On the perturbative S-matrix of generalized sine-Gordon models

Motivated by its relation to the Pohlmeyer reduction of AdS_5 x S^5 superstring theory we continue the investigation of the generalized sine-Gordon model defined by SO(N+1)/SO(N) gauged WZW theory with an integrable potential. Extending our previous work (arXiv:0912.2958) we compute the one-loop two-particle S-matrix for the elementary massive excitations. In the N = 2 case corresponding to the complex sine-Gordon theory it agrees with the charge-one sector of the quantum soliton S-matrix proposed in hep-th/9410140. In the case of N > 2 when the gauge group SO(N) is non-abelian we find a curious anomaly in the Yang-Baxter equation which we interpret as a gauge artifact related to the fact that the scattered particles are not singlets under the residual global subgroup of the gauge group

Enhancement of Both Long-Term Depression Induction and Optokinetic Response Adaptation in Mice Lacking Delphilin

In the cerebellum, Delphilin is expressed selectively in Purkinje cells (PCs) and is localized exclusively at parallel fiber (PF) synapses, where it interacts with glutamate receptor (GluR) δ2 that is essential for long-term depression (LTD), motor learning and cerebellar wiring. Delphilin ablation exerted little effect on the synaptic localization of GluRδ2. There were no detectable abnormalities in cerebellar histology, PC cytology and PC synapse formation in contrast to GluRδ2 mutant mice. However, LTD induction was facilitated at PF-PC synapses in Delphilin mutant mice. Intracellular Ca2+ required for the induction of LTD appeared to be reduced in the mutant mice, while Ca2+ influx through voltage-gated Ca2+ channels and metabotropic GluR1-mediated slow synaptic response were similar between wild-type and mutant mice. We further showed that the gain-increase adaptation of the optokinetic response (OKR) was enhanced in the mutant mice. These findings are compatible with the idea that LTD induction at PF-PC synapses is a crucial rate-limiting step in OKR gain-increase adaptation, a simple form of motor learning. As exemplified in this study, enhancing synaptic plasticity at a specific synaptic site of a neural network is a useful approach to understanding the roles of multiple plasticity mechanisms at various cerebellar synapses in motor control and learning

A path- and label-cost propagation approach to speedup the training of the optimum-path forest classifier

In general, pattern recognition techniques require a high computational burden for learning the discriminating functions that are responsible to separate samples from distinct classes. As such, there are several studies that make effort to employ machine learning algorithms in the context of 'big data' classification problems. The research on this area ranges from Graphics Processing Units-based implementations to mathematical optimizations, being the main drawback of the former approaches to be dependent on the graphic video card. Here, we propose an architecture-independent optimization approach for the optimum-path forest (OPF) classifier, that is designed using a theoretical formulation that relates the minimum spanning tree with the minimum spanning forest generated by the OPF over the training dataset. The experiments have shown that the approach proposed can be faster than the traditional one in five public datasets, being also as accurate as the original OPF. (C) 2014 Elsevier B. V. All rights reserved.40121127Fundacao para a Ciencia e a Tecnologia (FCT) in Portugal [PTDC/BBB-BMD/3088/2012]Fundacao para a Ciencia e a Tecnologia (FCT) in Portugal [PTDC/BBB-BMD/3088/2012