The Largest Laplacian and Signless Laplacian H-Eigenvalues of a Uniform Hypergraph

In this paper, we show that the largest Laplacian H-eigenvalue of a $k$-uniform nontrivial hypergraph is strictly larger than the maximum degree when $k$ is even. A tight lower bound for this eigenvalue is given. For a connected even-uniform hypergraph, this lower bound is achieved if and only if it is a hyperstar. However, when $k$ is odd, it happens that the largest Laplacian H-eigenvalue is equal to the maximum degree, which is a tight lower bound. On the other hand, tight upper and lower bounds for the largest signless Laplacian H-eigenvalue of a $k$-uniform connected hypergraph are given. For a connected $k$-uniform hypergraph, the upper (respectively lower) bound of the largest signless Laplacian H-eigenvalue is achieved if and only if it is a complete hypergraph (respectively a hyperstar). The largest Laplacian H-eigenvalue is always less than or equal to the largest signless Laplacian H-eigenvalue. When the hypergraph is connected, the equality holds here if and only if $k$ is even and the hypergraph is odd-bipartite.Comment: 26 pages, 3 figure

The clique and coclique numbers' bounds based on the H-eigenvalues of uniform hypergraphs

Abstract In this paper, some inequality relations between the Laplacian/signless Laplacian H-eigenvalues and the clique/coclique numbers of uniform hypergraphs are presented. For a connected uniform hypergraph, some tight lower bounds on the largest Laplacian H + -eigenvalue and signless Laplacian H-eigenvalue related to the clique/coclique numbers are given. And some upper and lower bounds on the clique/coclique numbers related to the largest Laplacian/signless Laplacian H-eigenvalues are obtained. Also some bounds on the sum of the largest/smallest adjacency/Laplacian/signless Laplacian H-eigenvalues of a hypergraph and its complement hypergraph are showed. All these bounds are consistent with what we have known when k is equal to 2

Frequent itemset based event detection in uncertain sensor networks

Conference Name:2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013. Conference Address: Beijing, China. Time:August 20, 2013 - August 23, 2013.IEEE; IEEE Computer Society; IEEE Technical Committee on Scalable Computing (TCSC); Ministry of Industry and Information Technology; of the People's Republic of China; Natural Science Foundation of China (NSFC)More and more sensor networks are deployed for the detection of events. Yet due to the resource-constraint nature of nodes, the readings are inherently inaccurate, imprecise and are distributed among the nodes, so it is a challenging task to detect events in such a kind of networks. In this paper, we study the problem of uncertain event detection in sensor networks, and propose an efficient detection algorithm Fibed. We use a possible world semantics to interpret the uncertain data, and events are defined based on computing the frequent item sets. A polynomial is constructed to calculate the probability of each frequent item, and the coefficient vector of the polynomial is merged and updated when it is routed towards the base station. Early decisions could be made for the events, and lots of items could be pruned to save unnecessary transmissions as their probability do not meet the probability threshold. Experimental studies show that Fibed is efficient in detecting the uncertain events and cutting down the incurred transmissions. ? 2013 IEEE

The Emerging Roles of Protein Interactions with O-GlcNAc Cycling Enzymes in Cancer

The dynamic O-GlcNAc modification of intracellular proteins is an important nutrient sensor for integrating metabolic signals into vast networks of highly coordinated cellular activities. Dysregulation of the sole enzymes responsible for O-GlcNAc cycling, O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA), and the associated cellular O-GlcNAc profile is a common feature across nearly every cancer type. Many studies have investigated the effects of aberrant OGT/OGA expression on global O-GlcNAcylation activity in cancer cells. However, recent studies have begun to elucidate the roles of protein–protein interactions (PPIs), potentially through regions outside of the immediate catalytic site of OGT/OGA, that regulate greater protein networks to facilitate substrate-specific modification, protein translocalization, and the assembly of larger biomolecular complexes. Perturbation of OGT/OGA PPI networks makes profound changes in the cell and may directly contribute to cancer malignancies. Herein, we highlight recent studies on the structural features of OGT and OGA, as well as the emerging roles and molecular mechanisms of their aberrant PPIs in rewiring cancer networks. By integrating complementary approaches, the research in this area will aid in the identification of key protein contacts and functional modules derived from OGT/OGA that drive oncogenesis and will illuminate new directions for anti-cancer drug development

MKANet: An Efficient Network with Sobel Boundary Loss for Land-Cover Classification of Satellite Remote Sensing Imagery

