arXiv subjects

Rui Guo

Publications and source records attributed to Rui Guo.

At least 73 records · Page 4Linked to original sources

arXiv2023

GANmouflage: 3D Object Nondetection with Texture Fields

We propose a method that learns to camouflage 3D objects within scenes. Given an object's shape and a distribution of viewpoints from which it will be seen, we estimate a texture that will make it difficult to detect. Successfully solving this task requires a model that can accurately reproduce textures from the scene, while simultaneously dealing with the highly conflicting constraints imposed by each viewpoint. We address these challenges with a model based on texture fields and adversarial learning. Our model learns to camouflage a variety of object shapes from randomly sampled locations and viewpoints within the input scene, and is the first to address the problem of hiding complex object shapes. Using a human visual search study, we find that our estimated textures conceal objects significantly better than previous methods. Project site: https://rrrrrguo.github.io/ganmouflage/

cs.CV↗

arXiv2023

The unexpected magnetism in 2D group-IV-doped GaN for spintronic applications

In this study, the structural and magnetic properties of group-IV-doped monolayer GaN were systematically investigated by first-principles calculations. Among all the group-IV dopants, only Ge and Sn atoms prefer to substitute the Ga atom of monolayer GaN and form a buckling structure with a magnetic moment of 1 $μ_B$ per dopant. The N-rich growth conditions are more desirable for such a substitution process than the Ga-rich grow conditions. With a large diffusion barrier vertical to the monolayer GaN, both Ge and Sn atoms tend to stay on the same side of monolayer GaN with an antiferromagnetic coupling between them. When intrinsic vacancies exist in monolayer GaN, the magnetic moments of group-IV dopants vanish due to the charge transferring from the dopants to Ga or N vacancies. The precondition creation of Ga vacancies, a plentiful supply of Ge or Sn dopants, and the N-rich conditions can be adopted to maintain the magnetic properties of group-IV-doped monolayer GaN. These theoretical results help to promote the applications of 2D GaN-based materials in spintronics.

cond-mat.mtrl-sci↗

arXiv2023

2D-XY ferromagnetism with high transition temperature in Janus monolayer V$_{2}$XN (X = P, As)

Two-dimensional (2D) XY magnets with easy magnetization planes support the nontrivial topological spin textures whose dissipationless transport is highly desirable for 2D spintronic devices. Here, we predicted that Janus monolayer V$_{2}$XN (X = P, As) with a square lattice are 2D-XY ferromagnets by first-principles calculations. Both the magnetocrystalline anisotropy and magnetic shape anisotropy favor an in-plane magnetization, leading to an easy magnetization $xy$-plane in Janus monolayer V$_{2}$XN. Resting on the Monte Carlo simulations, we observed the Berezinskii-Kosterlitz-Thouless (BKT) phase transition in monolayer V$_{2}$XN with transition temperature $T_{\rm BKT}$ being above the room temperature. Especially, monolayer V$_{2}$AsN has a magnetic anisotropy energy (MAE) of 292.0 $μ$eV per V atom and a $T_{\rm BKT}$ of 434 K, which is larger than that of monolayer V$_{2}$PN. Moreover, a tensile strain of 5\% can further improve the $T_{\rm BKT}$ of monolayer V$_{2}$XN to be above 500 K. Our results indicated that Janus monolayer V$_{2}$XN (X = P, As) were candidate materials to realize high-temperature 2D-XY ferromagnetism for spintronics applications.

cond-mat.mtrl-sci↗

arXiv2022

Analysis of Degrees of Freedom in Scattered Fields for Nonlinear Inverse Scattering Problems

In the study of nonlinear inverse scattering problems (ISPs), the traditional method for estimating the number of degrees of freedom (NDoF) of the scattered field does not consider the effect of the scatterer contrast. In this work, we study the relationship between NDoF and scatterer contrast by modifying the radiation matrix. Specifically, three numerical methods for calculating the NDoF are presented. The first NDoF is defined as the number of principle singular values of the external radiation matrix, which is used as a benchmark. The second NDoF is defined based on the modified radiation matrix that incorporates the contrast of domain of investigation (DoI) with the external radiation matrix. By exploring the relationship between the second NDoF and contrast, an approximate upper bound of the second NDoF is obtained, that is, the third NDoF. The accuracy of the proposed NDoF is verified by interpolation experiments on both synthetic and experimental data using frequency-domain zero-padding method. Results show that the second NDoF is more accurate than traditional NDoF for high-contrast scatterers, and the third NDoF can be easily estimated by the traditional NDoF plus a positive constant, which provides a simple way to estimate the upper bound of NDoF.

