arXiv subjects

Yanfeng Zhang

Publications and source records attributed to Yanfeng Zhang.

At least 73 records · Page 4Linked to original sources

arXiv2021

Universal Non-Volatile Resistive Switching Behavior in 2D Metal Dichalcogenides Featuring Unique Conductive-Point Random Access Memory Effect

Two-dimensional materials have been discovered to exhibit non-volatile resistive switching (NVRS) phenomenon. In our work, we reported the universal NVRS behavior in a dozen metal dichalcogenides, featuring low switching voltage, large on/off ratio, fast switching speed and forming free characteristics. A unique conductive-point random access memory (CPRAM) effect is used to explain the switching mechanisms, supported by experimental results from current-sweep measurements.

cond-mat.mes-hall↗

arXiv2021

Single-crystal hexagonal boron nitride monolayer epitaxially grown on Cu (111) thin film across a wafer

We demonstrate single crystal growth of wafer-scale hexagonal boron nitride (hBN), an insulating atomic thin monolayer, on high-symmetry index surface plane Cu(111). The unidirectional epitaxial growth is guaranteed by large binding energy difference, ~0.23 eV, between A- and B-steps edges on Cu(111) docking with B6N7 clusters, confirmed by density functional theory calculations.

cond-mat.mtrl-sci↗

arXiv2021

What Have We Learned from OpenReview?

Anonymous peer review is used by the great majority of computer science conferences. OpenReview is such a platform that aims to promote openness in peer review process. The paper, (meta) reviews, rebuttals, and final decisions are all released to public. We collect 5,527 submissions and their 16,853 reviews from the OpenReview platform. We also collect these submissions' citation data from Google Scholar and their non-peer-reviewed versions from arXiv.org. By acquiring deep insights into these data, we have several interesting findings that could help understand the effectiveness of the public-accessible double-blind peer review process. Our results can potentially help writing a paper, reviewing it, and deciding on its acceptance.

cs.DL↗

arXiv2020

MxPool: Multiplex Pooling for Hierarchical Graph Representation Learning

How to utilize deep learning methods for graph classification tasks has attracted considerable research attention in the past few years. Regarding graph classification tasks, the graphs to be classified may have various graph sizes (i.e., different number of nodes and edges) and have various graph properties (e.g., average node degree, diameter, and clustering coefficient). The diverse property of graphs has imposed significant challenges on existing graph learning techniques since diverse graphs have different best-fit hyperparameters. It is difficult to learn graph features from a set of diverse graphs by a unified graph neural network. This motivates us to use a multiplex structure in a diverse way and utilize a priori properties of graphs to guide the learning. In this paper, we propose MxPool, which concurrently uses multiple graph convolution/pooling networks to build a hierarchical learning structure for graph representation learning tasks. Our experiments on numerous graph classification benchmarks show that our MxPool has superiority over other state-of-the-art graph representation learning methods.

cs.LG↗

arXiv2020

Bandgap Control in Two-Dimensional Semiconductors via Coherent Doping of Plasmonic Hot Electrons

Bandgap control is of central importance for semiconductor technologies. The traditional means of control is to dope the lattice chemically, electrically or optically with charge carriers. Here, we demonstrate for the first time a widely tunable bandgap (renormalisation up to 650 meV at room-temperature) in two-dimensional (2D) semiconductors by coherently doping the lattice with plasmonic hot electrons. In particular, we integrate tungsten-disulfide (WS$_2$) monolayers into a self-assembled plasmonic crystal, which enables coherent coupling between semiconductor excitons and plasmon resonances. Accompanying this process, the plasmon-induced hot electrons can repeatedly fill the WS$_2$ conduction band, leading to population inversion and a significant reconstruction in band structures and exciton relaxations. Our findings provide an innovative and effective measure to engineer optical responses of 2D semiconductors, allowing a great flexiblity in design and optimisation of photonic and optoelectronic devices.

physics.optics↗

arXiv2019

GANCoder: An Automatic Natural Language-to-Programming Language Translation Approach based on GAN

We propose GANCoder, an automatic programming approach based on Generative Adversarial Networks (GAN), which can generate the same functional and logical programming language codes conditioned on the given natural language utterances. The adversarial training between generator and discriminator helps generator learn distribution of dataset and improve code generation quality. Our experimental results show that GANCoder can achieve comparable accuracy with the state-of-the-art methods and is more stable when programming languages.

