This paper tackles the challenge of accurately estimating shower duration from non-intrusive multi-modal sensor data to facilitate efficient water management. Efficient water usage is a critical environmental challenge, and showering contributes significantly to domestic water consumption. Developing accurate, accessible monitoring solutions is essential for promoting sustainability . Utilizing data from humidity, temperature, sound average, and sound peak sensors, we explore suitable data processing steps and the application of machine learning models to estimate shower duration. Our approach includes the design of a bidirectional Long Short-Term Memory model and the application of an existing Gated Transformer Network model to address the multivariate time series classification task . Our analysis reveals that both models are highly effective in this context, also compared to baseline models, and humidity emerges as a particularly powerful predictor either on its own or when combined with the temperature sensor . This work not only showcases the potential of using machine learning methods for multivariate time series classification in the domain of water consumption but also underscores the implications for adopting such technologies in promoting sustainable water use.

Topic models are widely used for discovering latent thematic structures in large text corpora, yet traditional unsupervised methods often struggle to align with pre-defined conceptual domains. This paper introduces seeded Poisson factorization (SPF), a novel approach that extends the Poisson factorization (PF) framework by incorporating domain knowledge through seed words. SPF enables a structured topic discovery by modifying the prior distribution of topic-specific term intensities, assigning higher initial rates to pre-defined seed words. The model is estimated using variational inference with stochastic gradient optimization, ensuring scalability to large datasets. We present in detail the results of applying SPF to an Amazon customer feedback dataset, leveraging pre-defined product categories as guiding structures. SPF achieves superior performance compared to alternative guided probabilistic topic models in terms of computational efficiency and classification performance. Robustness checks highlight SPF’s ability to adaptively balance domain knowledge and data-driven topic discovery, even in case of imperfect seed word selection. Further applications of SPF to four additional benchmark datasets, where the corpus varies in size and the number of topics differs, demonstrate its general superior classification performance compared to the unseeded PF model.

Abstract RNA sequencing of time-course experiments results in three-way count data where the dimensions are the genes, the time points and the biological units. Clustering RNA-seq data allows to extract groups of co-expressed genes over time. After standardisation, the normalised counts of individual genes across time points and biological units have similar properties as compositional data. We propose the following procedure to suitably cluster three-way RNA-seq data: (1) pre-process the RNA-seq data by calculating the normalised expression profiles, (2) transform the data using the additive log ratio transform to map the composition in the D-part Aitchison simplex to a $$D-1$$ D - 1 -dimensional Euclidean vector, (3) cluster the transformed RNA-seq data using matrix-variate Gaussian mixture models and (4) assess the quality of the overall cluster solution and of individual clusters based on cluster separation in the transformed space using density-based silhouette information and on compactness of the cluster in the original space using cluster maps as a suitable visualisation. The proposed procedure is illustrated on RNA-seq data from fission yeast and results are also compared to an analogous two-way approach after flattening out the biological units.

UNLABELLED: A model class of finite mixtures of linear additive models is presented. The component-specific parameters in the regression models are estimated using regularized likelihood methods. The advantages of the regularization are that (i) the pre-specified maximum degrees of freedom for the splines is less crucial than for unregularized estimation and that (ii) for each component individually a suitable degree of freedom is selected in an automatic way. The performance is evaluated in a simulation study with artificial data as well as on a yeast cell cycle dataset of gene expression levels over time. AVAILABILITY: The latest release version of the R package flexmix is available from CRAN (http://cran.r-project.org/).

SUMMARY: Finite mixture models are routinely applied to time course microarray data. Due to the complexity and size of this type of data, the choice of good starting values plays an important role. So far initialization strategies have only been investigated for data from a mixture of multivariate normal distributions. In this work several initialization procedures are evaluated for mixtures of regression models with and without random effects in an extensive simulation study on different artificial datasets. Finally, these procedures are also applied to a real dataset from Escherichia coli. AVAILABILITY: The latest release versions of R packages flexmix, gcExplorer and kernlab are always available from CRAN (http://cran.r-project.org/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.