@inproceedings{2956,
abstract = {Two-player games on graphs are central in many problems in formal verification and program analysis such as synthesis and verification of open systems. In this work we consider solving recursive game graphs (or pushdown game graphs) that can model the control flow of sequential programs with recursion. While pushdown games have been studied before with qualitative objectives, such as reachability and parity objectives, in this work we study for the first time such games with the most well-studied quantitative objective, namely, mean payoff objectives. In pushdown games two types of strategies are relevant: (1) global strategies, that depend on the entire global history; and (2) modular strategies, that have only local memory and thus do not depend on the context of invocation, but only on the history of the current invocation of the module. Our main results are as follows: (1) One-player pushdown games with mean-payoff objectives under global strategies are decidable in polynomial time. (2) Two-player pushdown games with mean-payoff objectives under global strategies are undecidable. (3) One-player pushdown games with mean-payoff objectives under modular strategies are NP-hard. (4) Two-player pushdown games with mean-payoff objectives under modular strategies can be solved in NP (i.e., both one-player and two-player pushdown games with mean-payoff objectives under modular strategies are NP-complete). We also establish the optimal strategy complexity showing that global strategies for mean-payoff objectives require infinite memory even in one-player pushdown games; and memoryless modular strategies are sufficient in two-player pushdown games. Finally we also show that all the problems have the same computational complexity if the stack boundedness condition is added, where along with the mean-payoff objective the player must also ensure that the stack height is bounded.},
author = {Chatterjee, Krishnendu and Velner, Yaron},
booktitle = {Proceedings of the 2012 27th Annual ACM/IEEE Symposium on Logic in Computer Science},
location = {Dubrovnik, Croatia },
publisher = {IEEE},
title = {{Mean payoff pushdown games}},
doi = {10.1109/LICS.2012.30},
year = {2012},
}
@inproceedings{2957,
abstract = {We consider probabilistic automata on infinite words with acceptance defined by parity conditions. We consider three qualitative decision problems: (i) the positive decision problem asks whether there is a word that is accepted with positive probability; (ii) the almost decision problem asks whether there is a word that is accepted with probability 1; and (iii) the limit decision problem asks whether words are accepted with probability arbitrarily close to 1. We unify and generalize several decidability results for probabilistic automata over infinite words, and identify a robust (closed under union and intersection) subclass of probabilistic automata for which all the qualitative decision problems are decidable for parity conditions. We also show that if the input words are restricted to lasso shape (regular) words, then the positive and almost problems are decidable for all probabilistic automata with parity conditions. For most decidable problems we show an optimal PSPACE-complete complexity bound.},
author = {Chatterjee, Krishnendu and Tracol, Mathieu},
booktitle = {Proceedings of the 2012 27th Annual ACM/IEEE Symposium on Logic in Computer Science},
location = {Dubrovnik, Croatia },
publisher = {IEEE},
title = {{Decidable problems for probabilistic automata on infinite words}},
doi = {10.1109/LICS.2012.29},
year = {2012},
}
@article{2958,
abstract = {The activity of hippocampal pyramidal cells reflects both the current position of the animal and information related to its current behavior. Here we investigated whether single hippocampal neurons can encode several independent features defining trials during a memory task. We also tested whether task-related information is represented by partial remapping of the place cell population or, instead, via firing rate modulation of spatially stable place cells. To address these two questions, the activity of hippocampal neurons was recorded in rats performing a conditional discrimination task on a modified T-maze in which the identity of a food reward guided behavior. When the rat was on the central arm of the maze, the firing rate of pyramidal cells changed depending on two independent factors: (1) the identity of the food reward given to the animal and (2) the previous location of the animal on the maze. Importantly, some pyramidal cells encoded information relative to both factors. This trial-type specific and retrospective coding did not interfere with the spatial representation of the maze: hippocampal cells had stable place fields and their theta-phase precession profiles were unaltered during the task, indicating that trial-related information was encoded via rate remapping. During error trials, encoding of both trial-related information and spatial location was impaired. Finally, we found that pyramidal cells also encode trial-related information via rate remapping during the continuous version of the rewarded alternation task without delays. These results suggest that hippocampal neurons can encode several task-related cognitive aspects via rate remapping.},
author = {Allen, Kevin and Rawlins, J Nick and Bannerman, David and Csicsvari, Jozsef L},
journal = {Journal of Neuroscience},
number = {42},
pages = {14752 -- 14766},
publisher = {Society for Neuroscience},
title = {{Hippocampal place cells can encode multiple trial-dependent features through rate remapping}},
doi = {10.1523/JNEUROSCI.6175-11.2012},
volume = {32},
year = {2012},
}
@article{2959,
abstract = {We study maximum likelihood estimation in Gaussian graphical models from a geometric point of view. An algebraic elimination criterion allows us to find exact lower bounds on the number of observations needed to ensure that the maximum likelihood estimator (MLE) exists with probability one. This is applied to bipartite graphs, grids and colored graphs. We also study the ML degree, and we present the first instance of a graph for which the MLE exists with probability one, even when the number of observations equals the treewidth.},
author = {Uhler, Caroline},
journal = {Annals of Statistics},
number = {1},
pages = {238 -- 261},
publisher = {Institute of Mathematical Statistics},
title = {{Geometry of maximum likelihood estimation in Gaussian graphical models}},
doi = {10.1214/11-AOS957},
volume = {40},
year = {2012},
}
@article{2962,
abstract = {The choice of summary statistics is a crucial step in approximate Bayesian computation (ABC). Since statistics are often not sufficient, this choice involves a trade-off between loss of information and reduction of dimensionality. The latter may increase the efficiency of ABC. Here, we propose an approach for choosing summary statistics based on boosting, a technique from the machine learning literature. We consider different types of boosting and compare them to partial least squares regression as an alternative. To mitigate the lack of sufficiency, we also propose an approach for choosing summary statistics locally, in the putative neighborhood of the true parameter value. We study a demographic model motivated by the re-introduction of Alpine ibex (Capra ibex) into the Swiss Alps. The parameters of interest are the mean and standard deviation across microsatellites of the scaled ancestral mutation rate (θanc = 4 Ne u), and the proportion of males obtaining access to matings per breeding season (ω). By simulation, we assess the properties of the posterior distribution obtained with the various methods. According to our criteria, ABC with summary statistics chosen locally via boosting with the L2-loss performs best. Applying that method to the ibex data, we estimate θanc ≈ 1.288, and find that most of the variation across loci of the ancestral mutation rate u is between 7.7×10−4 and 3.5×10−3 per locus per generation. The proportion of males with access to matings is estimated to ω ≈ 0.21, which is in good agreement with recent independent estimates.},
author = {Aeschbacher, Simon and Beaumont, Mark and Futschik, Andreas},
journal = {Genetics},
number = {3},
pages = {1027 -- 1047},
publisher = {Genetics Society of America},
title = {{A novel approach for choosing summary statistics in approximate Bayesian computation}},
doi = {10.1534/genetics.112.143164},
volume = {192},
year = {2012},
}