cs.CL↗

arXiv2019

An Efficient Interval Uncertainty Optimization Approach Based on Quasi-sparse Response Surface

The structure uncertainty optimization problem is usually treated as double-loop optimization process, which is computation-intensive. In this paper, an efficient interval uncertainty optimization approach based on Quasi-sparse response surface (QSRS) is proposed for structure uncertainty optimization. In which, 1) with l_1 norm and l_2 norm penalty method, a few appropriate basis functions are selected form a large number of basis functions to construct QSRS accurately and only a few sampling points is required, 2) as the orthogonal chebyshev polynomials is employed as QSRS basis functions, the local uncertainty can be evaluated by the combination of QSRS and interval arithmetic. Hence, the inner optimization process is eliminated. One mathematical problem and one engineering problem are used to validate the efficiency of the proposed approach. The results show that only 25% sampling points is needed than recently published approach.

math.OC↗

arXiv2019

Sparse Representation Based Efficient Radiation Symmetry Analysis Method for Cylindrical Model of Inertial Confinement Fusion

Radiation symmetry evaluation is critical to the laser driven Inertial Confinement Fusion (ICF), which is usually done by solving a view-factor equation model. The model is nonlinear, and the number of equations can be very large when the size of discrete mesh element is very small to achieve a prescribed accuracy, which may lead to an intensive equation solving process. In this paper, an efficient radiation symmetry analysis approach based on sparse representation is presented, in which, 1) the Spherical harmonics, annular Zernike polynomials and Legendre-Fourier polynomials are employed to sparsely represent the radiation flux on the capsule and cylindrical cavity, and the nonlinear energy equilibrium equations are transformed into the equations with sparse coefficients, which means there are many redundant equations, 2) only a few equations are selected to recover such sparse coefficients with Latin hypercube sampling, 3) a Conjugate Gradient Subspace Thresholding Pursuit (CGSTP) algorithm is then given to rapidly obtain such sparse coefficients equation with as few iterations as possible. Finally, the proposed method is validated with two experiment targets for Shenguang II and Shenguang III laser facility in China. The results show that only one tenth of computation time is required to solve one tenth of equations to achieve the radiation flux with comparable accuracy. Further more, the solution is much more efficient as the size of discrete mesh element decreases, in which, only 1.2\% computation time is required to obtain the accurate result.

eess.SP↗

arXiv2018

Revealing Strong Plasmon-Exciton Coupling Between Nano-gap Resonators and Two-Dimensional Semiconductors at Ambient Conditions

Strong coupling of two-dimensional semiconductor excitons with plasmonic resonators enables control of light-matter interaction at the subwavelength scale. Here we develop strong coupling in plasmonic nano-gap resonators that allow modification of exciton number contributing to the coupling. Using this system, we not only demonstrate a large vacuum Rabi splitting up to 163 meV and splitting features in photoluminescence spectra, but also reveal that the exciton number can be reduced down to single-digit level (N<10), which is an order lower than that of traditional systems, close to single-exciton based strong coupling. In addition, we prove that the strong coupling process is not affected by the large exciton coherence size that was previously believed to be detrimental to the formation of plasmon-exciton interaction. Our work provides a deeper understanding of storng coupling in two-dimensional semiconductors, paving the way for room temperature quantum optics applications.

physics.optics↗

arXiv2018

V$_5$S$_8$: a Kondo lattice based on intercalation of van der Waals layered transition metal dichalcogenide

