arXiv subjects

Hui Li

Publications and source records attributed to Hui Li.

At least 505 records · Page 28Linked to original sources

arXiv2023

An Effective and Differentially Private Protocol for Secure Distributed Cardinality Estimation

Counting the number of distinct elements distributed over multiple data holders is a fundamental problem with many real-world applications ranging from crowd counting to network monitoring. Although a number of space and computational efficient sketch methods (e.g., the Flajolet-Martin sketch and the HyperLogLog sketch) for cardinality estimation have been proposed to solve the above problem, these sketch methods are insecure when considering privacy concerns related to the use of each data holder's personal dataset. Despite a recently proposed protocol that successfully implements the well-known Flajolet-Martin (FM) sketch on a secret-sharing based multiparty computation (MPC) framework for solving the problem of private distributed cardinality estimation (PDCE), we observe that this MPC-FM protocol is not differentially private. In addition, the MPC-FM protocol is computationally expensive, which limits its applications to data holders with limited computation resources. To address the above issues, in this paper we propose a novel protocol DP-DICE, which is computationally efficient and differentially private for solving the problem of PDCE. Experimental results show that our DP-DICE achieves orders of magnitude speedup and reduces the estimation error by several times in comparison with state-of-the-arts under the same security requirements.

cs.CR↗

arXiv2023

Out-of-Time-Order-Correlator for van der Waals potential

The quantum-to-classical correspondence is often quantified in dynamics by a quantity referred to as the out-of-time-order correlator (OTOC). In chaotic systems, the OTOC is expected to grow exponentially at early time, characteristic of a Lyapunov exponent, however, exponential growth can also occur for integrable systems. Here we investigate the OTOC for realistic diatomic molecular potentials in one degree of freedom, finding that the OTOC can grow exponentially near the dissociation energy of the moelcule. Further, this dynamics is tied to the classical dynamics of the atoms at the outer classical turning point of the potential. These results should serve to guide and interpret dynamical chaos in more complex molecules.

quant-ph↗

arXiv2023

A Novel Solution for Uu Interface Based C-V2X

C-V2X includes two solutions: Uu interface based C-V2X and PC5 interface based C-V2X. The Uu interface based C-V2X has challenges such as latency and message congestion. This paper introduces a risk filtering algorithm through the V2X capability classification, and proposes a software-defined C-V2X solution (SUC-V2X) to systematically solve problems of Uu interface based C-V2X. This article further verifies the performance of the solution.

eess.SY↗

arXiv2023

Atlas of dynamic spectra of fast radio burst FRB 20201124A

Fast radio bursts (FRBs) are highly dispersed millisecond-duration radio bursts, of which the physical origin is still not fully understood. FRB 20201124A is one of the most actively repeating FRBs. In this paper, we present the collection of 1863 burst dynamic spectra of FRB 20201124A measured with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The current collection, taken from the observation during the FRB active phase from April to June 2021, is the largest burst sample detected in any FRB so far. The standard PSRFITs format is adopted, including dynamic spectra of the burst, and the time information of the dynamic spectra, in addition, mask files help readers to identify the pulse positions are also provided.

astro-ph.HE↗

arXiv2023

In-situ monitoring additive manufacturing process with AI edge computing

In-situ monitoring system can be used to monitor the quality of additive manufacturing (AM) processes. In the case of digital image correlation (DIC) based in-situ monitoring systems, high-speed cameras were used to capture images of high resolutions. This paper proposed a novel in-situ monitoring system to accelerate the process of digital images using artificial intelligence (AI) edge computing board. It built a visual transformer based video super resolution (ViTSR) network to reconstruct high resolution (HR) videos frames. Fully convolutional network (FCN) was used to simultaneously extract the geometric characteristics of molten pool and plasma arc during the AM processes. Compared with 6 state-of-the-art super resolution methods, ViTSR ranks first in terms of peak signal to noise ratio (PSNR). The PSNR of ViTSR for 4x super resolution reached 38.16 dB on test data with input size of 75 pixels x 75 pixels. Inference time of ViTSR and FCN was optimized to 50.97 ms and 67.86 ms on AI edge board after operator fusion and model pruning. The total inference time of the proposed system was 118.83 ms, which meets the requirement of real-time quality monitoring with low cost in-situ monitoring equipment during AM processes. The proposed system achieved an accuracy of 96.34% on the multi-objects extraction task and can be applied to different AM processes.

