arXiv subjects

Yan Li

Publications and source records attributed to Yan Li.

At least 559 records · Page 31Linked to original sources

arXiv2018

A photonic interface of chiral cavity quantum electrodynamics

Cavity quantum electrodynamics studies light-matter interactions at single quanta level. Chiral photon-emitter coupling in photonic structures is characterized as unidirectional propagation locked by the local polarization of light. However, to realize the strong interaction of photon-emitter with direction-locked propagation, which will bring novel applications in nonreciprocal quantum devices, has not been reported yet. Here, we propose the coupled photonic crystal and metallic nanoparticle structure, where through strong local field with high helicity, the rate of circularly polarized photons emitting into photonic crystal waveguide is one order larger than that without the nanoparticle and the linewidth of Rabi splitting spectra is about one-tenth of that with only the nanoparticle, both with $\sim$ 95$\%$ photons propagating unidirectionally, which can be utilized in directional quantum light sources.

physics.optics↗

arXiv2018

On Menon-Sury's identity with several Dirichlet characters

The Menon-Sury's identity is as follows: \begin{equation*} \sum_{\substack{1 \leq a, b_1, b_2, \ldots, b_r \leq n\\\mathrm{gcd}(a,n)=1}} \mathrm{gcd}(a-1,b_1, b_2, \ldots, b_r,n)=φ(n) σ_r(n), \end{equation*} where $φ$ is Euler's totient function and $σ_r(n)=\sum_{d\mid n}{d^r}$. Recently, Li, Hu and Kim \cite{L-K} extended the above identity to a multi-variable case with a Dirichlet character, that is, they proved \begin{equation*} \sum_{\substack{a\in\Bbb Z_n^\ast \\ b_1, \ldots, b_r\in\Bbb Z_n}} \mathrm{gcd}(a-1,b_1, b_2, \ldots, b_r,n)χ(a)=φ(n)σ_r{\left(\frac{n}{d}\right)}, \end{equation*} where $χ$ is a Dirichlet character modulo $n$ and $d$ is the conductor of $χ$. In this paper, we explicitly compute the sum \begin{equation*}\sum_{\substack{a_1, \ldots, a_s\in\Bbb Z_n^\ast \\ b_1, ..., b_r\in\Bbb Z_n}}\gcd(a_1-1, \ldots, a_s-1,b_1, \ldots, b_r, n)χ_{1}(a_1) \cdots χ_{s}(a_s).\end{equation*} where $χ_{i} (1\leq i\leq s)$ are Dirichlet characters mod $n$ with conductor $d_i$. A special but common case of our main result reads like this : \begin{equation*}\sum_{\substack{a_1, \ldots, a_s\in\Bbb Z_n^\ast \\ b_1, ..., b_r\in\Bbb Z_n}}\gcd(a_1-1, \ldots, a_s-1,b_1, \ldots, b_r, n)χ_{1}(a_1) \cdots χ_{s}(a_s)=φ(n)σ_{s+r-1}\left(\frac{n}{d}\right)\end{equation*} if $d$ and $n$ have exactly the same prime factors, where $d={\rm lcm}(d_1,\ldots,d_s)$ is the least common multiple of $d_1,\ldots,d_s$. Our result generalizes the above Menon-Sury's identity and Li-Hu-Kim's identity.

math.NT↗

arXiv2018

Generalized spatial differentiation from spin Hall effect of light

Optics naturally provides us with some powerful mathematical operations. Here we experimentally demonstrate that during reflection or refraction at a single optical planar interface, the optical computing of spatial differentiation can be realized by analyzing specific orthogonal polarization states of light. We show that the spatial differentiation is intrinsically due to the spin Hall effect of light and generally accompanies light reflection and refraction at any planar interface, regardless of material composition or incident angles. The proposed spin-optical method takes advantages of a simple and common structure to enable vectorial-field computation and perform edge detection for ultra-fast and energy-efficient image processing.

physics.optics↗

arXiv2018

Geometric approach of degree of polarization in 3D polarimetry

The geometric approach presented in this paper concerns the degree of polarization (DoP) of a random 3D statistical electromagnetic field. We use that a 3*3 coherency matrix can always be decomposed into an incoherent superposition of three specific coherency matrices, to construct the first orthant of a spherical shell(0<x,y,z<1,sqrt(3)/3<r<1) . For a random 3D statistical electromagnetic field, the polarimetric result of the can be expressed in terms of a point located in the first orthant of this spherical shell physically. Furthermore, based on the intrinsic relationship between the defined parameters (DoP3D_P,DoP3D_PP) and DoP, a spatially quadric surface is obtained to quantify the values of DoP the for all physically reachable points contained in the first orthant of this spherical shell. Within this geometric approach, two examples are cited to demonstrate the applicability of 3D polarimetry to intuitively display the polarimetric result of a random 3D statistical electromagnetic field.

