Research on 2D general feature based SLAM algorithm for mobile robot

University of Technology Sydney. Faculty of Engineering and Information Technology.Simultaneous Localization and Mapping (SLAM) is a fundamental research problem for autonomous robot navigation and map construction. This thesis studied the problem of improving the performance of localization and mapping for mobile robots, including pre-fitting features with ellipse representation, representing features with implicit functions, parameterization in Fourier series, and submap joining. The main contributions include three aspects: (1) a SLAM algorithm with pre-fitted conic features via 2D lidar is presented, which is named as Pre-fit SLAM and can be adapted to an open environment nicely; (2) a post-count framework for 2D lidar SLAM with implicit functions on general features is studied; (3) a 2D laser SLAM approach with Fourier series based feature parameterization (called Fourier-SLAM) and submap joining is studied

A Novel Slope Failure Operator for a Non-Equilibrium Sediment Transport Model

Complex transport mechanism and interaction between fluid and sediment make the mathematical and numerical modeling of sediment transport very challenging. Different types of models can lead to different results. This paper investigates a non-equilibrium sediment transport model based on the total load. In this type of model, it is assumed that a bed slide will occur if the bed slope reaches a critical angle. This is enabled by means of a slope failure operator. Existing slope failure operators usually suffer from the high computational cost and may fail at wet/dry interfaces. The main contribution of this work is the development of a novel slope failure operator for the total load transport model, based on a modified mass balance approach. The proposed approach is verified in three test cases, involving bank failure, dyke overtopping and a two-dimensional bank failure. It is shown that the proposed approach yields good agreement with analytical results and measurement data

Care for the Mind Amid Chronic Diseases: An Interpretable AI Approach Using IoT

Health sensing for chronic disease management creates immense benefits for social welfare. Existing health sensing studies primarily focus on the prediction of physical chronic diseases. Depression, a widespread complication of chronic diseases, is however understudied. We draw on the medical literature to support depression prediction using motion sensor data. To connect human expertise in the decision-making, safeguard trust for this high-stake prediction, and ensure algorithm transparency, we develop an interpretable deep learning model: Temporal Prototype Network (TempPNet). TempPNet is built upon the emergent prototype learning models. To accommodate the temporal characteristic of sensor data and the progressive property of depression, TempPNet differs from existing prototype learning models in its capability of capturing the temporal progression of depression. Extensive empirical analyses using real-world motion sensor data show that TempPNet outperforms state-of-the-art benchmarks in depression prediction. Moreover, TempPNet interprets its predictions by visualizing the temporal progression of depression and its corresponding symptoms detected from sensor data. We further conduct a user study to demonstrate its superiority over the benchmarks in interpretability. This study offers an algorithmic solution for impactful social good - collaborative care of chronic diseases and depression in health sensing. Methodologically, it contributes to extant literature with a novel interpretable deep learning model for depression prediction from sensor data. Patients, doctors, and caregivers can deploy our model on mobile devices to monitor patients' depression risks in real-time. Our model's interpretability also allows human experts to participate in the decision-making by reviewing the interpretation of prediction outcomes and making informed interventions.Comment: 39 pages, 12 figure

Modeling Shallow Water Flow And Transport Processes With Small Water Depths Using The Hydroinformatics Modelling System

In hydro- and environmental systems modelling, there are several application cases where very small water depths occur, for example rainfall and runoff in natural or urban catchments, possibly associated with tracer transport. In these cases, the water depth may be in the range of millimeters to a few centimeters. The numerical simulation of the associated processes is complex, therefore robust numerical schemes are required. Two test cases using high resolution topography data are investigated with the Hydroinformatics Modelling System (HMS). In the first case, the influence of microtopography and local depressions were analyzed in an idealized urban catchment; both had a strong impact on the hydrograph. In the second one, rainfall runoff experiments, which were carried out by Mügler et al. [10] were simulated. Through parameter optimization an overall good agreement between computed and measured breakthrough curves was achieved

GripRank: Bridging the Gap between Retrieval and Generation via the Generative Knowledge Improved Passage Ranking

