arXiv subjects

Zhan Zhang

Publications and source records attributed to Zhan Zhang.

At least 55 records · Page 3Linked to original sources

arXiv2021

Observation of coexisting weak localization and superconducting fluctuations in strained Sn1-xInxTe thin films

Topological superconductors have attracted tremendous excitement as they are predicted to host Majorana zero modes that can be utilized for topological quantum computing. Candidate topological superconductor Sn1-xInxTe thin films (0<x<0.3) grown by molecular beam epitaxy and strained in the (111) plane are shown to host three coexisting quantum effects: localization, antilocalization and superconducting fluctuations above the critical temperature Tc. An analysis of the normal state magnetoresistance reveals these effects. Weak localization is consistently observed in superconducting samples, indicating that superconductivity originates dominantly from trivial valence band states that may be strongly spin-orbit split. A large enhancement of the conductivity is observed above Tc, indicating that quantum coherent quasiparticle effects coexist with superconducting fluctuations. Our results motivate a re-examination of the debated pairing symmetry of this material when subjected to quantum confinement and lattice strain.

cond-mat.supr-con↗

arXiv2021

Effect of Buffer Termination on Intermixing and Conductivity in LaTiO$_3$/SrTiO$_3$ Heterostructures Integrated on Si(100)

The control of chemical exchange across heterointerfaces formed between ultra-thin functional transition-metal oxide layers provides an effective route to manipulate the electronic properties of these systems. We show that cationic exchange across the interface between the Mott insulator, LaTiO$_3$(LTO) grown epitaxially on SrTiO$_3$(STO)-buffered Silicon by molecular beam epitaxy depends strongly on the surface termination of the strained STO buffer. Using a combination of temperature-dependent transport and synchrotron X-ray crystal truncation rods and reciprocal space mapping, an enhanced conductivity in STO/LTO/SrO- terminated STO buffers compared to heterostructures with TiO$_2$-terminated STO buffers is correlated with La/Sr exchange and the formation of metallic La$_{1-x}$Sr$_x$TiO$_3$. La/Sr exchange effectively reduces the strain energy of the system due to the large lattice mismatch between the nominal oxide layers and the Si substrate.

cond-mat.mtrl-sci↗

arXiv2021

Gaze-Contingent Retinal Speckle Suppression for Perceptually-Matched Foveated Holographic Displays

Computer-generated holographic (CGH) displays show great potential and are emerging as the next-generation displays for augmented and virtual reality, and automotive heads-up displays. One of the critical problems harming the wide adoption of such displays is the presence of speckle noise inherent to holography, that compromises its quality by introducing perceptible artifacts. Although speckle noise suppression has been an active research area, the previous works have not considered the perceptual characteristics of the Human Visual System (HVS), which receives the final displayed imagery. However, it is well studied that the sensitivity of the HVS is not uniform across the visual field, which has led to gaze-contingent rendering schemes for maximizing the perceptual quality in various computer-generated imagery. Inspired by this, we present the first method that reduces the "perceived speckle noise" by integrating foveal and peripheral vision characteristics of the HVS, along with the retinal point spread function, into the phase hologram computation. Specifically, we introduce the anatomical and statistical retinal receptor distribution into our computational hologram optimization, which places a higher priority on reducing the perceived foveal speckle noise while being adaptable to any individual's optical aberration on the retina. Our method demonstrates superior perceptual quality on our emulated holographic display. Our evaluations with objective measurements and subjective studies demonstrate a significant reduction of the human perceived noise.

cs.HC↗

arXiv2021

Room-temperature continuous-wave Dirac-vortex topological lasers on silicon

Robust laser sources are a fundamental building block for contemporary information technologies. Originating from condensed-matter physics, the concept of topology has recently entered the realm of optics, offering fundamentally new design principles for lasers with enhanced robustness. In analogy to the well-known Majorana fermions in topological superconductors, Dirac-vortex states have recently been investigated in passive photonic systems and are now considered as a promising candidate for single-mode large-area lasers. Here, we experimentally realize the first Dirac-vortex topological lasers in InAs/InGaAs quantum-dot materials monolithically grown on a silicon substrate. We observe room-temperature continuous-wave single-mode linearly polarized vertical laser emission at a telecom wavelength. Most importantly, we confirm that the wavelength of the Dirac-vortex laser is topologically robust against variations in the cavity size, and its free spectral range defies the universal inverse scaling law with the cavity size. These lasers will play an important role in CMOS-compatible photonic and optoelectronic systems on a chip.

