arXiv subjects

Hui Liu

Publications and source records attributed to Hui Liu.

At least 433 records · Page 24Linked to original sources

arXiv2022

Discovery of ATLAS17jrp as an Optical, X-ray and Infrared Bright TDE in a Star-forming Galaxy

We hereby report the discovery of ATLAS17jrp as an extraordinary TDE in star-forming galaxy SDSSJ162034.99+240726.5 in our recent sample of mid-infrared outbursts in nearby galaxies. Its optical/UV light curves rise to a peak luminosity $\sim1.06\times10^{44}\rm\,erg\,s^{-1}$ in about a month and then decay as $\rm t^{-5/3}$ with a roughly constant temperature around 19000~K, and the optical spectra show a blue continuum and very broad Balmer lines with FWHM$\sim$15000 km/s which gradually narrowed to 1400 km/s within 4 years, all agreeing well with other optical TDEs. A delayed and rapidly rising X-ray flare with a peak luminosity $\rm \sim 1.27\times10^{43}\,erg\,s^{-1}$ was detected at $\rm \sim$ 170 days after the optical peak. The high MIR luminosity of ATLAS17jrp ($\sim2\times10^{43} \rm\,erg\,s^{-1}$) has revealed a distinctive dusty environment with covering factor as high as $\sim0.2$, that is comparable with that of torus in active galactic nuclei but at least one order of magnitude higher than normal optical TDEs. Therefore, ATLAS17jrp turns out to be one of the rare unambiguous TDE found in star-forming galaxies and its high dust covering factor implies that the dust extinction could play an important role in the absence of optical TDEs in star-forming galaxies.

astro-ph.GA↗

arXiv2022

Attention-wise masked graph contrastive learning for predicting molecular property

Accurate and efficient prediction of the molecular properties of drugs is one of the fundamental problems in drug research and development. Recent advancements in representation learning have been shown to greatly improve the performance of molecular property prediction. However, due to limited labeled data, supervised learning-based molecular representation algorithms can only search limited chemical space, which results in poor generalizability. In this work, we proposed a self-supervised representation learning framework for large-scale unlabeled molecules. We developed a novel molecular graph augmentation strategy, referred to as attention-wise graph mask, to generate challenging positive sample for contrastive learning. We adopted the graph attention network (GAT) as the molecular graph encoder, and leveraged the learned attention scores as masking guidance to generate molecular augmentation graphs. By minimization of the contrastive loss between original graph and masked graph, our model can capture important molecular structure and higher-order semantic information. Extensive experiments showed that our attention-wise graph mask contrastive learning exhibit state-of-the-art performance in a couple of downstream molecular property prediction tasks.

q-bio.BM↗

arXiv2022

Evidence for charge and spin order in single crystals of La$_3$Ni$_2$O$_7$ and La$_3$Ni$_2$O$_6$

Charge and spin order is intimately related to superconductivity in copper oxide superconductors. To elucidate the competing orders in various nickel oxide compounds are crucial given the fact that superconductivity has been discovered in Nd$_{0.8}$Sr$_{0.2}$NiO$_2$ films. Herein, we report structural, electronic transport, magnetic, and thermodynamic characterizations on single crystals of La$_3$Ni$_2$O$_7$ and La$_3$Ni$_2$O$_6$. La$_3$Ni$_2$O$_7$ is metallic with mixed Ni$^{2+}$ and Ni$^{3+}$ valent states. Resistivity measurements yield two transition-like kinks at $\sim$110 and 153 K, respectively. The kink at 153 K is further revealed from magnetization and specific heat measurements, indicative of the formation of charge and spin order. La$_3$Ni$_2$O$_6$ single crystals obtained from topochemical reduction of La$_3$Ni$_2$O$_7$ are insulating and show an anomaly at $\sim$176 K on magnetic susceptibility. The transition-like behaviors of La$_3$Ni$_2$O$_7$ and La$_3$Ni$_2$O$_6$ are analogous to the charge and spin order observed in La$_4$Ni$_3$O$_{10}$ and La$_4$Ni$_3$O$_8$, suggesting charge and spin order is a common feature in the ternary La-Ni-O system with mixed-valent states of nickel.

