arXiv subjects

Xiaobo Lu

Publications and source records attributed to Xiaobo Lu.

At least 55 records · Page 3Linked to original sources

arXiv2021

PTeacher: a Computer-Aided Personalized Pronunciation Training System with Exaggerated Audio-Visual Corrective Feedback

Second language (L2) English learners often find it difficult to improve their pronunciations due to the lack of expressive and personalized corrective feedback. In this paper, we present Pronunciation Teacher (PTeacher), a Computer-Aided Pronunciation Training (CAPT) system that provides personalized exaggerated audio-visual corrective feedback for mispronunciations. Though the effectiveness of exaggerated feedback has been demonstrated, it is still unclear how to define the appropriate degrees of exaggeration when interacting with individual learners. To fill in this gap, we interview 100 L2 English learners and 22 professional native teachers to understand their needs and experiences. Three critical metrics are proposed for both learners and teachers to identify the best exaggeration levels in both audio and visual modalities. Additionally, we incorporate the personalized dynamic feedback mechanism given the English proficiency of learners. Based on the obtained insights, a comprehensive interactive pronunciation training course is designed to help L2 learners rectify mispronunciations in a more perceptible, understandable, and discriminative manner. Extensive user studies demonstrate that our system significantly promotes the learners' learning efficiency.

cs.HC↗

arXiv2021

Measuring local moiré lattice heterogeneity of twisted bilayer graphene

We introduce a new method to continuously map inhomogeneities of a moiré lattice and apply it to large-area topographic images we measure on open-device twisted bilayer graphene (TBG). We show that the variation in the twist angle of a TBG device, which is frequently conjectured to be the reason for differences between devices with a supposed similar twist angle, is about 0.08° around the average of 2.02° over areas of several hundred nm, comparable to devices encapsulated between hBN slabs. We distinguish between an effective twist angle and local anisotropy and relate the latter to heterostrain. Our results imply that for our devices, twist angle heterogeneity has a roughly equal effect to the electronic structure as local strain. The method introduced here is applicable to results from different imaging techniques, and on different moiré materials.

cond-mat.mes-hall↗

arXiv2020

Competing zero-field Chern insulators in Superconducting Twisted Bilayer Graphene

The discovery of magic angle twisted bilayer graphene (MATBG) has unveiled a rich variety of superconducting, magnetic and topologically nontrivial phases. The existence of all these phases in one material, and their tunability, has opened new pathways for the creation of unusual gate tunable junctions. However, the required conditions for their creation - gate induced transitions between phases in zero magnetic field - have so far not been achieved. Here, we report on the first experimental demonstration of a device that is both a zero-field Chern insulator and a superconductor. The Chern insulator occurs near moire cell filling factor v = +1 in a hBN non-aligned MATBG device and manifests itself via an anomalous Hall effect. The insulator has Chern number C = +-1 and a relatively high Curie temperature of Tc = 4.5 K. Gate tuning away from this state exposes strong superconducting phases with critical temperatures of up to Tc = 3.5 K. In a perpendicular magnetic field above B > 0.5 T we observe a transition of the /C/= +1 Chern insulator from Chern number C = +-1 to C = 3, characterized by a quantized Hall plateau with Ryx = h/3e2. These observations show that interaction-induced symmetry breaking in MATBG leads to zero-field ground states that include almost degenerate and closely competing Chern insulators, and that states with larger Chern numbers couple most strongly to the B-field. By providing the first demonstration of a system that allows gate-induced transitions between magnetic and superconducting phases, our observations mark a major milestone in the creation of a new generation of quantum electronics.

cond-mat.mes-hall↗

arXiv2020

Ultra-fast calorimetric measurements of the electronic heat capacity of graphene

Heat capacity is an invaluable quantity in condensed matter physics, yet it has been so far experimentally inaccessible in two-dimensional (2D) van der Waals (vdW) materials, owing to their ultra-fast thermal relaxation times and the lack of suitable nano-scale thermometers. Here, we demonstrate a novel thermal relaxation calorimetry scheme that allows the first measurements of the electronic heat capacity of graphene Ce. It is enabled by the grouping of a radio-frequency Johnson noise thermometer, which can measure the electronic temperature Te with a measurement sensitivity of δTe ~ 20 mK, and an ultra-fast photo-mixed optical heater, which can simultaneously modulate Te with a frequency of up to Ω=0.2 THz. This combination allows record sensitive and record fast measurements of the electronic heat capacity Ce < 10^(-19) J/K, with an electronic thermal relaxation time τe < 10^(-13), representing orders of magnitude improvements as compared to previous state-of-the-art calorimeters. These features embody a breakthrough in heat capacity metrology of nano-scale and low-dimensional systems, and provide a new avenue for the investigation of their thermodynamic quantities.

