Television drama series’ incorporation of film narrative innovation: the case of 24

Joyard (2003) refers to the past decade as the Golden Age of the American series, mostly in connection with their narrative features and their capacity to arouse emotions. 24 (2001) by Joel Surnow and Robert Cochran illustrates perfectly these innovative capacities in dramatic series. The series concept is everything, making 24 an instant cult object. It is presented as the nearest to real time that any artistic work can achieve. The continuous flow of events from 24 enters our homes through our TV sets permitting us to follow an apparent reality, projected week by week at the same hour, but making us feel a contemporaneous experience from a use of a space/time that struggles against illusion. Creative liberty has permitted the development of new narrative trends (Thompson, 2003), just as unusual aesthetic forms new to television (Nelson, 2001) have striven to deliver greater degrees of realism. Narrative complexity is increasing, becoming more intricate not only at the plot level but also at the level of character development, which might lead us to believe that television series are positioning themselves in the vanguard of visual media narrative

Gossip vs. Markov Chains, and Randomness-Efficient Rumor Spreading

We study gossip algorithms for the rumor spreading problem which asks one node to deliver a rumor to all nodes in an unknown network. We present the first protocol for any expander graph $G$ with $n$ nodes such that, the protocol informs every node in $O(\log n)$ rounds with high probability, and uses $\tilde{O}(\log n)$ random bits in total. The runtime of our protocol is tight, and the randomness requirement of $\tilde{O}(\log n)$ random bits almost matches the lower bound of $\Omega(\log n)$ random bits for dense graphs. We further show that, for many graph families, polylogarithmic number of random bits in total suffice to spread the rumor in $O(\mathrm{poly}\log n)$ rounds. These results together give us an almost complete understanding of the randomness requirement of this fundamental gossip process. Our analysis relies on unexpectedly tight connections among gossip processes, Markov chains, and branching programs. First, we establish a connection between rumor spreading processes and Markov chains, which is used to approximate the rumor spreading time by the mixing time of Markov chains. Second, we show a reduction from rumor spreading processes to branching programs, and this reduction provides a general framework to derandomize gossip processes. In addition to designing rumor spreading protocols, these novel techniques may have applications in studying parallel and multiple random walks, and randomness complexity of distributed algorithms.Comment: 41 pages, 1 figure. arXiv admin note: substantial text overlap with arXiv:1304.135

A Multi Agent Based Organizational Architecture for Dynamic Pickup and Delivery Problem

Pickup and Delivery Problem (PDP) consists of searching an optimal set of vehicles and an optimal set of routes, one route by each vehicle, in order to pickup items from a set of origins and deliver them to another set of destinations. Pickup and delivery problem is a class of complex systems whose complexity is NP Hard. In PDP real life applications, heuristics and meta heuristics methods are used in order to obtain an acceptable solution in reasonable execution time. When unpredictable events, like for example path cut and vehicles failure, may occur during the PDP schedule execution, we say that the PDP is dynamic (DPDP) and in this case we have to revise this schedule. In this paper, we propose a multi agent architecture for DPDP based on an organizational architecture. Supported by a formal framework, the proposed architecture allows us to show, through a case study that computed solution for the studied problem could be done in a parallel manner which attenuates substantially the problem complexity

Parameterizable Byzantine Broadcast in Loosely Connected Networks

We consider the problem of reliably broadcasting information in a multihop asynchronous network, despite the presence of Byzantine failures: some nodes are malicious and behave arbitrarly. We focus on non-cryptographic solutions. Most existing approaches give conditions for perfect reliable broadcast (all correct nodes deliver the good information), but require a highly connected network. A probabilistic approach was recently proposed for loosely connected networks: the Byzantine failures are randomly distributed, and the correct nodes deliver the good information with high probability. A first solution require the nodes to initially know their position on the network, which may be difficult or impossible in self-organizing or dynamic networks. A second solution relaxed this hypothesis but has much weaker Byzantine tolerance guarantees. In this paper, we propose a parameterizable broadcast protocol that does not require nodes to have any knowledge about the network. We give a deterministic technique to compute a set of nodes that always deliver authentic information, for a given set of Byzantine failures. Then, we use this technique to experimentally evaluate our protocol, and show that it significantely outperforms previous solutions with the same hypotheses. Important disclaimer: these results have NOT yet been published in an international conference or journal. This is just a technical report presenting intermediary and incomplete results. A generalized version of these results may be under submission

Multi-hop Byzantine reliable broadcast with honest dealer made practical

We revisit Byzantine tolerant reliable broadcast with honest dealer algorithms in multi-hop networks. To tolerate Byzantine faulty nodes arbitrarily spread over the network, previous solutions require a factorial number of messages to be sent over the network if the messages are not authenticated (e.g., digital signatures are not available). We propose modifications that preserve the safety and liveness properties of the original unauthenticated protocols, while highly decreasing their observed message complexity when simulated on several classes of graph topologies, potentially opening to their employment

Asimovian Adaptive Agents

The goal of this research is to develop agents that are adaptive and predictable and timely. At first blush, these three requirements seem contradictory. For example, adaptation risks introducing undesirable side effects, thereby making agents' behavior less predictable. Furthermore, although formal verification can assist in ensuring behavioral predictability, it is known to be time-consuming. Our solution to the challenge of satisfying all three requirements is the following. Agents have finite-state automaton plans, which are adapted online via evolutionary learning (perturbation) operators. To ensure that critical behavioral constraints are always satisfied, agents' plans are first formally verified. They are then reverified after every adaptation. If reverification concludes that constraints are violated, the plans are repaired. The main objective of this paper is to improve the efficiency of reverification after learning, so that agents have a sufficiently rapid response time. We present two solutions: positive results that certain learning operators are a priori guaranteed to preserve useful classes of behavioral assurance constraints (which implies that no reverification is needed for these operators), and efficient incremental reverification algorithms for those learning operators that have negative a priori results