arXiv subjects

Chang Liu

Publications and source records attributed to Chang Liu.

At least 1,009 records · Page 56Linked to original sources

arXiv2020

Triaxially-deformed Freely-precessing Neutron Stars: Continuous electromagnetic and gravitational radiation

The shape of a neutron star (NS) is closely linked to its internal structure and the equation of state of supranuclear matters. A rapidly rotating, asymmetric NS in the Milky Way undergoes free precession, making it a potential source for multimessenger observation. The free precession could manifest in (i) the spectra of continuous gravitational waves (GWs) in the kilohertz band for ground-based GW detectors, and (ii) the timing behavior and pulse-profile characteristics if the NS is monitored as a pulsar with radio and/or X-ray telescopes. We extend previous work and investigate in great detail the free precession of a triaxially deformed NS with analytical and numerical approaches. In particular, its associated continuous GWs and pulse signals are derived. Explicit examples are illustrated for the continuous GWs, as well as timing residuals in both time and frequency domains. These results are ready to be used for future multimessenger observation of triaxially-deformed freely-precessing NSs, in order to extract scientific implication as much as possible.

astro-ph.HE↗

arXiv2020

Heating and Eruption of a Solar Circular Ribbon Flare

We studied a circular-ribbon flare, SOL2014-12-17T04:51, with emphasis on its thermal evolution as determined by the Differential Emission Measure (DEM) inversion analysis of the extreme ultraviolet (EUV) images of the Atmospheric Imaging Assembly (AIA) instrument onboard the Solar Dynamics Observatory (SDO). Both temperature and emission measure start to rise much earlier than the flare, along with an eruption and formation of a hot halo over the fan structure. In the main flare phase, another set of ribbons forms inside the circular ribbon, and expands as expected for ribbons at the footpoints of a postflare arcade. An additional heating event further extends the decay phase, which is also characteristic of some eruptive flares. The basic magnetic configuration appears to be a fan-spine topology, rooted in a minority-polarity patch surrounded by majority-polarity flux. We suggest that reconnection at the null point begins well before the impulsive phase, when the null is distorted into a breakout current sheet, and that both flare and breakout reconnection are necessary in order to explain the subsequent local thermal evolution and the eruptive activities in this confined magnetic structure. Using local DEMs, we found a postflare temperature increase inside the fan surface, indicating that the so-called EUV late phase is due to continued heating in the flare loops.

astro-ph.SR↗

arXiv2020

SCNet: A Neural Network for Automated Side-Channel Attack

The side-channel attack is an attack method based on the information gained about implementations of computer systems, rather than weaknesses in algorithms. Information about system characteristics such as power consumption, electromagnetic leaks and sound can be exploited by the side-channel attack to compromise the system. Much research effort has been directed towards this field. However, such an attack still requires strong skills, thus can only be performed effectively by experts. Here, we propose SCNet, which automatically performs side-channel attacks. And we also design this network combining with side-channel domain knowledge and different deep learning model to improve the performance and better to explain the result. The results show that our model achieves good performance with fewer parameters. The proposed model is a useful tool for automatically testing the robustness of computer systems.

cs.CR↗

arXiv2020

Empirical priors and posterior concentration in a piecewise polynomial sequence model

Inference on high-dimensional parameters in structured linear models is an important statistical problem. This paper focuses on the case of a piecewise polynomial Gaussian sequence model, and we develop a new empirical Bayes solution that enjoys adaptive minimax posterior concentration rates and improved structure learning properties compared to existing methods. Moreover, thanks to the conjugate form of the empirical prior, posterior computations are fast and easy. Numerical examples also highlight the method's strong finite-sample performance compared to existing methods across a range of different scenarios.

math.ST↗

arXiv2020

An Eruptive Circular-ribbon Flare with Extended Remote Brightenings

We study an eruptive X1.1 circular-ribbon flare on 2013 November 10, combining multiwavelength observations with a coronal field reconstruction using a non-force-free field method. In the first stage, a filament forms via magnetic reconnection between two mildly twisted sheared arcades, which are embedded under the fan dome associated with a null point. This reconnection seems to be driven by photospheric shearing and converging flows around the inner two arcade footpoints, consistent with the flare-related changes of transverse field. The southern portion of the filament rises upward due to torus instability and pushes against the null point. The induced null point reconnection then generates the circular ribbon and the initial remote brightening in the west, as accelerated electrons precipitate along the fan and propagate outward along quasi-separatix surfaces with high values of the squashing factor (Q) in the envelope fields, which have a curtain-like shape here. In the second stage, the southern end of the flux rope breaks away from the surface, sequentially disrupts the dome and overlying fields, and erupts in a whipping-like fashion to become a partial halo coronal mass ejection. This leads to an enhanced flare emission and fast-moving remote brightenings at the footpoints of the magnetic curtain, which span a remarkably broad region and are also associated with coronal dimmings. This is a rare example of eruptive circular-ribbon flares, in which the evolution of a flux rope from its formation to successful eruption out of the dome and the resulting unusually extended remote brightenings are completely observed.

