Luan C. S. M. Ozelim | Searcharxiv

arXiv2025

A general class of continuous asymmetric distributions with positive support

In order to better fit real-world datasets, studying asymmetric distribution is of great interest. In this work, we derive several mathematical properties of a general class of asymmetric distributions with positive support which shows up as a unified framework for Extreme Value Theory asymptotic results. The new model generalizes some well-known distribution models such as Generalized Gamma, Inverse Gamma, Weibull, Fréchet, Half-normal, Modified half-normal, Rayleigh, and Erlang. To highlight the applicability of our results, the performance of the analytical models is evaluated through real-life dataset modeling.

math.ST↗

arXiv2024

Non-Random Data Encodes its Geometric and Topological Dimensions

Based on the principles of information theory, measure theory, and theoretical computer science, we introduce a signal deconvolution method with a wide range of applications to coding theory, particularly in zero-knowledge one-way communication channels, such as in deciphering messages (i.e., objects embedded into multidimensional spaces) from unknown generating sources about which no prior knowledge is available and to which no return message can be sent. Our multidimensional space reconstruction method from an arbitrary received signal is proven to be agnostic vis-à-vis the encoding-decoding scheme, computation model, programming language, formal theory, the computable (or semi-computable) method of approximation to algorithmic complexity, and any arbitrarily chosen (computable) probability measure. The method derives from the principles of an approach to Artificial General Intelligence (AGI) capable of building a general-purpose model of models independent of any arbitrarily assumed prior probability distribution. We argue that this optimal and universal method of decoding non-random data has applications to signal processing, causal deconvolution, topological and geometric properties encoding, cryptography, and bio- and technosignature detection.

cs.IT↗