The height dependence of temperature - velocity correlation in the solar photosphere

We derive correlation coefficients between temperature and line-of-sight velocity as a function of optical depth throughout the solar photosphere for the non-magnetic photosphere and a small area of enhanced magnetic activity. The maximum anticorrelation of about -0.6 between temperature and line-of-sight velocity in the non-magnetic photosphere occurs at log tau5 = -0.4. The magnetic field is another decorrelating factor along with 5-min oscillations and seeing.Comment: In press,"Modern Solar Facilities - Advanced Solar Science", (Gottingen), Universitatsverlag Gottingen, 139-142, 200

Working with OpenCL to Speed Up a Genetic Programming Financial Forecasting Algorithm: Initial Results

The genetic programming tool EDDIE has been shown to be a successful financial forecasting tool, however it has suffered from an increase in execution time as new features have been added. Speed is an important aspect in financial problems, especially in the field of algorithmic trading, where a delay in taking a decision could cost millions. To offset this performance loss, EDDIE has been modified to take advantage of multi-core CPUs and dedicated GPUs. This has been achieved by modifying the candidate solution evaluation to use an OpenCL kernel, allowing the parallel evaluation of solutions. Our computational results have shown improvements in the running time of EDDIE when the evaluation was delegated to the OpenCL kernel running on a multi-core CPU, with speed ups up to 21 times faster than the original EDDIE algorithm. While most previous works in the literature reported significantly improvements in performance when running an OpenCL kernel on a GPU device, we did not observe this in our results. Further investigation revealed that memory copying overheads and branching code in the kernel are potentially causes of the (under-)performance of the OpenCL kernel when running on the GPU device

A Comparative Study on the Use of Classification Algorithms in Financial Forecasting

Financial forecasting is a vital area in computational finance, where several studies have taken place over the years. One way of viewing financial forecasting is as a classification problem, where the goal is to find a model that represents the predictive relationships between predictor attribute values and class attribute values. In this paper we present a comparative study between two bio-inspired classification algorithms, a genetic programming algorithm especially designed for financial forecasting, and an ant colony optimization one, which is designed for classification problems. In addition, we compare the above algorithms with two other state-of-the-art classification algorithms, namely C4.5 and RIPPER. Results show that the ant colony optimization classification algorithm is very successful, significantly outperforming all other algorithms in the given classification problems, which provides insights for improving the design of specific financial forecasting algorithms

Spectral Characteristics of the He I D3 Line in a Quiescent Prominence Observed by THEMIS

We analyze the observations of a quiescent prominence acquired by the Telescope Heliographique pour l'Etude du Magnetisme et des Instabilites Solaires (THEMIS) in the He I 5876 A (He I D3) multiplet aiming to measure the spectral characteristics of the He I D3 profiles and to find for them an adequate fitting model. The component characteristics of the He I D3 Stokes I profiles are measured by the fitting system approximating them with a double Gaussian. This model yields an He I D3 component peak intensity ratio of $5.5\pm0.4$, which differs from the value of 8 expected in the optically thin limit. Most of the measured Doppler velocities lie in the interval $\pm5$ km/s, with a standard deviation of $\pm1.7$ km/s around the peak value of 0.4 km/s. The wide distribution of the full-width at half maximum has two maxima at 0.25 A and 0.30 A for the He I D3 blue component and two maxima at 0.22 A and 0.31 A for the red component. The width ratio of the components is $1.04\pm0.18$. We show that the double-Gaussian model systematically underestimates the blue wing intensities. To solve this problem, we invoke a two-temperature multi-Gaussian model, consisting of two double-Gaussians, which provides a better representation of He I D3 that is free of the wing intensity deficit. This model suggests temperatures of 11.5 kK and 91 kK, respectively, for the cool and the hot component of the target prominence. The cool and hot components of a typical He I D3 profile have component peak intensity ratios of 6.6 and 8, implying a prominence geometrical width of 17 Mm and an optical thickness of 0.3 for the cool component, while the optical thickness of the hot component is negligible. These prominence parameters seem to be realistic, suggesting the physical adequacy of the multi-Gaussian model with important implications for interpreting He I D3 spectropolarimetry by current inversion codes.Comment: 25 pages,1 movie, 10 figures, 2 tables, 2 equations. The final publication is available at Springer via http://dx.doi.org/10.1007/s11207-017-1118-z The supplementary movie is available for viewing and download at https://www.dropbox.com/s/7tskvnc593tlbyv/Prominence_HeID3_GONG_AIA.mpg?dl=