physics.optics↗

arXiv2021

Accent Recognition with Hybrid Phonetic Features

The performance of voice-controlled systems is usually influenced by accented speech. To make these systems more robust, the frontend accent recognition (AR) technologies have received increased attention in recent years. As accent is a high-level abstract feature that has a profound relationship with the language knowledge, AR is more challenging than other language-agnostic audio classification tasks. In this paper, we use an auxiliary automatic speech recognition (ASR) task to extract language-related phonetic features. Furthermore, we propose a hybrid structure that incorporates the embeddings of both a fixed acoustic model and a trainable acoustic model, making the language-related acoustic feature more robust. We conduct several experiments on the Accented English Speech Recognition Challenge (AESRC) 2020 dataset. The results demonstrate that our approach can obtain a 6.57% relative improvement on the validation set. We also get a 7.28% relative improvement on the final test set for this competition, showing the merits of the proposed method.

eess.AS↗

arXiv2021

Text-Conditioned Transformer for Automatic Pronunciation Error Detection

Automatic pronunciation error detection (APED) plays an important role in the domain of language learning. As for the previous ASR-based APED methods, the decoded results need to be aligned with the target text so that the errors can be found out. However, since the decoding process and the alignment process are independent, the prior knowledge about the target text is not fully utilized. In this paper, we propose to use the target text as an extra condition for the Transformer backbone to handle the APED task. The proposed method can output the error states with consideration of the relationship between the input speech and the target text in a fully end-to-end fashion.Meanwhile, as the prior target text is used as a condition for the decoder input, the Transformer works in a feed-forward manner instead of autoregressive in the inference stage, which can significantly boost the speed in the actual deployment. We set the ASR-based Transformer as the baseline APED model and conduct several experiments on the L2-Arctic dataset. The results demonstrate that our approach can obtain 8.4\% relative improvement on the $F_1$ score metric.

eess.AS↗

arXiv2021

CASS: Towards Building a Social-Support Chatbot for Online Health Community

Chatbots systems, despite their popularity in today's HCI and CSCW research, fall short for one of the two reasons: 1) many of the systems use a rule-based dialog flow, thus they can only respond to a limited number of pre-defined inputs with pre-scripted responses; or 2) they are designed with a focus on single-user scenarios, thus it is unclear how these systems may affect other users or the community. In this paper, we develop a generalizable chatbot architecture (CASS) to provide social support for community members in an online health community. The CASS architecture is based on advanced neural network algorithms, thus it can handle new inputs from users and generate a variety of responses to them. CASS is also generalizable as it can be easily migrate to other online communities. With a follow-up field experiment, CASS is proven useful in supporting individual members who seek emotional support. Our work also contributes to fill the research gap on how a chatbot may influence the whole community's engagement.

cs.HC↗

arXiv2021

"Brilliant AI Doctor" in Rural China: Tensions and Challenges in AI-Powered CDSS Deployment

Artificial intelligence (AI) technology has been increasingly used in the implementation of advanced Clinical Decision Support Systems (CDSS). Research demonstrated the potential usefulness of AI-powered CDSS (AI-CDSS) in clinical decision making scenarios. However, post-adoption user perception and experience remain understudied, especially in developing countries. Through observations and interviews with 22 clinicians from 6 rural clinics in China, this paper reports the various tensions between the design of an AI-CDSS system ("Brilliant Doctor") and the rural clinical context, such as the misalignment with local context and workflow, the technical limitations and usability barriers, as well as issues related to transparency and trustworthiness of AI-CDSS. Despite these tensions, all participants expressed positive attitudes toward the future of AI-CDSS, especially acting as "a doctor's AI assistant" to realize a Human-AI Collaboration future in clinical settings. Finally we draw on our findings to discuss implications for designing AI-CDSS interventions for rural clinical contexts in developing countries.

