arXiv subjects

Fei Gao

Publications and source records attributed to Fei Gao.

At least 487 records · Page 27Linked to original sources

arXiv2019

Learning to Rank for Blind Image Quality Assessment

Blind image quality assessment (BIQA) aims to predict perceptual image quality scores without access to reference images. State-of-the-art BIQA methods typically require subjects to score a large number of images to train a robust model. However, subjective quality scores are imprecise, biased, and inconsistent, and it is challenging to obtain a large scale database, or to extend existing databases, because of the inconvenience of collecting images, training the subjects, conducting subjective experiments, and realigning human quality evaluations. To combat these limitations, this paper explores and exploits preference image pairs (PIPs) such as "the quality of image $I_a$ is better than that of image $I_b$" for training a robust BIQA model. The preference label, representing the relative quality of two images, is generally precise and consistent, and is not sensitive to image content, distortion type, or subject identity; such PIPs can be generated at very low cost. The proposed BIQA method is one of learning to rank. We first formulate the problem of learning the mapping from the image features to the preference label as one of classification. In particular, we investigate the utilization of a multiple kernel learning algorithm based on group lasso (MKLGL) to provide a solution. A simple but effective strategy to estimate perceptual image quality scores is then presented. Experiments show that the proposed BIQA method is highly effective and achieves comparable performance to state-of-the-art BIQA algorithms. Moreover, the proposed method can be easily extended to new distortion categories.

cs.CV↗

arXiv2019

Efficient Simulation Budget Allocation for Subset Selection Using Regression Metamodels

This research considers the ranking and selection (R&S) problem of selecting the optimal subset from a finite set of alternative designs. Given the total simulation budget constraint, we aim to maximize the probability of correctly selecting the top-m designs. In order to improve the selection efficiency, we incorporate the information from across the domain into regression metamodels. In this research, we assume that the mean performance of each design is approximately quadratic. To achieve a better fit of this model, we divide the solution space into adjacent partitions such that the quadratic assumption can be satisfied within each partition. Using the large deviation theory, we propose an approximately optimal simulation budget allocation rule in the presence of partitioned domains. Numerical experiments demonstrate that our approach can enhance the simulation efficiency significantly.

math.OC↗

arXiv2019

Realization of an acoustic third-order topological insulator

The recent discovery of higher-order topological insulators (TIs) has opened new possibilities in the search for novel topological materials and metamaterials. Second-order TIs have been implemented in two-dimensional (2D) systems exhibiting topological 'corner states', as well as three-dimensional (3D) systems having one-dimensional (1D) topological 'hinge states'. Third-order TIs, which have topological states three dimensions lower than the bulk (which must thus be 3D or higher), have not yet been reported. Here, we describe the realization of a third-order TI in an anisotropic diamond-lattice acoustic metamaterial. The bulk acoustic bandstructure has nontrivial topology characterized by quantized Wannier centers. By direct acoustic measurement, we observe corner states at two corners of a rhombohedron-like structure, as predicted by the quantized Wannier centers. This work extends topological corner states from 2D to 3D, and may find applications in novel acoustic devices.

cond-mat.mes-hall↗

arXiv2019

Higher-order photonic topological states in surface-wave photonic crystals

Photonic topological states have revolutionized our understanding on the propagation and scattering of light. Recent discovery of higher-order photonic topological insulators opens an emergent horizon for zero-dimensional topological corner states. However, the previous realizations of higher-order photonic topological insulators suffer from either a limited operational frequency range due to the lumped components involved or a bulky structure with a large footprint, which are unfavorable for future integrated photonics. To overcome these limitations, we hereby experimentally demonstrate a planar surface-wave photonic crystal realization of two-dimensional higher-order topological insulators. The surface-wave photonic crystals exhibit a very large bulk bandgap (a bandwidth of 28%) due to multiple Bragg scatterings and host one-dimensional gapped edge states described by massive Dirac equations. The topology of those higher-dimensional photonic bands leads to the emergence of zero-dimensional corner states, which provide a route toward robust cavity modes for scalable, integrated photonic chips and an interface for the control of light-matter interaction.