cond-mat.str-el↗

arXiv2022

Single crystal growth and superconductivity in RbNi$_2$Se$_2$

We report the synthesis and characterization of RbNi$_2$Se$_2$, an analog of the iron chalcogenide superconductor Rb$_x$Fe$_2$Se$_2$, via transport, angle resolved photoemission spectroscopy, and density functional theory calculations. A superconducting transition at $T_{c}$ = 1.20 K is identified. In normal state, RbNi$_2$Se$_2$ shows paramagnetic and Fermi liquid behaviors. A large Sommerfeld coefficient yields a heavy effective electron mass of $m^{*}\approx6m_{e}$. In the superconducting state, zero-field electronic specific-heat data $C_{es}$ can be described by a two-gap BCS model, indicating that RbNi$_2$Se$_2$ is a multi-gap superconductor. Our density functional theory calculations and angle resolved photoemission spectroscopy measurements demonstrate that RbNi$_2$Se$_2$ exhibits relatively weak correlations and multi-band characteristics, consistent with the multi-gap superconductivity.

cond-mat.supr-con↗

arXiv2022

RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition

Millimeter-wave radars are being increasingly integrated into commercial vehicles to support new advanced driver-assistance systems by enabling robust and high-performance object detection, localization, as well as recognition - a key component of new environmental perception. In this paper, we propose a novel radar multiple-perspectives convolutional neural network (RAMP-CNN) that extracts the location and class of objects based on further processing of the range-velocity-angle (RVA) heatmap sequences. To bypass the complexity of 4D convolutional neural networks (NN), we propose to combine several lower-dimension NN models within our RAMP-CNN model that nonetheless approaches the performance upper-bound with lower complexity. The extensive experiments show that the proposed RAMP-CNN model achieves better average recall and average precision than prior works in all testing scenarios. Besides, the RAMP-CNN model is validated to work robustly under nighttime, which enables low-cost radars as a potential substitute for pure optical sensing under severe conditions.

eess.SP↗

arXiv2022

Contrastive learning-based computational histopathology predict differential expression of cancer driver genes

Digital pathological analysis is run as the main examination used for cancer diagnosis. Recently, deep learning-driven feature extraction from pathology images is able to detect genetic variations and tumor environment, but few studies focus on differential gene expression in tumor cells. In this paper, we propose a self-supervised contrastive learning framework, HistCode, to infer differential gene expressions from whole slide images (WSIs). We leveraged contrastive learning on large-scale unannotated WSIs to derive slide-level histopathological feature in latent space, and then transfer it to tumor diagnosis and prediction of differentially expressed cancer driver genes. Our extensive experiments showed that our method outperformed other state-of-the-art models in tumor diagnosis tasks, and also effectively predicted differential gene expressions. Interestingly, we found the higher fold-changed genes can be more precisely predicted. To intuitively illustrate the ability to extract informative features from pathological images, we spatially visualized the WSIs colored by the attentive scores of image tiles. We found that the tumor and necrosis areas were highly consistent with the annotations of experienced pathologists. Moreover, the spatial heatmap generated by lymphocyte-specific gene expression patterns was also consistent with the manually labeled WSI.

cs.CV↗

arXiv2022

Learning to Detect Open Carry and Concealed Object with 77GHz Radar

Detecting harmful carried objects plays a key role in intelligent surveillance systems and has widespread applications, for example, in airport security. In this paper, we focus on the relatively unexplored area of using low-cost 77GHz mmWave radar for the carried objects detection problem. The proposed system is capable of real-time detecting three classes of objects - laptop, phone, and knife - under open carry and concealed cases where objects are hidden with clothes or bags. This capability is achieved by the initial signal processing for localization and generating range-azimuth-elevation image cubes, followed by a deep learning-based prediction network and a multi-shot post-processing module for detecting objects. Extensive experiments for validating the system performance on detecting open carry and concealed objects have been presented with a self-built radar-camera testbed and collected dataset. Additionally, the influence of different input formats, factors, and parameters on system performance is analyzed, providing an intuitive understanding of the system. This system would be the very first baseline for other future works aiming to detect carried objects using 77GHz radar.

