136 research outputs found
Load-Balancing for Parallel Delaunay Triangulations
Computing the Delaunay triangulation (DT) of a given point set in
is one of the fundamental operations in computational geometry.
Recently, Funke and Sanders (2017) presented a divide-and-conquer DT algorithm
that merges two partial triangulations by re-triangulating a small subset of
their vertices - the border vertices - and combining the three triangulations
efficiently via parallel hash table lookups. The input point division should
therefore yield roughly equal-sized partitions for good load-balancing and also
result in a small number of border vertices for fast merging. In this paper, we
present a novel divide-step based on partitioning the triangulation of a small
sample of the input points. In experiments on synthetic and real-world data
sets, we achieve nearly perfectly balanced partitions and small border
triangulations. This almost cuts running time in half compared to
non-data-sensitive division schemes on inputs exhibiting an exploitable
underlying structure.Comment: Short version submitted to EuroPar 201
Formal and model driven design of the bright light therapy system Luxamet
Seasonal depression seriously diminishes the quality of life for many patients. To improve their condition, we propose LUXAMET, a bright light therapy system. This system has the potential to relieve patients from some of the symptoms caused by seasonal depression. The system was designed with a formal and model driven design methodology. This methodology enabled us to minimize systemic hazards, like blinding patients with an unhealthy dose of light. This was achieved by controlling race conditions and memory leaks, during design time. We prove that the system specification is deadlock as well as livelock free and there are no invariant violations. These proofs, together with the similarity between specification model and implementation code, make us confident that the implemented system is a reliable tool which can help patients during seasonal depression
Robust Detection of Hierarchical Communities from Escherichia coli Gene Expression Data
Determining the functional structure of biological networks is a central goal
of systems biology. One approach is to analyze gene expression data to infer a
network of gene interactions on the basis of their correlated responses to
environmental and genetic perturbations. The inferred network can then be
analyzed to identify functional communities. However, commonly used algorithms
can yield unreliable results due to experimental noise, algorithmic
stochasticity, and the influence of arbitrarily chosen parameter values.
Furthermore, the results obtained typically provide only a simplistic view of
the network partitioned into disjoint communities and provide no information of
the relationship between communities. Here, we present methods to robustly
detect coregulated and functionally enriched gene communities and demonstrate
their application and validity for Escherichia coli gene expression data.
Applying a recently developed community detection algorithm to the network of
interactions identified with the context likelihood of relatedness (CLR)
method, we show that a hierarchy of network communities can be identified.
These communities significantly enrich for gene ontology (GO) terms, consistent
with them representing biologically meaningful groups. Further, analysis of the
most significantly enriched communities identified several candidate new
regulatory interactions. The robustness of our methods is demonstrated by
showing that a core set of functional communities is reliably found when
artificial noise, modeling experimental noise, is added to the data. We find
that noise mainly acts conservatively, increasing the relatedness required for
a network link to be reliably assigned and decreasing the size of the core
communities, rather than causing association of genes into new communities.Comment: Due to appear in PLoS Computational Biology. Supplementary Figure S1
was not uploaded but is available by contacting the author. 27 pages, 5
figures, 15 supplementary file
Recent Advances in Graph Partitioning
We survey recent trends in practical algorithms for balanced graph
partitioning together with applications and future research directions
A preliminary dictionary of Maori gainwords compiled on historical principles
This thesis is a preliminary dictionary of Maori gainwords compiled on historical principles. It will serve as the starting point for a fully fledged historical dictionary of Maori gainwords. The sources are a selection of all those Maori language publications printed between the dates 1815 and 1899. A large number of source items were photocopied from other institutions, and the binding and subsequent availability of these was not always in the order wished for. The research therefore has its limitations (clearly indicated by the use of the word 'preliminary' in the thesis title). Full coverage of all printed Maori publications between 1815 and 1899 has not been possible. Despite this, this preliminary dictionary offers a good indication of the extent of new gainword vocabulary introduced within the time frame. This thesis suggests that the terms loanword and borrowing should
be replaced by the new term gainword or gain, and that the process
by which new items of vocabulary enter a language should be known
as gaining .. 'Gaining' is a positive process, and the word 'gainword' is
normally devoid of any negative connotations or implications of
cultural imperialism.
This thesis is the first extended scholarly research into Maori
gainword lexicography. Although 'preliminary', the dictionary is the
first devoted solely to Maori gainwords - previous dictionaries of
Maori have had gainwords as appendices, or have listed small
numbers of gainwords in their general corpus. This dictionary builds
on those earlier dictionaries by giving gainwords their own
dictionary.
This thesis will indicate that nearly all new items of vocabulary
introduced into Maori language during the period researched were
introduced by English-speaking Pakeha. English-speaking (and some
few French-speaking) Pakeha controlled the printed word for some
considerable time - up until the first Maori-controlled publication,
Te Hokioi in 1861, in fact most gainwords were therefore imposed.
The frequency count for Maori-driven gains done for this thesis will
give only some slight indication of Maori use and acceptance of gains
between 1815 and 1899
A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems
International audienceSzeliski et al. published an influential study in 2006 on energy minimization methods for Markov Random Fields (MRF). This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenomenal success of random field models means that the kinds of inference problems that have to be solved changed significantly. Specifically , the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different car-dinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of more than 27 state-of-the-art optimization techniques on a corpus of 2,453 energy minimization instances from diverse applications in computer vision. To ensure reproducibility, we evaluate all methods in the OpenGM 2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types
Enveloping Sophisticated Tools into Process-Centered Environments
We present a tool integration strategy based on enveloping pre-existing tools without source code modifications or recompilation, and without assuming an extension language, application programming interface, or any other special capabilities on the part of the tool. This Black Box enveloping (or wrapping) idea has existed for a long time, but was previously restricted to relatively simple tools. We describe the design and implementation of, and experimentation with, a new Black Box enveloping facility intended for sophisticated tools --- with particular concern for the emerging class of groupware applications
Automatically Selecting Inference Algorithms for Discrete Energy Minimisation
Minimisation of discrete energies defined over factors is an important
problem in computer vision, and a vast number of MAP inference algorithms have
been proposed. Different inference algorithms perform better on factor graph
models (GMs) from different underlying problem classes, and in general it is
difficult to know which algorithm will yield the lowest energy for a given GM.
To mitigate this difficulty, survey papers advise the practitioner on what
algorithms perform well on what classes of models. We take the next step
forward, and present a technique to automatically select the best inference
algorithm for an input GM. We validate our method experimentally on an extended
version of the OpenGM2 benchmark, containing a diverse set of vision problems.
On average, our method selects an inference algorithm yielding labellings with
96% of variables the same as the best available algorithm
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