In this paper, we present a new concept of the adjacent-vertex-distinguishing total coloring of graphs (briefly, AVDTC of graphs) and, meanwhile, have obtained the adjacent-vertex-distinguishing total chromatic number...In this paper, we present a new concept of the adjacent-vertex-distinguishing total coloring of graphs (briefly, AVDTC of graphs) and, meanwhile, have obtained the adjacent-vertex-distinguishing total chromatic number of some graphs such as cycle, complete graph, complete bipartite graph, fan, wheel and tree.展开更多
For any vertex u ? V(G), let T N (u) = {u} ∪ {uυ|uυ ? E(G), υ ? υ(G)} ∪ {υ ? υ(G)|uυ ? E(G) and let f be a total k-coloring of G. The total-color neighbor of a vertex u of G is the color set C f(u) = {f(x) | ...For any vertex u ? V(G), let T N (u) = {u} ∪ {uυ|uυ ? E(G), υ ? υ(G)} ∪ {υ ? υ(G)|uυ ? E(G) and let f be a total k-coloring of G. The total-color neighbor of a vertex u of G is the color set C f(u) = {f(x) | x ? T N (u)}. For any two adjacent vertices x and y of V(G) such that C f(x) ≠ C f(y), we refer to f as a k-avsdt-coloring of G (“avsdt” is the abbreviation of “ adjacent-vertex-strongly-distinguishing total”). The avsdt-coloring number of G, denoted by χast(G), is the minimal number of colors required for a avsdt-coloring of G. In this paper, the avsdt-coloring numbers on some familiar graphs are studied, such as paths, cycles, complete graphs, complete bipartite graphs and so on. We prove Δ(G) + 1 ? χast(G) ? Δ(G) + 2 for any tree or unique cycle graph G.展开更多
A new concept of the D(β)-vertex-distinguishing total coloring of graphs, i.e., the proper total coloring such that any two vertices whose distance is not larger than β have different color sets, where the color set...A new concept of the D(β)-vertex-distinguishing total coloring of graphs, i.e., the proper total coloring such that any two vertices whose distance is not larger than β have different color sets, where the color set of a vertex is the set composed of all colors of the vertex and the edges incident to it, is proposed in this paper. The D(2)-vertex-distinguishing total colorings of some special graphs are discussed, meanwhile, a conjecture and an open problem are presented.展开更多
3-N-butylphthalide is an ettectwe drug for acute iscemlc stroke. However, its effects on cnromc cerebral ischemia-induced neuronal injury remain poorly understood. Therefore, this study li- gated bilateral carotid art...3-N-butylphthalide is an ettectwe drug for acute iscemlc stroke. However, its effects on cnromc cerebral ischemia-induced neuronal injury remain poorly understood. Therefore, this study li- gated bilateral carotid arteries in 15-month-old rats to simulate chronic cerebral ischemia in aged humans. Aged rats were then intragastrically administered 3-n-butylphthalide. 3-N-butylphtha- lide administration improved the neuronal morphology in the cerebral cortex and hippocampus of rats with chronic cerebral ischemia, increased choline acetyltransferase activity, and decreased malondialdehyde and amyloid beta levels, and greatly improved cognitive function. These findings suggest that 3-n-butylphthalide alleviates oxidative stress caused by chronic cerebral ischemia, improves cholinergic function, and inhibits amyloid beta accumulation, thereby im- proving cerebral neuronal injury and cognitive deficits.展开更多
In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Unleashing the power of knowledge in multi-view data is very importan...In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Unleashing the power of knowledge in multi-view data is very important in big data mining and analysis. This calls for advanced techniques that consider the diversity of different views,while fusing these data. Multi-view Clustering(MvC) has attracted increasing attention in recent years by aiming to exploit complementary and consensus information across multiple views. This paper summarizes a large number of multi-view clustering algorithms, provides a taxonomy according to the mechanisms and principles involved, and classifies these algorithms into five categories, namely, co-training style algorithms, multi-kernel learning, multiview graph clustering, multi-view subspace clustering, and multi-task multi-view clustering. Therein, multi-view graph clustering is further categorized as graph-based, network-based, and spectral-based methods. Multi-view subspace clustering is further divided into subspace learning-based, and non-negative matrix factorization-based methods. This paper does not only introduce the mechanisms for each category of methods, but also gives a few examples for how these techniques are used. In addition, it lists some publically available multi-view datasets.Overall, this paper serves as an introductory text and survey for multi-view clustering.展开更多