physics.optics↗

arXiv2018

D-shaped photonic crystal fiber plasmonic refractive index sensor based on gold grating

In this work, we proposed a high resolution D-shaped photonic crystal fiber (PCF) surface plasmon resonance (SPR) sensor based on gold grating. Gold grating is introduced to modulate the resonance wavelength and enhance the refractive index (RI) sensitivity. Structure parameters of PCF and gold grating are analyzed by finite element method (FEM) for optimizing the SPR sensor. The simulation results indicate that air hole pitch, air hole diameter and gold thickness and grating constant have little influence on the sensitivity of refractive index, which reduces the requirement of precise processing. For improving the resolution of RI sensing, two-feature (2F) interrogation method which combines wavelength interrogation and amplitude interrogation is used and the maximum theoretical resolution of SPR sensor reaches to 5.98*10-6 RIU in range of 1.36-1.38 and the wavelength sensitivity reaches to 3340nm/RIU. The proposed SPR sensor shows potential applications on developing a high-sensitivity, real time and fast-response SPR-RI sensor.

physics.optics↗

arXiv2018

Convective overshooting in sdB stars using the $k$-$ω$ model

Mixing in the convective core is quite uncertain in core helium burning stars. In order to explore the overshooting mixing beyond the convective core, we incorporated the $k$-$ω$ proposed by Li (2012, 2017) into MESA, and investigated the overshooting mixing in evolution of sdB models. We found that the development of the convective core can be divided into three stages. When the radiative temperature gradient $\nabla_{\rm rad}$ monotonically decreases from the stellar center, the overshooting mixing presents a behaviour of exponential decay similar with Herwig (2000), and the overshooting distance is to make $\nabla_{\rm rad} \simeq \nabla_{\rm ad}$ at the boundary of the convective core, in agreement with the prediction of the self-driving mechanism Castellani, Giannone \& Renzini (1971a). When the radiative temperature gradient $\nabla_{\rm rad}$ shows a minimum value in the convective core, the convective core may be divided into two convection zones if the minimum value of $\nabla_{\rm rad}$ is smaller than the adiabatic temperature gradient $\nabla_{\rm ad}$. For the single-zone case, the overshooting mixing still shows an exponential decay behaviour, but the overshooting distance is much smaller than in the initial stage. For the double-zone case, the overshooting mixing is similar to that of the single-case beyond the convective core, while it almost stops on both sides of the above convective shell. Our overshooting mixing scheme is similar to the maximal overshoot scheme of Constantino et al. (2015). In the final stage, continuous injection of fresh helium into the convective core by the overshooting mixing happens, which is similar to the "core breathing pulses" (Sweigart \& Demarque 1973; Castellani et al. 1985).

astro-ph.SR↗

arXiv2018

Object detection and tracking benchmark in industry based on improved correlation filter

Real-time object detection and tracking have shown to be the basis of intelligent production for industrial 4.0 applications. It is a challenging task because of various distorted data in complex industrial setting. The correlation filter (CF) has been used to trade off the low-cost computation and high performance. However, traditional CF training strategy can not get satisfied performance for the various industrial data; because the simple sampling(bagging) during training process will not find the exact solutions in a data space with a large diversity. In this paper, we propose Dijkstra-distance based correlation filters (DBCF), which establishes a new learning framework that embeds distribution-related constraints into the multi-channel correlation filters (MCCF). DBCF is able to handle the huge variations existing in the industrial data by improving those constraints based on the shortest path among all solutions. To evaluate DBCF, we build a new dataset as the benchmark for industrial 4.0 application. Extensive experiments demonstrate that DBCF produces high performance and exceeds the state-of-the-art methods. The dataset and source code can be found at https://github.com/bczhangbczhang

cs.CV↗

arXiv2018

Revealing photons behaviors in a birefringent interferometer

The interferometer is one of the most important devices for revealing the nature of light and for precision optical metrology. Though lots of experiments were performed for probing photons behaviors in various configurations, a complete study of photons behavior in a birefringent interferometer has not ever been performed. Based on an environmental turbulence immune Mach-Zehnder interferometer, tunable photonic beatings by rotating a birefringent crystal versus the temperature of the crystal for both single-photon and two-photon are observed. Furthermore, the two-photon interference fringes beat two times faster than the single-photon interference fringes. This beating effect is used to determine the thermal dispersion coefficients of the two principal refractive axes with a single measurement, the two-photon interference shows super-resolution and high sensitivity. Obvious differences between two-photon and single photon interference are also revealed in an unbalanced situations. In addition, influences of the photon bandwidth to the beating behaviors that come from polarization-dependent decoherence are also investigated. Our findings will be important for better understanding the behavior of two-photon interference in a birefringent interferometer and for precision optical metrology with quantum enhancement.