Since the discovery of graphene, a tremendous amount of two dimensional (2D) materials have surfaced. Their electronic properties can usually be well understood without considering correlations between electrons. On the other hand, strong electronic correlations are known to give rise to a variety of exotic properties and new quantum phases, for instance, high temperature superconductivity, heavy fermions and quantum spin liquids. The study of these phenomena has been one of the main focuses of condensed matter physics. There is a strong incentive to introduce electronic correlations into 2D materials. Via intercalating a van der Waals layered compound VS$_2$, we show an emergence of a Kondo lattice, an extensively studied strongly correlated system, by magnetic, specific heat, electrical and thermoelectric transport studies. In particular, an exceptionally large Sommerfeld coefficient, 440 mJ$\cdot$K$^{-2}\cdot$mol$^{-1}$, indicates a strong electron correlation. The obtained Kadowaki-Woods ratio, $2.7\times 10^{-6}$ $μΩ\cdot$cm$\cdot$mol$^2\cdot$K$^2\cdot$mJ$^{-2}$, also supports the strong electron-electron interaction. The temperature dependence of the resistivity and thermopower corroborate the Kondo lattice picture. The intercalated compound is one of a few rare examples of $d$-electron Kondo lattices. We further show that the Kondo physics persists in ultra-thin films. This work thus demonstrates a route to generate strong correlations in 2D materials.

cond-mat.mtrl-sci↗

arXiv2018

Spiral Transformation for High-Resolution and Efficient Sorting of Optical Vortex Modes

Mode sorting is an essential function for optical systems exploiting the orthogonality of photonic orbital angular momentum mode space. The familiar log-polar optical transformation provides an efficient yet simple approach, however with its resolution restricted by a considerable overlap between adjacent modes resulting from the limited excursion of the phase along one complete circle around the optical vortex axis that is transformed into a line segment with finite length. We propose and experimentally verify a new optical transformation that maps spirals instead of closed concentric circles to parallel lines. As the phase excursion along a spiral in the transverse plane of orbital angular momentum modes is theoretically unlimited, this new optical transformation separates vortex modes with superior resolution while maintaining unity efficiency.

physics.optics↗

arXiv2017

Spin transport in two-layer-CVD-hBN/graphene/hBN heterostructures

We study room temperature spin transport in graphene devices encapsulated between a layer-by-layer-stacked two-layer-thick chemical vapour deposition (CVD) grown hexagonal boron nitride (hBN) tunnel barrier, and a few-layer-thick exfoliated-hBN substrate. We find mobilities and spin-relaxation times comparable to that of SiO$_2$ substrate based graphene devices, and obtain a similar order of magnitude of spin relaxation rates for both the Elliott-Yafet and D'Yakonov-Perel' mechanisms. The behaviour of ferromagnet/two-layer-CVD-hBN/graphene/hBN contacts ranges from transparent to tunneling due to inhomogeneities in the CVD-hBN barriers. Surprisingly, we find both positive and negative spin polarizations for high-resistance two-layer-CVD-hBN barrier contacts with respect to the low-resistance contacts. Furthermore, we find that the differential spin injection polarization of the high-resistance contacts can be modulated by DC bias from -0.3 V to +0.3 V with no change in its sign, while its magnitude increases at higher negative bias. These features mark a distinctive spin injection nature of the two-layer-CVD-hBN compared to the bilayer-exfoliated-hBN tunnel barriers.

cond-mat.mes-hall↗

arXiv2017

Demonstration of a genetic-algorithm-optimized cavity-based waveguide crossing

A compact, single-etch silicon photonics waveguide crossing based on low quality factor cavity and optimized by a genetic algorithm is demonstrated. The device has 0.1~0.3 dB insertion loss and < -35 dB crosstalk per crossing for fundamental TE mode in the 1550-1600 nm wavelength band, with a footprint of 5 x 5 μm$^2$.

physics.app-ph↗

arXiv2017

On-chip radially and azimuthally polarized vortex beam generation utilizing shallow-etched gratings

Cylindrical vector vortex (CVV) beams, complex light fields that exhibit a vector nature and carry quantized orbital angular momentum states, have been widely investigated due to their rich applications. Current technologies to generate CVV beams using individual polarization and spatial phase manipulations suffer from bulky size and low configurability. In this work, we propose and experimentally demonstrate an approach to generate CVV beams with an integrated device based on silicon nitride microring resonator and embedded top-gratings. The device is capable of generating broad-band radially and azimuthally polarized vortex beams by simply switching the polarization and wavelength of the excited whispering-gallery-modes. In addition, we develop a method to fabricate the devices of shallow-etched top-gratings with only one-step etching. This novel method provides new capabilities to develop on-chip integrated devices with great ease and flexibility.

