arXiv subjects

Feng Li

Publications and source records attributed to Feng Li.

At least 289 records · Page 16Linked to original sources

arXiv2020

Ultrahigh-speed optical coherence tomography based on a 100 MHz and 100 nm swept source

We demonstrate an ultrahigh-speed optical coherence tomography (OCT) based on a 100 MHz swept source (SS). An all polarization-maintaining figure-9 mode-locked fiber laser is used as the seed laser. After nonlinear spectral expansion in an Erbium-doped fiber amplifier, a flat top spectrum with respectively 1-dB and 10-dB bandwidths of 73.7 nm and 106 nm is obtained. The broadband femtosecond pulse is time stretched to a swept signal in a section of dispersion compensation fiber with a total dispersion of -84 ps/nm. With the swept source, the axial resolution of the SS-OCT is measured to be 21 um with a 6 dB sensitivity roll-off length of 3 mm. A tomographic image of an encoding disk and a hard disk jointly rotating at 17,000 rpm was acquired by using the SS-OCT with a high imaging quality.

physics.optics↗

arXiv2020

Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image Restoration

Recently, convolutional neural network (CNN) has demonstrated significant success for image restoration (IR) tasks (e.g., image super-resolution, image deblurring, rain streak removal, and dehazing). However, existing CNN based models are commonly implemented as a single-path stream to enrich feature representations from low-quality (LQ) input space for final predictions, which fail to fully incorporate preceding low-level contexts into later high-level features within networks, thereby producing inferior results. In this paper, we present a deep interleaved network (DIN) that learns how information at different states should be combined for high-quality (HQ) images reconstruction. The proposed DIN follows a multi-path and multi-branch pattern allowing multiple interconnected branches to interleave and fuse at different states. In this way, the shallow information can guide deep representative features prediction to enhance the feature expression ability. Furthermore, we propose asymmetric co-attention (AsyCA) which is attached at each interleaved node to model the feature dependencies. Such AsyCA can not only adaptively emphasize the informative features from different states, but also improves the discriminative ability of networks. Our presented DIN can be trained end-to-end and applied to various IR tasks. Comprehensive evaluations on public benchmarks and real-world datasets demonstrate that the proposed DIN perform favorably against the state-of-the-art methods quantitatively and qualitatively.

cs.CV↗

arXiv2020

"Tiny galaxies" from "little bang"

We show, by solving the linearized Vlasov equations, that in the presence of an attractive density-dependent vector, or tensor, mean field potential, under a portion of the spinodal unstable phase region where the attractions among the quarks are more dominant, the transverse modes, i.e., the fluctuations of the current densities perpendicular to the wave vector, are activated due to relativity. In particular, the current densities in both the transverse directions oscillate with the same frequency, indicating the existence of the spontaneous rotations of the dense clusters. These rotating clusters would certainly create a local vortical flow field which, according to chiral (or spin) vortical effect, leads to the macroscopic separation of the axial charges or spins due to the spin-orbital interactions. Hence, for the first time, the connection between the axial charge or spin in-homogeneity and the first-order phase transition is proposed.

nucl-th↗

arXiv2020

No Perfect Outdoors: Towards A Deep Profiling of GNSS-based Location Contexts

While both outdoor and indoor localization methods are flourishing, how to properly marry them to offer pervasive localizability in urban areas remains open. Recently proposals on indoor-outdoor detection make the first step towards such an integration, yet complicated urban environments render such a binary classification incompetent. In this paper, we intend to fully explore raw GNSS measurements in order to better characterize the diversified urban environments. Essentially, we tackle the challenges introduced by the complex GNSS data and apply a deep learning model to identify representations for respective location contexts. We further develop two preliminary applications of our deep profiling. On one hand, we offer a more fine-grained semantic classification than binary indoor-outdoor detection. On the other hand, we derive a GPS error indicator more meaningful than that provided by Google Maps. These results are all corroborated by our extensive data collection and trace-driven evaluations.

