In this paper, the effect of noise on Grover's algorithm is analyzed, modeled as a total depolarizing channel (TDCh) and a local depolarizing channel (LDCh) in each qubit. The focus was not in error correction (e....
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In this paper, the effect of noise on Grover's algorithm is analyzed, modeled as a total depolarizing channel (TDCh) and a local depolarizing channel (LDCh) in each qubit. The focus was not in error correction (e.g. by the fault-tolerant method), but to provide an insight to the kind of error, or degradation, that needs to be corrected. In the last years, analytical results regarding mainly the TDCh model have been obtained. In this paper, we extend these previous results to the local case, concluding that the degradation of Grover's algorithm with the latter is worse than the former. It has been shown that for both cases with an N-dependent small enough error-width, smaller than 1/root N for total error and 1/(root Nlog(2)N) for the local case, correction is not needed.
We study competitions structured as hierarchically shaped single elimination tournaments. We define optimal tournaments by maximizing attractiveness such that topmost players will have the chance to meet in higher sta...
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ISBN:
(纸本)9783030975494;9783030975487
We study competitions structured as hierarchically shaped single elimination tournaments. We define optimal tournaments by maximizing attractiveness such that topmost players will have the chance to meet in higher stages of the tournament. We propose a dynamic programming algorithm for computing optimal tournaments and we provide its sound complexity analysis.
The paper describes an algorithm that constructs approximate decision trees (alpha-decision trees), which are optimal relatively to one of the following complexity measures: depth, total path length or number of nodes...
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ISBN:
(纸本)9783642135286
The paper describes an algorithm that constructs approximate decision trees (alpha-decision trees), which are optimal relatively to one of the following complexity measures: depth, total path length or number of nodes. The algorithm uses dynamic programming and extends methods described in [4] to constructing approximate decision trees. Adjustable approximation rate allows controlling algorithm complexity. The algorithm is applied to build optimal alpha-decision trees for two data sets from UCI Machine Learning Repository [1].
In this paper we present a generalization of the classic Firm's Profit Maximization Problem, using the linear model for the production function, considering a decreasing price w(i)(x(i)) = b(i) - c(i)x(i) and maxi...
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In this paper we present a generalization of the classic Firm's Profit Maximization Problem, using the linear model for the production function, considering a decreasing price w(i)(x(i)) = b(i) - c(i)x(i) and maximum constraints for the inputs or, equivalently, considering inputs that are in turn outputs in economies of scale with quadratic concave cost functions. We formulate the problem by previously calculating the analytical minimum cost function in the quadratic concave case. This minimum cost function will be calculated for each production level via the infimal convolution of quadratic concave functions whose result is a piecewise quadratic concave function. (C) 2012 Elsevier B.V. All rights reserved.
This article proposes a new algorithm applicable to H. 264 based on motion vector coding length and high-pixel accuracy filter operator instead of linear interpolation. After half-pixel accuracy motion estimation, a h...
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ISBN:
(纸本)9783038351153
This article proposes a new algorithm applicable to H. 264 based on motion vector coding length and high-pixel accuracy filter operator instead of linear interpolation. After half-pixel accuracy motion estimation, a high accuracy motion estimation can be calculated with the intermediate results. This algorithm can achieve high accuracy estimation and compensation while decreasing algorithm complexity. The computer simulation results show that when the new proposed algorithm for 1/8-pixel accuracy is used in QCIF, CIF, CCIR format pictures, total computing time reduces more than 20% and computing time used for fractional-pixel motion estimation reduces more than 50% while coding length only increases less than 10% and PSNR-Y is approximately equal.
The paper analyzes existing factorization algorithms and classifies them. The elaboration of the factorization algorithm which allows working with both small and large numbers is presented. Whereas the replacement of ...
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ISBN:
(纸本)9781728140681
The paper analyzes existing factorization algorithms and classifies them. The elaboration of the factorization algorithm which allows working with both small and large numbers is presented. Whereas the replacement of operations of addition and multiplication by the operation of addition, in comparison with the existing algorithms, a large gain of time is achieved. The initiation of operation of inequality solution allows cutting up to N-1/4 of possible candidates for a single calculation step. The evaluation of the time of the algorithm was conducted. It is comparable to the best known.
This paper presents a performance analysis of a procedure for Quality of Service (QoS) negotiation in distributed multimedia applications. The numerical complexity of the procedure is regarded as a measure of its perf...
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ISBN:
(纸本)0780379632
This paper presents a performance analysis of a procedure for Quality of Service (QoS) negotiation in distributed multimedia applications. The numerical complexity of the procedure is regarded as a measure of its performance. The results are derived by combining formal analysis with computer simulation. It is shown that the performance of the considered procedure is better than of some known existing solutions.
While machine learning algorithms become more and more accurate in image processing tasks, their computation complexity becomes less important because they can be run on more and more powerful hardware. In this work, ...
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ISBN:
(纸本)9781538626337
While machine learning algorithms become more and more accurate in image processing tasks, their computation complexity becomes less important because they can be run on more and more powerful hardware. In this work, we are considering the computation complexity of a machine learning algorithm training/classification phase as the major criterion. The main aim is given to the Principal Component Analysis algorithm, which is examined, its drawbacks are point-out and suppressed by the proposed combination with the F-transform technique. We show that the training phase of such a combination is very fast, which is caused by the fact that both PCA and F-transform algorithms reduce dimensionality. In the designed benchmark, we show that the success rate of the fast hybrid algorithm is the same as the original PCA, due to F-transform ability to capture spatial information and reduction of noise/distortion in an image. Finally, we demonstrate that PCA+FT is faster and can achieve a higher success rate than a standard Convolution Neural Network and nevertheless, it is slightly less accurate as a Capsule Neural Network for the chosen dataset, its training phase is 100000 x faster and classification time is faster 9x.
As the 5G technology is applied indoors, the antenna array aperture and the distance from the source to the antenna array become comparable, which makes the far-field signal model no longer valid. In this case, the co...
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ISBN:
(纸本)9781728135175
As the 5G technology is applied indoors, the antenna array aperture and the distance from the source to the antenna array become comparable, which makes the far-field signal model no longer valid. In this case, the complexity of signal model is increased because the channel parameters are not only the angle of arrival, but also the distance. In this paper, a near-field signal model based on a uniform circular array (UCA) is studied to avoid the estimation error caused by the plane wave model. Moreover, channel estimation algorithms based on near-field signal model are investigated and compared in terms of estimation accuracy and algorithm complexity. Finally based on these two aspects, the algorithm that is more suitable for indoor communication will be selected.
Recurrent neural networks have found their application in a wide range of tasks related to the processing of sequential data sets, because of using of an internal state, which depends on both the current input data an...
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Recurrent neural networks have found their application in a wide range of tasks related to the processing of sequential data sets, because of using of an internal state, which depends on both the current input data and the state at the previous iteration. DeepMind group proposed a new approach combining the attention mechanism and external memory. It became a big step in the development of this class of networks. The architecture called the Neural Turing Machine, as well as the later improved Differential Neural Computer (DNC) model, are an alternative to the classical Turing machine, with the exception that all its operations are differential. During all the opportunities that open, researchers faced with incomplete knowledge of its fundamental capabilities. In this thesis proposed a research method based on the use of DNC to solve basic elementary algorithms and analyzing the obtained characteristics of the model in comparison with classical algorithms for a Turing machine. The results obtained in this paper provide researchers with both practical advice on the use of DNC, shows weaknesses, and open new directions for the further improvement of this neural network architecture.
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