cond-mat.mes-hall↗

arXiv2020

Untying the insulating and superconducting orders in magic-angle graphene

The coexistence of superconducting and correlated insulating states in magic-angle twisted bilayer graphene prompts fascinating questions about the relationship of these orders. Independent control of the microscopic mechanisms governing these phases could help uncover their individual roles and shed light on their intricate interplay. Here we report on direct tuning of electronic interactions in this system by changing its separation from a metallic screening layer. We observe quenching of correlated insula-tors in devices with screening layer separations that are smaller than a typical Wannier orbital size of 15nm, and with the twist angles slightly deviating from the magic value 1.10 plus(minus) 0.05 degrees. Upon extinction of the insulating orders, the vacated phase space is taken over by superconducting domes that feature critical temperatures comparable to those in the devices with strong insulators. In addition, we find that insulators at half-filling can reappear in small out-of-plane magnetic fields of 0.4 T, giving rise to quantized Hall states with a Chern number of 2. Our study suggests reexamination of the often-assumed mother-child relation between the insulating and superconducting phases in moire graphene, and illustrates a new approach to directly probe microscopic mechanisms of superconductivity in strongly-correlated systems.

cond-mat.supr-con↗

arXiv2020

Multiple Flat Bands and Topological Hofstadter Butterfly in Twisted Bilayer Graphene Close to the Second Magic Angle

Moiré superlattices in two-dimensional (2D) van der Waals (vdW) heterostructures provide 20 an efficient way to engineer electron band properties. The recent discovery of exotic quantum phases and their interplay in twisted bilayer graphene (tBLG) has built this moiré system one of the most renowned condensed matter platforms (1-10). So far the studies of tBLG has been mostly focused on the lowest two flat moiré bands at the first magic angle θm1 ~ 1.1°, leaving high-order moiré bands and magic angles largely unexplored. Here we report 25 an observation of multiple well-isolated flat moiré bands in tBLG close to the second magic angle θm2 ~ 0.5°, which cannot be explained without considering electron-election interactions. With high magnetic field magneto-transport measurements, we further reveal a qualitatively new, energetically unbound Hofstadter butterfly spectrum in which continuously extended quantized Landau level gaps cross all trivial band-gaps. The 30 connected Hofstadter butterfly strongly evidences the topologically nontrivial textures of the multiple moiré bands. Overall, our work provides a new perspective for understanding the quantum phases in tBLG and the fractal Hofstadter spectra of multiple topological bands.

cond-mat.mes-hall↗

arXiv2020

Visual-speech Synthesis of Exaggerated Corrective Feedback

To provide more discriminative feedback for the second language (L2) learners to better identify their mispronunciation, we propose a method for exaggerated visual-speech feedback in computer-assisted pronunciation training (CAPT). The speech exaggeration is realized by an emphatic speech generation neural network based on Tacotron, while the visual exaggeration is accomplished by ADC Viseme Blending, namely increasing Amplitude of movement, extending the phone's Duration and enhancing the color Contrast. User studies show that exaggerated feedback outperforms non-exaggerated version on helping learners with pronunciation identification and pronunciation improvement.

