We introduce a hierarchy of semidefinite relaxations the set quantum correlations in generalised contextuality scenarios. This constitutes simple and versatile tool for bounding magnitude contextuality. To illustrate its utility, we use it to determine maximal violation several noncontextuality inequalities whose maximum violations were previously unknown. then go further prove that certain pre...

Using panel data on micro-enterprises from Peru, we find evidence suggesting that firm dynamics explain ‘formality’, i.e. the decision to participate in societal institutions. In particular, we find that participation increases with firm size and age, implying transition of firms to formality as they grow. The distinct characteristics of sub-groups, segmented according to life-cycle criteria, f...

We discuss the differential graded Lie algebra (DGLA) of Drinfeld modeled on the tensor algebra ⊗ Ug of the universal enveloping algebra of a Lie algebra g over any field K of characteristic zero. We explicitly analyze the first obstruction to the existence of the formality quasi-isomorphism for this DGLA. Our analysis implies the formality of the DGLA ⊗ Ub of Drinfeld associated to the twodime...

We conjecture an explicit formula for a cyclic analog of the Formality L∞-morphism [K]. We prove that its first Taylor component, the cyclic Hochschild-Kostant-Rosenberg map, is in fact a morphism (and a quasiisomorphism) of the complexes. To prove it we construct a cohomological version of the Connes-Tsygan bicomplex in cyclic homology. As an application of the cyclic Formality conjecture, we ...

The Klyachko, Can, Binicioglu, and Shumovsky (KCBS) inequality is an important contextuality inequality in three-level system, which has been demonstrated experimentally by using quantum states. Using the path and polarization degrees of freedom of classical optics fields, we have constructed the classical trit (cetrit), tested the KCBS inequality and its geometrical form (Wright's inequality) ...

Traditionally categorical data analysis (e.g. generalized linear models) works with simple, flat datasets akin to a single table in a database with no notion of missing data or conflicting versions. In contrast, modern data analysis must deal with distributed databases with many partial local tables that need not always agree. The computational agents tabulating these tables are spatially separ...