Fair clustering enjoyed a surge of interest recently. One appealing way of integrating fairness aspects into classical clustering problems is by introducing multiple covering constraints. This is a natural generalizat...
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Given n points in dp, we consider the problem of partitioning points into k clusters with associated centers. The cost of a clustering is the sum of pth powers of distances of points to their cluster centers. For p ∈...
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The rapid development of low earth orbit (LEO) satellite communication has driven the deployment of artificial intelligence (AI) in space, providing various intelligent services like real-time disaster navigation, glo...
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Three-Bar Charts Packing Problem is to pack the bar charts consisting of three bars each into the horizontal unit-height strip of minimal length. The bars of each bar chart may move vertically within the strip, but it...
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Given n points in d-dimensional space and a parameter z, we study the problem of finding the smallest axis- aligned bounding box that covers at least n - z points and labels the remaining points as outliers. We consid...
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In this letter, considering the lack of core and drilling cuttings, an interpretable semisupervised classification method (ISSCM) under multiple smoothness assumptions is proposed and applied to lithology identificati...
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In this letter, considering the lack of core and drilling cuttings, an interpretable semisupervised classification method (ISSCM) under multiple smoothness assumptions is proposed and applied to lithology identification. The contribution is threefold. First, the novel semisupervised learning algorithm is developed based on the decision tree, the interpretability of which is highly beneficial to solve risk-aware problems. Second, both smoothness in the feature space and depth is utilized to generate pseudo-labels for the unlabelled data by using label propagation. Third, an algorithm to approximate the optimal affinity matrix is added to avoid degradation rendered by inappropriate manual settings under multiple smoothness assumptions. All these contributions could yield a classification model that is interpretable, accurate, and insusceptible to imprecise empirical settings. In the experiment, the proposed method is applied to lithology identification and verified by real-world data.
We show that the use of Schur complement lemma to derive equivalent convex constraints to those non-convex in (54) and (55) of the above paper is not valid. In this comment, an alternative approach is presented to con...
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We show that the use of Schur complement lemma to derive equivalent convex constraints to those non-convex in (54) and (55) of the above paper is not valid. In this comment, an alternative approach is presented to convexify those constraints.
For industrial applications of the artificial intelligence of things, mechanical control usually affects the overall product output and production schedule. Recently, more and more engineers have applied the deep rein...
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For industrial applications of the artificial intelligence of things, mechanical control usually affects the overall product output and production schedule. Recently, more and more engineers have applied the deep reinforcement learning method to mechanical control to improve the company's profit. However, the problem of deep reinforcement learning training stage is that overfitting often occurs, which results in accidental control and increases the risk of overcontrol. In order to address this problem, in this article, an expected advantage learning method is proposed for moderating the maximum value of expectation-based deep reinforcement learning for industrial applications. With the tanh softmax policy of the softmax function, we replace the sigmod function with the tanh function as the softmax function activation value. It makes it so that the proposed expectation-based method can successfully decrease the value overfitting in cognitive computing. In the experimental results, the performance of the Deep Q Network algorithm, advantage learning algorithm, and propose expected advantage learning method were evaluated in every episodes with the four criteria: the total score, total step, average score, and highest score. Comparing with the AL algorithm, the total score of the proposed expected advantage learning method is increased by 6% in the same number of trainings. This shows that the action probability distribution of the proposed expected advantage learning method has better performance than the traditional soft-max strategy for the optimal setting control of industrial applications.
In Network Function Virtualization (NFV), a multicast data flow must be traversed through a Service Function Chain (SFC) that is a predefined sequence of Virtual Network Functions (VNFs). In this letter, we formulate ...
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In Network Function Virtualization (NFV), a multicast data flow must be traversed through a Service Function Chain (SFC) that is a predefined sequence of Virtual Network Functions (VNFs). In this letter, we formulate the problem of optimal embedding of NFV-Enabled multicast service chains as a mixed binary linear program. Then to solve the problem in a reasonable time, we use the penalty-based successive upper bound minimization algorithm. Also, a heuristic solution based on the decomposition of the problem into smaller sub-problems that can be solved iteratively and sequentially, is proposed. Simulation results demonstrate the effectiveness of the proposed solution methods.
An implicit function can be represented only in numerical format. For example, the figure of the geoid of the Earth as a surface of equal potentials in the final version is provided by a set of numerical data, based o...
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