Prefrontal white matter abnormalities in young adult with major depressive disorder: A diffusion tensor imaging study

Prefrontal impairments have been hypothesized to be most strongly associated with the cognitive and emotional dysfunction in depression. Recently, white matter microstructural abnormalities in prefrontal lobe have been reported in elderly patients with ma

Biologically Inspired Tensor Features

According to the research results reported in the past decades, it is well acknowledged that face recognition is not a trivial task. With the development of electronic devices, we are gradually revealing the secret of object recognition in the primate's visual cortex. Therefore, it is time to reconsider face recognition by using biologically inspired features. In this paper, we represent face images by utilizing the C1 units, which correspond to complex cells in the visual cortex, and pool over S1 units by using a maximum operation to reserve only the maximum response of each local area of S1 units. The new representation is termed C1 Face. Because C1 Face is naturally a third-order tensor (or a three dimensional array), we propose three-way discriminative locality alignment (TWDLA), an extension of the discriminative locality alignment, which is a top-level discriminate manifold learning-based subspace learning algorithm. TWDLA has the following advantages: (1) it takes third-order tensors as input directly so the structure information can be well preserved; (2) it models the local geometry over every modality of the input tensors so the spatial relations of input tensors within a class can be preserved; (3) it maximizes the margin between a tensor and tensors from other classes over each modality so it performs well for recognition tasks and (4) it has no under sampling problem. Extensive experiments on YALE and FERET datasets show (1) the proposed C1Face representation can better represent face images than raw pixels and (2) TWDLA can duly preserve both the local geometry and the discriminative information over every modality for recognition.

Spin and spin-isospin instabilities and Landau parameters of Skyrme interactions with tensor correlations

The Landau parameters of Skyrme interactions in the spin and spin-isospin channels are studied using various Skyrme effective interactions with and without tensor correlations. We focus on the role of the tensor terms on the spin and spin-isospin instabilities that can occur in nuclear matter above saturation density. We point out that these instabilities are realized in nuclear matter at the critical density of about two times the saturation density for all the adopted parameter sets. The critical density is shown to be very much dependent not only on the choice of the Skyrme parameter set, but also on the inclusion of the tensor terms.

A Unified Tensor Level Set for Image Segmentation

This paper presents a new region-based unified tensor level set model for image segmentation. This model introduces a three-order tensor to comprehensively depict features of pixels, e.g., gray value and the local geometrical features, such as orientation and gradient, and then, by defining a weighted distance, we generalized the representative region-based level set method from scalar to tensor. The proposed model has four main advantages compared with the traditional representative method as follows. First, involving the Gaussian filter bank, the model is robust against noise, particularly the salt-and pepper-type noise. Second, considering the local geometrical features, e. g., orientation and gradient, the model pays more attention to boundaries and makes the evolving curve stop more easily at the boundary location. Third, due to the unified tensor pixel representation representing the pixels, the model segments images more accurately and naturally. Fourth, based on a weighted distance definition, the model possesses the capacity to cope with data varying from scalar to vector, then to high-order tensor. We apply the proposed method to synthetic, medical, and natural images, and the result suggests that the proposed method is superior to the available representative region-based level set method.

Robust Tensor Analysis With L1-Norm

Tensor analysis plays an important role in modern image and vision computing problems. Most of the existing tensor analysis approaches are based on the Frobenius norm, which makes them sensitive to outliers. In this paper, we propose L1-norm-based tensor analysis (TPCA-L1), which is robust to outliers. Experimental results upon face and other datasets demonstrate the advantages of the proposed approach.

Tensor density matrix theory for determining population and alignment of symmetric top molecule using rotationally unresolved fluorescence

General expressions used for transforming raw laser-induced fluorescence (LIF) intensity into the population and alignment parameters of a symmetric top molecule are derived by employing the density matrix approach. The molecular population and alignment are described by molecular state multipoles. The results are presented for a general excitation-detection geometry and then applied to some special geometries. In general cases, the LIF intensity is a complex function of the initial molecular state multipoles, the dynamic factors and the excitation-detection geometrical factors. It contains a population and 14 alignment multipoles. How to extract all initial state multipoles from the rotationally unresolved emission LIF intensity is discussed in detail.