In constrained solution spaces with a huge number of homotopy classes, stand-alone sampling-based kinodynamic planners suffer low efficiency in convergence. Local optimization is integrated to alleviate this problem. In this paper, we propose to thri
ve the trajectory tree growing by optimizing the tree in the forms of deformation units, and each unit contains one tree node and all the edges connecting it. The deformation proceeds both spatially and temporally by optimizing the node state and edge time durations efficiently. The unit only changes the tree locally yet improves the overall quality of a corresponding sub-tree. Further, variants to deform different tree parts considering the computation burden and optimizing level are studied and compared, all showing much faster convergence. The proposed deformation is compatible with different RRT-based kinodynamic planning methods, and numerical experiments show that integrating the spatio-temporal deformation greatly accelerates the convergence and outperforms the spatial-only deformation.
For aerial swarms, navigation in a prescribed formation is widely practiced in various scenarios. However, the associated planning strategies typically lack the capability of avoiding obstacles in cluttered environments. To address this deficiency, w
e present an optimization-based method that ensures collision-free trajectory generation for formation flight. In this paper, a novel differentiable metric is proposed to quantify the overall similarity distance between formations. We then formulate this metric into an optimization framework, which achieves spatial-temporal planning using polynomial trajectories. Minimization over collision penalty is also incorporated into the framework, so that formation preservation and obstacle avoidance can be handled simultaneously. To validate the efficiency of our method, we conduct benchmark comparisons with other cutting-edge works. Integrated with an autonomous distributed aerial swarm system, the proposed method demonstrates its efficiency and robustness in real-world experiments with obstacle-rich surroundings. We will release the source code for the reference of the community.
This paper proposes Elastic Tracker, a flexible trajectory planning framework that can deal with challenging tracking tasks with guaranteed safety and visibility. Firstly, an object detection and intension-free motion prediction method is designed. T
hen an occlusion-aware path finding method is proposed to provide a proper topology. A smart safe flight corridor generation strategy is designed with the guiding path. An analytical occlusion cost is evaluated. Finally, an effective trajectory optimization approach enables to generate a spatio-temporal optimal trajectory within the resultant flight corridor. Particular formulations are designed to guarantee both safety and visibility, with all the above requirements optimized jointly. The experimental results show that our method works more robustly but with less computation than the existing methods, even in some challenging tracking tasks.
In this work, we compute masses and magnetic moments of the heavy baryons and tetraquarks with one and two open heavy flavors in a unified framework of MIT bag model. Using the parameters of MIT bag model, we confirm that an extra binding energy, whi
ch is supposed to exist between heavy quarks ($c$ and $b$) and between heavy and strange quarks in literatures, is required to reconcile light hadrons with heavy hadrons. Numerical calculations are made for all light mesons, heavy hadrons with one and two open heavy flavors, predicting the mass of doubly charmed baryons to be $M(Xi _{cc})=3.604$ GeV, $M(Xi _{cc}^{ast })=3.714$ GeV, and that of the strange isosinglet tetraquark $udbar{s}bar{c}$ with $J^{P}=0^{+}$ to be $Mleft( udbar{s}bar{c},0^{+}right) =2.934$ GeV. The state mixing due to chromomagnetic interaction is shown to be sizable for the strange scalar tetraquark $nnbar{s}bar{c}$.
Distributed pose graph optimization (DPGO) is one of the fundamental techniques of swarm robotics. Currently, the sub-problems of DPGO are built on the native poses. Our validation proves that this approach may introduce an imbalance in the sizes of
the sub-problems in real-world scenarios, which affects the speed of DPGO optimization, and potentially increases communication requirements. In addition, the coherence of the estimated poses is not guaranteed when the robots in the swarm fail, or partial robots are disconnected. In this paper, we propose BDPGO, a balanced distributed pose graph optimization framework using the idea of decoupling the robot poses and DPGO. BDPGO re-distributes the poses in the pose graph to the robot swarm in a balanced way by introducing a two-stage graph partitioning method to build balanced subproblems. Our validation demonstrates that BDPGO significantly improves the optimization speed without changing the specific algorithm of DPGO in realistic datasets. Whats more, we also validate that BDPGO is robust to robot failure, changes in the wireless network. BDPGO has capable of keeps the coherence of the estimated poses in these situations. The framework also has the potential to be applied to other collaborative simultaneous localization and mapping (CSLAM) problems involved in distributedly solving the factor graph.
