A reciprocal relationship between repetitive negative thinking (RNT) and negative affect (NA) has been found in various types of psychopathology. Recent studies have suggested that the magnitude of this association ca...
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A reciprocal relationship between repetitive negative thinking (RNT) and negative affect (NA) has been found in various types of psychopathology. Recent studies have suggested that the magnitude of this association can vary across time and individuals, which may inform future psychopathology. Here, we explored how these dynamics and interplays are manifested in student and general populations using a statistical clustering algorithm. Across three experience-sampling data sets, our clustering analyses consistently identified two groups of individuals;one group had a higher bidirectional association between RNT and NA (and also higher inertia) than the other group. Furthermore, a prospective analysis revealed that the group with the higher bidirectional association is at risk of developing depressive symptoms during the 3-month follow-up period if they had experienced high levels of NA over the experience-sampling phase. These findings suggest that the dysfunctional affective and cognitive dynamics would be a promising target of preventive intervention.
In recent years, measurement or collection of heterogeneous sets of data such as those containing scalars, waveform signals, images, and even structured point clouds, has become more common. Statistical models based o...
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In recent years, measurement or collection of heterogeneous sets of data such as those containing scalars, waveform signals, images, and even structured point clouds, has become more common. Statistical models based on such heterogeneous sets of data that represent the behavior of an underlying system can be used in the monitoring, control, and optimization of the system. Unfortunately, available methods mainly focus on the scalars and profiles and do not provide a general framework for integrating different sources of data to construct a model. This article addresses the problem of estimating a process output, measured by a scalar, curve, image, or structured point cloud by a set of heterogeneous process variables such as scalar process setting, profile sensor readings, and images. We introduce a general multiple tensor-on-tensor regression approach in which each set of input data (predictor) and output measurements are represented by tensors. We formulate a linear regression model between the input and output tensors and estimate the parameters by minimizing a leastsquare loss function. To avoid overfitting and reduce the number of parameters to be estimated, we decompose the model parameters using several basis matrices that span the input and output spaces, and provide efficient optimization algorithms for learning the basis and coefficients. Through several simulation and case studies, we evaluate the performance of the proposed method. The results reveal the advantage of the proposed method over some benchmarks in the literature in terms of the mean square prediction error. for this article are available online.
Over the last two decades, tensor-based methods have received growing attention in the signal processing community. In this work, the authors proposed a comprehensive overview of tensor-based models and methods for mu...
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Over the last two decades, tensor-based methods have received growing attention in the signal processing community. In this work, the authors proposed a comprehensive overview of tensor-based models and methods for multisensor signal processing. They presented for instance the Tucker decomposition, the canonical polyadic decomposition, the tensor-train decomposition (TTD), the structured TTD, including nested Tucker train, as well as the associated optimisation strategies. More precisely, they gave synthetic descriptions of state-of-the-art estimators as the alternatingleastsquare (ALS) algorithm, the high-order singular value decomposition (HOSVD), and of more advanced algorithms as the rectified ALS, the TT-SVD/TT-HSVD and the Joint dImensionally Reduction and Factor retrieval Estimator scheme. They illustrated the efficiency of the introduced methodological and algorithmic concepts in the context of three important and timely signal processing-based applications: the direction-of-arrival estimation based on sensor arrays, multidimensional harmonic retrieval and multiple-input-multiple-output wireless communication systems.
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