Mind the Gap – Passenger Arrival Patterns in Multi-agent Simulations

In most studies mathematical models are developed finding the expected waiting time to be a function of the headway. These models have in common that the proportion of passengers that arrive randomly at a public transport stop is less as headway in-creases. Since there are several factors of influence, such as social demographic or regional aspects, the reliability of public transport service and the level of passenger information, the threshold headway for the transition from random to coordinated passenger arrivals vary from study to study. This study's objective is to investigate if an agent-based model exhibits realistic passenger arrival behavior at transit stops. This objective is approached by exploring the sensitivity of the agents' arrival behavior towards (1) the degree of learning, (2) the reliability of the experienced transit service, and (3) the service headway. The simulation experiments for a simple transit corridor indicate that the applied model is capable of representing the complex passenger arrival behavior observed in reality. (1) For higher degrees of learning, the agents tend to over-optimize, i.e. they try to obtain the latest possible departure time exact to the second. An approach is presented which increases the diversity in the agents' travel alternatives and results in a more realistic behavior. (2) For a less reliable service the agents' time adaptation changes in that a buffer time is added between their arrival at the stop and the actual departure of the vehicle. (3) For the modification of the headway the simulation outcome is consistent with the literature on arrival patterns. Smaller headways yield a more equally distributed arrival pattern whereas larger headways result in more coordinated arrival patterns

A neural blackboard architecture of sentence structure

We present a neural architecture for sentence representation. Sentences are represented in terms of word representations as constituents. A word representation consists of a neural assembly distributed over the brain. Sentence representation does not result from associations between neural word assemblies. Instead, word assemblies are embedded in a neural architecture, in which the structural (thematic) relations between words can be represented. Arbitrary thematic relations between arguments and verbs can be represented. Arguments can consist of nouns and phrases, as in sentences with relative clauses. A number of sentences can be stored simultaneously in this architecture. We simulate how probe questions about thematic relations can be answered. We discuss how differences in sentence complexity, such as the difference between subject-extracted versus object-extracted relative clauses and the difference between right-branching versus center-embedded structures, can be related to the underlying neural dynamics of the model. Finally, we illustrate how memory capacity for sentence representation can be related to the nature of reverberating neural activity, which is used to store information temporarily in this architecture

Locally embedded presages of global network bursts

Spontaneous, synchronous bursting of neural population is a widely observed phenomenon in nervous networks, which is considered important for functions and dysfunctions of the brain. However, how the global synchrony across a large number of neurons emerges from an initially non-bursting network state is not fully understood. In this study, we develop a new state-space reconstruction method combined with high-resolution recordings of cultured neurons. This method extracts deterministic signatures of upcoming global bursts in "local" dynamics of individual neurons during non-bursting periods. We find that local information within a single-cell time series can compare with or even outperform the global mean field activity for predicting future global bursts. Moreover, the inter-cell variability in the burst predictability is found to reflect the network structure realized in the non-bursting periods. These findings demonstrate the deterministic mechanisms underlying the locally concentrated early-warnings of the global state transition in self-organized networks

Verifying service continuity in a satellite reconfiguration procedure: application to a satellite

The paper discusses the use of the TURTLE UML profile to model and verify service continuity during dynamic reconfiguration of embedded software, and space-based telecommunication software in particular. TURTLE extends UML class diagrams with composition operators, and activity diagrams with temporal operators. Translating TURTLE to the formal description technique RT-LOTOS gives the profile a formal semantics and makes it possible to reuse verification techniques implemented by the RTL, the RT-LOTOS toolkit developed at LAAS-CNRS. The paper proposes a modeling and formal validation methodology based on TURTLE and RTL, and discusses its application to a payload software application in charge of an embedded packet switch. The paper demonstrates the benefits of using TURTLE to prove service continuity for dynamic reconfiguration of embedded software