arXiv subjects

Hui Chen

Publications and source records attributed to Hui Chen.

At least 343 records · Page 19Linked to original sources

arXiv2020

Active suppression of temperature oscillation from a pulse-tube cryocooler in a cryogen-free cryostat: Part 2. Experimental realization

A cryogen-free cryostat cooled by a closed cycle cryocooler is compact, can provide uninterrupted long-term operation (up to ten thousand hours) and is suited to temperatures from 3 K to 300 K. Its intrinsic temperature oscillation, however, limits its application in experiments requiring high thermal stability at low temperature (below 77 K). Passive suppression methods are effective but all suffer from drawbacks. We describe a novel, active suppression scheme more efficient than traditional proportional-integral (PI) control. The experimental results show that it can reduce the standard deviation of the temperature oscillation by a further 30% compared with PI feedback. To the best of our knowledge, this is the first time such active suppression of temperature oscillations has been implemented with the cryogen-free cryostat. The results also show, however, that an unwanted lower frequency thermal noise will be generated, which appears to be the limit of the method. Nevertheless, the approach could be used to improve the temperature stability in all cryogen-free cryostats.

physics.ins-det↗

arXiv2020

Active suppression of temperature oscillation from a pulse-tube cryocooler in a cryogen-free cryostat: Part 1. Simulation modeling from thermal response characteristics

A cryogen-free cryostat cooled using a 4 K commercial GM or pulse tube cryocooler (PTC) displays temperature oscillations caused by the intrinsic working principle of the regenerative cryocooler. To dampen such oscillations usually requires either a large heat capacity or a large thermal resistance. To understand this phenomenon better and suppress it more effectively, both the step response characteristic and the intrinsic oscillation characteristic of cryostat have been used to obtain the complete transfer functions of a simulation model. The latter is used to test and optimize traditional PID feedback control. The results showed this approach has almost no effect on the temperature oscillation amplitude. Based on this simulation model, a novel active method was proposed and tested numerically. Simulation results predict the method should suppress the amplitude of the original temperature oscillation by a factor of two.

physics.ins-det↗

arXiv2020

Nearly quantized conductance plateau of vortex zero mode in an iron-based superconductor

Majorana zero-modes (MZMs) are spatially-localized zero-energy fractional quasiparticles with non-Abelian braiding statistics that hold a great promise for topological quantum computing. Due to its particle-antiparticle equivalence, an MZM exhibits robust resonant Andreev reflection and 2e2/h quantized conductance at low temperature. By utilizing variable-tunnel-coupled scanning tunneling spectroscopy, we study tunneling conductance of vortex bound states on FeTe0.55Se0.45 superconductors. We report observations of conductance plateaus as a function of tunnel coupling for zero-energy vortex bound states with values close to or even reaching the 2e2/h quantum conductance. In contrast, no such plateau behaviors were observed on either finite energy Caroli-de Genne-Matricon bound states or in the continuum of electronic states outside the superconducting gap. This unique behavior of the zero-mode conductance reaching a plateau strongly supports the existence of MZMs in this iron-based superconductor, which serves as a promising single-material platform for Majorana braiding at a relatively high temperature.

cond-mat.supr-con↗

arXiv2019

An Iterative Polishing Framework based on Quality Aware Masked Language Model for Chinese Poetry Generation

Owing to its unique literal and aesthetical characteristics, automatic generation of Chinese poetry is still challenging in Artificial Intelligence, which can hardly be straightforwardly realized by end-to-end methods. In this paper, we propose a novel iterative polishing framework for highly qualified Chinese poetry generation. In the first stage, an encoder-decoder structure is utilized to generate a poem draft. Afterwards, our proposed Quality-Aware Masked Language Model (QAMLM) is employed to polish the draft towards higher quality in terms of linguistics and literalness. Based on a multi-task learning scheme, QA-MLM is able to determine whether polishing is needed based on the poem draft. Furthermore, QAMLM is able to localize improper characters of the poem draft and substitute with newly predicted ones accordingly. Benefited from the masked language model structure, QAMLM incorporates global context information into the polishing process, which can obtain more appropriate polishing results than the unidirectional sequential decoding. Moreover, the iterative polishing process will be terminated automatically when QA-MLM regards the processed poem as a qualified one. Both human and automatic evaluation have been conducted, and the results demonstrate that our approach is effective to improve the performance of encoder-decoder structure.