How Noisy Data Affects Geometric Semantic Genetic Programming

Noise is a consequence of acquiring and pre-processing data from the environment, and shows fluctuations from different sources---e.g., from sensors, signal processing technology or even human error. As a machine learning technique, Genetic Programming (GP) is not immune to this problem, which the field has frequently addressed. Recently, Geometric Semantic Genetic Programming (GSGP), a semantic-aware branch of GP, has shown robustness and high generalization capability. Researchers believe these characteristics may be associated with a lower sensibility to noisy data. However, there is no systematic study on this matter. This paper performs a deep analysis of the GSGP performance over the presence of noise. Using 15 synthetic datasets where noise can be controlled, we added different ratios of noise to the data and compared the results obtained with those of a canonical GP. The results show that, as we increase the percentage of noisy instances, the generalization performance degradation is more pronounced in GSGP than GP. However, in general, GSGP is more robust to noise than GP in the presence of up to 10% of noise, and presents no statistical difference for values higher than that in the test bed.Comment: 8 pages, In proceedings of Genetic and Evolutionary Computation Conference (GECCO 2017), Berlin, German

Magnetic loop emergence within a granule

We investigate the temporal evolution of magnetic flux emerging within a granule in the quiet-Sun internetwork at disk center. We combined IR spectropolarimetry performed in two Fe I lines at 1565 nm with speckle-reconstructed G-band imaging. We determined the magnetic field parameters by a LTE inversion of the full Stokes vector using the SIR code, and followed their evolution in time. To interpret the observations, we created a geometrical model of a rising loop in 3D. The relevant parameters of the loop were matched to the observations where possible. We then synthesized spectra from the 3D model for a comparison to the observations. We found signatures of magnetic flux emergence within a growing granule. In the early phases, a horizontal magnetic field with a distinct linear polarization signal dominated the emerging flux. Later on, two patches of opposite circular polarization signal appeared symmetrically on either side of the linear polarization patch, indicating a small loop-like structure. The mean magnetic flux density of this loop was roughly 450 G, with a total magnetic flux of around 3x10^17 Mx. During the ~12 min episode of loop occurrence, the spatial extent of the loop increased from about 1 to 2 arcsec. The middle part of the appearing feature was blueshifted during its occurrence, supporting the scenario of an emerging loop. The temporal evolution of the observed spectra is reproduced to first order by the spectra derived from the geometrical model. The observed event can be explained as a case of flux emergence in the shape of a small-scale loop.Comment: 10 pages, 13 figures; accepted for Astronomy and Astrophysics; ps and eps figures in full resolution are available at http://www.astro.sk/~koza/figures/aa2009_loop

Fractal dimension of transport coefficients in a deterministic dynamical system

In many low-dimensional dynamical systems transport coefficients are very irregular, perhaps even fractal functions of control parameters. To analyse this phenomenon we study a dynamical system defined by a piece-wise linear map and investigate the dependence of transport coefficients on the slope of the map. We present analytical arguments, supported by numerical calculations, showing that both the Minkowski-Bouligand and Hausdorff fractal dimension of the graphs of these functions is 1 with a logarithmic correction, and find that the exponent $\gamma$ controlling this correction is bounded from above by 1 or 2, depending on some detailed properties of the system. Using numerical techniques we show local self-similarity of the graphs. The local self-similarity scaling transformations turn out to depend (irregularly) on the values of the system control parameters.Comment: 17 pages, 6 figures; ver.2: 18 pages, 7 figures (added section 5.2, corrected typos, etc.