eess.AS↗

arXiv2020

STCNet: Spatio-Temporal Cross Network for Industrial Smoke Detection

Industrial smoke emissions present a serious threat to natural ecosystems and human health. Prior works have shown that using computer vision techniques to identify smoke is a low cost and convenient method. However, industrial smoke detection is a challenging task because industrial emission particles are often decay rapidly outside the stacks or facilities and steam is very similar to smoke. To overcome these problems, a novel Spatio-Temporal Cross Network (STCNet) is proposed to recognize industrial smoke emissions. The proposed STCNet involves a spatial pathway to extract texture features and a temporal pathway to capture smoke motion information. We assume that spatial and temporal pathway could guide each other. For example, the spatial path can easily recognize the obvious interference such as trees and buildings, and the temporal path can highlight the obscure traces of smoke movement. If the two pathways could guide each other, it will be helpful for the smoke detection performance. In addition, we design an efficient and concise spatio-temporal dual pyramid architecture to ensure better fusion of multi-scale spatiotemporal information. Finally, extensive experiments on public dataset show that our STCNet achieves clear improvements on the challenging RISE industrial smoke detection dataset against the best competitors by 6.2%. The code will be available at: https://github.com/Caoyichao/STCNet.

cs.CV↗

arXiv2020

Face Hallucination with Finishing Touches

Obtaining a high-quality frontal face image from a low-resolution (LR) non-frontal face image is primarily important for many facial analysis applications. However, mainstreams either focus on super-resolving near-frontal LR faces or frontalizing non-frontal high-resolution (HR) faces. It is desirable to perform both tasks seamlessly for daily-life unconstrained face images. In this paper, we present a novel Vivid Face Hallucination Generative Adversarial Network (VividGAN) for simultaneously super-resolving and frontalizing tiny non-frontal face images. VividGAN consists of coarse-level and fine-level Face Hallucination Networks (FHnet) and two discriminators, i.e., Coarse-D and Fine-D. The coarse-level FHnet generates a frontal coarse HR face and then the fine-level FHnet makes use of the facial component appearance prior, i.e., fine-grained facial components, to attain a frontal HR face image with authentic details. In the fine-level FHnet, we also design a facial component-aware module that adopts the facial geometry guidance as clues to accurately align and merge the frontal coarse HR face and prior information. Meanwhile, two-level discriminators are designed to capture both the global outline of a face image as well as detailed facial characteristics. The Coarse-D enforces the coarsely hallucinated faces to be upright and complete while the Fine-D focuses on the fine hallucinated ones for sharper details. Extensive experiments demonstrate that our VividGAN achieves photo-realistic frontal HR faces, reaching superior performance in downstream tasks, i.e., face recognition and expression classification, compared with other state-of-the-art methods.

cs.CV↗

arXiv2020

Meron-Like Topological Spin Defects in Monolayer CrCl3

Noncollinear spin textures in low-dimensional magnetic systems have been studied for decades because of their extraordinary properties and promising applications derived from the chirality and topological nature. However, material realizations of topological spin states are still limited. Employing first-principles and Monte Carlo simulations, we propose that monolayer chromium trichloride (CrCl3) can be a promising candidate for observing the vortex/antivortex type of topological defects, so-called merons. The numbers of vortices and antivortices are found to be the same, maintaining an overall integer topological unit. By perturbing with external magnetic fields, we show the robustness of these meron pairs and reveal a rich phase space to tune the hybridization between the ferromagnetic order and meron-like defects. The signatures of topological excitations under external magnetic field also provide crucial information for experimental justifications. Our study predicts that two-dimensional magnets with weak spin-orbit coupling can be a promising family for realizing meron-like spin textures.

cond-mat.mes-hall↗

arXiv2020

High-order minibands and interband Landau level reconstruction in graphene moire superlattice

The propagation of Dirac fermions in graphene through a long-period periodic potential would result in a band folding together with the emergence of a series of cloned Dirac points (DPs). In highly aligned graphene/hexagonal boron nitride (G/hBN) heterostructures, the lattice mismatch between the two atomic crystals generates a unique kind of periodic structure known as a moiré superlattice. Of particular interests is the emergent phenomena related to the reconstructed band-structure of graphene, such as the Hofstadter butterfly, topological currents, gate dependent pseudospin mixing, and ballistic miniband conduction. However, most studies so far have been limited to the lower-order minibands, e.g. the 1st and 2nd minibands counted from charge neutrality, and consequently the fundamental nature of the reconstructed higher-order miniband spectra still remains largely unknown. Here we report on probing the higher-order minibands of precisely aligned graphene moiré superlattices by transport spectroscopy. Using dual electrostatic gating, the edges of these high-order minibands, i.e. the 3rd and 4th minibands, can be reached. Interestingly, we have observed interband Landau level (LL) crossinginducing gap closures in a multiband magneto-transport regime, which originates from band overlap between the 2nd and 3rd minibands. As observed high-order minibands and LL reconstruction qualitatively match our simulated results. Our findings highlight the synergistic effect of minibands in transport, thus presenting a new opportunity for graphene electronic devices.

