Physics III/astronomy

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XDFA - Software To Evaluate Detrended Fluctuation Analysis And Related Time Series Measures

xdfa is a software package to compute Detrended Fluctuation Analysis (DFA), and related methods, on time series. The core of the package is written in C++ for performance reasons with interfaces available for python, octave and R. The purpose of the package is to provide a uniform calling convention together with ensuring the numerical stability of the methods used.

Structural analysis of semiconductor a-SiC:H thin-films alloys based on statistical physics

The use of a-SiC:H thin-films alloys in p-i-n (a-SiC:H(p)/a-Si:H(i)/a-Si:H(n)) photovoltaic components has widely attracted the research interest, especially towards the direction of optimizing the optoelectronic attributes of a-SiC:H that aim to the exploitation of a wider range of the solar spectrum. A major effort towards this direction concerns the increase of the carbon concentration in the a-SiC:H thin-films alloys, which causes a consequent increase of their optical energy-band gap up to a critical ceiling value, while afterwards the energy-band gap decreases.

Self-Organization In Stellar Evolution: Size-Complexity Rule

Complexity Theory is highly interdisciplinary, therefore any regularities must hold on all levels of organization, independent on the nature of the system. An open question in science is how complex systems self-organize to produce emergent structures and properties, a branch of non-equilibrium thermodynamics. It has long been known that there is a quantity-quality transition in natural systems. This is to say that the properties of a system depend on its size.

Thermodynamic Machine Learning through Maximum Work Production

Adaptive thermodynamic systems---such as a biological organism attempting to gain survival advantage, an autonomous robot performing a functional task, or a motor protein transporting intracellular nutrients---can improve their
performance by effectively modeling the regularities and stochasticity in their environments. Analogously, but in a purely computational realm, machine learning algorithms seek to estimate models that capture predictable structure

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