Autoencoder for Text Generationwith Discrete Latent Bottleneck

Variational autoencoders (VAEs) are essential tools in end-to-end representation learning. However, the sequential text generation common pitfall with VAEs is that the model tends to ignore latent variables with a strong auto-regressive decoder. In this paper, we propose a principled approach to alleviate this issue by applying a discretized bottleneck to enforce an implicit latent feature matching in a more compact latent space. We impose a shared discrete latent space where each input is learned to choose a combination of latent atoms as a regularized latent representation. Our model endows a promising capability to model underlying semantics of discrete sequences and thus provide more interpretative latent structures. Empirically, we demonstrate our model's efficiency and effectiveness on a broad range of tasks, including language modeling, unaligned text style transfer, dialog response generation, and neural machine translation.

cs.LG↗

arXiv2021

SDA: Improving Text Generation with Self Data Augmentation

Data augmentation has been widely used to improve deep neural networks in many research fields, such as computer vision. However, less work has been done in the context of text, partially due to its discrete nature and the complexity of natural languages. In this paper, we propose to improve the standard maximum likelihood estimation (MLE) paradigm by incorporating a self-imitation-learning phase for automatic data augmentation. Unlike most existing sentence-level augmentation strategies, which are only applied to specific models, our method is more general and could be easily adapted to any MLE-based training procedure. In addition, our framework allows task-specific evaluation metrics to be designed to flexibly control the generated sentences, for example, in terms of controlling vocabulary usage and avoiding nontrivial repetitions. Extensive experimental results demonstrate the superiority of our method on two synthetic and several standard real datasets, significantly improving related baselines.

cs.CL↗

arXiv2020

ReMP: Rectified Metric Propagation for Few-Shot Learning

Few-shot learning features the capability of generalizing from a few examples. In this paper, we first identify that a discriminative feature space, namely a rectified metric space, that is learned to maintain the metric consistency from training to testing, is an essential component to the success of metric-based few-shot learning. Numerous analyses indicate that a simple modification of the objective can yield substantial performance gains. The resulting approach, called rectified metric propagation (ReMP), further optimizes an attentive prototype propagation network, and applies a repulsive force to make confident predictions. Extensive experiments demonstrate that the proposed ReMP is effective and efficient, and outperforms the state of the arts on various standard few-shot learning datasets.

cs.CV↗

arXiv2020

Structure-Aware Human-Action Generation

Generating long-range skeleton-based human actions has been a challenging problem since small deviations of one frame can cause a malformed action sequence. Most existing methods borrow ideas from video generation, which naively treat skeleton nodes/joints as pixels of images without considering the rich inter-frame and intra-frame structure information, leading to potential distorted actions. Graph convolutional networks (GCNs) is a promising way to leverage structure information to learn structure representations. However, directly adopting GCNs to tackle such continuous action sequences both in spatial and temporal spaces is challenging as the action graph could be huge. To overcome this issue, we propose a variant of GCNs to leverage the powerful self-attention mechanism to adaptively sparsify a complete action graph in the temporal space. Our method could dynamically attend to important past frames and construct a sparse graph to apply in the GCN framework, well-capturing the structure information in action sequences. Extensive experimental results demonstrate the superiority of our method on two standard human action datasets compared with existing methods.

cs.GR↗

arXiv2020

Feature Quantization Improves GAN Training

The instability in GAN training has been a long-standing problem despite remarkable research efforts. We identify that instability issues stem from difficulties of performing feature matching with mini-batch statistics, due to a fragile balance between the fixed target distribution and the progressively generated distribution. In this work, we propose Feature Quantization (FQ) for the discriminator, to embed both true and fake data samples into a shared discrete space. The quantized values of FQ are constructed as an evolving dictionary, which is consistent with feature statistics of the recent distribution history. Hence, FQ implicitly enables robust feature matching in a compact space. Our method can be easily plugged into existing GAN models, with little computational overhead in training. We apply FQ to 3 representative GAN models on 9 benchmarks: BigGAN for image generation, StyleGAN for face synthesis, and U-GAT-IT for unsupervised image-to-image translation. Extensive experimental results show that the proposed FQ-GAN can improve the FID scores of baseline methods by a large margin on a variety of tasks, achieving new state-of-the-art performance.

cs.LG↗

arXiv2019

Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions

Human-motion generation is a long-standing challenging task due to the requirement of accurately modeling complex and diverse dynamic patterns. Most existing methods adopt sequence models such as RNN to directly model transitions in the original action space. Due to high dimensionality and potential noise, such modeling of action transitions is particularly challenging. In this paper, we focus on skeleton-based action generation and propose to model smooth and diverse transitions on a latent space of action sequences with much lower dimensionality. Conditioned on a latent sequence, actions are generated by a frame-wise decoder shared by all latent action-poses. Specifically, an implicit RNN is defined to model smooth latent sequences, whose randomness (diversity) is controlled by noise from the input. Different from standard action-prediction methods, our model can generate action sequences from pure noise without any conditional action poses. Remarkably, it can also generate unseen actions from mixed classes during training. Our model is learned with a bi-directional generative-adversarial-net framework, which not only can generate diverse action sequences of a particular class or mix classes, but also learns to classify action sequences within the same model. Experimental results show the superiority of our method in both diverse action-sequence generation and classification, relative to existing methods.