cs.CL↗

arXiv2019

UER: An Open-Source Toolkit for Pre-training Models

Existing works, including ELMO and BERT, have revealed the importance of pre-training for NLP tasks. While there does not exist a single pre-training model that works best in all cases, it is of necessity to develop a framework that is able to deploy various pre-training models efficiently. For this purpose, we propose an assemble-on-demand pre-training toolkit, namely Universal Encoder Representations (UER). UER is loosely coupled, and encapsulated with rich modules. By assembling modules on demand, users can either reproduce a state-of-the-art pre-training model or develop a pre-training model that remains unexplored. With UER, we have built a model zoo, which contains pre-trained models based on different corpora, encoders, and targets (objectives). With proper pre-trained models, we could achieve new state-of-the-art results on a range of downstream datasets.

cs.CL↗

arXiv2019

PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression

Existing methods on visual emotion analysis mainly focus on coarse-grained emotion classification, i.e. assigning an image with a dominant discrete emotion category. However, these methods cannot well reflect the complexity and subtlety of emotions. In this paper, we study the fine-grained regression problem of visual emotions based on convolutional neural networks (CNNs). Specifically, we develop a Polarity-consistent Deep Attention Network (PDANet), a novel network architecture that integrates attention into a CNN with an emotion polarity constraint. First, we propose to incorporate both spatial and channel-wise attentions into a CNN for visual emotion regression, which jointly considers the local spatial connectivity patterns along each channel and the interdependency between different channels. Second, we design a novel regression loss, i.e. polarity-consistent regression (PCR) loss, based on the weakly supervised emotion polarity to guide the attention generation. By optimizing the PCR loss, PDANet can generate a polarity preserved attention map and thus improve the emotion regression performance. Extensive experiments are conducted on the IAPS, NAPS, and EMOTIC datasets, and the results demonstrate that the proposed PDANet outperforms the state-of-the-art approaches by a large margin for fine-grained visual emotion regression. Our source code is released at: https://github.com/ZizhouJia/PDANet.

cs.CV↗

arXiv2019

Bifrequency 3D Ghost Imaging with Haar Wavelet Transform

Recently, ghost imaging has been attracting attentions because its mechanism would lead to many applications inaccessible to conventional imaging methods. However, it is challenging for high contrast and high resolution imaging, due to its low signal-to-noise ratio (SNR) and the demand of high sampling rate in detection. To circumvent these challenges, we here propose a ghost imaging scheme that exploits Haar wavelets as illuminating patterns with a bi-frequency light projecting system and frequency-selecting single-pixel detectors. This method provides a theoretically 100% image contrast and high detection SNR, which reduces the requirement of high dynamic range of detectors, enabling high resolution ghost imaging. Moreover, it can highly reduce the sampling rate (far below Nyquist limit) for a sparse object by adaptively abandoning unnecessary patterns during the measurement. These characteristics are experimentally verified with a resolution of 512 times 512 and a sampling rate lower than 5%. A high-resolution (1000 times 1000 times 1000) 3D reconstruction of an object is also achieved from multi-angle images.

eess.IV↗

arXiv2019

Modeling Hierarchical Usage Context for Software Exceptions based on Interaction Data

Traces of user interactions with a software system, captured in production, are commonly used as an input source for user experience testing. In this paper, we present an alternative use, introducing a novel approach of modeling user interaction traces enriched with another type of data gathered in production - software fault reports consisting of software exceptions and stack traces. The model described in this paper aims to improve developers' comprehension of the circumstances surrounding a specific software exception and can highlight specific user behaviors that lead to a high frequency of software faults. Modeling the combination of interaction traces and software crash reports to form an interpretable and useful model is challenging due to the complexity and variance in the combined data source. Therefore, we propose a probabilistic unsupervised learning approach, adapting the Nested Hierarchical Dirichlet Process, which is a Bayesian non-parametric topic model commonly applied to natural language data. This model infers a tree of topics, each of whom describes a set of commonly co-occurring commands and exceptions. The topic tree can be interpreted hierarchically to aid in categorizing the numerous types of exceptions and interactions. We apply the proposed approach to large scale datasets collected from the ABB RobotStudio software application, and evaluate it both numerically and with a small survey of the RobotStudio developers.