astro-ph.SR↗

arXiv2020

Learning to Match Distributions for Domain Adaptation

When the training and test data are from different distributions, domain adaptation is needed to reduce dataset bias to improve the model's generalization ability. Since it is difficult to directly match the cross-domain joint distributions, existing methods tend to reduce the marginal or conditional distribution divergence using predefined distances such as MMD and adversarial-based discrepancies. However, it remains challenging to determine which method is suitable for a given application since they are built with certain priors or bias. Thus they may fail to uncover the underlying relationship between transferable features and joint distributions. This paper proposes Learning to Match (L2M) to automatically learn the cross-domain distribution matching without relying on hand-crafted priors on the matching loss. Instead, L2M reduces the inductive bias by using a meta-network to learn the distribution matching loss in a data-driven way. L2M is a general framework that unifies task-independent and human-designed matching features. We design a novel optimization algorithm for this challenging objective with self-supervised label propagation. Experiments on public datasets substantiate the superiority of L2M over SOTA methods. Moreover, we apply L2M to transfer from pneumonia to COVID-19 chest X-ray images with remarkable performance. L2M can also be extended in other distribution matching applications where we show in a trial experiment that L2M generates more realistic and sharper MNIST samples.

cs.LG↗

arXiv2020

Impact of an Active Sgr A* on the Synthesis of Water and Organic Molecules Throughout the Milky Way

Sgr A*, the supermassive black hole (SMBH) in our Galaxy, is dormant today, but it should have gone through multiple gas-accretion episodes in the past billions of years to grow to its current mass of $4\times10^6\,M_\odot$. Each episode temporarily ignites the SMBH and turns the Galactic Center into an active galactic nucleus (AGN). Recently, we showed that the AGN could produce large amount of hard X-rays that can penetrate the dense interstellar medium in the Galactic plane. Here we further study the impact of the X-rays on the molecular chemistry in our Galaxy. We use a chemical reaction network to simulate the evolution of several molecular species including H2O, CH3OH, and H2CO, both in the gas phase and on the surface of dust grains. We find that the X-ray irradiation could significantly enhance the abundances of these species. The effect is the most significant in those young, high-density molecular clouds, and could be prominent at a Galactic distance of $8$ kpc or smaller. The imprint in the chemical abundance is visible even several million years after the AGN turns off.

astro-ph.GA↗

arXiv2020

Progressive Cluster Purification for Unsupervised Feature Learning

In unsupervised feature learning, sample specificity based methods ignore the inter-class information, which deteriorates the discriminative capability of representation models. Clustering based methods are error-prone to explore the complete class boundary information due to the inevitable class inconsistent samples in each cluster. In this work, we propose a novel clustering based method, which, by iteratively excluding class inconsistent samples during progressive cluster formation, alleviates the impact of noise samples in a simple-yet-effective manner. Our approach, referred to as Progressive Cluster Purification (PCP), implements progressive clustering by gradually reducing the number of clusters during training, while the sizes of clusters continuously expand consistently with the growth of model representation capability. With a well-designed cluster purification mechanism, it further purifies clusters by filtering noise samples which facilitate the subsequent feature learning by utilizing the refined clusters as pseudo-labels. Experiments on commonly used benchmarks demonstrate that the proposed PCP improves baseline method with significant margins. Our code will be available at https://github.com/zhangyifei0115/PCP.

cs.CV↗

arXiv2020

Freestanding n-Doped Graphene via Intercalation of Calcium and Magnesium into the Buffer Layer - SiC(0001) Interface

The intercalation of epitaxial graphene on SiC(0001) with Ca has been studied extensively, yet precisely where the Ca resides remains elusive. Furthermore, the intercalation of Mg underneath epitaxial graphene on SiC(0001) has not been reported. Here, we use low energy electron diffraction, x-ray photoelectron spectroscopy, secondary electron cut-off photoemission and scanning tunneling microscopy to elucidate the physical and electronic structure of both Ca- and Mg-intercalated epitaxial graphene on 6H-SiC(0001). We find that Ca intercalates underneath the buffer layer and bonds to the Si-terminated SiC surface, breaking the C-Si bonds of the buffer layer i.e. 'freestanding' the buffer layer to form Ca-intercalated quasi-freestanding bilayer graphene (Ca-QFSBLG). The situation is similar for the Mg-intercalation of epitaxial graphene on SiC(0001), where an ordered Mg-terminated reconstruction at the SiC surface and Mg bonds to the Si-terminated SiC surface are formed, resulting in Mg-intercalated quasi-freestanding bilayer graphene (Mg-QFSBLG). Ca-intercalation underneath the buffer layer has not been considered in previous studies of Ca-intercalated epitaxial graphene. Furthermore, we find no evidence that either Ca or Mg intercalates between graphene layers. However, we do find that both Ca-QFSBLG and Mg-QFSBLG exhibit very low workfunctions of 3.68 and 3.78 eV, respectively, indicating high n-type doping. Upon exposure to ambient conditions, we find Ca-QFSBLG degrades rapidly, whereas Mg-QFSBLG remains remarkably stable.

