pauleve-journals.bib

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@article{PMR10-TSE,
  author = {{P}aulev{\'e}, {L}o{\"i}c and {M}agnin, {M}organ and {R}oux, {O}livier},
  title = {{T}uning {T}emporal {F}eatures within the {S}tochastic $\pi$-{C}alculus},
  journal = {IEEE Transactions on Software Engineering},
  year = {2010},
  volume = {99},
  number = {PrePrints},
  address = {Los Alamitos, CA, USA},
  class = {journal},
  doi = {10.1109/TSE.2010.95},
  issn = {0098-5589},
  publisher = {IEEE Computer Society}
}

@article{PR10-CRAS,
  author = {Lo{\"i}c Paulev{\'e} and Adrien Richard},
  title = {{T}opological {F}ixed {P}oints in {B}oolean {N}etworks},
  journal = {Comptes Rendus de l'Acad\'{e}mie des Sciences - Series I - Mathematics},
  year = {2010},
  volume = {348},
  pages = {825 - 828},
  number = {15-16},
  abstract = {We introduce the notion of a topological fixed point in Boolean Networks:
	a fixed point of Boolean network F is said to be topologic if it
	is a fixed point of every Boolean network with the same interaction
	graph as the one of F. Then, we characterize the number of topological
	fixed points of a Boolean network according to the structure of its
	interaction graph.},
  class = {journal},
  doi = {10.1016/j.crma.2010.07.014},
  file = {PR10-CRAS.pdf:PR10-CRAS.pdf:PDF}
}

@incollection{PMR10-TCSB,
  author = {{P}aulev{\'e}, {L}o{\"i}c and {M}agnin, {M}organ and {R}oux, {O}livier},
  title = {Refining Dynamics of Gene Regulatory Networks in a Stochastic $\pi$-Calculus
	Framework},
  booktitle = {Transactions on Computational Systems Biology XIII},
  publisher = {Springer Berlin / Heidelberg},
  year = {2011},
  editor = {Priami, Corrado and Back, Ralph-Johan and Petre, Ion and de Vink,
	Erik},
  volume = {6575},
  series = {Lecture Notes in Computer Science},
  pages = {171-191},
  affiliation = {IRCCyN, UMR CNRS 6597, École Centrale de Nantes, France},
  class = {journal},
  doi = {10.1007/978-3-642-19748-2_8},
  file = {PMR10-TCSB.pdf:PMR10-TCSB.pdf:PDF}
}

@article{PJA10,
  author = {Lo{\"i}c Paulev{\'e} and Herv{\'e} J{\'e}gou and Laurent Amsaleg},
  title = {Locality sensitive hashing: A comparison of hash function types and
	querying mechanisms},
  journal = {Pattern Recognition Letters},
  year = {2010},
  volume = {31},
  pages = {1348 - 1358},
  number = {11},
  abstract = {It is well known that high-dimensional nearest neighbor retrieval
	is very expensive. Dramatic performance gains are obtained using
	approximate search schemes, such as the popular Locality-Sensitive
	Hashing (LSH). Several extensions have been proposed to address the
	limitations of this algorithm, in particular, by choosing more appropriate
	hash functions to better partition the vector space. All the proposed
	extensions, however, rely on a structured quantizer for hashing,
	poorly fitting real data sets, limiting its performance in practice.
	In this paper, we compare several families of space hashing functions
	in a real setup, namely when searching for high-dimension SIFT descriptors.
	The comparison of random projections, lattice quantizers, k-means
	and hierarchical k-means reveal that unstructured quantizer significantly
	improves the accuracy of LSH, as it closely fits the data in the
	feature space. We then compare two querying mechanisms introduced
	in the literature with the one originally proposed in LSH, and discuss
	their respective merits and limitations.},
  class = {journal},
  doi = {10.1016/j.patrec.2010.04.004},
  file = {PJA10.pdf:PJA10.pdf:PDF},
  issn = {0167-8655},
  keywords = {Search methods},
  timestamp = {2010.04.05}
}