Integrating Specialized Classifiers Based on Continuous Time Markov Chain

Specialized classifiers, namely those dedicated to a subset of classes, are often adopted in real-world recognition systems. However, integrating such classifiers is nontrivial. Existing methods, e.g. weighted average, usually implicitly assume that all constituents of an ensemble cover the same set of classes. Such methods can produce misleading predictions when used to combine specialized classifiers. This work explores a novel approach. Instead of combining predictions from individual classifiers directly, it first decomposes the predictions into sets of pairwise preferences, treating them as transition channels between classes, and thereon constructs a continuous-time Markov chain, and use the equilibrium distribution of this chain as the final prediction. This way allows us to form a coherent picture over all specialized predictions. On large public datasets, the proposed method obtains considerable improvement compared to mainstream ensemble methods, especially when the classifier coverage is highly unbalanced.Comment: Published at IJCAI-17, typo fixe

Bis[3-dimethyl­amino-1-(pyridin-2-yl)prop-2-en-1-one-κ2 N 1,O]tris­(nitrato-κ2 O,O)gadolinium(III) ethanol disolvate

In the title compound, [Gd(NO3)3(C10H12N2O)2]·2C2H5OH, the GdIII ion and one nitrate anion are located on a twofold rotation axis. The GdIII ion is ten-coordinated by two N and two O atoms from two bidentate 3-(N,N-dimethyl­amino)-1-(2-pyrid­yl)prop-2-en-1-one) ligands and six O atoms from three nitrate anions in a distorted bicapped square-anti­prismatic geometry. In the crystal, the components are linked by O—H⋯O hydrogen bonds. The ethanol solvent mol­ecule is disordered over two positions in a ratio 0.615 (16):0.385 (16)

Residual Degradation Learning Unfolding Framework with Mixing Priors across Spectral and Spatial for Compressive Spectral Imaging

To acquire a snapshot spectral image, coded aperture snapshot spectral imaging (CASSI) is proposed. A core problem of the CASSI system is to recover the reliable and fine underlying 3D spectral cube from the 2D measurement. By alternately solving a data subproblem and a prior subproblem, deep unfolding methods achieve good performance. However, in the data subproblem, the used sensing matrix is ill-suited for the real degradation process due to the device errors caused by phase aberration, distortion; in the prior subproblem, it is important to design a suitable model to jointly exploit both spatial and spectral priors. In this paper, we propose a Residual Degradation Learning Unfolding Framework (RDLUF), which bridges the gap between the sensing matrix and the degradation process. Moreover, a Mix$S^2$ Transformer is designed via mixing priors across spectral and spatial to strengthen the spectral-spatial representation capability. Finally, plugging the Mix$S^2$ Transformer into the RDLUF leads to an end-to-end trainable neural network RDLUF-Mix$S^2$. Experimental results establish the superior performance of the proposed method over existing ones.Comment: 10 pages, 5 figure