physics.comp-ph↗

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

A Monotonicity Constrained Attention Module for Emotion Classification with Limited EEG Data

In this work, a parameter-efficient attention module is presented for emotion classification using a limited, or relatively small, number of electroencephalogram (EEG) signals. This module is called the Monotonicity Constrained Attention Module (MCAM) due to its capability of incorporating priors on the monotonicity when converting features' Gram matrices into attention matrices for better feature refinement. Our experiments have shown that MCAM's effectiveness is comparable to state-of-the-art attention modules in boosting the backbone network's performance in prediction while requiring less parameters. Several accompanying sensitivity analyses on trained models' prediction concerning different attacks are also performed. These attacks include various frequency domain filtering levels and gradually morphing between samples associated with multiple labels. Our results can help better understand different modules' behaviour in prediction and can provide guidance in applications where data is limited and are with noises.

eess.SP↗

arXiv2022

Near-real-time estimates of daily CO2 emissions from 1500 cities worldwide

Building on near-real-time and spatially explicit estimates of daily carbon dioxide (CO2) emissions, here we present and analyze a new city-level dataset of fossil fuel and cement emissions. Carbon Monitor Cities provides daily, city-level estimates of emissions from January 2019 through December 2021 for 1500 cities in 46 countries, and disaggregates five sectors: power generation, residential (buildings), industry, ground transportation, and aviation. The goal of this dataset is to improve the timeliness and temporal resolution of city-level emission inventories and includes estimates for both functional urban areas and city administrative areas that are consistent with global and regional totals. Comparisons with other datasets (i.e. CEADs, MEIC, Vulcan, and CDP) were performed, and we estimate the overall uncertainty to be 21.7%. Carbon Monitor Cities is a near-real-time, city-level emission dataset that includes cities around the world, including the first estimates for many cities in low-income countries. A more complete description of this dataset is published in Scientific Data (https://doi.org/10.1038/s41597-022-01657-z).

physics.soc-ph↗

arXiv2022

HI content of massive red spiral galaxies observed by FAST

A sample of 279 massive red spirals was selected optically by Guo et al. (2020), among which 166 galaxies have been observed by the ALFALFA survey. In this work, we observe HI content of the rest 113 massive red spiral galaxies using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). 75 of the 113 galaxies have HI detection with a signal-to-noise ratio (S/N) greater than 4.7. Compared with the red spirals in the same sample that have been observed by the ALFALFA survey, galaxies observed by FAST have on average a higher S/N, and reach to a lower HI mass. To investigate why many red spirals contain a significant amount of HI mass, we check color profiles of the massive red spirals using images observed by the DESI Legacy Imaging Surveys. We find that galaxies with HI detection have bluer outer disks than the galaxies without HI detection, for both ALFALFA and FAST samples. For galaxies with HI detection, there exists a clear correlation between galaxy HI mass and g-r color at outer radius: galaxies with higher HI masses have bluer outer disks. The results indicate that optically selected massive red spirals are not fully quenched, and the HI gas observed in many of the galaxies may exist in their outer blue disks.

astro-ph.GA↗

arXiv2022

The North/South Asymmetry of the Galaxy: Possible Connection to the Vertical Phase Space Snail

