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In this paper, we present the Intra- and Inter-Human Relation Networks (I^2R-Net) for Multi-Person Pose Estimation. It involves two basic modules. First, the Intra-Human Relation Module operates on a single person and aims to capture Intra-Human dependencies. Second, the Inter-Human Relation Module considers the relation between multiple instances and focuses on capturing Inter-Human interactions. The Inter-Human Relation Module can be designed very lightweight by reducing the resolution of feature map, yet learn useful relation information to significantly boost the performance of the Intra-Human Relation Module. Even without bells and whistles, our method can compete or outperform current competition winners. We conduct extensive experiments on COCO, CrowdPose, and OCHuman datasets. The results demonstrate that the proposed model surpasses all the state-of-the-art methods. Concretely, the proposed method achieves 77.4% AP on CrowPose dataset and 67.8% AP on OCHuman dataset respectively, outperforming existing methods by a large margin. Additionally, the ablation study and visualization analysis also prove the effectiveness of our model., Comment: Accepected by IJCAI 2022

A new injection scheme using the interference of two coaxial laser pulses is proposed for generating high quality beams in laser wakefield accelerators. In this scheme, a relatively loosely focused laser pulse drives the plasma wakefield, and a tightly focused laser pulse with similar intensity triggers interference ring pattern which creates onion-like multi sheaths in the plasma wakefield. Due to the wavefront curvature change after the focal position of the tightly focused laser, the innermost sheath of the wakefield expands, which slows down the effective phase velocity of the wakefield and triggers injection of plasma electrons. Particle-in-cell simulations show that high quality electron beams with low energy spread (a few per mill), high charge (hundred picocoulomb) and small emittance (sub millimeter milliradian) at the same time can be generated using moderate laser parameters for properly chosen phase differences between the two lasers., Comment: 5 pages, 4 figures

Recently, silicon photomultipliers (SiPMs) have been used in several space-borne missions, owing to their solid state, compact size, low operating voltage, and insensitivity to magnetic fields. However, operating SiPMs in space results in radiation damage and degraded performance. In-orbit quantitative studies on these effects are limited. In this study, we present in-orbit SiPM characterization results obtained by the second detector of the Gamma-Ray Integrated Detectors (GRID-02), which was launched on 6 November 2020. An increase in dark current of $\sim$100 $\mu$A/year per SiPM chip (model MicroFJ-60035-TSV) at 28.5 V and 5 $^{\circ}$C was observed. Consequently, the overall noise level (sigma) of the GRID-02 detector increased by $\sim$7.5 keV/year. The estimate of this increase is $\sim$40 $\mu$A/year per SiPM chip at -20 $^{\circ}$C, highlighting the positive effect of using a cooling system., Comment: final manuscript, 20 pages, 8 figures, published on NIM-A

Due to the highly nonlinear nature of the beam-loading, it is at present not possible to analytically determine the beam parameters needed in a two-bunch plasma wakefield accelerator for maintaining a low energy spread. Therefore in this paper, by using the Broyden-Fletcher-Goldfarb-Shanno algorithm for the parameter scanning with the code QuickPIC and the polynomial regression together with k-fold cross-validation method, we obtain two fitting formulas for calculating the parameters of tri-Gaussian electron beams when minimizing the energy spread based on the beam-loading effect in a nonlinear plasma wakefield accelerator. One formula allows the optimization of the normalized charge per unit length of a trailing beam to achieve the minimal energy spread, i.e. the optimal beam-loading. The other one directly gives the transformer ratio when the trailing beam achieves the optimal beam-loading. A simple scaling law for charges of drive beams and trailing beams is obtained from the fitting formula, which indicates that the optimal beam-loading is always achieved for a given charge ratio of the two beams when the length and separation of two beams and the plasma density are fixed. The formulas can also help obtain the optimal plasma densities for the maximum accelerated charge and the maximum acceleration efficiency under the optimal beam-loading respectively. These two fitting formulas will significantly enhance the efficiency for designing and optimizing a two-bunch plasma wakefield acceleration stage.

In this paper, we investigate the physical layer security in the reconfigurable intelligent surface (RIS)-aided cell-free networks. A maximum weighted sum secrecy rate problem is formulated by jointly optimizing the active beamforming (BF) at the base stations and passive BF at the RISs. To handle this non-trivial problem, we adopt the alternating optimization to decouple the original problem into two sub-ones, which are solved using the semidefinite relaxation and continuous convex approximation theory. To decrease the complexity for obtaining overall channel state information (CSI), we extend the proposed framework to the case that only requires part of the RIS' CSI. This is achieved via deliberately discarding the RIS that has a small contribution to the user's secrecy rate. Based on this, we formulate a mixed integer non-linear programming problem, and the linear conic relaxation is used to obtained the solutions. Finally, the simulation results show that the proposed schemes can obtain a higher secrecy rate than the existing ones.