cond-mat.mes-hall↗

arXiv2020

Symmetry broken Chern insulators and magic series of Rashba-like Landau level crossings in magic angle bilayer graphene

Flat-bands in magic angle twisted bilayer graphene (MATBG) have recently emerged as a rich platform to explore strong correlations, superconductivity and mag-netism. However, the phases of MATBG in magnetic field, and what they reveal about the zero-field phase diagram remain relatively unchartered. Here we use magneto-transport and Hall measurements to reveal a rich sequence of wedge-like regions of quantized Hall conductance with Chern numbers C = +(-)1, +(-)2, +(-)3, +(-)4 which nucleate from integer fillings of the moire unit cell v = +(-)3, +(-)2, +(-)1, 0 correspondingly. We interpret these phases as spin and valley polarized Chern insulators, equivalent to quantum Hall ferro-magnets. The exact sequence and correspondence of Chern numbers and filling factors suggest that these states are driven directly by electronic interactions which specifically break time-reversal symmetry in the system. We further study quantum magneto-oscillation in the yet unexplored higher energy dispersive bands with a Rashba-like dis-persion. Analysis of Landau level crossings enables a parameter-free comparison to a newly derived magic series of level crossings in magnetic field and provides constraints on the parameters w0 and w1 of the Bistritzer-MacDonald MATBG Hamiltonian. Over-all, our data provides direct insights into the complex nature of symmetry breaking in MATBG and allows for quantitative tests of the proposed microscopic scenarios for its electronic phases.

cond-mat.str-el↗

arXiv2020

Modulation Doping via a 2d Atomic Crystalline Acceptor

Two-dimensional (2d) nano-electronics, plasmonics, and emergent phases require clean and local charge control, calling for layered, crystalline acceptors or donors. Our Raman, photovoltage, and electrical conductance measurements combined with \textit{ab initio} calculations establish the large work function and narrow bands of $α$-RuCl$_3$ enable modulation doping of exfoliated, chemical vapor deposition (CVD), and molecular beam epitaxy (MBE) materials. Short-ranged lateral doping (${\leq}65\ \text{nm}$) and high homogeneity are achieved in proximate materials with a single layer of \arucl. This leads to the highest monolayer graphene (mlg) mobilities ($4,900\ \text{cm}^2/ \text{Vs}$) at these high hole densities ($3\times10^{13}\ \text{cm}^{-2}$); and yields larger charge transfer to bilayer graphene (blg) ($6\times10^{13}\ \text{cm}^{-2}$). We further demonstrate proof of principle optical sensing, control via twist angle, and charge transfer through hexagonal boron nitride (hBN).

cond-mat.mtrl-sci↗

arXiv2020

Intrinsic Spin Photogalvanic Effect in Nonmagnetic Insulator

We show that with the help of spin-orbit coupling, nonlinear light-matter interactions can efficiently couple with spin and valley degrees of freedom. This revealed spin photogalvanic effect can generate the long-time pursued intrinsic pure spin current (PSC) in non-centrosymmetric nonmagnetic insulators. Different from the spin and valley Hall effect, such a photo-driven spin current is universal and can be generated without external bias field. Using first-principles simulation, we study monolayer transition metal dichalcogenides (TMDs) to demonstrate this effect and confirm an enhanced PSC under linearly polarized photoexcitation. The amplitude of the PSC is one order larger than that of the charge current observed in monolayer TMDs. This exotic nonlinear light-spin interaction indicates that light can be utilized as a rapid fashion to manipulate the spin-polarized current, which is crucial for future low-dissipation nanodevices.

