@techreport{5398,
abstract = {This document is created as a part of the project “Repository for Research Data on IST Austria”. It summarises the actual state of research data at IST Austria, based on survey results. It supports the choice of appropriate software, which would best fit the requirements of their users, the researchers.},
author = {Porsche, Jana},
publisher = {IST Austria},
title = {{Actual state of research data @ ISTAustria}},
year = {2012},
}
@inbook{5745,
author = {Gupta, Ashutosh},
booktitle = {Automated Technology for Verification and Analysis},
isbn = {9783642333859},
issn = {0302-9743},
location = {Thiruvananthapuram, Kerala, India},
pages = {107--121},
publisher = {Springer Berlin Heidelberg},
title = {{Improved Single Pass Algorithms for Resolution Proof Reduction}},
doi = {10.1007/978-3-642-33386-6_10},
volume = {7561},
year = {2012},
}
@inproceedings{2715,
abstract = {We consider Markov decision processes (MDPs) with specifications given as Büchi (liveness) objectives. We consider the problem of computing the set of almost-sure winning vertices from where the objective can be ensured with probability 1. We study for the first time the average case complexity of the classical algorithm for computing the set of almost-sure winning vertices for MDPs with Büchi objectives. Our contributions are as follows: First, we show that for MDPs with constant out-degree the expected number of iterations is at most logarithmic and the average case running time is linear (as compared to the worst case linear number of iterations and quadratic time complexity). Second, for the average case analysis over all MDPs we show that the expected number of iterations is constant and the average case running time is linear (again as compared to the worst case linear number of iterations and quadratic time complexity). Finally we also show that given that all MDPs are equally likely, the probability that the classical algorithm requires more than constant number of iterations is exponentially small.},
author = {Chatterjee, Krishnendu and Joglekar, Manas and Shah, Nisarg},
location = {Hyderabad, India},
pages = {461 -- 473},
publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik},
title = {{Average case analysis of the classical algorithm for Markov decision processes with Büchi objectives}},
doi = {10.4230/LIPIcs.FSTTCS.2012.461},
volume = {18},
year = {2012},
}
@article{2848,
abstract = {We study evolutionary game theory in a setting where individuals learn from each other. We extend the traditional approach by assuming that a population contains individuals with different learning abilities. In particular, we explore the situation where individuals have different search spaces, when attempting to learn the strategies of others. The search space of an individual specifies the set of strategies learnable by that individual. The search space is genetically given and does not change under social evolutionary dynamics. We introduce a general framework and study a specific example in the context of direct reciprocity. For this example, we obtain the counter intuitive result that cooperation can only evolve for intermediate benefit-to-cost ratios, while small and large benefit-to-cost ratios favor defection. Our paper is a step toward making a connection between computational learning theory and evolutionary game dynamics.},
author = {Chatterjee, Krishnendu and Zufferey, Damien and Nowak, Martin},
journal = {Journal of Theoretical Biology},
pages = {161 -- 173},
publisher = {Elsevier},
title = {{Evolutionary game dynamics in populations with different learners}},
doi = {10.1016/j.jtbi.2012.02.021},
volume = {301},
year = {2012},
}
@article{2849,
author = {Edelsbrunner, Herbert and Strelkova, Nataliya},
journal = {Russian Mathematical Surveys},
number = {6},
pages = {1167 -- 1168},
publisher = {IOP Publishing Ltd.},
title = {{On the configuration space of Steiner minimal trees}},
doi = {10.1070/RM2012v067n06ABEH004820},
volume = {67},
year = {2012},
}
@inproceedings{2891,
abstract = {Quantitative automata are nondeterministic finite automata with edge weights. They value a
run by some function from the sequence of visited weights to the reals, and value a word by its
minimal/maximal run. They generalize boolean automata, and have gained much attention in
recent years. Unfortunately, important automaton classes, such as sum, discounted-sum, and
limit-average automata, cannot be determinized. Yet, the quantitative setting provides the potential
of approximate determinization. We define approximate determinization with respect to
a distance function, and investigate this potential.
We show that sum automata cannot be determinized approximately with respect to any
distance function. However, restricting to nonnegative weights allows for approximate determinization
with respect to some distance functions.
Discounted-sum automata allow for approximate determinization, as the influence of a word’s
suffix is decaying. However, the naive approach, of unfolding the automaton computations up
to a sufficient level, is shown to be doubly exponential in the discount factor. We provide an
alternative construction that is singly exponential in the discount factor, in the precision, and
in the number of states. We prove matching lower bounds, showing exponential dependency on
each of these three parameters.
