TY - CONF
AB - Linearizability of concurrent data structures is usually proved by monolithic simulation arguments relying on identifying the so-called linearization points. Regrettably, such proofs, whether manual or automatic, are often complicated and scale poorly to advanced non-blocking concurrency patterns, such as helping and optimistic updates.
In response, we propose a more modular way of checking linearizability of concurrent queue algorithms that does not involve identifying linearization points. We reduce the task of proving linearizability with respect to the queue specification to establishing four basic properties, each of which can be proved independently by simpler arguments. As a demonstration of our approach, we verify the Herlihy and Wing queue, an algorithm that is challenging to verify by a simulation proof.
AU - Henzinger, Thomas A
AU - Sezgin, Ali
AU - Vafeiadis, Viktor
ID - 2328
TI - Aspect-oriented linearizability proofs
VL - 8052
ER -
TY - CONF
AB - 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 both finite-state game graphs, and recursive game graphs (or pushdown game graphs) that model the control flow of sequential programs with recursion. The objectives we study are multidimensional mean-payoff objectives, where the goal of player 1 is to ensure that the mean-payoff is non-negative in all dimensions. 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. Our main contributions are as follows: (1) We show that finite-state multidimensional mean-payoff games can be solved in polynomial time if the number of dimensions and the maximal absolute value of the weights are fixed; whereas if the number of dimensions is arbitrary, then the problem is known to be coNP-complete. (2) We show that pushdown graphs with multidimensional mean-payoff objectives can be solved in polynomial time. For both (1) and (2) our algorithms are based on hyperplane separation technique. (3) For pushdown games under global strategies both one and multidimensional mean-payoff objectives problems are known to be undecidable, and we show that under modular strategies the multidimensional problem is also undecidable; under modular strategies the one-dimensional problem is NP-complete. We show that if the number of modules, the number of exits, and the maximal absolute value of the weights are fixed, then pushdown games under modular strategies with one-dimensional mean-payoff objectives can be solved in polynomial time, and if either the number of exits or the number of modules is unbounded, then the problem is NP-hard. (4) Finally we show that a fixed parameter tractable algorithm for finite-state multidimensional mean-payoff games or pushdown games under modular strategies with one-dimensional mean-payoff objectives would imply the fixed parameter tractability of parity games.
AU - Chatterjee, Krishnendu
AU - Velner, Yaron
ID - 2329
TI - Hyperplane separation technique for multidimensional mean-payoff games
VL - 8052
ER -
TY - JOUR
AB - Here, we describe a novel virulent bacteriophage that infects Bacillus weihenstephanensis, isolated from soil in Austria. It is the first phage to be discovered that infects this species. Here, we present the complete genome sequence of this podovirus.
AU - Fernandes Redondo, Rodrigo A
AU - Kupczok, Anne
AU - Stift, Gertraud
AU - Bollback, Jonathan P
ID - 2410
IS - 3
JF - Genome Announcements
TI - Complete genome sequence of the novel phage MG-B1 infecting bacillus weihenstephanensis
VL - 1
ER -
TY - JOUR
AB - Background: The CRISPR/Cas system is known to act as an adaptive and heritable immune system in Eubacteria and Archaea. Immunity is encoded in an array of spacer sequences. Each spacer can provide specific immunity to invasive elements that carry the same or a similar sequence. Even in closely related strains, spacer content is very dynamic and evolves quickly. Standard models of nucleotide evolutioncannot be applied to quantify its rate of change since processes other than single nucleotide changes determine its evolution.Methods We present probabilistic models that are specific for spacer content evolution. They account for the different processes of insertion and deletion. Insertions can be constrained to occur on one end only or are allowed to occur throughout the array. One deletion event can affect one spacer or a whole fragment of adjacent spacers. Parameters of the underlying models are estimated for a pair of arrays by maximum likelihood using explicit ancestor enumeration.Results Simulations show that parameters are well estimated on average under the models presented here. There is a bias in the rate estimation when including fragment deletions. The models also estimate times between pairs of strains. But with increasing time, spacer overlap goes to zero, and thus there is an upper bound on the distance that can be estimated. Spacer content similarities are displayed in a distance based phylogeny using the estimated times.We use the presented models to analyze different Yersinia pestis data sets and find that the results among them are largely congruent. The models also capture the variation in diversity of spacers among the data sets. A comparison of spacer-based phylogenies and Cas gene phylogenies shows that they resolve very different time scales for this data set.Conclusions The simulations and data analyses show that the presented models are useful for quantifying spacer content evolution and for displaying spacer content similarities of closely related strains in a phylogeny. This allows for comparisons of different CRISPR arrays or for comparisons between CRISPR arrays and nucleotide substitution rates.
AU - Kupczok, Anne
AU - Bollback, Jonathan P
ID - 2412
IS - 1
JF - BMC Evolutionary Biology
TI - Probabilistic models for CRISPR spacer content evolution
VL - 13
ER -
TY - CHAP
AB - Progress in understanding the global brain dynamics has remained slow to date in large part because of the highly multiscale nature of brain activity. Indeed, normal brain dynamics is characterized by complex interactions between multiple levels: from the microscopic scale of single neurons to the mesoscopic level of local groups of neurons, and finally to the macroscopic level of the whole brain. Among the most difficult tasks are those of identifying which scales are significant for a given particular function and describing how the scales affect each other. It is important to realize that the scales of time and space are linked together, or even intertwined, and that causal inference is far more ambiguous between than within levels. We approach this problem from the perspective of our recent work on simultaneous recording from micro- and macroelectrodes in the human brain. We propose a physiological description of these multilevel interactions, based on phaseâ€“amplitude coupling of neuronal oscillations that operate at multiple frequencies and on different spatial scales. Specifically, the amplitude of the oscillations on a particular spatial scale is modulated by phasic variations in neuronal excitability induced by lower frequency oscillations that emerge on a larger spatial scale. Following this general principle, it is possible to scale up or scale down the multiscale brain dynamics. It is expected that large-scale network oscillations in the low-frequency range, mediating downward effects, may play an important role in attention and consciousness.
AU - Valderrama, Mario
AU - Botella Soler, Vicente
AU - Le Van Quyen, Michel
ED - Meyer, Misha
ED - Pesenson, Z.
ID - 2413
SN - 9783527411986
T2 - Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain
TI - Neuronal oscillations scale up and scale down the brain dynamics
ER -