arXiv subjects

Lei Gao

Publications and source records attributed to Lei Gao.

At least 109 records · Page 6Linked to original sources

arXiv2020

Phase-gradient metasurfaces based on local Fabry-Perot resonances

In this work we present a new mechanism for designing phase-gradient metasurfaces (PGMs) to control an electromagnetic wavefront with high efficiency. Specifically, we design a transmission-type PGM formed by a periodic subwavelength metallic slit array filled with identical dielectrics of different heights. It is found that when Fabry-Perot (FP) resonances occur locally inside the dielectric regions, in addition to the common phenomenon of complete transmission, the transmitted phase differences between two adjacent slits are exactly the same, being a non-zero constant. These local FP resonances ensure total phase shift across a supercell that can fully cover the range of 0 to 2Pi, satisfying the design requirements of PGMs. More studies reveal that due to local FP resonances, there is a one-to-one correspondence between the phase difference and the permittivity of the filled dielectric. A similar approach can be extended to the reflection-type case and other wavefront transformation, creating new opportunities for wave manipulation.

physics.optics↗

arXiv2020

Switchable bifunctional metasurfaces: nearly perfect retroreflection and absorption at THz regime

Here we make use of vanadium dioxide (VO2) to design a bifunctional metasurface working at the same targeted frequency. With the increase of temperature, the functionality of the designed metasurface can switch from a multi-channel retroreflector to a perfect absorber, caused by the phase transition of VO2 from insulator to conductor. Different from traditional bifunctional metasurfaces designed by simple composition of two functionalities, our proposed bifunctional metasurface is based on the interaction between two functionalities. The device shows good potential for the combination of wavefront manipulation and optical absorption, therefore providing a promising approach for switchable detection and anti-detection devices.

physics.optics↗

arXiv2020

Dynamic Partial Removal: A Neural Network Heuristic for Large Neighborhood Search

This paper presents a novel neural network design that learns the heuristic for Large Neighborhood Search (LNS). LNS consists of a destroy operator and a repair operator that specify a way to carry out the neighborhood search to solve the Combinatorial Optimization problems. The proposed approach in this paper applies a Hierarchical Recurrent Graph Convolutional Network (HRGCN) as a LNS heuristic, namely Dynamic Partial Removal, with the advantage of adaptive destruction and the potential to search across a large scale, as well as the context-awareness in both spatial and temporal perspective. This model is generalized as an efficient heuristic approach to different combinatorial optimization problems, especially to the problems with relatively tight constraints. We apply this model to vehicle routing problem (VRP) in this paper as an example. The experimental results show that this approach outperforms the traditional LNS heuristics on the same problem as well. The source code is available at \href{https://github.com/water-mirror/DPR}{https://github.com/water-mirror/DPR}.

cs.NE↗

arXiv2020

Learn to Design the Heuristics for Vehicle Routing Problem

This paper presents an approach to learn the local-search heuristics that iteratively improves the solution of Vehicle Routing Problem (VRP). A local-search heuristics is composed of a destroy operator that destructs a candidate solution, and a following repair operator that rebuilds the destructed one into a new one. The proposed neural network, as trained through actor-critic framework, consists of an encoder in form of a modified version of Graph Attention Network where node embeddings and edge embeddings are integrated, and a GRU-based decoder rendering a pair of destroy and repair operators. Experiment results show that it outperforms both the traditional heuristics algorithms and the existing neural combinatorial optimization for VRP on medium-scale data set, and is able to tackle the large-scale data set (e.g., over 400 nodes) which is a considerable challenge in this area. Moreover, the need for expertise and handcrafted heuristics design is eliminated due to the fact that the proposed network learns to design the heuristics with a better performance. Our implementation is available online.

cs.NE↗

arXiv2019

Field of View Extension in Computed Tomography Using Deep Learning Prior

In computed tomography (CT), data truncation is a common problem. Images reconstructed by the standard filtered back-projection algorithm from truncated data suffer from cupping artifacts inside the field-of-view (FOV), while anatomical structures are severely distorted or missing outside the FOV. Deep learning, particularly the U-Net, has been applied to extend the FOV as a post-processing method. Since image-to-image prediction neglects the data fidelity to measured projection data, incorrect structures, even inside the FOV, might be reconstructed by such an approach. Therefore, generating reconstructed images directly from a post-processing neural network is inadequate. In this work, we propose a data consistent reconstruction method, which utilizes deep learning reconstruction as prior for extrapolating truncated projections and a conventional iterative reconstruction to constrain the reconstruction consistent to measured raw data. Its efficacy is demonstrated in our study, achieving small average root-mean-square error of 24 HU inside the FOV and a high structure similarity index of 0.993 for the whole body area on a test patient's CT data.

