11,031 research outputs found
Extending Hybrid CSP with Probability and Stochasticity
Probabilistic and stochastic behavior are omnipresent in computer controlled
systems, in particular, so-called safety-critical hybrid systems, because of
fundamental properties of nature, uncertain environments, or simplifications to
overcome complexity. Tightly intertwining discrete, continuous and stochastic
dynamics complicates modelling, analysis and verification of stochastic hybrid
systems (SHSs). In the literature, this issue has been extensively
investigated, but unfortunately it still remains challenging as no promising
general solutions are available yet. In this paper, we give our effort by
proposing a general compositional approach for modelling and verification of
SHSs. First, we extend Hybrid CSP (HCSP), a very expressive and process
algebra-like formal modeling language for hybrid systems, by introducing
probability and stochasticity to model SHSs, which is called stochastic HCSP
(SHCSP). To this end, ordinary differential equations (ODEs) are generalized by
stochastic differential equations (SDEs) and non-deterministic choice is
replaced by probabilistic choice. Then, we extend Hybrid Hoare Logic (HHL) to
specify and reason about SHCSP processes. We demonstrate our approach by an
example from real-world.Comment: The conference version of this paper is accepted by SETTA 201
Optimal matching between spatial datasets under capacity constraints
Consider a set of customers (e.g., WiFi receivers) and a set of service providers (e.g., wireless access points), where each provider has a capacity and the quality of service offered to its customers is anti-proportional to their distance. The Capacity Constrained Assignment (CCA) is a matching between the two sets such that (i) each customer is assigned to at most one provider, (ii) every provider serves no more customers than its capacity, (iii) the maximum possible number of customers are served, and (iv) the sum of Euclidean distances within the assigned provider-customer pairs is minimized. Although max-flow algorithms are applicable to this problem, they require the complete distance-based bipartite graph between the customer and provider sets. For large spatial datasets, this graph is expensive to compute and it may be too large to fit in main memory. Motivated by this fact, we propose efficient algorithms for optimal assignment that employ novel edge-pruning strategies, based on the spatial properties of the problem. Additionally, we develop incremental techniques that maintain an optimal assignment (in the presence of updates) with a processing cost several times lower than CCA recomputation from scratch. Finally, we present approximate (i.e., suboptimal) CCA solutions that provide a tunable trade-off between result accuracy and computation cost, abiding by theoretical quality guarantees. A thorough experimental evaluation demonstrates the efficiency and practicality of the proposed techniques. © 2010 ACM.postprin
Depletion of Arabidopsis ACYL-COA-BINDING PROTEIN3 affects fatty acid composition in the phloem
published_or_final_versio
How to suppress undesired synchronization
It is delightful to observe the emergence of synchronization in the blinking
of fireflies to attract partners and preys. Other charming examples of
synchronization can also be found in a wide range of phenomena such as, e.g.,
neurons firing, lasers cascades, chemical reactions, and opinion formation.
However, in many situations the formation of a coherent state is not pleasant
and should be mitigated. For example, the onset of synchronization can be the
root of epileptic seizures, traffic congestion in communication networks, and
the collapse of constructions. Here we propose the use of contrarians to
suppress undesired synchronization. We perform a comparative study of different
strategies, either requiring local or total knowledge of the system, and show
that the most efficient one solely requires local information. Our results also
reveal that, even when the distribution of neighboring interactions is narrow,
significant improvement in mitigation is observed when contrarians sit at the
highly connected elements. The same qualitative results are obtained for
artificially generated networks as well as two real ones, namely, the Routers
of the Internet and a neuronal network
Measurement of the Branching Fraction of J/psi --> pi+ pi- pi0
Using 58 million J/psi and 14 million psi' decays obtained by the BESII
experiment, the branching fraction of J/psi --> pi+ pi- pi0 is determined. The
result is (2.10+/-0.12)X10^{-2}, which is significantly higher than previous
measurements.Comment: 9 pages, 8 figures, RevTex
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