The Galaxy is found to be in disequilibrium based on recent findings of the North/South (N/S) asymmetry and the phase mixing signatures, such as a phase spiral (snail) structure in the vertical phase space ($z-V_{z}$). We show that the N/S asymmetry in a tracer population of dwarfs may be quantitatively modeled with a simple phase snail model superimposed on a smooth equilibrium background. As the phase snail intersects with the $z$ axis, the number density is enhanced, and the velocity dispersion ($σ_{z}$) is decreased relative to the other side of the Galactic plane. Fitting only to the observed asymmetric N/S $σ_{z}$ profiles, we obtain reasonable parameters for the phase space snail and the potential utilized in modeling the background, despite the complex dependence of the model on the potential parameters and the significant selection effects of the data. Both the snail shape and the N/S number density difference given by our best-fit model are consistent with previous observations. The equilibrium background implies a local dark matter density of $0.0151^{+0.0050}_{-0.0051}$ ${\rm M}_{\odot}\,{\rm pc}^{-3}$. The vertical bulk motion of our model is similar to the observation, but with a $\sim$1.2 $\rm km\,s^{-1}$ shift. Our work demonstrates the strong correlation between the phase space snail and the N/S asymmetry. Future observational constraints will facilitate more comprehensive snail models to unravel the Milky Way potential and the perturbation history encoded in the snail feature.

astro-ph.GA↗

arXiv2022

Physics Embedded Machine Learning for Electromagnetic Data Imaging

Electromagnetic (EM) imaging is widely applied in sensing for security, biomedicine, geophysics, and various industries. It is an ill-posed inverse problem whose solution is usually computationally expensive. Machine learning (ML) techniques and especially deep learning (DL) show potential in fast and accurate imaging. However, the high performance of purely data-driven approaches relies on constructing a training set that is statistically consistent with practical scenarios, which is often not possible in EM imaging tasks. Consequently, generalizability becomes a major concern. On the other hand, physical principles underlie EM phenomena and provide baselines for current imaging techniques. To benefit from prior knowledge in big data and the theoretical constraint of physical laws, physics embedded ML methods for EM imaging have become the focus of a large body of recent work. This article surveys various schemes to incorporate physics in learning-based EM imaging. We first introduce background on EM imaging and basic formulations of the inverse problem. We then focus on three types of strategies combining physics and ML for linear and nonlinear imaging and discuss their advantages and limitations. Finally, we conclude with open challenges and possible ways forward in this fast-developing field. Our aim is to facilitate the study of intelligent EM imaging methods that will be efficient, interpretable and controllable.

physics.comp-ph↗

arXiv2022

Robust and accurate depth estimation by fusing LiDAR and Stereo

Depth estimation is one of the key technologies in some fields such as autonomous driving and robot navigation. However, the traditional method of using a single sensor is inevitably limited by the performance of the sensor. Therefore, a precision and robust method for fusing the LiDAR and stereo cameras is proposed. This method fully combines the advantages of the LiDAR and stereo camera, which can retain the advantages of the high precision of the LiDAR and the high resolution of images respectively. Compared with the traditional stereo matching method, the texture of the object and lighting conditions have less influence on the algorithm. Firstly, the depth of the LiDAR data is converted to the disparity of the stereo camera. Because the density of the LiDAR data is relatively sparse on the y-axis, the converted disparity map is up-sampled using the interpolation method. Secondly, in order to make full use of the precise disparity map, the disparity map and stereo matching are fused to propagate the accurate disparity. Finally, the disparity map is converted to the depth map. Moreover, the converted disparity map can also increase the speed of the algorithm. We evaluate the proposed pipeline on the KITTI benchmark. The experiment demonstrates that our algorithm has higher accuracy than several classic methods.

cs.CV↗

arXiv2022

An interactive music infilling interface for pop music composition

Artificial intelligence (AI) has been widely applied to music generation topics such as continuation, melody/harmony generation, genre transfer and music infilling application. Although with the burst interest to apply AI to music, there are still few interfaces for the musicians to take advantage of the latest progress of the AI technology. This makes those tools less valuable in practice and harder to find its advantage/drawbacks without utilizing them in the real scenario. This work builds a max patch for interactive music infilling application with different levels of control, including track density/polyphony/occupation rate and bar tonal tension control. The user can select the melody/bass/harmony track as the infilling content up to 16 bars. The infilling algorithm is based on the author's previous work, and the interface sends/receives messages to the AI system hosted in the cloud. This interface lowers the barrier of AI technology and can generate different variations of the selected content. Those results can give several alternatives to the musicians' composition, and the interactive process realizes the value of the AI infilling system.

cs.SD↗

arXiv2022

MusIAC: An extensible generative framework for Music Infilling Applications with multi-level Control

