Vehicular ad hoc network (VANET) and road-side sensors are used to improve driving safety in many applications. Sensor nodes deployed along the roadside are used to sense road conditions and then deliver information a...
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Vehicular ad hoc network (VANET) and road-side sensors are used to improve driving safety in many applications. Sensor nodes deployed along the roadside are used to sense road conditions and then deliver information about dangerous conditions to vehicles. In hybrid sensor-vehicular networks, new challenges arise and should be addressed. Geocasting can be used to perform the regional broadcast to deliver geographic-related safety, commercials, and advertisements messages. The challenging problem in geocasting is how to deliver packets to all the nodes within the geocast region with high efficiency but low overhead. Network coding is a special in-network data-processing technique that can potentially increase the network capacity and packet throughput in wireless networking environments. In this paper, a network coding based rarest-first packet recovery algorithm for transmitting geocast packets over hybrid sensor-vehicular-networks is proposed. The proposed algorithm can increase packet delivered ratio at each mobile node. As a result, the safety and transmission efficiency can be achieved simultaneously.
In a recent work, Markham and Sanders proposed a framework to study quantum secret-sharing (QSS) schemes using graph states. This framework unified three classes of QSS protocols, namely, sharing classical secrets ove...
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In a recent work, Markham and Sanders proposed a framework to study quantum secret-sharing (QSS) schemes using graph states. This framework unified three classes of QSS protocols, namely, sharing classical secrets over private and public channels, and sharing quantum secrets. However, previous work on graph-state secret sharing mostly focused on threshold schemes. In this paper, we focus on general access structures. We show how to realize a large class of arbitrary access structures using the graph-state formalism. We show an equivalence between [[n,1]] binary quantum codes and graph-state secret-sharing schemes sharing one bit. We also establish a similar (but restricted) equivalence between a class of [[n,1]] Calderbank-Shor-Steane codes and graph-state QSS schemes sharing one qubit. With these results we are able to construct a large class of graph-state quantum secret-sharing schemes with arbitrary access structures.
We study random-number-expansion protocols based on the n→1 quantum random access codes (QRACs). We consider them in the semi-device-independent scenario where the inner workings of the devices are unknown to us but ...
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We study random-number-expansion protocols based on the n→1 quantum random access codes (QRACs). We consider them in the semi-device-independent scenario where the inner workings of the devices are unknown to us but we can certify the dimensions of the systems being communicated. This approach does not require the use of entanglement and makes the physical realization of these protocols much easier than in the standard device-independent scenario. In our work, we propose a protocol for randomness expansion, compute min-entropy for the semi-device-independent protocol, and investigate n→1 QRACs with a view to their use in randomness-expansion protocols. We also calculate the dependence of the effectiveness of the randomness generation on n and find it optimal for n=3, and provide the explanation for this fact.
Experimental and computational studies emphasize the role of the millisecond precision of neuronal spike times as an important coding mechanism for transmitting and representing information in the central nervous syst...
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Experimental and computational studies emphasize the role of the millisecond precision of neuronal spike times as an important coding mechanism for transmitting and representing information in the central nervous system. We investigate the spike time precision of a multicompartmental pyramidal neuron model of the CA3 region of the hippocampus under the influence of various sources of neuronal noise. We describe differences in the contribution to noise originating from voltage-gated ion channels, synaptic vesicle release, and vesicle quantal size. We analyze the effect of interspike intervals and the voltage course preceding the firing of spikes on the spike-timing jitter. The main finding of this study is the ranking of different noise sources according to their contribution to spike time precision. The most influential is synaptic vesicle release noise, causing the spike jitter to vary from 1ms to 7 ms of a mean value 2.5 ms. Of second importance was the noise incurred by vesicle quantal size variation causing the spike time jitter to vary from 0.03 ms to 0.6 ms. Least influential was the voltage-gated channel noise generating spike jitter from 0.02 ms to 0.15 ms.
We propose a method to simulate a Dirac or Majorana equation evolving under particular potentials with the use of the corresponding free evolution, while the potential dynamics is encoded in a static transformation up...
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We propose a method to simulate a Dirac or Majorana equation evolving under particular potentials with the use of the corresponding free evolution, while the potential dynamics is encoded in a static transformation upon the initial state. We extend our results to interacting two-body systems.
The effect of cellular heterogeneity on the coding properties of neural populations is studied analytically and numerically. We find that heterogeneity decreases the threshold for synchronization, and its strength is ...
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The effect of cellular heterogeneity on the coding properties of neural populations is studied analytically and numerically. We find that heterogeneity decreases the threshold for synchronization, and its strength is nonlinearly related to the network mean firing rate. In addition, conditions are shown under which heterogeneity optimizes network information transmission for either temporal or rate coding, with high input frequencies leading to different effects for each coding strategy. The results are shown to be robust for more realistic conditions.
Good software engineering practice demands generalization and abstraction, whereas high performance demands specialization and concretization. These goals are at odds, and compilers can only rarely translate expressiv...
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Good software engineering practice demands generalization and abstraction, whereas high performance demands specialization and concretization. These goals are at odds, and compilers can only rarely translate expressive high-level programs to modern hardware platforms in a way that makes best use of the available resources. Generative programming is a promising alternative to fully automatic translation. Instead of writing down the target program directly, developers write a program generator, which produces the target program as its output. The generator can be written in a high-level, generic style and can still produce efficient, specialized target programs. In practice, however, developing high-quality program generators requires a very large effort that is often hard to amortize. We present lightweight modular staging (LMS), a generative programming approach that lowers this effort significantly. LMS seamlessly combines program generator logic with the generated code in a single program, using only types to distinguish the two stages of execution. Through extensive use of component technology, LMS makes a reusable and extensible compiler framework available at the library level, allowing programmers to tightly integrate domain-specific abstractions and optimizations into the generation process, with common generic optimizations provided by the framework. LMS is well suited to develop embedded domain-specific languages (DSLs) and has been used to develop powerful performance-oriented DSLs for demanding domains such as machine learning, with code generation for heterogeneous platforms including GPUs. LMS has also been used to generate SQL for embedded database queries and JavaScript for web applications.
Many theories of perception are anchored in the central notion that the brain continuously updates an internal model of the world to infer the probable causes of sensory events. In this framework, the brain needs not ...
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Many theories of perception are anchored in the central notion that the brain continuously updates an internal model of the world to infer the probable causes of sensory events. In this framework, the brain needs not only to predict the causes of sensory input, but also when they are most likely to happen. In this article, we review the neurophysiological bases of sensory predictions of "what' (predictive coding) and 'when' (predictive timing), with an emphasis on low-level oscillatory mechanisms. We argue that neural rhythms offer distinct and adapted computational solutions to predicting 'what' is going to happen in the sensory environment and 'when'.
We introduce a novel Medium Access Control (MAC) protocol for Automatic Repeat reQuest-based (ARQ-based) cooperative wireless sensor networks. Using network coding techniques, we achieve a better network performance i...
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We introduce a novel Medium Access Control (MAC) protocol for Automatic Repeat reQuest-based (ARQ-based) cooperative wireless sensor networks. Using network coding techniques, we achieve a better network performance in terms of energy efficiency without compromising the offered Quality of Service (QoS). The proposed solution is compared to other cooperative schemes, while analytical and simulation results are provided to evaluate our protocol.
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