12,855 research outputs found
Distributed Connectivity Decomposition
We present time-efficient distributed algorithms for decomposing graphs with
large edge or vertex connectivity into multiple spanning or dominating trees,
respectively. As their primary applications, these decompositions allow us to
achieve information flow with size close to the connectivity by parallelizing
it along the trees. More specifically, our distributed decomposition algorithms
are as follows:
(I) A decomposition of each undirected graph with vertex-connectivity
into (fractionally) vertex-disjoint weighted dominating trees with total weight
, in rounds.
(II) A decomposition of each undirected graph with edge-connectivity
into (fractionally) edge-disjoint weighted spanning trees with total
weight , in
rounds.
We also show round complexity lower bounds of
and
for the above two decompositions,
using techniques of [Das Sarma et al., STOC'11]. Moreover, our
vertex-connectivity decomposition extends to centralized algorithms and
improves the time complexity of [Censor-Hillel et al., SODA'14] from
to near-optimal .
As corollaries, we also get distributed oblivious routing broadcast with
-competitive edge-congestion and -competitive
vertex-congestion. Furthermore, the vertex connectivity decomposition leads to
near-time-optimal -approximation of vertex connectivity: centralized
and distributed . The former moves
toward the 1974 conjecture of Aho, Hopcroft, and Ullman postulating an
centralized exact algorithm while the latter is the first distributed vertex
connectivity approximation
Content Distribution by Multiple Multicast Trees and Intersession Cooperation: Optimal Algorithms and Approximations
In traditional massive content distribution with multiple sessions, the
sessions form separate overlay networks and operate independently, where some
sessions may suffer from insufficient resources even though other sessions have
excessive resources. To cope with this problem, we consider the universal
swarming approach, which allows multiple sessions to cooperate with each other.
We formulate the problem of finding the optimal resource allocation to maximize
the sum of the session utilities and present a subgradient algorithm which
converges to the optimal solution in the time-average sense. The solution
involves an NP-hard subproblem of finding a minimum-cost Steiner tree. We cope
with this difficulty by using a column generation method, which reduces the
number of Steiner-tree computations. Furthermore, we allow the use of
approximate solutions to the Steiner-tree subproblem. We show that the
approximation ratio to the overall problem turns out to be no less than the
reciprocal of the approximation ratio to the Steiner-tree subproblem.
Simulation results demonstrate that universal swarming improves the performance
of resource-poor sessions with negligible impact to resource-rich sessions. The
proposed approach and algorithm are expected to be useful for
infrastructure-based content distribution networks with long-lasting sessions
and relatively stable network environment
Coding limits on the number of transcription factors
Transcription factor proteins bind specific DNA sequences to control the
expression of genes. They contain DNA binding domains which belong to several
super-families, each with a specific mechanism of DNA binding. The total number
of transcription factors encoded in a genome increases with the number of genes
in the genome. Here, we examined the number of transcription factors from each
super-family in diverse organisms.
We find that the number of transcription factors from most super-families
appears to be bounded. For example, the number of winged helix factors does not
generally exceed 300, even in very large genomes. The magnitude of the maximal
number of transcription factors from each super-family seems to correlate with
the number of DNA bases effectively recognized by the binding mechanism of that
super-family. Coding theory predicts that such upper bounds on the number of
transcription factors should exist, in order to minimize cross-binding errors
between transcription factors. This theory further predicts that factors with
similar binding sequences should tend to have similar biological effect, so
that errors based on mis-recognition are minimal. We present evidence that
transcription factors with similar binding sequences tend to regulate genes
with similar biological functions, supporting this prediction.
The present study suggests limits on the transcription factor repertoire of
cells, and suggests coding constraints that might apply more generally to the
mapping between binding sites and biological function.Comment: http://www.weizmann.ac.il/complex/tlusty/papers/BMCGenomics2006.pdf
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1590034/
http://www.biomedcentral.com/1471-2164/7/23
Convolutional Neural Networks over Tree Structures for Programming Language Processing
Programming language processing (similar to natural language processing) is a
hot research topic in the field of software engineering; it has also aroused
growing interest in the artificial intelligence community. However, different
from a natural language sentence, a program contains rich, explicit, and
complicated structural information. Hence, traditional NLP models may be
inappropriate for programs. In this paper, we propose a novel tree-based
convolutional neural network (TBCNN) for programming language processing, in
which a convolution kernel is designed over programs' abstract syntax trees to
capture structural information. TBCNN is a generic architecture for programming
language processing; our experiments show its effectiveness in two different
program analysis tasks: classifying programs according to functionality, and
detecting code snippets of certain patterns. TBCNN outperforms baseline
methods, including several neural models for NLP.Comment: Accepted at AAAI-1
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