eess.SP↗

arXiv2022

Attacking Black-box Recommendations via Copying Cross-domain User Profiles

Recently, recommender systems that aim to suggest personalized lists of items for users to interact with online have drawn a lot of attention. In fact, many of these state-of-the-art techniques have been deep learning based. Recent studies have shown that these deep learning models (in particular for recommendation systems) are vulnerable to attacks, such as data poisoning, which generates users to promote a selected set of items. However, more recently, defense strategies have been developed to detect these generated users with fake profiles. Thus, advanced injection attacks of creating more `realistic' user profiles to promote a set of items is still a key challenge in the domain of deep learning based recommender systems. In this work, we present our framework CopyAttack, which is a reinforcement learning based black-box attack method that harnesses real users from a source domain by copying their profiles into the target domain with the goal of promoting a subset of items. CopyAttack is constructed to both efficiently and effectively learn policy gradient networks that first select, and then further refine/craft, user profiles from the source domain to ultimately copy into the target domain. CopyAttack's goal is to maximize the hit ratio of the targeted items in the Top-$k$ recommendation list of the users in the target domain. We have conducted experiments on two real-world datasets and have empirically verified the effectiveness of our proposed framework and furthermore performed a thorough model analysis.

cs.IR↗

arXiv2022

Adaptive Attribute and Structure Subspace Clustering Network

Deep self-expressiveness-based subspace clustering methods have demonstrated effectiveness. However, existing works only consider the attribute information to conduct the self-expressiveness, which may limit the clustering performance. In this paper, we propose a novel adaptive attribute and structure subspace clustering network (AASSC-Net) to simultaneously consider the attribute and structure information in an adaptive graph fusion manner. Specifically, we first exploit an auto-encoder to represent input data samples with latent features for the construction of an attribute matrix. We also construct a mixed signed and symmetric structure matrix to capture the local geometric structure underlying data samples. Then, we perform self-expressiveness on the constructed attribute and structure matrices to learn their affinity graphs separately. Finally, we design a novel attention-based fusion module to adaptively leverage these two affinity graphs to construct a more discriminative affinity graph. Extensive experimental results on commonly used benchmark datasets demonstrate that our AASSC-Net significantly outperforms state-of-the-art methods. In addition, we conduct comprehensive ablation studies to discuss the effectiveness of the designed modules. The code will be publicly available at https://github.com/ZhihaoPENG-CityU.

cs.CV↗

arXiv2022

Non-autoregressive Model for Full-line Code Completion

Code completion tools are frequently used by software developers to accelerate software development by suggesting the following code elements. Completing a sequence of code tokens (e.g., a full line of code) has been proved more efficient than predicting a single token at a time. To complete the code sequence, researchers are employing AutoRegressive (AR) decoders to generate tokens in a left-to-right, token-by-token fashion. Consequently, the prediction of the next token depends on all previously generated tokens, which leads to high latency in inference. To improve the efficiency and accuracy of full-line code completion, in this paper, we propose a Non-AutoRegressive (NAR) model for code completion boosted by a syntax-aware sampling strategy. Our experimental results on two widely used datasets suggest that our model outperforms both AR and NAR baselines on full-line code completion, and it is faster than the AR model with up to 9 times speed-up.

cs.SE↗

arXiv2022

The Co-alignment of Winged Hα Data Observed by the New Vacuum Solar Telescop

The New Vacuum Solar Telescope (NVST) has been releasing its novel winged Ha data (WHD) since April 2021, namely the Ha imaging spectroscopic data. Compared with the prior released version, the new data are further co-aligned among the off-band images and packaged into a standard solar physics community format. In this study, we illustrate the alignment algorithm used by the novel WHD, which is mainly based on the optical flow method to obtain the translation offset between the winged images. To quantitatively evaluate the alignment results of two images with different similarities, we calculate the alignment accuracies between the images of different off-band and line center, respectively. The result shows that our alignment algorithm could reach up to the accuracy of about 0.1 "when the off-band of winged image is lower than 0.6 Ȧ. In addition, we introduce the final product of the WHD in detail, which can provide convenience for the solar physicists to use high-resolution Hα imaging spectroscopic data of NVST.

