Nowadays, social network plays an important role in human life. Besides all advantages of social networks, the dissemination of rumors becomes a major concern for users, so it is important to find a way to limit the spread of misinformation as much as possible. Influence blocking maximization (IBM) is the problem of finding k nodes in a social graph to minimize the spread of rumor source at the end of a propagation process. In this article, we propose a two-step method called influence blocking maximization using martingale (IBMM) to solve IBM problem under competitive independent cascade model (ICM) with both (1-1/e-ε)-approximation guarantee and practical runtime efficiency. In the proposed method, first we calculate the number of required samples using a set of estimation techniques based on martingale; and then we generate the samples and find top-k savior nodes. We perform extensive experiments on three real-world data sets and three rumor sets with different behaviors. We both experimentally and theoretically show that the effectiveness of IBMM is close to greedy. The results also show that IBMM is very fast, in particular, for a network with 265,214 nodes, 420,045 edges, and a set of 50 high influential nodes as rumor, when k = 50, l=1, and ϵ = 0.5 IBMM returns the solution within 3.5 s.

Fusing a low spatial resolution hyperspectral image (HSI) with a high spatial resolution multispectral image (MSI), aiming to produce a super-resolution hyperspectral image, has recently attracted increasing research interest. In this paper, a novel approach based on coupled non-negative tensor decomposition is proposed. The proposed method performs a tucker tensor factorization of a low resolution hyperspectral image and a high resolution multispectral image under the constraint of non-negative tensor decomposition (NTD). The conventional matrix factorization methods essentially lose spatio-spectral structure information when stacking the 3D data structure of a hyperspectral image into a matrix form. Moreover, the spectral, spatial, or their joint structural features have to be imposed from the outside as a constraint to well pose the matrix factorization problem. The proposed method has the advantage of preserving the spatio-spectral structure of hyperspectral images. In this paper, the NTD is directly imposed on the coupled tensors of the HSI and MSI. Hence, the intrinsic spatio-spectral structure of the HSI is represented without loss, and spatial and spectral information can be interdependently exploited. Furthermore, multilinear interactions of different modes of the HSIs can be exactly modeled with the core tensor of the Tucker tensor decomposition. The proposed method is straightforward and easy to implement. Unlike other state-of-the-art approaches, the complexity of the proposed approach is linear with the size of the HSI cube. Experiments on two well-known datasets give promising results when compared with some recent methods from the literature.

Nuclear pleomorphism is one of the criteria for diagnosing and grading breast cancer. The grading that is made by pathologist is subjective and prone to inter, intra observer variations. Furthermore, pathologists may need a huge time for evaluating all cases per day. Therefore, there is a necessity to provide an automatic system for a better diagnosis and detection. This paper proposes an automatic system for detecting and segmenting cancerous nuclei, which is partly different from healthy nuclei segmentation systems. In contrast, our system detects critical nuclei with any shape, border and chromatin density even in higher scores. This system avoids segmenting healthy cell nuclei. It only detects and segments a high percentage of deformed cell nuclei, which are necessary for nuclear pleomorphism scoring even cells with vesicular nuclei that are not detected in any other algorithms.

Online social networks (OSNs) are one of the most popular services available on the Internet. They are also considered as the primary source of news for many people. In spite of all the advantages of OSNs, dissemination of rumors has become a major concern. Influence blocking maximization (IBM) is the problem of determining savior users to limit the spread of misinformation as much as possible. Although the IBM problem has been widely studied, most of existing works neglect the effect of deadline and delay. In this article, we study Temporal IBM and propose a sampling-based approach (TIBM-M) with both theoretical guarantee and practical runtime efficiency. We perform extensive experiments on four real-world datasets and both experimentally and theoretically show that TIBM-M consistently matches the greedy algorithm in terms of effectiveness while significantly improves the efficiency. The results also indicate that TIBM-M has better blocking effect than other methods in all conditions.