Average and limit-average automata are shown to prohibit approximate determinization with
respect to any distance function, and this is the case even for two weights, 0 and 1.},
author = {Boker, Udi and Henzinger, Thomas A},
booktitle = {Leibniz International Proceedings in Informatics},
location = {Hyderabad, India},
pages = {362 -- 373},
publisher = {Schloss Dagstuhl - Leibniz-Zentrum für Informatik},
title = {{Approximate determinization of quantitative automata}},
doi = {10.4230/LIPIcs.FSTTCS.2012.362},
volume = {18},
year = {2012},
}
@inproceedings{2903,
abstract = {In order to enjoy a digital version of the Jordan Curve Theorem, it is common to use the closed topology for the foreground and the open topology for the background of a 2-dimensional binary image. In this paper, we introduce a single topology that enjoys this theorem for all thresholds decomposing a real-valued image into foreground and background. This topology is easy to construct and it generalizes to n-dimensional images.},
author = {Edelsbrunner, Herbert and Symonova, Olga},
location = {New Brunswick, NJ, USA },
pages = {41 -- 48},
publisher = {IEEE},
title = {{The adaptive topology of a digital image}},
doi = {10.1109/ISVD.2012.11},
year = {2012},
}
@article{2904,
abstract = {Generalized van der Corput sequences are onedimensional, infinite sequences in the unit interval. They are generated from permutations in integer base b and are the building blocks of the multi-dimensional Halton sequences. Motivated by recent progress of Atanassov on the uniform distribution behavior of Halton sequences, we study, among others, permutations of the form P(i) = ai (mod b) for coprime integers a and b. We show that multipliers a that either divide b - 1 or b + 1 generate van der Corput sequences with weak distribution properties. We give explicit lower bounds for the asymptotic distribution behavior of these sequences and relate them to sequences generated from the identity permutation in smaller bases, which are, due to Faure, the weakest distributed generalized van der Corput sequences.},
author = {Pausinger, Florian},
issn = {2118-8572},
journal = {Journal de Theorie des Nombres des Bordeaux},
number = {3},
pages = {729 -- 749},
publisher = {Universite de Bordeaux},
title = {{Weak multipliers for generalized van der Corput sequences}},
doi = {10.5802/jtnb.819},
volume = {24},
year = {2012},
}
@inproceedings{2916,
abstract = {The classical (boolean) notion of refinement for behavioral interfaces of system components is the alternating refinement preorder. In this paper, we define a quantitative measure for interfaces, called interface simulation distance. It makes the alternating refinement preorder quantitative by, intu- itively, tolerating errors (while counting them) in the alternating simulation game. We show that the interface simulation distance satisfies the triangle inequality, that the distance between two interfaces does not increase under parallel composition with a third interface, and that the distance between two interfaces can be bounded from above and below by distances between abstractions of the two interfaces. We illustrate the framework, and the properties of the distances under composition of interfaces, with two case studies.},
author = {Cerny, Pavol and Chmelik, Martin and Henzinger, Thomas A and Radhakrishna, Arjun},
booktitle = {Electronic Proceedings in Theoretical Computer Science},
location = {Napoli, Italy},
pages = {29 -- 42},
publisher = {EPTCS},
title = {{Interface Simulation Distances}},
doi = {10.4204/EPTCS.96.3},
volume = {96},
year = {2012},
}
@unpublished{2928,
abstract = { This paper addresses the problem of approximate MAP-MRF inference in general graphical models. Following [36], we consider a family of linear programming relaxations of the problem where each relaxation is specified by a set of nested pairs of factors for which the marginalization constraint needs to be enforced. We develop a generalization of the TRW-S algorithm [9] for this problem, where we use a decomposition into junction chains, monotonic w.r.t. some ordering on the nodes. This generalizes the monotonic chains in [9] in a natural way. We also show how to deal with nested factors in an efficient way. Experiments show an improvement over min-sum diffusion, MPLP and subgradient ascent algorithms on a number of computer vision and natural language processing problems. },
author = {Kolmogorov, Vladimir and Schoenemann, Thomas},
booktitle = {arXiv},
pages = {16},
publisher = {ArXiv},
title = {{Generalized sequential tree-reweighted message passing}},
year = {2012},
}