Terahertz (THz) communication is a promising technology for future wireless networks due to its ultra-wide bandwidth. However, THz signals suffer from severe attenuation and poor diffraction capability, making it vulnerable to blocking obstacles. To compensate for these two shortcomings and improve the system performance, an intelligent reflecting surface (IRS) can be exploited to change the propagation direction and enhance the signal strength. In this article, we investigate this promising ultra wide band (UWB) THz IRS communication paradigm. We start by motivating our research and describing several potential application scenarios. Then, we identify major challenges faced by UWB THz IRS communications. To overcome these challenges, several effective key techniques are developed, i.e., the time delayer-based sparse radio frequency antenna structure, delay hybrid precoding and IRS deployment. Simulation results are also presented to compare the system performance for these proposed techniques, thus demonstrating their effectiveness. Finally, we highlight several open issues and research opportunities for UWB THz IRS communications.

How to learn a universal facial representation that boosts all face analysis tasks? This paper takes one step toward this goal. In this paper, we study the transfer performance of pre-trained models on face analysis tasks and introduce a framework, called FaRL, for general Facial Representation Learning in a visual-linguistic manner. On one hand, the framework involves a contrastive loss to learn high-level semantic meaning from image-text pairs. On the other hand, we propose exploring low-level information simultaneously to further enhance the face representation, by adding a masked image modeling. We perform pre-training on LAION-FACE, a dataset containing large amount of face image-text pairs, and evaluate the representation capability on multiple downstream tasks. We show that FaRL achieves better transfer performance compared with previous pre-trained models. We also verify its superiority in the low-data regime. More importantly, our model surpasses the state-of-the-art methods on face analysis tasks including face parsing and face alignment., Comment: CVPR2022 Oral; 16 pages, 6 figures, 14 tables

We report the detection of X-ray polarization in the neutron star low mass X-ray binary Scorpius (Sco) X-1 with PolarLight. The result is energy dependent, with a non-detection in 3-4 keV but a 4$\sigma$ detection in 4-8 keV; it is also flux dependent in the 4-8 keV band, with a non-detection when the source displays low fluxes but a 5$\sigma$ detection during high fluxes, in which case we obtain a polarization fraction of $0.043 \pm 0.008$ and a polarization angle of $52.6^\circ \pm 5.4^\circ$. This confirms a previous marginal detection with OSO-8 in the 1970s, and marks Sco X-1 the second astrophysical source with a significant polarization measurement in the keV band. The measured polarization angle is in line with the jet orientation of the source on the sky plane ($54^\circ$), which is supposedly the symmetric axis of the system. Combining previous spectral analysis, our measurements suggest that an optically thin corona is located in the transition layer under the highest accretion rates, and disfavor the extended accretion disk corona model., Comment: Submitted to AAS journals. We are grateful to some experts for useful comments

We propose to use a frequency doubled pulse colliding with the driving pulse at an acute angle to trigger ionization injection in a laser wakefield accelerator. This scheme effectively reduces the duration that injection occurs, thus high injection quality is obtained. Three-dimensional particle-in-cell simulations show that electron beams with energy of ~500 MeV, charge of ~40 pC, energy spread of ~1% and normalized emittance of a few millimeter milliradian can be produced by ~100 TW laser pulses. By adjusting the angle between the two pulses, the intensity of the trigger pulse and the gas dope ratio, the charge and energy spread of the electron beam can be controlled., Comment: 12 pages, 6 figures

The cluster slave-spin method is implemented to research the ground state properties of the honeycomb lattice Hubbard model with doping $\delta$ and coupling $U$ being its parameters. At half-filling, a single direct and continuous phase transition between the semi-metal and antiferromagnetic (AFM) insulator is found at $U_{\text{AFM}}=2.43t$ that is in the Gross-Neveu-Yukawa universality class, where a relation between the staggered magnetization $M$ and the AFM energy gap $\Delta_{\text{AFM}}$ is established as $M \propto \Delta_{\text{AFM}}$, compared to $M \propto \Delta_{\text{AFM}} ( \ln{\Delta_{\text{AFM}}})^2$ in the square lattice case. A first-order semi-metal to the underlying paramagnetic (PM) insulator Mott transition is corroborated at $U_{\text{Mott}}=8.36t$, which is responsible for a broad crossover around $U_{c} = 5.4t$ between the weak- and strong-coupling regimes in the AFM state that increases with $\delta$, in contrast to the square lattice case. In the doped system, the compressibility $\kappa$ near the van Hove singularity at $\delta=1/4$ is suppressed substantially by the interaction before the semi-metal to AFM transition occurs, whereas $\kappa$ near the Dirac points is very close to the noninteracting one, indicating that the Dirac cone structure of the energy dispersion is rather robust. An overall phase diagram in the $U$-$\delta$ plane is presented, consisting of four regimes: the AFM insulator at $\delta=0$ for $U> U_{\text{AFM}}$, the AFM metal with compressibility $\kappa>0$ or $\kappa<0$, and the PM semi-metal, and the AFM metal with $\kappa<0$ only exists in an extremely small area near the phase boundary between the AFM and PM state., Comment: 11 Figures and 12 Pages. The version that accepted for publication as a Regular Article in Physical Review B

