We investigate the 1/f noise of the Five-hundred-meter Aperture Spherical Telescope (FAST) receiver system using drift-scan data from an intensity mapping pilot survey. All the 19 beams have 1/f fluctuations with similar structures. Both the temporal
and the 2D power spectrum densities are estimated. The correlations directly seen in the time series data at low frequency $f$ are associated with the sky signal, perhaps due to a coupling between the foreground and the system response. We use Singular Value Decomposition (SVD) to subtract the foreground. By removing the strongest components, the measured 1/f noise power can be reduced significantly. With 20 modes subtraction, the knee frequency of the 1/f noise in a 10 MHz band is reduced to $1.8 times 10^{-3}Hz$, well below the thermal noise over 500-seconds time scale. The 2D power spectra show that the 1/f-type variations are restricted to a small region in the time-frequency space and the correlations in frequency can be suppressed with SVD modes subtraction. The residual 1/f noise after the SVD mode subtraction is uncorrelated in frequency, and a simple noise diode frequency-independent calibration of the receiver gain at 8s interval does not affect the results. The 1/f noise can be important for HI intensity mapping, we estimate that the 1/f noise has a knee frequency $(f_{k}) sim$ 6 $times$ 10$^{-4}$Hz, and time and frequency correlation spectral indices $(alpha) sim 0.65$, $(beta) sim 0.8$ after the SVD subtraction of 30 modes. This can bias the HI power spectrum measurement by 10 percent.
Error entropy is a important nonlinear similarity measure, and it has received increasing attention in many practical applications. The default kernel function of error entropy criterion is Gaussian kernel function, however, which is not always the b
est choice. In our study, a novel concept, called generalized error entropy, utilizing the generalized Gaussian density (GGD) function as the kernel function is proposed. We further derivate the generalized minimum error entropy (GMEE) criterion, and a novel adaptive filtering called GMEE algorithm is derived by utilizing GMEE criterion. The stability, steady-state performance, and computational complexity of the proposed algorithm are investigated. Some simulation indicate that the GMEE algorithm performs well in Gaussian, sub-Gaussian, and super-Gaussian noises environment, respectively. Finally, the GMEE algorithm is applied to acoustic echo cancelation and performs well.
Recently, scientists have made great progresses in experiments in searching for the excited states of $Xi_{b}$ and $Lambda_{b}$ baryons such as the $Lambda_{b}(6072)$, $Lambda_{b}(6146)$, $Lambda_{b}(6152)$, $Xi_{b}(6227)$, $Xi_{b}(6100)$, $Xi_{b}(63
27)$ and $Xi_{b}(6333)$. Stimulated by these progresses, we give a systematical analysis about the $1D$ and $2D$ states of $Xi_{b}$ and $Lambda_{b}$ baryons with the method of QCD sum rules. By constructing three types of interpolating currents, we calculate the masses and pole residues of these heavy baryons with different excitation modes $(L_{rho},L_{lambda})=(0,2)$, $(2,0)$ and $(1,1)$. As a result, we decode the inner structures of $Lambda_{b}(6146)$, $Lambda_{b}(6152)$, $Xi_{b}(6327)$ and $Xi_{b}(6333)$, and favor assigning these states as the $1D$ baryons with the quantum numbers $(L_{rho},L_{lambda})=(0,2)$ and $frac{3}{2}^{+}$, $frac{5}{2}^{+}$, $frac{3}{2}^{+}$ and $frac{5}{2}^{+}$, respectively. In addition, the predictions about the masses and pole residues of the other $1D$ and $2D$ states of $Xi_{b}$ and $Lambda_{b}$ baryons in this paper are helpful in studying the D-wave bottom baryons in experiments in the future.
Optical non-reciprocity, a phenomenon that allows unidirectional flow of optical field is pivoted on the time reversal symmetry breaking. The symmetry breaking happens in the cavity optomechanical system (COS) due to non uniform radiation pressure as
a result of light-matter interaction, and is crucial in building non-reciprocal optical devices. In our proposed COS, we study the non-reciprocal transport of optical signals across two ports via three optical modes optomechanically coupled to the mechanical excitations of two nano-mechanical resonators (NMRs) under the influence of strong classical drive fields and weak probe fields. By tuning different system parameters, we discover the conversion of reciprocal to non-reciprocal signal transmission. We reveal perfect nonreciprocal transmission of output fields when the effective cavity detuning parameters are near resonant to the NMRs frequencies. The unidirectional non-reciprocal signal transport is robust to the optomechanical coupling parameters at resonance conditions. Moreover, the cavities photon loss rates play an inevitable role in the unidirectional flow of signal across the two ports. Bidirectional transmission can be fully controlled by the phase changes associated with the incoming probe and drive fields via two ports. Our scheme may provide a foundation for the compact non-reciprocal communication and quantum information processing, thus enabling new devices that route photons in unconventional ways such as all-optical diodes, optical transistors and optical switches.
