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Machine learning is an efficient method for analysing and interpreting the increasing amount of astronomical data that are available. In this study, we show a pedagogical approach that should benefit anyone willing to experiment with deep learning techniques in the context of stellar parameter determination. Using the convolutional neural network architecture, we give a step-by-step overview of how to select the optimal parameters for deriving the most accurate values for the stellar parameters of stars: T eff {T}_{{\rm{eff}}} , log g \log g , [M/H], and v e sin i {v}_{e}\sin i . Synthetic spectra with random noise were used to constrain this method and to mimic the observations. We found that each stellar parameter requires a different combination of network hyperparameters and the maximum accuracy reached depends on this combination as well as the signal-to-noise ratio of the observations, and the architecture of the network. We also show that this technique can be applied to other spectral-types in different wavelength ranges after the technique has been optimized.

In this paper, we present a scheme that protects legitimate traffic from the large volume of attackers packets during a DDoS attack. Legitimate packets can be recognized by the tokens they carry in the IP header. Obtaining a token does not require protocol additions or changes, rather it is automatically obtained when a TCP connection is established. We believe that the Implicit Token Scheme (ITS) has numerous advantages: (1) It is totally transparent to clients. (2) No new protocols or modification of existing ones is needed to implement ITS. (3) Operations required by intermediate routers are computationally not more intensive than a couple of addition operations which could be easily done at wire-speed. (4) Does not lead to false positives. (5) Can sustain server availability even during attacks involving hundreds of thousands of attackers.

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The nonequilibrium grand ensemble method previously reported is rigorously implemented for a nonequilibrium dilute gas mixture sheared in plane Couette flow geometry and analytic results are presented for the nonequilibrium thermodynamic quantities of the sheared gas. The calortropy is shown to contain all the constitutive information of the system. The notions of temperature and pressure for the nonequilibrium gas are examined on the basis of the calortropy calculated from the nonequilibrium grand partition function. The shear rate dependence of the nonlinear shear and first normal stress coefficients is calculated numerically and also by means of an iterative method. The first iterative solutions are found to give a qualitatively correct behavior for all Peclet numbers.