cs.HC↗

arXiv2020

Interface-driven magnetic anisotropy in relaxed La$_{0.7}$Sr$_{0.3}$CrO$_3$/La$_{0.7}$Sr$_{0.3}$MnO$_3$ heterostructures on MgO

We investigate the structural and magnetic properties of La$_{0.7}$Sr$_{0.3}$CrO$_3$ (LSCO)/La$_{0.7}$Sr$_{0.3}$MnO$_3$(LSMO) heterostructures grown on (001)-oriented MgO by molecular beam epitaxy. Due to the large film-substrate lattice mismatch, strain relaxation is found to occur within the first 2-3 unit cells (uc) of the film as evidenced by reflection high energy electron diffraction and high-resolution synchrotron X-ray reciprocal space mapping. We find that the presence of the LSCO spacer and capping layers leads to ferromagnetism in ultra-thin LSMO layers with thicknesses on the order of 2 uc with the magnetic easy axis oriented in the film plane. Net magnetic moments of 1.4 and 2.4 $μ_B$/Mn are measured for [2 uc LSCO/ 2 uc LSMO] and [2 uc LSCO/ 4 uc LSMO] superlattices, respectively by SQUID magnetometry. The effective magnetic anisotropy of the relaxed [2 uc LSCO/ 4 uc LSMO] heterostructure is found to be an order of magnitude higher than bulk LSMO highlighting the critical role of interfacial magnetic exchange interactions in tuning magnetic anisotropy at complex oxide interfaces.

cond-mat.mtrl-sci↗

arXiv2020

Pressure-dependent Intermediate Magnetic Phase in Thin Fe$_3$GeTe$_2$ Flakes

We investigated the evolution of ferromagnetism in layered Fe$_3$GeTe$_2$ flakes under different pressures and temperatures using in situ magnetic circular dichroism (MCD) spectroscopy. We found that the rectangle shape of hysteretic loop under an out-of-plane magnetic field sweep can sustain below 7 GPa. Above that pressure, an intermediate state appears at low temperature region signaled by an 8-shaped skew hysteretic loop. Meanwhile, the coercive field and Curie temperature decrease with increasing pressures, implying the decrease of the exchange interaction and the magneto-crystalline anisotropy under pressures. The intermediate phase has a labyrinthine domain structure, which is attributed to the increase of ratio of exchange interaction to magneto-crystalline anisotropy based on Jagla's theory. Moreover, our calculation results reveal a weak structural transition around 6 GPa, which leads to a drop of the magnetic momentum of Fe ions.

cond-mat.mes-hall↗

arXiv2019

First performance test of 30 mm iron-based superconductor single pancake coil under 24 T background field

The iron-based superconductor (IBS) single pancake coil (SPC) with 30 mm inner diameter was firstly fabricated and tested under 24 T background field. This SPC was successfully made using the 7-filamentary Ba1-xKxFe2As2 (Ba122) tape by wind-and-react method. This IBS coil show the highest Ic value at magnetic field reported so far. For example, the transport critical current of this Ba122 SPC achieved 35 A at 4.2 K and 10 T, which is about half of that of short sample. This indicates that the non-insulation winding process together with the stainless-steel tape is suitable to the iron-based superconductor. Even more encouraging is the fact that the Ic of this SPC is still as high as 26 A under 24 T background field, which is still about 40% of that at zero external magnetic field. These results clearly demonstrate that the iron-based superconductors are very promising for high-field magnet applications.

cond-mat.supr-con↗

arXiv2018

Reducing the Upfront Cost of Private Clouds with Clairvoyant Virtual Machine Placement