cs.NI↗

arXiv2020

On-chip higher-order topological micromechanical metamaterials

Higher-order topological insulators exhibit multidimensional topological physics and unique application values due to their ability of integrating stable boundary states at multiple dimensions in a single chip. However, for signal-processing applications in high-frequency mechanical systems, the current realizations of higher-order topological mechanical materials are still limited to large-scale systems for kilohertz or lower frequencies. Here, we report the experimental observation of a on-chip micromechanical metamaterial as higher-order topological insulator for high-frequency mechanical waves. The higher-order topological phononic band gap is induced by the band inversion at the Brillouin zone corner which is achieved by configuring the orientations of the elliptic pillars etched on the silicon chip. With consistent experiments and theory, we demonstrate the coexistence of topological edge and corner states in the megahertz frequency regime as induced by the higher-order band topology. The experimental realization of on-chip micromechanical metamaterials with higher-order topology opens a regime for applications based on megahertz mechanical waves in an integrated platform where the edge and corner states act as stable waveguides and resonators, respectively.

physics.app-ph↗

arXiv2020

A New Screening Method for COVID-19 based on Ocular Feature Recognition by Machine Learning Tools

The Coronavirus disease 2019 (COVID-19) has affected several million people. With the outbreak of the epidemic, many researchers are devoting themselves to the COVID-19 screening system. The standard practices for rapid risk screening of COVID-19 are the CT imaging or RT-PCR (real-time polymerase chain reaction). However, these methods demand professional efforts of the acquisition of CT images and saliva samples, a certain amount of waiting time, and most importantly prohibitive examination fee in some countries. Recently, some literatures have shown that the COVID-19 patients usually accompanied by ocular manifestations consistent with the conjunctivitis, including conjunctival hyperemia, chemosis, epiphora, or increased secretions. After more than four months study, we found that the confirmed cases of COVID-19 present the consistent ocular pathological symbols; and we propose a new screening method of analyzing the eye-region images, captured by common CCD and CMOS cameras, could reliably make a rapid risk screening of COVID-19 with very high accuracy. We believe a system implementing such an algorithm should assist the triage management or the clinical diagnosis. To further evaluate our algorithm and approved by the Ethics Committee of Shanghai public health clinic center of Fudan University, we conduct a study of analyzing the eye-region images of 303 patients (104 COVID-19, 131 pulmonary, and 68 ocular patients), as well as 136 healthy people. Remarkably, our results of COVID-19 patients in testing set consistently present similar ocular pathological symbols; and very high testing results have been achieved in terms of sensitivity and specificity. We hope this study can be inspiring and helpful for encouraging more researches in this topic.

eess.IV↗

arXiv2020

Déjà vu: A data-centric forecasting approach through time series cross-similarity

Accurate forecasts are vital for supporting the decisions of modern companies. Forecasters typically select the most appropriate statistical model for each time series. However, statistical models usually presume some data generation process while making strong assumptions about the errors. In this paper, we present a novel data-centric approach -- `forecasting with similarity', which tackles model uncertainty in a model-free manner. Existing similarity-based methods focus on identifying similar patterns within the series, i.e., `self-similarity'. In contrast, we propose searching for similar patterns from a reference set, i.e., `cross-similarity'. Instead of extrapolating, the future paths of the similar series are aggregated to obtain the forecasts of the target series. Building on the cross-learning concept, our approach allows the application of similarity-based forecasting on series with limited lengths. We evaluate the approach using a rich collection of real data and show that it yields competitive accuracy in both points forecasts and prediction intervals.

stat.ME↗

arXiv2020

Transverse optical binding for a dual dipolar dielectric nanoparticle dimer

The physical origins of the transverse optical binding force and torque beyond the Rayleigh approximation have not been clearly expressed to date. Here, we present analytical expressions of the force and torque for a dual dipolar dielectric dimer illuminated by a plane wave propagating perpendicularly to the dimer axis. Using this analytical model, we explore the roles of the hybridized electric dipolar, magnetic dipolar, and electric-magnetic dipolar coupling interactions in the total force and torque on the particles. We find significant departures from the predictions of the Rayleigh approximation, particularly for high-refractive-index particles, where the force is dominated by the magnetic interaction. This results in an enhancement of the dimer stability by one to four orders of magnitude compared to the predictions of the Rayleigh approximation. For the case of torque, this is dominated by the coupling interaction and increases by an order of magnitude. Our results will help to guide future experimental work in optical binding of high-refractive-index dielectric particles.

