Component SPD Matrices: A lower-dimensional discriminative data descriptor for image set classification

In the domain of pattern recognition, using the SPD (Symmetric Positive Definite) matrices to represent data and taking the metrics of resulting Riemannian manifold into account have been widely used for the task of image set classification. In this paper, we propose a new data representation framework for image sets named CSPD (Component Symmetric Positive Definite). Firstly, we obtain sub-image sets by dividing the image set into square blocks with the same size, and use traditional SPD model to describe them. Then, we use the results of the Riemannian kernel on SPD matrices as similarities of corresponding sub-image sets. Finally, the CSPD matrix appears in the form of the kernel matrix for all the sub-image sets, and CSPDi,j denotes the similarity between i-th sub-image set and j-th sub-image set. Here, the Riemannian kernel is shown to satisfy the Mercer's theorem, so our proposed CSPD matrix is symmetric and positive definite and also lies on a Riemannian manifold. On three benchmark datasets, experimental results show that CSPD is a lower-dimensional and more discriminative data descriptor for the task of image set classification.Comment: 8 pages,5 figures, Computational Visual Media, 201

Thermoelectric effect in an Aharonov-Bohm ring with an embedded quantum dot

Thermoelectric effect is studied in an Aharonov-Bohm interferometer with an embedded quantum dot (QD) in the Coulomb blockade regime. The electrical conductance, electron thermal conductance, thermopower, and thermoelectric figure-of-merit are calculated by using the Keldysh Green's function method. It is found that the figure-of-merit ZT of the QD ring may be quite high due to the Fano effect originated from the quantum interference effect. Moreover, the thermoelectric efficiency is sensitive to the magnitude of the dot-lead and inter-lead coupling strengthes. The effect of intradot Coulomb repulsion on ZT is significant in the weak-coupling regime, and then large ZT values can be obtained at rather high temperature

Riemannian kernel based Nystr\"om method for approximate infinite-dimensional covariance descriptors with application to image set classification

In the domain of pattern recognition, using the CovDs (Covariance Descriptors) to represent data and taking the metrics of the resulting Riemannian manifold into account have been widely adopted for the task of image set classification. Recently, it has been proven that infinite-dimensional CovDs are more discriminative than their low-dimensional counterparts. However, the form of infinite-dimensional CovDs is implicit and the computational load is high. We propose a novel framework for representing image sets by approximating infinite-dimensional CovDs in the paradigm of the Nystr\"om method based on a Riemannian kernel. We start by modeling the images via CovDs, which lie on the Riemannian manifold spanned by SPD (Symmetric Positive Definite) matrices. We then extend the Nystr\"om method to the SPD manifold and obtain the approximations of CovDs in RKHS (Reproducing Kernel Hilbert Space). Finally, we approximate infinite-dimensional CovDs via these approximations. Empirically, we apply our framework to the task of image set classification. The experimental results obtained on three benchmark datasets show that our proposed approximate infinite-dimensional CovDs outperform the original CovDs.Comment: 6 pages, 3 figures, International Conference on Pattern Recognition 201

Anti-inflammatory activity and chemical composition of the essential oils from Senecio flammeus

Many species from Senecio genus have been used in traditional medicine, and their pharmacological activities have been demonstrated. This study investigated the chemical composition and anti-inflammatory activities of essential oils from Senecio flammeus. A total of 48 components representing 98.41 % of the total oils were identified. The main compounds in the oils were α-farnesene (11.26 %), caryophyllene (8.69 %), n-hexadecanoic acid (7.23 %), and α-pinene (6.36 %). The anti-inflammatory activity of the essential oils was evaluated in rodents (10–90 mg/kg bw) in classical models of inflammation [carrageenan-induced paw edema, 12-O-tetradecanoyl-phorbol-13-acetate (TPA)-induced ear edema, and cotton pellet-induced granuloma]. The essential oils at doses of 10, 30, and 90 mg/kg bw significantly reduced carrageenan-induced paw edema by 17.42 % (P < 0.05), 52.90 % (P < 0.05), and 66.45 % (P < 0.05) 4 h after carrageenan injection, respectively, and significantly reduced myeloperoxidase activity (P < 0.05). The essential oils (10, 30, and 90 mg/kg) also produced asignificant dose-dependent response to reduce TPA-induced ear edema by 20.27 % (P < 0.05), 33.06 % (P < 0.05), and 53.90 % (P < 0.05), respectively. The essential oils produced significant dose-response anti-inflammatory activity against cotton pellet-induced granuloma that peaked at the highest dose of 90 mg/kg (49.08 % wet weight and 47.29 % dry weight). Results demonstrate that the essential oils of S. flammeus were effective in the treatment of both acute and chronic inflammatory conditions, there by supporting the traditional use of this herb