TY - JOUR AB - We study the Fokker-Planck equation derived in the large system limit of the Markovian process describing the dynamics of quantitative traits. The Fokker-Planck equation is posed on a bounded domain and its transport and diffusion coefficients vanish on the domain's boundary. We first argue that, despite this degeneracy, the standard no-flux boundary condition is valid. We derive the weak formulation of the problem and prove the existence and uniqueness of its solutions by constructing the corresponding contraction semigroup on a suitable function space. Then, we prove that for the parameter regime with high enough mutation rate the problem exhibits a positive spectral gap, which implies exponential convergence to equilibrium.Next, we provide a simple derivation of the so-called Dynamic Maximum Entropy (DynMaxEnt) method for approximation of observables (moments) of the Fokker-Planck solution, which can be interpreted as a nonlinear Galerkin approximation. The limited applicability of the DynMaxEnt method inspires us to introduce its modified version that is valid for the whole range of admissible parameters. Finally, we present several numerical experiments to demonstrate the performance of both the original and modified DynMaxEnt methods. We observe that in the parameter regimes where both methods are valid, the modified one exhibits slightly better approximation properties compared to the original one. AU - Bodova, Katarina AU - Haskovec, Jan AU - Markowich, Peter ID - 607 JF - Physica D: Nonlinear Phenomena TI - Well posedness and maximum entropy approximation for the dynamics of quantitative traits VL - 376-377 ER - TY - JOUR AB - We developed a method to calculate two-photon processes in quantum mechanics that replaces the infinite summation over the intermediate states by a perturbation expansion. This latter consists of a series of commutators that involve position, momentum, and Hamiltonian quantum operators. We analyzed several single- and many-particle cases for which a closed-form solution to the perturbation expansion exists, as well as more complicated cases for which a solution is found by convergence. Throughout the article, Rayleigh and Raman scattering are taken as examples of two-photon processes. The present method provides a clear distinction between the Thomson scattering, regarded as classical scattering, and quantum contributions. Such a distinction lets us derive general results concerning light scattering. Finally, possible extensions to the developed formalism are discussed. AU - Fratini, Filippo AU - Safari, Laleh AU - Amaro, Pedro AU - Santos, José ID - 294 IS - 4 JF - Physical Review A - Atomic, Molecular, and Optical Physics TI - Two-photon processes based on quantum commutators VL - 97 ER - TY - JOUR AB - We establish the existence of a global solution for a new family of fluid-like equations, which are obtained in certain regimes in as the mean-field evolution of the supercurrent density in a (2D section of a) type-II superconductor with pinning and with imposed electric current. We also consider general vortex-sheet initial data, and investigate the uniqueness and regularity properties of the solution. For some choice of parameters, the equation under investigation coincides with the so-called lake equation from 2D shallow water fluid dynamics, and our analysis then leads to a new existence result for rough initial data. AU - Duerinckx, Mitia AU - Fischer, Julian L ID - 606 IS - 5 JF - Annales de l'Institut Henri Poincare (C) Non Linear Analysis TI - Well-posedness for mean-field evolutions arising in superconductivity VL - 35 ER - TY - CONF AB - Formalizing properties of systems with continuous dynamics is a challenging task. In this paper, we propose a formal framework for specifying and monitoring rich temporal properties of real-valued signals. We introduce signal first-order logic (SFO) as a specification language that combines first-order logic with linear-real arithmetic and unary function symbols interpreted as piecewise-linear signals. We first show that while the satisfiability problem for SFO is undecidable, its membership and monitoring problems are decidable. We develop an offline monitoring procedure for SFO that has polynomial complexity in the size of the input trace and the specification, for a fixed number of quantifiers and function symbols. We show that the algorithm has computation time linear in the size of the input trace for the important fragment of bounded-response specifications interpreted over input traces with finite variability. We can use our results to extend signal temporal logic with first-order quantifiers over time and value parameters, while preserving its efficient monitoring. We finally demonstrate the practical appeal of our logic through a case study in the micro-electronics domain. AU - Bakhirkin, Alexey AU - Ferrere, Thomas AU - Henzinger, Thomas A AU - Nickovicl, Deian ID - 5959 SN - 9781538655603 T2 - 2018 International