208 research outputs found
Minimum multicuts and Steiner forests for Okamura-Seymour graphs
We study the problem of finding minimum multicuts for an undirected planar
graph, where all the terminal vertices are on the boundary of the outer face.
This is known as an Okamura-Seymour instance. We show that for such an
instance, the minimum multicut problem can be reduced to the minimum-cost
Steiner forest problem on a suitably defined dual graph. The minimum-cost
Steiner forest problem has a 2-approximation algorithm. Hence, the minimum
multicut problem has a 2-approximation algorithm for an Okamura-Seymour
instance.Comment: 6 pages, 1 figur
Analyzing Cascading Failures in Smart Grids under Random and Targeted Attacks
We model smart grids as complex interdependent networks, and study targeted
attacks on smart grids for the first time. A smart grid consists of two
networks: the power network and the communication network, interconnected by
edges. Occurrence of failures (attacks) in one network triggers failures in the
other network, and propagates in cascades across the networks. Such cascading
failures can result in disintegration of either (or both) of the networks.
Earlier works considered only random failures. In practical situations, an
attacker is more likely to compromise nodes selectively.
We study cascading failures in smart grids, where an attacker selectively
compromises the nodes with probabilities proportional to their degrees; high
degree nodes are compromised with higher probability. We mathematically analyze
the sizes of the giant components of the networks under targeted attacks, and
compare the results with the corresponding sizes under random attacks. We show
that networks disintegrate faster for targeted attacks compared to random
attacks. A targeted attack on a small fraction of high degree nodes
disintegrates one or both of the networks, whereas both the networks contain
giant components for random attack on the same fraction of nodes.Comment: Accepted for publication in 28th IEEE International Conference on
Advanced Information Networking and Applications (AINA) 201
Direct observation of valley-hybridization and universal symmetry of graphene with mesoscopic conductance fluctuations
In graphene, the valleys represent spin-like quantities and can act as a
physical resource in valley-based electronics to novel quantum computation
schemes. Here we demonstrate a direct route to tune and read the valley quantum
states of disordered graphene by measuring the mesoscopic conductance
fluctuations. We show that the conductance fluctuations in graphene at low
temperatures are reduced by a factor of four when valley triplet states are
gapped in the presence of short range potential scatterers at high carrier
densities. We also show that this implies a gate tunable universal symmetry
class which outlines a fundamental feature arising from graphene's unique
crystal structure.Comment: 5 pages, 5 figure
Scheduling Resources for Executing a Partial Set of Jobs
In this paper, we consider the problem of choosing a minimum cost set of
resources for executing a specified set of jobs. Each input job is an interval,
determined by its start-time and end-time. Each resource is also an interval
determined by its start-time and end-time; moreover, every resource has a
capacity and a cost associated with it. We consider two versions of this
problem. In the partial covering version, we are also given as input a number
k, specifying the number of jobs that must be performed. The goal is to choose
k jobs and find a minimum cost set of resources to perform the chosen k jobs
(at any point of time the capacity of the chosen set of resources should be
sufficient to execute the jobs active at that time). We present an O(log
n)-factor approximation algorithm for this problem.
We also consider the prize collecting version, wherein every job also has a
penalty associated with it. The feasible solution consists of a subset of the
jobs, and a set of resources, to perform the chosen subset of jobs. The goal is
to find a feasible solution that minimizes the sum of the costs of the selected
resources and the penalties of the jobs that are not selected. We present a
constant factor approximation algorithm for this problemComment: Full version of paper accepted to FSTTCS'201
Parallel and Distributed Machine Learning Algorithms for Scalable Big Data Analytics
This editorial is for the Special Issue of the journal Future Generation Computing Systems, consisting of the selected papers of the 6th International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics (ParLearning 2017). In this editorial, we have given a high-level overview of the 4 papers contained in this special issue, along with references to some of the related works
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