Second-harmonic generation circular dichroism spectroscopy from tripod-like chiral molecular films

The second-harmonic generation （SHG） circular dichroism in the light of reflection from chiral films of tripod-like chiral molecules is investigated. The expressions of the second-harmonic generation circular dichroism are derived from our presented three-coupled-oscillator model for the tripod-like chiral molecules. Spectral dependence of the circular dichroism of SHG from film surface composed of tripod-like chiral molecules is simulated numerically and analysed. Influence of ehiral parameters on the second-harmonic generation circular dichroism spectrum in chiral films is studied. The result shows that the second-harmonic generation circular dichroism is a sensitive method of detecting chirality compared with the ordinary circular dichroism in linear optics. All of our work indicates that the classical molecular models are very effective to explain the second-harmonic generation circular dichroism of chiral molecular system. The classical molecular model theory can give us a clear physical picture and brings us very instructive information about the link between the molecular configuration and the nonlinear processes.

Co-Periodicity Isomorphisms between Forests of Finite <I>p</I>-Groups

Based on a general theory of descendant trees of finite p-groups and the virtual periodicity isomorphisms between the branches of a coclass subtree, the behavior of algebraic invariants of the tree vertices and their automorphism groups under these isomorphisms is described with simple transformation laws. For the tree of finite 3-groups with elementary bicyclic commutator qu-otient, the information content of each coclass subtree with metabelian main-line is shown to be finite. As a striking novelty in this paper, evidence is provided of co-periodicity isomorphisms between coclass forests which reduce the information content of the entire metabelian skeleton and a significant part of non-metabelian vertices to a finite amount of data.

Predicting Effectiveness of Generate-and-Validate Patch Generation Systems Using Random Forest

One way to improve practicability of automatic program repair（APR） techniques is to build prediction models which can predict whether an application of a APR technique on a bug is effective or not. Existing prediction models have some limitations. First, the prediction models are built with hand crafted features which usually fail to capture the semantic characteristics of program repair task. Second, the performance of the prediction models is only evaluated on Genprog, a genetic-programming based APR technique. This paper develops prediction models, i.e., random forest prediction models for SPR, another kind of generate-and-validate APR technique, which can distinguish ineffective repair instances from effective repair instances. Rather than handcrafted features, we use features automatically learned by deep belief network（DBN） to train the prediction models. The empirical results show that compared to the baseline models, that is, all effective models, our proposed models can at least improve the F1 by 9% and AUC（area under the receiver operating characteristics curve） by 19%. At the same time, the prediction model using learned features at least outperforms the one using hand-crafted features in terms of F1 by 11%.