physics.app-ph↗

arXiv2019

Quantum algorithm for visual tracking

Visual tracking (VT) is the process of locating a moving object of interest in a video. It is a fundamental problem in computer vision, with various applications in human-computer interaction, security and surveillance, robot perception, traffic control, etc. In this paper, we address this problem for the first time in the quantum setting, and present a quantum algorithm for VT based on the framework proposed by Henriques et al. [IEEE Trans. Pattern Anal. Mach. Intell., 7, 583 (2015)]. Our algorithm comprises two phases: training and detection. In the training phase, in order to discriminate the object and background, the algorithm trains a ridge regression classifier in the quantum state form where the optimal fitting parameters of ridge regression are encoded in the amplitudes. In the detection phase, the classifier is then employed to generate a quantum state whose amplitudes encode the responses of all the candidate image patches. The algorithm is shown to be polylogarithmic in scaling, when the image data matrices have low condition numbers, and therefore may achieve exponential speedup over the best classical counterpart. However, only quadratic speedup can be achieved when the algorithm is applied to implement the ultimate task of Henriques's framework, i.e., detecting the object position. We also discuss two other important applications related to VT: (1) object disappearance detection and (2) motion behavior matching, where much more significant speedup over the classical methods can be achieved. This work demonstrates the power of quantum computing in solving computer vision problems.

quant-ph↗

arXiv2019

Quantum Conditional Random Field

Conditional random field (CRF) is an important probabilistic machine learning model for labeling sequential data, which is widely utilized in natural language processing, bioinformatics and computer vision. However, training the CRF model is computationally intractable when large scale training samples are processed. Since little work has been done for labeling sequential data in the quantum settings, we in this paper construct a quantum CRF (QCRF) model by introducing well-defined Hamiltonians and measurements, and present a quantum algorithm to train this model. It is shown that the algorithm achieves an exponential speed-up over its classical counterpart. Furthermore, we also demonstrate that the QCRF model possesses higher Vapnik Chervonenkis dimension than the classical CRF model, which means QCRF is equipped with a higher learning ability.

quant-ph↗

arXiv2018

Valley-Hall photonic topological insulators with dual-band kink states

Extensive researches have revealed that valley, a binary degree of freedom (DOF), can be an excellent candidate of information carrier. Recently, valley DOF has been introduced into photonic systems, and several valley-Hall photonic topological insulators (PTIs) have been experimentally demonstrated. However, in the previous valley-Hall PTIs, topological kink states only work at a single frequency band, which limits potential applications in multiband waveguides, filters, communications, and so on. To overcome this challenge, here we experimentally demonstrate a valley-Hall PTI, where the topological kink states exist at two separated frequency bands, in a microwave substrate-integrated circuitry. Both the simulated and experimental results demonstrate the dual-band valley-Hall topological kink states are robust against the sharp bends of the internal domain wall with negligible inter-valley scattering. Our work may pave the way for multi-channel substrate-integrated photonic devices with high efficiency and high capacity for information communications and processing.

physics.app-ph↗

arXiv2018

Distribution Amplitudes of Heavy-Light Mesons

A symmetry-preserving approach to the continuum bound-state problem in quantum field theory is used to calculate the masses, leptonic decay constants and light-front distribution amplitudes of empirically accessible heavy-light mesons. The inverse moment of the $B$-meson distribution is particularly important in treatments of exclusive $B$-decays using effective field theory and the factorisation formalism; and its value is therefore computed: $λ_B(ζ= 2\,{\rm GeV}) = 0.54(3)\,$GeV. As an example and in anticipation of precision measurements at new-generation $B$-factories, the branching fraction for the rare $B\to γ(E_γ) \ell ν_\ell$ radiative decay is also calculated, retaining $1/m_B^2$ and $1/E_γ^2$ corrections to the differential decay width, with the result $Γ_{B\to γ\ell ν_\ell}/Γ_B = 0.47(15)$ on $E_γ> 1.5\,$GeV.

nucl-th↗

arXiv2018

Quantum data compression by principal component analysis

Data compression can be achieved by reducing the dimensionality of high-dimensional but approximately low-rank datasets, which may in fact be described by the variation of a much smaller number of parameters. It often serves as a preprocessing step to surmount the curse of dimensionality and to gain efficiency, and thus it plays an important role in machine learning and data mining. In this paper, we present a quantum algorithm that compresses an exponentially large high-dimensional but approximately low-rank dataset in quantum parallel, by dimensionality reduction (DR) based on principal component analysis (PCA), the most popular classical DR algorithm. We show that the proposed algorithm achieves exponential speedup over the classical PCA algorithm when the original dataset are projected onto a polylogarithmically low-dimensional space. The compressed dataset can then be further processed to implement other tasks of interest, with significantly less quantum resources. As examples, we apply this algorithm to reduce data dimensionality for two important quantum machine learning algorithms, quantum support vector machine and quantum linear regression for prediction. This work demonstrates that quantum machine learning can be released from the curse of dimensionality to solve problems of practical importance.

