arXiv subjects

Yun Zhang

Publications and source records attributed to Yun Zhang.

At least 109 records · Page 6Linked to original sources

arXiv2021

The Excited Spin State of Dimorphos Resulting from the DART Impact

The NASA Double Asteroid Redirection Test (DART) mission is a planetary defense-driven test of a kinetic impactor on Dimorphos, the satellite of the binary asteroid 65803 Didymos. DART will intercept Dimorphos at a relative speed of ${\sim}6.5 \text{ km s}^{-1}$, perturbing Dimorphos's orbital velocity and changing the binary orbital period. We present three independent methods (one analytic and two numerical) to investigate the post-impact attitude stability of Dimorphos as a function of its axial ratios, $a/b$ and $b/c$ ($a \ge b \ge c$), and the momentum transfer efficiency $β$. The first method uses a novel analytic approach in which we assume a circular orbit and a point-mass primary that identifies four fundamental frequencies of motion corresponding to the secondary's mean motion, libration, precession, and nutation frequencies. At resonance locations among these four frequencies, we find that attitude instabilities are possible. Using two independent numerical codes, we recover many of the resonances predicted by the analytic model and indeed show attitude instability. With one code, we use fast Lyapunov indicators to show that the secondary's attitude can evolve chaotically near the resonance locations. Then, using a high-fidelity numerical model, we find that Dimorphos enters a chaotic tumbling state near the resonance locations and is especially prone to unstable rotation about its long axis, which can be confirmed by ESA's Hera mission arriving at Didymos in late 2026. We also show that a fully coupled treatment of the spin and orbital evolution of both bodies is crucial to accurately model the long-term evolution of the secondary's spin state and libration amplitude. Finally, we discuss the implications of a post-impact tumbling or rolling state, including the possibility of terminating BYORP evolution if Dimorphos is no longer in synchronous rotation.

astro-ph.EP↗

arXiv2021

Orbital migration and circularization of tidal debris by Alfvén-wave drag: circumstellar debris and pollution around white dwarfs

A significant fraction of white dwarfs (WDs) exhibit signs of ongoing accretion of refractory elements at rates $\sim10^3$--$10^7$ kg s$^{-1}$, among which, 37 WDs were detected to harbor dusty debris disks. Such a concurrence requires not only fertile reservoirs of planetary material, but also a high duty cycle of metal delivery. It has been commonly suggested that this material could be supplied by Solar System analogs of Main Belt asteroids or Kuiper Belt objects. Here we consider the primary progenitors of WD pollutants as a population of residual high-eccentricity planetesimals, de-volatilized during the stellar giant phases. Equivalent to the Solar System's long-period comets, they are scattered to the proximity of WDs by perturbations from remaining planets, Galactic tides, passing molecular clouds, and nearby stars. These objects undergo downsizing when they venture within the tidal disruption limit. We show quantitatively how the breakup condition and fragment sizes are determined by material strength and gravity. Thereafter, the fragments' semi-major axes need to decay by at least $\sim$6 orders of magnitude before their constituents are eventually accreted onto the surface of WDs. We investigate the orbital evolution of these fragments around WDs and show that WDs' magnetic fields induce an Alfvén-wave drag during their periastron passages and rapidly circularize their orbits. This process could be responsible for the observed accretion rates of heavy-elements and the generation of circum-WD debris disks. A speculative implication is that giant planets may be common around WDs' progenitors and they may still be bound to some WDs today.

astro-ph.EP↗

arXiv2020

Explainable Machine Learning based Transform Coding for High Efficiency Intra Prediction