Although public clouds still occupy the largest portion of the total cloud infrastructure, private clouds are attracting increasing interest from both industry and academia because of their better security and privacy control. According to the existing studies, the high upfront cost is among the most critical challenges associated with private clouds. To reduce cost and improve performance, virtual machine placement (VMP) methods have been extensively investigated, however, few of these methods have focused on private clouds. This paper proposes a heterogeneous and multidimensional clairvoyant dynamic bin packing (CDBP) model, in which the scheduler can conduct more efficient VMP processes using additional information on the arrival time and duration of virtual machines to reduce the datacenter scale and thereby decrease the upfront cost of private clouds. In addition, a novel branch-and-bound algorithm with a divide-and-conquer strategy (DCBB) is proposed to effectively and efficiently handle the derived problem. One state-of-the-art and several classic VMP methods are also modified to adapt to the proposed model to observe their performance and compare with our proposed algorithm. Extensive experiments are conducted on both real-world and synthetic workloads to evaluate the accuracy and efficiency of the algorithms. The experimental results demonstrate that DCBB delivers near-optimal solutions with a convergence rate that is much faster than those of the other search-based algorithms evaluated. In particular, DCBB yields the optimal solution for a real-world workload with an execution time that is an order of magnitude shorter than that required by the original branch-and-bound (BB) algorithm.

cs.DC↗

arXiv2018

Joint Estimation of DOA and Frequency with Sub-Nyquist Sampling in a Binary Array Radar System

Recently, several array radar structures combined with sub-Nyquist techniques and corresponding algorithms have been extensively studied. Carrier frequency and direction-of-arrival (DOA) estimations of multiple narrow-band signals received by array radars at the sub-Nyquist rates are considered in this paper. We propose a new sub-Nyquist array radar architecture (a binary array radar separately connected to a multi-coset structure with M branches) and an efficient joint estimation algorithm which can match frequencies up with corresponding DOAs. We further come up with a delay pattern augmenting method, by which the capability of the number of identifiable signals can increase from M-1 to Q-1 (Q is extended degrees of freedom). We further conclude that the minimum total sampling rate 2MB is sufficient to identify $ {K \leq Q-1}$ narrow-band signals of maximum bandwidth $B$ inside. The effectiveness and performance of the estimation algorithm together with the augmenting method have been verified by simulations.

eess.SP↗

arXiv2018

Computer-Aided Diagnosis of Label-Free 3-D Optical Coherence Microscopy Images of Human Cervical Tissue

Objective: Ultrahigh-resolution optical coherence microscopy (OCM) has recently demonstrated its potential for accurate diagnosis of human cervical diseases. One major challenge for clinical adoption, however, is the steep learning curve clinicians need to overcome to interpret OCM images. Developing an intelligent technique for computer-aided diagnosis (CADx) to accurately interpret OCM images will facilitate clinical adoption of the technology and improve patient care. Methods: 497 high-resolution 3-D OCM volumes (600 cross-sectional images each) were collected from 159 ex vivo specimens of 92 female patients. OCM image features were extracted using a convolutional neural network (CNN) model, concatenated with patient information (e.g., age, HPV results), and classified using a support vector machine classifier. Ten-fold cross-validations were utilized to test the performance of the CADx method in a five-class classification task and a binary classification task. Results: An 88.3 plus or minus 4.9% classification accuracy was achieved for five fine-grained classes of cervical tissue, namely normal, ectropion, low-grade and high-grade squamous intraepithelial lesions (LSIL and HSIL), and cancer. In the binary classification task (low-risk [normal, ectropion and LSIL] vs. high-risk [HSIL and cancer]), the CADx method achieved an area-under-the-curve (AUC) value of 0.959 with an 86.7 plus or minus 11.4% sensitivity and 93.5 plus or minus 3.8% specificity. Conclusion: The proposed deep-learning based CADx method outperformed three human experts. It was also able to identify morphological characteristics in OCM images that were consistent with histopathological interpretations. Significance: Label-free OCM imaging, combined with deep-learning based CADx methods, hold a great promise to be used in clinical settings for the effective screening and diagnosis of cervical diseases.

