@inproceedings{780,
abstract = {Population protocols are networks of finite-state agents, interacting randomly, and updating their states using simple rules. Despite their extreme simplicity, these systems have been shown to cooperatively perform complex computational tasks, such as simulating register machines to compute standard arithmetic functions. The election of a unique leader agent is a key requirement in such computational constructions. Yet, the fastest currently known population protocol for electing a leader only has linear convergence time, and it has recently been shown that no population protocol using a constant number of states per node may overcome this linear bound. In this paper, we give the first population protocol for leader election with polylogarithmic convergence time, using polylogarithmic memory states per node. The protocol structure is quite simple: each node has an associated value, and is either a leader (still in contention) or a minion (following some leader). A leader keeps incrementing its value and “defeats” other leaders in one-to-one interactions, and will drop from contention and become a minion if it meets a leader with higher value. Importantly, a leader also drops out if it meets a minion with higher absolute value. While these rules are quite simple, the proof that this algorithm achieves polylogarithmic convergence time is non-trivial. In particular, the argument combines careful use of concentration inequalities with anti-concentration bounds, showing that the leaders’ values become spread apart as the execution progresses, which in turn implies that straggling leaders get quickly eliminated. We complement our analysis with empirical results, showing that our protocol converges extremely fast, even for large network sizes.},
author = {Alistarh, Dan-Adrian and Gelashvili, Rati},
pages = {479 -- 491},
publisher = {Springer},
title = {{Polylogarithmic-time leader election in population protocols}},
doi = {10.1007/978-3-662-47666-6_38},
volume = {9135},
year = {2015},
}
@inproceedings{784,
abstract = {We demonstrate an optical switch design that can scale up to a thousand ports with high per-port bandwidth (25 Gbps+) and low switching latency (40 ns). Our design uses a broadcast and select architecture, based on a passive star coupler and fast tunable transceivers. In addition we employ time division multiplexing to achieve very low switching latency. Our demo shows the feasibility of the switch data plane using a small testbed, comprising two transmitters and a receiver, connected through a star coupler.},
author = {Alistarh, Dan-Adrian and Ballani, Hitesh and Costa, Paolo and Funnell, Adam and Benjamin, Joshua and Watts, Philip and Thomsen, Benn},
isbn = {978-1-4503-3542-3},
location = {London, United Kindgdom},
pages = {367 -- 368},
publisher = {ACM},
title = {{A high-radix, low-latency optical switch for data centers}},
doi = {10.1145/2785956.2790035},
year = {2015},
}
@article{473,
abstract = {We prove that nonlinear Gibbs measures can be obtained from the corresponding many-body, grand-canonical, quantum Gibbs states, in a mean-field limit where the temperature T diverges and the interaction strength behaves as 1/T. We proceed by characterizing the interacting Gibbs state as minimizing a functional counting the free-energy relatively to the non-interacting case. We then perform an infinite-dimensional analogue of phase-space semiclassical analysis, using fine properties of the quantum relative entropy, the link between quantum de Finetti measures and upper/lower symbols in a coherent state basis, as well as Berezin-Lieb type inequalities. Our results cover the measure built on the defocusing nonlinear Schrödinger functional on a finite interval, as well as smoother interactions in dimensions d 2.},
author = {Lewin, Mathieu and Phan Thanh, Nam and Rougerie, Nicolas},
journal = {Journal de l'Ecole Polytechnique - Mathematiques},
pages = {65 -- 115},
publisher = {Ecole Polytechnique},
title = {{Derivation of nonlinear gibbs measures from many-body quantum mechanics}},
doi = {10.5802/jep.18},
volume = {2},
year = {2015},
}
@article{477,
abstract = {Dendritic cells are potent antigen-presenting cells endowed with the unique ability to initiate adaptive immune responses upon inflammation. Inflammatory processes are often associated with an increased production of serotonin, which operates by activating specific receptors. However, the functional role of serotonin receptors in regulation of dendritic cell functions is poorly understood. Here, we demonstrate that expression of serotonin receptor 5-HT7 (5-HT7TR) as well as its downstream effector Cdc42 is upregulated in dendritic cells upon maturation. Although dendritic cell maturation was independent of 5-HT7TR, receptor stimulation affected dendritic cell morphology through Cdc42-mediated signaling. In addition, basal activity of 5-HT7TR was required for the proper expression of the chemokine receptor CCR7, which is a key factor that controls dendritic cell migration. Consistent with this, we observed that 5-HT7TR enhances chemotactic motility of dendritic cells in vitro by modulating their directionality and migration velocity. Accordingly, migration of dendritic cells in murine colon explants was abolished after pharmacological receptor inhibition. Our results indicate that there is a crucial role for 5-HT7TR-Cdc42-mediated signaling in the regulation of dendritic cell morphology and motility, suggesting that 5-HT7TR could be a new target for treatment of a variety of inflammatory and immune disorders.},
author = {Holst, Katrin and Guseva, Daria and Schindler, Susann and Sixt, Michael K and Braun, Armin and Chopra, Himpriya and Pabst, Oliver and Ponimaskin, Evgeni},