Machine learning techniques provide a chance to explore the coding performance potential of transform. In this work, we propose an explainable transform based intra video coding to improve the coding efficiency. Firstly, we model machine learning based transform design as an optimization problem of maximizing the energy compaction or decorrelation capability. The explainable machine learning based transform, i.e., Subspace Approximation with Adjusted Bias (Saab) transform, is analyzed and compared with the mainstream Discrete Cosine Transform (DCT) on their energy compaction and decorrelation capabilities. Secondly, we propose a Saab transform based intra video coding framework with off-line Saab transform learning. Meanwhile, intra mode dependent Saab transform is developed. Then, Rate Distortion (RD) gain of Saab transform based intra video coding is theoretically and experimentally analyzed in detail. Finally, three strategies on integrating the Saab transform and DCT in intra video coding are developed to improve the coding efficiency. Experimental results demonstrate that the proposed 8$\times$8 Saab transform based intra video coding can achieve Bjønteggard Delta Bit Rate (BDBR) from -1.19% to -10.00% and -3.07% on average as compared with the mainstream 8$\times$8 DCT based coding scheme.

eess.IV↗

arXiv2020

Critical spin periods of sub-km-sized cohesive rubble-pile asteroids: dependencies on material parameters

In this work, we employ a soft-sphere discrete element method with a cohesion implementation to model the dynamical process of sub-km-sized cohesive rubble piles under continuous spinup. The dependencies of critical spin periods $T_c$ on several material parameters for oblate rubble piles with different bulk diameters $D$ are explored. Our numerical simulations show that both the increase of interparticle cohesion and particle shape parameter in our model can strengthen the bodies, especially for the smaller ones. In addition, we find there exists some critical diameter $D_{cri,ρ}$ at which the variation trend of $T_c$ with the bulk density $ρ$ reverses. Though a greater static friction coefficient $μ_S$ can strengthen the body, this effect attains a minimum at a critical diameter $D_{cri,ϕ}$ close to $D_{cri,ρ}$. The continuum theory (analytical method) is used for comparison and two equivalent critical diameters are obtained. The numerical results were fitted with the analytical method and the ratio of the interparticle cohesion $c$ to the bulk cohesion $C$ is estimated to be roughly 88.3. We find this ratio keeps constant for different $c$ and $ρ$, while it strongly depends on the friction angle $ϕ$. Also, our numerical results further show that the dependency of $T_c$ on $ϕ$ is opposite from that predicted by the continuum theory when $D$ < $D_{cri,ϕ}$. Finally, we find that the two critical diameters happen to be close to the diameter when the mean normal stress of the body equals zero, which is the separation between the compressive regime and the tensile regime.

astro-ph.EP↗

arXiv2020

Attention Distillation for Learning Video Representations

We address the challenging problem of learning motion representations using deep models for video recognition. To this end, we make use of attention modules that learn to highlight regions in the video and aggregate features for recognition. Specifically, we propose to leverage output attention maps as a vehicle to transfer the learned representation from a motion (flow) network to an RGB network. We systematically study the design of attention modules, and develop a novel method for attention distillation. Our method is evaluated on major action benchmarks, and consistently improves the performance of the baseline RGB network by a significant margin. Moreover, we demonstrate that our attention maps can leverage motion cues in learning to identify the location of actions in video frames. We believe our method provides a step towards learning motion-aware representations in deep models. Our project page is available at https://aptx4869lm.github.io/AttentionDistillation/

cs.CV↗

arXiv2020

Terahertz Strong-Field Physics in Light-Emitting Diodes for Terahertz Detection and Imaging

Intense terahertz (THz) electromagnetic fields have been utilized to reveal a variety of extremely nonlinear optical effects in many materials through nonperturbative driving of elementary and collective excitations. However, such nonlinear photoresponses have not yet been discovered in light-emitting diodes (LEDs), letting alone employing them as fast, cost effective,compact, and room-temperature-operating THz detectors and cameras. Here we report ubiquitously available LEDs exhibited gigantic and fast photovoltaic signals with excellent signal-to-noise ratios when being illuminated by THz field strengths >50 kV/cm. We also successfully demonstrated THz-LED detectors and camera prototypes. These unorthodox THz detectors exhibited high responsivities (>1 kV/W) with response time shorter than those of pyroelectric detectors by four orders of magnitude. The detection mechanism was attributed to THz-field-induced nonlinear impact ionization and Schottky contact. These findings not only help deepen our understanding of strong THz field-matter interactions but also greatly contribute to the applications of strong-field THz diagnosis.

