No abstract

No abstract

No abstract

No abstract

Summary This paper concerns the stability analysis of systems with interval time‐varying delay. A Lyapunov‐Krasovskii functional containing an augmented quadratic term and certain triple integral terms is constructed to integrate features of the truncated Bessel‐Legendre inequality less conservative than Wirtinger inequality that encompasses Jensen inequality, respectively, and to exploit merits of the newly developed double integral inequalities tighter than auxiliary function‐based, Wirtinger, and Jensen double integral inequalities. A new quadratic convex lemma is proposed to derive delay and its derivative dependent sufficient stability conditions in terms of linear matrix inequalities synthetically with reciprocal convex approach and affine convex combination. The efficiency of the presented method is illustrated on some classical numerical examples.

No abstract

In this paper, we propose to learn shared semantic space with correlation alignment (S 3 CA) for multimodal data representations, which aligns nonlinear correlations of multimodal data distributions in deep neural networks designed for heterogeneous data. In the context of cross-modal (event) retrieval, we design a neural network with convolutional layers and fully-connected layers to extract features for images, including images on Flickr-like social media. Simultaneously, we exploit a fully-connected neural network to extract semantic features for texts, including news articles from news media. In particular, nonlinear correlations of layer activations in the two neural networks are aligned with correlation alignment during the joint training of the networks. Furthermore, we project the multimodal data into a shared semantic space for cross-modal (event) retrieval, where the distances between heterogeneous data samples can be measured directly. In addition, we contribute a Wiki-Flickr Event dataset, where the multimodal data samples are not describing each other in pairs like the existing paired datasets, but all of them are describing semantic events. Extensive experiments conducted on both paired and unpaired datasets manifest the effectiveness of S 3 CA, outperforming the state-of-the-art methods.

The traveling thief problem (TTP) is a challenging combinatorial optimization problem that has attracted many scholars, the problem interconnects two well-known NP-hard problems: the traveling salesman problem and the 0-1 knapsack problem. Various approaches have increasingly been proposed to solve this novel problem that combines two interdependent subproblems. In this paper, the TTP is investigated theoretically and empirically. This paper proposed an approach based on item selection weight and reverse-order allocation. A novel method to calculate the score value of each item for item selection, which expands the effect of the item's weight, was introduced. Furthermore, the approach adopted reverse-order allocation, which selects items in an inverse order according to the traveling route. Different approaches for solving the TTP are compared and analyzed. The experimental investigations suggest that our proposed approach is competitive for many instances of various sizes and types compared to other heuristics.