STR: Seasonal-Trend Decomposition Using Regression

We propose a new method for decomposing seasonal data: a seasonal-trend decomposition using regression (STR). Unlike other decomposition methods, STR allows for multiple seasonal and cyclic components, covariates, seasonal patterns that may have noninteger periods, and seasonality with complex topology. It can be used for time series with any regular time index, including hourly, daily, weekly, monthly, or quarterly data. It is competitive with existing methods when they exist and tackles many more decomposition problems than other methods allow. STR is based on a regularized optimization and so is somewhat related to ridge regression. Because it is based on a statistical model, we can easily compute confidence intervals for components, something that is not possible with most existing decomposition methods (such as seasonal-trend decomposition using Loess, X-12-ARIMA, SEATS-TRAMO, etc.). Our model is implemented in the R package stR, so it can be applied by anyone to their own data.

Real-time recognition of team behaviors by multisensory graph-embedded robot learning

Awareness of team behaviors (e.g., individual activities and team intents) plays a critical role in human–robot teaming. Autonomous robots need to be aware of the overall intent of the team they are collaborating with in order to effectively aid their human peers or augment the team’s capabilities. Team intents encode the goal of the team, which cannot be simply identified from a collection of individual activities. Instead, teammate relationships must also be encoded for team intent recognition. In this article, we introduce a novel representation learning approach to recognizing team intent awareness in real-time, based upon both individual human activities and the relationship between human peers in the team. Our approach formulates the task of robot learning for team intent recognition as a joint regularized optimization problem, which encodes individual activities as latent variables and represents teammate relationships through graph embedding. In addition, we design a new algorithm to efficiently solve the formulated regularized optimization problem, which possesses a theoretical guarantee to converge to the optimal solution. To evaluate our approach’s performance on team intent recognition, we test our approach on a public benchmark group activity dataset and a multisensory underground search and rescue team behavior dataset newly collected from robots in an underground environment, as well as perform a proof-of-concept case study on a physical robot. The experimental results have demonstrated both the superior accuracy of our proposed approach and its suitability for real-time applications on mobile robots.

Regularization by denoising sub-sampled Newton method for spectral CT multi-material decomposition

Spectral Computed Tomography (CT) is an emerging technology that enables us to estimate the concentration of basis materials within a scanned object by exploiting different photon energy spectra. In this work, we aim at efficiently solving a model-based maximum-a-posterior problem to reconstruct multi-materials images with application to spectral CT. In particular, we propose to solve a regularized optimization problem based on a plug-in image-denoising function using a randomized second order method. By approximating the Newton step using a sketching of the Hessian of the likelihood function, it is possible to reduce the complexity while retaining the complex prior structure given by the data-driven regularizer. We exploit a non-uniform block sub-sampling of the Hessian with inexact but efficient conjugate gradient updates that require only Jacobian-vector products for denoising term. Finally, we show numerical and experimental results for spectral CT materials decomposition. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 1’.

Diabetes prediction using an improved machine learning approach

This paper deals with a machine-learning model arising from the healthcare sector, namely diabetes progression. The model is reformulated into a regularized optimization problem. The term of the fidelity is the L1 norm and the optimization space of the minimum is constructed by a reproducing kernel Hilbert space (RKSH). The numerical approximation of the model is realized by the Adam method, which shows its success in the numerical experiments (if compared to the stochastic gradient descent (SGD) algorithm).