Searcharxiv⌕ Search

arXiv subjects

Jie Liu

Publications and source records attributed to Jie Liu.

At least 505 records · Page 28Linked to original sources

Privacy protection based on mask template

Powerful recognition algorithms are widely used in the Internet or important medical systems, which poses a serious threat to personal privacy. Although the law provides for diversity protection, e.g. The General Data Protection Regulation (GDPR) in Europe and Articles 1032 to 1039 of the civil code in China. However, as an important privacy disclosure event, biometric data is often hidden, which is difficult for the owner to detect and trace to the source. Human biometrics generally exist in images. In order to avoid the disclosure of personal privacy, we should prevent unauthorized recognition algorithms from acquiring the real features of the original image.

cs.CR↗

Application of Color Block Code in Image Scaling

Aiming at the high cost of embedding annotation watermark in a narrow small area and the information distortion caused by the change of annotation watermark image resolution, this paper proposes a color block code technology, which uses location information and color code to form recognizable graphics, which can not only simplify the annotation graphics, but also ensure the recognition efficiency. First, the constituent elements of color block code are designed, and then the coding and decoding method of color block code is proposed. Experiments show that color block code has high anti-scaling and anti-interference, and can be widely used in the labeling of small object surface and low resolution image.

cs.GR↗

A practical algorithm to minimize the overall error in FEM computations

Using the standard finite element method (FEM) to solve general partial differential equations, the round-off error is found to be proportional to $N^{β_{\rm R}}$, with $N$ the number of degrees of freedom (DoFs) and $β_{\rm R}$ a coefficient. A method which uses a few cheap numerical experiments is proposed to determine the coefficient of proportionality and $β_{\rm R}$ in various space dimensions and FEM packages. Using the coefficients obtained above, the strategy put forward in \cite{liu386balancing} for predicting the highest achievable accuracy $E_{\rm min}$ and the associated optimal number of DoFs $N_{\rm opt}$ for specific problems is extended to general problems. This strategy allows predicting $E_{\rm min}$ accurately for general problems, with the CPU time for obtaining the solution with the highest accuracy $E_{\rm min}$ typically reduced by 60\%--90\%.

math.NA↗

Reducing circuit depth in adaptive variational quantum algorithms via effective Hamiltonian theories

Electronic structure simulation is an anticipated application for quantum computers. Due to high-dimensional quantum entanglement in strongly correlated systems, the quantum resources required to perform such simulations are far beyond the capacity of current quantum devices. To reduce the quantum circuit complexity, it has been suggested to incorporate a part of the electronic correlation into an effective Hamiltonian, which is often obtained from a similarity transformation of the electronic Hamiltonian. In this work, we introduce a new transformation in the form of a product of a linear combination of excitation operators to construct the effective Hamiltonian with finite terms. To demonstrate its accuracy, we also consider an equivalent adaptive variational algorithm with this transformation and show that it can obtain an accurate ground state wave function. The effective Hamiltonian defined with this new transformation is incorporated into the adaptive variational quantum algorithms to maintain constant-size quantum circuits. The new computational scheme is assessed by performing numerical simulations for small molecules. Chemical accuracy is achieved with a much shallower circuit depth.

quant-ph↗

Resonant tunneling of deuteron-triton fusion in strong high-frequency electromagnetic fields

We investigate deuteron-triton (DT) fusion in the presence of linearly polarized strong electromagnetic fields in high-frequency limit, in which a complex spherical square-well potential is exploited to describe the nuclear potential. Within the framework of the Kramers-Henneberger (KH) transformation, we have calculated the total and angular differential fusion cross sections by investigating the asymptotical phase shifts of the Coulomb wavefunctions. With introducing a dimensionless quantity of $n_d$ representing the ratio of the particle quiver oscillation amplitude to the radius of nuclear potential, we find that, even though the tunneling probability of passing through the Coulomb repulsive potential keeps almost identical to that in the absence of electromagnetic fields, the peak of total fusion sections shows an apparent shift from the well known value of 110 keV to 78 keV for $n_d=0.01$. The angular differential cross sections also show some resonance peaks that shift from zero inclination angle to $π/2$ with increasing the parameter $n_d$. The corresponding astrophysical $S$-factors are found to be enhanced by several times in amplitudes. With the help of Wentzel-Kramers-Brillouin (WKB) approximate wavefunctions, the shape-resonance tunneling mechanism of the above findings are uncovered and some implications are discussed.

