arXiv subjects

Xing Wei

Publications and source records attributed to Xing Wei.

At least 73 records · Page 4Linked to original sources

arXiv2022

TESS Timings of 31 Hot Jupiters with Ephemeris Uncertainties

A precise transit ephemeris serves as the premise for follow-up exoplanet observations. We compare TESS Objects of Interest (TOI) transit timings of 262 hot Jupiters with the archival ephemeris and find 31 of them having TOI timing offsets, among which WASP-161b shows the most significant offset of -203.7$\pm$4.1 minutes. The median value of these offsets is 17.8 minutes, equivalent to 3.6 $σ$. We generate TESS timings in each sector for these 31 hot Jupiters, using a self-generated pipeline. The pipeline performs photometric measurements to TESS images and produces transit timings by fitting the light curves. We refine and update the previous ephemeris, based on these TESS timings (uncertainty $\sim$ 1 minute) and a long timing baseline ($\sim 10$ years). Our refined ephemeris gives the transit timing at a median precision of 0.82 minutes until 2025 and 1.21 minutes until 2030. We regard the timing offsets mainly originating from the underestimated ephemeris uncertainty. All the targets with timing offset larger than 10$σ$ present earlier timings than the prediction, which cannot be due to underestimated ephemeris uncertainty, apsidal precision, or R$ø$mer effect as those effects should be unsigned. For some particular targets, timing offsets are likely due to tidal dissipation. Our sample leads to the detection of period decaying candidates of WASP-161b and XO-3b reported previously.

astro-ph.EP↗

arXiv2022

IR2Net: Information Restriction and Information Recovery for Accurate Binary Neural Networks

Weight and activation binarization can efficiently compress deep neural networks and accelerate model inference, but cause severe accuracy degradation. Existing optimization methods for binary neural networks (BNNs) focus on fitting full-precision networks to reduce quantization errors, and suffer from the trade-off between accuracy and computational complexity. In contrast, considering the limited learning ability and information loss caused by the limited representational capability of BNNs, we propose IR$^2$Net to stimulate the potential of BNNs and improve the network accuracy by restricting the input information and recovering the feature information, including: 1) information restriction: for a BNN, by evaluating the learning ability on the input information, discarding some of the information it cannot focus on, and limiting the amount of input information to match its learning ability; 2) information recovery: due to the information loss in forward propagation, the output feature information of the network is not enough to support accurate classification. By selecting some shallow feature maps with richer information, and fusing them with the final feature maps to recover the feature information. In addition, the computational cost is reduced by streamlining the information recovery method to strike a better trade-off between accuracy and efficiency. Experimental results demonstrate that our approach still achieves comparable accuracy even with $ \sim $10x floating-point operations (FLOPs) reduction for ResNet-18. The models and code are available at https://github.com/pingxue-hfut/IR2Net.

cs.CV↗

arXiv2022

Construct a realistic stellar model with polytropic relation

The aim of this study is to construct a simple stellar model with non-uniform polytropic index. We find that the Emden equation cannot deal with the polytrope gas sphere with non-uniform polytropic index in a real star, and then we construct a realistic stellar model. The key point is that we should solve the two independent equations for density and pressure due to the essence of polytropic relation, but not the Emden equation which combines the hydrostatic balance and polytropic relation. We take the Sun for a computational example to find that this simple model yields quite a good result compared to the MESA code. The advantage of this simple model lies in its much simpler equation of state than that in the standard stellar model.

astro-ph.SR↗

arXiv2022

Magnetic effect on equilibrium tides and its influence on the orbital evolution of binary systems

In the standard theory of equilibrium tides, hydrodynamic turbulence is considered. In this paper we study the effect of magnetic fields on equilibrium tides. We find that the turbulent Ohmic dissipation associated with a tidal flow is much stronger than the turbulent viscous dissipation such that a magnetic field can greatly speed up the tidal evolution of a binary system. We then apply the theory to three binary systems: the orbital migration of 51 Pegasi b, the orbital decay of WASP-12b, and the circularization of close binary stars. Theoretical predictions are in good agreement with observations, which cannot be clearly interpreted with hydrodynamic equilibrium tides.