eess.IV↗

arXiv2022

eDIG-CHANGES I: Extended Hα Emission from the Extraplanar Diffuse Ionized Gas (eDIG) around CHANG-ES Galaxies

The extraplanar diffuse ionized gas (eDIG) represents the cool/warm ionized gas reservoir around galaxies. We present a spatial analysis of H$α$ images of 22 nearby edge-on spiral galaxies from the CHANG-ES sample (the eDIG-CHANGES project), taken with the APO 3.5m telescope, in order to study their eDIG. We conduct an exponential fit to the vertical intensity profiles of the sample galaxies, of which 16 can be decomposed into a thin disk plus an extended thick disk component. The median value of the scale height (h) of the extended component is $1.13\pm 0.14$ kpc. We find a tight sublinear correlation between h and the SFR. Moreover, the offset of individual galaxies from the best-fit SFR-h relation shows significant anti-correlation with SFR_SD. This indicates that galaxies with more intense star formation tend to have disproportionately extended eDIG. Combined with data from the literature, we find that the correlations between the eDIG properties and the galaxies' properties extend to broader ranges. We further compare the vertical extension of the eDIG to multi-wavelength measurements of other CGM phases. We find the eDIG to be slightly more extended than the neutral gas (HI 21-cm line), indicating the existence of some extended ionizing sources. Most galaxies have an X-ray scale height smaller than the h, suggesting that the majority of the X-ray emission detected in shallow observations are actually from the thick disk. The h is comparable to the L-band radio continuum scale height, both slightly larger than that at higher frequencies (C-band), where the cooling is stronger and the thermal contribution may be larger. The comparable H$α$ and L-band scale height indicates that the thermal and non-thermal electrons have similar spatial distributions. This further indicates that the thermal gas, the cosmics rays, and the magnetic field may be close to energy equipartition.

astro-ph.GA↗

arXiv2022

Strongly-overdoped La$_{2-x}$Sr$_x$CuO$_4$: Evidence for Josephson-coupled grains of strongly-correlated superconductor

The interpretation of how superconductivity disappears in cuprates at large hole doping has been controversial. To address this issue, we present an experimental study of single-crystal and thin film samples of La$_{2-x}$Sr$_x$CuO$_4$ (LSCO) with $x\ge0.25$. In particular, measurements of bulk susceptibility on LSCO crystals with $x=0.25$ indicate an onset of diamagnetism at $T_{c1}=38.5$ K, with a sharp transition to a phase with full bulk shielding at $T_{c2}=18$ K, independent of field direction. Strikingly, the in-plane resistivity only goes to zero at $T_{c2}$. Inelastic neutron scattering on $x=0.25$ crystals confirms the presence of low-energy incommensurate magnetic excitations with reduced strength compared to lower doping levels. The ratio of the spin gap to $T_{c2}$ is anomalously large. Our results are consistent with a theoretical prediction for strongly overdoped cuprates by Spivak, Oreto, and Kivelson, in which superconductivity initially develops within disconnected self-organized grains characterized by a reduced hole concentration, with bulk superconductivity occurring only after superconductivity is induced by proximity effect in the surrounding medium of higher hole concentration. Beyond the superconducting-to-metal transition, local differential conductance measurements on an LSCO thin film suggest that regions with pairing correlations survive, but are too dilute to support superconducting order. Future experiments will be needed to test the degree to which these results apply to overdoped cuprates in general.

cond-mat.supr-con↗

arXiv2022

Baryon Number Transport, Strangeness Conservation and $Ω$-hadron Correlations

Although strange quarks are produced in $s\bar{s}$ pairs, the ratio of $Ω^{-}$ to ${\barΩ}^{+}$ is greater than one in heavy-ion collisions at lower RHIC energies. Thus the produced $Ω$ hyperons must carry net baryon quantum numbers from the colliding nuclei. We present results of $K$-$Ω$ correlations from AMPT model simulations of Au+Au collisions at $\sqrt{s_{NN}}$ = 14.6 GeV, to probe dynamics for baryon number transport to mid-rapidities at this beam energy. We use both the default and string-melting versions to illustrate how hadronization schemes of quark coalescence and string fragmentations could leave imprints on such correlations. Implications on the measurements of these correlations with the STAR experiment at RHIC will also be discussed.