cond-mat.mtrl-sci↗

arXiv2020

Improved YOLOv3 Object Classification in Intelligent Transportation System

The technology of vehicle and driver detection in Intelligent Transportation System(ITS) is a hot topic in recent years. In particular, the driver detection is still a challenging problem which is conductive to supervising traffic order and maintaining public safety. In this paper, an algorithm based on YOLOv3 is proposed to realize the detection and classification of vehicles, drivers, and people on the highway, so as to achieve the purpose of distinguishing driver and passenger and form a one-to-one correspondence between vehicles and drivers. The proposed model and contrast experiment are conducted on our self-build traffic driver's face database. The effectiveness of our proposed algorithm is validated by extensive experiments and verified under various complex highway conditions. Compared with other advanced vehicle and driver detection technologies, the model has a good performance and is robust to road blocking, different attitudes, and extreme lighting.

cs.CV↗

arXiv2020

Deep Attentive Generative Adversarial Network for Photo-Realistic Image De-Quantization

Most of current display devices are with eight or higher bit-depth. However, the quality of most multimedia tools cannot achieve this bit-depth standard for the generating images. De-quantization can improve the visual quality of low bit-depth image to display on high bit-depth screen. This paper proposes DAGAN algorithm to perform super-resolution on image intensity resolution, which is orthogonal to the spatial resolution, realizing photo-realistic de-quantization via an end-to-end learning pattern. Until now, this is the first attempt to apply Generative Adversarial Network (GAN) framework for image de-quantization. Specifically, we propose the Dense Residual Self-attention (DenseResAtt) module, which is consisted of dense residual blocks armed with self-attention mechanism, to pay more attention on high-frequency information. Moreover, the series connection of sequential DenseResAtt modules forms deep attentive network with superior discriminative learning ability in image de-quantization, modeling representative feature maps to recover as much useful information as possible. In addition, due to the adversarial learning framework can reliably produce high quality natural images, the specified content loss as well as the adversarial loss are back-propagated to optimize the training of model. Above all, DAGAN is able to generate the photo-realistic high bit-depth image without banding artifacts. Experiment results on several public benchmarks prove that the DAGAN algorithm possesses ability to achieve excellent visual effect and satisfied quantitative performance.

eess.IV↗

arXiv2020

Adaptive Multiscale Illumination-Invariant Feature Representation for Undersampled Face Recognition

This paper presents an novel illumination-invariant feature representation approach used to eliminate the varying illumination affection in undersampled face recognition. Firstly, a new illumination level classification technique based on Singular Value Decomposition (SVD) is proposed to judge the illumination level of input image. Secondly, we construct the logarithm edgemaps feature (LEF) based on lambertian model and local near neighbor feature of the face image, applying to local region within multiple scales. Then, the illumination level is referenced to construct the high performance LEF as well realize adaptive fusion for multiple scales LEFs for the face image, performing JLEF-feature. In addition, the constrain operation is used to remove the useless high-frequency interference, disentangling useful facial feature edges and constructing AJLEF-face. Finally, the effects of the our methods and other state-of-the-art algorithms including deep learning methods are tested on Extended Yale B, CMU PIE, AR as well as our Self-build Driver database (SDB). The experimental results demonstrate that the JLEF-feature and AJLEF-face outperform other related approaches for undersampled face recognition under varying illumination.

cs.CV↗

arXiv2020

Copy and Paste GAN: Face Hallucination from Shaded Thumbnails

Existing face hallucination methods based on convolutional neural networks (CNN) have achieved impressive performance on low-resolution (LR) faces in a normal illumination condition. However, their performance degrades dramatically when LR faces are captured in low or non-uniform illumination conditions. This paper proposes a Copy and Paste Generative Adversarial Network (CPGAN) to recover authentic high-resolution (HR) face images while compensating for low and non-uniform illumination. To this end, we develop two key components in our CPGAN: internal and external Copy and Paste nets (CPnets). Specifically, our internal CPnet exploits facial information residing in the input image to enhance facial details; while our external CPnet leverages an external HR face for illumination compensation. A new illumination compensation loss is thus developed to capture illumination from the external guided face image effectively. Furthermore, our method offsets illumination and upsamples facial details alternately in a coarse-to-fine fashion, thus alleviating the correspondence ambiguity between LR inputs and external HR inputs. Extensive experiments demonstrate that our method manifests authentic HR face images in a uniform illumination condition and outperforms state-of-the-art methods qualitatively and quantitatively.

cs.CV↗