We analyze the computational complexity of optimally playing the two-player board game Push Fight, generalized to an arbitrary board and number of pieces. We prove that the game is PSPACE-hard to decide who will win f...
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Unlike most researches focus on computation reduction, the fast algorithm proposed in this paper aims at maintaining the coding efficiency as high as possible. In the proposed algorithm we employ support vector machin...
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ISBN:
(纸本)9781509040452
Unlike most researches focus on computation reduction, the fast algorithm proposed in this paper aims at maintaining the coding efficiency as high as possible. In the proposed algorithm we employ support vector machine (SVM) that uses three parameters as features: variances, lowfrequency AC components of DCT and spatially neighboring CU levels for fast CU size decision. In addition, based upon RMD cost we propose an adaptive mode candidates method for further RDO computation. Experimental results demonstrate that average 22% reduction of computational complexity can be achieved but only with 0.09% BD bit rate increase.
We propose hybridization of sub-propositional fragments of Halpern-Shoham logic as a way of obtaining expressive and decidable referential interval temporal logics. In the paper, we hybridize a Horn fragment of Halper...
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ISBN:
(纸本)9783662540695;9783662540688
We propose hybridization of sub-propositional fragments of Halpern-Shoham logic as a way of obtaining expressive and decidable referential interval temporal logics. In the paper, we hybridize a Horn fragment of Halpern-Shoham logic whose language is restricted in its modal part to necessity modalities, and prove that satisfiability problem in this fragment is NP-complete over reflexive or an irreflexive and dense underlying structure of time.
In this paper, we propose a new distributed optimization method that computes a Lasso estimator for logistic regression in the case when two parties have explanatory variables corresponding to distinct attributes. An ...
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ISBN:
(纸本)9781728145693
In this paper, we propose a new distributed optimization method that computes a Lasso estimator for logistic regression in the case when two parties have explanatory variables corresponding to distinct attributes. An existing protocol using the alternating direction method of multipliers (ADMM) for linear regression can be applied to logistic regression. However, this protocol needs an underlying iterative method such as the gradient method. We show that the proposed protocol using the generalized Bregman ADMM, which removes the necessity to use the underlying iterative method, requires lower computational and communication complexity.
In this paper, we solve a long-standing graph partition problem under vertex-compaction that has been of interest since about 1999. The graph partition problem that we consider in this paper is to decide whether or no...
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The last decade has seen the rise of neuromorphic architectures based on artificial spiking neural networks, such as the SpiNNaker, TrueNorth, and Loihi systems. The massive parallelism and co-locating of computation ...
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The computational complexity required in the channel estimation plays an important role in underwater acoustic communications (UAC) with orthogonal frequency duplex access (OFDM), especially when the channel is sparse...
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The computational complexity required in the channel estimation plays an important role in underwater acoustic communications (UAC) with orthogonal frequency duplex access (OFDM), especially when the channel is sparse. The authors develop an algorithm to carry out the orthogonal matching pursuit (OMP) for the sparse channel estimation based on the compressive sensing, where the goal is to obtain the minimum computational complexity. It is discovered that the inter-carrier interference (ICI) mainly depends on the adjacent subcarriers since the ICI interferences become more and more marginable with the increase of the distance from the other subcarriers to the current desired subcarrier in the frequency domain, which can be utilised to reduce the complexity of the design over the sparse channel estimator. By exploiting this property, the authors propose that the diagonal band of the ICI channel matrix is employed in the calculation of the objective function to minimise the required computational complexity, which develops an adaptive algorithm that is theoretically proved to be a faster algorithm. Numerical simulations are demonstrated for the typical UAC system that the proposed algorithm achieves the remarkable gain of the computational complexity compared to the existing algorithm.
We consider Web services defined by orchestrations in the Orc language and two natural quality of services measures, the number of outputs and a discrete version of the first response time. We analyse first those subf...
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We consider Web services defined by orchestrations in the Orc language and two natural quality of services measures, the number of outputs and a discrete version of the first response time. We analyse first those subfamilies of finite orchestrations in which the measures are well defined and consider their evaluation in both reliable and probabilistic unreliable environments. On those subfamilies in which the QoS measures are well defined, we consider a set of natural related problems and analyse its computational complexity. In general our results show a clear picture of the difficulty of computing the proposed QoS measures with respect to the expressiveness of the subfamilies of Orc. Only in few cases the problems are solvable in polynomial time pointing out the computational difficulty of evaluating QoS measures even in simplified models.
The outcomes of quantum mechanical measurements are inherently random. It is therefore necessary to develop stringent methods for quantifying the degree of statistical uncertainty about the results of quantum experime...
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The outcomes of quantum mechanical measurements are inherently random. It is therefore necessary to develop stringent methods for quantifying the degree of statistical uncertainty about the results of quantum experiments. For the particularly relevant task of quantum state tomography, it has been shown that a significant reduction in uncertainty can be achieved by taking the positivity of quantum states into account. However-the large number of partial results and heuristics notwithstanding-no efficient general algorithm is known that produces an optimal uncertainty region from experimental data, while making use of the prior constraint of positivity. Here, we provide a precise formulation of this problem and show that the general case is NP-hard. Our result leaves room for the existence of efficient approximate solutions, and therefore does not in itself imply that the practical task of quantum uncertainty quantification is intractable. However, it does show that there exists a non-trivial trade-off between optimality and computational efficiency for error regions. Weprove two versions of the result: one for frequentist and one for Bayesian statistics.
Spinal codes, a new class of rateless codes, have received considerable attention for their capacity-approaching performance over noisy channels. Hash function is the core of a spinal encoder to generate infinite code...
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Spinal codes, a new class of rateless codes, have received considerable attention for their capacity-approaching performance over noisy channels. Hash function is the core of a spinal encoder to generate infinite coded symbols, which has higher hardware complexity. In this study, the authors propose a multiplicative repetition-based method instead of the hash function to generate innumerable symbols with low encoding complexity. Furthermore, both frozen-aided and cyclic redundancy check-aided decoding methods are proposed to improve the spectral efficiency. Simulation results show that the proposed spinal codes have lower computational complexity and can achieve higher spectral efficiency in the high signal-to-noise ratio region compared with the conventional ones over both additive white Gaussian noise and Rayleigh fading channels.
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