CP Violation and Extra Dimensions

It is shown that the new sources of CP violation can be generated in the models with more than one extra dimensions. In the supersymmetric models on the space-time $M^4\times T^2/Z_2$, where the radius moduli have auxiliary vacuum expectation values and the supersymmetry breaking is mediated by the Kaluza-Klein states of gauge supermultiplets, we analyze the gaugino masses and trilinear couplings for two scenarios and obtain that there exist relative CP violating phases among the gaugino masses and trilinear couplings.Comment: Latex, 7 page

Genetic regulation of the development of mating projections in Candida albicans.

Candida albicans is a major human fungal pathogen, capable of switching among a range of morphological types, such as the yeast form, including white and opaque cell types and the GUT (gastrointestinally induced transition) cell type, the filamentous form, including hyphal and pseudohyphal cell types, and chlamydospores. This ability is associated with its commensal and pathogenic life styles. In response to pheromone, C. albicans cells are able to form long mating projections resembling filaments. This filamentous morphology is required for efficient sexual mating. In the current study, we report the genetic regulatory mechanisms controlling the development of mating projections in C. albicans. Ectopic expression of MTLα1 in "a" cells induces the secretion of α-pheromone and promotes the development of mating projections. Using this inducible system, we reveal that members of the pheromone-sensing pathway (including the pheromone receptor), the Ste11-Hst7-Cek1/2 mediated MAPK signalling cascade, and the RAM pathway are essential for the development of mating projections. However, the cAMP/PKA signalling pathway and a number of key regulators of filamentous growth such as Hgc1, Efg1, Flo8, Tec1, Ume6, and Rfg1 are not required for mating projection formation. Therefore, despite the phenotypic similarities between filaments and mating projections in C. albicans, distinct mechanisms are involved in the regulation of these two morphologies

A deep-neural-network-based hybrid method for semi-supervised classification of polarimetric SAR data

This paper proposes a deep-neural-network-based semi-supervised method for polarimetric synthetic aperture radar (PolSAR) data classification. The proposed method focuses on achieving a well-trained deep neural network (DNN) when the amount of the labeled samples is limited. In the proposed method, the probability vectors, where each entry indicates the probability of a sample associated with a category, are first evaluated for the unlabeled samples, leading to an augmented training set. With this augmented training set, the parameters in the DNN are learned by solving the optimization problem, where the log-likelihood cost function and the class probability vectors are used. To alleviate the “salt-and-pepper” appearance in the classification results of PolSAR images, the spatial interdependencies are incorporated by introducing a Markov random field (MRF) prior in the prediction step. The experimental results on two realistic PolSAR images demonstrate that the proposed method effectively incorporates the spatial interdependencies and achieves the good classification accuracy with a limited number of labeled samples