Calculating excited states of molecular aggregates by the renormalized excitonic method

In this paper, we apply the recently developed ab initio renormalized excitonic method (REM) to the excitation energy calculations of various molecular aggregates, through the extension of REM to the time-dependent density functional theory (TDDFT). Tested molecular aggregate systems include one-dimensional hydrogen-bonded water chains, ring crystals with $\pi$-$\pi$ stacking or van-der Waals interactions and the general aqueous systems with polar and non-polar solutes. The basis set factor as well as the effect of the exchange-correlation functionals are also investigated. The results indicate that the REM-TDDFT method with suitable basis set and exchange-correlation functionals can give good descriptions of excitation energies and excitation area for lowest electronic excitations in the molecular aggregate systems with economic computational costs. It's shown that the deviations of REM-TDDFT excitation energies from those by standard TDDFT are much less than 0.1 eV and the computational time can be reduced by one order

Constraints on the CKM angle alpha in the B --> rho rho decays

Using a data sample of 122 million Upsilon(4S) -> BBbar decays collected with BaBar detector at the PEP-II asymmetric B factory at SLAC, we measure the time-dependent-asymmetry parameters of the longitudinally polarized component in the B0 -> rho^+ rho^- decay as C_L = -0.23 +/- 0.24 (stat) +/- 0.14 (syst) and S_L = -0.19 +/- 0.33 (stat) +/- 0.11 (syst). The B0 -> rho0 rho0 decay mode is also searched for in a data sample of about 227 million BBbar pairs. No significant signal is observed, and an upper limit of 1.1 * 10-6 (90% C.L.) on the branching fraction is set. The penguin contribution to the CKM angle $\alpha$ uncertainty is measured to be 11 degree. All results are preliminary.Comment: 3 pages, 1 postscript figues, submitted to DPF200

FSMJ: Feature Selection with Maximum Jensen-Shannon Divergence for Text Categorization

In this paper, we present a new wrapper feature selection approach based on Jensen-Shannon (JS) divergence, termed feature selection with maximum JS-divergence (FSMJ), for text categorization. Unlike most existing feature selection approaches, the proposed FSMJ approach is based on real-valued features which provide more information for discrimination than binary-valued features used in conventional approaches. We show that the FSMJ is a greedy approach and the JS-divergence monotonically increases when more features are selected. We conduct several experiments on real-life data sets, compared with the state-of-the-art feature selection approaches for text categorization. The superior performance of the proposed FSMJ approach demonstrates its effectiveness and further indicates its wide potential applications on data mining.Comment: 8 pages, 6 figures, World Congress on Intelligent Control and Automation, 201

Leveraging Mid-Level Deep Representations For Predicting Face Attributes in the Wild

Predicting facial attributes from faces in the wild is very challenging due to pose and lighting variations in the real world. The key to this problem is to build proper feature representations to cope with these unfavourable conditions. Given the success of Convolutional Neural Network (CNN) in image classification, the high-level CNN feature, as an intuitive and reasonable choice, has been widely utilized for this problem. In this paper, however, we consider the mid-level CNN features as an alternative to the high-level ones for attribute prediction. This is based on the observation that face attributes are different: some of them are locally oriented while others are globally defined. Our investigations reveal that the mid-level deep representations outperform the prediction accuracy achieved by the (fine-tuned) high-level abstractions. We empirically demonstrate that the midlevel representations achieve state-of-the-art prediction performance on CelebA and LFWA datasets. Our investigations also show that by utilizing the mid-level representations one can employ a single deep network to achieve both face recognition and attribute prediction.Comment: In proceedings of 2016 International Conference on Image Processing (ICIP