arXiv subjects

Feng Liu

Publications and source records attributed to Feng Liu.

At least 631 records · Page 35Linked to original sources

arXiv2019

Asynchronous Distributed Power Control of Multi-Microgrid Systems Based on the Operator Splitting Approach

Forming (hybrid) AC/DC microgrids (MGs) has become a promising manner for the interconnection of various kinds of distributed generators that are inherently AC or DC electric sources. This paper addresses the distributed asynchronous power control problem of hybrid microgrids, considering imperfect communication due to non-identical sampling rates and communication delays. To this end, we first formulate the optimal power control problem of MGs and devise a synchronous algorithm. Then, we analyze the impact of asynchrony on optimal power control and propose an asynchronous iteration algorithm based on the synchronous version. By introducing a random clock at each iteration, different types of asynchrony are fitted into a unified framework, where the asynchronous algorithm is converted into a fixed-point problem based on the operator splitting method, leading to a convergence proof. We further provide an upper bound estimation of the time delay in the communication. Moreover, the real-time implementation of the proposed algorithm in both AC and DC MGs is introduced. By taking the power system as a solver, the controller is simplified by reducing one order and the power loss can be considered. Finally, a benchmark MG is utilized to verify the effectiveness and advantages of the proposed algorithm.

eess.SY↗

arXiv2019

Fermionic analogue of black hole radiation with a super high Hawking temperature

Measurement of gravitational Hawking radiation of black hole (BH) is prohibitive because of an extremely low Hawking temperature (TH). Here we demonstrate a fermionic analog of BH with a super high TH ~ 3 K, which is several orders of magnitude higher than previous works. We propose that Floquet-Dirac states, formed in a periodically laser driven two-dimensional black phosphorous thin film, can be designed with a spatial gradient to mimic the "gravity" felt by fermionic quasiparticles as that for a Schwarzschild BH (SBH). Quantum tunneling of electrons from a type-II Dirac cone (inside BH) to a type-I Dirac cone (outside) emits a SBH-like Hawking radiation spectrum.

cond-mat.mes-hall↗

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

Electron-Nuclear Hyperfine Coupling in Quantum Kagome Antiferromagnets from First-Principles Calculation and a Reflection of the Defect Effect

The discovery of ideal spin-1/2 kagome antiferromagnets Herbertsmithite and Zn-doped Barlowite represents a breakthrough in the quest for quantum spin liquids (QSLs), and nuclear magnetic resonance (NMR) spectroscopy plays a prominent role in revealing the quantum paramagnetism in these compounds. However, interpretation of NMR data that is often masked by defects can be controversial. Here, we show that the most significant interaction strength for NMR, i.e. the hyperfine coupling (HFC) strength, can be reasonably reproduced by first-principles calculations for these proposed QSLs. Applying this method to a supercell containing Cu-Zn defects enables us to map out the variation and distribution of HFC at different nuclear sites. This predictive power is expected to bridge the missing link in the analysis of the low-temperature NMR data.

cond-mat.str-el↗

arXiv2019

Anomalous Dirac Plasmons in 1D Topological Electrides

Plasmon opens up the possibility to efficiently couple light and matter at sub-wavelength scales. In general, the plasmon frequency is dependent of carrier density. This dependency, however, renders fundamentally a weak plasmon intensity at low frequency, especially for Dirac plasmon (DP) widely studied in graphene. Here we demonstrate a new type of DP, excited by a Dirac nodal-surface state, which exhibits an anomalously density-independent frequency. Remarkably, we predict realization of anomalous DP (ADP) in 1D topological electrides, such as Ba3CrN3 and Sr3CrN3, by first-principles calculations. The ADPs in both systems have a density-independent frequency and high intensity, and their frequency can be tuned from terahertz to mid-infrared by changing the excitation direction. Furthermore, the intrinsic weak electron-phonon coupling of anionic electrons in electrides affords an added advantage of ultra-low phonon-assisted damping and hence a long lifetime of the ADPs. Our work paves the way to developing novel plasmonic and optoelectronic devices by combining topological physics with electride materials.