eess.IV↗

arXiv2019

Superpixel-Based Background Recovery from Multiple Images

In this paper, we propose an intuitive method to recover background from multiple images. The implementation consists of three stages: model initialization, model update, and background output. We consider the pixels whose values change little in all input images as background seeds. Images are then segmented into superpixels with simple linear iterative clustering. When the number of pixels labelled as background in a superpixel is bigger than a predefined threshold, we label the superpixel as background to initialize the background candidate masks. Background candidate images are obtained from input raw images with the masks. Combining all candidate images, a background image is produced. The background candidate masks, candidate images, and the background image are then updated alternately until convergence. Finally, ghosting artifacts is removed with the k-nearest neighbour method. An experiment on an outdoor dataset demonstrates that the proposed algorithm can achieve promising results.

cs.CV↗

arXiv2019

High coherent frequency-entangled photons generated by parametric instability in active fiber ring cavity

High coherent frequency-entangled photons at telecom band are critical in quantum information protocols and quantum tele-communication. While photon pairs generated by spontaneous parametric down-conversion in nonlinear crystal or modulation instability in optical fiber exhibit random fluctuations, making the photons distinguishable among consecutive roundtrips. Here, we demonstrate a frequency-entangled photons based on parametric instability in an active fiber ring cavity, where periodic modulation of dispersion excites parametric resonance. The characteristic wave number in parametric instability is selected by the periodic modulation of resonator, and stable patterns with symmetric gains are formed. We find that the spectra of parametric instability sidebands possess a high degree of coherence, which is verified by the background-free autocorrelation of single-shot spectra. Two photon interference is performed by a fiber-based Mach-Zehnder interferometer without any stabilization. We obtain a Hong-Ou-Mandel interference visibility of 86.3% with a dip width of 4.3 mm. The correlation time measurement exhibits a linewidth of 68.36 MHz, indicating high coherence and indistinguishability among the photon pairs. Our results proves that the parametric instability in active fiber cavity is effective to generate high coherent frequency-entangled photon pairs, which would facilitate subsequent quantum applications.

physics.optics↗

arXiv2019

Orbital design of topological insulators from two-dimensional semiconductors

Two-dimensional (2D) materials have attracted much recent attention because they exhibit various distinct intrinsic properties/functionalities, which are, however, usually not interchangeable. Interestingly, here we propose a generic approach to convert 2D semiconductors, which are amply abundant, to 2D topological insulators (TIs), which are less available, via selective atomic adsorption and strain engineering. The approach is underlined by an orbital design principle that involves introducing an extrinsic s-orbital state into the intrinsic sp-bands of a 2D semiconductor, so as to induce s-p band inversion for a TI phase, as demonstrated by tight-binding model analyses. Remarkably, based on first-principles calculations, we apply this approach to convert the semiconducting monolayer CuS and CuTe into a TI by adsorbing Na and K respectively with a proper s-level energy, and CuSe into a TI by adsorbing a mixture of Na and K with a tuned s-level energy or by adsorbing either Na or K on a strained CuSe with a tuned p-level valence band edge. Our findings open a new door to the discovery of TIs by a predictive materials design, beyond finding a preexisting 2D TI.

cond-mat.mtrl-sci↗

arXiv2019

Measuring Road Network Topology Vulnerability by Ricci Curvature

Describing the basic properties of road network systems, such as their robustness, vulnerability, and reliability, has been a very important research topic in the field of urban transportation. Current research mainly uses several statistical indicators of complex networks to analyze the road network systems. However, these methods are essentially node-based. These node-based methods are more concerned with the number of connections between nodes, and lack of consideration for interactions. So, this leads to the well-known node paradox problem, and their ability of characterizing the local and intrinsic properties of a network is weak. From the perspective of network intrinsic geometry, this paper proposes a method for measuring road network vulnerability using a discrete Ricci curvature, which can identify the key sections of a road network and indicate its fragile elements. The results show that our method performs better than complex network statistics on measuring the vulnerability of a road network. Additionally, it can characterize the evolution of the road network vulnerability among different periods of time in the same city through our method. Finally, we compare our method with the previous method of centrality and show the different between them. This article provides a new perspective on a geometry to analyze the vulnerability of a road network and describes the inherent nature of the vulnerability of a road system from a new perspective. It also contributes to enriching the analytical methods of complex road networks.

