arXiv subjects

Zhenyu Wang

Publications and source records attributed to Zhenyu Wang.

At least 199 records · Page 11Linked to original sources

arXiv2020

Super-resolved optical mapping of reactive sulfur-vacancy in 2D transition metal dichalcogenides

Transition metal dichalcogenides (TMDs) represent an entire new class of semiconducting 2D materials with exciting properties. Defects in 2D TMDs can crucially affect their physical and chemical properties. However, characterization of the presence and spatial distribution of defects is limited either in throughput or in resolution. Here, we demonstrate large area mapping of reactive sulfur-deficient defects in 2D-TMDs coupling single-molecule localization microscopy with fluorescence labeling using thiol chemistry. Our method, reminiscent of PAINT strategies, relies on the specific binding by reversible physisorption of fluorescent probes to sulfur-vacancies via a thiol group and their intermittent emission to apply localization of the labeled defects with a precision down to 15 nm. Tuning the distance between the fluorophore and the docking thiol site allows us to control Föster Resonance Energy Transfer (FRET) process and reveal large structural defects such as grain boundaries and line defects, due to the local irregular lattice structure. Our methodology provides a simple and fast alternative for large-scale mapping of non-radiative defects in 2D materials and paves the way for in-situ and spatially resolved monitoring of the interaction between chemical agent and the defects in 2D materials that has general implications for defect engineering in aqueous condition.

physics.app-ph↗

arXiv2020

Effect of Dzyaloshinskii-Moriya interaction on magnetic vortex switching driven by radial spin waves

We theoretically investigate the radial-spin-wave induced magnetic vortex switching in the presence of Dzyaloshinskii-Moriya interaction (DMI). From micromagnetic simulations, we observe a circular-to-radial vortex phase transition by increasing the DMI strength. The radial spin-wave excitation spectrum for each magnetization configuration is analyzed, showing that the frequency of spin-wave mode with a given radial node number monotonically increases (decreases) with the DMI parameter of the radial (circular) vortex. Interestingly, we find that the DMI can significantly facilitate the polarity switching of the circular vortex driven by radial spin waves. Our work provides a new insight into the DMI effect on the vortex dynamics and is helpful for designing fast all-magnonic memory devices.

cond-mat.mes-hall↗

arXiv2020

Twisted magnon as a magnetic tweezer

Wave fields with spiral phase dislocations carrying orbital angular momentum (OAM) have been realized in many branches of physics, such as for photons, sound waves, electron beams, and neutrons. However, the OAM states of magnons (spin waves)$-$the building block of modern magnetism$-$and particularly their implications have yet to be addressed. Here, we theoretically investigate the twisted spin-wave generation and propagation in magnetic nanocylinders. The OAM nature of magnons is uncovered by showing that the spin-wave eigenmode is also the eigenstate of the OAM operator in the confined geometry. Inspired by optical tweezers, we predict an exotic "magnetic tweezer" effect by showing skyrmion gyrations under twisted magnons in exchange coupled nanocylinder$|$nanodisk heterostructure, as a practical demonstration of magnonic OAM to manipulate topological spin defects. Our study paves the way for the emerging magnetic manipulations by harnessing the OAM degree of freedom of magnons.

cond-mat.mes-hall↗

arXiv2020

AIBench: An Agile Domain-specific Benchmarking Methodology and an AI Benchmark Suite

Domain-specific software and hardware co-design is encouraging as it is much easier to achieve efficiency for fewer tasks. Agile domain-specific benchmarking speeds up the process as it provides not only relevant design inputs but also relevant metrics, and tools. Unfortunately, modern workloads like Big data, AI, and Internet services dwarf the traditional one in terms of code size, deployment scale, and execution path, and hence raise serious benchmarking challenges. This paper proposes an agile domain-specific benchmarking methodology. Together with seventeen industry partners, we identify ten important end-to-end application scenarios, among which sixteen representative AI tasks are distilled as the AI component benchmarks. We propose the permutations of essential AI and non-AI component benchmarks as end-to-end benchmarks. An end-to-end benchmark is a distillation of the essential attributes of an industry-scale application. We design and implement a highly extensible, configurable, and flexible benchmark framework, on the basis of which, we propose the guideline for building end-to-end benchmarks, and present the first end-to-end Internet service AI benchmark. The preliminary evaluation shows the value of our benchmark suite---AIBench against MLPerf and TailBench for hardware and software designers, micro-architectural researchers, and code developers. The specifications, source code, testbed, and results are publicly available from the web site \url{http://www.benchcouncil.org/AIBench/index.html}.