cond-mat.mtrl-sci↗

arXiv2020

Relative Entropy Regularised TDLAS Tomography for Robust Temperature Imaging

Tunable Diode Laser Absorption Spectroscopy (TDLAS) tomography has been widely used for in situ combustion diagnostics, yielding images of both species concentration and temperature. The temperature image is generally obtained from the reconstructed absorbance distributions for two spectral transitions, i.e. two-line thermometry. However, the inherently ill-posed nature of tomographic data inversion leads to noise in each of the reconstructed absorbance distributions. These noise effects propagate into the absorbance ratio and generate artefacts in the retrieved temperature image. To address this problem, we have developed a novel algorithm, which we call Relative Entropy Tomographic RecOnstruction (RETRO), for TDLAS tomography. A relative entropy regularisation is introduced for high-fidelity temperature image retrieval from jointly reconstructed two-line absorbance distributions. We have carried out numerical simulations and proof-of-concept experiments to validate the proposed algorithm. Compared with the well-established Simultaneous Algebraic Reconstruction Technique (SART), the RETRO algorithm significantly improves the quality of the tomographic temperature images, exhibiting excellent robustness against TDLAS tomographic measurement noise. RETRO offers great potential for industrial field applications of TDLAS tomography, where it is common for measurements to be performed in very harsh environments.

eess.IV↗

arXiv2020

Modeling Lost Information in Lossy Image Compression

Lossy image compression is one of the most commonly used operators for digital images. Most recently proposed deep-learning-based image compression methods leverage the auto-encoder structure, and reach a series of promising results in this field. The images are encoded into low dimensional latent features first, and entropy coded subsequently by exploiting the statistical redundancy. However, the information lost during encoding is unfortunately inevitable, which poses a significant challenge to the decoder to reconstruct the original images. In this work, we propose a novel invertible framework called Invertible Lossy Compression (ILC) to largely mitigate the information loss problem. Specifically, ILC introduces an invertible encoding module to replace the encoder-decoder structure to produce the low dimensional informative latent representation, meanwhile, transform the lost information into an auxiliary latent variable that won't be further coded or stored. The latent representation is quantized and encoded into bit-stream, and the latent variable is forced to follow a specified distribution, i.e. isotropic Gaussian distribution. In this way, recovering the original image is made tractable by easily drawing a surrogate latent variable and applying the inverse pass of the module with the sampled variable and decoded latent features. Experimental results demonstrate that with a new component replacing the auto-encoder in image compression methods, ILC can significantly outperform the baseline method on extensive benchmark datasets by combining with the existing compression algorithms.

cs.CV↗

arXiv2020

Discretization-Aware Architecture Search

The search cost of neural architecture search (NAS) has been largely reduced by weight-sharing methods. These methods optimize a super-network with all possible edges and operations, and determine the optimal sub-network by discretization, \textit{i.e.}, pruning off weak candidates. The discretization process, performed on either operations or edges, incurs significant inaccuracy and thus the quality of the final architecture is not guaranteed. This paper presents discretization-aware architecture search (DA\textsuperscript{2}S), with the core idea being adding a loss term to push the super-network towards the configuration of desired topology, so that the accuracy loss brought by discretization is largely alleviated. Experiments on standard image classification benchmarks demonstrate the superiority of our approach, in particular, under imbalanced target network configurations that were not studied before.

cs.CV↗

arXiv2020

Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks

This paper investigates capabilities of Privacy-Preserving Deep Learning (PPDL) mechanisms against various forms of privacy attacks. First, we propose to quantitatively measure the trade-off between model accuracy and privacy losses incurred by reconstruction, tracing and membership attacks. Second, we formulate reconstruction attacks as solving a noisy system of linear equations, and prove that attacks are guaranteed to be defeated if condition (2) is unfulfilled. Third, based on theoretical analysis, a novel Secret Polarization Network (SPN) is proposed to thwart privacy attacks, which pose serious challenges to existing PPDL methods. Extensive experiments showed that model accuracies are improved on average by 5-20% compared with baseline mechanisms, in regimes where data privacy are satisfactorily protected.