We present a novel music generation framework for music infilling, with a user friendly interface. Infilling refers to the task of generating musical sections given the surrounding multi-track music. The proposed transformer-based framework is extensible for new control tokens as the added music control tokens such as tonal tension per bar and track polyphony level in this work. We explore the effects of including several musically meaningful control tokens, and evaluate the results using objective metrics related to pitch and rhythm. Our results demonstrate that adding additional control tokens helps to generate music with stronger stylistic similarities to the original music. It also provides the user with more control to change properties like the music texture and tonal tension in each bar compared to previous research which only provided control for track density. We present the model in a Google Colab notebook to enable interactive generation.

cs.AI↗

arXiv2021

Asynchronous Collaborative Localization by Integrating Spatiotemporal Graph Learning with Model-Based Estimation

Collaborative localization is an essential capability for a team of robots such as connected vehicles to collaboratively estimate object locations from multiple perspectives with reliant cooperation. To enable collaborative localization, four key challenges must be addressed, including modeling complex relationships between observed objects, fusing observations from an arbitrary number of collaborating robots, quantifying localization uncertainty, and addressing latency of robot communications. In this paper, we introduce a novel approach that integrates uncertainty-aware spatiotemporal graph learning and model-based state estimation for a team of robots to collaboratively localize objects. Specifically, we introduce a new uncertainty-aware graph learning model that learns spatiotemporal graphs to represent historical motions of the objects observed by each robot over time and provides uncertainties in object localization. Moreover, we propose a novel method for integrated learning and model-based state estimation, which fuses asynchronous observations obtained from an arbitrary number of robots for collaborative localization. We evaluate our approach in two collaborative object localization scenarios in simulations and on real robots. Experimental results show that our approach outperforms previous methods and achieves state-of-the-art performance on asynchronous collaborative localization.

cs.RO↗

arXiv2021

Star formation histories of massive red spiral galaxies in the local universe

We investigate the star formation histories (SFHs) of massive red spiral galaxies with stellar mass $M_\ast>10^{10.5}M_\odot$, and make comparisons with blue spirals and red ellipticals of similar masses. We make use of the integral field spectroscopy from the SDSS-IV/DR15 MaNGA sample, and estimate spatially resolved SFHs and stellar population properties of each galaxy by applying a Bayesian spectral fitting code to the MaNGA spectra. We find that both red spirals and red ellipticals have experienced only one major star formation episode at early times, and the result is independent of the adopted SFH model. On average, more than half of their stellar masses were formed $>$10 Gyrs ago, and more than 90\% were formed $>6$ Gyrs ago. The two types of galaxies show similarly flat profiles in a variety of stellar population parameters: old stellar ages indicated by $D4000$ (the spectral break at around 4000Å), high stellar metallicities, large Mgb/Fe ratios indicating fast formation, and little stellar dust attenuation. In contrast, although blue spirals also formed their central regions $>$10 Gyrs ago, both their central regions and outer disks continuously form stars over a long timescale. Our results imply that, massive red spirals are likely to share some common processes of formation (and possibly quenching) with massive red ellipticals in the sense that both types were formed at $z > 2$ through a fast formation process.Possible mechanisms for the formation and quenching of massive red spirals are discussed.

astro-ph.GA↗

arXiv2021

An Empirical Bayesian Approach to Limb-darkening in Modeling WASP-121b Transit Light Curves

We present a novel, iterative method using an empirical Bayesian approach for modeling the limb darkened WASP-121b transit from the TESS light curve. Our method is motivated by the need to improve $R_{p}/R_{\ast}$ estimates for exoplanet atmosphere modeling, and is particularly effective with the limb darkening (LD) quadratic law requiring no prior central value from stellar atmospheric models. With the non-linear LD law, the method has all the advantages of not needing atmospheric models but does not converge. The iterative method gives a different $R_{p}/R_{\ast}$ for WASP-121b at a significance level of 1$σ$ when compared with existing non-iterative methods. To assess the origins and implications of this difference, we generate and analyze light curves with known values of the limb darkening coefficients (LDCs). We find that non-iterative modeling with LDC priors from stellar atmospheric models results in an inconsistent $R_{p}/R_{\ast}$ at 1.5$σ$ level when the known LDC values are as those previously found when modeling real data by the iterative method. In contrast, the LDC values from the iterative modeling yields the correct value of $R_{p}/R_{\ast}$ to within 0.25$σ$. For more general cases with different known inputs, Monte Carlo simulations show that the iterative method obtains unbiased LDCs and correct $R_{p}/R_{\ast}$ to within a significance level of 0.3$σ$. Biased LDC priors can cause biased LDC posteriors and lead to bias in the $R_{p}/R_{\ast}$ of up to 0.82$\%$, 2.5$σ$ for the quadratic law and 0.32$\%$, 1.0$σ$ for the non-linear law. Our improvement in $R_{p}/R_{\ast}$ estimation is important when analyzing exoplanet atmospheres.