cs.PF↗

arXiv2020

Momentum Resolved Superconducting Energy Gaps of Sr$_2$RuO$_4$ from Quasiparticle Interference Imaging

Sr$_2$RuO$_4$ has long been the focus of intense research interest because of conjectures that it is a correlated topological superconductor. It is the momentum space (k-space) structure of the superconducting energy gap $Δ_i(\mathbf{k})$ on each band $i$ that encodes its unknown superconducting order-parameter. But, because the energy scales are so low, it has never been possible to directly measure the $Δ_i(\mathbf{k})$ of Sr$_2$RuO$_4$. Here we implement Bogoliubov quasiparticle interference (BQPI) imaging, a technique capable of high-precision measurement of multiband $Δ_i(\mathbf{k})$. At T=90 mK we visualize a set of Bogoliubov scattering interference wavevectors $q_j:j=1-5$ consistent with eight gap nodes/minima, that are all closely aligned to the $(\pm1,\pm1)$ crystal-lattice directions on both the $α$-and $β$-bands. Taking these observations in combination with other very recent advances in directional thermal conductivity (E. Hassinger et al. Phys. Rev. X 7, 011032 (2017)), temperature dependent Knight shift (A. Pustogow et al. Nature 574, 72 (2019)), time-reversal symmetry conservation (S. Kashiwaya et al. arXiv:1907.030939) and theory (A.T. Romer et al. Phys. Rev. Lett. 123, 247001 (2019); H. S. Roising et al. Phys. Rev. Research 1, 033108 (2019),O. Gingras et al. Phys. Rev. Lett. 123, 217005 (2019)), the BQPI signature of Sr$_2$RuO$_4$ appears most consistent with $Δ_i(\mathbf{k})$ having $d_{x^2-y^2}$ $(B_{1g})$ symmetry.

cond-mat.supr-con↗

arXiv2020

Off-axial focusing of spin-wave lens in the presence of Dzyaloshinskii-Moriya interaction

We theoretically study the effect of Dzyaloshinskii-Moriya interaction (DMI) on the focusing of a spin-wave lens that is constructed by a circular interface between two magnetic films. We analytically derive the generalized Snell's law in the curved geometry and the position of the focal point which exhibits a peculiar off-axial focusing behavior. We uncover a strong dependence of the focal point on both the material parameters and the frequency of incident spin waves. Full micromagnetic simulations compare well with theoretical predictions. Our findings would be helpful to manipulate spin waves in chiral magnets and to design functional magnonic devices.

cond-mat.mes-hall↗

arXiv2020

Mapping the Dirac fermions in intrinsic antiferromagnetic topological insulators (MnBi$_2$Te$_4$)(Bi$_2$Te$_3$)$_n$ (n=0, 1)

Topological surface states with intrinsic magnetic ordering in the MnBi$_2$Te$_4$(Bi$_2$Te$_3$)$_n$ compounds have been predicted to host rich topological phenomena including quantized anomalous Hall effect and axion insulator state. Here we use scanning tunneling microscopy to image the surface Dirac fermions in MnBi$_2$Te$_4$ and MnBi$_4$Te$_7$. We have determined the energy dispersion and helical spin texture of the surface states through quasiparticle interference patterns far above Dirac energy, which confirms its topological nature. Approaching the Dirac point, the native defects in the MnBi$_2$Te$_4$ septuple layer give rise to resonance states which extend spatially and potentially hinder the detection of a mass gap in the spectra. Our results demonstrate that regulating defects is essential to realize exotic topological states at higher temperatures in these compounds.

cond-mat.mtrl-sci↗

arXiv2019

Microscopic evidence for a chiral superconducting order parameter in the heavy fermion superconductor UTe2

Spin-triplet superconductivity is a condensate of electron pairs with spin-1 and an odd-parity wavefunction. A particularly interesting manifestation of triplet pairing is a chiral p-wave state which is topologically non-trivial and a natural platform for realizing Majorana edge modes. Triplet pairing is however rare in solid state systems and so far, no unambiguous identification has been made in any bulk compound. Since pairing is most naturally mediated by ferromagnetic spin fluctuations, uranium based heavy fermion systems containing f electron elements that can harbor both strong correlations and magnetism are considered ideal candidate spin-triplet superconductors. In this work we present scanning tunneling microscopy (STM) studies of the newly discovered heavy fermion superconductor, UTe2 with a T$_{SC}$ of 1.6 K. We find signatures of coexisting Kondo effect and superconductivity which show competing spatial modulations within one unit-cell. STM spectroscopy at step edges show signatures of chiral in-gap states, predicted to exist at the boundaries of a topological superconductor. Combined with existing data indicating triplet pairing, the presence of chiral edge states suggests that UTe2 is a strong candidate material for chiral-triplet topological superconductivity.