cond-mat.mtrl-sci↗

arXiv2019

Towards High-fidelity Nonlinear 3D Face Morphable Model

Embedding 3D morphable basis functions into deep neural networks opens great potential for models with better representation power. However, to faithfully learn those models from an image collection, it requires strong regularization to overcome ambiguities involved in the learning process. This critically prevents us from learning high fidelity face models which are needed to represent face images in high level of details. To address this problem, this paper presents a novel approach to learn additional proxies as means to side-step strong regularizations, as well as, leverages to promote detailed shape/albedo. To ease the learning, we also propose to use a dual-pathway network, a carefully-designed architecture that brings a balance between global and local-based models. By improving the nonlinear 3D morphable model in both learning objective and network architecture, we present a model which is superior in capturing higher level of details than the linear or its precedent nonlinear counterparts. As a result, our model achieves state-of-the-art performance on 3D face reconstruction by solely optimizing latent representations.

cs.CV↗

arXiv2019

Theory of Epitaxial Growth of Borophene on Layered Electride: Thermodynamic Stability and Kinetic Pathway

Based on first-principles calculations, we propose that a layered electride Mg2O can serve as an effective substrate, in place of metal substrate, to grow honeycomb borophene (h-borophene). We first confirm the thermodynamic stability of h-B@Mg2O heterostructure by energetics analysis and dynamic stability by absence of imaginary frequency in phonon spectra. Then, kinetically, we identify the atomistic pathways for a preferred 2D growth mode over 3D growth, and reveal a growth transition from 2D compact islands to h-borophene at ~13-atom cluster size, indicating the feasibility of epitaxial growth of h-borophene on Mg2O. The h-borophene is found to be stabilized by nearly one electron transfer from Mg2O to h-borophene based on the Bader charge analysis. Moreover, the intrinsic band structure of the free-standing h-borophene is only weakly perturbed by the Mg2O substrate, a significant advantage over metal substrate. We envision that layered electrides provide an attractive family of substrates for epitaxial growth of a range of 2D materials that can otherwise only be grown on undesirable metal substrates.

cond-mat.mtrl-sci↗

arXiv2019

Elliptic flow of transported and non-transported proton in Au+Au collisions within the UrQMD model

With the framework of UrQMD model, by tracing the number of initial quarks in protons, we study the elliptic flow of transported protons, poduced protons and anti-protons in Au+Au collisions at $\sqrt{s_{NN}}$ = 7.7, 11.5, 39, 200 GeV. The difference of elliptic flow between transported protons and anti-protons is observed. The results of UrQMD model show a good agreement with the STAR results at low energies, but are deviate at high energies. It partly explains the $v_{2}$ difference between proton and anti-proton observed in the Beam Energy Scan program in Relativistic Heavy Ion Collider~(RHIC).

nucl-th↗

arXiv2019

Update of a Multi-Phase Transport Model with Modern Parton Distribution Functions and Nuclear Shadowing

A multi-phase transport (AMPT) model has been successful in explaining a wide range of observables in relativistic heavy ion collisions. In this work, we implement a modern set of free proton parton distribution functions and an impact parameter-dependent nuclear shadowing in the AMPT model. After refitting the parameters of the two-component initial condition model to the experimental data on $pp$ and $p \bar p$ total and inelastic cross sections from $\sqrt s \sim $ 4 GeV to 13 TeV, we study particle productions in $pp$ and $AA$ collisions. We show that the updated AMPT model with string melting can reasonably describe the overall particle yields and transverse momentum spectra for both $pp$ and $AA$ collisions at RHIC and LHC energies after we introduce a nuclear scaling of the minijet transverse momentum cutoff for $AA$ collisions at LHC energies that is motivated by the color glass condensate. Since heavy flavor and high-$p_{\rm T}$ particles are produced by perturbative-QCD processes and thus directly depend on parton distribution functions of nuclei, the updated AMPT model is expected to provide a more reliable description of these observables.

