Connecting in the Dirac Equation the Clifford Algebra of Lorentz Invariance with the Lie Algebra of SU(N) Gauge Symmetry

In this paper, we study possible mathematical connections of the Clifford algebra with the su(N)-Lie algebra, or in more physical terms the links between space-time symmetry (Lorentz invariance) and internal SU(N) gauge-symmetry for a massive spin one-half fermion described by the Dirac equation. The related matrix algebra is worked out in particular for the SU(2) symmetry and outlined as well for the color gauge group SU(3). Possible perspectives of this approach to unification of symmetries are briefly discussed. The calculations make extensive use of tensor multiplication of the matrices involved, whereby our focus is on revisiting the Coleman–Mandula theorem. This permits us to construct unified symmetries between Lorentz invariance and gauge symmetry in a direct product sense.

Towards a Framework for Observational Causality from Time Series: When Shannon Meets Turing

We propose a tensor based approach to infer causal structures from time series. An information theoretical analysis of transfer entropy (TE) shows that TE results from transmission of information over a set of communication channels. Tensors are the mathematical equivalents of these multichannel causal channels. The total effect of subsequent transmissions, i.e., the total effect of a cascade, can now be expressed in terms of the tensors of these subsequent transmissions using tensor multiplication. With this formalism, differences in the underlying structures can be detected that are otherwise undetectable using TE or mutual information. Additionally, using a system comprising three variables, we prove that bivariate analysis suffices to infer the structure, that is, bivariate analysis suffices to differentiate between direct and indirect associations. Some results translate to TE. For example, a Data Processing Inequality (DPI) is proven to exist for transfer entropy.

Strongly Connected Multivariate Digraphs

Generalizing the idea of viewing a digraph as a model of a linear map, we suggest a multi-variable analogue of a digraph, called a hydra, as a model of a multi-linear map. Walks in digraphs correspond to usual matrix multiplication while walks in hydras correspond to the tensor multiplication introduced by Robert Grone in 1987. By viewing matrix multiplication as a special case of this tensor multiplication, many concepts on strongly connected digraphs are generalized to corresponding ones for hydras, including strongly connectedness, periods and primitiveness, etc. We explore the structure of all possible periods of strongly connected hydras, which turns out to be related to the existence of certain kind of combinatorial designs. We also provide estimates of largest primitive exponents and largest diameters of relevant hydras. Much existing research on tensors are based on some other definitions of multiplications of tensors and so our work here supplies new perspectives for understanding irreducible and primitive nonnegative tensors.