physics.optics↗

arXiv2017

Hybrid light-emitting polymer/SiNx platform for photonic integration

Organic semiconductors have potentials for a broad range of applications; however, it is difficult to be integrated with traditional inorganic material to meet the need of further application. Based on low-temperature silicon nitride (SiNx) deposition technique, here we demonstrate a hybrid structure fabricated by directly depositing high-quality SiNx on organic polymer film Poly[2-(2',5'-bis(2"-ethylhexyloxy)- phenyl) -1,4-phenylene vinylene] (BEHP-PPV). Stacked BEHP-PPV/SiNx hybrid structures with different periods are obtained and their optical properties are systematically characterized. Moreover, a group of BEHP/PPV embedded SiNx micro-disk is fabricated and amplification of spontaneous emission (ASE) is observed under optical pumping, further confirming that the gain properties of BEHP/PPV are well preserved. Our technique offers a platform to fabricate organic/inorganic hybrid optical devices compatible with integrated components.

physics.optics↗

arXiv2017

Surface Plasmon Enhanced Strong Exciton-Photon Coupling in Hybrid Inorganic-Organic Perovskites Nanowires

Manipulating strong light-matter interaction in semiconductor microcavities is crucial for developing high-performance exciton polariton devices with great potentials in next-generation all-solid state quantum technologies. In this work, we report surface plasmon enhanced strong exciton-photon interaction in CH3NH3PbBr3 perovskite nanowires. Characteristic anti-crossing behaviors, indicating Rabi splitting energy up to ~ 560 meV, are observed near exciton resonance in hybrid semiconductor-insulator-metallic waveguide cavity at room temperature. An exciton-photon coupling strength enhancement factor of ~ 1.4 times is evaluated, which is mainly attributed to surface plasmon induced localized excitation field redistribution. Further, systematic studies on nanowires and insulator dimension dependence of exciton-photon interaction are presented. These results provide new avenues to achieve extremely high coupling strengths and push forward the development of electrically pumped and ultra-low threshold small lasers.

cond-mat.mes-hall↗

arXiv2017

Maiter: An Asynchronous Graph Processing Framework for Delta-based Accumulative Iterative Computation

Myriad of graph-based algorithms in machine learning and data mining require parsing relational data iteratively. These algorithms are implemented in a large-scale distributed environment in order to scale to massive data sets. To accelerate these large-scale graph-based iterative computations, we propose delta-based accumulative iterative computation (DAIC). Different from traditional iterative computations, which iteratively update the result based on the result from the previous iteration, DAIC updates the result by accumulating the "changes" between iterations. By DAIC, we can process only the "changes" to avoid the negligible updates. Furthermore, we can perform DAIC asynchronously to bypass the high-cost synchronous barriers in heterogeneous distributed environments. Based on the DAIC model, we design and implement an asynchronous graph processing framework, Maiter. We evaluate Maiter on local cluster as well as on Amazon EC2 Cloud. The results show that Maiter achieves as much as 60x speedup over Hadoop and outperforms other state-of-the-art frameworks.

cs.DC↗

arXiv2017

Clustering Stream Data by Exploring the Evolution of Density Mountain

Stream clustering is a fundamental problem in many streaming data analysis applications. Comparing to classical batch-mode clustering, there are two key challenges in stream clustering: (i) Given that input data are changing continuously, how to incrementally update clustering results efficiently? (ii) Given that clusters continuously evolve with the evolution of data, how to capture the cluster evolution activities? Unfortunately, most of existing stream clustering algorithms can neither update the cluster result in real time nor track the evolution of clusters. In this paper, we propose an stream clustering algorithm EDMStream by exploring the Evolution of Density Mountain. The density mountain is used to abstract the data distribution, the changes of which indicate data distribution evolution. We track the evolution of clusters by monitoring the changes of density mountains. We further provide efficient data structures and filtering schemes to ensure the update of density mountains in real time, which makes online clustering possible. The experimental results on synthetic and real datasets show that, comparing to the state-of-the-art stream clustering algorithms, e.g., D-Stream, DenStream, DBSTREAM and MR-Stream, our algorithm can response to a cluster update much faster (say 7-15x faster than the best of the competitors) and at the same time achieve comparable cluster quality. Furthermore, EDMStream can successfully capture the cluster evolution activities.

cs.DB↗