cs.SE↗

arXiv2019

GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition

The dominant approaches for named entity recognition (NER) mostly adopt complex recurrent neural networks (RNN), e.g., long-short-term-memory (LSTM). However, RNNs are limited by their recurrent nature in terms of computational efficiency. In contrast, convolutional neural networks (CNN) can fully exploit the GPU parallelism with their feedforward architectures. However, little attention has been paid to performing NER with CNNs, mainly owing to their difficulties in capturing the long-term context information in a sequence. In this paper, we propose a simple but effective CNN-based network for NER, i.e., gated relation network (GRN), which is more capable than common CNNs in capturing long-term context. Specifically, in GRN we firstly employ CNNs to explore the local context features of each word. Then we model the relations between words and use them as gates to fuse local context features into global ones for predicting labels. Without using recurrent layers that process a sentence in a sequential manner, our GRN allows computations to be performed in parallel across the entire sentence. Experiments on two benchmark NER datasets (i.e., CoNLL2003 and Ontonotes 5.0) show that, our proposed GRN can achieve state-of-the-art performance with or without external knowledge. It also enjoys lower time costs to train and test.We have made the code publicly available at https://github.com/HuiChen24/NER-GRN.

cs.CL↗

arXiv2019

A new Majorana platform in an Fe-As bilayer superconductor

Recently, iron-chalcogenide superconductors have emerged as a new and promising platform for studying and manipulating Majorana zero mode (MZM). By combining topological band structure and superconductivity in a multiband material, they provide significant advantages such as higher superconducting transition temperature (Tc) and isolated Majorana mode. However, iron-chalcogenide superconductors, especially Fe(Te,Se), suffer from strong inhomogeneity which may hamper their practical application. On the other hand, some iron-pnictide (Fe-As) superconductors, such as LiFeAs, have been demonstrated to have a similar topological band structure, yet no MZM has been observed in its vortex cores, raising a question of universality of MZM presence in iron-based superconductors. In this work, by using high-resolution angle-resolved photoemission spectroscopy and scanning tunneling microscopy/spectroscopy, we identify the first Fe-As superconductor CaKFe4As4 (Tc = 35 K) which has both the superconducting Dirac surface states and the MZMs inside its vortex cores. The topological band inversion is largely due to the down-shift of the pz band caused by the bilayer band folding in this material. More strikingly, the energies and spatial line profiles of MZM and multiple quantized Caroli-de Gennes-Matricon bound states observed inside the topological vortex can be accurately reproduced by a simple theoretical model derived from a surface Dirac cone, firmly establishing Majorana nature of the zero mode.

cond-mat.supr-con↗

arXiv2019

Ultra-high-speed color imaging with single-pixel detectors under low light level

Single-pixel imaging is suitable for low light level scenarios because a bucket detector is employed to maximally collect the light from an object. However, one of the challenges is its slow imaging speed, mainly due to the slow light modulation technique. We here demonstrate 1.4MHz video imaging based on computational ghost imaging with a RGB LED array having a full-range frame rate up to 100MHz. With this method, the motion of a high speed propeller is observed. Moreover, by exploiting single-photon detectors to increase the detection efficiency, this method is developed for ultra-high-speed imaging under low light level.

physics.ins-det↗

arXiv2019

Naked-Eye Ghost Imaging via Photoelectric-Feedback

Based on optical correlations, ghost imaging is usually reconstructed by computer algorithm from the acquired data. We here proposed an alternatively high contrast naked-eye ghost imaging scheme which avoids computer algorithm processing. Instead, the proposed scheme uses a photoelectric feedback loop to realize the multiplication process of traditional ghost imaging. Meanwhile, it exploits the vision persistence effect to implement integral process and to generate negative images observed by naked eyes. To realize high contrast naked-eye ghost imaging, a special pattern-scanning architecture on a low-speed light-modulation mask is designed, which enables high-resolution imaging with lower-order Hadamard vectors and boosts the imaging speed as well. Moreover, two kinds of feedback circuits, the digital circuit and the analog circuit, are presented respectively, which can achieve high-speed feedback operation on the light intensity. With this approach, we demonstrate high-contrast real-time imaging for moving objects.

eess.IV↗

arXiv2019

All-optical naked-eye ghost imaging

Ghost imaging is usually based on optoelectronic process and eletronic computing. We here propose a new ghost imaging scheme, which avoids any optoelectronic or electronic process. Instead, the proposed scheme exploits all-optical correlation via the light-light interaction and the vision persistence effect to generate images observed by naked eyes. To realize high contrast naked-eye ghost imaging, a special pattern-scanning architecture on a low-speed light-modulation disk is designed, which also enables high-resolution imaging with lower-order Hadamard vectors and boosts the imaging speed. With this approach, we realize high-contrast real-time ghost imaging for moving colored objects.

eess.IV↗

arXiv2019

Observation of half-integer level shift of vortex bound states in an iron-based superconductor