nucl-th↗

arXiv2022

Galaxy Image Classification using Hierarchical Data Learning with Weighted Sampling and Label Smoothing

With the development of a series of Galaxy sky surveys in recent years, the observations increased rapidly, which makes the research of machine learning methods for galaxy image recognition a hot topic. Available automatic galaxy image recognition researches are plagued by the large differences in similarity between categories, the imbalance of data between different classes, and the discrepancy between the discrete representation of Galaxy classes and the essentially gradual changes from one morphological class to the adjacent class (DDRGC). These limitations have motivated several astronomers and machine learning experts to design projects with improved galaxy image recognition capabilities. Therefore, this paper proposes a novel learning method, ``Hierarchical Imbalanced data learning with Weighted sampling and Label smoothing" (HIWL). The HIWL consists of three key techniques respectively dealing with the above-mentioned three problems: (1) Designed a hierarchical galaxy classification model based on an efficient backbone network; (2) Utilized a weighted sampling scheme to deal with the imbalance problem; (3) Adopted a label smoothing technique to alleviate the DDRGC problem. We applied this method to galaxy photometric images from the Galaxy Zoo-The Galaxy Challenge, exploring the recognition of completely round smooth, in between smooth, cigar-shaped, edge-on and spiral. The overall classification accuracy is 96.32\%, and some superiorities of the HIWL are shown based on recall, precision, and F1-Score in comparing with some related works. In addition, we also explored the visualization of the galaxy image features and model attention to understand the foundations of the proposed scheme.

astro-ph.IM↗

arXiv2022

Automated Optical Inspection of FAST's Reflector Surface using Drones and Computer Vision

The Five-hundred-meter Aperture Spherical radio Telescope (FAST) is the world's largest single-dish radio telescope. Its large reflecting surface achieves unprecedented sensitivity but is prone to damage, such as dents and holes, caused by naturally-occurring falling objects. Hence, the timely and accurate detection of surface defects is crucial for FAST's stable operation. Conventional manual inspection involves human inspectors climbing up and examining the large surface visually, a time-consuming and potentially unreliable process. To accelerate the inspection process and increase its accuracy, this work makes the first step towards automating the inspection of FAST by integrating deep-learning techniques with drone technology. First, a drone flies over the surface along a predetermined route. Since surface defects significantly vary in scale and show high inter-class similarity, directly applying existing deep detectors to detect defects on the drone imagery is highly prone to missing and misidentifying defects. As a remedy, we introduce cross-fusion, a dedicated plug-in operation for deep detectors that enables the adaptive fusion of multi-level features in a point-wise selective fashion, depending on local defect patterns. Consequently, strong semantics and fine-grained details are dynamically fused at different positions to support the accurate detection of defects of various scales and types. Our AI-powered drone-based automated inspection is time-efficient, reliable, and has good accessibility, which guarantees the long-term and stable operation of FAST.

cs.CV↗

arXiv2022

Fully and Weakly Supervised Referring Expression Segmentation with End-to-End Learning

Referring Expression Segmentation (RES), which is aimed at localizing and segmenting the target according to the given language expression, has drawn increasing attention. Existing methods jointly consider the localization and segmentation steps, which rely on the fused visual and linguistic features for both steps. We argue that the conflict between the purpose of identifying an object and generating a mask limits the RES performance. To solve this problem, we propose a parallel position-kernel-segmentation pipeline to better isolate and then interact the localization and segmentation steps. In our pipeline, linguistic information will not directly contaminate the visual feature for segmentation. Specifically, the localization step localizes the target object in the image based on the referring expression, and then the visual kernel obtained from the localization step guides the segmentation step. This pipeline also enables us to train RES in a weakly-supervised way, where the pixel-level segmentation labels are replaced by click annotations on center and corner points. The position head is fully-supervised and trained with the click annotations as supervision, and the segmentation head is trained with weakly-supervised segmentation losses. To validate our framework on a weakly-supervised setting, we annotated three RES benchmark datasets (RefCOCO, RefCOCO+ and RefCOCOg) with click annotations.Our method is simple but surprisingly effective, outperforming all previous state-of-the-art RES methods on fully- and weakly-supervised settings by a large margin. The benchmark code and datasets will be released.

