In the fields of environmental science and medicine it is increasingly common to have access to data collected on subjects over time. Given a sufficiently dense sampling, these data can often be smoothed and analyzed as functional variables. While functional variables can be used as covariates in regression models, traditional methods, such as the functional linear model, impose constraints that limit the usefulness of functional covariates as predictors. In this paper we introduce Basis-Expanded Bayesian Additive Regression Trees (BBART), an adaptation to the original BART model that allows for the inclusion of functional covariates. By leveraging the BART model, BBART inherits many attractive features, including requiring no assumption of additivity or smooth effects and enabling posterior inference with MCMC samples.
Monitoring a group of animals would greatly benefit from automated animal re-identification from video. Multiple Object Tracking alone does not provide a sufficiently good re-identification, hence we propose to augment the process by further clustering the output tracks. Unlike datasets for person and vehicle identification, existing animal datasets are not substantial enough to train an advanced model for conventional clustering. In this paper, we present a Classification-Based Clustering method (CBC) which employs track labels and temporal constraints to train a bespoke model for each video dataset. Our proposed method works better than using the tracks alone as animal identities. It also outperforms 13 alternative clustering methods applied to the tracking results.
Tabular data, prevalent in relational databases and spreadsheets, is fundamental across fields like healthcare, engineering, and finance. Despite significant advances in tabular data learning, critical challenges remain: handling missing values, addressing class imbalance, enabling transfer learning, and facilitating feature incremental learning beyond traditional supervised paradigms. We introduce TabMixer, an innovative model that enhances the multilayer perceptron (MLP) mixer architecture to address these challenges. TabMixer incorporates a self-attention mechanism, making it versatile across various learning scenarios including supervised learning, transfer learning, and feature incremental learning. Extensive experiments on eight public datasets demonstrate TabMixer's superior performance over existing state-of-the-art methods. Notably, TabMixer achieved substantial improvements in ANOVA AUC across all scenarios: a 4% increase in supervised learning (0.840 to 0.881), 8% in transfer learning (0.803 to 0.872), and 4% in feature incremental learning (0.806 to 0.843). TabMixer demonstrates high computational efficiency and scalability through reduced floating-point operations and learnable parameters. Moreover, it exhibits strong resilience to missing values and class imbalances through both its architectural design and optional preprocessing enhancements. These results establish TabMixer as a promising model for tabular data analysis and a valuable tool for diverse applications.
In this paper, a subsequence time-series clustering algorithm is proposed to identify the strongly coupled aftershocks sequences and Poissonian background activity from earthquake catalogs of active regions. The proposed method considers the inter-event time statistics between the successive pair of events for characterizing the nature of temporal sequences and observing their relevance with earthquake epicenters and magnitude information simultaneously. This approach categorizes the long-earthquake time series into the finite meaningful temporal sequences and then applies the clustering mechanism to the selective sequences. The proposed approach is built on two phases: (1) a Gaussian kernel-based density estimation for finding the optimal subsequence of given earthquake time-series, and (2) inter-event time ( t ) and distance-based observation of each subsequence for checking the presence of highly correlated aftershock sequences (hot-spots) in it. The existence of aftershocks is determined based on the coefficient of variation (COV). A sliding temporal window on t with earthquake’s magnitude M is applied on the selective subsequence to filter out the presence of time-correlated events and make the meaningful time stationary Poissonian subsequences. This proposed approach is applied to the regional Sumatra-Andaman (2000–2021) and worldwide ISC-GEM (2000–2016) earthquake catalog. Simulation results indicate that meaningful subsequences (background events) can be modeled by a homogeneous Poisson process after achieving a linear cumulative rate and time-independent λ in the exponential distribution of t . The relations COV_a(T)>COV_o(T)> (COV_b(T)≈ 1) and COV_a(d)>COV_o(d)>COV_b(d) are achieved for both studied catalogs. Comparative analysis justifies the competitive performance of the proposed approach to the state-of-art approaches and recently introduced methods.
Interval-valued data have been commonly encountered in practice, and Symbolic Data Analysis provides a solution to the statistical treatment of these data. Regression analysis for interval-valued symbolic data is a topic that has been widely investigated in the literature of symbolic data analysis, and several models from different paradigms have been proposed. There are basic regression assumptions, and it is essential to validate them. This paper introduces an approach to check interval regression model adequacy based on residual analysis. Concepts of ordinary and standardized interval residual are presented, and graphical analysis of these residuals is also proposed. To show the usefulness of the proposed approach, an application for estimating school dropout in the scenario of Brazilian municipalities is performed. We observed some outliers from the interval residuals analysis, and interval robust regression models are more suitable for estimating school dropout.