astro-ph.SR↗

arXiv2022

Thermally enhanced photoluminescence and temperature sensing properties of Sc$_2$W$_3$O$_{12}$:Eu$^{3+}$ phosphors

Currently,lanthanide ions doped luminescence materials applying as optical thermometers have arose much concern. Basing on the different responses of two emissions to temperature, the fluorescence intensity ratio (FIR) technique can be executed and further estimate the sensitivities to assess the optical thermometry performances. In this study, we introduce different doping concentrations of Eu$^{3+}$ ions into negative expansion material Sc$_2$W$_3$O$_{12}$:Eu$^{3+}$, accessing to the thermal enhanced luminescence from 373 to 548 K, and investigate the temperature sensing properties in detail. All samples exhibit good thermally enhanced luminescence behavior. The emission intensity of Sc$_2$W$_3$O$_{12}$: 6 mol% Eu$^{3+}$ phosphors reaches at 147.81% of initial intensity at 473 K. As the Eu doping concentration increases, the resistance of the samples to thermal quenching decreases. The FIR technique based on the transitions 5D0-7F1 (592 nm) and 5D0-7F2 (613 nm) of Eu$^{3+}$ ions demonstrate a maximum relative temperature sensitivity of 3.063% K-1 at 298 K for Sc$_2$W$_3$O$_{12}$:Eu$^{3+}$: 6 mol% Eu$^{3+}$ phosphors. The sensitivity of sample decreases with the increase of Eu$^{3+}$ concentration. Benefiting from the thermal enhanced luminescence performance and good temperature sensing properties, the Sc$_2$W$_3$O$_{12}$:Eu$^{3+}$: Eu$^{3+}$ phosphors can be applies as optical thermometers.

cond-mat.mtrl-sci↗

arXiv2022

Contact three-manifolds with exactly two simple Reeb orbits

It is known that every contact form on a closed three-manifold has at least two simple Reeb orbits, and a generic contact form has infinitely many. We show that if there are exactly two simple Reeb orbits, then the contact form is nondegenerate. Combined with a previous result, this implies that the three-manifold is diffeomorphic to the three-sphere or a lens space, and the two simple Reeb orbits are the core circles of a genus one Heegaard splitting. We also obtain further information about the Reeb dynamics and the contact structure. For example the Reeb flow has a disk-like global surface of section and so its dynamics are described by a pseudorotation; the contact struture is universally tight; and in the case of the three-sphere, the contact volume and the periods and rotation numbers of the simple Reeb orbits satisfy the same relations as for an irrational ellipsoid.

math.SG↗

arXiv2022

Light Nuclei Production in Au+Au Collisions at $\sqrt{s_{\mathrm{NN}}}$ = 3 GeV from the STAR experiment

Light nuclei production is expected to be sensitive to baryon density fluctuations and can be used to probe the signatures of QCD critical point and/or a first-order phase transition in heavy-ion collisions. In this proceedings, we present the spectra and yields of protons ($p$) and light nuclei ($d$, $t$, $^{3}\mathrm{He}$, $^{4}\mathrm{He}$) in Au+Au collisions at $\sqrt{s_{\mathrm{NN}}}$ = 3 GeV by the STAR experiment. Finally, it is found that the kinetic freeze-out dynamics (temperature $T_{kin}$ $vs.$ average radial flow velocity $\langleβ_{T}\rangle$) at 3 GeV extracted with the blast-wave model deviate from the trends at high energies ($\sqrt{s_{\mathrm{NN}}}$ = 7.7 - 200 GeV), indicating a different medium equation of state.

nucl-ex↗

arXiv2022

Refinements of Franks' theorem and applications in Reeb dynamics

In this article, we give two refinements of Franks' theorem: For orientation and area preserving homeomorphisms of the closed or open annulus, the existence of $k$-periodic orbits ($(k,n_0)=1$) forces the existence of infinitely many periodic orbits with periods prime to $n_0$. Moreover, if $f$ is reversible, the periodic orbits above could be symmetric. Our improvements of Franks' theorem can be applied to Reeb dynamics and celestial mechanics, for example, we give some precise information about the symmetries of periodic orbits found in Hofer, Wysocki and Zehnder's dichotomy theorem when the tight 3-sphere is equipped with some additional symmetries, and also the symmetries of periodic orbits on the energy level of H$\acute{e}$non-Heiles system in celestial mechanics.

