arXiv subjects

Hui Li

Publications and source records attributed to Hui Li.

At least 541 records · Page 30Linked to original sources

arXiv2022

Tighter sum uncertainty relations via metric-adjusted skew information

In this paper, we first provide three general norm inequalities, which are used to give new uncertainty relations of any finite observables and quantum channels via metric-adjusted skew information. The results are applicable to its special cases as Wigner-Yanase-Dyson skew information. In quantifying the uncertainty of channels, we discuss two types of lower bounds and compare the tightness between them, meanwhile, a tight lower bound is given. The uncertainty relations obtained by us are stronger than the existing ones. To illustrate our results, we give several specific examples.

quant-ph↗

arXiv2022

Simulation of classical Ising-like magnetism with a Mott insulator of paired atoms

Quantum simulation of the XXZ model with a two-component Bose or Fermi Hubbard model based on a Mott insulator background has been widely used in the investigations of quantum magnetism with ultracold neutral atoms. In most cases, the diagonal spin-spin interaction is always accompanied by a large spin-exchange interaction which hinders the formation of long-range magnetic order at low temperature. Here we show that the spin-exchange interaction can be strongly reduced in a Mott insulator of paired atoms, while the diagonal spin-spin interaction remains unaffected. Thus, the effective magnetic model is quite close to an exact classical Ising model in the textbook. And we analysed an experimentally achievable three-component Fermi-Hubbard model of $\mathrm{{}^{6}Li}$ with two hyperfine levels of atoms paired in the lattice. We find the long-range antiferromagnetic order of such a three-component Fermi-Hubbard model can be much stronger than that of a typical two-component Fermi-Hubbard model at low temperature. And we discussed the possiblity of simulating an exact two-dimensional ferromagnetic Ising model in a Mott insulator of paired bosonic atoms. Our results may be useful for experimental investigation of the long-range Ising-like magnetism with ultracold neutral atoms under thermal equilibrium.

cond-mat.quant-gas↗

arXiv2022

Nature and Scalings of Density Fluctuations of Compressible MHD Turbulence with Applications to the Solar Wind

The solar wind is a magnetized and turbulent plasma. Its turbulence is often dominated by Alfvénic fluctuations and often deemed as nearly incompressible far away from the Sun, as shown by in-situ measurements near 1AU. However, for solar wind closer to the Sun, the plasma $β$ decreases (often lower than unity) while the turbulent Mach number $M_t$ increases (can approach unity, e.g., transonic fluctuations). These conditions could produce significantly more compressible effects, characterized by enhanced density fluctuations, as seen by several space missions. In this paper, a series of 3D MHD simulations of turbulence are carried out to understand the properties of compressible turbulence, particularly the generation of density fluctuations. We find that, over a broad range of parameter space in plasma $β$, cross helicity and polytropic index, the turbulent density fluctuations scale linearly as a function of $M_t$, with the scaling coefficients showing weak dependence on parameters. Furthermore, through detailed spatio-temporal analysis, we show that the density fluctuations are dominated by low-frequency nonlinear structures, rather than compressible MHD eigen-waves. These results could be important for understanding how compressible turbulence contributes to solar wind heating near the Sun.

astro-ph.SR↗

arXiv2022

Whistler Waves As a Signature of Converging Magnetic Holes in Space Plasmas

Magnetic holes are plasma structures that trap a large number of particles in a magnetic field that is weaker than the field in its surroundings. The unprecedented high time-resolution observations by NASA's Magnetospheric Multi-Scale (MMS) mission enable us to study the particle dynamics in magnetic holes in the Earth's magnetosheath in great detail. We reveal the local generation mechanism of whistler waves by a combination of Landau-resonant and cyclotron-resonant wave-particle interactions of electrons in response to the large-scale evolution of a magnetic hole. As the magnetic hole converges, a pair of counter-streaming electron beams form near the hole's center as a consequence of the combined action of betatron and Fermi effects. The beams trigger the generation of slightly-oblique whistler waves. Our conceptual prediction is supported by a remarkable agreement between our observations and numerical predictions from the Arbitrary Linear Plasma Solver (ALPS). Our study shows that wave-particle interactions are fundamental to the evolution of magnetic holes in space and astrophysical plasmas.