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arXiv2022

Tidal dynamo in solar-like close binary stars

Thermal convection is commonly believed to be the energy source of stellar or planetary dynamo. In this short paper we provide another possibility, namely large-scale tidal flow. In close binary stars, say, solar-like stars with orbital period at 2 or 3 days, large-scale tidal flow is comparable to or even stronger than convective flow, and it can induce magnetic dynamo action. Based on dynamo equation and tidal theory we estimate the magnetic energy induced by large-scale tidal flow, which is proportional to the cube of orbital frequency. Our estimation can be tested by the future spectropolarimetric observations and numerical simulations for close binary stars.

astro-ph.SR↗

arXiv2022

InsPose: Instance-Aware Networks for Single-Stage Multi-Person Pose Estimation

Multi-person pose estimation is an attractive and challenging task. Existing methods are mostly based on two-stage frameworks, which include top-down and bottom-up methods. Two-stage methods either suffer from high computational redundancy for additional person detectors or they need to group keypoints heuristically after predicting all the instance-agnostic keypoints. The single-stage paradigm aims to simplify the multi-person pose estimation pipeline and receives a lot of attention. However, recent single-stage methods have the limitation of low performance due to the difficulty of regressing various full-body poses from a single feature vector. Different from previous solutions that involve complex heuristic designs, we present a simple yet effective solution by employing instance-aware dynamic networks. Specifically, we propose an instance-aware module to adaptively adjust (part of) the network parameters for each instance. Our solution can significantly increase the capacity and adaptive-ability of the network for recognizing various poses, while maintaining a compact end-to-end trainable pipeline. Extensive experiments on the MS-COCO dataset demonstrate that our method achieves significant improvement over existing single-stage methods, and makes a better balance of accuracy and efficiency compared to the state-of-the-art two-stage approaches. The code and models are available at \url{https://github.com/hikvision-research/opera}.

cs.CV↗

arXiv2022

Privacy Leakage in Proactive VR Streaming: Modeling and Tradeoff

Proactive tile-based virtual reality (VR) video streaming employs the viewpoint of a user to predict the tiles to be requested, renders and delivers the predicted tiles before playback. Recently, it has been found that the identity and preference of the user can be inferred from the trace of viewpoint uploaded for proactive streaming, which indicates that viewpoint leakage incurs privacy leakage. In this paper, we strive to answer the following questions regarding viewpoint leakage during proactive VR video streaming. When is the viewpoint leaked? Can privacy-preserving approaches (e.g., federated or individual training, using predictors with no need for training, or predicting locally) avoid viewpoint leakage? We find that if the prediction error or the quality of experience (QoE) metric is uploaded for adaptive streaming, the real viewpoint can be inferred even with the privacy-preserving approaches. Then, we define viewpoint leakage probability to characterize the accuracy of the inferred viewpoint, and respectively derive the probability when uploading prediction error and QoE metric. We find that the viewpoint leakage probability can be reduced by sacrificing QoE or increasing resources. Simulation with the state-of-the-art predictor over a real dataset shows that such a tradeoff does not exist only in rare cases.

cs.MM↗

arXiv2022

Transit Timing Variation of XO-3b: Evidence for Tidal Evolution of Hot Jupiter with High Eccentricity

Observed transit timing variation (TTV) potentially reveals the period decay caused by star-planet tidal interaction which can explain the orbital migration of hot Jupiters. We report the TTV of XO-3b, using TESS observed timings and archival timings. We generate a photometric pipeline to produce light curves from raw TESS images and find the difference between our pipeline and TESS PDC is negligible for timing analysis. TESS timing presents a shift of 17.6 minutes (80 $σ$), earlier than the prediction from the previous ephemeris. The best linear fit for all timings available gives a Bayesian Information Criterion (BIC) value of 439. A quadratic function is a better model with a BIC of 56. The period derivative obtained from a quadratic function is -6.2$\times$10$^{-9}$$\pm$2.9$\times$10$^{-10}$ per orbit, indicating an orbital decay timescale 1.4 Myr. We find that the orbital period decay can be well explained by tidal interaction. The `modified tidal quality factor' $Q_{p}'$ would be 1.8$\times$10$^{4}$$\pm$8$\times$10$^{2}$ if we assume the decay is due to the tide in the planet; whereas $Q_{*}'$ would be 1.5$\times$10$^{5}$$\pm$6$\times$10$^{3}$ if tidal dissipation is predominantly in the star. The precession model is another possible origin to explain the observed TTVs. We note that the follow-up observations of occultation timing and radial velocity monitoring are needed for fully discriminating the different models.