The bistable states and separation hysteresis in curved compression ramp (CCR) flows, and the corresponding aerothermal characteristics (including wall friction, pressure and heat flux), are studied numerically and theoretically. Direct numerical sim
ulations of separation hysteresis induced by variation of turning angle, as well as the influence of inflow Mach number and wall temperature on hysteresis loops, are carried out. Distributions of wall friction, pressure and heat flux are analyzed. Further, emergence of wall frictions first and second minima in the separation bubble is interpreted, revealing it is dominated by the adverse pressure gradient induced by separation and reattachment shocks. The present results and analysis indicate that the reversed-flow singularity of Smith (Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences, 1988, 420: 21-52) is less likely to occur in CCR flows. The prediction of peak pressure of separation states confirms the model based on the minimum viscous dissipation theorem (Physics of Fluids, 2020, 32(10):101702). While the pressure overshoot can be analyzed by shock-polars with pressure match of compression and expansion process. The correlation between peak heat flux and peak pressure rise of both separation and attachment states is also discussed in terms of the classical power relations.
In the present work, we investigate the axialvector doubly-charmed tetraquark molecular states without strange, with strange and with doubly-strange via the QCD sum rules, and try to make assignment of the $T^+_{cc}$ from the LHCb collaboration in th
e scenario of molecular states. The predictions favor assigning the $T^+_{cc}$ to be the heavier $DD^{*}$ molecular state with the spin-parity $J^P=1^+$, while the lighter $DD^{*}$ molecular state with the spin-parity $J^P=1^+$ still escapes experimental detections. All the predicted doubly-charmed tetraquark molecular states can be confronted to the experimental data in the future.
In previous work [1], three TAIJI orbital deployments have been proposed to compose alternative LISA-TAIJI networks, TAIJIm (leading the Earth by $20^circ$ and $-60^circ$ inclined with respect to ecliptic plane), TAIJIp (leading the Earth by $20^circ
$ and $+60^circ$ inclined), TAIJIc (colocated and coplanar with LISA) with respect to LISA mission (trailing the Earth by $20^circ$ and $+60^circ$ inclined). And the LISA-TAIJIm network has been identified as the most capable configuration for massive black hole binary observation. In this work, we examine the performance of three networks to the stochastic gravitational wave background (SGWB) especially for the comparison of two eligible configurations, LISA-TAIJIm and LISA-TAIJIp. This investigation shows that the detectability of LISA-TAIJIm is competitive with the LISA-TAIJIp network for some specific SGWB spectral shapes. And the capability of LISA-TAIJIm is also identical to LISA-TAIJIp to separate the SGWB components by determining the parameters of signals. Considering the performances on SGWB and massive black hole binaries observations, the TAIJIm could be recognized as an optimal option to fulfill joint observations with LISA.
Deep learning technique has yielded significant improvements in point cloud completion with the aim of completing missing object shapes from partial inputs. However, most existing methods fail to recover realistic structures due to over-smoothing of
fine-grained details. In this paper, we develop a voxel-based network for point cloud completion by leveraging edge generation (VE-PCN). We first embed point clouds into regular voxel grids, and then generate complete objects with the help of the hallucinated shape edges. This decoupled architecture together with a multi-scale grid feature learning is able to generate more realistic on-surface details. We evaluate our model on the publicly available completion datasets and show that it outperforms existing state-of-the-art approaches quantitatively and qualitatively. Our source code is available at https://github.com/xiaogangw/VE-PCN.
The 2nd Anti-UAV Workshop & Challenge aims to encourage research in developing novel and accurate methods for multi-scale object tracking. The Anti-UAV dataset used for the Anti-UAV Challenge has been publicly released. There are two subsets in the d
ataset, $i.e.$, the test-dev subset and test-challenge subset. Both subsets consist of 140 thermal infrared video sequences, spanning multiple occurrences of multi-scale UAVs. Around 24 participating teams from the globe competed in the 2nd Anti-UAV Challenge. In this paper, we provide a brief summary of the 2nd Anti-UAV Workshop & Challenge including brief introductions to the top three methods.The submission leaderboard will be reopened for researchers that are interested in the Anti-UAV challenge. The benchmark dataset and other information can be found at: https://anti-uav.github.io/.
Stereo-based 3D detection aims at detecting 3D object bounding boxes from stereo images using intermediate depth maps or implicit 3D geometry representations, which provides a low-cost solution for 3D perception. However, its performance is still inf
erior compared with LiDAR-based detection algorithms. To detect and localize accurate 3D bounding boxes, LiDAR-based models can encode accurate object boundaries and surface normal directions from LiDAR point clouds. However, the detection results of stereo-based detectors are easily affected by the erroneous depth features due to the limitation of stereo matching. To solve the problem, we propose LIGA-Stereo (LiDAR Geometry Aware Stereo Detector) to learn stereo-based 3D detectors under the guidance of high-level geometry-aware representations of LiDAR-based detection models. In addition, we found existing voxel-based stereo detectors failed to learn semantic features effectively from indirect 3D supervisions. We attach an auxiliary 2D detection head to provide direct 2D semantic supervisions. Experiment results show that the above two strategies improved the geometric and semantic representation capabilities. Compared with the state-of-the-art stereo detector, our method has improved the 3D detection performance of cars, pedestrians, cyclists by 10.44%, 5.69%, 5.97% mAP respectively on the official KITTI benchmark. The gap between stereo-based and LiDAR-based 3D detectors is further narrowed.