Land cover classification is a multiclass segmentation task to classify each pixel into a certain natural or human-made category of the earth’s surface, such as water, soil, natural vegetation, crops, and human infrastructure. Limited by hardware computational resources and memory capacity, most existing studies preprocessed original remote sensing images by downsampling or cropping them into small patches less than 512 × 512 pixels before sending them to a deep neural network. However, downsampling incurs a spatial detail loss, renders small segments hard to discriminate, and reverses the spatial resolution progress obtained by decades of efforts. Cropping images into small patches causes a loss of long-range context information, and restoring the predicted results to their original size brings extra latency. In response to the above weaknesses, we present an efficient lightweight semantic segmentation network termed MKANet. Aimed at the characteristics of top view high-resolution remote sensing imagery, MKANet utilizes sharing kernels to simultaneously and equally handle ground segments of inconsistent scales, and also employs a parallel and shallow architecture to boost inference speed and friendly support image patches more than 10× larger. To enhance boundary and small segment discrimination, we also propose a method that captures category impurity areas, exploits boundary information, and exerts an extra penalty on boundaries and small segment misjudgments. Both visual interpretations and quantitative metrics of extensive experiments demonstrate that MKANet obtains a state-of-the-art accuracy on two land-cover classification datasets and infers 2× faster than other competitive lightweight networks. All these merits highlight the potential of MKANet in practical applications

MKANet: An Efficient Network with Sobel Boundary Loss for Land-Cover Classification of Satellite Remote Sensing Imagery

Land cover classification is a multiclass segmentation task to classify each pixel into a certain natural or human-made category of the earth’s surface, such as water, soil, natural vegetation, crops, and human infrastructure. Limited by hardware computational resources and memory capacity, most existing studies preprocessed original remote sensing images by downsampling or cropping them into small patches less than 512 × 512 pixels before sending them to a deep neural network. However, downsampling incurs a spatial detail loss, renders small segments hard to discriminate, and reverses the spatial resolution progress obtained by decades of efforts. Cropping images into small patches causes a loss of long-range context information, and restoring the predicted results to their original size brings extra latency. In response to the above weaknesses, we present an efficient lightweight semantic segmentation network termed MKANet. Aimed at the characteristics of top view high-resolution remote sensing imagery, MKANet utilizes sharing kernels to simultaneously and equally handle ground segments of inconsistent scales, and also employs a parallel and shallow architecture to boost inference speed and friendly support image patches more than 10× larger. To enhance boundary and small segment discrimination, we also propose a method that captures category impurity areas, exploits boundary information, and exerts an extra penalty on boundaries and small segment misjudgments. Both visual interpretations and quantitative metrics of extensive experiments demonstrate that MKANet obtains a state-of-the-art accuracy on two land-cover classification datasets and infers 2× faster than other competitive lightweight networks. All these merits highlight the potential of MKANet in practical applications

Recent Advances in the Reutilization of Granite Waste in Various Fields

Quarrying and processing of granite produce large amounts of waste residues. Besides being a loss of resources, improper disposal of these wastes results in pollution of the soil, water and air around the dumpsites. The main components of granite waste are quartz, feldspars and a small amount of biotite. Due to its hard and dense texture, high strength, corrosion resistance and wear resistance, granite waste may be recycled into building materials, composite materials and fine ceramics, effectively improving their mechanical properties and durability. By using the flotation process, high value-added products such as potash feldspar and albite may be retrieved from granite waste. Also, granite waste has the potential for application in soil remediation and sewage treatment. This review presents recent advances in granite waste reutilization, and points out the problems associated with its use, and the related countermeasures, indicating the scale of high value-added reutilization of granite waste

Cryogel-Templated Fabrication of n-Al/PVDF Superhydrophobic Energetic Films with Exceptional Underwater Ignition Performance

The rapid heat loss and corrosion of nano-aluminum limits the energy performance of metastable intermolecular composites (MICs) in aquatic conditions. In this work, superhydrophobic n-Al/PVDF films were fabricated by the cryogel-templated method. The underwater ignition performance of the energetic films was investigated. The preparation process of energetic materials is relatively simple, and avoids excessively high temperatures, ensuring the safety of the entire experimental process. The surface of the n-Al/PVDF energetic film exhibits super-hydrophobicity. Because the aluminum nanoparticles are uniformly encased in the hydrophobic energetic binder, the film is more waterproof and anti-aging. Laser-induced underwater ignition experiments show that the superhydrophobic modification can effectively induce the ignition of energetic films underwater. The results suggest that the cryogel-templated method provides a feasible route for underwater applications of energetic materials, especially nanoenergetics-on-a-chip in underwater micro-scale energy-demanding systems