astro-ph.EP↗

arXiv2022

Detecting and Monitoring Tidal Dissipation of Hot Jupiters in the Era of SiTian

Transit Timing Variation (TTV) of hot Jupiters provides direct observational evidence of planet tidal dissipation. Detecting tidal dissipation through TTV needs high precision transit timings and long timing baselines. In this work, we predict and discuss the potential scientific contribution of SiTian Survey in detecting and analyzing exoplanet TTV. We develop a tidal dissipation detection pipeline for SiTian Survey that aims at time-domain astronomy with 72 1-meter optical telescopes. The pipeline includes the modules of light curve deblending, transit timing obtaining, and TTV modeling. SiTian is capable to detect more than 25,000 exoplanets among which we expect $\sim$50 sources showing evidence of tidal dissipation. We present detection and analysis of tidal dissipating targets, based on simulated SiTian light curves of XO-3b and WASP-161b. The transit light curve modeling gives consistent results within 1$σ$ to input values of simulated light curves. Also, the parameter uncertainties predicted by Monte-Carlo Markov Chain are consistent with the distribution obtained from simulating and modeling the light curve 1000 times. The timing precision of SiTian observations is $\sim$ 0.5 minutes with one transit visit. We show that differences between TTV origins, e.g., tidal dissipation, apsidal precession, multiple planets, would be significant, considering the timing precision and baseline. The detection rate of tidal dissipating hot Jupiters would answer a crucial question of whether the planet migrates at an early formation stage or random stages due to perturbations, e.g., planet scattering, secular interaction. SiTian identified targets would be constructive given that the sample would extend tenfold.

astro-ph.EP↗

arXiv2022

Why hot Jupiters can be large but not too large

Tidal heating is often used to interpret "radius anomaly" of hot Jupiters (i.e. radii of a large fraction of hot Jupiters are in excess of 1.2 Jupiter radius which cannot be interpreted by the standard theory of planetary evolution). In this paper we find that tidal heating induces another phenomenon "runaway inflation" (i.e. planet inflation becomes unstable and out of control when tidal heating rate is above its critical value). With sufficiently strong tidal heating, luminosity initially increases with inflation, but across its peak it decreases with inflation such that heating is stronger than cooling and runaway inflation occurs. In this mechanism, the opacity near radiative-convective boundary (RCB) scales approximately as temperature to the fourth power and heat cannot efficiently radiate away from planet interior, which induces runaway inflation (similar to a tight lid on a boiling pot). Based on this mechanism, we find that radii of hot Jupiters cannot exceed $2.2R_J$, which is in good agreement with the observations. We also give an upper limit for orbital eccentricity of hot Jupiters. Moreover, by comparison to the observations we infer that tidal heating locates near RCB.

astro-ph.EP↗

arXiv2022

Estimations and scaling laws for stellar magnetic fields

In rapidly rotating turbulence (Rossby number much less than unity), the standard mixing length theory for turbulent convection breaks and Coriolis force enters the force balance such that magnetic field eventually depends on rotation. By simplifying the self-sustained magnetohydrodynamics dynamo equations of electrically conducting fluid motion, with the aid of theory of isotropic non-rotating or anisotropic rotating turbulence driven by thermal convection, we make estimations and derive scaling laws for stellar magnetic fields with slow and fast rotation. Our scaling laws are in good agreement with the observations.

