Highly selective electrochemical hydrogenation of alkynes: Rapid construction of mechanochromic materials

Electrochemical hydrogenation has emerged as an environmentally benign and operationally simple alternative to traditional catalytic reduction of organic compounds. Here, we have disclosed for the first time the electrochemical hydrogenation of alkynes to a library of synthetically important Z-alkenes under mild conditions with great selectivity and efficiency. The deuterium and control experiments of electrochemical hydrogenation suggest that the hydrogen source comes from the solvent, supporting electrolyte, and base. The scanning electron microscopy and x-ray diffraction experiments demonstrate that palladium nanoparticles generated in the electrochemical reaction act as a chemisorbed hydrogen carrier. Moreover, complete reduction of alkynes to saturated alkanes can be achieved through slightly modified conditions. Furthermore, a series of novel mechanofluorochromic materials have been efficiently constructed with this protocol that showed blue-shifted mechanochromism. This discovery represents the first example of cis-olefins-based organic mechanochromic materials

A Local Density-Based Approach for Local Outlier Detection

This paper presents a simple but effective density-based outlier detection approach with the local kernel density estimation (KDE). A Relative Density-based Outlier Score (RDOS) is introduced to measure the local outlierness of objects, in which the density distribution at the location of an object is estimated with a local KDE method based on extended nearest neighbors of the object. Instead of using only $k$ nearest neighbors, we further consider reverse nearest neighbors and shared nearest neighbors of an object for density distribution estimation. Some theoretical properties of the proposed RDOS including its expected value and false alarm probability are derived. A comprehensive experimental study on both synthetic and real-life data sets demonstrates that our approach is more effective than state-of-the-art outlier detection methods.Comment: 22 pages, 14 figures, submitted to Pattern Recognition Letter

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