187 research outputs found
Euclidean Greedy Drawings of Trees
Greedy embedding (or drawing) is a simple and efficient strategy to route
messages in wireless sensor networks. For each source-destination pair of nodes
s, t in a greedy embedding there is always a neighbor u of s that is closer to
t according to some distance metric. The existence of greedy embeddings in the
Euclidean plane R^2 is known for certain graph classes such as 3-connected
planar graphs. We completely characterize the trees that admit a greedy
embedding in R^2. This answers a question by Angelini et al. (Graph Drawing
2009) and is a further step in characterizing the graphs that admit Euclidean
greedy embeddings.Comment: Expanded version of a paper to appear in the 21st European Symposium
on Algorithms (ESA 2013). 24 pages, 20 figure
A New Perspective on Clustered Planarity as a Combinatorial Embedding Problem
The clustered planarity problem (c-planarity) asks whether a hierarchically
clustered graph admits a planar drawing such that the clusters can be nicely
represented by regions. We introduce the cd-tree data structure and give a new
characterization of c-planarity. It leads to efficient algorithms for
c-planarity testing in the following cases. (i) Every cluster and every
co-cluster (complement of a cluster) has at most two connected components. (ii)
Every cluster has at most five outgoing edges.
Moreover, the cd-tree reveals interesting connections between c-planarity and
planarity with constraints on the order of edges around vertices. On one hand,
this gives rise to a bunch of new open problems related to c-planarity, on the
other hand it provides a new perspective on previous results.Comment: 17 pages, 2 figure
Splitting Clusters To Get C-Planarity
In this paper we introduce a generalization of the c-planarity testing problem for clustered graphs. Namely, given a clustered graph, the goal of the S PLIT-C-P LANARITY problem is to split as few clusters as possible in order to make the graph c-planar. Determining whether zero splits are enough coincides with testing c-planarity. We show that S PLIT-C-P LANARITY is NP-complete for c-connected clustered triangulations and for non-c-connected clustered paths and cycles. On the other hand, we present a polynomial-time algorithm for flat c-connected clustered graphs whose underlying graph is a biconnected seriesparallel graph, both in the fixed and in the variable embedding setting, when the splits are assumed to maintain the c-connectivity of the clusters
Advances on Testing C-Planarity of Embedded Flat Clustered Graphs
We show a polynomial-time algorithm for testing c-planarity of embedded flat
clustered graphs with at most two vertices per cluster on each face.Comment: Accepted at GD '1
Planar and Poly-Arc Lombardi Drawings
In Lombardi drawings of graphs, edges are represented as circular arcs, and
the edges incident on vertices have perfect angular resolution. However, not
every graph has a Lombardi drawing, and not every planar graph has a planar
Lombardi drawing. We introduce k-Lombardi drawings, in which each edge may be
drawn with k circular arcs, noting that every graph has a smooth 2-Lombardi
drawing. We show that every planar graph has a smooth planar 3-Lombardi drawing
and further investigate topics connecting planarity and Lombardi drawings.Comment: Expanded version of paper appearing in the 19th International
Symposium on Graph Drawing (GD 2011). 16 pages, 8 figure
Indexing Information for Data Forensics
We introduce novel techniques for organizing the indexing structures of how data is stored so that alterations from an original version can be detected and the changed values specifically identified. We give forensic constructions for several fundamental data structures, including arrays, linked lists, binary search trees, skip lists, and hash tables. Some of our constructions are based on a new reduced-randomness construction for nonadaptive combinatorial group testing
Measurements of long-range near-side angular correlations in TeV proton-lead collisions in the forward region
Two-particle angular correlations are studied in proton-lead collisions at a
nucleon-nucleon centre-of-mass energy of TeV, collected
with the LHCb detector at the LHC. The analysis is based on data recorded in
two beam configurations, in which either the direction of the proton or that of
the lead ion is analysed. The correlations are measured in the laboratory
system as a function of relative pseudorapidity, , and relative
azimuthal angle, , for events in different classes of event
activity and for different bins of particle transverse momentum. In
high-activity events a long-range correlation on the near side, , is observed in the pseudorapidity range . This
measurement of long-range correlations on the near side in proton-lead
collisions extends previous observations into the forward region up to
. The correlation increases with growing event activity and is found
to be more pronounced in the direction of the lead beam. However, the
correlation in the direction of the lead and proton beams are found to be
compatible when comparing events with similar absolute activity in the
direction analysed.Comment: All figures and tables, along with any supplementary material and
additional information, are available at
https://lhcbproject.web.cern.ch/lhcbproject/Publications/LHCbProjectPublic/LHCb-PAPER-2015-040.htm
Study of the production of and hadrons in collisions and first measurement of the branching fraction
The product of the () differential production
cross-section and the branching fraction of the decay () is
measured as a function of the beauty hadron transverse momentum, ,
and rapidity, . The kinematic region of the measurements is and . The measurements use a data sample
corresponding to an integrated luminosity of collected by the
LHCb detector in collisions at centre-of-mass energies in 2011 and in 2012. Based on previous LHCb
results of the fragmentation fraction ratio, , the
branching fraction of the decay is
measured to be \begin{equation*} \mathcal{B}(\Lambda_b^0\rightarrow J/\psi
pK^-)= (3.17\pm0.04\pm0.07\pm0.34^{+0.45}_{-0.28})\times10^{-4},
\end{equation*} where the first uncertainty is statistical, the second is
systematic, the third is due to the uncertainty on the branching fraction of
the decay , and the
fourth is due to the knowledge of . The sum of the
asymmetries in the production and decay between and
is also measured as a function of and .
The previously published branching fraction of , relative to that of , is updated.
The branching fractions of are determined.Comment: 29 pages, 19figures. All figures and tables, along with any
supplementary material and additional information, are available at
https://lhcbproject.web.cern.ch/lhcbproject/Publications/LHCbProjectPublic/LHCb-PAPER-2015-032.htm
Evidence for the strangeness-changing weak decay
Using a collision data sample corresponding to an integrated luminosity
of 3.0~fb, collected by the LHCb detector, we present the first search
for the strangeness-changing weak decay . No
hadron decay of this type has been seen before. A signal for this decay,
corresponding to a significance of 3.2 standard deviations, is reported. The
relative rate is measured to be
, where and
are the and fragmentation
fractions, and is the branching
fraction. Assuming is bounded between 0.1 and
0.3, the branching fraction would lie
in the range from to .Comment: 7 pages, 2 figures, All figures and tables, along with any
supplementary material and additional information, are available at
https://lhcbproject.web.cern.ch/lhcbproject/Publications/LHCbProjectPublic/LHCb-PAPER-2015-047.htm
flavour tagging using charm decays at the LHCb experiment
An algorithm is described for tagging the flavour content at production of
neutral mesons in the LHCb experiment. The algorithm exploits the
correlation of the flavour of a meson with the charge of a reconstructed
secondary charm hadron from the decay of the other hadron produced in the
proton-proton collision. Charm hadron candidates are identified in a number of
fully or partially reconstructed Cabibbo-favoured decay modes. The algorithm is
calibrated on the self-tagged decay modes and using of data collected by the LHCb
experiment at centre-of-mass energies of and
. Its tagging power on these samples of
decays is .Comment: All figures and tables, along with any supplementary material and
additional information, are available at
http://lhcbproject.web.cern.ch/lhcbproject/Publications/LHCbProjectPublic/LHCb-PAPER-2015-027.htm
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