cs.CV↗

arXiv2022

Self-supervised Graph Representation Learning for Black Market Account Detection

Nowadays, Multi-purpose Messaging Mobile App (MMMA) has become increasingly prevalent. MMMAs attract fraudsters and some cybercriminals provide support for frauds via black market accounts (BMAs). Compared to fraudsters, BMAs are not directly involved in frauds and are more difficult to detect. This paper illustrates our BMA detection system SGRL (Self-supervised Graph Representation Learning) used in WeChat, a representative MMMA with over a billion users. We tailor Graph Neural Network and Graph Self-supervised Learning in SGRL for BMA detection. The workflow of SGRL contains a pretraining phase that utilizes structural information, node attribute information and available human knowledge, and a lightweight detection phase. In offline experiments, SGRL outperforms state-of-the-art methods by 16.06%-58.17% on offline evaluation measures. We deploy SGRL in the online environment to detect BMAs on the billion-scale WeChat graph, and it exceeds the alternative by 7.27% on the online evaluation measure. In conclusion, SGRL can alleviate label reliance, generalize well to unseen data, and effectively detect BMAs in WeChat.

cs.SI↗

arXiv2022

The essential role of multi-point measurements in investigations of turbulence, three-dimensional structure, and dynamics: the solar wind beyond single scale and the Taylor Hypothesis

Space plasmas are three-dimensional dynamic entities. Except under very special circumstances, their structure in space and their behavior in time are not related in any simple way. Therefore, single spacecraft in situ measurements cannot unambiguously unravel the full space-time structure of the heliospheric plasmas of interest in the inner heliosphere, in the Geospace environment, or the outer heliosphere. This shortcoming leaves numerous central questions incompletely answered. Deficiencies remain in at least two important subjects, Space Weather and fundamental plasma turbulence theory, due to a lack of a more complete understanding of the space-time structure of dynamic plasmas. Only with multispacecraft measurements over suitable spans of spatial separation and temporal duration can these ambiguities be resolved. We note that these characterizations apply to turbulence across a wide range of scales, and also equally well to shocks, flux ropes, magnetic clouds, current sheets, stream interactions, etc. In the following, we will describe the basic requirements for resolving space-time structure in general, using turbulence' as both an example and a principal target or study. Several types of missions are suggested to resolve space-time structure throughout the Heliosphere.

physics.space-ph↗

arXiv2022

Multiple gas phases in supernova remnant IC 443: mapping shocked H$_2$ with VLT/KMOS

Supernovae and their remnants provide energetic feedback to the ambient interstellar medium (ISM), which is often distributed in multiple gas phases. Among them, warm molecular hydrogen (H$_2$) often dominates the cooling of the shocked molecular ISM, which has been observed with the H$_2$ emission lines at near-infrared wavelengths. Such studies, however, were either limited in narrow filter imaging or sparsely sampled mid-infrared spectroscopic observations with relatively poor angular resolutions. Here we present near-infrared ($H$- and $K$-band) spectroscopic mosaic observations towards the A, B, C, and G regions of the supernova remnant (SNR) IC 443, with the K-band Multi-Object Spectrograph (KMOS) onboard the Very Large Telescope (VLT). We detected 20 ro-vibrational transitions of H$_2$, one H line (Br$γ$), and two [Fe II] lines, which dominate broadband images at both $H$- and $K$-band. The spatial distribution of H$_2$ lines at all regions are clumpy on scales from $\sim 0.1$ pc down to $\sim 0.008$ pc. The fitted excitation temperature of H$_2$ is between 1500 K and 2500 K, indicating warm shocked gas in these regions. The multi-gas-phase comparison shows stratified shock structures in all regions, which explains the co-existence of multiple types of shocks in the same regions. Last, we verify the candidates of young stellar objects previously identified in these regions with our spectroscopic data, and find none of them are associated with young stars. This sets challenges to the previously proposed scenario of triggered star formation by SNR shocks in IC~443.

