11,627 research outputs found
Drawing Binary Tanglegrams: An Experimental Evaluation
A binary tanglegram is a pair of binary trees whose leaf sets are in
one-to-one correspondence; matching leaves are connected by inter-tree edges.
For applications, for example in phylogenetics or software engineering, it is
required that the individual trees are drawn crossing-free. A natural
optimization problem, denoted tanglegram layout problem, is thus to minimize
the number of crossings between inter-tree edges.
The tanglegram layout problem is NP-hard and is currently considered both in
application domains and theory. In this paper we present an experimental
comparison of a recursive algorithm of Buchin et al., our variant of their
algorithm, the algorithm hierarchy sort of Holten and van Wijk, and an integer
quadratic program that yields optimal solutions.Comment: see
http://www.siam.org/proceedings/alenex/2009/alx09_011_nollenburgm.pd
WARP: Wavelets with adaptive recursive partitioning for multi-dimensional data
Effective identification of asymmetric and local features in images and other
data observed on multi-dimensional grids plays a critical role in a wide range
of applications including biomedical and natural image processing. Moreover,
the ever increasing amount of image data, in terms of both the resolution per
image and the number of images processed per application, requires algorithms
and methods for such applications to be computationally efficient. We develop a
new probabilistic framework for multi-dimensional data to overcome these
challenges through incorporating data adaptivity into discrete wavelet
transforms, thereby allowing them to adapt to the geometric structure of the
data while maintaining the linear computational scalability. By exploiting a
connection between the local directionality of wavelet transforms and recursive
dyadic partitioning on the grid points of the observation, we obtain the
desired adaptivity through adding to the traditional Bayesian wavelet
regression framework an additional layer of Bayesian modeling on the space of
recursive partitions over the grid points. We derive the corresponding
inference recipe in the form of a recursive representation of the exact
posterior, and develop a class of efficient recursive message passing
algorithms for achieving exact Bayesian inference with a computational
complexity linear in the resolution and sample size of the images. While our
framework is applicable to a range of problems including multi-dimensional
signal processing, compression, and structural learning, we illustrate its work
and evaluate its performance in the context of 2D and 3D image reconstruction
using real images from the ImageNet database. We also apply the framework to
analyze a data set from retinal optical coherence tomography
NXNSAttack: Recursive DNS Inefficiencies and Vulnerabilities
This paper exposes a new vulnerability and introduces a corresponding attack,
the NoneXistent Name Server Attack (NXNSAttack), that disrupts and may paralyze
the DNS system, making it difficult or impossible for Internet users to access
websites, web e-mail, online video chats, or any other online resource. The
NXNSAttack generates a storm of packets between DNS resolvers and DNS
authoritative name servers. The storm is produced by the response of resolvers
to unrestricted referral response messages of authoritative name servers. The
attack is significantly more destructive than NXDomain attacks (e.g., the Mirai
attack): i) It reaches an amplification factor of more than 1620x on the number
of packets exchanged by the recursive resolver. ii) In addition to the negative
cache, the attack also saturates the 'NS' section of the resolver caches. To
mitigate the attack impact, we propose an enhancement to the recursive resolver
algorithm, MaxFetch(k), that prevents unnecessary proactive fetches. We
implemented the MaxFetch(1) mitigation enhancement on a BIND resolver and
tested it on real-world DNS query datasets. Our results show that MaxFetch(1)
degrades neither the recursive resolver throughput nor its latency. Following
the discovery of the attack, a responsible disclosure procedure was carried
out, and several DNS vendors and public providers have issued a CVE and patched
their systems
Task mapping on a dragonfly supercomputer
The dragonfly network topology has recently gained traction in the design of high performance computing (HPC) systems and has been implemented in large-scale supercomputers. The impact of task mapping, i.e., placement of MPI ranks onto compute cores, on the communication performance of applications on dragonfly networks has not been comprehensively investigated on real large-scale systems. This paper demonstrates that task mapping affects the communication overhead significantly in dragonflies and the magnitude of this effect is sensitive to the application, job size, and the OpenMP settings. Among the three task mapping algorithms we study (in-order, random, and recursive coordinate bisection), selecting a suitable task mapper reduces application communication time by up to 47%
Bayesian model averaging over tree-based dependence structures for multivariate extremes
Describing the complex dependence structure of extreme phenomena is
particularly challenging. To tackle this issue we develop a novel statistical
algorithm that describes extremal dependence taking advantage of the inherent
hierarchical dependence structure of the max-stable nested logistic
distribution and that identifies possible clusters of extreme variables using
reversible jump Markov chain Monte Carlo techniques. Parsimonious
representations are achieved when clusters of extreme variables are found to be
completely independent. Moreover, we significantly decrease the computational
complexity of full likelihood inference by deriving a recursive formula for the
nested logistic model likelihood. The algorithm performance is verified through
extensive simulation experiments which also compare different likelihood
procedures. The new methodology is used to investigate the dependence
relationships between extreme concentration of multiple pollutants in
California and how these pollutants are related to extreme weather conditions.
Overall, we show that our approach allows for the representation of complex
extremal dependence structures and has valid applications in multivariate data
analysis, such as air pollution monitoring, where it can guide policymaking
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