Comparative Research on Robot Path Planning Based on GA-ACA and ACA-GA

The path planning for mobile robots is one of the core contents in the field of robotics research with complex, restrictive and nonlinear characteristics. It consists of automatically determining a path from an initial position of the robot to its final position. Due to classic approaches have several drawbacks, evolutionary methods such as Ant Colony Optimization Algorithm (ACA) and Genetic Algorithm (GA) are employed to solve the path planning efficiently

ElixirNet: Relation-aware Network Architecture Adaptation for Medical Lesion Detection

Most advances in medical lesion detection network are limited to subtle modification on the conventional detection network designed for natural images. However, there exists a vast domain gap between medical images and natural images where the medical image detection often suffers from several domain-specific challenges, such as high lesion/background similarity, dominant tiny lesions, and severe class imbalance. Is a hand-crafted detection network tailored for natural image undoubtedly good enough over a discrepant medical lesion domain? Is there more powerful operations, filters, and sub-networks that better fit the medical lesion detection problem to be discovered? In this paper, we introduce a novel ElixirNet that includes three components: 1) TruncatedRPN balances positive and negative data for false positive reduction; 2) Auto-lesion Block is automatically customized for medical images to incorporate relation-aware operations among region proposals, and leads to more suitable and efficient classification and localization. 3) Relation transfer module incorporates the semantic relationship and transfers the relevant contextual information with an interpretable the graph thus alleviates the problem of lack of annotations for all types of lesions. Experiments on DeepLesion and Kits19 prove the effectiveness of ElixirNet, achieving improvement of both sensitivity and precision over FPN with fewer parameters.Comment: 7 pages, 5 figure, AAAI202

Transaction-filtering data mining and a predictive model for intelligent data management

This thesis, first of all, proposes a new data mining paradigm (transaction-filtering association rule mining) addressing a time consumption issue caused by the repeated scans of original transaction databases in conventional associate rule mining algorithms. An in-memory transaction filter is designed to discard those infrequent items in the pruning steps. This filter is a data structure to be updated at the end of each iteration. The results based on an IBM benchmark show that an execution time reduction of 10% - 19% is achieved compared with the base case. Next, a data mining-based predictive model is then established contributing to intelligent data management within the context of Centre for Grid Computing. The capability of discovering unseen rules, patterns and correlations enables data mining techniques favourable in areas where massive amounts of data are generated. The past behaviours of two typical scenarios (network file systems and Data Grids) have been analyzed to build the model. The future popularity of files can be forecasted with an accuracy of 90% by deploying the above predictor based on the given real system traces. A further step towards intelligent policy design is achieved by analyzing the prediction results of files’ future popularity. The real system trace-based simulations have shown improvements of 2-4 times in terms of data response time in network file system scenario and 24% mean job time reduction in Data Grids compared with conventional cases.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

The early posterior cortex pixel value ratio: a novel reliable indicator for distraction osteogenesis

AimsWe aimed to explore the associations of the early PVR in four cortices with Healing Index (HI), Lengthening Index (LI), and External Fixator Index (EFI) in the bone union and non-union groups.MethodsA total of 52 patients, including 39 bone union and 13 bone non-union subjects, were recruited in this study. The general characteristics and PVR in four cortices in each group were explored. Afterward, the early PVR in four cortices, including medial, lateral, anterior, and posterior sides, were compared. Finally, the associations of the early PVR in four cortices with HI, LI, and EFI were also investigated.ResultsThe general characteristics of these patients were consistent, except for HI (31.54 ± 12.24 vs. 45.08 ± 27.10, P = 0.018) and EFI (57.63 ± 18.15 vs. 71.29 ± 24.60, P = 0.046). The growth of regenerated callus was asymmetrical in the bone union group (the posterior PVR seems to grow faster), whereas no statistical difference was obtained in the bone non-union group. Furthermore, the posterior PVR in the bone union group was significantly higher than that in the bone non-union group (the first month: 0.96 ± 0.17 vs. 0.86 ± 0.06, p = 0.047; the second month: 0.98 ± 0.14 vs. 0.89 ± 0.09, p = 0.041; the third month: 1.00 ± 0.12 vs. 0.92 ± 0.09, p = 0.039). Most importantly, the posterior PVR was inversely associated with HI, LI, and EFI (the first month: r = −0.343, p = 0.041; r = −0.346, p = 0.042; r = −0.352, p = 0.041; the second month: r = −0.459, p = 0.004; r = −0.277, p = 0.101; r = −0.511, p = 0.002; the third month: r = −0.479, p = 0.003; r = −0.398, p = 0.018; r = −0.551, p = 0.001) in the bone union group, respectively. However, this finding was lost in the bone non-union group.ConclusionThe early posterior cortex PVR seems to grow faster than the medial, lateral, and anterior sides in the bone union group, which represents an asymmetrical development pattern. Moreover, the posterior cortex PVR was negatively associated with HI, LI, and EFI, respectively. The posterior cortex PVR may be a novel and reliable detection index in the process of DO