Conference on Embedded Software TI - Keynote: The first-order logic of signals ER - TY - CONF AB - Stochastic Gradient Descent (SGD) is a fundamental algorithm in machine learning, representing the optimization backbone for training several classic models, from regression to neural networks. Given the recent practical focus on distributed machine learning, significant work has been dedicated to the convergence properties of this algorithm under the inconsistent and noisy updates arising from execution in a distributed environment. However, surprisingly, the convergence properties of this classic algorithm in the standard shared-memory model are still not well-understood. In this work, we address this gap, and provide new convergence bounds for lock-free concurrent stochastic gradient descent, executing in the classic asynchronous shared memory model, against a strong adaptive adversary. Our results give improved upper and lower bounds on the "price of asynchrony'' when executing the fundamental SGD algorithm in a concurrent setting. They show that this classic optimization tool can converge faster and with a wider range of parameters than previously known under asynchronous iterations. At the same time, we exhibit a fundamental trade-off between the maximum delay in the system and the rate at which SGD can converge, which governs the set of parameters under which this algorithm can still work efficiently. AU - Alistarh, Dan-Adrian AU - De Sa, Christopher AU - Konstantinov, Nikola H ID - 5962 SN - 9781450357951 T2 - Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC '18 TI - The convergence of stochastic gradient descent in asynchronous shared memory ER - TY - JOUR AB - A major problem for evolutionary theory is understanding the so-called open-ended nature of evolutionary change, from its definition to its origins. Open-ended evolution (OEE) refers to the unbounded increase in complexity that seems to characterize evolution on multiple scales. This property seems to be a characteristic feature of biological and technological evolution and is strongly tied to the generative potential associated with combinatorics, which allows the system to grow and expand their available state spaces. Interestingly, many complex systems presumably displaying OEE, from language to proteins, share a common statistical property: the presence of Zipf's Law. Given an inventory of basic items (such as words or protein domains) required to build more complex structures (sentences or proteins) Zipf's Law tells us that most of these elements are rare whereas a few of them are extremely common. Using algorithmic information theory, in this paper we provide a fundamental definition for open-endedness, which can be understood as postulates. Its statistical counterpart, based on standard Shannon information theory, has the structure of a variational problem which is shown to lead to Zipf's Law as the expected consequence of an evolutionary process displaying OEE. We further explore the problem of information conservation through an OEE process and we conclude that statistical information (standard Shannon information) is not conserved, resulting in the paradoxical situation in which the increase of information content has the effect of erasing itself. We prove that this paradox is solved if we consider non-statistical forms of information. This last result implies that standard information theory may not be a suitable theoretical framework to explore the persistence and increase of the information content in OEE systems. AU - Corominas-Murtra, Bernat AU - Seoane, Luís F. AU - Solé, Ricard ID - 5860 IS - 149 JF - Journal of the Royal Society Interface SN - 17425689 TI - Zipf's Law, unbounded complexity and open-ended evolution VL - 15 ER - TY - JOUR AB - In this paper we present a reliable method to verify the existence of loops along the uncertain trajectory of a robot, based on proprioceptive measurements only, within a bounded-error context. The loop closure detection is one of the key points in simultaneous localization and mapping (SLAM) methods, especially in homogeneous environments with difficult scenes recognitions. The proposed approach is generic and could be coupled with conventional SLAM algorithms to reliably reduce their computing burden, thus improving the localization and mapping processes in the most challenging environments such as unexplored underwater extents. To prove that a robot performed a loop whatever the uncertainties in its evolution, we employ the notion of topological degree that originates in the field of differential topology. We show that a verification tool based on the topological degree is an optimal method for proving robot loops. This is demonstrated both on datasets from real missions involving autonomous underwater vehicles and by a mathematical discussion. AU - Rohou, Simon AU - Franek, Peter AU - Aubry, Clément AU - Jaulin, Luc ID - 5960 IS - 12 JF - The International Journal of Robotics