quant-ph↗

arXiv2018

PID2018 Benchmark Challenge: learning feedforward control

The design and application of learning feedforward controllers (LFFC) for the one-staged refrigeration cycle model described in the PID2018 Benchmark Challenge is presented, and its effectiveness is evaluated. The control system consists of two components: 1) a preset PID component and 2) a learning feedforward component which is a function approximator that is adapted on the basis of the feedback signal. A B-spline network based LFFC and a low-pass filter based LFFC are designed to track the desired outlet temperature of evaporator secondary flux and the superheating degree of refrigerant at evaporator outlet. Encouraging simulation results are included. Qualitative and quantitative comparison results evaluations show that, with little effort, a high-performance control system can be obtained with this approach. Our initial simple attempt of low-pass filter based LFFC and B-spline network based LFFC give J=0.4902 and J=0.6536 relative to the decentralized PID controller, respectively. Besides, the initial attempt of a combination controller of our optimized PI controller and low-pass filter LFFC gives J=0.6947 relative to the multi-variable PID controller.

eess.SY↗

arXiv2018

Information Encoding with Optical Dielectric Metasurface via Independent Multichannels

Information encryption and security is a prerequisite for information technology which can be realized by optical metasurface owing to its arbitrary manipulation over the wavelength, polarization, phase and amplitude of light. So far information encoding can be implemented by the metasurface in one dimensional (1D) mode (either wavelength or polarization) only with several combinations of independent channels. Here we successfully apply dielectric metasurfaces in a 2D mode (both wavelength and polarization) with far more combinations of independent channels to encrypt information, which therefore enhances the encryption security dramatically. Six independent channels by two circular polarization states (RCP and LCP) and three visible wavelengths (633 nm, 532 nm and 473 nm) in 2D mode can produce 63 combinations available to information encoding, in sharp contrast with 7 combinations by 3 independent channels in 1D mode. This 2D mode encoding strategy paves a novel pathway for escalating the security level of information in multichannel information encryption, anti-counterfeiting, optical data storage, and information processing.

physics.optics↗

arXiv2018

Communication-Efficient Projection-Free Algorithm for Distributed Optimization

Distributed optimization has gained a surge of interest in recent years. In this paper we propose a distributed projection free algorithm named Distributed Conditional Gradient Sliding(DCGS). Compared to the state-of-the-art distributed Frank-Wolfe algorithm, our algorithm attains the same communication complexity under much more realistic assumptions. In contrast to the consensus based algorithm, DCGS is based on the primal-dual algorithm, yielding a modular analysis that can be exploited to improve linear oracle complexity whenever centralized Frank-Wolfe can be improved. We demonstrate this advantage and show that the linear oracle complexity can be reduced to almost the same order of magnitude as the communication complexity, when the feasible set is polyhedral. Finally we present experimental results on Lasso and matrix completion, demonstrating significant performance improvement compared to the existing distributed Frank-Wolfe algorithm.

math.OC↗

arXiv2018

Projection-Free Algorithms in Statistical Estimation

Frank-Wolfe algorithm (FW) and its variants have gained a surge of interests in machine learning community due to its projection-free property. Recently people have reduced the gradient evaluation complexity of FW algorithm to $\log(\frac{1}ε)$ for the smooth and strongly convex objective. This complexity result is especially significant in learning problem, as the overwhelming data size makes a single evluation of gradient computational expensive. However, in high-dimensional statistical estimation problems, the objective is typically not strongly convex, and sometimes even non-convex. In this paper, we extend the state-of-the-art FW type algorithms for the large-scale, high-dimensional estimation problem. We show that as long as the objective satisfies {\em restricted strong convexity}, and we are not optimizing over statistical limit of the model, the $\log(\frac{1}ε)$ gradient evaluation complexity could still be attained.