cs.SI↗

arXiv2019

Coherent optical modulation of partially mode-locked fiber laser based on coherent population oscillation in reduced oxide graphene

Optical control of graphene-based photonic devices and systems has been under extensive explorations, nevertheless, the requirement of high power pump laser due to incoherent modulation makes those schemes low efficient. Here, we demonstrate coherent manipulation of the operating states of partially mode-locked fiber laser based on coherent population oscillation in reduced graphene oxide for the first time. We couple a much weaker continuous wave laser into the resonator operating with parametric instability state, and observe significant depression/enhancement of the sidebands when the coherent population oscillation conditions are satisfied. Besides, significant depression of partially mode-locked fiber laser is achieved. The experimental results reveal that the coherent population oscillation in reduced graphene oxide is highly effective in manipulating mode-locked fiber laser system, and the induced phase variation is highly asymmetrical. The discovery of coherent population oscillation in reduced graphene oxide facilitates coherent optical modulation of reduced graphene oxide-based photonic devices and systems with a much weaker controlling laser.

physics.optics↗

arXiv2018

Polarization evolution dynamics of dissipative soliton fiber laser

Dissipative solitons are localized solutions in non-integrable and non-conservative nonlinear system, due to a balance of nonlinearity, dispersion, filtering, and loss/gain. Different from conventional soliton, they exhibit extremely complex and striking dynamics, and are more universal in the far-from-equilibrium natural system. Taking advantage of dispersive Fourier transform and time lens, the spatio-temporal transient dynamics of dissipative solitons have been characterized with both amplitude and phase. Yet, a full avenue of the buildup process of dissipative soliton is full of debris for the lack of the polarization evolution information. Here, we characterize the probabilistic polarization distributions in the buildup of dissipative soliton in a net-normal dispersion fiber laser system mode-locked by single-wall carbon nanotubes. The lasing system operates from random amplified spontaneous emission to stable dissipative soliton when the cavity gain is increasing, and state of polarization of each wavelength convergence exponentially towards a fixed point. To reveal the invariant polarization relationship among various wavelengths, the phase diagram of the ellipticity angle and spherical orientation angle is introduced. We find that the polarization of each filtered wavelength in the central region of the dissipative soliton is linearly spaced, while those in the two fronts are spatially varying. Increasing cavity gain leads to spectrum broadening, meanwhile, the polarizations of the new generated frequencies are extending to scatter. Further increasing pump power results into dissipative soliton explosion, during which a kind of polarization optical rogue waves is identified. Those experimental results provide more information for deeper insights into the transient dynamics of dissipative soliton fiber laser.

physics.optics↗

arXiv2018

Coupling of RF Antennas to Large Volume Helicon Plasma

Large volume helicon plasma sources are of particular interest for large scale semiconductor processing, high power plasma propulsion and recently plasma-material interaction under fusion conditions. This work is devoted to studying the coupling of four typical RF antennas to helicon plasma with infinite length and diameter of $0.5$~m, and exploring its frequency dependence in the range of $13.56-70$~MHz for coupling optimization. It is found that loop antenna is more efficient than half helix, Boswell and Nagoya III antennas for power absorption; radially parabolic density profile overwhelms Gaussian density profile in terms of antenna coupling for low-density plasma, but the superiority reverses for high-density plasma. Increasing the driving frequency results in power absorption more near plasma edge, but the overall power absorption increases with frequency. Perpendicular stream plots of wave magnetic field, wave electric field and perturbed current are also presented. This work can serve as an important reference for the experimental design of large volume helicon plasma source with high RF power.

physics.plasm-ph↗

arXiv2018

Ultrafast cryptography with indefinitely switchable optical nanoantennas

Bistability is widely exploited to demonstrate all-optical signal processing and light-based computing. The standard paradigm of switching between two steady states corresponding to '0" and '1" bits is based on the rule that a transition occurs when the signal pulse intensity overcomes the bistability threshold, and otherwise, the system remains in the initial state. Here, we break with this concept by revealing the phenomenon of indefinite switching in which the eventual steady state of a resonant bistable system is transformed into a nontrivial function of signal pulse parameters for moderately intense signal pulses. The essential nonlinearity of the indefinite switching allows realization of well-protected cryptographic algorithms with a single bistable element in contrast to software-assisted cryptographic protocols that require thousands of logic gates. As a proof of concept, we demonstrate stream deciphering of the word 'enigma' by means of an indefinitely switchable optical nanoantenna. An extremely high bitrate ranging from ~0.1 to 1 terabits per second and a small size make such systems promising as basic elements for all-optical cryptographic architectures.

