Resource-Efficient Cooperative Online Scalar Field Mapping via Distributed Sparse Gaussian Process Regression

Cooperative online scalar field mapping is an important task for multi-robot systems. Gaussian process regression is widely used to construct a map that represents spatial information with confidence intervals. However, it is difficult to handle cooperative online mapping tasks because of its high computation and communication costs. This letter proposes a resource-efficient cooperative online field mapping method via distributed sparse Gaussian process regression. A novel distributed online Gaussian process evaluation method is developed such that robots can cooperatively evaluate and find observations of sufficient global utility to reduce computation. The bounded errors of distributed aggregation results are guaranteed theoretically, and the performances of the proposed algorithms are validated by real online light field mapping experiments

CARE: Confidence-rich Autonomous Robot Exploration using Bayesian Kernel Inference and Optimization

In this paper, we consider improving the efficiency of information-based autonomous robot exploration in unknown and complex environments. We first utilize Gaussian process (GP) regression to learn a surrogate model to infer the confidence-rich mutual information (CRMI) of querying control actions, then adopt an objective function consisting of predicted CRMI values and prediction uncertainties to conduct Bayesian optimization (BO), i.e., GP-based BO (GPBO). The trade-off between the best action with the highest CRMI value (exploitation) and the action with high prediction variance (exploration) can be realized. To further improve the efficiency of GPBO, we propose a novel lightweight information gain inference method based on Bayesian kernel inference and optimization (BKIO), achieving an approximate logarithmic complexity without the need for training. BKIO can also infer the CRMI and generate the best action using BO with bounded cumulative regret, which ensures its comparable accuracy to GPBO with much higher efficiency. Extensive numerical and real-world experiments show the desired efficiency of our proposed methods without losing exploration performance in different unstructured, cluttered environments. We also provide our open-source implementation code at https://github.com/Shepherd-Gregory/BKIO-Exploration.Comment: Full version for the paper accepted by IEEE Robotics and Automation Letters (RA-L) 2023. arXiv admin note: text overlap with arXiv:2301.0052

Distributed Target Tracking with Fading Channels over Underwater Wireless Sensor Networks

This paper investigates the problem of distributed target tracking via underwater wireless sensor networks (UWSNs) with fading channels. The degradation of signal quality due to wireless channel fading can significantly impact network reliability and subsequently reduce the tracking accuracy. To address this issue, we propose a modified distributed unscented Kalman filter (DUKF) named DUKF-Fc, which takes into account the effects of measurement fluctuation and transmission failure induced by channel fading. The channel estimation error is also considered when designing the estimator and a sufficient condition is established to ensure the stochastic boundedness of the estimation error. The proposed filtering scheme is versatile and possesses wide applicability to numerous real-world scenarios, e.g., tracking a maneuvering underwater target with acoustic sensors. Simulation results demonstrate the effectiveness of the proposed filtering algorithm. In addition, considering the constraints of network energy resources, the issue of investigating a trade-off between tracking performance and energy consumption is discussed accordingly.Comment: 12 pages, 6 figures, 6 table

Five-Tiered Route Planner for Multi-AUV Accessing Fixed Nodes in Uncertain Ocean Environments

This article introduces a five-tiered route planner for accessing multiple nodes with multiple autonomous underwater vehicles (AUVs) that enables efficient task completion in stochastic ocean environments. First, the pre-planning tier solves the single-AUV routing problem to find the optimal giant route (GR), estimates the number of required AUVs based on GR segmentation, and allocates nodes for each AUV to access. Second, the route planning tier plans individual routes for each AUV. During navigation, the path planning tier provides each AUV with physical paths between any two points, while the actuation tier is responsible for path tracking and obstacle avoidance. Finally, in the stochastic ocean environment, deviations from the initial plan may occur, thus, an auction-based coordination tier drives online task coordination among AUVs in a distributed manner. Simulation experiments are conducted in multiple different scenarios to test the performance of the proposed planner, and the promising results show that the proposed method reduces AUV usage by 7.5% compared with the existing methods. When using the same number of AUVs, the fleet equipped with the proposed planner achieves a 6.2% improvement in average task completion rate

Physics-informed Neural Network Combined with Characteristic-Based Split for Solving Navier-Stokes Equations

In this paper, physics-informed neural network (PINN) based on characteristic-based split (CBS) is proposed, which can be used to solve the time-dependent Navier-Stokes equations (N-S equations). In this method, The output parameters and corresponding losses are separated, so the weights between output parameters are not considered. Not all partial derivatives participate in gradient backpropagation, and the remaining terms will be reused.Therefore, compared with traditional PINN, this method is a rapid version. Here, labeled data, physical constraints and network outputs are regarded as priori information, and the residuals of the N-S equations are regarded as posteriori information. So this method can deal with both data-driven and data-free problems. As a result, it can solve the special form of compressible N-S equations -- -Shallow-Water equations, and incompressible N-S equations. As boundary conditions are known, this method only needs the flow field information at a certain time to restore the past and future flow field information. We solve the progress of a solitary wave onto a shelving beach and the dispersion of the hot water in the flow, which show this method's potential in the marine engineering. We also use incompressible equations with exact solutions to prove this method's correctness and universality. We find that PINN needs more strict boundary conditions to solve the N-S equation, because it has no computational boundary compared with the finite element method