cond-mat.mtrl-sci↗

arXiv2019

Signature of Dispersing 1D Majorana Channels in an Iron-based Superconductor

The possible realization of Majorana fermions as quasiparticle excitations in condensed matter physics has created much excitement. Most recent studies have focused on Majorana bound states which can serve as topological qubits. More generally, akin to elementary particles, Majorana fermions can propagate and display linear dispersion. These excitations have not yet been directly observed, and can also be used for quantum information processing. One route to realizing this is in a line junction between two phase-shifted superconductors coupled to topological surface states. Recent theory indicates that in iron-based superconductors, a particular type of crystalline defect, i.e., a domain wall (DW) between two regions with a half-unit cell shift between them, should create a $π$-phase shift in the superconducting order parameter. Combined with recent data showing topological surface states in FeSe$_x$Te$_{1-x}$ we find that this is the ideal system to realize helical 1D-dispersing Majorana modes. Here we report scanning tunneling spectroscopic (STS) measurements of crystalline DWs in FeSe$_{0.45}$Te$_{0.55}$. By analyzing large-area superconducting gap maps, we identify the gap in the topological surface state, demonstrating that our sample is an effective Fu-Kane proximitized topological system. We further locate DWs across which the atoms shift by half a unit cell. STS data on these DWs reveal a flat density of states inside the superconducting gap, a hallmark of linearly dispersing modes in 1D. This unique signature is absent in DWs in the related superconductor, FeSe which is not in the topological phase. Our combined data are consistent with the observation of dispersing Majorana states at a $π$-phase shift DW in a proximitized topological material.

cond-mat.supr-con↗

arXiv2019

AIBench: An Industry Standard Internet Service AI Benchmark Suite

Today's Internet Services are undergoing fundamental changes and shifting to an intelligent computing era where AI is widely employed to augment services. In this context, many innovative AI algorithms, systems, and architectures are proposed, and thus the importance of benchmarking and evaluating them rises. However, modern Internet services adopt a microservice-based architecture and consist of various modules. The diversity of these modules and complexity of execution paths, the massive scale and complex hierarchy of datacenter infrastructure, the confidential issues of data sets and workloads pose great challenges to benchmarking. In this paper, we present the first industry-standard Internet service AI benchmark suite---AIBench with seventeen industry partners, including several top Internet service providers. AIBench provides a highly extensible, configurable, and flexible benchmark framework that contains loosely coupled modules. We identify sixteen prominent AI problem domains like learning to rank, each of which forms an AI component benchmark, from three most important Internet service domains: search engine, social network, and e-commerce, which is by far the most comprehensive AI benchmarking effort. On the basis of the AIBench framework, abstracting the real-world data sets and workloads from one of the top e-commerce providers, we design and implement the first end-to-end Internet service AI benchmark, which contains the primary modules in the critical paths of an industry scale application and is scalable to deploy on different cluster scales. The specifications, source code, and performance numbers are publicly available from the benchmark council web site http://www.benchcouncil.org/AIBench/index.html.

cs.CV↗

arXiv2019

Goos-Hänchen effect of spin waves at heterochiral interfaces

We theoretically investigate the Goos-Hänchen (GH) effect of spin-wave beams reflected from the interface between two ferromagnetic films with different Dzyaloshinskii-Moriya interactions (DMIs). The formula of the GH shift as functions of the incident angle and material parameters is derived analytically. We show that the GH effect occurs only when spin waves are totally reflected at the interface and vanishes otherwise. We further explore the GH shift of spin waves by narrow DMI strips of different widths. It is found that the induced shift is independent of the strip width down to $10$ nm, offering a novel approach to measure the DMI strength of ultra-narrow magnetic strips which is out the scope of current technology. Full micromagnetic simulations compare well with our theoretical findings. Strong distortion of edge magnetizations for narrower strips however generates a width dependence of the GH shift. The results presented in this work are helpful for understanding the GH effect in chiral magnets and for quantifying the DMI parameter in magnetic strips of sub-$50$ nm scales.

