Natural Four-Generation Mass Textures in MSSM Brane Worlds

A fourth generation of Standard Model (SM) fermions is usually considered unlikely due to constraints from direct searches, electroweak precision measurements, and perturbative unitarity. We show that fermion mass textures consistent with all constraints may be obtained naturally in a model with four generations constructed from intersecting D6 branes on a T^6/(Z_2 x Z_2) orientifold. The Yukawa matrices of the model are rank 2, so that only the third- and fourth-generation fermions obtain masses at the trilinear level. The first two generations obtain masses via higher-order couplings and are therefore naturally lighter. In addition, we find that the third and fourth generation automatically split in mass, but do not mix at leading order. Furthermore, the SM gauge couplings automatically unify at the string scale, and all the hidden-sector gauge groups become confining in the range 10^{13}--10^{16} GeV, so that the model becomes effectively a four-generation MSSM at low energies.Comment: Accepted for publication in Physical Review

Robust federated learning with noisy communication

Federated learning is a communication-efficient training process that alternate between local training at the edge devices and averaging of the updated local model at the center server. Nevertheless, it is impractical to achieve perfect acquisition of the local models in wireless communication due to the noise, which also brings serious effect on federated learning. To tackle this challenge in this paper, we propose a robust design for federated learning to decline the effect of noise. Considering the noise in two aforementioned steps, we first formulate the training problem as a parallel optimization for each node under the expectation-based model and worst-case model. Due to the non-convexity of the problem, regularizer approximation method is proposed to make it tractable. Regarding the worst-case model, we utilize the sampling-based successive convex approximation algorithm to develop a feasible training scheme to tackle the unavailable maxima or minima noise condition and the non-convex issue of the objective function. Furthermore, the convergence rates of both new designs are analyzed from a theoretical point of view. Finally, the improvement of prediction accuracy and the reduction of loss function value are demonstrated via simulation for the proposed designs