135,157 research outputs found
Identifying Design Requirements for Wireless Routing Link Metrics
In this paper, we identify and analyze the requirements to design a new
routing link metric for wireless multihop networks. Considering these
requirements, when a link metric is proposed, then both the design and
implementation of the link metric with a routing protocol become easy.
Secondly, the underlying network issues can easily be tackled. Thirdly, an
appreciable performance of the network is guaranteed. Along with the existing
implementation of three link metrics Expected Transmission Count (ETX), Minimum
Delay (MD), and Minimum Loss (ML), we implement inverse ETX; invETX with
Optimized Link State Routing (OLSR) using NS-2.34. The simulation results show
that how the computational burden of a metric degrades the performance of the
respective protocol and how a metric has to trade-off between different
performance parameters
Computational identification of signalling pathways in Plasmodium falciparum
Malaria is one of the worldâs most common and serious diseases causing death of about 3 million people
each year. Its most severe occurrence is caused by the protozoan Plasmodium falciparum. Reports have
shown that the resistance of the parasite to existing drugs is increasing. Therefore, there is a huge and
urgent need to discover and validate new drug or vaccine targets to enable the development of new
treatments for malaria. The ability to discover these drug or vaccine targets can only be enhanced from
our deep understanding of the detailed biology of the parasite, for example how cells function and how
proteins organize into modules such as metabolic, regulatory and signal transduction pathways. It has
been noted that the knowledge of signalling transduction pathways in Plasmodium is fundamental to aid
the design of new strategies against malaria. This work uses a linear-time algorithm for finding paths in a
network under modified biologically motivated constraints. We predicted several important signalling
transduction pathways in Plasmodium falciparum. We have predicted a viable signalling pathway
characterized in terms of the genes responsible that may be the PfPKB pathway recently elucidated in
Plasmodium falciparum. We obtained from the FIKK family, a signal transduction pathway that ends up on
a chloroquine resistance marker protein, which indicates that interference with FIKK proteins might
reverse Plasmodium falciparum from resistant to sensitive phenotype. We also proposed a hypothesis
that showed the FIKK proteins in this pathway as enabling the resistance parasite to have a mechanism
for releasing chloroquine (via an efflux process). Furthermore, we also predicted a signalling pathway
that may have been responsible for signalling the start of the invasion process of Red Blood Cell (RBC) by
the merozoites. It has been noted that the understanding of this pathway will give insight into the
parasite virulence and will facilitate rational vaccine design against merozoites invasion. And we have a
host of other predicted pathways, some of which have been used in this work to predict the functionality
of some proteins
Optimizing expected word error rate via sampling for speech recognition
State-level minimum Bayes risk (sMBR) training has become the de facto
standard for sequence-level training of speech recognition acoustic models. It
has an elegant formulation using the expectation semiring, and gives large
improvements in word error rate (WER) over models trained solely using
cross-entropy (CE) or connectionist temporal classification (CTC). sMBR
training optimizes the expected number of frames at which the reference and
hypothesized acoustic states differ. It may be preferable to optimize the
expected WER, but WER does not interact well with the expectation semiring, and
previous approaches based on computing expected WER exactly involve expanding
the lattices used during training. In this paper we show how to perform
optimization of the expected WER by sampling paths from the lattices used
during conventional sMBR training. The gradient of the expected WER is itself
an expectation, and so may be approximated using Monte Carlo sampling. We show
experimentally that optimizing WER during acoustic model training gives 5%
relative improvement in WER over a well-tuned sMBR baseline on a 2-channel
query recognition task (Google Home)
NeuRoute: Predictive Dynamic Routing for Software-Defined Networks
This paper introduces NeuRoute, a dynamic routing framework for Software
Defined Networks (SDN) entirely based on machine learning, specifically, Neural
Networks. Current SDN/OpenFlow controllers use a default routing based on
Dijkstra algorithm for shortest paths, and provide APIs to develop custom
routing applications. NeuRoute is a controller-agnostic dynamic routing
framework that (i) predicts traffic matrix in real time, (ii) uses a neural
network to learn traffic characteristics and (iii) generates forwarding rules
accordingly to optimize the network throughput. NeuRoute achieves the same
results as the most efficient dynamic routing heuristic but in much less
execution time.Comment: Accepted for CNSM 201
K shortest paths in stochastic time-dependent networks
A substantial amount of research has been devoted to the shortest path problem in networks where travel times are stochastic or (deterministic and) time-dependent. More recently, a growing interest has been attracted by networks that are both stochastic and time-dependent. In these networks, the best route choice is not necessarily a path, but rather a time-adaptive strategy that assigns successors to nodes as a function of time. In some particular cases, the shortest origin-destination path must nevertheless be chosen a priori, since time-adaptive choices are not allowed. Unfortunately, finding the a priori shortest path is NP-hard, while the best time-adaptive strategy can be found in polynomial time. In this paper, we propose a solution method for the a priori shortest path problem, and we show that it can be easily adapted to the ranking of the first K shortest paths. Moreover, we present a computational comparison of time-adaptive and a priori route choices, pointing out the effect of travel time and cost distributions. The reported results show that, under realistic distributions, our solution methods are effectiveShortest paths; K shortest paths; stochastic time-dependent networks; routing; directed hypergraphs
On the Size and the Approximability of Minimum Temporally Connected Subgraphs
We consider temporal graphs with discrete time labels and investigate the
size and the approximability of minimum temporally connected spanning
subgraphs. We present a family of minimally connected temporal graphs with
vertices and edges, thus resolving an open question of (Kempe,
Kleinberg, Kumar, JCSS 64, 2002) about the existence of sparse temporal
connectivity certificates. Next, we consider the problem of computing a minimum
weight subset of temporal edges that preserve connectivity of a given temporal
graph either from a given vertex r (r-MTC problem) or among all vertex pairs
(MTC problem). We show that the approximability of r-MTC is closely related to
the approximability of Directed Steiner Tree and that r-MTC can be solved in
polynomial time if the underlying graph has bounded treewidth. We also show
that the best approximation ratio for MTC is at least and at most , for
any constant , where is the number of temporal edges and
is the maximum degree of the underlying graph. Furthermore, we prove
that the unweighted version of MTC is APX-hard and that MTC is efficiently
solvable in trees and -approximable in cycles
Searchability of Networks
We investigate the searchability of complex systems in terms of their
interconnectedness. Associating searchability with the number and size of
branch points along the paths between the nodes, we find that scale-free
networks are relatively difficult to search, and thus that the abundance of
scale-free networks in nature and society may reflect an attempt to protect
local areas in a highly interconnected network from nonrelated communication.
In fact, starting from a random node, real-world networks with higher order
organization like modular or hierarchical structure are even more difficult to
navigate than random scale-free networks. The searchability at the node level
opens the possibility for a generalized hierarchy measure that captures both
the hierarchy in the usual terms of trees as in military structures, and the
intrinsic hierarchical nature of topological hierarchies for scale-free
networks as in the Internet.Comment: 9 pages, 10 figure
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