journal = {Journal of Cell Science},
number = {15},
pages = {2866 -- 2880},
publisher = {Company of Biologists},
title = {{The serotonin receptor 5-HT7R regulates the morphology and migratory properties of dendritic cells}},
doi = {10.1242/jcs.167999},
volume = {128},
year = {2015},
}
@article{523,
abstract = {We consider two-player games played on weighted directed graphs with mean-payoff and total-payoff objectives, two classical quantitative objectives. While for single-dimensional games the complexity and memory bounds for both objectives coincide, we show that in contrast to multi-dimensional mean-payoff games that are known to be coNP-complete, multi-dimensional total-payoff games are undecidable. We introduce conservative approximations of these objectives, where the payoff is considered over a local finite window sliding along a play, instead of the whole play. For single dimension, we show that (i) if the window size is polynomial, deciding the winner takes polynomial time, and (ii) the existence of a bounded window can be decided in NP ∩ coNP, and is at least as hard as solving mean-payoff games. For multiple dimensions, we show that (i) the problem with fixed window size is EXPTIME-complete, and (ii) there is no primitive-recursive algorithm to decide the existence of a bounded window.},
author = {Chatterjee, Krishnendu and Doyen, Laurent and Randour, Mickael and Raskin, Jean},
journal = {Information and Computation},
number = {6},
pages = {25 -- 52},
publisher = {Elsevier},
title = {{Looking at mean-payoff and total-payoff through windows}},
doi = {10.1016/j.ic.2015.03.010},
volume = {242},
year = {2015},
}
@article{524,
abstract = {We consider concurrent games played by two players on a finite-state graph, where in every round the players simultaneously choose a move, and the current state along with the joint moves determine the successor state. We study the most fundamental objective for concurrent games, namely, mean-payoff or limit-average objective, where a reward is associated to each transition, and the goal of player 1 is to maximize the long-run average of the rewards, and the objective of player 2 is strictly the opposite (i.e., the games are zero-sum). The path constraint for player 1 could be qualitative, i.e., the mean-payoff is the maximal reward, or arbitrarily close to it; or quantitative, i.e., a given threshold between the minimal and maximal reward. We consider the computation of the almost-sure (resp. positive) winning sets, where player 1 can ensure that the path constraint is satisfied with probability 1 (resp. positive probability). Almost-sure winning with qualitative constraint exactly corresponds to the question of whether there exists a strategy to ensure that the payoff is the maximal reward of the game. Our main results for qualitative path constraints are as follows: (1) we establish qualitative determinacy results that show that for every state either player 1 has a strategy to ensure almost-sure (resp. positive) winning against all player-2 strategies, or player 2 has a spoiling strategy to falsify almost-sure (resp. positive) winning against all player-1 strategies; (2) we present optimal strategy complexity results that precisely characterize the classes of strategies required for almost-sure and positive winning for both players; and (3) we present quadratic time algorithms to compute the almost-sure and the positive winning sets, matching the best known bound of the algorithms for much simpler problems (such as reachability objectives). For quantitative constraints we show that a polynomial time solution for the almost-sure or the positive winning set would imply a solution to a long-standing open problem (of solving the value problem of turn-based deterministic mean-payoff games) that is not known to be solvable in polynomial time.},
author = {Chatterjee, Krishnendu and Ibsen-Jensen, Rasmus},
journal = {Information and Computation},
number = {6},
pages = {2 -- 24},
publisher = {Elsevier},
title = {{Qualitative analysis of concurrent mean payoff games}},
doi = {10.1016/j.ic.2015.03.009},
volume = {242},
year = {2015},
}
@article{532,
abstract = {Ethylene is a gaseous phytohormone that plays vital roles in plant growth and development. Previous studies uncovered EIN2 as an essential signal transducer linking ethylene perception on ER to transcriptional regulation in the nucleus through a “cleave and shuttle” model. In this study, we report another mechanism of EIN2-mediated ethylene signaling, whereby EIN2 imposes the translational repression of EBF1 and EBF2 mRNA. We find that the EBF1/2 3′ UTRs mediate EIN2-directed translational repression and identify multiple poly-uridylates (PolyU) motifs as functional cis elements of 3′ UTRs. Furthermore, we demonstrate that ethylene induces EIN2 to associate with 3′ UTRs and target EBF1/2 mRNA to cytoplasmic processing-body (P-body) through interacting with multiple P-body factors, including EIN5 and PABs. Our study illustrates translational regulation as a key step in ethylene signaling and presents mRNA 3′ UTR functioning as a “signal transducer” to sense and relay cellular signaling in plants.},
author = {Li, Wenyang and Ma, Mengdi and Feng, Ying and Li, Hongjiang and Wang, Yichuan and Ma, Yutong and Li, Mingzhe and An, Fengying and Guo, Hongwei},
journal = {Cell},
number = {3},
pages = {670 -- 683},
publisher = {Cell Press},
title = {{EIN2-directed translational regulation of ethylene signaling in arabidopsis}},
doi = {10.1016/j.cell.2015.09.037},
volume = {163},
year = {2015},
}
@misc{5429,
abstract = {We consider Markov decision processes (MDPs) with multiple limit-average (or mean-payoff) objectives.