nucl-th↗

Cross-domain User Preference Learning for Cold-start Recommendation

Cross-domain cold-start recommendation is an increasingly emerging issue for recommender systems. Existing works mainly focus on solving either cross-domain user recommendation or cold-start content recommendation. However, when a new domain evolves at its early stage, it has potential users similar to the source domain but with much fewer interactions. It is critical to learn a user's preference from the source domain and transfer it into the target domain, especially on the newly arriving contents with limited user feedback. To bridge this gap, we propose a self-trained Cross-dOmain User Preference LEarning (COUPLE) framework, targeting cold-start recommendation with various semantic tags, such as attributes of items or genres of videos. More specifically, we consider three levels of preferences, including user history, user content and user group to provide reliable recommendation. With user history represented by a domain-aware sequential model, a frequency encoder is applied to the underlying tags for user content preference learning. Then, a hierarchical memory tree with orthogonal node representation is proposed to further generalize user group preference across domains. The whole framework updates in a contrastive way with a First-In-First-Out (FIFO) queue to obtain more distinctive representations. Extensive experiments on two datasets demonstrate the efficiency of COUPLE in both user and content cold-start situations. By deploying an online A/B test for a week, we show that the Click-Through-Rate (CTR) of COUPLE is superior to other baselines used on Taobao APP. Now the method is serving online for the cross-domain cold micro-video recommendation.

cs.IR↗

AdaDM: Enabling Normalization for Image Super-Resolution

Normalization like Batch Normalization (BN) is a milestone technique to normalize the distributions of intermediate layers in deep learning, enabling faster training and better generalization accuracy. However, in fidelity image Super-Resolution (SR), it is believed that normalization layers get rid of range flexibility by normalizing the features and they are simply removed from modern SR networks. In this paper, we study this phenomenon quantitatively and qualitatively. We found that the standard deviation of the residual feature shrinks a lot after normalization layers, which causes the performance degradation in SR networks. Standard deviation reflects the amount of variation of pixel values. When the variation becomes smaller, the edges will become less discriminative for the network to resolve. To address this problem, we propose an Adaptive Deviation Modulator (AdaDM), in which a modulation factor is adaptively predicted to amplify the pixel deviation. For better generalization performance, we apply BN in state-of-the-art SR networks with the proposed AdaDM. Meanwhile, the deviation amplification strategy in AdaDM makes the edge information in the feature more distinguishable. As a consequence, SR networks with BN and our AdaDM can get substantial performance improvements on benchmark datasets. Extensive experiments have been conducted to show the effectiveness of our method.

eess.IV↗

Auto robust relative radiometric normalization via latent change noise modelling

Relative radiometric normalization(RRN) of different satellite images of the same terrain is necessary for change detection, object classification/segmentation, and map-making tasks. However, traditional RRN models are not robust, disturbing by object change, and RRN models precisely considering object change can not robustly obtain the no-change set. This paper proposes auto robust relative radiometric normalization methods via latent change noise modeling. They utilize the prior knowledge that no change points possess small-scale noise under relative radiometric normalization and that change points possess large-scale radiometric noise after radiometric normalization, combining the stochastic expectation maximization method to quickly and robustly extract the no-change set to learn the relative radiometric normalization mapping functions. This makes our model theoretically grounded regarding the probabilistic theory and mathematics deduction. Specifically, when we select histogram matching as the relative radiometric normalization learning scheme integrating with the mixture of Gaussian noise(HM-RRN-MoG), the HM-RRN-MoG model achieves the best performance. Our model possesses the ability to robustly against clouds/fogs/changes. Our method naturally generates a robust evaluation indicator for RRN that is the no-change set root mean square error. We apply the HM-RRN-MoG model to the latter vegetation/water change detection task, which reduces the radiometric contrast and NDVI/NDWI differences on the no-change set, generates consistent and comparable results. We utilize the no-change set into the building change detection task, efficiently reducing the pseudo-change and boosting the precision.