physics.plasm-ph↗

arXiv2022

Absolute phase measurement method based on bidirectional coding patterns

The stair phase coding patterns have been widely used to determine the fringe order for phase unwrapping of the wrapped phase in 3D shape measurement. Although the special coding sequence algorithm can achieve with a large number of codewords, it needs current codeword and its adjacent codewords to jointly determine the fringe order. If any codeword of the grouped adjacent codewords is incorrectly recognized, it will result in false fringe order.Therefore, it is challenging to significantly increase the number of codewords. When it is necessary to simultaneously measure more than two isolated objects with large size differences, if the fringe frequency is high, the number of fringes on the smaller objects is too few to determine the fringe orders. On the other hand, if the fringe frequency is low, the image can not contain all isolated objects at the same time. To solve this problem, we propose an absolute phase measurement method based on bidirectional coding patterns. The wrapped phase of the object is obtained by fou step phase shifting patterns, and the fringe orders is obtained by two bidirectional coded patterns. When coding the bidirectional patterns, we code two groups of stair phase with different frequency along horizontal direction, which respectively represent local fringe order information and partition information, then, we alternately repeated the two groups of stair phase along the vertical direction to obtain the bidirectional coding patterns in the whole pattern. Each local fringe order information and the corresponding partition information in small region jointly determine the fringe order of each position in the wrapped phase. To verify the effectiveness of our method, we did simulations and three experiments. Simulation and experimental results show that our method is effective for complex surfaces and isolated objects with different sizes.

physics.optics↗

arXiv2022

Shilling Black-box Recommender Systems by Learning to Generate Fake User Profiles

Due to the pivotal role of Recommender Systems (RS) in guiding customers towards the purchase, there is a natural motivation for unscrupulous parties to spoof RS for profits. In this paper, we study Shilling Attack where an adversarial party injects a number of fake user profiles for improper purposes. Conventional Shilling Attack approaches lack attack transferability (i.e., attacks are not effective on some victim RS models) and/or attack invisibility (i.e., injected profiles can be easily detected). To overcome these issues, we present Leg-UP, a novel attack model based on the Generative Adversarial Network. Leg-UP learns user behavior patterns from real users in the sampled ``templates'' and constructs fake user profiles. To simulate real users, the generator in Leg-UP directly outputs discrete ratings. To enhance attack transferability, the parameters of the generator are optimized by maximizing the attack performance on a surrogate RS model. To improve attack invisibility, Leg-UP adopts a discriminator to guide the generator to generate undetectable fake user profiles. Experiments on benchmarks have shown that Leg-UP exceeds state-of-the-art Shilling Attack methods on a wide range of victim RS models. The source code of our work is available at: https://github.com/XMUDM/ShillingAttack.

cs.IR↗

arXiv2022

FD-CAM: Improving Faithfulness and Discriminability of Visual Explanation for CNNs

Class activation map (CAM) has been widely studied for visual explanation of the internal working mechanism of convolutional neural networks. The key of existing CAM-based methods is to compute effective weights to combine activation maps in the target convolution layer. Existing gradient and score based weighting schemes have shown superiority in ensuring either the discriminability or faithfulness of the CAM, but they normally cannot excel in both properties. In this paper, we propose a novel CAM weighting scheme, named FD-CAM, to improve both the faithfulness and discriminability of the CAM-based CNN visual explanation. First, we improve the faithfulness and discriminability of the score-based weights by performing a grouped channel switching operation. Specifically, for each channel, we compute its similarity group and switch the group of channels on or off simultaneously to compute changes in the class prediction score as the weights. Then, we combine the improved score-based weights with the conventional gradient-based weights so that the discriminability of the final CAM can be further improved. We perform extensive comparisons with the state-of-the-art CAM algorithms. The quantitative and qualitative results show our FD-CAM can produce more faithful and more discriminative visual explanations of the CNNs. We also conduct experiments to verify the effectiveness of the proposed grouped channel switching and weight combination scheme on improving the results. Our code is available at https://github.com/crishhh1998/FD-CAM.

