Connecting speeds, directions and arrival times of 22 coronal mass ejections from the Sun to 1 AU

Forecasting the in situ properties of coronal mass ejections (CMEs) from remote images is expected to strongly enhance predictions of space weather, and is of general interest for studying the interaction of CMEs with planetary environments. We study the feasibility of using a single heliospheric imager (HI) instrument, imaging the solar wind density from the Sun to 1 AU, for connecting remote images to in situ observations of CMEs. We compare the predictions of speed and arrival time for 22 CMEs (in 2008-2012) to the corresponding interplanetary coronal mass ejection (ICME) parameters at in situ observatories (STEREO PLASTIC/IMPACT, Wind SWE/MFI). The list consists of front- and backsided, slow and fast CMEs (up to $2700 \: km \: s^{-1}$). We track the CMEs to $34.9 \pm 7.1$ degrees elongation from the Sun with J-maps constructed using the SATPLOT tool, resulting in prediction lead times of $-26.4 \pm 15.3$ hours. The geometrical models we use assume different CME front shapes (Fixed-$\Phi$, Harmonic Mean, Self-Similar Expansion), and constant CME speed and direction. We find no significant superiority in the predictive capability of any of the three methods. The absolute difference between predicted and observed ICME arrival times is $8.1 \pm 6.3$ hours ($rms$ value of 10.9h). Speeds are consistent to within $284 \pm 288 \: km \: s^{-1}$. Empirical corrections to the predictions enhance their performance for the arrival times to $6.1 \pm 5.0$ hours ($rms$ value of 7.9h), and for the speeds to $53 \pm 50 \: km \: s^{-1}$. These results are important for Solar Orbiter and a space weather mission positioned away from the Sun-Earth line.Comment: 19 pages, 13 figures, accepted for publication in the Astrophysical Journa

Evolutionary Synthesis Of Stellar Population In Elliptical Galaxies .1. Ingredients, Broad-Band Colors, And Infrared Features

NSF MPS 73-04673 A01, GP-40482, GP-3143, MPS 75-01398Astronom

A cognitive based Intrusion detection system

Intrusion detection is one of the primary mechanisms to provide computer networks with security. With an increase in attacks and growing dependence on various fields such as medicine, commercial, and engineering to give services over a network, securing networks have become a significant issue. The purpose of Intrusion Detection Systems (IDS) is to make models which can recognize regular communications from abnormal ones and take necessary actions. Among different methods in this field, Artificial Neural Networks (ANNs) have been widely used. However, ANN-based IDS, has two main disadvantages: 1- Low detection precision. 2- Weak detection stability. To overcome these issues, this paper proposes a new approach based on Deep Neural Network (DNN. The general mechanism of our model is as follows: first, some of the data in dataset is properly ranked, afterwards, dataset is normalized with Min-Max normalizer to fit in the limited domain. Then dimensionality reduction is applied to decrease the amount of both useless dimensions and computational cost. After the preprocessing part, Mean-Shift clustering algorithm is the used to create different subsets and reduce the complexity of dataset. Based on each subset, two models are trained by Support Vector Machine (SVM) and deep learning method. Between two models for each subset, the model with a higher accuracy is chosen. This idea is inspired from philosophy of divide and conquer. Hence, the DNN can learn each subset quickly and robustly. Finally, to reduce the error from the previous step, an ANN model is trained to gain and use the results in order to be able to predict the attacks. We can reach to 95.4 percent of accuracy. Possessing a simple structure and less number of tunable parameters, the proposed model still has a grand generalization with a high level of accuracy in compared to other methods such as SVM, Bayes network, and STL.Comment: 18 pages, 6 figure

Algorithmic patterns for H\mathcal{H}-matrices on many-core processors

In this work, we consider the reformulation of hierarchical ($\mathcal{H}$) matrix algorithms for many-core processors with a model implementation on graphics processing units (GPUs). $\mathcal{H}$ matrices approximate specific dense matrices, e.g., from discretized integral equations or kernel ridge regression, leading to log-linear time complexity in dense matrix-vector products. The parallelization of $\mathcal{H}$ matrix operations on many-core processors is difficult due to the complex nature of the underlying algorithms. While previous algorithmic advances for many-core hardware focused on accelerating existing $\mathcal{H}$ matrix CPU implementations by many-core processors, we here aim at totally relying on that processor type. As main contribution, we introduce the necessary parallel algorithmic patterns allowing to map the full $\mathcal{H}$ matrix construction and the fast matrix-vector product to many-core hardware. Here, crucial ingredients are space filling curves, parallel tree traversal and batching of linear algebra operations. The resulting model GPU implementation hmglib is the, to the best of the authors knowledge, first entirely GPU-based Open Source $\mathcal{H}$ matrix library of this kind. We conclude this work by an in-depth performance analysis and a comparative performance study against a standard $\mathcal{H}$ matrix library, highlighting profound speedups of our many-core parallel approach