Federated learning (FL) is experiencing a fast booming with the wave of distributed machine learning and ever-increasing privacy concerns. In the FL paradigm, global model aggregation is handled by a centralized aggregate server based on local update
d gradients trained on local nodes, which mitigates privacy leakage caused by the collection of sensitive information. With the increased computing and communicating capabilities of edge and IoT devices, applying FL on heterogeneous devices to train machine learning models becomes a trend. The synchronous aggregation strategy in the classic FL paradigm cannot effectively use the resources, especially on heterogeneous devices, due to its waiting for straggler devices before aggregation in each training round. Furthermore, in real-world scenarios, the disparity of data dispersed on devices (i.e. data heterogeneity) downgrades the accuracy of models. As a result, many asynchronous FL (AFL) paradigms are presented in various application scenarios to improve efficiency, performance, privacy, and security. This survey comprehensively analyzes and summarizes existing variants of AFL according to a novel classification mechanism, including device heterogeneity, data heterogeneity, privacy and security on heterogeneous devices, and applications on heterogeneous devices. Finally, this survey reveals rising challenges and presents potentially promising research directions in this under-investigated field.
Prominence eruption is closely related to coronal mass ejections and is an important topic in solar physics. Spectroscopic observation is an effective way to explore the plasma properties, but the spectral observations of eruptive prominences are rar
e. In this paper we will introduce an eruptive polar crown prominence with spectral observations from the Interface Region Imaging Spectrograph (IRIS), and try to explain some phenomena that are rarely reported in previous works. The eruptive prominence experiences a slow-rise and fast-rise phase, while the line-of-sight motions of the prominence plasma could be divided into three periods: two hours before the fast-rise phase, opposite Doppler shifts are found at the two sides of the prominence axis;then, red shifts dominate the prominence gradually; in the fast-rise phase, the prominence gets to be blue-shifted. During the second period, a faint component appears in Mg II k window with a narrow line width and a large red shift. A faint region is also found in AIA 304-angstrom images along the prominence spine, and the faint region gets darker during the expansion of the spine. We propose that the opposite Doppler shifts in the first period is a feature of the polar crown prominence that we studied. The red shifts in the second period is possibly due to mass drainage during the elevation of the prominence spine, which could accelerate the eruption in return. The blue shifts in the third period is due to that the prominence erupts toward the observer. We suggest that the faint component appears due to the decreasing of the plasma density, and the latter results from the expansion of the prominence spine.
We study a non-Hermitian AA model with the long-range hopping, $1/r^a$, and different choices of the quasi-periodic parameters $beta$ to be the member of the metallic mean family. We find that when the power-law exponent is in the $a<1$ regime, the s
ystem displays a delocalized-to-multifractal (DM) edge in its eigenstate spectrum. For the $a>1$ case, it exists a delocalized-to-localized (DL) edge, also called the mobility edge. While a striking feature of the Hermitian AA model with the long-range hopping is that the fraction of delocalized states can be obtained from a general sequence manifesting a mathematical feature of the metallic mean family, we find that the DM or DL edge for the non-Hermitian cases is independent of the mathematical feature of the metallic mean family. To understand this difference, we consider a specific case of the non-Hermitian long-range AA model with $a=2$, for which we can apply the Sarnak method to analytically derive its localization transition points and the exact expression of the DL edge. Our analytical result clearly demonstrates that the mobility edge is independent of the quasi-periodic parameter $beta$, which confirms our numerical result. Finally, an optical setup is proposed to realize the non-Hermitian long-range AA model.
Mixture-of-Experts (MoE) with sparse conditional computation has been proved an effective architecture for scaling attention-based models to more parameters with comparable computation cost. In this paper, we propose Sparse-MLP, scaling the recent ML
P-Mixer model with sparse MoE layers, to achieve a more computation-efficient architecture. We replace a subset of dense MLP blocks in the MLP-Mixer model with Sparse blocks. In each Sparse block, we apply two stages of MoE layers: one with MLP experts mixing information within channels along image patch dimension, one with MLP experts mixing information within patches along the channel dimension. Besides, to reduce computational cost in routing and improve expert capacity, we design Re-represent layers in each Sparse block. These layers are to re-scale image representations by two simple but effective linear transformations. When pre-training on ImageNet-1k with MoCo v3 algorithm, our models can outperform dense MLP models by 2.5% on ImageNet Top-1 accuracy with fewer parameters and computational cost. On small-scale downstream image classification tasks, i.e. Cifar10 and Cifar100, our Sparse-MLP can still achieve better performance than baselines.
Implicit neural rendering techniques have shown promising results for novel view synthesis. However, existing methods usually encode the entire scene as a whole, which is generally not aware of the object identity and limits the ability to the high-l
evel editing tasks such as moving or adding furniture. In this paper, we present a novel neural scene rendering system, which learns an object-compositional neural radiance field and produces realistic rendering with editing capability for a clustered and real-world scene. Specifically, we design a novel two-pathway architecture, in which the scene branch encodes the scene geometry and appearance, and the object branch encodes each standalone object conditioned on learnable object activation codes. To survive the training in heavily cluttered scenes, we propose a scene-guided training strategy to solve the 3D space ambiguity in the occluded regions and learn sharp boundaries for each object. Extensive experiments demonstrate that our system not only achieves competitive performance for static scene novel-view synthesis, but also produces realistic rendering for object-level editing.
