Design and implementation of an efficient priority queue

Boas, Peter van Emde; Kaas, R.; Zijlstra, E.E.

doi:10.1007/bf01683268

Cited by 379 publications

(34 citation statements)

References 3 publications

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“…In computer science, efficient data structures to maintain an ordered list of time-stamped events are grouped under the name priority queues (Cormen et al 2001). The topic of priority queues is dense and well documented; examples are binary heaps, Fibonacci heaps (Cormen et al 2001), calendar queues (Brown 1988;Claverol et al 2002) or van Emde Boas trees (van Emde Boas et al 1976) (see also Connollly et al 2003, in which various priority queues are compared). Using an efficient priority queue is a crucial element of a good event-driven algorithm.…”

Section: Instantaneous Synaptic Interactions-mentioning

confidence: 99%

Simulation of networks of spiking neurons: A review of tools and strategies

et al. 2007

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We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-andfire models, interacting with current-based or conductance-based synapses, using clock-driven or NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.

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Section: Instantaneous Synaptic Interactions-mentioning

confidence: 99%

Simulation of networks of spiking neurons: A review of tools and strategies

et al. 2007

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“…Thus, we need an execution sequence to handle the order of the decision processes of these pedestrians. We use a priority queue to solve this issue [24]. Whenever p i makes a decision for an outgoing edge, the pedestrian is put into the queue.…”

Section: Decision Process and Network Reductionmentioning

confidence: 99%

Oppilatio+ - A data and cognitive science based approach to analyze pedestrian flows in networks

et al. 2017

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Public transport services are a widespread and environmentally friendly option for mobility. In the majority of cases, passengers of public transport services will have to walk from a subway, train, or bus station to their desired travel destination. In an urban environment with a network of narrow streets, this can lead to crowd congestions during rush hour, due to the fact that passengers tend to arrive in waves. In order to monitor and analyze such crowding behavior, city planners, crowd managers, and organizers of public events must ascertain which routes these pedestrians will take from the respective station to their destination. The Oppilatio + approach is suitable for solving this problem. It is an easy-to-apply approach to predict way-finding behavior with a minimal set of information. The necessary data includes the schedule of incoming transport vehicles at the stations and the time-stamped count of pedestrians at the respective destinations. Under these conditions, the Oppilatio + approach is suitable for estimating the distribution of pedestrians on all possible walkways between stations and destinations. This information helps crowd control experts to recognize weak spots in the infrastructure and help event organizers to ensure an undisturbed arrival at their event. We validated our approach using two field experiments. The first one was a field study on a public event, and the second one was a case study for a large Swiss train station.

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“…Sorting by z-coordinate takes O(n log log n) time in total [13]. Finding d i takes O(log log n) time [22] and thus O(n log log n) time in total. It turns out the rest of the algorithm consumes linear time.…”

Section: Introductionmentioning

confidence: 99%

Deterministic Rectangle Enclosure and Offline Dominance Reporting on the RAM

Afshani

Chan

Tsakalidis

2014

Automata, Languages, and Programming

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Abstract. We revisit a classical problem in computational geometry that has been studied since the 1980s: in the rectangle enclosure problem we want to report all k enclosing pairs of n input rectangles in 2D. We present the first deterministic algorithm that takes O(n log n + k) worst-case time and O(n) space in the word-RAM model. This improves previous deterministic algorithms with O((n log n + k) log log n) running time. We achieve the result by derandomizing the algorithm of Chan, Larsen and Pȃtraşcu [SoCG'11] that attains the same time complexity but in expectation. The 2D rectangle enclosure problem is related to the offline dominance range reporting problem in 4D, and our result leads to the currently fastest deterministic algorithm for offline dominance reporting in any constant dimension d ≥ 4. A key tool behind Chan et al.'s previous randomized algorithm is shallow cuttings for 3D dominance ranges. Recently, Afshani and Tsakalidis [SODA'14] obtained a deterministic O(n log n)-time algorithm to construct such cuttings. We first present an improved deterministic construction algorithm that runs in O(n log log n) time in the word-RAM; this result is of independent interest. Many additional ideas are then incorporated, including a linear-time algorithm for merging shallow cuttings and an algorithm for an offline tree point location problem.

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Design and implementation of an efficient priority queue

Cited by 379 publications

References 3 publications

Simulation of networks of spiking neurons: A review of tools and strategies

Simulation of networks of spiking neurons: A review of tools and strategies

Oppilatio+ - A data and cognitive science based approach to analyze pedestrian flows in networks

Deterministic Rectangle Enclosure and Offline Dominance Reporting on the RAM

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