Retrieval-enhanced text generation, which aims to leverage passages retrieved from a large passage corpus for delivering a proper answer given the input query, has shown remarkable progress on knowledge-intensive language tasks such as open-domain question answering and knowledge-enhanced dialogue generation. However, the retrieved passages are not ideal for guiding answer generation because of the discrepancy between retrieval and generation, i.e., the candidate passages are all treated equally during the retrieval procedure without considering their potential to generate the proper answers. This discrepancy makes a passage retriever deliver a sub-optimal collection of candidate passages to generate answers. In this paper, we propose the GeneRative Knowledge Improved Passage Ranking (GripRank) approach, addressing the above challenge by distilling knowledge from a generative passage estimator (GPE) to a passage ranker, where the GPE is a generative language model used to measure how likely the candidate passages can generate the proper answer. We realize the distillation procedure by teaching the passage ranker learning to rank the passages ordered by the GPE. Furthermore, we improve the distillation quality by devising a curriculum knowledge distillation mechanism, which allows the knowledge provided by the GPE can be progressively distilled to the ranker through an easy-to-hard curriculum, enabling the passage ranker to correctly recognize the provenance of the answer from many plausible candidates. We conduct extensive experiments on four datasets across three knowledge-intensive language tasks. Experimental results show advantages over the state-of-the-art methods for both passage ranking and answer generation on the KILT benchmark.Comment: 11 pages, 4 figure

Chinese herbal compound for multidrug-resistant or extensively drug-resistant bacterial pneumonia: a meta-analysis and trial sequential analysis with association rule mining to identify core herb combinations

Purpose: Antibiotic-resistant bacterial pneumonia poses a significant therapeutic challenge. In China, Chinese herbal compound (CHC) is commonly used to treat bacterial pneumonia. We aimed to evaluate the efficacy and safety of CHC and identify core herb combinations for the treatment of multidrug-resistant or extensively drug-resistant bacterial pneumonia.Methods: Stata 16 and TSA 0.9.5.10 beta software were used for meta-analysis and trial sequential analysis (TSA), respectively. Exploring the sources of heterogeneity through meta-regression and subgroup analysis.Results: Thirty-eight studies involving 2890 patients were included in the analyses. Meta-analysis indicated that CHC combined with antibiotics improved the response rate (RR = 1.24; 95% CI: 1.19–1.28; p < 0.0001) and microbiological eradication (RR = 1.41; 95% CI: 1.27–1.57; p < 0.0001), lowered the white blood cell count (MD = −2.09; 95% CI: −2.65 to −1.53; p < 0.0001), procalcitonin levels (MD = −0.49; 95% CI: −0.59 to −0.40; p < 0.0001), C-reactive protein levels (MD = −11.80; 95% CI: −15.22 to −8.39; p < 0.0001), Clinical Pulmonary Infection Scores (CPIS) (MD = −1.97; 95% CI: −2.68 to −1.26; p < 0.0001), and Acute Physiology and Chronic Health Evaluation (APACHE)-II score (MD = −4.08; 95% CI: −5.16 to −3.00; p < 0.0001), shortened the length of hospitalization (MD = −4.79; 95% CI: −6.18 to −3.40; p < 0.0001), and reduced the number of adverse events. TSA indicated that the response rate and microbiological eradication results were robust. Moreover, Scutellaria baicalensis Georgi, Fritillaria thunbergii Miq, Lonicera japonica Thunb, and Glycyrrhiza uralensis Fisch were identified as core CHC prescription herbs.Conclusion: Compared with antibiotic treatment, CHC + antibiotic treatment was superior in improving response rate, microbiological eradication, inflammatory response, CPIS, and APACHE-II score and shortening the length of hospitalization. Association rule analysis identified four core herbs as promising candidates for treating antibiotic-resistant bacterial pneumonia. However, large-scale clinical studies are still required.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/, identifier CRD42023410587

A novel T-cell exhaustion-related feature can accurately predict the prognosis of OC patients

The phenomenon of T Cell exhaustion (TEX) entails a progressive deterioration in the functionality of T cells within the immune system during prolonged conflicts with chronic infections or tumors. In the context of ovarian cancer immunotherapy, the development, and outcome of treatment are closely linked to T-cell exhaustion. Hence, gaining an in-depth understanding of the features of TEX within the immune microenvironment of ovarian cancer is of paramount importance for the management of OC patients. To this end, we leveraged single-cell RNA data from OC to perform clustering and identify T-cell marker genes utilizing the Unified Modal Approximation and Projection (UMAP) approach. Through GSVA and WGCNA in bulk RNA-seq data, we identified 185 TEX-related genes (TEXRGs). Subsequently, we transformed ten machine learning algorithms into 80 combinations and selected the most optimal one to construct TEX-related prognostic features (TEXRPS) based on the mean C-index of the three OC cohorts. In addition, we explored the disparities in clinicopathological features, mutational status, immune cell infiltration, and immunotherapy efficacy between the high-risk (HR) and low-risk (LR) groups. Upon the integration of clinicopathological features, TEXRPS displayed robust predictive power. Notably, patients in the LR group exhibited a superior prognosis, higher tumor mutational load (TMB), greater immune cell infiltration abundance, and enhanced sensitivity to immunotherapy. Lastly, we verified the differential expression of the model gene CD44 using qRT-PCR. In conclusion, our study offers a valuable tool to guide clinical management and targeted therapy of OC