astro-ph.SR↗

arXiv2021

Scene-Adaptive Attention Network for Crowd Counting

In recent years, significant progress has been made on the research of crowd counting. However, as the challenging scale variations and complex scenes existed in crowds, neither traditional convolution networks nor recent Transformer architectures with fixed-size attention could handle the task well. To address this problem, this paper proposes a scene-adaptive attention network, termed SAANet. First of all, we design a deformable attention in-built Transformer backbone, which learns adaptive feature representations with deformable sampling locations and dynamic attention weights. Then we propose the multi-level feature fusion and count-attentive feature enhancement modules further, to strengthen feature representation under the global image context. The learned representations could attend to the foreground and are adaptive to different scales of crowds. We conduct extensive experiments on four challenging crowd counting benchmarks, demonstrating that our method achieves state-of-the-art performance. Especially, our method currently ranks No.1 on the public leaderboard of the NWPU-Crowd benchmark. We hope our method could be a strong baseline to support future research in crowd counting. The source code will be released to the community.

cs.CV↗

arXiv2021

SOIT: Segmenting Objects with Instance-Aware Transformers

This paper presents an end-to-end instance segmentation framework, termed SOIT, that Segments Objects with Instance-aware Transformers. Inspired by DETR \cite{carion2020end}, our method views instance segmentation as a direct set prediction problem and effectively removes the need for many hand-crafted components like RoI cropping, one-to-many label assignment, and non-maximum suppression (NMS). In SOIT, multiple queries are learned to directly reason a set of object embeddings of semantic category, bounding-box location, and pixel-wise mask in parallel under the global image context. The class and bounding-box can be easily embedded by a fixed-length vector. The pixel-wise mask, especially, is embedded by a group of parameters to construct a lightweight instance-aware transformer. Afterward, a full-resolution mask is produced by the instance-aware transformer without involving any RoI-based operation. Overall, SOIT introduces a simple single-stage instance segmentation framework that is both RoI- and NMS-free. Experimental results on the MS COCO dataset demonstrate that SOIT outperforms state-of-the-art instance segmentation approaches significantly. Moreover, the joint learning of multiple tasks in a unified query embedding can also substantially improve the detection performance. Code is available at \url{https://github.com/yuxiaodongHRI/SOIT}.

cs.CV↗

arXiv2021

Online Continual Learning via Multiple Deep Metric Learning and Uncertainty-guided Episodic Memory Replay -- 3rd Place Solution for ICCV 2021 Workshop SSLAD Track 3A Continual Object Classification

Online continual learning in the wild is a very difficult task in machine learning. Non-stationarity in online continual learning potentially brings about catastrophic forgetting in neural networks. Specifically, online continual learning for autonomous driving with SODA10M dataset exhibits extra problems on extremely long-tailed distribution with continuous distribution shift. To address these problems, we propose multiple deep metric representation learning via both contrastive and supervised contrastive learning alongside soft labels distillation to improve model generalization. Moreover, we exploit modified class-balanced focal loss for sensitive penalization in class imbalanced and hard-easy samples. We also store some samples under guidance of uncertainty metric for rehearsal and perform online and periodical memory updates. Our proposed method achieves considerable generalization with average mean class accuracy (AMCA) 64.01% on validation and 64.53% AMCA on test set.

cs.CV↗

arXiv2021

LTD064402+245919: A Subgiant with a 1-3 M$_{\odot}$ Undetected Companion Identified from LAMOST-TD Data

Single-line spectroscopic binaries recently contribute to the stellar-mass black hole discovery, independently of the X-ray transient method. We report the identification of a single-line binary system LTD064402+245919, with an orbital period of 14.50 days. The observed component is a subgiant with a mass of 2.77$\pm$0.68M$_{\odot}$, radius 15.5$\pm$2.5R$_{\odot}$, effective temperature $T_{\rm eff}$ 4500$\pm$200K, and surface gravity log\emph{g} 2.5$\pm$0.25dex. The discovery makes use of the LAMOST time-domain (LAMOST-TD) and ZTF survey. Our general-purpose software pipeline applies the Lomb-Scargle periodogram to determine the orbital period and uses machine-learning to classify the variable type from the folded light curves. We apply a combined model to estimate the orbital parameters from both the light and radial velocity curves, taking constraints on the primary star mass, mass function, and detection limit of secondary luminosity into consideration. We obtain a radial velocity semi-amplitude of 44.6$\pm$1.5 km s$^{-1}$, mass ratio of 0.73$\pm$0.07, and an undetected component mass of 2.02$\pm$0.49M$_{\odot}$ when the type of the undetected component is not set. We conclude that the inclination is not well constrained, and that the secondary mass is larger than 1M$_{\odot}$ when the undetected component is modelled as a compact object. According to our investigations using an MCMC simulation, increasing the spectra SNR by a factor of 3 would enable the secondary light to be distinguished (if present). The algorithm and software in this work are able to serve as general-purpose tools for the identification of compact objects quiescent in X-rays.