cs.CV↗

Algebraic fibre spaces with strictly nef relative anti-log canonical divisor

Let $(X,Δ)$ be a projective klt pair, and $f:X\to Y$ a fibration to a smooth projective variety $Y$ with strictly nef relative anti-log canonical divisor $-(K_{X/Y}+Δ)$. We prove that $f$ is a locally constant fibration with rationally connected fibres, and the base $Y$ is a canonically polarized hyperbolic projective manifold. In particular, when $Y$ is a single point, we establish that $X$ is rationally connected. Moreover, when $\dim X=3$ and $-(K_X+Δ)$ is strictly nef, we prove that $-(K_X+Δ)$ is ample, which confirms the singular version of a conjecture of Campana-Peternell for threefolds.

math.AG↗

An Eclipsing Black Widow Pulsar in NGC 6712

We report the discovery of the first radio pulsar associated with NGC 6712, an eclipsing black widow (BW) pulsar, J1853$-$0842A, found by high-sensitivity searches using the Five-hundred-meter Aperture Spherical radio Telescope. This 2.15 ms pulsar is in a 3.56 hr compact circular orbit with a very low mass companion likely of mass 0.018 to 0.036 $M_{\rm \odot}$ and exhibits eclipsing of the pulsar signal. Though the distance to PSR J1853$-$0842A predicted from its dispersion measure ($155.125 \pm 0.004$ cm$^{-3}$ pc) and Galactic free electron density models are about 30\% smaller than that of NGC 6712 obtained from interstellar reddening measurements, this is likely due to limited knowledge about the spiral arms and Scutum stellar cloud in this direction. Follow-up timing observations spanning 445 days allow us to localize the pulsar's position to be 0.14 core radii from the center of NGC 6712 and measure a negative spin-down rate for this pulsar of $-2.39(2)\times10^{-21}\rm s s^{-1}$. The latter cannot be explained without the acceleration of the GC and decisively supports the association between PSR J1853--0842A and NGC 6712. Considering the maximum GC acceleration, Galactic acceleration, and Shklovskii effect, we place an upper limit on the intrinsic spin-down rate to be $1.11\times10^{-20}\rm~s~s^{-1}$. From an analysis of the eclipsing observations, we estimate the electron density of the eclipse region to be about $1.88\times10^6\rm cm^{-3}$. We also place an upper limit of the accretion rate from the companion is about $3.05\times10^{-13}~M_{\rm \odot}\rm~yr^{-1}$ which is comparable with some other BWs.

astro-ph.HE↗

Observations of Forbush Decreases of cosmic ray electrons and positrons with the Dark Matter Particle Explorer

The Forbush Decrease (FD) represents the rapid decrease of the intensities of charged particles accompanied with the coronal mass ejections (CMEs) or high-speed streams from coronal holes. It has been mainly explored with ground-based neutron monitors network which indirectly measure the integrated intensities of all species of cosmic rays by counting secondary neutrons produced from interaction between atmosphere atoms and cosmic rays. The space-based experiments can resolve the species of particles but the energy ranges are limited by the relative small acceptances except for the most abundant particles like protons and helium. Therefore, the FD of cosmic ray electrons and positrons have just been investigated by the PAMELA experiment in the low energy range ($<5$ GeV) with limited statistics. In this paper, we study the FD event occurred in September, 2017, with the electron and positron data recorded by the Dark Matter Particle Explorer. The evolution of the FDs from 2 GeV to 20 GeV with a time resolution of 6 hours are given. We observe two solar energetic particle events in the time profile of the intensity of cosmic rays, the earlier and weak one has not been shown in the neutron monitor data. Furthermore, both the amplitude and recovery time of fluxes of electrons and positrons show clear energy-dependence, which is important in probing the disturbances of the interplanetary environment by the coronal mass ejections.