nucl-th↗

arXiv2019

On Computation and Generalization of GANs with Spectrum Control

Generative Adversarial Networks (GANs), though powerful, is hard to train. Several recent works (brock2016neural,miyato2018spectral) suggest that controlling the spectra of weight matrices in the discriminator can significantly improve the training of GANs. Motivated by their discovery, we propose a new framework for training GANs, which allows more flexible spectrum control (e.g., making the weight matrices of the discriminator have slow singular value decays). Specifically, we propose a new reparameterization approach for the weight matrices of the discriminator in GANs, which allows us to directly manipulate the spectra of the weight matrices through various regularizers and constraints, without intensively computing singular value decompositions. Theoretically, we further show that the spectrum control improves the generalization ability of GANs. Our experiments on CIFAR-10, STL-10, and ImageNet datasets confirm that compared to other methods, our proposed method is capable of generating images with competitive quality by utilizing spectral normalization and encouraging the slow singular value decay.

cs.LG↗

arXiv2019

Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment

Continuous integration is an indispensable step of modern software engineering practices to systematically manage the life cycles of system development. Developing a machine learning model is no difference - it is an engineering process with a life cycle, including design, implementation, tuning, testing, and deployment. However, most, if not all, existing continuous integration engines do not support machine learning as first-class citizens. In this paper, we present ease.ml/ci, to our best knowledge, the first continuous integration system for machine learning. The challenge of building ease.ml/ci is to provide rigorous guarantees, e.g., single accuracy point error tolerance with 0.999 reliability, with a practical amount of labeling effort, e.g., 2K labels per test. We design a domain specific language that allows users to specify integration conditions with reliability constraints, and develop simple novel optimizations that can lower the number of labels required by up to two orders of magnitude for test conditions popularly used in real production systems.

cs.LG↗

arXiv2019

Topological Edge States Induced by Zak's Phase in A3B Monolayers

In crystalline systems, charge polarization is related to Zak's phase determined by bulk band topology. Nontrivial charge polarization induces robust edge states accompanied with fractional charge. In Su-Schrieffer-Heeger (SSH) model, it is known that the strong modulation of electron hopping causes nontrivial charge polarization even in the presence of inversion symmetry. Here, we consider a bi-atomic honeycomb lattice to introduce such strong modulation, i.e. A$_3$B sheet. By tuning hopping ratio and onsite potential difference between A and B atoms, we show that topological phase transition characterized by Zak's phase occurs. Furthermore, we propose that C$_3$N and BC$_3$ are the possible realistic materials on the basis of first-principles calculations. Both of them display topological edge states induced by Zak's phase without spin-orbital couplings and external fields unlike conventional topological insulators.

cond-mat.mes-hall↗

arXiv2019

Joint Stabilization and Direction of 360°Videos

360° video provides an immersive experience for viewers, allowing them to freely explore the world by turning their head. However, creating high-quality 360° video content can be challenging, as viewers may miss important events by looking in the wrong direction, or they may see things that ruin the immersion, such as stitching artifacts and the film crew. We take advantage of the fact that not all directions are equally likely to be observed; most viewers are more likely to see content located at ``true north'', i.e. in front of them, due to ergonomic constraints. We therefore propose 360° video direction, where the video is jointly optimized to orient important events to the front of the viewer and visual clutter behind them, while producing smooth camera motion. Unlike traditional video, viewers can still explore the space as desired, but with the knowledge that the most important content is likely to be in front of them. Constraints can be user guided, either added directly on the equirectangular projection or by recording ``guidance'' viewing directions while watching the video in a VR headset, or automatically computed, such as via visual saliency or forward motion direction. To accomplish this, we propose a new motion estimation technique specifically designed for 360° video which outperforms the commonly used 5-point algorithm on wide angle video. We additionally formulate the direction problem as an optimization where a novel parametrization of spherical warping allows us to correct for some degree of parallax effects. We compare our approach to recent methods that address stabilization-only and converting 360° video to narrow field-of-view video.

cs.GR↗

arXiv2019

Closed-loop Control of Compensation Point in the K-Rb-$^{21}$Ne Comagnetometer

We investigate the real-time closed-loop control of compensation point in the K-Rb-$^{21}$Ne comagnetometer operated in the spin-exchange relaxation-free regime. By locking the electron resonance, the alkali metal electrons are free from the fluctuations of the longitudinal ambient magnetic field and nuclear magnetization, which could improve the systematic stability, enlarge the linear measuring range, and suppress the cross-talk error of the comagnetometer. This is the first demonstration of closed-loop control of magnetic field in the single nuclear species comagnetometer, which will be of great significance for rotation sensing as gyroscopes and other high precision metrology applications of the comagnetometer.