Vortices in topological superconductors host Majorana zero modes (MZMs), which are proposed to be building blocks of fault-tolerant topological quantum computers. Recently, a new single-material platform for realizing MZM has been discovered in iron-based superconductors, without involving hybrid semiconductor-superconductor structures. Here we report on a detailed scanning tunneling spectroscopy study of a FeTe0.55Se0.45 single crystal, revealing two distinct classes of vortices present in this system which differ by a half-integer level shift in the energy spectra of the vortex bound states. This level shift is directly tied with the presence or absence of zero-bias peak and also alters the ratios of higher energy levels from integer to half-odd-integer. Our model calculations fully reproduce the spectra of these two types of vortex bound states, suggesting the presence of topological and conventional superconducting regions that coexist within the same crystal. Our findings provide strong evidence for the topological nature of superconductivity in FeTe0.55Se0.45 and establish it as an excellent platform for further studies on MZMs.

cond-mat.supr-con↗

arXiv2018

Observation of nonreciprocal wave propagation in a dynamic phononic lattice

Acoustic waves in a linear time-invariant medium are generally reciprocal; however, reciprocity can break down in a time-variant system. In this Letter, we report on an experimental demonstration of nonreciprocity in a dynamic one-dimensional phononic crystal, where the local elastic properties are dependent on time. The system consists of an array of repelling magnets, and the on-site elastic potentials of the constitutive elements are modulated by an array of electromagnets. The modulation in time breaks time-reversal symmetry and opens a directional band gap in the dispersion relation. As shown by experimental and numerical results, nonreciprocal mechanical systems like the one presented here offer opportunities to create phononic diodes that can serve for rectification applications.

physics.app-ph↗

arXiv2018

High-Performance Hybrid Silicon and Lithium Niobate Mach-Zehnder Modulators for 100 Gbit/s and Beyond

Optical modulators are at the heart of optical communication links. Ideally, they should feature low insertion loss, low drive voltage, large modulation bandwidth, high linearity, compact footprint and low manufacturing cost. Unfortunately, these criteria have only been achieved on separate occasions.Based on a Silicon and Lithium Niobate hybrid integration platform, we demonstrate Mach-Zehnder modulators that simultaneously fulfill these criteria. The presented device exhibits an insertion loss of 2.5 dB, voltage-length product of 2.2 Vcm, high linearity, electro-optic bandwidth of at least 70 GHz and modulation rates up to 112 Gbit/s. The high-performance modulator is realized by seamless integration of high-contrast waveguide based on Lithium Niobate - the most mature modulator material - with compact, low-loss silicon circuits. The hybrid platform demonstrated here allows for the combination of 'best-in-breed' active and passive components, opening up new avenues for enabling future high-speed, energy efficient and cost-effective optical communication networks.

physics.app-ph↗

arXiv2018

Geometry-Contrastive GAN for Facial Expression Transfer

In this paper, we propose a Geometry-Contrastive Generative Adversarial Network (GC-GAN) for transferring continuous emotions across different subjects. Given an input face with certain emotion and a target facial expression from another subject, GC-GAN can generate an identity-preserving face with the target expression. Geometry information is introduced into cGANs as continuous conditions to guide the generation of facial expressions. In order to handle the misalignment across different subjects or emotions, contrastive learning is used to transform geometry manifold into an embedded semantic manifold of facial expressions. Therefore, the embedded geometry is injected into the latent space of GANs and control the emotion generation effectively. Experimental results demonstrate that our proposed method can be applied in facial expression transfer even there exist big differences in facial shapes and expressions between different subjects.

cs.CV↗

arXiv2018

Non-invasive dynamic or wide-field imaging through opaque layers and around corners

In turbid media, scattering of light scrambles information of the incident beam and represents an obstacle to optical imaging. Noninvasive imaging through opaque layers is challenging for dynamic and wide-field objects due to unreliable image reconstruction processes. We here propose a new perspective to solve these problems: rather than using the full point-spread-function (PSF), the wave distortions in scattering layers can be characterized with only the phase of the optical-transfer-function (OTF, the Fourier transform of PSF), with which diffraction-limit images can be analytically solved. We then develop a method that exploits the redundant information dynamic objects, and can reliably and rapidly recover OTFs' phases within several iterations. It enables not only noninvasive video imaging at 25 ~ 200 Hz of a moving object hidden inside turbid media, but also imaging under weak illumination that is inaccessible with previous methods. Furthermore, by scanning a localized illumination on the object plane, we propose a wide-field imaging approach, with which we demonstrate an application where a photoluminescent sample hidden behind four-layers of opaque polythene films is imaged with a modified multi-photon excitation microscopy setup.

physics.optics↗