astro-ph.HE↗

Optical potential parameters of light nuclear fusion based on precise Coulomb wave functions

Based on precise Coulomb wave functions (CWFs), we attempt to calculate the fusion cross sections of light nuclei in a complex spherical square-well nuclear potential (i.e., optical potential model). Comparing with experimental benchmark cross section data, we can calibrate optical potential parameters associated with D+D, D+T, D+3He, p+D, p+6Li and p+7Li fusion reactions. Surprisingly, we find that our calculated optical potential parameters are quite different from those of many previous results (e.g., Phys. Rev. C. 61 (2000) 024610, Nucl. Phys. A 986 (2019) 98, etc.), in which approximate Coulomb wave functions (ACWFs) with only retaining the leading terms are exploited for the continuity conditions at the radius of nuclear potential. Furthermore, with the obtained optical potential parameters, we compare the fusion cross sections and astrophysical S-factors with that formulated from ACWFs approach, and also find apparent deviations especially for the fusion reactions with resonance peaks such as D+T and D+3He fusion reactions. We then calculate the phase diagrams of the fusion cross sections with respect to the optical potential parameters and demonstrate several narrow shape resonance belts. It implies that a small deviation of ACWFs from the exact CWFs at nuclear radius might lead to fall off the resonance regimes and therefore causes the big difference on the optical parameters as well as the cross sections.

nucl-th↗

Anchor-based Plain Net for Mobile Image Super-Resolution

Along with the rapid development of real-world applications, higher requirements on the accuracy and efficiency of image super-resolution (SR) are brought forward. Though existing methods have achieved remarkable success, the majority of them demand plenty of computational resources and large amount of RAM, and thus they can not be well applied to mobile device. In this paper, we aim at designing efficient architecture for 8-bit quantization and deploy it on mobile device. First, we conduct an experiment about meta-node latency by decomposing lightweight SR architectures, which determines the portable operations we can utilize. Then, we dig deeper into what kind of architecture is beneficial to 8-bit quantization and propose anchor-based plain net (ABPN). Finally, we adopt quantization-aware training strategy to further boost the performance. Our model can outperform 8-bit quantized FSRCNN by nearly 2dB in terms of PSNR, while satisfying realistic needs at the same time. Code is avaliable at https://github.com/NJU- Jet/SR_Mobile_Quantization.

eess.IV↗

NUMA-aware FFT-based Convolution on ARMv8 Many-core CPUs

Convolutional Neural Networks (CNNs), one of the most representative algorithms of deep learning, are widely used in various artificial intelligence applications. Convolution operations often take most of the computational overhead of CNNs. The FFT-based algorithm can improve the efficiency of convolution by reducing its algorithm complexity, there are a lot of works about the high-performance implementation of FFT-based convolution on many-core CPUs. However, there is no optimization for the non-uniform memory access (NUMA) characteristics in many-core CPUs. In this paper, we present a NUMA-aware FFT-based convolution implementation on ARMv8 many-core CPUs with NUMA architectures. The implementation can reduce a number of remote memory access through the data reordering of FFT transformations and the three-level parallelization of the complex matrix multiplication. The experiment results on a ARMv8 many-core CPU with NUMA architectures demonstrate that our NUMA-aware implementation has much better performance than the state-of-the-art work in most cases.