physics.optics↗

arXiv2020

Tidal distortion and disruption of rubble-pile bodies revisited -- Soft-sphere discrete element analyses

During close approaches to planets or stars, the morphological and dynamical properties of rubble-pile small bodies can be modified, and some may catastrophically break up. This phenomenon is of particular interest for understanding the evolution and population of small bodies, and for making predictions for future encounters. Previous numerical explorations typically used methods that do not adequately represent the nature of rubble piles. The encounter outcomes and influence factors are still poorly constrained. Based on recent advances in modeling rubble piles, we aim to provide a better understanding of the tidal encounter processes through soft-sphere discrete element modeling (SSDEM) and to establish a database of encounter outcomes and the dependencies on encounter conditions and rubble-pile properties. We performed thousands of numerical simulations using the SSDEM implemented in the N-body code pkdgrav to study the dynamical evolution of rubble piles during close encounters with the Earth. The effects of encounter conditions, material strength, arrangement, and resolution of constituent particles are explored. Three typical tidal encounter outcomes are classified, i.e., deformation, mass shedding, and disruption, ranging from mild modifications to severe damages of the progenitor. The encounter speed and distance required for causing disruption events are much smaller than those predicted by previous studies, indicating a smaller creation rate of tidally disrupted small body populations. Extremely elongated fragments with axis ratios ~1:6 can be formed by moderate encounters. Our analyses of the spin-shape evolution of the largest remnants reveal reshaping mechanisms of rubble piles in response to tidal forces, consistent with stable rubble-pile configurations derived by continuum theory. A case study for SL9 suggests a low bulk density (0.2-0.3 g/cc) for its progenitor.

astro-ph.EP↗

arXiv2020

Kondo scenario of the γ-α phase transition in single crystalline Cerium thin films

The physical mechanism driving the $γ$-$α$ phase transition of face-centre-cubic (fcc) cerium (Ce) remains controversial until now. In this work, high quality single crystalline fcc-Ce thin films were grown on Graphene/6$H$-SiC(0001) substrate, and explored by XRD and ARPES measurement. XRD spectra showed a clear $γ$-$α$ phase transition at $T_{γ-α}\approx$ 50 K, which is retarded by strain effect from substrate comparing with $T_{γ-α}$ (about 140 K) of the bulk Ce metal. However, APRES spectra did not show any signature of $α$-phase emerging in the surface-layer from 300 K to 17 K, which implied that $α$-phase might form at the bulk-layer of our Ce thin films. Besides, an evident Kondo dip near Fermi energy was observed in the APRES spectrum at 80 K, indicting the formation of Kondo singlet states in $γ$-Ce. Furthermore, the DFT+DMFT calculations were performed to simulate the electronic structures and the theoretical spectral functions agreed well with the experimental ARPES spectra. In $γ$-Ce, the behavior of the self-energy's imaginary part at low frequency not only confirmed that the Kondo singlet states emerged at $T_{\rm KS} \geq 80$ K, but also implied that they became coherent states at a lower characteristic temperature ($T_{\rm coh}\sim 40$ K) due to the indirect RKKY interaction among $f$-$f$ electrons. Besides, $T_{\rm coh}$ from the theoretical simulation was close to $T_{γ-α}$ from the XRD spectra. These issues suggested that the Kondo scenario might play an important role in the $γ$-$α$ phase transition of cerium thin films.

cond-mat.str-el↗

arXiv2020

Cost-effectiveness Analysis of Antiepidemic Policies and Global Situation Assessment of COVID-19