physics.optics↗

arXiv2018

Recognizing Explicit and Implicit Hate Speech Using a Weakly Supervised Two-path Bootstrapping Approach

In the wake of a polarizing election, social media is laden with hateful content. To address various limitations of supervised hate speech classification methods including corpus bias and huge cost of annotation, we propose a weakly supervised two-path bootstrapping approach for an online hate speech detection model leveraging large-scale unlabeled data. This system significantly outperforms hate speech detection systems that are trained in a supervised manner using manually annotated data. Applying this model on a large quantity of tweets collected before, after, and on election day reveals motivations and patterns of inflammatory language.

cs.CL↗

arXiv2018

Detecting Online Hate Speech Using Context Aware Models

In the wake of a polarizing election, the cyber world is laden with hate speech. Context accompanying a hate speech text is useful for identifying hate speech, which however has been largely overlooked in existing datasets and hate speech detection models. In this paper, we provide an annotated corpus of hate speech with context information well kept. Then we propose two types of hate speech detection models that incorporate context information, a logistic regression model with context features and a neural network model with learning components for context. Our evaluation shows that both models outperform a strong baseline by around 3% to 4% in F1 score and combining these two models further improve the performance by another 7% in F1 score.

cs.CL↗

arXiv2018

Optical tristability and ultrafast Fano switching in nonlinear magneto-plasmonic nanoparticles

We consider light scattering by a coated magneto-plasmonic nanoparticle (MPNP) with a Kerr-type nonlinear plasmonic shell and a magneto-optic core. Such structure features two plasmon dipole modes, associated with electronic oscillations on the inner and outer surfaces of the shell. Driven in a nonlinear regime, each mode exhibits a bistable response. Bistability of an inner plasmon leads to switching between this state and a Fano resonance (Fano switching). Once the external light intensity exceeds the critical value, the bistability zones of both eigen modes overlap yielding optical tristability characterized by three stable steady states for a given wavelength and light intensity. We develop a dynamic theory of transitions between nonlinear steady states and estimate the characteristic switching time as short as 0.5 ps. We also show that the magneto-optical (MO) effect allows red- and blue- spectral shift of the Fano profile for right- and left- circular polarizations of the external light, rendering Fano switching sensitive to the light polarization. Specifically, one can reach Fano switching for the right circular polarization while cancelling it for the left circular polarization. Our results point to a novel class of ultrafast Fano switchers tunable by magnetic field for applications in nanophotonics.

physics.optics↗

arXiv2017

Optical puff mediated laminar-turbulent polarization transition

Various physical structures exhibit a fundamentally probabilistic nature over diverse scales in space and time, to the point that the demarcation line between quantum and classic laws gets blurred. Here, we characterize the probability of intermittency in the laminar-turbulence transition of a partially mode-locked fiber laser system, whose degree of coherence is deteriorated by multiple mode mixing. Two competing processes, namely the proliferation and the decay of an optical turbulent puff, determine a critical behavior for the onset of turbulence in such a nonlinear dissipative system. A new kind of optical rogue waves, polarization rogue waves, is introduced at the point of transition to polarization turbulence. The probabilistic description of the puff-mediated laminar-turbulence polarization transition provides an additional degree of freedom for our understanding of the complex physics of lasers.

physics.optics↗

arXiv2017

All-optical wavelength-tunable narrow-linewidth fiber laser

Parameter regulations of narrow-linewidth fiber lasers in frequency domain has drawn considerable interests for widespread applications in the light quantum computing, precise coherent detection, and generation of micro-waves. All-optical methods provide compact, precise and fast accesses to achieving these lasers with wavelength-tunability. Here, the optical-thermal effects of graphene is utilized to precisely control operations of free-running lasers with a tuning speed of 140 MHz/ms. Assisted by the single-longitude-mode operation and linewidth suppression of stimulated Brillouin backscattering, we obtain an optical-controllable ~750 Hz fiber laser with a wavelength-tuning range of 3.7 nm.

physics.optics↗