Search results for "General topology"
showing 10 items of 131 documents
"Table 1" of "C-12 (gamma,p) B-11 cross-section from 80 to 157 MeV"
1995
EX IN 0 MEV REGION (FROM 0 TO 1.5 MEV). ANGULAR BINS OF 5 DEG WIDTH.
Dimension estimates on circular (s,t)-Furstenberg sets
2023
In this paper, we show that circular $(s,t)$-Furstenberg sets in $\mathbb R^2$ have Hausdorff dimension at least $$\max\{\frac{t}3+s,(2t+1)s-t\} \text{ for all $0<s,t\le 1$}.$$ This result extends the previous dimension estimates on circular Kakeya sets by Wolff.
Cardinal Invariants for the $G_\delta$ topology
2017
We prove upper bounds for the spread, the Lindel\"of number and the weak Lindel\"of number of the $G_\delta$-topology on a topological space and apply a few of our bounds to give a short proof to a recent result of Juh\'asz and van Mill regarding the cardinality of a $\sigma$-countably tight homogeneous compactum.
The Links-Gould invariants as generalizations of the Alexander polynomial
2016
In this thesis we focus on the connections that exist between two link invariants: first the Alexander-Conway invariant ∆ that was the first polynomial link invariant to be discovered, and one of the most thoroughly studied since alongside with the Jones polynomial, and on the other hand the family of Links-Gould invariants LGn,m that are quantum link invariants derived from super Hopf algebras Uqgl(n|m). We prove a case of the De Wit-Ishii-Links conjecture: in some cases we can recover powers of the Alexander polynomial as evaluations of the Links-Gould invariants. So the LG polynomials are generalizations of the Alexander invariant. Moreover we give evidence that these invariants should s…
Connecting Granular and Topological Relations through Description Logics
2021
Granularity deals with organizing in greater or lesser detail data, information, and knowledge that resides at a granular level. This organization is carried out according to certain criteria, which thereby provide a context view or dimension also called granular perspective. Topological relations express spatial associations among geospatial features (points, polylines, and polygons); they represent a horizontal spatial analysis. The two domains allow scientists to conceive different perspectives of the world. In this article, we aim to combine the two representations through Description Logics (DL) rules to relate granular (vertical representation) and geospatial topological (horizontal r…
"Table 162" of "Measurements of $t\bar{t}$ differential cross-sections of highly boosted top quarks decaying to all-hadronic final states in $pp$ col…
2019
${\Delta\phi}(t_1,t_2)$ correlation matrix for the normalized differential cross-section at parton level
"Table 159" of "Measurements of $t\bar{t}$ differential cross-sections of highly boosted top quarks decaying to all-hadronic final states in $pp$ col…
2019
${\Delta\phi}(t_1,t_2)$ covariance matrix for the absolute differential cross-section at parton level
"Table 161" of "Measurements of $t\bar{t}$ differential cross-sections of highly boosted top quarks decaying to all-hadronic final states in $pp$ col…
2019
${\Delta\phi}(t_1,t_2)$ covariance matrix for the normalized differential cross-section at parton level
"Table 160" of "Measurements of $t\bar{t}$ differential cross-sections of highly boosted top quarks decaying to all-hadronic final states in $pp$ col…
2019
${\Delta\phi}(t_1,t_2)$ correlation matrix for the absolute differential cross-section at parton level
Model Identification of a Network as Compressing Sensing
2013
In many applications, it is important to derive information about the topology and the internal connections of dynamical systems interacting together. Examples can be found in fields as diverse as Economics, Neuroscience and Biochemistry. The paper deals with the problem of deriving a descriptive model of a network, collecting the node outputs as time series with no use of a priori insight on the topology, and unveiling an unknown structure as the estimate of a "sparse Wiener filter". A geometric interpretation of the problem in a pre-Hilbert space for wide-sense stochastic processes is provided. We cast the problem as the optimization of a cost function where a set of parameters are used t…