Autonomous navigation with constrained consistency for C-Ranger

Autonomous underwater vehicles (AUVs) have become the most widely used tools for undertaking complex exploration tasks in marine environments. Their synthetic ability to carry out localization autonomously and build an environmental map concurrently, in other words, simultaneous localization and mapping (SLAM), are considered to be pivotal requirements for AUVs to have truly autonomous navigation. However, the consistency problem of the SLAM system has been greatly ignored during the past decades. In this paper, a consistency constrained extended Kalman filter (EKF) SLAM algorithm, applying the idea of local consistency, is proposed and applied to the autonomous navigation of the C-Ranger AUV, which is developed as our experimental platform. The concept of local consistency (LC) is introduced after an explicit theoretical derivation of the EKF-SLAM system. Then, we present a locally consistency-constrained EKF-SLAM design, LC-EKF, in which the landmark estimates used for linearization are fixed at the beginning of each local time period, rather than evaluated at the latest landmark estimates. Finally, our proposed LC-EKF algorithm is experimentally verified, both in simulations and sea trials. The experimental results show that the LC-EKF performs well with regard to consistency, accuracy and computational efficiency

Note On Certain Inequalities for Neuman Means

In this paper, we give the explicit formulas for the Neuman means $N_{AH}$, $N_{HA}$, $N_{AC}$ and $N_{CA}$, and present the best possible upper and lower bounds for theses means in terms of the combinations of harmonic mean $H$, arithmetic mean $A$ and contraharmonic mean $C$.Comment: 9 page

EAGAN: Efficient Two-stage Evolutionary Architecture Search for GANs

Generative adversarial networks (GANs) have proven successful in image generation tasks. However, GAN training is inherently unstable. Although many works try to stabilize it by manually modifying GAN architecture, it requires much expertise. Neural architecture search (NAS) has become an attractive solution to search GANs automatically. The early NAS-GANs search only generators to reduce search complexity but lead to a sub-optimal GAN. Some recent works try to search both generator (G) and discriminator (D), but they suffer from the instability of GAN training. To alleviate the instability, we propose an efficient two-stage evolutionary algorithm-based NAS framework to search GANs, namely EAGAN. We decouple the search of G and D into two stages, where stage-1 searches G with a fixed D and adopts the many-to-one training strategy, and stage-2 searches D with the optimal G found in stage-1 and adopts the one-to-one training and weight-resetting strategies to enhance the stability of GAN training. Both stages use the non-dominated sorting method to produce Pareto-front architectures under multiple objectives (e.g., model size, Inception Score (IS), and Fr\'echet Inception Distance (FID)). EAGAN is applied to the unconditional image generation task and can efficiently finish the search on the CIFAR-10 dataset in 1.2 GPU days. Our searched GANs achieve competitive results (IS=8.81$\pm$0.10, FID=9.91) on the CIFAR-10 dataset and surpass prior NAS-GANs on the STL-10 dataset (IS=10.44$\pm$0.087, FID=22.18). Source code: https://github.com/marsggbo/EAGAN.Comment: Accepted in ECCV2022, Guohao Yin and Xin He contributed equall

Stationary distribution for a three-dimensional stochastic viral infection model with general distributed delay

This work examines a stochastic viral infection model with a general distributed delay. We transform the model with weak kernel case into an equivalent system through the linear chain technique. First, we establish that a global positive solution to the stochastic system exists and is unique. We establish the existence of a stationary distribution of a positive solution under the stochastic condition R^s > 0 , also referred to as a stationary solution, by building appropriate Lyapunov functions. Finally, numerical simulation is proved to verify our analytical result and reveals the impact of stochastic perturbations on disease transmission

Searching for doubly charmed tetraquark candidates TccT_{cc} and TccsˉT_{cc\bar{s}} in BcB_c decays

In this work, we propose to search for the exotic doubly charmed meson $T_{cc}^+$ and its analog $T_{cc\bar{s}}^+$ in $B_c^+$ decays, which provide a good environment for the formation of the exotic state containing double charm quarks. Within the molecular scheme, the production of $T_{cc}^+$ and $T_{cc\bar{s}}^+$ through various rescattering processes with different intermediate states are investigated. For the moderate values of model parameters, the branching ratios of $B_c^+$ decaying into $T_{cc}^+ \bar{D}^{0}$, $T_{cc}^+ \bar{D}^{*0}$, $T_{cc\bar{s}}^+ \bar{D}^{0}$ and $T_{cc\bar{s}}^+ \bar{D}^{*0}$ are estimated to be of the order of $10^{-7}$, $10^{-5}$, $10^{-6}$ and $10^{-4}$, respectively, which may be tested by future experiments.Comment: 9 pages, 8 figure

Poly[μ2-aqua-diaqua­(μ8-3-nitro­benzene-1,2-dicarboxylato)(μ6-3-nitro­benzene-1,2-dicarboxylato)tetra­sodium]

In the title layered coordination polymer, [Na4(C8H3NO6)2(H2O)3]n, the doubly deprotonated 3-nitro­benzene-1,2-dicarboxyl­ate ligands exhibit μ8- and μ6-coordination modes to the sodium ions, generating sheets lying parallel to (001). The coordination environments of the sodium ions are distorted octa­hedral, distorted trigonal-bipyramidal and moncapped trigonal-prismatic. One of the nitro groups is disordered over two sets of sites with site-occupancy factors 0.580 (8):0.419 (2). A network of O—H⋯O and O—H⋯N hydrogen bonds helps to establish the packing