51,568 research outputs found
Event-Related Outputs of Computations in P Systems
We briefly investigate the idea to consider as the result of a computation in a P system the number of steps elapsed between two events produced during the computation. Specifically, we first consider the case when the result of a computation is defined in terms of events related to using rules, introducing objects, or meeting objects. Universality is easily obtained in each case for symport/antiport P systems. Then, we address the case when the number computed by a system is the length of a computation itself. We obtain a few results both for catalytic multiset-rewriting and for symport/antiport systems (in each case, also with using membrane dissolution) showing that non-semilinear sets of vectors can be computed in this way. A general non-universality result is proved for this case – no system, of any type, can have as the length of its halting computations all sets of numbers computable by Turing machines. The general problem, of characterizing the sets of numbers computed in this way, remains open
The Swapping Constraint
Triviality arguments against the computational theory of mind claim that computational implementation is trivial and thus does not serve as an adequate metaphysical basis for mental states. It is common to take computational implementation to consist in a mapping from physical states to abstract computational states. In this paper, I propose a novel constraint on the kinds of physical states that can implement computational states, which helps to specify what it is for two physical states to non-trivially implement the same computational state
Distributed measurement-based quantum computation
We develop a formal model for distributed measurement-based quantum
computations, adopting an agent-based view, such that computations are
described locally where possible. Because the network quantum state is in
general entangled, we need to model it as a global structure, reminiscent of
global memory in classical agent systems. Local quantum computations are
described as measurement patterns. Since measurement-based quantum computation
is inherently distributed, this allows us to extend naturally several concepts
of the measurement calculus, a formal model for such computations. Our goal is
to define an assembly language, i.e. we assume that computations are
well-defined and we do not concern ourselves with verification techniques. The
operational semantics for systems of agents is given by a probabilistic
transition system, and we define operational equivalence in a way that it
corresponds to the notion of bisimilarity. With this in place, we prove that
teleportation is bisimilar to a direct quantum channel, and this also within
the context of larger networks.Comment: 17 page
Sensor placement for fault location identification in water networks: A minimum test cover approach
This paper focuses on the optimal sensor placement problem for the
identification of pipe failure locations in large-scale urban water systems.
The problem involves selecting the minimum number of sensors such that every
pipe failure can be uniquely localized. This problem can be viewed as a minimum
test cover (MTC) problem, which is NP-hard. We consider two approaches to
obtain approximate solutions to this problem. In the first approach, we
transform the MTC problem to a minimum set cover (MSC) problem and use the
greedy algorithm that exploits the submodularity property of the MSC problem to
compute the solution to the MTC problem. In the second approach, we develop a
new \textit{augmented greedy} algorithm for solving the MTC problem. This
approach does not require the transformation of the MTC to MSC. Our augmented
greedy algorithm provides in a significant computational improvement while
guaranteeing the same approximation ratio as the first approach. We propose
several metrics to evaluate the performance of the sensor placement designs.
Finally, we present detailed computational experiments for a number of real
water distribution networks
spChains: A Declarative Framework for Data Stream Processing in Pervasive Applications
Pervasive applications rely on increasingly complex streams of sensor data continuously captured from the physical world. Such data is crucial to enable applications to ``understand'' the current context and to infer the right actions to perform, be they fully automatic or involving some user decisions. However, the continuous nature of such streams, the relatively high throughput at which data is generated and the number of sensors usually deployed in the environment, make direct data handling practically unfeasible. Data not only needs to be cleaned, but it must also be filtered and aggregated to relieve higher level algorithms from near real-time handling of such massive data flows. We propose here a stream-processing framework (spChains), based upon state-of-the-art stream processing engines, which enables declarative and modular composition of stream processing chains built atop of a set of extensible stream processing blocks. While stream processing blocks are delivered as a standard, yet extensible, library of application-independent processing elements, chains can be defined by the pervasive application engineering team. We demonstrate the flexibility and effectiveness of the spChains framework on two real-world applications in the energy management and in the industrial plant management domains, by evaluating them on a prototype implementation based on the Esper stream processo
Fast Local Computation Algorithms
For input , let denote the set of outputs that are the "legal"
answers for a computational problem . Suppose and members of are
so large that there is not time to read them in their entirety. We propose a
model of {\em local computation algorithms} which for a given input ,
support queries by a user to values of specified locations in a legal
output . When more than one legal output exists for a given
, the local computation algorithm should output in a way that is consistent
with at least one such . Local computation algorithms are intended to
distill the common features of several concepts that have appeared in various
algorithmic subfields, including local distributed computation, local
algorithms, locally decodable codes, and local reconstruction.
We develop a technique, based on known constructions of small sample spaces
of -wise independent random variables and Beck's analysis in his algorithmic
approach to the Lov{\'{a}}sz Local Lemma, which under certain conditions can be
applied to construct local computation algorithms that run in {\em
polylogarithmic} time and space. We apply this technique to maximal independent
set computations, scheduling radio network broadcasts, hypergraph coloring and
satisfying -SAT formulas.Comment: A preliminary version of this paper appeared in ICS 2011, pp. 223-23
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