cs.CV↗

arXiv2022

A change-point detection method for detecting and locating the abrupt changes in distributions of damage-sensitive features of SHM data, with application to structural condition assessment

Diagnosing the changes of structural behaviors using monitoring data is an important objective of structural health monitoring (SHM). The changes in structural behaviors are usually manifested as the feature changes in monitored structural responses; thus, developing effective methods for automatically detecting such changes is of considerable significance. Existing methods for change detection in SHM are mainly used for scalar or vector data, thus incapable of detecting the changes of the features represented by complex data, e.g., the probability density functions (PDFs). Detecting the abrupt changes occurred in the distributions (represented by PDFs) associated with the damage-sensitive features extracted from SHM data are usually of crucial interest for structural condition assessment; however, the SHM community still lacks effective diagnostic tools for detecting such changes. In this study, a change-point detection method is developed in the functional data-analytic framework for PDF-valued sequence, and it is leveraged to diagnose the distributional information break encountered in structural condition assessment. A major challenge in PDF-valued data modeling or analysis is that the PDFs are special functional data subjecting to nonlinear constraints. To tackle this issue, the PDFs are embedded into the Bayes space, and the associated change-point model is constructed by using the linear structure of the Bayes space; then, a hypothesis testing procedure is presented for distributional change-point detection based on the isomorphic mapping between the Bayes space and a functional linear space. Comprehensive simulation studies are conducted to validate the effectiveness of the proposed method as well as demonstrate its superiority over the competing method. Finally, an application to real SHM data illustrates its practical utility in structural condition assessment.

stat.ME↗

arXiv2022

Learning Dense Reward with Temporal Variant Self-Supervision

Rewards play an essential role in reinforcement learning. In contrast to rule-based game environments with well-defined reward functions, complex real-world robotic applications, such as contact-rich manipulation, lack explicit and informative descriptions that can directly be used as a reward. Previous effort has shown that it is possible to algorithmically extract dense rewards directly from multimodal observations. In this paper, we aim to extend this effort by proposing a more efficient and robust way of sampling and learning. In particular, our sampling approach utilizes temporal variance to simulate the fluctuating state and action distribution of a manipulation task. We then proposed a network architecture for self-supervised learning to better incorporate temporal information in latent representations. We tested our approach in two experimental setups, namely joint-assembly and door-opening. Preliminary results show that our approach is effective and efficient in learning dense rewards, and the learned rewards lead to faster convergence than baselines.

cs.LG↗

arXiv2022

Tighter monogamy relations in multiparty quantum systems

We investigate tight monogamy relations of multiparty quantum entanglement for any quantum state in this paper. First, we obtain a class of lower bounds for multiparty quantum systems which improve the previous results. Next, we establish a class of tighter monogamy relations in tripartite quantum systems by means of the new inequality. Furthermore, we generalize this relations to multiparty quantum systems. And then we prove the lower bounds we obtained are larger than the existing ones. Detailed examples are provided at last.

quant-ph↗

arXiv2022

Pushing the Performance Limit of Scene Text Recognizer without Human Annotation

Scene text recognition (STR) attracts much attention over the years because of its wide application. Most methods train STR model in a fully supervised manner which requires large amounts of labeled data. Although synthetic data contributes a lot to STR, it suffers from the real-tosynthetic domain gap that restricts model performance. In this work, we aim to boost STR models by leveraging both synthetic data and the numerous real unlabeled images, exempting human annotation cost thoroughly. A robust consistency regularization based semi-supervised framework is proposed for STR, which can effectively solve the instability issue due to domain inconsistency between synthetic and real images. A character-level consistency regularization is designed to mitigate the misalignment between characters in sequence recognition. Extensive experiments on standard text recognition benchmarks demonstrate the effectiveness of the proposed method. It can steadily improve existing STR models, and boost an STR model to achieve new state-of-the-art results. To our best knowledge, this is the first consistency regularization based framework that applies successfully to STR.

cs.CV↗

arXiv2022

Spherical contact toric manifolds

Let $(M, α)$ be a $2n+1$-dimensional connected compact contact toric manifold of Reeb type. Suppose the contact form $α$ is regular, we find conditions under which $M$ is homeomorphic to $S^{2n+1}$.