Improved Heuristics for Low-latency Implementations of Linear Layers

In many applications, low area and low latency are required for the chip-level implementation of cryptographic primitives. The low-cost implementations of linear layers usually play a crucial role for symmetric ciphers. Some heuristic methods, such as the forward search and the backward search, minimize the number of XOR gates of the linear layer under the minimum latency limitation. For the sake of achieving further optimization for such implementation of the linear layer, we put forward a new general search framework attaching the division optimization and extending base techniques in this paper. In terms of the number of XOR gates and the searching time, our new search algorithm is better than the previous heuristics, including the forward search and the backward search when testing matrices provided by them. We obtain an improved implementation of AES MixColumns requiring only 102 XORs under minimum latency, which outdoes the previous best record provided by the forward search

Activation of Wnt signaling reduces high-glucose mediated damages on skin fibroblast cells

Objective(s): High-glucose (HG) stress, a mimic of diabetes mellitus (DM) in culture cells, alters expression of a large number of genes including Wnt and NF-κB signaling-related genes; however, the role of Wnt signaling during HG-mediated fibroblast damage and the relationship between Wnt and NF-κB signaling have not been understood. In this study, we aimed to investigate the ffects of Wnt signaling on HG-mediated damages. Materials and Methods: Wnt3a was treated to HG-stressed human primary foreskin fibroblasts and the levels of Wnt signaling markers and cell proliferation were monitored. In addition, Wnt3a and NF-κB signaling inhibitor were assisted to analyze the relationship between two pathways. Results: The results indicated that HG treatment repressed β-catenin level, and Wnt3a treatment increased the levels of β-catenin and FZD8 as well as cell proliferation. RNA-seq based transcriptome analysis identified 207 up-regulated and 200 down-regulated genes upon Wnt3a supply. These altered genes are distributed into 20 different pathways. In addition, gene ontology (GO) analysis indicates that 20 GO terms are enriched. Wnt signaling genes were further verified by qRT-PCR and the results were similar with RNA-seq assay. Since NF-κB signaling negatively regulates Wnt marker gene expression, Bay117082, a typical NF-κB signaling inhibitor and Wnt3a were supplemented for testing β-catenin and phosphorylated IκBα (p-IκBα), respectively. Conclusion: HG positively inhibits Wnt signaling, and signaling activation via supplementation of Wnt3a rescued the defect caused by HG. NF-κB signaling negatively regulates accumulation of β-catenin, but Wnt signaling has no effects on IκBα activation

Involvement of serotonergic system in the antidepressant-like effect of hyperoside from apocynum venetum leaves

The present study investigated the antidepressant-like effect of hyperoside extracted from Apocynum venetum leaves in mice using the tail suspension test (TST) and forced swimming test (FST). Hyperoside administration at 10, 20 and 30 mg/kg (p.o.) for 10 days reduced immobility time in both tests. This effect is dose-dependent without influencing the animals’ locomotor activity. Additionally, the monoaminergic mechanisms involved in the antidepressant-like effect of hyperoside in the mouse forced swimming test (FST) were evaluated. The results showed that hyperoside produced an antidepressant-like effect in the FST (10-30 mg/kg, i.g.) and in the TST (10–30 mg/kg, i.g.), without accompanying changes in ambulation distance when assessed in the open-field test. The antidepressant-like effect of hyperoside (20 mg/kg, i.g.) was prevented by the pretreatment of mice with ketanserin (5 mg/kg, s.c., a serotonin 5-HT2A receptor antagonist), cyproheptadine (3 mg/kg, i.g., a serotonin 5-HT2 receptor antagonist). On the other hand, the pretreatment of mice with WAY 100635 (0.1 mg/kg, s.c., a serotonin 5-HT1A receptor antagonist) did not block the antidepressant-like effect of hyperoside in the TST. It may be concluded that the hyperoside produces an antidepressant-like effect in the FST and in the TST that is dependent on its interaction with the serotonergic (5-HT2A and 5-HT2 receptors) systems. Taken together, our results suggested that hyperoside deserves further investigation as a putative alternative therapeutic tool that could help the conventional pharmacotherapy of depression.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Improved Quantum Circuits for AES: Reducing the Depth and the Number of Qubits

Quantum computers hold the potential to solve problems that are intractable for classical computers, thereby driving increased interest in the development of new cryptanalytic ciphers. In NIST\u27s post-quantum standardization process, the security categories are defined by the costs of quantum key search against AES. However, the cost estimates provided by Grassl et al. for the search are high. NIST has acknowledged that these initial classifications should be approached cautiously, since the costs of the most advanced attacks can be significantly reduced. Therefore, accurate resource estimations are crucial for evaluating the security of ciphers against quantum adversaries. This paper presents a set of generic techniques for implementing AES quantum oracles, which are essential for quantum attacks such as Grover\u27s algorithms. Firstly, we introduce the mixing-XOR technique to reuse the ancilla qubits. At ASIACRYPT 2022, Huang et al. proposed an S-box structure with 120 ancilla qubits. We are able to reduce the number of ancilla qubits to 83 without increasing the T-depth. Secondly, we propose the combined pipeline architecture with the share technique to combine the S-box and its reverse, which achieves it with only 98 ancilla qubits, resulting in a significant reduction of 59% compared to the independent structure. Thirdly, we use a general algorithm to determine the depth of quantum circuits, searching for the in-place circuit of AES MixColumns with depth 16. Applying these improvements, we achieve the lower quantum depth of AES circuits, obtaining more precise resource estimates for Grover\u27s algorithm. For AES-128, -192, and -256, we only require the depth of 730, 876, and 1,018, respectively. Recently, the community has also focused on the trade-off of the time and space cost of quantum circuits for AES. In this regard, we present quantum implementations of AES circuits with a lower DW-cost on the zig-zag architecture. Compared with the circuit proposed by Huang et al., the DW-cost is reduced by 35%