PKE-RRT: Efficient Multi-Goal Path Finding Algorithm Driven by Multi-Task Learning Model

Multi-goal path finding (MGPF) aims to find a closed and collision-free path to visit a sequence of goals orderly. As a physical travelling salesman problem, an undirected complete graph with accurate weights is crucial for determining the visiting order. Lack of prior knowledge of local paths between vertices poses challenges in meeting the optimality and efficiency requirements of algorithms. In this study, a multi-task learning model designated Prior Knowledge Extraction (PKE), is designed to estimate the local path length between pairwise vertices as the weights of the graph. Simultaneously, a promising region and a guideline are predicted as heuristics for the path-finding process. Utilizing the outputs of the PKE model, a variant of Rapidly-exploring Random Tree (RRT) is proposed known as PKE-RRT. It effectively tackles the MGPF problem by a local planner incorporating a prioritized visiting order, which is obtained from the complete graph. Furthermore, the predicted region and guideline facilitate efficient exploration of the tree structure, enabling the algorithm to rapidly provide a sub-optimal solution. Extensive numerical experiments demonstrate the outstanding performance of the PKE-RRT for the MGPF problem with a different number of goals, in terms of calculation time, path cost, sample number, and success rate.Comment: 9 pages, 12 figure

Neural-Network-Driven Method for Optimal Path Planning via High-Accuracy Region Prediction

Sampling-based path planning algorithms suffer from heavy reliance on uniform sampling, which accounts for unreliable and time-consuming performance, especially in complex environments. Recently, neural-network-driven methods predict regions as sampling domains to realize a non-uniform sampling and reduce calculation time. However, the accuracy of region prediction hinders further improvement. We propose a sampling-based algorithm, abbreviated to Region Prediction Neural Network RRT* (RPNN-RRT*), to rapidly obtain the optimal path based on a high-accuracy region prediction. First, we implement a region prediction neural network (RPNN), to predict accurate regions for the RPNN-RRT*. A full-layer channel-wise attention module is employed to enhance the feature fusion in the concatenation between the encoder and decoder. Moreover, a three-level hierarchy loss is designed to learn the pixel-wise, map-wise, and patch-wise features. A dataset, named Complex Environment Motion Planning, is established to test the performance in complex environments. Ablation studies and test results show that a high accuracy of 89.13% is achieved by the RPNN for region prediction, compared with other region prediction models. In addition, the RPNN-RRT* performs in different complex scenarios, demonstrating significant and reliable superiority in terms of the calculation time, sampling efficiency, and success rate for optimal path planning.Comment: 9 pages, 8 figure

Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution

Flow-based methods have demonstrated promising results in addressing the ill-posed nature of super-resolution (SR) by learning the distribution of high-resolution (HR) images with the normalizing flow. However, these methods can only perform a predefined fixed-scale SR, limiting their potential in real-world applications. Meanwhile, arbitrary-scale SR has gained more attention and achieved great progress. Nonetheless, previous arbitrary-scale SR methods ignore the ill-posed problem and train the model with per-pixel L1 loss, leading to blurry SR outputs. In this work, we propose "Local Implicit Normalizing Flow" (LINF) as a unified solution to the above problems. LINF models the distribution of texture details under different scaling factors with normalizing flow. Thus, LINF can generate photo-realistic HR images with rich texture details in arbitrary scale factors. We evaluate LINF with extensive experiments and show that LINF achieves the state-of-the-art perceptual quality compared with prior arbitrary-scale SR methods.Comment: CVPR 2023 camera-ready versio

Incidence Rates of Enterovirus 71 Infections in Young Children during a Nationwide Epidemic in Taiwan, 2008–09

Enterovirus 71 (EV71) was first isolated in California, USA, in 1969. Since then, EV71 has been identified globally. Recently, EV71 caused several life-threatening outbreaks in young children in tropical Asia. Development of EV71 vaccines becomes national priority in several Asia countries including Taiwan. To design clinical trials of EV71 vaccines, age-specific incidence rates of EV71 infections are required to identify target populations, estimate disease burdens, select endpoints of clinical efficacy, and estimate sample size. In Taiwan, nationwide EV71 epidemics occurred every 3–4 years but age-specific incidences of EV71 infection are not available. In 2006, we initiated a prospective cohort study in northern Taiwan to recruit neonates and follow up them. In 2008–09, a nationwide EV71 epidemic occurred and we found that age-specific incidence rates of EV71 infection increased from 1.71 per 100 person-years at 0–6 months of age to 4.09, 5.74, and 4.97 per 100 person-years at 7–12, 13–24, and 25–36 months of age, respectively. The cumulative incidence rate was 15% by 36 months of age, and 29% of EV71 infections were asymptomatic in young children. These findings would be helpful to development of EV71 vaccines in Taiwan and other Asian tropical countries

Potential Therapeutic Role of Hispidulin in Gastric Cancer through Induction of Apoptosis via NAG-1 Signaling

Gastric cancer is one of the most common malignant cancers due to poor prognoses and high mortality rates worldwide. However, an effective chemotherapeutic drug without side effects remains lacking. Saussurea involucrata (SI) Kar. et Kir., also known as snow lotus, grows in mountainous rocky habitats at 2600 m elevation in the Tian Shan and A’er Tai regions of China. The ethyl acetate extract of SI had been shown to inhibit proliferation and induce apoptosis in various tumor cells. In this study, we demonstrated that Hispidulin, active ingredients in SI, inhibits the growth of AGS gastric cancer cells. After Hispidulin treatment, NAG-1 remained highly expressed, whereas COX-2 expression was downregulated. Flow cytometric analysis indicated that Hispidulin induces G1/S phase arrest and apoptosis in time- and concentration-dependent manners. G1/S arrest correlated with upregulated p21/WAF1 and p16 and downregulated cyclin D1 and cyclin E, independent of p53 pathway. In addition, Hispidulin can elevate Egr-1 expression and ERK1/2 activity, whereas ERK1/2 inhibitor markedly attenuated NAG-1 mediated apoptosis. Taken together, Hispidulin can efficiently activate ERK1/2 signaling followed by NAG-1 constitutive expression and trigger cell cycle arrest as well as apoptosis in cancer cell. It can be a potential compound for combination therapy of gastric cancer in the future