physics.optics↗

arXiv2020

Effects of QCD critical point on light nuclei production

Using the nucleon coalescence model, which can naturally take into account the correlations in the nucleon density distribution, we study the effects of QCD critical point on light nuclei production in relativistic heavy-ion collisions. We find that the yield ratio $N_tN_p/N_d^2$ of proton ($p$), deuteron ($d$) and triton ($t$) increases monotonically with the nucleon density correlation length, which is expected to increase significantly near the critical point in the QCD phase diagram. Our study thus demonstrates that the yield ratio $N_tN_p/N_d^2$ can be used as a sensitive probe of the QCD critical phenomenon. We further discuss the relation between the QCD phase transitions in heavy-ion collisions and the possible non-monotonic behavior of \pdt~in its collision energy dependence.

nucl-th↗

arXiv2020

Rabi oscillation of azimuthons in weakly nonlinear waveguides

Rabi oscillation, an inter-band oscillation, depicts the periodic flopping between two states that belong to different energy levels in the presence of an oscillatory driving field. In photonics, Rabi oscillation can be mimicked by applying a weak longitudinal periodic modulation to the refractive index change of the system. However, the Rabi oscillation of nonlinear states has yet to be discussed. We report Rabi oscillations of azimuthons---spatially modulated vortex solitons---in weakly nonlinear waveguides with different symmetries, both numerically and theoretically. The period of Rabi oscillation can be determined by applying the coupled mode theory, which largely depends on the modulation strength. Whether the Rabi oscillation between two states can be obtained or not is determined by the spatial symmetry of the azimuthons and the modulating potential. In this paper we succeeded in obtaining the Rabi oscillation of azimuthons in the weakly nonlinear waveguides with different symmetries. Our results not only enrich the Rabi oscillation phenomena, but also provide a new avenue in the study of pattern formation and spatial field manipulation in nonlinear optical systems.

physics.optics↗

arXiv2020

The Sloan Digital Sky Survey Reverberation Mapping Project: Photometric g and i Light Curves

The Sloan Digital Sky Survey Reverberation Mapping (SDSS-RM) program monitors 849 active galactic nuclei (AGN) both spectroscopically and photometrically. The photometric observations used in this work span over four years and provide an excellent baseline for variability studies of these objects. We present the photometric light curves from 2014 to 2017 obtained by the Steward Observatory's Bok telescope and the CFHT telescope with MegaCam. We provide details on the data acquisition and processing of the data from each telescope, the difference imaging photometry used to produce the light curves, and the calculation of a variability index to quantify each AGN's variability. We find that the Welch-Stetson J-index provides a useful characterization of AGN variability and can be used to select AGNs for further study.

astro-ph.GA↗

arXiv2020

Enhanced yield ratio of light nuclei in heavy ion collisions with a first-order QCD phase transition

Using a transport model that includes a first-order chiral phase transition between the partonic and the hadronic matter, we study the development of density fluctuations in the matter produced in heavy ion collisions as it undergoes the phase transition, and their time evolution in later hadronic stage of the collisions. Using the coalescence model to describe the production of deuterons and tritons from nucleons at the kinetic freeze out, we find that the yield ratio $ N_\text{t}N_\text{p}/ N_\text{d}^2$, where $N_\text{p}$, $N_\text{d}$, and $N_\text{t}$ are, respectively, the proton, deuteron, and triton numbers, is enhanced if the evolution trajectory of the produced matter in the QCD phase diagram passes through the spinodal region of a first-order chiral phase transition.

nucl-th↗

arXiv2020

Forecasting with time series imaging

Feature-based time series representations have attracted substantial attention in a wide range of time series analysis methods. Recently, the use of time series features for forecast model averaging has been an emerging research focus in the forecasting community. Nonetheless, most of the existing approaches depend on the manual choice of an appropriate set of features. Exploiting machine learning methods to extract features from time series automatically becomes crucial in state-of-the-art time series analysis. In this paper, we introduce an automated approach to extract time series features based on time series imaging. We first transform time series into recurrence plots, from which local features can be extracted using computer vision algorithms. The extracted features are used for forecast model averaging. Our experiments show that forecasting based on automatically extracted features, with less human intervention and a more comprehensive view of the raw time series data, yields highly comparable performances with the best methods in the largest forecasting competition dataset (M4) and outperforms the top methods in the Tourism forecasting competition dataset.