With a two-layer contact-dispersion model and data in China, we analyze the cost-effectiveness of three types of antiepidemic measures for COVID-19: regular epidemiological control, local social interaction control, and inter-city travel restriction. We find that: 1) intercity travel restriction has minimal or even negative effect compared to the other two at the national level; 2) the time of reaching turning point is independent of the current number of cases, and only related to the enforcement stringency of epidemiological control and social interaction control measures; 3) strong enforcement at the early stage is the only opportunity to maximize both antiepidemic effectiveness and cost-effectiveness; 4) mediocre stringency of social interaction measures is the worst choice. Subsequently, we cluster countries/regions into four groups based on their control measures and provide situation assessment and policy suggestions for each group.

physics.soc-ph↗

arXiv2020

Tidal fragmentation as the origin of 1I/2017 U1 ('Oumuamua)

The first discovered interstellar object (ISO), `Oumuamua (1I/2017 U1) shows a dry and rocky surface, an unusually elongated short-to-long axis ratio $c/a \lesssim 1/6$, a low velocity relative to the local standard of rest ($\sim 10$ km s$^{-1}$), non-gravitational accelerations, and tumbles on a few hours timescale. The inferred number density ($\sim 3.5 \times 10^{13} - 2 \times 10^{15}$ pc$^{-3}$) for a population of asteroidal ISOs outnumbers cometary ISOs by $\geq 10^3$, in contrast to the much lower ratio ($\lesssim 10^{-2}$) of rocky/icy Kuiper belt objects. Although some scenarios can cause the ejection of asteroidal ISOs, a unified formation theory has yet to comprehensively link all `Oumuamua's puzzling characteristics and to account for the population. Here we show by numerical simulations that `Oumuamua-like ISOs can be prolifically produced through extensive tidal fragmentation and ejected during close encounters of their volatile-rich parent bodies with their host stars. Material strength enhanced by the intensive heating during periastron passages enables the emergence of extremely elongated triaxial ISOs with shape $c/a \lesssim 1/10$, sizes $a \sim 100$ m, and rocky surfaces. Although volatiles with low sublimation temperature (such as CO) are concurrently depleted, H$_2$O buried under surfaces is preserved in these ISOs, providing an outgassing source without measurable cometary activities for `Oumuamua's non-gravitational accelerations during its passage through the inner Solar System. We infer that the progenitors of `Oumuamua-like ISOs may be km-sized long-period comets from Oort clouds, km-sized residual planetesimals from debris disks, or planet-size bodies at a few AU, orbiting around low-mass main-sequence stars or white dwarfs. These provide abundant reservoirs to account for `Oumuamua's occurrence rate.

astro-ph.EP↗

arXiv2020

SUR-FeatNet: Predicting the Satisfied User Ratio Curvefor Image Compression with Deep Feature Learning

The satisfied user ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, characterizes the complementary cumulative distribution function of the just noticeable difference (JND), the smallest distortion level that can be perceived by a subject when a reference image is compared to a distorted one. A sequence of JNDs can be defined with a suitable successive choice of reference images. We propose the first deep learning approach to predict SUR curves. We show how to apply maximum likelihood estimation and the Anderson-Darling test to select a suitable parametric model for the distribution function. We then use deep feature learning to predict samples of the SUR curve and apply the method of least squares to fit the parametric model to the predicted samples. Our deep learning approach relies on a siamese convolutional neural network, transfer learning, and deep feature learning, using pairs consisting of a reference image and a compressed image for training. Experiments on the MCL-JCI dataset showed state-of-the-art performance. For example, the mean Bhattacharyya distances between the predicted and ground truth first, second, and third JND distributions were 0.0810, 0.0702, and 0.0522, respectively, and the corresponding average absolute differences of the peak signal-to-noise ratio at a median of the first JND distribution were 0.58, 0.69, and 0.58 dB. Further experiments on the JND-Pano dataset showed that the method transfers well to high resolution panoramic images viewed on head-mounted displays.

