Robust Spectral Clustering via Sparse Representation

Clustering high-dimensional data has been a challenging problem in data mining and machining learning. Spectral clustering via sparse representation has been proposed for clustering high-dimensional data. A critical step in spectral clustering is to effectively construct a weight matrix by assessing the proximity between each pair of objects. While sparse representation proves its effectiveness for compressing high-dimensional signals, existing spectral clustering algorithms based on sparse representation use those sparse coefficients directly. We believe that the similarity measure exploiting more global information from the coefficient vectors will provide more truthful similarity among data objects. The intuition is that the sparse coefficient vectors corresponding to two similar objects are similar and those of two dissimilar objects are also dissimilar. In particular, we propose two approaches of weight matrix construction according to the similarity of the sparse coefficient vectors. Experimental results on several real-world high-dimensional data sets demonstrate that spectral clustering based on the proposed similarity matrices outperforms existing spectral clustering algorithms via sparse representation

To Be or Not To Be Humorous? Cross Cultural Perspectives on Humor

open access articleHumor seems to manifest differently in Western and Eastern cultures, although little is known about how culture shapes humor perceptions. The authors suggest that Westerners regard humor as a common and positive disposition; the Chinese regard humor as a special disposition particular to humorists, with controversial aspects. In Study 1, Hong Kong participants primed with Western culture evaluate humor more positively than they do when primed with Chinese culture. In Study 2a, Canadians evaluate humor as being more important in comparison with Chinese participants. In Study 2b, Canadians expect ordinary people to possess humor, while Chinese expect specialized comedians to be humorous. The implications and limitations are discussed

Can you forgive? It depends on how happy you are

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This paper examined how individual group status and happiness influence forgiveness. In Study 1, happiness was treated as a trait difference: highly happy people, compared with very unhappy people, were found to be more willing to forgive murderers. More important, an interaction effect between happiness and group status on forgiveness was found, that is, highly happy people tended to be more forgiving when either ingroup or outgroup mem- bers were killed; unhappy people, however, tended to be less forgiving about murder when ingroup rather than outgroup members were killed. In Study 2, happiness was treated as an emotional state difference: happiness, rather than sadness, was found to bring greater forgiveness. Moreover, consistent with the interaction effect displayed in Study 1, happy participants tended to forgive more when ingroup or outgroup members were hurt; sad partici- pants tended to forgive less when ingroup members rather than outgroup members were hurt. Implications for connections between happiness, group membership, and forgiveness are discussed

Depth Assisted Full Resolution Network for Single Image-based View Synthesis

Researches in novel viewpoint synthesis majorly focus on interpolation from multi-view input images. In this paper, we focus on a more challenging and ill-posed problem that is to synthesize novel viewpoints from one single input image. To achieve this goal, we propose a novel deep learning-based technique. We design a full resolution network that extracts local image features with the same resolution of the input, which contributes to derive high resolution and prevent blurry artifacts in the final synthesized images. We also involve a pre-trained depth estimation network into our system, and thus 3D information is able to be utilized to infer the flow field between the input and the target image. Since the depth network is trained by depth order information between arbitrary pairs of points in the scene, global image features are also involved into our system. Finally, a synthesis layer is used to not only warp the observed pixels to the desired positions but also hallucinate the missing pixels with recorded pixels. Experiments show that our technique performs well on images of various scenes, and outperforms the state-of-the-art techniques

How Belief in a Just World Benefits Mental Health: The Effects of Optimism and Gratitude

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Past research suggests that individuals’ belief in a just world (BJW) is closely connected with their mental health. To clarify the underlying mechanism, the current study proposes that BJW encourages optimism and gratitude which then mediates the relation- ship between BJW and mental health as indicated by subjective well-being (SWB) and depression. A sample of 1,200 undergraduates yields results indicating that (a) BJW influences optimism, gratitude, SWB, and depression after controlling for gender, age, income, and personality; (b) optimism and gratitude mediate BJW effects by increasing SWB and decreasing depression. The issues of BJW’s adaptive functions are discussed

Productivity analysis for a vertically fractured well under non- Darcy flow condition

This work was supported by the National Natural Science Foundation of China No. 51204148 and the Fundamental Research Funds for the Central UniversitiesPeer reviewedPostprin