We study a generalization of classical combinatorial graph spanners to the spectral setting. Given a set of vectorswhere for two matrices A, B ∈ R d×d we writeWe show that any set V has an Õ(k)-spectral spanner of size Õ(k) and this bound is almost optimal in the worst case.We use spectral spanners to study composable core-sets for spectral problems. We show that for many objective functions one can use a spectral spanner, independent of the underlying function, as a core-set and obtain almost optimal composable core-sets. For example, for the k-determinant maximization problem, we obtain an Õ(k) k -composable core-set, and we show that this is almost optimal in the worst case.Our algorithm is a spectral analogue of the classical greedy algorithm for finding (combinatorial) spanners in graphs. We expect that our spanners find many other applications in distributed or parallel models of computation. Our proof is spectral. As a side result of our techniques, we show that the rank of diagonally dominant lower-triangular matrices are robust under "small perturbations" which could be of independent interests.
This research aims to estimate the overflow capacity of a curved labyrinth using different intelligent prediction models, namely the adaptive neural-fuzzy inference system, the support vector machine, the M5 model tree, the least-squares support vector machine and the least-squares support vector machine-bat algorithm (LSSVM-BA). A total of 355 empirical data for 6 different congressional overflow models were extracted from the results of a laboratory study on labyrinth overflow models. The parameters of the upstream water head to overflow ratio, the lateral wall angle and the curvature angle were used to estimate the discharge coefficient of curved labyrinth overflows. Based on various statistical evaluation indicators, the results show that those input parameters can be relied upon to predict the discharge coefficient. Specifically, the LSSVM-BA model showed the best prediction accuracy during the training and test phases. Such a low-cost prediction model may have a remarkable practical implication as it could be an economic alternative to the expensive laboratory solution, which is costly and time-consuming.
We prove that the permanent of nonnegative matrices can be deterministically approximated within a factor of √ 2 n in polynomial time, improving upon the previous deterministic approximations. We show this by proving that the Bethe approximation of the permanent, a quantity computable in polynomial time, is at least as large as the permanent divided by √ 2 n .This resolves a conjecture of Gurvits [Gur11]. Our bound is tight, and when combined with previously known inequalities lower bounding the permanent, fully resolves the quality of Bethe approximation for permanent. 1 We remark that for any matrix A, |per(A)| ≤ A n .
Abstract-The computational intelligence such as artificial neural network (ANN) and fuzzy inference system (FIS) is a strong tool for prediction and simulation in engineering applications. In this paper, radial basis function (RBF) network and adaptive neuro-fuzzy inference system (ANFIS) are used for prediction of IC50 (the 50% inhibitory concentration) values evaluated by the MTT assay in human cancer cell lines. For developing of the proposed models, the input parameters are the concentration of the drug and the types of cell lines and the output is IC50 values in the A549, H157, H460 and H1975 cell lines. The predicted IC50 values using the proposed RBF and ANFIS models are compared with the experimental data. The obtained results show that both RBF and ANFIS models have achieved good agreement with the experimental data. Therefore, the proposed RBF and ANFIS models are useful, reliable, fast and cheap tools to predict the IC50 values determined by the MTT assay in human cancer cell lines.
Maximum power point tracking (MPPT) is a technique that grid tie inverters, solar battery chargers and similar devices use to get the maximum possible power from one or more solar panels. In this paper the simulation result of the P&O algorithm and the conventional maximum power point tracking technique compared with Intelligent Control (fuzzy logic controller-FLC). In fact the target of this manuscript is study the effect of the environmental conditions like variation of solar intensity and temperature on output power of photovoltaic module. Solarex MSX-83 PV module and MATLAB/SIMULINK software are used for simulation. In this paper, the construction of stand-alone photovoltaic system consists of PV module, boost converter, maximum power point tracker and Pulse Width Modulator.
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