cs.CV↗

arXiv2018

Interfacial Structure of SrZr$_{x}$Ti$_{1-x}$O$_3$ films on Ge

The interfacial structure of SrZr$_{x}$Ti$_{1-x}$O$_3$ films grown on semiconducting Ge substrates are investigated by synchrotron X-ray diffraction and first-principles density functional theory. By systematically tuning Zr content x, the effects of bonding at the interface and epitaxial strain on the physical structure of the film can be distinguished. The interfacial perovskite layers are found to be polarized as a result of cation-anion ionic displacements perpendicular to the perovskite/semiconductor interface. We find a correlation between the observed buckling and valence band offsets at the SrZr$_{x}$Ti$_{1-x}$O$_3$/Ge interface. The theoretical valence band offsets for the polar structures are in agreement with reported X-ray photoelectron spectroscopy measurements. These results have important implications for the integration of functional oxide materials with established semiconductor based technologies.

cond-mat.mtrl-sci↗

arXiv2018

Bolt Detection Signal Analysis Method Based on ICEEMD

The construction quality of the bolt is directly related to the safety of the project, and as such, it must be tested. In this paper, the improved complete ensemble empirical mode decomposition (ICEEMD) method is introduced to the bolt detection signal analysis. The ICEEMD is used in order to decompose the anchor detection signal according to the approximate entropy of each intrinsic mode function (IMF). The noise of the IMFs is eliminated by the wavelet soft threshold de-noising technique. Based on the approximate entropy, and the wavelet de-noising principle, the ICEEMD-De anchor signal analysis method is proposed. From the analysis of the vibration analog signal, as well as the bolt detection signal, the result shows that the ICEEMD-De method is capable of correctly separating the different IMFs under noisy conditions, and also that the IMF can effectively identify the reflection signal of the end of the bolt.

eess.SP↗

arXiv2016

A deep learning approach for predicting the quality of online health expert question-answering services

Currently, a growing number of health consumers are asking health-related questions online, at any time and from anywhere, which effectively lowers the cost of health care. The most common approach is using online health expert question-answering (HQA) services, as health consumers are more willing to trust answers from professional physicians. However, these answers can be of varying quality depending on circumstance. In addition, as the available HQA services grow, how to predict the answer quality of HQA services via machine learning becomes increasingly important and challenging. In an HQA service, answers are normally short texts, which are severely affected by the data sparsity problem. Furthermore, HQA services lack community features such as best answer and user votes. Therefore, the wisdom of the crowd is not available to rate answer quality. To address these problems, in this paper, the prediction of HQA answer quality is defined as a classification task. First, based on the characteristics of HQA services and feedback from medical experts, a standard for HQA service answer quality evaluation is defined. Next, based on the characteristics of HQA services, several novel non-textual features are proposed, including surface linguistic features and social features. Finally, a deep belief network (DBN)-based HQA answer quality prediction framework is proposed to predict the quality of answers by learning the high-level hidden semantic representation from the physicians' answers. Our results prove that the proposed framework overcomes the problem of overly sparse textual features in short text answers and effectively identifies high-quality answers.

cs.IR↗

arXiv2016

Correlated Disorder in van der Waals Heterostructures

The individual building blocks of van der Waals (vdW) heterostructures host fascinating physical phenomena, ranging from ballistic electron transport in graphene to striking optical properties of MoSe2 sheets. The presence of bonded and non-bonded cohesive interactions in a vdW heterostructure, promotes diversity in their structural arrangements, which in turn profoundly modulate the properties of their individual constituents. Here, we report on the presence of correlated structural disorder coexisting with the nearly perfect crystallographic order along the growth direction of epitaxial vdW heterostructures of Bi2Se3/graphene/SiC. Using the depth penetration of X-ray diffraction microscopy and scattering, we probed their crystal structure from atomic to mesoscopic length scales, to reveal that their structural diversity is underpinned by spatially correlated disorder states. The presence of the latter induces on a system, widely considered to behave as a collection of nearly independent 2-dimensional units, a pseudo-3-dimensional character, when subjected to epitaxial constraints and ordered substrate interactions. These findings shed new light on the nature of the vast structural landscape of vdW heterostructures and could enable new avenues in modulating their unique properties by correlated disorder.

cond-mat.dis-nn↗