Primordial Black Holes from Sound Speed Resonance during Inflation

We report on a novel phenomenon of the resonance effect of primordial density perturbations arisen from a sound speed parameter with an oscillatory behavior, which can generically lead to the formation of primordial black holes in the early Universe. For a general inflaton field, it can seed primordial density fluctuations and their propagation is governed by a parameter of sound speed square. Once if this parameter achieves an oscillatory feature for a while during inflation, a significant non-perturbative resonance effect on the inflaton field fluctuations takes place around a critical length scale, which results in significant peaks in the primordial power spectrum. By virtue of this robust mechanism, primordial black holes with specific mass function can be produced with a sufficient abundance for dark matter in sizable parameter ranges.Comment: 6 pages, 4 figures; v2: figures replotted with corrections, analysis extended, version accepted by Phys.Rev.Let

Chaos on Phase Noise of Van Der Pol Oscillator

 Phase noise is the most important parameter in many oscillators. The proposed method in this paper is based on nonlinear stochastic differential equation for phase noise analysis approach. The influences of two different sources of noise in the Van Der Pol oscillator adopted this method are compared. The source of noise is a white noise process which is a genuinely stochastic process and the other is actually a deterministic system, which exhibits chaotic behavior in some regions. The behavior of the oscillator under different conditions is investigated numerically. It is shown that the phase noise of the oscillator is affected by a noise arising from chaos than a noise arising from the genuine stochastic process at the same noise intensity

Measuring Overall Heterogeneity in Meta-Analyses: Application to CSF Biomarker Studies in Alzheimer’s Disease

The interpretations of statistical inferences from meta-analyses depend on the degree of heterogeneity in the meta-analyses. Several new indices of heterogeneity in meta-analyses are proposed, and assessed the variation/difference of these indices through a large simulation study. The proposed methods are applied to biomakers of Alzheimer’s disease

Numerical Approach to Dynamical Structure Factor of Dusty Plasmas

The dynamical structure factor [S(k,ω)] gives the information about static and dynamic properties of complex dusty plasma (CDPs). We have used the equilibrium molecular dynamic (EMD) simulations for the investigation of S(k,ω) of strongly coupled CDPs (SCCDPs). In this work, we have computed all possible values of dynamical density with increasing and decreasing sequences of plasma frequency (ωp) and wave number (k) over a wide range of different combinations of the plasma parameters (κ, Γ). Our new simulation results show that the fluctuation of S(k,ω) increases with increasing Г and it decreases with an increase of κ and N. Moreover, investigation shows that the amplitude of S(k,ω) increases by increasing screening (κ) and wave number (k), and it decreases with increasing Г. Our EMD simulation shows that dynamical density of SCCDPs is slightly dependent on N; however, it is nearly independent of other parameters. The presented results obtained through EMD approach are in reasonable agreement with earlier known results based on different numerical methods and plasma states. It is demonstrated that the presented model is the best tool for estimating the density fluctuation in the SCCDPs over a suitable range of parameters

Intelligent resource scheduling for 5G radio access network slicing

It is widely acknowledged that network slicing can tackle the diverse use cases and connectivity services of the forthcoming next-generation mobile networks (5G). Resource scheduling is of vital importance for improving resource-multiplexing gain among slices while meeting specific service requirements for radio access network (RAN) slicing. Unfortunately, due to the performance isolation, diversified service requirements, and network dynamics (including user mobility and channel states), resource scheduling in RAN slicing is very challenging. In this paper, we propose an intelligent resource scheduling strategy (iRSS) for 5G RAN slicing. The main idea of an iRSS is to exploit a collaborative learning framework that consists of deep learning (DL) in conjunction with reinforcement learning (RL). Specifically, DL is used to perform large time-scale resource allocation, whereas RL is used to perform online resource scheduling for tackling small time-scale network dynamics, including inaccurate prediction and unexpected network states. Depending on the amount of available historical traffic data, an iRSS can flexibly adjust the significance between the prediction and online decision modules for assisting RAN in making resource scheduling decisions. Numerical results show that the convergence of an iRSS satisfies online resource scheduling requirements and can significantly improve resource utilization while guaranteeing performance isolation between slices, compared with other benchmark algorithms