Intrinsic Reduced Attitude Formation with Ring Inter-Agent Graph

This paper investigates the reduced attitude formation control problem for a group of rigid-body agents using feedback based on relative attitude information. Under both undirected and directed cycle graph topologies, it is shown that reversing the sign of a classic consensus protocol yields asymptotical convergence to formations whose shape depends on the parity of the group size. Specifically, in the case of even parity the reduced attitudes converge asymptotically to a pair of antipodal points and distribute equidistantly on a great circle in the case of odd parity. Moreover, when the inter-agent graph is an undirected ring, the desired formation is shown to be achieved from almost all initial states

Spatial second-order positive and asymptotic preserving filtered PNP_N schemes for nonlinear radiative transfer equations

A spatial second-order scheme for the nonlinear radiative transfer equations is introduced in this paper. The discretization scheme is based on the filtered spherical harmonics ($FP_N$) method for the angular variable and the unified gas kinetic scheme (UGKS) framework for the spatial and temporal variables respectively. In order to keep the scheme positive and second-order accuracy, firstly, we use the implicit Monte Carlo linearization method [6] in the construction of the UGKS numerical boundary fluxes. Then, by carefully analyzing the constructed second-order fluxes involved in the macro-micro decomposition, which is induced by the $FP_N$ angular discretization, we establish the sufficient conditions that guarantee the positivity of the radiative energy density and material temperature. Finally, we employ linear scaling limiters for the angular variable in the $P_N$ reconstruction and for the spatial variable in the piecewise linear slopes reconstruction respectively, which are shown to be realizable and reasonable to enforce the sufficient conditions holding. Thus, the desired scheme, called the $PPFP_N$-based UGKS, is obtained. Furthermore, in the regime $\epsilon\ll 1$ and the regime $\epsilon=O(1)$, a simplified spatial second-order scheme, called the $PPFP_N$-based SUGKS, is presented, which possesses all the properties of the non-simplified one. Inheriting the merit of UGKS, the proposed schemes are asymptotic preserving. By employing the $FP_N$ method for the angular variable, the proposed schemes are almost free of ray effects. To our best knowledge, this is the first time that spatial second-order, positive, asymptotic preserving and almost free of ray effects schemes are constructed for the nonlinear radiative transfer equations without operator splitting. Various numerical experiments are included to validate the properties of the proposed schemes

Defective Dynamics Of Mitochondria In Amyotrophic Lateral Sclerosis And Huntington\u27s Disease

Mitochondria play important roles in neuronal function and survival, including ATP production, Ca2+ buffering, and apoptosis. Mitochondrial dysfunction is a common event in the pathogenesis of neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) and Huntington\u27s disease (HD); however, what causes the mitochondrial dysfunction remains unclear. Mitochondrial fission is mediated by dynamin-related protein 1 (DRP1) and fusion by mitofusin 1/2 (MFN1/2) and optic atrophy 1 (OPA1), which are essential for mitochondrial function. Mutations in the mitochondrial fission and fusion machinery lead to neurodegeneration. Thus, whether defective mitochondrial dynamics participates in ALS and HD requires further investigation. ALS is a fatal neurodegenerative disease characterized by upper and lower motor neuron loss. Mutations in Cu/Zn superoxide dismutase (SOD1) cause the most common familiar form of ALS by mechanisms not fully understood. Here, a new motor neuron-astrocyte coculture system was created and live-cell imaging was used to evaluate mitochondrial dynamics. Excessive mitochondrial fission was observed in mutant SOD1G93A motor neurons, correlating with impaired axonal transport and neuronal cell death. Inhibition of mitochondrial fission restored mitochondrial dynamics and protected neurons against SOD1G93A -induced mitochondrial fragmentation and neuronal cell death, implicating defects in mitochondrial dynamics in ALS pathogenesis. iv HD is an inherited neurodegenerative disorder caused by glutamine (Q) expansion in the polyQ region of the huntingtin (HTT) protein. In the current work, mutant HTT caused mitochondrial fragmentation in a polyQ-dependent manner in both primary cortical neurons and fibroblasts from human patients. An abnormal interaction between DRP1 and HTT was observed in mutant HTT mice and inhibition of mitochondrial fission or promotion of mitochondrial fusion restored mitochondrial dynamics and protected neurons against mutant HTT-induced cell death. Thus, mutant HTT may increase mitochondrial fission by elevating DRP1 GTPase activity, suggesting that mitochondrial dynamics plays a causal role in HD. In summary, rebalanced mitochondrial fission and fusion rescues neuronal cell death in ALS and HD, suggesting that mitochondrial dynamics could be the molecular mechanism underlying these diseases. Furthermore, DRP1 might be a therapeutic target to delay or prevent neurodegeneration