math.SG↗

arXiv2022

Formation and evolution of young massive clusters in galaxy mergers: the SMUGGLE view

Galaxy mergers are known to host abundant young massive cluster (YMC) populations, whose formation mechanism is still not well-understood. Here, we present a high-resolution galaxy merger simulation with explicit star formation and stellar feedback prescriptions to investigate how mergers affect the properties of the interstellar medium and YMCs. Compared with a controlled simulation of an isolated galaxy, the mass fraction of dense and high-pressure gas is much higher in mergers. Consequently, the mass function of both molecular clouds and YMCs becomes shallower and extends to higher masses. Moreover, cluster formation efficiency is significantly enhanced and correlates positively with the star formation rate surface density and gas pressure. We track the orbits of YMCs and investigate the time evolution of tidal fields during the course of the merger. At an early stage of the merger, the tidal field strength correlates positively with YMC mass, $λ_{\rm tid}\propto M^{0.71}$, which systematically affects the shape of the mass function and age distribution of the YMCs. At later times, most YMCs closely follow the orbits of their host galaxies, gradually sinking into the center of the merger remnant due to dynamical friction, and are quickly dissolved via efficient tidal disruption. Interestingly, YMCs formed during the first passage, mostly in tidal tails and bridges, are distributed over a wide range of galactocentric radii, greatly increasing their survivability because of the much weaker tidal field in the outskirts of the merger system. These YMCs are promising candidates for globular clusters that survive to the present day.

astro-ph.GA↗

arXiv2022

Chromospheric recurrent jets in a sunspot group and their inter-granular origin

We report on high resolution observations of recurrent fan-like jets by the Goode Solar telescope (GST) in multi-wavelengths inside a sunspot group. The dynamics behaviour of the jets is derived from the Ha line profiles. Quantitative values for one well-identified event have been obtained showing a maximum projected velocity of 42 km s^-1 and a Doppler shift of the order of 20 km s^-1. The footpoints/roots of the jets have a lifted center on the Ha line profile compared to the quiet sun suggesting a long lasting heating at these locations. The magnetic field between the small sunspots in the group shows a very high resolution pattern with parasitic polarities along the inter-granular lanes accompanied by high velocity converging flows (4 km s^-1) in the photosphere. Magnetic cancellations between the opposite polarities are observed in the vicinity of the footpoints of the jets. Along the inter-granular lanes horizontal magnetic field around 1000 Gauss is generated impulsively. Overall, all the kinetic features at the different layers through photosphere and chromosphere favor a convection-driven reconnection scenario for the recurrent fan-like jets, and evidence a site of reconnection between the photosphere and chromosphere corresponding to the inter-granular lanes.

astro-ph.SR↗

arXiv2022

Visual Encoding and Debiasing for CTR Prediction

Extracting expressive visual features is crucial for accurate Click-Through-Rate (CTR) prediction in visual search advertising systems. Current commercial systems use off-the-shelf visual encoders to facilitate fast online service. However, the extracted visual features are coarse-grained and/or biased. In this paper, we present a visual encoding framework for CTR prediction to overcome these problems. The framework is based on contrastive learning which pulls positive pairs closer and pushes negative pairs apart in the visual feature space. To obtain fine-grained visual features,we present contrastive learning supervised by click through data to fine-tune the visual encoder. To reduce sample selection bias, firstly we train the visual encoder offline by leveraging both unbiased self-supervision and click supervision signals. Secondly, we incorporate a debiasing network in the online CTR predictor to adjust the visual features by contrasting high impression items with selected items with lower impressions.We deploy the framework in the visual sponsor search system at Alibaba. Offline experiments on billion-scale datasets and online experiments demonstrate that the proposed framework can make accurate and unbiased predictions.

