We present Tesla-Rapture, a gesture recognition system for sparse point clouds generated by mmWave Radars. State of the art gesture recognition models are either too resource consuming or not sufficiently accurate for the integration into real-life scenarios using wearable or constrained equipment such as IoT devices (e.g. Raspberry PI), XR hardware (e.g. HoloLens), or smart-phones. To tackle this issue, we have developed Tesla, a Message Passing Neural Network (MPNN) graph convolution approach for mmWave radar point clouds. The model outperforms the state of the art on three datasets in terms of accuracy while reducing the computational complexity and, hence, the execution time. In particular, the approach, is able to predict a gesture almost 8 times faster than the most accurate competitor. Our performance evaluation in different scenarios (environments, angles, distances) shows that Tesla generalizes well and improves the accuracy up to 20% in challenging scenarios, such as a through-wall setting and sensing at extreme angles. Utilizing Tesla, we develop Tesla-Rapture, a real-time implementation using a mmWave Radar on a Raspberry PI 4 and evaluate its accuracy and time-complexity. We also publish the source code, the trained models, and the implementation of the model for embedded devices.
Background Molecular interaction networks summarize complex biological processes as graphs, whose structure is informative of biological function at multiple scales. Simultaneously, omics technologies measure the variation or activity of genes, proteins, or metabolites across individuals or experimental conditions. Integrating the complementary viewpoints of biological networks and omics data is an important task in bioinformatics, but existing methods treat networks as discrete structures, which are intrinsically difficult to integrate with continuous node features or activity measures. Graph neural networks map graph nodes into a low-dimensional vector space representation, and can be trained to preserve both the local graph structure and the similarity between node features. Results We studied the representation of transcriptional, protein–protein and genetic interaction networks in E. coli and mouse using graph neural networks. We found that such representations explain a large proportion of variation in gene expression data, and that using gene expression data as node features improves the reconstruction of the graph from the embedding. We further proposed a new end-to-end Graph Feature Auto-Encoder framework for the prediction of node features utilizing the structure of the gene networks, which is trained on the feature prediction task, and showed that it performs better at predicting unobserved node features than regular MultiLayer Perceptrons. When applied to the problem of imputing missing data in single-cell RNAseq data, the Graph Feature Auto-Encoder utilizing our new graph convolution layer called FeatGraphConv outperformed a state-of-the-art imputation method that does not use protein interaction information, showing the benefit of integrating biological networks and omics data with our proposed approach. Conclusion Our proposed Graph Feature Auto-Encoder framework is a powerful approach for integrating and exploiting the close relation between molecular interaction networks and functional genomics data.
<p>Climate change is expected to alter the occurrence of floods in high latitude countries; evidence of earlier spring floods and more frequent rainfall-driven floods has already been detected in Norway. While the state-of-the-art hydrological climate-impact model chain embeds explicit assumptions about stationarity, machine learning offers a complementary approach to hydrological climate-impact modelling by facilitating direct downscaling from large-scale atmospheric variables to streamflow, thus making downscaling and bias-correction implicit. While applications of machine learning algorithms for streamflow and flood modelling are well documented in the scientific literature, few studies have linked large-scale atmospheric variables directly to streamflow without including observed streamflow as part of the input variable selection. Such autoregressive models have limited application for climate-impact studies, as future streamflow is yet to observe. Furthermore, most studies linking large-scale atmospheric forcing to catchment response have focused on monthly, seasonal, or annual streamflow. This study presents the application of feed-forward and recurrent neural networks for daily streamflow and flood reconstruction from atmospheric reanalysis data with comparable spatiotemporal resolution to global climate model outputs. Two widely applied neural network types, namely multilayer perceptron (MLP) and long short-term memory (LSTM), were benchmarked against gradient boost regression tree models. Catchment-specific, physically-based input variable selections representing the dominant flood-drivers were identified for 27 catchments in Norway. The selected catchments have low degrees of basin development and anthropogenic influence so that the established statistical links only reflect the forcing-response relationship between the atmosphere and the catchments. Overall, the LSTM obtained the highest accuracy, with a median Nash Sutcliffe Efficiency (NSE) of 0.88 on the training set (1950-2000) and 0.76 on the testing set (2006-2010). However, the MLP proved more robust, with a smaller drop in NSE from training (0.76) to testing (0.72), indicating that further restricting the input variables based on hydrological theory and physical interpretability may increase the robustness of neural networks in the context of daily streamflow modelling. The median NSE of the regression tree models was lower on both the training set (0.73) and the testing set (0.66). The results point to the potential of neural networks for hydrological climate-impact modelling in catchments where both snowmelt and rainfall constitute flood-drivers in the present climate. This research provides a springboard for future studies employing neural networks for hydrological climate-impact modelling in high latitude countries. Future research should assess the potential for regionalization by including catchment characteristics through clustering techniques like Kohonen Self-Organizing Maps.</p>
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