All industries are trying to leverage Artificial Intelligence (AI) based on their existing big data which is available in so called tabular form, where each record composed of a number heterogeneous continuous and categorical columns also known as features. Deep Learning (DL) has constituted major breakthrough for AI fields related human skills like natural language processing, but its applicability been more challenging. More classical Machine (ML) models tree-based ensemble ones usually...

Data imputation and data generation have important applications for many domains, like healthcare finance, where incomplete or missing can hinder accurate analysis decision-making. Diffusion models emerged as powerful generative capable of capturing complex distributions across various modalities such image, audio, time series data. Recently, they been also adapted to generate tabular In this paper, we propose a diffusion model that introduces three key enhancements: (1) conditioning...

Motivated by the electroweak hierarchy problem, we consider theories with two extra dimensions in which four-dimensional scalar fields are components of gauge boson full space, namely Gauge-Higgs unification framework. We briefly explain basics features "flux compactification", i.e. compactification presence a background (magnetic) flux. In particular recall how chirality and symmetry breaking can be obtained this context. More details, find catalogue all possible degenerate zero-energy...

We review the basic notions of compactification in presence a background flux. In extra-dimensional models with more than five dimensions, Scherk and Schwarz boundary conditions have to satisfy 't Hooft consistency conditions. Different vacuum configurations can be obtained, depending whether trivial or nontrivial flux is considered. The magnetic provides, addition, mechanism for producing four-dimensional chiral fermions. Particularizing six-dimensional case, we calculate one-loop effective...

In this paper, we study the problem of locating a predefined sequence patterns in time series. particular, studied scenario assumes theoretical model is available that contains expected locations patterns. This found several contexts, and it commonly solved by first synthesizing series from model, then aligning to true through dynamic warping. We propose technique increases similarity both before them, mapping them into latent correlation space. The learned data machine-learning setup....

All industries are trying to leverage Artificial Intelligence (AI) based on their existing big data which is available in so called tabular form, where each record composed of a number heterogeneous continuous and categorical columns also known as features. Deep Learning (DL) has constituted major breakthrough for AI fields related human skills like natural language processing, but its applicability been more challenging. More classical Machine (ML) models tree-based ensemble ones usually...

Views Icon Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Twitter Facebook Reddit LinkedIn Tools Reprints and Permissions Cite Search Site Citation M. Salvatori; Symmetry breaking from continous discrete Scherk‐Schwarz periodicity conditions. AIP Conf. Proc. 20 April 2007; 903 (1): 403–406. https://doi.org/10.1063/1.2735209 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search...

In this paper, we study the problem of locating a predefined sequence patterns in time series. particular, studied scenario assumes theoretical model is available that contains expected locations patterns. This found several contexts, and it commonly solved by first synthesizing series from model, then aligning to true through dynamic warping. We propose technique increases similarity both before them, mapping them into latent correlation space. The learned data machine-learning setup....