Exploring the Higgs Sector of a Most Natural NMSSM and its Prediction on Higgs Pair Production at the LHC

As a most natural realization of the Next-to Minimal Supersymmetry Standard Model (NMSSM), {\lambda}-SUSY is parameterized by a large {\lambda} around one and a low tan$\beta$ below 10. In this work, we first scan the parameter space of {\lambda}-SUSY by considering various experimental constraints, including the limitation from the Higgs data updated by the ATLAS and CMS collaborations in the summer of 2014, then we study the properties of the Higgs bosons. We get two characteristic features of {\lambda}-SUSY in experimentally allowed parameter space. One is the triple self coupling of the SM-like Higgs boson may get enhanced by a factor over 10 in comparison with its SM prediction. The other is the pair production of the SM-like Higgs boson at the LHC may be two orders larger than its SM prediction. All these features seems to be unachievable in the Minimal Supersymmetric Standard Model and in the NMSSM with a low {\lambda}. Moreover, we also find that naturalness plays an important role in selecting the parameter space of {\lambda}-SUSY, and that the Higgs $\chi^2$ obtained with the latest data is usually significantly smaller than before due to the more consistency of the two collaboration measurements

A Task Allocation Algorithm for Profit Maximization in NFC-RAN

In this paper, we study a general Near-Far Computing Enhanced C-RAN (NFC-RAN), in which users can offload the tasks to the near edge cloud (NEC) or the far edge cloud (FEC).We aim to propose a profit-aware task allocation model by maximizing the profit of the edge cloud operators. We first prove that this problem can be transformed to a Multiple-Choice Multi-Dimensional 0-1 Knapsack Problem (MMKP), which is NP-hard. Then, we solve it by using a low complexity heuristic algorithm. The simulation results show that the proposed algorithm achieves a good tradeoff between the performance and the complexity compared with the benchmark algorithm

Koopman Spectral Linearization vs. Carleman Linearization: A Computational Comparison Study

Nonlinearity presents a significant challenge in problems involving dynamical systems, prompting the exploration of various linearization techniques, including the well-known Carleman Linearization. In this paper, we introduce the Koopman Spectral Linearization method tailored for nonlinear autonomous dynamical systems. This innovative linearization approach harnesses the Chebyshev differentiation matrix and the Koopman Operator to yield a lifted linear system. It holds the promise of serving as an alternative approach that can be employed in scenarios where Carleman linearization is traditionally applied. Numerical experiments demonstrate the effectiveness of this linearization approach for several commonly used nonlinear dynamical systems.Comment: 17 pages, 7 figure