physics.atom-ph↗

arXiv2019

An Accurate and Transferable Machine-Learning Interatomic Potential for Silicon

The development of modern ab initio methods has rapidly increased our understanding of physics, chemistry and materials science. Unfortunately, intensive ab initio calculations are intractable for large and complex systems. On the other hand, empirical force fields are less accurate with poor transferability even though they are efficient to handle large and complex systems. The recent development of machine-learning based neural-network (NN) for local atomic environment representation of density functional theory (DFT) has offered a promising solution to this long-standing challenge. Si is one of the most important elements in science and technology, however, an accurate and transferable interatomic potential for Si is still lacking. Here, we develop a generalized NN potential for Si, which correctly predicts the Si(111)-(7x7) ground-state surface reconstruction for the first time and accurately reproduces the DFT results in a wide range of complex Si structures. We envision similar developments will be made for a wide range of materials systems in the near future.

cond-mat.mtrl-sci↗

arXiv2018

Photonic crystal nanocavity based on a topological corner state

Topological phonics has emerged as a novel approach to engineer the flow of light and provides unprecedented means for developing diverse photonic elements, including robust optical waveguides immune to structural imperfections. However, the development of nanoscale standing-wave cavities in topological photonics is rather slow, despite its importance when building densely-integrated photonic integrated circuits. In this Letter, we report a photonic crystal nanocavity based on a topological corner state, supported at a 90-degrees-angled rim of a two dimensional photonic crystal. A combination of the bulk-edge and edge-corner correspondences guarantees the presence of the higher-order topological state in a hierarchical manner. We experimentally observed a corner mode that is tightly localized in space while supporting a high Q factor over 2,000, verifying its promise as a nanocavity. These results cast new light on the way to introduce nanocavities in topological photonics platforms.

cond-mat.mes-hall↗

arXiv2018

Joint Face Alignment and 3D Face Reconstruction with Application to Face Recognition

Face alignment and 3D face reconstruction are traditionally accomplished as separated tasks. By exploring the strong correlation between 2D landmarks and 3D shapes, in contrast, we propose a joint face alignment and 3D face reconstruction method to simultaneously solve these two problems for 2D face images of arbitrary poses and expressions. This method, based on a summation model of 3D faces and cascaded regression in 2D and 3D shape spaces, iteratively and alternately applies two cascaded regressors, one for updating 2D landmarks and the other for 3D shape. The 3D shape and the landmarks are correlated via a 3D-to-2D mapping matrix, which is updated in each iteration to refine the location and visibility of 2D landmarks. Unlike existing methods, the proposed method can fully automatically generate both pose-and-expression-normalized (PEN) and expressive 3D faces and localize both visible and invisible 2D landmarks. Based on the PEN 3D faces, we devise a method to enhance face recognition accuracy across poses and expressions. Both linear and nonlinear implementations of the proposed method are presented and evaluated in this paper. Extensive experiments show that the proposed method can achieve the state-of-the-art accuracy in both face alignment and 3D face reconstruction, and benefit face recognition owing to its reconstructed PEN 3D face.

cs.CV↗

arXiv2018

Helical Topological Edge States in a Quadrupole Phase

Topological electric quadrupole is a recently proposed concept that extends the theory of electric polarization of crystals to higher orders. Such a quadrupole phase supports topological states localized on both edges and corners. In this work, we show that in a quadrupole phase of honeycomb lattice, topological helical edge states and pseudo-spin-polarized corner states appear by making use of a pseudo-spin degree of freedom related to point group symmetry. Furthermore, we argue that a general condition for emergence of helical edge states in a (pseudo-)spinful quadrupole phase is mirror or time-reversal symmetry. Our results offers a way of generating topological helical edge states without spin-orbital couplings.

cond-mat.mes-hall↗