stat.ML↗

arXiv2020

Deep Interleaved Network for Image Super-Resolution With Asymmetric Co-Attention

Recently, Convolutional Neural Networks (CNN) based image super-resolution (SR) have shown significant success in the literature. However, these methods are implemented as single-path stream to enrich feature maps from the input for the final prediction, which fail to fully incorporate former low-level features into later high-level features. In this paper, to tackle this problem, we propose a deep interleaved network (DIN) to learn how information at different states should be combined for image SR where shallow information guides deep representative features prediction. Our DIN follows a multi-branch pattern allowing multiple interconnected branches to interleave and fuse at different states. Besides, the asymmetric co-attention (AsyCA) is proposed and attacked to the interleaved nodes to adaptively emphasize informative features from different states and improve the discriminative ability of networks. Extensive experiments demonstrate the superiority of our proposed DIN in comparison with the state-of-the-art SR methods.

cs.CV↗

arXiv2020

The QCD critical point from the Nambu-Jona-Lasino model with a scalar-vector interaction

We study the critical point in the QCD phase diagram in the Nambu-Jona-Lasino (NJL) model by including a scalar-vector coupled interaction. We find that varying the strength of this interaction, which has no effect on the vacuum properties of QCD, can significantly affect the location of the critical point in the QCD phase diagram, particularly the value of the cirtical temperature. This provides a convenient way to use the NJL-based transport or hydrodynamic model to extract information about the QCD phase diagram from relativistic heavy-ion collisions.

nucl-th↗

arXiv2020

Acoustic spin-1 Weyl semimetal

Topological semimetal, hosting spin-1 Weyl point beyond Dirac and Weyl points, has attracted a great deal of attention. However, the spin-1 Weyl semimetal, which possesses exclusively the spin-1 Weyl points in a clean frequency window, without shadowed by any other nodal points, is yet to be discovered. Here, we report for the first time a spin-1 Weyl semimetal in a phononic crystal. Its spin-1 Weyl points, touched by two linear dispersions and an additional flat band, carry monopole charges (-2,0,2) or (2,0,-2) for the three bands from bottom to top, and result in double Fermi arcs existing both between the 1st and 2nd bands, as well as between the 2nd and 3rd bands. We further observe robust propagation against the multiple joints and topological negative refraction of acoustic surface arc wave. Our results pave the way to explore on the macroscopic scale the exotic properties of the spin-1 Weyl physics.

cond-mat.mes-hall↗

arXiv2020

Spin-orbit coupling in photonic graphene

We generate experimentally a honeycomb refractive index pattern in an atomic vapor cell using electromagnetically-induced transparency. We study experimentally and theoretically the propagation of polarized light beams in such "photonic graphene". We demonstrate that an effective spin-orbit coupling appears as a correction to the paraxial beam equations because of the strong spatial gradients of the permittivity. It leads to the coupling of spin and angular momentum at the Dirac points of the graphene lattice. Our results suggest that the polarization degree plays an important role in many configurations where it has been previously neglected.

cond-mat.mes-hall↗

arXiv2020

The Third Data Release of the Beijing-Arizona Sky Survey

The Beijing-Arizona Sky Survey (BASS) is a wide and deep imaging survey to cover a 5400 deg$^2$ area in the Northern Galactic Cap with the 2.3m Bok telescope using two filters ($g$ and $r$ bands). The Mosaic $z$-band Legacy Survey (MzLS) covers the same area in $z$ band with the 4m Mayall telescope. These two surveys will be used for spectroscopic targeting of the Dark Energy Spectroscopic Instrument (DESI). The BASS survey observations were completed in 2019 March. This paper describes the third data release (DR3) of BASS, which contains the photometric data from all BASS and MzLS observations between 2015 January and 2019 March. The median astrometric precision relative to {\it Gaia} positions is about 17 mas and the median photometric offset relative to the PanSTARRS1 photometry is within 5 mmag. The median $5σ$ AB magnitude depths for point sources are 24.2, 23.6, and 23.0 mag for $g$, $r$, and $z$ bands, respectively. The photometric depth within the survey area is highly homogeneous, with the difference between the 20\% and 80\% depth less than 0.3 mag. The DR3 data, including raw data, calibrated single-epoch images, single-epoch photometric catalogs, stacked images, and co-added photometric catalogs, are publicly accessible at \url{http://batc.bao.ac.cn/BASS/doku.php?id=datarelease:home}.

astro-ph.GA↗