cond-mat.mes-hall↗

arXiv2019

Dimensional crossover and topological nature of the thin films of a three-dimensional topological insulator by band gap engineering

Identification and control of topological phases in topological thin films offer great opportunity for fundamental research and the fabrication of topology-based devices. Here, combining molecular beam epitaxy, angle-resolved photoemission spectroscopy and ab-initio calculations, we investigate the electronic structure evolution in (Bi1-xInx)2Se3 films with thickness from 2 to 13 quintuple layers. We identify several phases with their characteristic topological nature and evolution between them, i.e., dimensional crossover from a three-dimensional topological insulator with gapless surface state to its two-dimensional counterpart with gapped surface state, and topological phase transition from topological insulator to a normal semiconductor with increasing In concentration x. Furthermore, by introducing In alloying as an external knob of band gap engineering, we experimentally demonstrated the trivial topological nature of Bi2Se3 thin films (below 6 quintuple layers) as two-dimensional gapped systems, in consistent with our theoretical calculations. Our results provide not only a comprehensive phase diagram of (Bi1-xInx)2Se3 and a route to control its phase evolution, but also a practical way to experimentally determine the topological properties of a gapped compound by topological phase transition and band gap engineering.

cond-mat.mtrl-sci↗

arXiv2019

Doping induced Mott collapse and the density wave instabilities in (Sr$_{1-x}$La$_x$)$_3$Ir$_2$O$_7$

The path from a Mott insulating phase to high temperature superconductivity encounters a rich set of unconventional phenomena involving the insulator-to-metal transition (IMT) such as emergent electronic orders and pseudogaps that ultimately affect the condensation of Cooper pairs. A huge hindrance to understanding the origin of these phenomena in the curates is the difficulty in accessing doping levels near the parent state. Recently, the J$_{eff}$=1/2 Mott state of the perovskite strontium iridates has revealed intriguing parallels to the cuprates, with the advantage that it provides unique access to the Mott transition. Here, we exploit this accessibility to study the IMT and the possible nearby electronic orders in the electron-doped bilayer iridate (Sr$_{1-x}$La$_x$)$_3$Ir$_2$O$_7$. Using spectroscopic imaging scanning tunneling microscopy, we image the La dopants in the top as well as the interlayer SrO planes. Surprisingly, we find a disproportionate distribution of La in these layers with the interlayer La being primarily responsible for the IMT, thereby revealing the distinct site-dependent effects of dopants on the electronic properties of bilayer systems. Furthermore, we discover the coexistence of two electronic orders generated by electron doping: a unidirectional electronic order with a concomitant structural distortion; and local resonant states forming a checkerboard-like pattern trapped by La. This provides evidence that multiple charge orders may exist simultaneously in Mott systems, even with only one band crossing the Fermi energy.

cond-mat.str-el↗

arXiv2019

Air Quality Measurement Based on Double-Channel Convolutional Neural Network Ensemble Learning

Environmental air quality affects people's life, obtaining real-time and accurate environmental air quality has a profound guiding significance for the development of social activities. At present, environmental air quality measurement mainly adopts the method that setting air quality detector at specific monitoring points in cities and timing sampling analysis, which is easy to be restricted by time and space factors. Some air quality measurement algorithms related to deep learning mostly adopt a single convolutional neural network to train the whole image, which will ignore the difference of different parts of the image. In this paper, we propose a method for air quality measurement based on double-channel convolutional neural network ensemble learning to solve the problem of feature extraction for different parts of environmental images. Our method mainly includes two aspects: ensemble learning of double-channel convolutional neural network and self-learning weighted feature fusion. We constructed a double-channel convolutional neural network, used each channel to train different parts of the environment images for feature extraction. We propose a feature weight self-learning method, which weights and concatenates the extracted feature vectors, and uses the fused feature vectors to measure air quality. Our method can be applied to the two tasks of air quality grade measurement and air quality index (AQI) measurement. Moreover, we build an environmental image dataset of random time and location condition. The experiments show that our method can achieve nearly 82% accuracy and a small mean absolute error (MAE) on our test dataset. At the same time, through comparative experiment, we proved that our proposed method gained considerable improvement in performance compared with single channel convolutional neural network air quality measurements.

cs.CV↗

arXiv2018

Probing the Dzyaloshinskii-Moriya interaction via the propagation of spin waves in ferromagnetic thin films