cs.MM↗

arXiv2020

DymSLAM:4D Dynamic Scene Reconstruction Based on Geometrical Motion Segmentation

Most SLAM algorithms are based on the assumption that the scene is static. However, in practice, most scenes are dynamic which usually contains moving objects, these methods are not suitable. In this paper, we introduce DymSLAM, a dynamic stereo visual SLAM system being capable of reconstructing a 4D (3D + time) dynamic scene with rigid moving objects. The only input of DymSLAM is stereo video, and its output includes a dense map of the static environment, 3D model of the moving objects and the trajectories of the camera and the moving objects. We at first detect and match the interesting points between successive frames by using traditional SLAM methods. Then the interesting points belonging to different motion models (including ego-motion and motion models of rigid moving objects) are segmented by a multi-model fitting approach. Based on the interesting points belonging to the ego-motion, we are able to estimate the trajectory of the camera and reconstruct the static background. The interesting points belonging to the motion models of rigid moving objects are then used to estimate their relative motion models to the camera and reconstruct the 3D models of the objects. We then transform the relative motion to the trajectories of the moving objects in the global reference frame. Finally, we then fuse the 3D models of the moving objects into the 3D map of the environment by considering their motion trajectories to obtain a 4D (3D+time) sequence. DymSLAM obtains information about the dynamic objects instead of ignoring them and is suitable for unknown rigid objects. Hence, the proposed system allows the robot to be employed for high-level tasks, such as obstacle avoidance for dynamic objects. We conducted experiments in a real-world environment where both the camera and the objects were moving in a wide range.

cs.CV↗

arXiv2019

Stereo-based Multi-motion Visual Odometry for Mobile Robots

With the development of computer vision, visual odometry is adopted by more and more mobile robots. However, we found that not only its own pose, but the poses of other moving objects are also crucial for the decision of the robot. In addition, the visual odometry will be greatly disturbed when a significant moving object appears. In this letter, a stereo-based multi-motion visual odometry method is proposed to acquire the poses of the robot and other moving objects. In order to obtain the poses simultaneously, a continuous motion segmentation module and a coordinate conversion module are applied to the traditional visual odometry pipeline. As a result, poses of all moving objects can be acquired and transformed into the ground coordinate system. The experimental results show that the proposed multi-motion visual odometry can effectively eliminate the influence of moving objects on the visual odometry, as well as achieve 10 cm in position and 3° in orientation RMSE (Root Mean Square Error) of each moving object.

cs.CV↗

arXiv2019

On Energy Compaction of 2D Saab Image Transforms

The block Discrete Cosine Transform (DCT) is commonly used in image and video compression due to its good energy compaction property. The Saab transform was recently proposed as an effective signal transform for image understanding. In this work, we study the energy compaction property of the Saab transform in the context of intra-coding of the High Efficiency Video Coding (HEVC) standard. We compare the energy compaction property of the Saab transform, the DCT, and the Karhunen-Loeve transform (KLT) by applying them to different sizes of intra-predicted residual blocks in HEVC. The basis functions of the Saab transform are visualized. Extensive experimental results are given to demonstrate the energy compaction capability of the Saab transform.

