On The Effect of Hyperedge Weights On Hypergraph Learning

Hypergraph is a powerful representation in several computer vision, machine learning and pattern recognition problems. In the last decade, many researchers have been keen to develop different hypergraph models. In contrast, no much attention has been paid to the design of hyperedge weights. However, many studies on pairwise graphs show that the choice of edge weight can significantly influence the performances of such graph algorithms. We argue that this also applies to hypegraphs. In this paper, we empirically discuss the influence of hyperedge weight on hypegraph learning via proposing three novel hyperedge weights from the perspectives of geometry, multivariate statistical analysis and linear regression. Extensive experiments on ORL, COIL20, JAFFE, Sheffield, Scene15 and Caltech256 databases verify our hypothesis. Similar to graph learning, several representative hyperedge weighting schemes can be concluded by our experimental studies. Moreover, the experiments also demonstrate that the combinations of such weighting schemes and conventional hypergraph models can get very promising classification and clustering performances in comparison with some recent state-of-the-art algorithms

Learning Hypergraph-regularized Attribute Predictors

We present a novel attribute learning framework named Hypergraph-based Attribute Predictor (HAP). In HAP, a hypergraph is leveraged to depict the attribute relations in the data. Then the attribute prediction problem is casted as a regularized hypergraph cut problem in which HAP jointly learns a collection of attribute projections from the feature space to a hypergraph embedding space aligned with the attribute space. The learned projections directly act as attribute classifiers (linear and kernelized). This formulation leads to a very efficient approach. By considering our model as a multi-graph cut task, our framework can flexibly incorporate other available information, in particular class label. We apply our approach to attribute prediction, Zero-shot and $N$-shot learning tasks. The results on AWA, USAA and CUB databases demonstrate the value of our methods in comparison with the state-of-the-art approaches.Comment: This is an attribute learning paper accepted by CVPR 201

Lithium atom storage in nanoporous cellulose via surface induced Li2\rm Li_2 breakage

We demonstrate a physical mechanism that enhances a splitting of diatomic $\rm Li_2$ at cellulose surfaces. The origin of this splitting is a possible surface induced diatomic excited state resonance repulsion. The atomic Li is then free to form either physical or chemical bonds with the cellulose surface and even diffuse into the cellulose layer structure. This allows for an enhanced storage capacity of atomic Li in nanoporous celluloseComment: 5 pages, 6 figure

Experiments on the Control of Soybean Aphid with Imidacloprid

The results of a plot experiment showed that imidacloprid at 15, 22.5, 30, 45g a.i. (active ingredient) per hectare gave good control effect against soybean aphid, Aphis glycines Matsumura. The control effect was over 70% four weeks after spraying. The results of demonstration field indicated its average control effect at 22.5, 30 g per hectare reached 54.0, 86.7% four weeks after spraying respectively? Yield increased by 9.1 and 10.9% respectively compared with that of omethoate treated plots.Originating text in Chinese.Citation: Huang, Cunda, Zhou, Jianfeng, Yang, Dan. (1998). Experiments on the Control of Soybean Aphid with Imidacloprid. Pesticides, 37(1), 44-45

Comparative genomic analyses of Lactobacillus rhamnosus isolated from Chinese subjects

peer-reviewedLactobacillus rhamnosus has been found in many niches, including human intestine, vagina, mouth and dairy products. To intensively investigate the genomic diversity of this species, draft genomes of 70 L. rhamnosus strains isolated from different Chinese subjects were sequenced and further investigated. The pan-genome of L. rhamnosus was open. And gene-trait matching (GTM) was done to explore the carbohydrate utilization ability and antibiotic resistance, and to establish a pattern of gene existence/absence and growth/absence. There were no significant correlations between genetic diversity of the strains and the age or region of the donors. The current results extend the understanding of L. rhamnosus, which could be used as a reference for subsequent research as well as mining and application of the species