quant-ph↗

arXiv2018

Tunable magnetism of a single-carbon vacancy in graphene

Removing a single-carbon vacancy introduces (quasi-)localized states for both and electrons in graphene. Interactions between the localized dangling bond and quasilocalized electrons of a single-carbon vacancy in graphene are predicted to control its magnetism. However, experimentally confirming this prediction through manipulating the interactions between the and electrons remains an outstanding challenge. Here we report the manipulation of magnetism of individual single-carbon vacancy in graphene by using a scanning tunnelling microscopy (STM) tip. Our spin-polarized STM measurements, complemented by density functional theory calculations, indicate that interactions between the localized and quasilocalized electrons could split the electrons into two states with opposite spins even when they are well above the Fermi level. Via the STM tip, we successfully manipulate both the magnitude and direction of magnetic moment of the electrons with respect to that of the electrons. Three different magnetic states of the single-carbon vacancy, exhibiting magnetic moments of about 1.6, 0.5, and 0 respectively, are realized in our experiment.

cond-mat.mtrl-sci↗

arXiv2018

Simultaneous measurement of the light and charge response of liquid xenon to low-energy nuclear recoils at multiple electric fields

Dual-phase liquid xenon (LXe) detectors lead the direct search for particle dark matter. Understanding the signal production process of nuclear recoils in LXe is essential for the interpretation of LXe based dark matter searches. Up to now, only two experiments have simultaneously measured both the light and charge yield at different electric fields, neither of which attempted to evaluate the processes leading to light and charge production. In this letter, results from a neutron calibration of liquid xenon with simultaneous light and charge detection are presented for energies from 3-74 keV, at electric fields of 0.19, 0.49, and 1.02 kV/cm. No significant field dependence of the yields is observed.

physics.ins-det↗

arXiv2018

Spin-valley-controlled photonic topological insulator

The discovery of photonic topological insulators (PTIs) has opened the door to fundamentally new topological states of light.Current time-reversal-invariant PTIs emulate either the quantum spin Hall (QSH) effect or the quantum valley Hall (QVH) effect in condensed-matter systems, in order to achieve topological transport of photons whose propagation is predetermined by either 'photonic pseudospin' (abbreviated as 'spin') or valley. Here we demonstrate a new class of PTIs, whose topological phase is not determined solely by spin or valley, but is controlled by the competition between their induced gauge fields. Such a competition is enabled by tuning the strengths of spin-orbit coupling (SOC) and inversion-symmetry breaking in a single PTI. An unprecedented topological transition between QSH and QVH phases that is hard to achieve in condensed-matter systems is demonstrated. Our study merges the emerging fields of spintronics and valleytronics in the same photonic platform, and offers novel PTIs with reconfigurable topological phases.

physics.optics↗

arXiv2018

SD-CNN: a Shallow-Deep CNN for Improved Breast Cancer Diagnosis

Breast cancer is the second leading cause of cancer death among women worldwide. Nevertheless, it is also one of the most treatable malignances if detected early. Screening for breast cancer with digital mammography (DM) has been widely used. However it demonstrates limited sensitivity for women with dense breasts. An emerging technology in the field is contrast-enhanced digital mammography (CEDM), which includes a low energy (LE) image similar to DM, and a recombined image leveraging tumor neoangiogenesis similar to breast magnetic resonance imaging (MRI). CEDM has shown better diagnostic accuracy than DM. While promising, CEDM is not yet widely available across medical centers. In this research, we propose a Shallow-Deep Convolutional Neural Network (SD-CNN) where a shallow CNN is developed to derive "virtual" recombined images from LE images, and a deep CNN is employed to extract novel features from LE, recombined or "virtual" recombined images for ensemble models to classify the cases as benign vs. cancer. To evaluate the validity of our approach, we first develop a deep-CNN using 49 CEDM cases collected from Mayo Clinic to prove the contributions from recombined images for improved breast cancer diagnosis (0.86 in accuracy using LE imaging vs. 0.90 in accuracy using both LE and recombined imaging). We then develop a shallow-CNN using the same 49 CEDM cases to learn the nonlinear mapping from LE to recombined images. Next, we use 69 DM cases collected from the hospital located at Zhejiang University, China to generate "virtual" recombined images. Using DM alone provides 0.91 in accuracy, whereas SD-CNN improves the diagnostic accuracy to 0.95.