cs.LG↗

arXiv2020

Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations

The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology, Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers, expert modelers, and computer/data scientists. Their objective was to discuss critical developments and prospects of the application of machine and/or deep learning techniques for data analysis, modeling and forecasting in Heliophysics, and to shape a strategy for further developments in the field. The workshop combined a set of plenary sessions featuring invited introductory talks interleaved with a set of open discussion sessions. The outcome of the discussion is encapsulated in this white paper that also features a top-level list of recommendations agreed by participants.

astro-ph.SR↗

arXiv2020

Video Playback Rate Perception for Self-supervisedSpatio-Temporal Representation Learning

In self-supervised spatio-temporal representation learning, the temporal resolution and long-short term characteristics are not yet fully explored, which limits representation capabilities of learned models. In this paper, we propose a novel self-supervised method, referred to as video Playback Rate Perception (PRP), to learn spatio-temporal representation in a simple-yet-effective way. PRP roots in a dilated sampling strategy, which produces self-supervision signals about video playback rates for representation model learning. PRP is implemented with a feature encoder, a classification module, and a reconstructing decoder, to achieve spatio-temporal semantic retention in a collaborative discrimination-generation manner. The discriminative perception model follows a feature encoder to prefer perceiving low temporal resolution and long-term representation by classifying fast-forward rates. The generative perception model acts as a feature decoder to focus on comprehending high temporal resolution and short-term representation by introducing a motion-attention mechanism. PRP is applied on typical video target tasks including action recognition and video retrieval. Experiments show that PRP outperforms state-of-the-art self-supervised models with significant margins. Code is available at github.com/yuanyao366/PRP

cs.CV↗

arXiv2020

A New Comprehensive Data Set of Solar Filaments of 100 yr Interval. I

Filaments are very common physical phenomena on the Sun and are often taken as important proxies of solar magnetic activities. The study of filaments has become a hot topic in the space weather research. For a more comprehensive understanding of filaments, especially for an understanding of solar activities of multiple solar cycles, it is necessary to perform a combined multifeature analysis by constructing a data set of multiple solar cycle data. To achieve this goal, we constructed a centennial data set that covers the H$α$ data from five observatories around the world. During the data set construction, we encountered varieties of problems, such as data fusion, accurate determination of the solar edge, classifying data by quality, dynamic threshold, and so on, which arose mainly due to multiple sources and a large time span of data. But fortunately, these problems were well solved. The data set includes seven types of data products and eight types of feature parameters with which we can implement the functions of data searching and statistical analyses. It has the characteristics of better continuity and highly complementary to space observation data, especially in the wavelengths not covered by space observations, and covers many solar cycles (including more than 60 yr of high-cadence data). We expect that this new comprehensive data set as well as the tools will help researchers to significantly speed up their search for features or events of interest, for either statistical or case study purposes, and possibly help them get a better and more comprehensive understanding of solar filament mechanisms.

astro-ph.SR↗

arXiv2020

Spin Excitations and Spin Wave Gap in the Ferromagnetic Weyl Semimetal Co$_3$Sn$_2$S$_2$

We report a comprehensive neutron scattering study on the spin excitations in the magnetic Weyl semimetal Co$_3$Sn$_2$S$_2$ with quasi-two-dimensional structure. Both in-plane and out-of-plane dispersions of the spin waves are revealed in the ferromagnetic state, similarly dispersive but damped spin excitations persist into the paramagnetic state. The effective exchange interactions have been estimated by a semi-classical Heisenberg model to consistently reproduce the experimental $T_C$ and spin stiffness. However, a full spin wave gap below $E_g=2.3$ meV is observed at $T=4$ K, much larger than the estimated magnetic anisotropy energy ($\sim0.6$ meV), while its temperature dependence indicates a significant contribution from the Weyl fermions. These results suggest that Co$_3$Sn$_2$S$_2$ is a three-dimensional correlated system with large spin stiffness, and the low-energy spin dynamics could interplay with the topological electron states.

cond-mat.str-el↗

arXiv2020

LRNNet: A Light-Weighted Network with Efficient Reduced Non-Local Operation for Real-Time Semantic Segmentation

The recent development of light-weighted neural networks has promoted the applications of deep learning under resource constraints and mobile applications. Many of these applications need to perform a real-time and efficient prediction for semantic segmentation with a light-weighted network. This paper introduces a light-weighted network with an efficient reduced non-local module (LRNNet) for efficient and realtime semantic segmentation. We proposed a factorized convolutional block in ResNet-Style encoder to achieve more lightweighted, efficient and powerful feature extraction. Meanwhile, our proposed reduced non-local module utilizes spatial regional dominant singular vectors to achieve reduced and more representative non-local feature integration with much lower computation and memory cost. Experiments demonstrate our superior trade-off among light-weight, speed, computation and accuracy. Without additional processing and pretraining, LRNNet achieves 72.2% mIoU on Cityscapes test dataset only using the fine annotation data for training with only 0.68M parameters and with 71 FPS on a GTX 1080Ti card.

cs.CV↗