math.DS↗

arXiv2022

Multiplicity of non-contractible closed geodesics on Finsler compact space forms

Let $M=S^n/ Γ$ and $h$ be a nontrivial element of finite order $p$ in $π_1(M)$, where the integer $n, p\geq2$, $Γ$ is a finite abelian group which acts freely and isometrically on the $n$-sphere and therefore $M$ is diffeomorphic to a compact space form. In this paper, we prove that for every irreversible Finsler compact space form $(M,F)$ with reversibility $λ$ and flag curvature $K$ satisfying \[ \frac{4p^2}{(p+1)^2} \big(\fracλ{λ+1} \big)^2 < K \leq 1,\;\;λ< \frac{p+1}{p-1}, \] there exist at least $n-1$ non-contractible closed geodesics of class $[h]$. In addition, if the metric $F$ is bumpy and \[ (\frac{4p}{2p+1})^2 (\fracλ{λ+1})^2 < K \leq 1,\;\;λ<\frac{2p+1}{2p-1}, \] then there exist at least $2[\frac{n+1}{2}]$ non-contractible closed geodesics of class $[h]$, which is the optimal lower bound due to Katok's example. For $C^4$-generic Finsler metrics, there are infinitely many non-contractible closed geodesics of class $[h]$ on $(M, F)$ if $\frac{λ^2}{(λ+1)^2} < K \leq 1$ with $n$ being odd, or $\frac{λ^2}{(λ+1)^2}\frac{4}{(n-1)^2} < K \leq 1$ with $n$ being even.

math.DG↗

arXiv2022

What Makes a Good Commit Message?

A key issue in collaborative software development is communication among developers. One modality of communication is a commit message, in which developers describe the changes they make in a repository. As such, commit messages serve as an "audit trail" by which developers can understand how the source code of a project has changed-and why. Hence, the quality of commit messages affects the effectiveness of communication among developers. Commit messages are often of poor quality as developers lack time and motivation to craft a good message. Several automatic approaches have been proposed to generate commit messages. However, these are based on uncurated datasets including considerable proportions of poorly phrased commit messages. In this multi-method study, we first define what constitutes a "good" commit message, and then establish what proportion of commit messages lack information using a sample of almost 1,600 messages from five highly active open source projects. We find that an average of circa 44% of messages could be improved, suggesting the use of uncurated datasets may be a major threat when commit message generators are trained with such data. We also observe that prior work has not considered semantics of commit messages, and there is surprisingly little guidance available for writing good commit messages. To that end, we develop a taxonomy based on recurring patterns in commit messages' expressions. Finally, we investigate whether "good" commit messages can be automatically identified; such automation could prompt developers to write better commit messages.

cs.SE↗

arXiv2022

Tick-Tock: The Imminent Merger of a Supermassive Black Hole Binary

Supermassive black hole binaries (SMBHs) are a fascinating byproduct of galaxy mergers in the hierarchical universe. In the last stage of their orbital evolution, gravitational wave radiation drives the binary inspiral and produces the loudest siren awaiting to be detected by gravitational wave observatories. Periodically varying emission from active galactic nuclei has been proposed as a powerful approach to probe such systems, although none of the identified candidates are close to their final coalescence such that the observed periods stay constant in time. In this work, we report on the first system with rapid decaying periods revealed by its optical and X-ray light curves, which has decreased from about one year to one month in three years. Together with its optical hydrogen line spectroscopy, we propose that the system is an uneven mass-ratio, highly eccentric SMBH binary which will merge within three years, as predicted by the trajectory evolution model. If the interpretation is true, coordinated, multi-band electromagnetic campaign should be planned for this first binary SMBH merger event observed in human history, together with possible neutrino measurements. Gravitational wave memory from this event may also be detectable by Pulsar Timing Array with additional five-to-ten year observation.

astro-ph.HE↗