cs.CV↗

arXiv2018

Acoustic higher-order topological insulator on a Kagome lattice

High-order topological insulators (TIs) are a family of recently-predicted topological phases of matter obeying an extended topological bulk-boundary correspondence principle. For example, a two-dimensional (2D) second-order TI does not exhibit gapless one-dimensional (1D) topological edge states, like a standard 2D TI, but instead has topologically-protected zero-dimensional (0D) corner states. So far, higher-order TIs have been demonstrated only in classical mechanical and electromagnetic metamaterials exhibiting quantized quadrupole polarization. Here, we experimentally realize a second-order TI in an acoustic metamaterial. This is the first experimental realization of a new type of higher-order TI, based on a breathing Kagome lattice, that has zero quadrupole polarization but nontrivial bulk topology characterized by quantized Wannier centers (WCs). Unlike previous higher-order TI realizations, the corner states depend not only on the bulk topology but also on the corner shape; we show experimentally that they exist at acute-angled corners of the Kagome lattice, but not at obtuse-angled corners. This shape dependence allows corner states to act as topologically-protected but reconfigurable local resonances.

cond-mat.mes-hall↗

arXiv2018

A modification of Faddeev-Popov approach free from Gribov ambiguity

We propose a modified version of the Faddeev-Popov quantization approach for non-Abelian gauge field theory to avoid the Gribov ambiguity. We show that by means of introducing a new method to insert the correct identity into the Yang-Mills generating functional and considering the identity generated by an integral through a subgroup of the gauge group, the problem of the Gribov ambiguity can be removed naturally. Meanwhile by handling the absolute value of the Faddeev-Popov determinant with the method introduced by Williams and collaborators, we lift the Jacobian determinant together with the absolute value and obtain a local Lagrangian. The new Lagrangian have a nilpotent symmetry which can be viewed as an analogue of the BRST symmetry.

hep-th↗

arXiv2018

Superlight inverse Doppler effect

There is a century-old tenet [1, 2] that the inverse Doppler frequency shift of light [3-13] is impossible in homogeneous systems with a positive refractive index. Here we break this long-held tenet by predicting a new kind of Doppler effect of light inside the Cherenkov cone. Ever since the classic work of Ginzburg and Frank, it has been known that a superlight (i.e., superluminal) normal Doppler effect [14-18] appears inside the Cherenkov cone when the velocity of the source v is larger than the phase velocity of light v_p. By further developing their theory we discover that an inverse Doppler frequency shift will arise when v>2v_p. We denote this as the superlight inverse Doppler effect. Moreover, we show that the superlight inverse Doppler effect can be spatially separated from the other Doppler effects by using highly squeezed polaritons (such as graphene plasmons), which may facilitate the experimental observation.

physics.optics↗

arXiv2018

A Parameter Estimation of Fractional Order Grey Model Based on Adaptive Dynamic Cat Swarm Algorithm

In this paper, we utilize ADCSO (Adaptive Dynamic Cat Swarm Optimization) to estimate the parameters of Fractional Order Grey Model. The parameters of Fractional Order Grey Model affect the prediction accuracy of the model. In order to solve the problem that general swarm intelligence algorithms easily fall into the local optimum and optimize the accuracy of the model, ADCSO is utilized to reduce the error of the model. Experimental results for the data of container throughput of Wuhan Port and marine capture productions of Zhejiang Province show that the different parameter values affect the prediction results. The parameters estimated by ADCSO make the prediction error of the model smaller and the convergence speed higher, and it is not easy to fall into the local convergence compared with PSO (Particle Swarm Optimization) and LSM (Least Square Method). The feasibility and advantage of ADCSO for the parameter estimation of Fractional Order Grey Model are verified.

math.OC↗

arXiv2018

Ge hole spin qubit

Holes confined in quantum dots have gained considerable interest in the past few years due to their potential as spin qubits. Here we demonstrate double quantum dot devices in Ge hut wires. Low temperature transport measurements reveal Pauli spin blockade. We demonstrate electric-dipole spin resonance by applying a radio frequency electric field to one of the electrodes defining the double quantum dot. Next, we induce coherent hole spin oscillations by varying the duration of the microwave burst. Rabi oscillations with frequencies reaching 140MHz are observed. Finally, Ramsey experiments reveal dephasing times of 130ns. The reported results emphasize the potential of Ge as a platform for fast and scalable hole spin qubit devices.

cond-mat.mes-hall↗