cs.IR↗

arXiv2022

Turbulence in Milky Way Star-Forming Regions Traced by Young Stars and Gas

The interstellar medium (ISM) is turbulent on all scales and in all phases. In this paper, we study turbulence with different tracers in four nearby star-forming regions: Orion, Ophiuchus, Perseus, and Taurus. We combine the APOGEE-2 and Gaia surveys to obtain the full 6-dimensional measurements of positions and velocities of young stars in these regions. The velocity structure functions (VSFs) of the stars show a universal scaling of turbulence. We also obtain Hα gas kinematics in these four regions from the Wisconsin H-Alpha Mapper. The VSFs of the Hα are more diverse compared to the stars. In regions with recent supernova activities, they show characteristics of local energy injections and higher amplitudes compared to the VSFs of stars and of CO from the literature. Such difference in amplitude of the VSFs can be explained by the different energy and momentum transport from supernovae into different phases of the ISM, thus resulting in higher levels of turbulence in the warm ionized phase traced by Hα. In regions without recent supernova activities, the VSFs of young stars, Hα, and CO are generally consistent, indicating well-coupled turbulence between different phases. Within individual regions, the brighter parts of the Hα gas tend to have a higher level of turbulence than the low-emission parts. Our findings support a complex picture of the Milky Way ISM, where turbulence can be driven at different scales and inject energy unevenly into different phases.

astro-ph.GA↗

arXiv2022

SwinFuse: A Residual Swin Transformer Fusion Network for Infrared and Visible Images

The existing deep learning fusion methods mainly concentrate on the convolutional neural networks, and few attempts are made with transformer. Meanwhile, the convolutional operation is a content-independent interaction between the image and convolution kernel, which may lose some important contexts and further limit fusion performance. Towards this end, we present a simple and strong fusion baseline for infrared and visible images, namely\textit{ Residual Swin Transformer Fusion Network}, termed as SwinFuse. Our SwinFuse includes three parts: the global feature extraction, fusion layer and feature reconstruction. In particular, we build a fully attentional feature encoding backbone to model the long-range dependency, which is a pure transformer network and has a stronger representation ability compared with the convolutional neural networks. Moreover, we design a novel feature fusion strategy based on $L_{1}$-norm for sequence matrices, and measure the corresponding activity levels from row and column vector dimensions, which can well retain competitive infrared brightness and distinct visible details. Finally, we testify our SwinFuse with nine state-of-the-art traditional and deep learning methods on three different datasets through subjective observations and objective comparisons, and the experimental results manifest that the proposed SwinFuse obtains surprising fusion performance with strong generalization ability and competitive computational efficiency. The code will be available at https://github.com/Zhishe-Wang/SwinFuse.

cs.CV↗

arXiv2022

H-alpha emission in local galaxies: star formation, time variability and the diffuse ionized gas

The nebular recombination line H$α$ is widely used as a star-formation rate (SFR) indicator in the local and high-redshift Universe. We present a detailed H$α$ radiative transfer study of high-resolution isolated Milky-Way and Large Magellanic Cloud simulations that include radiative transfer, non-equilibrium thermochemistry, and dust evolution. We focus on the spatial morphology and temporal variability of the H$α$ emission, and its connection to the underlying gas and star formation properties. The H$α$ and H$β$ radial and vertical surface brightness profiles are in excellent agreement with observations of nearby galaxies. We find that the fraction of H$α$ emission from collisional excitation amounts to $f_{\rm col}\sim5-10\%$, only weakly dependent on radius and vertical height, and that scattering boosts the H$α$ luminosity by $\sim40\%$. The dust correction via the Balmer decrement works well (intrinsic H$α$ emission recoverable within $25\%$), though the dust attenuation law depends on the amount of attenuation itself both on spatially resolved and integrated scales. Important for the understanding of the H$α$-SFR connection is the dust and helium absorption of ionizing radiation (Lyman continuum [LyC] photons), which are about $f_{\rm abs}\approx28\%$ and $f_{\rm He}\approx9\%$, respectively. Together with an escape fraction of $f_{\rm esc}\approx6\%$, this reduces the available budget for hydrogen line emission by nearly half ($f_{\rm H}\approx57\%$). We discuss the impact of the diffuse ionized gas, showing - among other things - that the extraplanar H$α$ emission is powered by LyC photons escaping the disc. Future applications of this framework to cosmological (zoom-in) simulations will assist in the interpretation of spectroscopy of high-redshift galaxies with the upcoming James Webb Space Telescope.

astro-ph.GA↗