The Dzyaloshinskii-Moriya interaction (DMI) has attracted considerable recent attention owing to the intriguing physics behind and the fundamental role it played in stabilizing magnetic solitons, such as magnetic skyrmions and chiral domain walls. A number of experimental efforts have been devoted to probe the DMI, among which the most popular method is the Brillouin light scattering spectroscopy (BLS) to measure the frequency difference of spin waves with opposite wave vectors $\pm\mathbf{k}$ perpendicular to the in-plane magnetization $\mathbf{m}$. Such a technique, however, is not applicable for the cases of $\mathbf{k}\parallel\mathbf{m}$, since the spin-wave reciprocity is recovered then. For a narrow magnetic strip, it is also difficult to measure the DMI strength using BLS because of the spatial resolution limit of lights. To fill these gaps, we propose to probe the DMI via the propagation of spin waves in ferromagnetic films. We show that the DMI can cause the non-collinearity of the group velocities of spin waves with $\pm\mathbf{k}\parallel\mathbf{m}$. In heterogeneous magnetic thin films with different DMIs, negative refractions of spin waves emerge at the interface under proper conditions. These findings enable us to quantify the DMI strength by measuring the angle between the two spin-wave beams with $\pm\mathbf{k}\parallel\mathbf{m}$ in homogeneous film and by measuring the incident and negative refraction angles in heterogeneous films. For a narrow magnetic strip, we propose a nonlocal scheme to determine the DMI strength via nonlinear three-magnon processes. We implement theoretical calculations and micromagnetic simulations to verify our ideas. The results presented here are helpful for future measurement of the DMI and for designing novel spin-wave spintronic devices.

cond-mat.mes-hall↗

arXiv2018

Disorder induced power-law gaps in an insulator-metal Mott transition

A correlated material in the vicinity of an insulator-metal transition (IMT) exhibits rich phenomenology and variety of interesting phases. A common avenue to induce IMTs in Mott insulators is doping, which inevitably leads to disorder. While disorder is well known to create electronic inhomogeneity, recent theoretical studies have indicated that it may play an unexpected and much more profound role in controlling the properties of Mott systems. Theory predicts that disorder might play a role in driving a Mott insulator across an IMT, with the emergent metallic state hosting a power law suppression of the density of states (with exponent close to 1; V-shaped gap) centered at the Fermi energy. Such V-shaped gaps have been observed in Mott systems but their origins are as yet unknown. To investigate this, we use scanning tunneling microscopy and spectroscopy to study isovalent Ru substitutions in Sr$_3$(Ir$_{1-x}$Ru$_x$)$_2$O$_7$ which drives the system into an antiferromagnetic, metallic state. Our experiments reveal that many core features of the IMT such as power law density of states, pinning of the Fermi energy with increasing disorder, and persistence of antiferromagnetism can be understood as universal features of a disordered Mott system near an IMT and suggest that V-shaped gaps may be an inevitable consequence of disorder in doped Mott insulators.

cond-mat.str-el↗

arXiv2018

Image Captioning based on Deep Reinforcement Learning

Recently it has shown that the policy-gradient methods for reinforcement learning have been utilized to train deep end-to-end systems on natural language processing tasks. What's more, with the complexity of understanding image content and diverse ways of describing image content in natural language, image captioning has been a challenging problem to deal with. To the best of our knowledge, most state-of-the-art methods follow a pattern of sequential model, such as recurrent neural networks (RNN). However, in this paper, we propose a novel architecture for image captioning with deep reinforcement learning to optimize image captioning tasks. We utilize two networks called "policy network" and "value network" to collaboratively generate the captions of images. The experiments are conducted on Microsoft COCO dataset, and the experimental results have verified the effectiveness of the proposed method.

cs.CV↗

arXiv2018

EMPHASIS: An Emotional Phoneme-based Acoustic Model for Speech Synthesis System

We present EMPHASIS, an emotional phoneme-based acoustic model for speech synthesis system. EMPHASIS includes a phoneme duration prediction model and an acoustic parameter prediction model. It uses a CBHG-based regression network to model the dependencies between linguistic features and acoustic features. We modify the input and output layer structures of the network to improve the performance. For the linguistic features, we apply a feature grouping strategy to enhance emotional and prosodic features. The acoustic parameters are designed to be suitable for the regression task and waveform reconstruction. EMPHASIS can synthesize speech in real-time and generate expressive interrogative and exclamatory speech with high audio quality. EMPHASIS is designed to be a multi-lingual model and can synthesize Mandarin-English speech for now. In the experiment of emotional speech synthesis, it achieves better subjective results than other real-time speech synthesis systems.

eess.AS↗