The Gamma-Ray Integrated Detectors (GRID) are a space project to monitor the transient gamma-ray sky in the multi-messenger astronomy era using multiple detectors on-board CubeSats. The second GRID detector, GRID-02, was launched in 2020. The performance of the detector, including the energy response, effective area, angular response, and temperature-bias dependence, is calibrated in the laboratory and presented here. These measurements are compared with particle tracing simulations and validate the Geant4 model that will be used for generating detector responses., Comment: final manuscript, 15 pages, 12 figures, published on Exp. Astron

In this paper, we propose a talking face generation method that takes an audio signal as input and a short target video clip as reference, and synthesizes a photo-realistic video of the target face with natural lip motions, head poses, and eye blinks that are in-sync with the input audio signal. We note that the synthetic face attributes include not only explicit ones such as lip motions that have high correlations with speech, but also implicit ones such as head poses and eye blinks that have only weak correlation with the input audio. To model such complicated relationships among different face attributes with input audio, we propose a FACe Implicit Attribute Learning Generative Adversarial Network (FACIAL-GAN), which integrates the phonetics-aware, context-aware, and identity-aware information to synthesize the 3D face animation with realistic motions of lips, head poses, and eye blinks. Then, our Rendering-to-Video network takes the rendered face images and the attention map of eye blinks as input to generate the photo-realistic output video frames. Experimental results and user studies show our method can generate realistic talking face videos with not only synchronized lip motions, but also natural head movements and eye blinks, with better qualities than the results of state-of-the-art methods., Comment: 10 pages, 9 figures. Accepted by ICCV 2021

Although current face manipulation techniques achieve impressive performance regarding quality and controllability, they are struggling to generate temporal coherent face videos. In this work, we explore to take full advantage of the temporal coherence for video face forgery detection. To achieve this, we propose a novel end-to-end framework, which consists of two major stages. The first stage is a fully temporal convolution network (FTCN). The key insight of FTCN is to reduce the spatial convolution kernel size to 1, while maintaining the temporal convolution kernel size unchanged. We surprisingly find this special design can benefit the model for extracting the temporal features as well as improve the generalization capability. The second stage is a Temporal Transformer network, which aims to explore the long-term temporal coherence. The proposed framework is general and flexible, which can be directly trained from scratch without any pre-training models or external datasets. Extensive experiments show that our framework outperforms existing methods and remains effective when applied to detect new sorts of face forgery videos., Comment: Accepted by ICCV 2021

The Chinese CubeSat Mission, Gamma Ray Integrated Detectors (GRID), recently detected its first gamma-ray burst, GRB 210121A, which was jointly observed by the Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM). This burst is confirmed by several other missions, including \fermi and \textit{Insight}-HXMT. We combined multi-mission observational data and performed a comprehensive analysis of the burst's temporal and spectral properties. Our results show that the burst is relatively special in its high peak energy, thermal-like low energy indices, and large fluence. By putting it to the $E_{\rm p}$-$E_{\rm\gamma, iso}$ relation diagram with assumed distance, we found this burst can be constrained at the redshift range of [0.3,3.0]. The thermal spectral component is also confirmed by the direct fit of the physical models to the observed spectra. Interestingly, the physical photosphere model also constrained a redshift of $z\sim$ 0.3 for this burst, which help us to identify a host galaxy candidate at such a distance within the location error box. Assuming the host galaxy is real, we found the burst can be best explained by the photosphere emission of a typical fireball with an initial radius of $r_0\sim$ 3.2 $\times 10^7$ cm., Comment: ApJ accpeted; 12 pages, 11 figures, 3 tables

Graph Neural Networks (GNNs) have been extensively used for mining graph-structured data with impressive performance. However, because these traditional GNNs do not distinguish among various downstream tasks, embeddings embedded by them are not always effective. Intuitively, paths in a graph imply different semantics for different downstream tasks. Inspired by this, we design a novel GNN solution, namely Customized Graph Neural Network with Path Reweighting (CustomGNN for short). Specifically, the proposed CustomGNN can automatically learn the high-level semantics for specific downstream tasks to highlight semantically relevant paths as well to filter out task-irrelevant noises in a graph. Furthermore, we empirically analyze the semantics learned by CustomGNN and demonstrate its ability to avoid the three inherent problems in traditional GNNs, i.e., over-smoothing, poor robustness, and overfitting. In experiments with the node classification task, CustomGNN achieves state-of-the-art accuracies on three standard graph datasets and four large graph datasets. The source code of the proposed CustomGNN is available at \url{https://github.com/cjpcool/CustomGNN}., Comment: 16 pages. Accepted by Information Sciences