Research SN - 0278-3649 TI - Proving the existence of loops in robot trajectories VL - 37 ER - TY - CONF AB - There has been significant progress in understanding the parallelism inherent to iterative sequential algorithms: for many classic algorithms, the depth of the dependence structure is now well understood, and scheduling techniques have been developed to exploit this shallow dependence structure for efficient parallel implementations. A related, applied research strand has studied methods by which certain iterative task-based algorithms can be efficiently parallelized via relaxed concurrent priority schedulers. These allow for high concurrency when inserting and removing tasks, at the cost of executing superfluous work due to the relaxed semantics of the scheduler. In this work, we take a step towards unifying these two research directions, by showing that there exists a family of relaxed priority schedulers that can efficiently and deterministically execute classic iterative algorithms such as greedy maximal independent set (MIS) and matching. Our primary result shows that, given a randomized scheduler with an expected relaxation factor of k in terms of the maximum allowed priority inversions on a task, and any graph on n vertices, the scheduler is able to execute greedy MIS with only an additive factor of \poly(k) expected additional iterations compared to an exact (but not scalable) scheduler. This counter-intuitive result demonstrates that the overhead of relaxation when computing MIS is not dependent on the input size or structure of the input graph. Experimental results show that this overhead can be clearly offset by the gain in performance due to the highly scalable scheduler. In sum, we present an efficient method to deterministically parallelize iterative sequential algorithms, with provable runtime guarantees in terms of the number of executed tasks to completion. AU - Alistarh, Dan-Adrian AU - Brown, Trevor A AU - Kopinsky, Justin AU - Nadiradze, Giorgi ID - 5963 SN - 9781450357951 T2 - Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing - PODC '18 TI - Relaxed schedulers can efficiently parallelize iterative algorithms ER - TY - CONF AB - Relaxed concurrent data structures have become increasingly popular, due to their scalability in graph processing and machine learning applications (\citeNguyen13, gonzalez2012powergraph ). Despite considerable interest, there exist families of natural, high performing randomized relaxed concurrent data structures, such as the popular MultiQueue~\citeMQ pattern for implementing relaxed priority queue data structures, for which no guarantees are known in the concurrent setting~\citeAKLN17. Our main contribution is in showing for the first time that, under a set of analytic assumptions, a family of relaxed concurrent data structures, including variants of MultiQueues, but also a new approximate counting algorithm we call the MultiCounter, provides strong probabilistic guarantees on the degree of relaxation with respect to the sequential specification, in arbitrary concurrent executions. We formalize these guarantees via a new correctness condition called distributional linearizability, tailored to concurrent implementations with randomized relaxations. Our result is based on a new analysis of an asynchronous variant of the classic power-of-two-choices load balancing algorithm, in which placement choices can be based on inconsistent, outdated information (this result may be of independent interest). We validate our results empirically, showing that the MultiCounter algorithm can implement scalable relaxed timestamps. AU - Alistarh, Dan-Adrian AU - Brown, Trevor A AU - Kopinsky, Justin AU - Li, Jerry Z. AU - Nadiradze, Giorgi ID - 5965 SN - 9781450357999 T2 - Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures - SPAA '18 TI - Distributionally linearizable data structures ER - TY - CONF AB - The Big Match is a multi-stage two-player game. In each stage Player 1 hides one or two pebbles in his hand, and his opponent has to guess that number; Player 1 loses a point if Player 2 is correct, and otherwise he wins a point. As soon as Player 1 hides one pebble, the players cannot change their choices in any future stage. Blackwell and Ferguson (1968) give an ε-optimal strategy for Player 1 that hides, in each stage, one pebble with a probability that depends on the entire past history. Any strategy that depends just on the clock or on a finite memory is worthless. The long-standing natural open problem has been whether every strategy that depends just on the clock and a finite memory is worthless. We prove that there is such a strategy that is ε-optimal. In fact, we show that just two states of memory are sufficient. AU - Hansen, Kristoffer Arnsfelt AU - Ibsen-Jensen, Rasmus AU - Neyman, Abraham ID - 5967 SN - 9781450358293 T2 - Proceedings of the 2018 ACM Conference on Economics and Computation - EC '18 TI - The Big Match with a clock and a bit of memory ER -