astro-ph.EP↗

arXiv2021

Unprecedented decarbonization of China's power system in the post-COVID era

In October of 2020, China announced that it aims to start reducing its carbon dioxide (CO2) emissions before 2030 and achieve carbon neutrality before 20601. The surprise announcement came in the midst of the COVID-19 pandemic which caused a transient drop in China's emissions in the first half of 2020. Here, we show an unprecedented de-carbonization of China's power system in late 2020: although China's power related carbon emissions were 0.5% higher in 2020 than 2019, the majority (92.9%) of the increased power demand was met by increases in low-carbon (renewables and nuclear) generation (increased by 9.3%), as compared to only 0.4% increase for fossil fuels. China's low-carbon generation in the country grew in the second half of 2020, supplying a record high of 36.7% (increased by 1.9% compared to 2019) of total electricity in 2020, when the fossil production dropped to a historical low of 63.3%. Combined, the carbon intensity of China's power sector decreased to an historical low of 519.9 tCO2/GWh in 2020. If the fast decarbonization and slowed down power demand growth from 2019 to 2020 were to continue, by 2030, over half (50.8%) of China's power demand could be provided by low carbon sources. Our results thus reveal that China made progress towards its carbon neutrality target during the pandemic, and suggest the potential for substantial further decarbonization in the next few years if the latest trends persist.

physics.soc-ph↗

arXiv2021

$\textbf{MyoMapNet}$: Accelerated Modified Look-Locker Inversion Recovery Myocardial T1 Mapping via Neural Networks

Purpose: To develop and evaluate MyoMapNet, a rapid myocardial T1 mapping approach that uses neural networks (NN) to estimate voxel-wise myocardial T1 and extracellular (ECV) from T1-weighted images collected after a single inversion pulse over 4-5 heartbeats. Method: MyoMapNet utilizes a simple fully-connected NN to estimate T1 values from 5 (native) or 4 (post-contrast) T1-weighted images. Native MOLLI-5(3)3 T1 was collected in 717 subjects (386 males, 55$\pm$16.5 years) and post-contrast MOLLI-4(1)3(1)2 in 535 subjects (232 male, 56.5$\pm$15 years). The dataset was divided into training (80%) and testing (20%), where 20% of the training set was used to optimize MyoMapNet architecture (size and loss functions). We used MyoMapNet to estimate T1 and ECV maps with the first 5 (native) or 4 (post-contrast) T1-weighted images from the corresponding MOLLI sequence compared to the conventional and an abbreviated MOLLI using similar number of T1-weighted images with 3-parameter curve-fitting. Results: In our preliminary optimizaiton step, we determined that a 5-layers NN trained using mean-absolute-error loss yields lower estimation errors and was used subsequently in independent testing study. The myocardial T1 by MyoMapNet was similar to MOLLI (1200$\pm$45ms vs. 1199$\pm$46ms; P=0.3 for native T1, and 27.3$\pm$3.5% vs. 27.1$\pm$4%; P=0.4 for ECV). MyoMapNet had significantly smaller errors in T1 estimations compared to abbreviated-MOLLI (1$\pm$17ms vs. 31$\pm$34ms, P<0.01 for in native T1, and 0.1$\pm$1.3% vs. 1.9$\pm$2.5%, P<0.01 for ECV). The duration of T1 estimation was approximately 2 ms per slice using MyoMapNet. Conclusion: MyoMapNet T1 mapping enables myocardial T1 quantification in 4-5 heartbeats with near-instantaneous map estimation time with similar accuracy and precision as MOLLI. Keywords: Myocardial T1 mapping, MOLLI, T1 reconstruction, Neural network, Deep Learning.

physics.med-ph↗