Motivated by some recent works on the topic of the Brown-Resnick process, we study the functional limit theorem for normalized pointwise maxima of dependent chi-processes. It is proven that the properly normalized poi...
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Motivated by some recent works on the topic of the Brown-Resnick process, we study the functional limit theorem for normalized pointwise maxima of dependent chi-processes. It is proven that the properly normalized pointwise maxima of those processes are attracted by the Brown-Resnick process.
The proper implementation of datascience and big data analytics has become an essential component for gaining valuable insight and driving innovation across a variety of industries in this era, which is characterized...
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Long-term multivariate time series forecasting is an important task in engineering applications. It helps grasp the future development trend of data in real-time, which is of great significance for a wide variety of f...
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Long-term multivariate time series forecasting is an important task in engineering applications. It helps grasp the future development trend of data in real-time, which is of great significance for a wide variety of fields. Due to the non-linear and unstable characteristics of multivariate time series, the existing methods encounter difficulties in analyzing complex high-dimensional data and capturing latent relationships between multivariates in time series, thus affecting the performance of long-term prediction. In this paper, we propose a novel time series forecasting model based on multilayer perceptron that combines spatio-temporal decomposition and doubly residual stacking, namely Spatio-Temporal Decomposition Neural Network (STDNet). We decompose the originally complex and unstable time series into two parts, temporal term and spatial term. We design temporal module based on auto-correlation mechanism to discover temporal dependencies at the sub-series level, and spatial module based on convolutional neural network and self-attention mechanism to integrate multivariate information from two dimensions, global and local, respectively. Then we integrate the results obtained from the different modules to get the final forecast. Extensive experiments on four real-world datasets show that STDNet significantly outperforms other state-of-the-art methods, which provides an effective solution for long-term time series forecasting.
Because of the combination of Internet of Things (IoT) technology and machine learning (ML) algorithms, quality control methods in the industrial sector are undergoing a revolution. This is because real-time monitorin...
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Let N be a maximal discrete nest on an infinite-dimensional separable Hilbert space H,ξ=∑^(∞)_(n=1)en/2n be a separating vector for the commutant N',E_(ξ)be the projection from H onto the subspace[Cξ]spanned ...
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Let N be a maximal discrete nest on an infinite-dimensional separable Hilbert space H,ξ=∑^(∞)_(n=1)en/2n be a separating vector for the commutant N',E_(ξ)be the projection from H onto the subspace[Cξ]spanned by the vectorξ,and Q be the projection from K=H⊕H⊕H onto the closed subspace{(η,η,η)^(T):η∈H}.Suppose that L is the projection lattice generated by the projections(E_(ξ) 0 0 0 0 0 0 0 0),{(E 0 0 0 0 0 0 0 0):E∈N},(I 0 0 0 I 0 0 0 0) and *** show that L is a Kadison-Singer lattice with the trivial ***,we prove that every n-th bounded cohomology group H~n(AlgL,B(K))with coefficients in B(K)is trivial for n≥1.
The Berry-Esseen bound provides an upper bound on the Kolmogorov distance between a random variable and the normal *** this paper,we establish Berry-Esseen bounds with optimal rates for self-normalized sums of locally...
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The Berry-Esseen bound provides an upper bound on the Kolmogorov distance between a random variable and the normal *** this paper,we establish Berry-Esseen bounds with optimal rates for self-normalized sums of locally dependent random variables,assuming only a second-moment *** proof leverages Stein's method and introduces a novel randomized concentration inequality,which may also be of independent interest for other *** main results have applied to self-normalized sums of m-dependent random variables and graph dependency models.
In this paper, we design a distributed stochastic source seeking algorithm based on time-delay measurements to implement source seeking and formation control, so that vehicles can achieve and maintain a specific forma...
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In this paper, we design a distributed stochastic source seeking algorithm based on time-delay measurements to implement source seeking and formation control, so that vehicles can achieve and maintain a specific formation during the source seeking process. First, we present continuous-time stochastic averaging theorems for nonlinear delay-differential systems with stochastic perturbations. Then, based on the stochastic extremum seeking method and the leaderless formation strategy,we design a distributed stochastic source seeking algorithm based on time-delay measurements to navigate multiple velocity-actuated vehicles to search for an unknown source while achieving and maintaining a predefined formation, and the effect of the delay is eliminated by adopting the one-stage sequential predictor approach. Moreover, based on our developed stochastic averaging theorems, we prove that the average position of vehicles exponentially converges to a small neighborhood of the source in the almost sure sense, and vehicles can achieve and maintain a predefined formation. Finally, we provide numerical examples to verify the effectiveness of our proposed algorithm.
This research study explores the new dynamics of employee-organization relationships (EOR) [6] using advanced datascience methodologies and presents findings through accessible visualizations. Leveraging a dataset pr...
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In this paper,we consider the regular s-level fractional factorial split-plot(FFSP)designs when the subplot(SP)factors are more *** idea of general minimum lower-order confounding criterion is applied to such designs,...
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In this paper,we consider the regular s-level fractional factorial split-plot(FFSP)designs when the subplot(SP)factors are more *** idea of general minimum lower-order confounding criterion is applied to such designs,and the general minimum lower-order confounding criterion of type SP(SP-GMC)is *** a finite projective geometric formulation,we derive explicit formulae connecting the key terms for the criterion with the complementary *** results are applied to choose optimal FFSP designs under the SP-GMC *** two-and three-level SP-GMC FFSP designs are constructed.
The emergence of cooperation in decentralized multi-agent systems is challenging;naive implementations of learning algorithms typically fail to converge or converge to equilibria without cooperation. Opponent modeling...
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