There have been two different views: (i) the expectation semantics, where the goal is to optimize the expected mean-payoff objective, and (ii) the satisfaction semantics, where the goal is to maximize the probability of runs such that the mean-payoff value stays above a given vector.
We consider the problem where the goal is to optimize the expectation under the constraint that the satisfaction semantics is ensured, and thus consider a generalization that unifies the existing semantics.
Our problem captures the notion of optimization with respect to strategies that are risk-averse (i.e., ensures certain probabilistic guarantee).
Our main results are algorithms for the decision problem which are always polynomial in the size of the MDP. We also show that an approximation of the Pareto-curve can be computed in time polynomial in the size of the MDP, and the approximation factor, but exponential in the number of dimensions.
Finally, we present a complete characterization of the strategy complexity (in terms of memory bounds and randomization) required to solve our problem.},
author = {Chatterjee, Krishnendu and Komarkova, Zuzana and Kretinsky, Jan},
issn = {2664-1690},
pages = {41},
publisher = {IST Austria},
title = {{Unifying two views on multiple mean-payoff objectives in Markov decision processes}},
doi = {10.15479/AT:IST-2015-318-v1-1},
year = {2015},
}
@misc{5430,
abstract = {We consider the core algorithmic problems related to verification of systems with respect to three classical quantitative properties, namely, the mean- payoff property, the ratio property, and the minimum initial credit for energy property. The algorithmic problem given a graph and a quantitative property asks to compute the optimal value (the infimum value over all traces) from every node of the graph. We consider graphs with constant treewidth, and it is well-known that the control-flow graphs of most programs have constant treewidth. Let n denote the number of nodes of a graph, m the number of edges (for constant treewidth graphs m = O ( n ) ) and W the largest absolute value of the weights. Our main theoretical results are as follows. First, for constant treewidth graphs we present an algorithm that approximates the mean-payoff value within a mul- tiplicative factor of ∊ in time O ( n · log( n/∊ )) and linear space, as compared to the classical algorithms that require quadratic time. Second, for the ratio property we present an algorithm that for constant treewidth graphs works in time O ( n · log( | a · b · n | )) = O ( n · log( n · W )) , when the output is a b , as compared to the previously best known algorithm with running time O ( n 2 · log( n · W )) . Third, for the minimum initial credit problem we show that (i) for general graphs the problem can be solved in O ( n 2 · m ) time and the associated decision problem can be solved in O ( n · m ) time, improving the previous known O ( n 3 · m · log( n · W )) and O ( n 2 · m ) bounds, respectively; and (ii) for constant treewidth graphs we present an algorithm that requires O ( n · log n ) time, improving the previous known O ( n 4 · log( n · W )) bound. We have implemented some of our algorithms and show that they present a significant speedup on standard benchmarks.},
author = {Chatterjee, Krishnendu and Ibsen-Jensen, Rasmus and Pavlogiannis, Andreas},
issn = {2664-1690},
pages = {31},
publisher = {IST Austria},
title = {{Faster algorithms for quantitative verification in constant treewidth graphs}},
doi = {10.15479/AT:IST-2015-319-v1-1},
year = {2015},
}
@misc{5431,
abstract = {We consider finite-state concurrent stochastic games, played by k>=2 players for an infinite number of rounds, where in every round, each player simultaneously and independently of the other players chooses an action, whereafter the successor state is determined by a probability distribution given by the current state and the chosen actions. We consider reachability objectives that given a target set of states require that some state in the target set is visited, and the dual safety objectives that given a target set require that only states in the target set are visited. We are interested in the complexity of stationary strategies measured by their patience, which is defined as the inverse of the smallest non-zero probability employed.
Our main results are as follows: We show that in two-player zero-sum concurrent stochastic games (with reachability objective for one player and the complementary safety objective for the other player): (i) the optimal bound on the patience of optimal and epsilon-optimal strategies, for both players is doubly exponential; and (ii) even in games with a single non-absorbing state exponential (in the number of actions) patience is necessary. In general we study the class of non-zero-sum games admitting epsilon-Nash equilibria. We show that if there is at least one player with reachability objective, then doubly-exponential patience is needed in general for epsilon-Nash equilibrium strategies, whereas in contrast if all players have safety objectives, then the optimal bound on patience for epsilon-Nash equilibrium strategies is only exponential.},
author = {Chatterjee, Krishnendu and Ibsen-Jensen, Rasmus and Hansen, Kristoffer},
issn = {2664-1690},
pages = {25},
publisher = {IST Austria},
title = {{The patience of concurrent stochastic games with safety and reachability objectives}},
doi = {10.15479/AT:IST-2015-322-v1-1},
year = {2015},
}