eess.IV↗

arXiv2019

A Novel Deep Neural Network Based Approach for Sparse Code Multiple Access

Sparse code multiple access (SCMA) has been one of non-orthogonal multiple access (NOMA) schemes aiming to support high spectral efficiency and ubiquitous access requirements for 5G wireless communication networks. Conventional SCMA approaches are confronting remarkable challenges in designing low complexity high accuracy decoding algorithm and constructing optimum codebooks. Fortunately, the recent spotlighted deep learning technologies are of significant potentials in solving many communication engineering problems. Inspired by this, we explore approaches to improve SCMA performances with the help of deep learning methods. We propose and train a deep neural network (DNN) called DL-SCMA to learn to decode SCMA modulated signals corrupted by additive white Gaussian noise (AWGN). Putting encoding and decoding together, an autoencoder called AE-SCMA is established and trained to generate optimal SCMA codewords and reconstruct original bits. Furthermore, by manipulating the mapping vectors, an autoencoder is able to generalize SCMA, thus a dense code multiple access (DCMA) scheme is proposed. Simulations show that the DNN SCMA decoder significantly outperforms the conventional message passing algorithm (MPA) in terms of bit error rate (BER), symbol error rate (SER) and computational complexity, and AE-SCMA also demonstrates better performances via constructing better SCMA codebooks. The performance of deep learning aided DCMA is superior to the SCMA.

eess.SP↗

arXiv2019

Experimental Evidence of the Topological Surface States in Mg3Bi2 Films Grown by Molecular Beam Epitaxy

Type-II nodal line semimetal (NLS) is a new quantum state hosting one-dimensional closed loops formed by the crossing of two bands which have the same sign in their slopes along the radial direction of the loop. According to the theoretical prediction, Mg3Bi2 is an ideal candidate for studying the type-II NLS by tuning its spin-orbit coupling (SOC). In this paper, high quality Mg3Bi2 films are grown by molecular beam epitaxy (MBE). By in-situ angle resolved photoemission spectroscopy (ARPES), a pair of surface resonance bands (SRBs) around Gamma point is clearly seen. It shows that Mg3Bi2 films grown by MBE is Mg(1)-terminated by comparing the ARPES data with the first principles calculations results. And, the temperature dependent weak anti-localization (WAL) effect in Mg3Bi2 films is observed under low magnetic field, which shows a clear two dimensional (2D) e-e scattering characteristics by fitting with the Hikami-Larkin-Nagaoka (HLN) model. Combining with ARPES, magneto-transport measurements and the first principles calculations, this work proves that Mg3Bi2 is a semimetal with topological surface states TSSs, which paves the way for Mg3Bi2 as an ideal materials platform for studying the exotic features of type-II nodal line semimetals (NLSs) and the topological phase transition by tuning its SOC.

cond-mat.mtrl-sci↗

arXiv2019

When an attacker meets a cipher-image in 2018: A Year in Review

This paper aims to review the encountered technical contradictions when an attacker meets the cipher-images encrypted by the image encryption schemes (algorithms) proposed in 2018 from the viewpoint of an image cryptanalyst. The most representative works among them are selected and classified according to their essential structures. Almost all image cryptanalysis works published in 2018 are surveyed due to their small number. The challenging problems on design and analysis of image encryption schemes are summarized to receive the attentions of both designers and attackers (cryptanalysts) of image encryption schemes, which may promote solving scenario-oriented image security problems with new technologies.

cs.CR↗

arXiv2019

Content-Preserving Image Stitching with Regular Boundary Constraints

This paper proposes an approach to content-preserving stitching of images with regular boundary constraints, which aims to stitch multiple images to generate a panoramic image with regular boundary. Existing methods treat image stitching and rectangling as two separate steps, which may result in suboptimal results as the stitching process is not aware of the further warping needs for rectangling. We address these limitations by formulating image stitching with regular boundaries in a unified optimization. Starting from the initial stitching results produced by traditional warping-based optimization, we obtain the irregular boundary from the warped meshes by polygon Boolean operations which robustly handle arbitrary mesh compositions, and by analyzing the irregular boundary construct a piecewise rectangular boundary. Based on this, we further incorporate straight line preserving and regular boundary constraints into the image stitching framework, and conduct iterative optimization to obtain an optimal piecewise rectangular boundary, thus can make the panoramic boundary as close as possible to a rectangle, while reducing unwanted distortions. We further extend our method to panoramic videos and selfie photography, by integrating the temporal coherence and portrait preservation into the optimization. Experiments show that our method efficiently produces visually pleasing panoramas with regular boundaries and unnoticeable distortions.

cs.GR↗