stat.ML↗

arXiv2018

Gated Recurrent Unit Based Acoustic Modeling with Future Context

The use of future contextual information is typically shown to be helpful for acoustic modeling. However, for the recurrent neural network (RNN), it's not so easy to model the future temporal context effectively, meanwhile keep lower model latency. In this paper, we attempt to design a RNN acoustic model that being capable of utilizing the future context effectively and directly, with the model latency and computation cost as low as possible. The proposed model is based on the minimal gated recurrent unit (mGRU) with an input projection layer inserted in it. Two context modules, temporal encoding and temporal convolution, are specifically designed for this architecture to model the future context. Experimental results on the Switchboard task and an internal Mandarin ASR task show that, the proposed model performs much better than long short-term memory (LSTM) and mGRU models, whereas enables online decoding with a maximum latency of 170 ms. This model even outperforms a very strong baseline, TDNN-LSTM, with smaller model latency and almost half less parameters.

cs.CL↗

arXiv2018

Which evolutionary status does the Blue Large-Amplitude Pulsators stay at?

Asteroseismology is a very useful tool for exploring the stellar interiors and evolutionary status and for determining stellar fundamental parameters, such as stellar mass, radius, surface gravity, and the stellar mean density. In the present work, we use it to preliminarily analyze the 14 new-type pulsating stars: Blue Large-Amplitude Pulsators (BLAPs) which is observed by OGLE project, to roughly analyze their evolutionary status. We adopt the theory of single star evolution and artificially set the mass loss rate of $\dot{M}=-2\times10^{-4}~{\rm M_{\odot}/year}$ and mass loss beginning at the radius of $R=40$ $\rm R_{\odot}$ on red giant branch to generate a series of theoretical models. Based on these theoretical models and the corresponding observations, we find that those BLAP stars are more likely to be the core helium burning stars. Most of them are in the middle and late phase of the helium burning.

astro-ph.SR↗

arXiv2018

Frequentist and Bayesian Quantum Phase Estimation

Frequentist and Bayesian phase estimation strategies lead to conceptually different results on the state of knowledge about the true value of the phase shift. We compare the two frameworks and their sensitivity bounds to the estimation of an interferometric phase shift limited by quantum noise, considering both the cases of a fixed and a fluctuating parameter. We point out that frequentist precision bounds, such as the Cramèr-Rao bound, for instance, do not apply to Bayesian strategies and vice-versa. Similarly, bounds for fluctuating parameters make no statement about the estimation of a fixed parameter.

quant-ph↗

arXiv2018

A Telecom Perspective on the Internet of Drones: From LTE-Advanced to 5G

Drones are driving numerous and evolving use cases, and creating transformative socio-economic benefits. Drone operation needs wireless connectivity for communication between drones and ground control systems, among drones, and between drones and air traffic management systems. Mobile networks are well positioned to identify, track, and control the growing fleet of drones. The wide-area, quality, and secure connectivity provided by mobile networks can enhance the efficiency and effectiveness of drone operations beyond visual line-of-sight range. In this article, we elaborate how the drone ecosystem can benefit from mobile technologies, summarize key capabilities required by drone applications, and analyze the service requirements on mobile networks. We present field trial results collected in LTE-Advanced networks to gain insights into the capabilities of the current 4G+ networks for connected drones and share our vision on how 5G networks can further support diversified drone applications.

cs.NI↗

arXiv2018

Effective numerical treatment of sub-diffusion equation with non-smooth solution

In this paper we investigate a sub-diffusion equation for simulating the anomalous diffusion phenomenon in real physical environment. Based on an equivalent transformation of the original sub-diffusion equation followed by the use of a smooth operator, we devise a high-order numerical scheme by combining the Nystrom method in temporal direction with the compact finite difference method and the spectral method in spatial direction. The distinct advantage of this approach in comparison with most current methods is its high convergence rate even though the solution of the anomalous sub-diffusion equation usually has lower regularity on the starting point. The effectiveness and efficiency of our proposed method are verified by several numerical experiments.

math.NA↗

arXiv2018

Discriminative Learning of Latent Features for Zero-Shot Recognition

Zero-shot learning (ZSL) aims to recognize unseen image categories by learning an embedding space between image and semantic representations. For years, among existing works, it has been the center task to learn the proper mapping matrices aligning the visual and semantic space, whilst the importance to learn discriminative representations for ZSL is ignored. In this work, we retrospect existing methods and demonstrate the necessity to learn discriminative representations for both visual and semantic instances of ZSL. We propose an end-to-end network that is capable of 1) automatically discovering discriminative regions by a zoom network; and 2) learning discriminative semantic representations in an augmented space introduced for both user-defined and latent attributes. Our proposed method is tested extensively on two challenging ZSL datasets, and the experiment results show that the proposed method significantly outperforms state-of-the-art methods.

cs.CV↗