astro-ph.GA↗

arXiv2022

Hydrodynamical Simulations of Black-Hole Binary Formation in AGN Disks

We study the close encounters between two single black holes (BHs) embedded in an AGN disk using a series of global 2D hydrodynamics simulations. We find that when the disk density is sufficiently high, bound BH binaries can be formed by the collision of their circum-single disks. Our analysis demonstrates that, after a BH pair passes the pericenter of their relative trajectory, a gas post-collision drag may slow down the BHs, possibly forcing the two BHs to stay tightly bound. A binary formed by a close encounter can have a compact semi-major axis, large eccentricity, and retrograde orbital angular momentum. We provide a fitting formula that can accurately predict whether a close encounter can form a binary based on the gas mass and the incoming energy of the encounter. This fitting formula can be easily implemented in other long-term simulations that study the dynamical evolution of BHs in AGN disks.

astro-ph.HE↗

arXiv2022

Non-Homogeneous Haze Removal via Artificial Scene Prior and Bidimensional Graph Reasoning

Due to the lack of natural scene and haze prior information, it is greatly challenging to completely remove the haze from a single image without distorting its visual content. Fortunately, the real-world haze usually presents non-homogeneous distribution, which provides us with many valuable clues in partial well-preserved regions. In this paper, we propose a Non-Homogeneous Haze Removal Network (NHRN) via artificial scene prior and bidimensional graph reasoning. Firstly, we employ the gamma correction iteratively to simulate artificial multiple shots under different exposure conditions, whose haze degrees are different and enrich the underlying scene prior. Secondly, beyond utilizing the local neighboring relationship, we build a bidimensional graph reasoning module to conduct non-local filtering in the spatial and channel dimensions of feature maps, which models their long-range dependency and propagates the natural scene prior between the well-preserved nodes and the nodes contaminated by haze. To the best of our knowledge, this is the first exploration to remove non-homogeneous haze via the graph reasoning based framework. We evaluate our method on different benchmark datasets. The results demonstrate that our method achieves superior performance over many state-of-the-art algorithms for both the single image dehazing and hazy image understanding tasks. The source code of the proposed NHRN is available on https://github.com/whrws/NHRNet.

cs.CV↗

arXiv2022

Four-body singlet potential energy surface for reactions of calcium monofluoride

A full six-dimensional Born-Oppenheimer singlet potential energy surface is constructed for the reaction CaF + CaF $\rightarrow$ CaF$_2$ + Ca using a multireference configuration interaction (MRCI) electronic structure calculation. The {\it ab initio} data thus calculated are interpolated by Gaussian process (GP) regression. The four-body potential energy surface features one $D_{2h}$ global minimum and one $C_s$ local minimum, connected by a barrierless transition state that lends insight to the reaction mechanism. This surface is intended to be of use in understanding ultracold chemistry of CaF molecules.

physics.atom-ph↗

arXiv2022

Assemble Them All: Physics-Based Planning for Generalizable Assembly by Disassembly

Assembly planning is the core of automating product assembly, maintenance, and recycling for modern industrial manufacturing. Despite its importance and long history of research, planning for mechanical assemblies when given the final assembled state remains a challenging problem. This is due to the complexity of dealing with arbitrary 3D shapes and the highly constrained motion required for real-world assemblies. In this work, we propose a novel method to efficiently plan physically plausible assembly motion and sequences for real-world assemblies. Our method leverages the assembly-by-disassembly principle and physics-based simulation to efficiently explore a reduced search space. To evaluate the generality of our method, we define a large-scale dataset consisting of thousands of physically valid industrial assemblies with a variety of assembly motions required. Our experiments on this new benchmark demonstrate we achieve a state-of-the-art success rate and the highest computational efficiency compared to other baseline algorithms. Our method also generalizes to rotational assemblies (e.g., screws and puzzles) and solves 80-part assemblies within several minutes.

cs.RO↗