R-PMAC: A Robust Preamble Based MAC Mechanism Applied in Industrial Internet of Things

This paper proposes a novel media access control (MAC) mechanism, called the robust preamble-based MAC mechanism (R-PMAC), which can be applied to power line communication (PLC) networks in the context of the Industrial Internet of Things (IIoT). Compared with other MAC mechanisms such as P-MAC and the MAC layer of IEEE1901.1, R-PMAC has higher networking speed. Besides, it supports whitelist authentication and functions properly in the presence of data frame loss. Firstly, we outline three basic mechanisms of R-PMAC, containing precise time difference calculation, preambles generation and short ID allocation. Secondly, we elaborate its networking process of single layer and multiple layers. Thirdly, we illustrate its robust mechanisms, including collision handling and data retransmission. Moreover, a low-cost hardware platform is established to measure the time of connecting hundreds of PLC nodes for the R-PMAC, P-MAC, and IEEE1901.1 mechanisms in a real power line environment. The experiment results show that R-PMAC outperforms the other mechanisms by achieving a 50% reduction in networking time. These findings indicate that the R-PMAC mechanism holds great potential for quickly and effectively building a PLC network in actual industrial scenarios.Comment: This paper has been accepted by IEEE Internet of Things Journa

A model-data asymptotic-preserving neural network method based on micro-macro decomposition for gray radiative transfer equations

We propose a model-data asymptotic-preserving neural network(MD-APNN) method to solve the nonlinear gray radiative transfer equations(GRTEs). The system is challenging to be simulated with both the traditional numerical schemes and the vanilla physics-informed neural networks(PINNs) due to the multiscale characteristics. Under the framework of PINNs, we employ a micro-macro decomposition technique to construct a new asymptotic-preserving(AP) loss function, which includes the residual of the governing equations in the micro-macro coupled form, the initial and boundary conditions with additional diffusion limit information, the conservation laws, and a few labeled data. A convergence analysis is performed for the proposed method, and a number of numerical examples are presented to illustrate the efficiency of MD-APNNs, and particularly, the importance of the AP property in the neural networks for the diffusion dominating problems. The numerical results indicate that MD-APNNs lead to a better performance than APNNs or pure data-driven networks in the simulation of the nonlinear non-stationary GRTEs

Boosting Video Object Segmentation via Space-time Correspondence Learning

Current top-leading solutions for video object segmentation (VOS) typically follow a matching-based regime: for each query frame, the segmentation mask is inferred according to its correspondence to previously processed and the first annotated frames. They simply exploit the supervisory signals from the groundtruth masks for learning mask prediction only, without posing any constraint on the space-time correspondence matching, which, however, is the fundamental building block of such regime. To alleviate this crucial yet commonly ignored issue, we devise a correspondence-aware training framework, which boosts matching-based VOS solutions by explicitly encouraging robust correspondence matching during network learning. Through comprehensively exploring the intrinsic coherence in videos on pixel and object levels, our algorithm reinforces the standard, fully supervised training of mask segmentation with label-free, contrastive correspondence learning. Without neither requiring extra annotation cost during training, nor causing speed delay during deployment, nor incurring architectural modification, our algorithm provides solid performance gains on four widely used benchmarks, i.e., DAVIS2016&2017, and YouTube-VOS2018&2019, on the top of famous matching-based VOS solutions.Comment: CVPR 2023; Project page: https://github.com/wenguanwang/VOS_Correspondenc