Sigref – A Symbolic Bisimulation Tool Box

We present a uniform signature-based approach to compute the most popular bisimulations. Our approach is implemented symbolically using BDDs, which enables the handling of very large transition systems. Signatures for the bisimulations are built up from a few generic building blocks, which naturally correspond to efficient BDD operations. Thus, the definition of an appropriate signature is the key for a rapid development of algorithms for other types of bisimulation. We provide experimental evidence of the viability of this approach by presenting computational results for many bisimulations on real-world instances. The experiments show cases where our framework can handle state spaces efficiently that are far too large to handle for any tool that requires an explicit state space description. This work was partly supported by the German Research Council (DFG) as part of the Transregional Collaborative Research Center “Automatic Verification and Analysis of Complex Systems” (SFB/TR 14 AVACS). See www.avacs.org for more information

The placement of the head that maximizes predictability. An information theoretic approach

The minimization of the length of syntactic dependencies is a well-established principle of word order and the basis of a mathematical theory of word order. Here we complete that theory from the perspective of information theory, adding a competing word order principle: the maximization of predictability of a target element. These two principles are in conflict: to maximize the predictability of the head, the head should appear last, which maximizes the costs with respect to dependency length minimization. The implications of such a broad theoretical framework to understand the optimality, diversity and evolution of the six possible orderings of subject, object and verb are reviewed.Comment: in press in Glottometric

LTLf/LDLf Non-Markovian Rewards

In Markov Decision Processes (MDPs), the reward obtained in a state is Markovian, i.e., depends on the last state and action. This dependency makes it difficult to reward more interesting long-term behaviors, such as always closing a door after it has been opened, or providing coffee only following a request. Extending MDPs to handle non-Markovian reward functions was the subject of two previous lines of work. Both use LTL variants to specify the reward function and then compile the new model back into a Markovian model. Building on recent progress in temporal logics over finite traces, we adopt LDLf for specifying non-Markovian rewards and provide an elegant automata construction for building a Markovian model, which extends that of previous work and offers strong minimality and compositionality guarantees

Synchronization Based Approach for Estimating All Model Parameters of Chaotic Systems

The problem of dynamic estimation of all parameters of a model representing chaotic and hyperchaotic systems using information from a scalar measured output is solved. The variational calculus based method is robust in the presence of noise, enables online estimation of the parameters and is also able to rapidly track changes in operating parameters of the experimental system. The method is demonstrated using the Lorenz, Rossler chaos and hyperchaos models. Its possible application in decoding communications using chaos is discussed.Comment: 13 pages, 4 figure

Extracting Boolean rules from CA patterns

A multiobjective genetic algorithm (GA) is introduced to identify both the neighborhood and the rule set in the form of a parsimonious Boolean expression for both one- and two-dimensional cellular automata (CA). Simulation results illustrate that the new algorithm performs well even when the patterns are corrupted by static and dynamic nois

Adaptive Power Allocation and Control in Time-Varying Multi-Carrier MIMO Networks

In this paper, we examine the fundamental trade-off between radiated power and achieved throughput in wireless multi-carrier, multiple-input and multiple-output (MIMO) systems that vary with time in an unpredictable fashion (e.g. due to changes in the wireless medium or the users' QoS requirements). Contrary to the static/stationary channel regime, there is no optimal power allocation profile to target (either static or in the mean), so the system's users must adapt to changes in the environment "on the fly", without being able to predict the system's evolution ahead of time. In this dynamic context, we formulate the users' power/throughput trade-off as an online optimization problem and we provide a matrix exponential learning algorithm that leads to no regret - i.e. the proposed transmit policy is asymptotically optimal in hindsight, irrespective of how the system evolves over time. Furthermore, we also examine the robustness of the proposed algorithm under imperfect channel state information (CSI) and we show that it retains its regret minimization properties under very mild conditions on the measurement noise statistics. As a result, users are able to track the evolution of their individually optimum transmit profiles remarkably well, even under rapidly changing network conditions and high uncertainty. Our theoretical analysis is validated by extensive numerical simulations corresponding to a realistic network deployment and providing further insights in the practical implementation aspects of the proposed algorithm.Comment: 25 pages, 4 figure

Finite size effects in Neutron Star and Nuclear matter simulations

In this work we study molecular dynamics simulations of symmetric nuclear matter using a semi-classical nucleon interaction model. We show that, at sub-saturation densities and low temperatures, the solutions are non-homogeneous structures reminiscent of the ``nuclear pasta'' phases expected in Neutron Star Matter simulations, but shaped by artificial aspects of the simulations. We explore different geometries for the periodic boundary conditions imposed on the simulation cell: cube, hexagonal prism and truncated octahedron. We find that different cells may yield different solutions for the same physical conditions (i.e. density and temperature). The particular shape of the solution at a given density can be predicted analytically by energy minimization. We also show that even if this behavior is due to finite size effects, it does not mean that it vanishes for very large systems and it actually is independent of the system size: The system size sets the only characteristic length scale for the inhomogeneities. We then include a screened Coulomb interaction, as a model of Neutron Star Matter, and perform simulations in the three cell geometries. In this case, the competition between competing interactions of different range produces the well known nuclear pasta, with (in most cases) several structures per cell. However, we find that the results are affected by finite size in different ways depending on the geometry of the cell. In particular, at the same physical conditions and system size, the hexagonal prism yields a single structure per cell while the cubic and truncated octahedron show consistent results with more than one structure per cell. In this case, the results in every cell are expected to converge for systems much larger than the characteristic length scale that arises from the competing interactions.Comment: 17 pages, 10 figure