astro-ph.SR↗

arXiv2021

FoV Privacy-aware VR Streaming

Proactive tile-based virtual reality (VR) video streaming can use the trace of FoV and eye movement to predict future requested tiles, then renders and delivers the predicted tiles before playback. The quality of experience (QoE) depends on the combined effect of tile prediction and consumed resources. Recently, it has been found that with the FoV and eye movement data collected for a user, one can infer the identity and preference of the user. Existing works investigate the privacy protection for eye movement, but never address how to protect the privacy in terms of FoV and how the privacy protection affects the QoE. In this paper, we strive to characterize and satisfy the FoV privacy requirement. We consider "trading resources for privacy". We first add camouflaged tile requests around the real FoV and define spatial degree of privacy (SDoP) as a normalized number of camouflaged tile requests. By consuming more resources to ensure SDoP, the real FoVs can be hidden. Then, we proceed to analyze the impacts of SDoP on the QoE by jointly optimizing the durations for prediction, computing, and transmission that maximizes the QoE given arbitrary predictor, configured resources, and SDoP. We find that a larger SDoP requires more resources but degrades the performance of tile prediction. Simulation with state-of-the-art predictors on a real dataset verifies the analysis and shows that a user requiring a larger SDoP can be served with better QoE.

cs.MM↗

arXiv2021

Convectively coupled equatorial trapped waves in stars and planets

In this paper, we have studied the convectively coupled equatorially trapped waves in rotating stars, with and without magnetic field. The equatorial trapped HD and MHD Poincaré, Rossby, mixed Rossby-Poincaré, and Kelvin waves were identified. The effects of stratification and non-traditional Coriolis force terms have been investigated. When the flow is strongly stratified, the wave frequencies of the convectively coupled model are almost the same as those of shallow water model. However, when the flow is weakly stratified, the wave frequencies are constrained by the buoyancy frequency. The non-traditional Coriolis terms affect the widths and phases of the equatorial waves. The width increases with the increasing non-traditional Coriolis parameter. Phase shift occurs when the non-traditional Coriolis parameter is included. Magnetic effect is significant when the magnetic field is strong. We have applied the model in the solar atmosphere and solar tachocline to explain the Rieger type periodicities. For the solar atmosphere, when magnetic effect is taken into account, we find that the magnetic field should be smaller than $5G$ in the solar photosphere. Otherwise, the Rieger type periodicities can be only attributed to long Rossby waves. For the solar tachocline, we find that magnetic field of the solar tachocline should be smaller than $50kG$ to observe the 160 days Rieger period. In addition, we find that the effect of the non-traditional Coriolis terms is not obvious in the solar photosphere, but its effect on the tachocline is significant.

astro-ph.SR↗

arXiv2021

Efficient Deep Image Denoising via Class Specific Convolution

Deep neural networks have been widely used in image denoising during the past few years. Even though they achieve great success on this problem, they are computationally inefficient which makes them inappropriate to be implemented in mobile devices. In this paper, we propose an efficient deep neural network for image denoising based on pixel-wise classification. Despite using a computationally efficient network cannot effectively remove the noises from any content, it is still capable to denoise from a specific type of pattern or texture. The proposed method follows such a divide and conquer scheme. We first use an efficient U-net to pixel-wisely classify pixels in the noisy image based on the local gradient statistics. Then we replace part of the convolution layers in existing denoising networks by the proposed Class Specific Convolution layers (CSConv) which use different weights for different classes of pixels. Quantitative and qualitative evaluations on public datasets demonstrate that the proposed method can reduce the computational costs without sacrificing the performance compared to state-of-the-art algorithms.

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