cs.CV↗

arXiv2019

SIMEX Estimation in Parametric Modal Regression with Measurement Error

For a class of parametric modal regression models with measurement error, a simulation extrapolation estimation procedure is proposed in this paper for estimating the modal regression coefficients. Large sample properties of the proposed estimation procedure, including the consistency and asymptotic normality, are thoroughly investigated. Simulation studies are conducted to evaluate its robustness to potential outliers and the effectiveness in reducing the bias caused by the measurement error.

stat.ME↗

arXiv2019

Generating Adversarial Examples With Conditional Generative Adversarial Net

Recently, deep neural networks have significant progress and successful application in various fields, but they are found vulnerable to attack instances, e.g., adversarial examples. State-of-art attack methods can generate attack images by adding small perturbation to the source image. These attack images can fool the classifier but have little impact to human. Therefore, such attack instances are difficult to generate by searching the feature space. How to design an effective and robust generating method has become a spotlight. Inspired by adversarial examples, we propose two novel generative models to produce adaptive attack instances directly, in which conditional generative adversarial network is adopted and distinctive strategy is designed for training. Compared with the common method, such as Fast Gradient Sign Method, our models can reduce the generating cost and improve robustness and has about one fifth running time for producing attack instance.

cs.LG↗

arXiv2019

TensorFlow.js: Machine Learning for the Web and Beyond

TensorFlow.js is a library for building and executing machine learning algorithms in JavaScript. TensorFlow.js models run in a web browser and in the Node.js environment. The library is part of the TensorFlow ecosystem, providing a set of APIs that are compatible with those in Python, allowing models to be ported between the Python and JavaScript ecosystems. TensorFlow.js has empowered a new set of developers from the extensive JavaScript community to build and deploy machine learning models and enabled new classes of on-device computation. This paper describes the design, API, and implementation of TensorFlow.js, and highlights some of the impactful use cases.

cs.LG↗

arXiv2017

Apparent Reversal of Molecular Orbitals Reveals Entanglement

The frontier orbital sequence of individual dicyanovinyl-substituted oligothiophene molecules is studied by means of scanning tunneling microscopy. On NaCl/Cu(111) the molecules are neutral and the two lowest unoccupied molecular states are observed in the expected order of increasing energy. On NaCl/Cu(311), where the molecules are negatively charged, the sequence of two observed molecular orbitals is reversed, such that the one with one more nodal plane appears lower in energy. These experimental results, in open contradiction with a single-particle interpretation, are explained by a many-body theory predicting a strongly entangled doubly charged ground state.

cond-mat.mes-hall↗

arXiv2014

Full control of polarization states and phase distributions of light with dual-metasurfaces

Control of the phase and polarization states of light is an important goal for nearly all optical research. The development of an efficient optical component that allows the simultaneous manipulation of the polarization and phase distribution is needed. Traditional methods require the combination of multiple optical devices, and a single optical device cannot easily realize full control of light. We theoretically predict and experimentally verify that our proposed dual-metasurfaces provide an excellent means to simultaneously manipulate the phase and polarization of transmission light at the nanoscale. By introducing a phase gradient along the interface, we achieved a near-perfect anomalous refraction with controllable polarization in the near-infrared region. On the basis of these properties, we created a dual-metasurface capable of generating radially polarized beam, demonstrating the power of full control of light. This work opens exciting avenues toward improving the degrees of freedom in the manipulation of light, including the propagation direction and distribution of the polarization and phase, and may profoundly affect a wide range of plasmonic applications.

physics.optics↗

arXiv2012

Genuine Entanglement of Four Qubit Cluster Diagonal States

We reduce the necessary and sufficient biseparable conditions of the four qubit cluster diagonal state to concise forms. Only 4 out of the 15 parameters are proved to be relevant in specifying the genuine entanglement of the state. Using the relative entropy of entanglement as the entanglement measure, we analytically find the genuine entanglement of all the four qubit cluster diagonal states. The formulas of the genuine entanglement are of five kinds, for seven different parameter regions of entanglement.

quant-ph↗

arXiv2012

Realignment Entanglement Criterion for Continuous Bipartite Symmetric Quantum States

The separability of bipartite non-Gaussian states is studied by applying the realignment criterion with the technique of functional analysis. The realignment criterion is given as one inequality in contrast to the infinitive number of inequalities based on the moments. We give the necessary and sufficient condition of inseparability for non-Gaussian states prepared by photon subtraction or addition from symmetric Gaussian states. The entanglement criterion of non-Gaussian states evolved in thermal noise and amplitude damping environment is also obtained.

quant-ph↗

arXiv2012

Entanglement and genuine entanglement of three qubit GHZ diagonal states

We analytically prove the necessary and sufficient criterion for the full separability of three-qubit Greenberger-Horne-Zeilinger (GHZ) diagonal states. The corresponding entanglement is exactly calculable for some GHZ diagonal states and is tractable for the others using the relative entropy of entanglement. We show that the biseparable criterion and the genuine entanglement are determined only by the biggest GHZ diagonal element regardless of all the other smaller diagonal elements. We have completely solved the entanglement problems of three-qubit GHZ diagonal states.

quant-ph↗