Hyperconnectivity of the lateral amygdala in long-term methamphetamine abstainers negatively correlated with withdrawal duration

Introduction: Several studies have reported structural and functional abnormalities of the amygdala caused by methamphetamine addiction. However, it is unknown whether abnormalities in amygdala function persist in long-term methamphetamine abstainers.Methods: In this study, 38 long-term male methamphetamine abstainers (>12 months) and 40 demographically matched male healthy controls (HCs) were recruited. Considering the heterogeneous nature of the amygdala structure and function, we chose 4 amygdala subregions (i.e., left lateral, left medial, right lateral, and right medial) as regions of interest (ROI) and compared the ROI-based resting-state functional connectivity (FC) at the whole-brain voxel-wise between the two groups. We explored the relationship between the detected abnormal connectivity, methamphetamine use factors, and the duration of withdrawal using correlation analyses. We also examined the effect of methamphetamine use factors, months of withdrawal, and sociodemographic data on detected abnormal connectivity through multiple linear regressions.Results: Compared with HCs, long-term methamphetamine abstainers showed significant hyperconnectivity between the left lateral amygdala and a continuous area extending to the left inferior/middle occipital gyrus and left middle/superior temporal gyrus. Abnormal connections negatively correlated with methamphetamine withdrawal time (r = −0.85, p < 0.001). The linear regression model further demonstrated that the months of withdrawal could identify the abnormal connectivity (βadj = −0.86, 95%CI: −1.06 to −0.65, p < 0.001).Discussion: The use of methamphetamine can impair the neural sensory system, including the visual and auditory systems, but this abnormal connectivity can gradually recover after prolonged withdrawal of methamphetamine. From a neuroimaging perspective, our results suggest that withdrawal is an effective treatment for methamphetamine

Genetic and metabolic links between the murine microbiome and memory.

BackgroundRecent evidence has linked the gut microbiome to host behavior via the gut-brain axis [1-3]; however, the underlying mechanisms remain unexplored. Here, we determined the links between host genetics, the gut microbiome and memory using the genetically defined Collaborative Cross (CC) mouse cohort, complemented with microbiome and metabolomic analyses in conventional and germ-free (GF) mice.ResultsA genome-wide association analysis (GWAS) identified 715 of 76,080 single-nucleotide polymorphisms (SNPs) that were significantly associated with short-term memory using the passive avoidance model. The identified SNPs were enriched in genes known to be involved in learning and memory functions. By 16S rRNA gene sequencing of the gut microbial community in the same CC cohort, we identified specific microorganisms that were significantly correlated with longer latencies in our retention test, including a positive correlation with Lactobacillus. Inoculation of GF mice with individual species of Lactobacillus (L. reuteri F275, L. plantarum BDGP2 or L. brevis BDGP6) resulted in significantly improved memory compared to uninoculated or E. coli DH10B inoculated controls. Untargeted metabolomics analysis revealed significantly higher levels of several metabolites, including lactate, in the stools of Lactobacillus-colonized mice, when compared to GF control mice. Moreover, we demonstrate that dietary lactate treatment alone boosted memory in conventional mice. Mechanistically, we show that both inoculation with Lactobacillus or lactate treatment significantly increased the levels of the neurotransmitter, gamma-aminobutyric acid (GABA), in the hippocampus of the mice.ConclusionTogether, this study provides new evidence for a link between Lactobacillus and memory and our results open possible new avenues for treating memory impairment disorders using specific gut microbial inoculants and/or metabolites. Video Abstract

Automatic Modulation Classification Using Hybrid Data Augmentation and Lightweight Neural Network

Automatic modulation classification (AMC) plays an important role in intelligent wireless communications. With the rapid development of deep learning in recent years, neural network-based automatic modulation classification methods have become increasingly mature. However, the high complexity and large number of parameters of neural networks make them difficult to deploy in scenarios and receiver devices with strict requirements for low latency and storage. Therefore, this paper proposes a lightweight neural network-based AMC framework. To improve classification performance, the framework combines complex convolution with residual networks. To achieve a lightweight design, depthwise separable convolution is used. To compensate for any performance loss resulting from a lightweight design, a hybrid data augmentation scheme is proposed. The simulation results demonstrate that the lightweight AMC framework reduces the number of parameters by approximately 83.34% and the FLOPs by approximately 83.77%, without a degradation in performance