cs.DC↗

Basic quantities of the Equation of State in isospin asymmetric nuclear matter

Based on the Hugenholtz-Van Hove theorem, six basic quantities of the EoS in isospin asymmetric nuclear matter are expressed in terms of the nucleon kinetic energy $t(k)$, the isospin symmetric and asymmetric parts of the single-nucleon potentials $U_0(ρ,k)$ and $U_{\text{\text{sym,i}}}(ρ,k)$. The six basic quantities include the quadratic symmetry energy $E_{\text{sym,2}}(ρ)$, the quartic symmetry energy $E_{\text{sym,4}}(ρ)$, their corresponding density slopes $L_2(ρ)$ and $L_4(ρ)$, and the incompressibility coefficients $K_2(ρ)$ and $K_4(ρ)$. By using four types of well-known effective nucleon-nucleon interaction models, namely the BGBD, MDI, Skyrme, and Gogny forces, the density- and isospin-dependent properties of these basic quantities are systematically calculated and their values at the saturation density $ρ_0$ are explicitly given. The contributions to these quantities from $t(k)$, $U_0(ρ,k)$, and $U_{\text{sym,i}}(ρ,k)$ are also analyzed at the normal nuclear density $ρ_0$. It is clearly shown that the first-order asymmetric term $U_{\text{sym,1}}(ρ,k)$ (also known as the symmetry potential in Lane potential) plays a vital role in determining the density dependence of the quadratic symmetry energy $E_{\text{sym,2}}(ρ)$. It is also shown that the contributions from high-order asymmetric parts of the single-nucleon potentials ($U_{\text{sym,i}}(ρ,k)$ with $i>1$) cannot be neglected in the calculations of the other five basic quantities. Moreover, by analyzing the properties of asymmetric nuclear matter at the exact saturation density $ρ_{\text{sat}}(δ)$, the corresponding quadratic incompressibility coefficient is found to have a simple empirical relation $K_{\text{sat,2}}=K_{2}(ρ_0)-4.14 L_2(ρ_0)$.

nucl-th↗

A Quantum Algorithm to Calculate Band Structure at the EOM Level of Theory

Band structure is a cornerstone to understand electronic properties of materials. Accurate band structure calculations using a high-level quantum-chemistry theory can be computationally very expensive. It is promising to speed up such calculations with a quantum computer. In this study, we present a quantum algorithm for band structure calculation based on the equation-of-motion (EOM) theory. First, we introduce a new variational quantum eigensolver algorithm named ADAPT-C for ground-state quantum simulation of solids, where the wave function is built adaptively from a complete set of anti-Hermitian operators. Then, on top of the ADAPT-C ground state, quasiparticle energies and the band structure can be calculated using the EOM theory in a quantum-subspace-expansion (QSE) style, where the projected excitation operators guarantee that the killer condition is satisfied. As a proof of principle, such an EOM-ADAPT-C protocol is used to calculate the band structures of silicon and diamond using a quantum computer simulator.

quant-ph↗

Numerical Eigensolver for Solving Eigenmodes of Cavity Resonators Filled With both Electric and Magnetic Lossy, Anisotropic Media

This article presents the numerical eigensolver to find the resonant frequencies of 3-D closed cavity resonators filled with both electric and magnetic lossy, anisotropic media. By introducing a dummy variable with zero value in the 3-D linear vector Maxwell eigenvalue problem for the electric field, we enforce the divergence-free condition for electric flux density in a weak sense. In addition, by introducing a dummy variable with constant value in the 3-D linear vector Maxwell eigenvalue problem for the magnetic field, we enforce the divergence-free condition for magnetic flux density in a weak sense. Moreover, it is theoretically proved that the novel method of introducing dummy variables can be free of all the spurious modes in solving eigenmodes of the 3-D closed cavity problem. Numerical experiments show that the numerical eigensolver supported by this article can eliminate all the spurious modes, including spurious dc modes.

math.NA↗