Information on the Internet is fragmented and presented in different data sources, which makes automatic knowledge harvesting and understanding formidable for ma- chines, and even for humans. Knowledge graphs have be-...Information on the Internet is fragmented and presented in different data sources, which makes automatic knowledge harvesting and understanding formidable for ma- chines, and even for humans. Knowledge graphs have be- come prevalent in both of industry and academic circles these years, to be one of the most efficient and effective knowledge integration approaches. Techniques for knowledge graph construction can mine information from either structured, semi-structured, or even unstructured data sources, and fi- nally integrate the information into knowledge, represented in a graph. Furthermore, knowledge graph is able to organize information in an easy-to-maintain, easy-to-understand and easy-to-use manner. In this paper, we give a summarization of techniques for constructing knowledge graphs. We review the existing knowledge graph systems developed by both academia and industry. We discuss in detail about the process of building knowledge graphs, and survey state-of-the-art techniques for automatic knowledge graph checking and expansion via log- ical inferring and reasoning. We also review the issues of graph data management by introducing the knowledge data models and graph databases, especially from a NoSQL point of view. Finally, we overview current knowledge graph sys- tems and discuss the future research directions.展开更多
Cyberattack forms are complex and varied, and the detection and prediction of dynamic types of attack are always challenging tasks. Research on knowledge graphs is becoming increasingly mature in many fields. At prese...Cyberattack forms are complex and varied, and the detection and prediction of dynamic types of attack are always challenging tasks. Research on knowledge graphs is becoming increasingly mature in many fields. At present, it is very significant that certain scholars have combined the concept of the knowledge graph with cybersecurity in order to construct a cybersecurity knowledge base. This paper presents a cybersecurity knowledge base and deduction rules based on a quintuple model. Using machine learning, we extract entities and build ontology to obtain a cybersecurity knowledge base. New rules are then deduced by calculating formulas and using the path-ranking algorithm. The Stanford named entity rec- ognizer （NER） is also used to train an extractor to extract useful information. Experimental results show that the Stanford NER provides many features and the useGazettes parameter may be used to train a rec- ognizer in the cybersecurity domain in preparation for future work.展开更多
文摘In this paper, we present a new concept of the adjacent-vertex-distinguishing total coloring of graphs (briefly, AVDTC of graphs) and, meanwhile, have obtained the adjacent-vertex-distinguishing total chromatic number of some graphs such as cycle, complete graph, complete bipartite graph, fan, wheel and tree.
基金the National Natural Science Foundation of China (Grant Nos. 10771091, 10661007)
文摘For any vertex u ? V(G), let T N (u) = {u} ∪ {uυ|uυ ? E(G), υ ? υ(G)} ∪ {υ ? υ(G)|uυ ? E(G) and let f be a total k-coloring of G. The total-color neighbor of a vertex u of G is the color set C f(u) = {f(x) | x ? T N (u)}. For any two adjacent vertices x and y of V(G) such that C f(x) ≠ C f(y), we refer to f as a k-avsdt-coloring of G (“avsdt” is the abbreviation of “ adjacent-vertex-strongly-distinguishing total”). The avsdt-coloring number of G, denoted by χast(G), is the minimal number of colors required for a avsdt-coloring of G. In this paper, the avsdt-coloring numbers on some familiar graphs are studied, such as paths, cycles, complete graphs, complete bipartite graphs and so on. We prove Δ(G) + 1 ? χast(G) ? Δ(G) + 2 for any tree or unique cycle graph G.
文摘A new concept of the D(β)-vertex-distinguishing total coloring of graphs, i.e., the proper total coloring such that any two vertices whose distance is not larger than β have different color sets, where the color set of a vertex is the set composed of all colors of the vertex and the edges incident to it, is proposed in this paper. The D(2)-vertex-distinguishing total colorings of some special graphs are discussed, meanwhile, a conjecture and an open problem are presented.
基金supported by Innovation Team Project of Hubei Province 2011 Plans,No.2011JH-2013CXTT06Momentous Scientific Research Funds of Hubei Provincial Education Ministry,No.D20102101Cultivating Funds of Country’s Projects of Hubei University of Medicine,No.2013GPY03
文摘3-N-butylphthalide is an ettectwe drug for acute iscemlc stroke. However, its effects on cnromc cerebral ischemia-induced neuronal injury remain poorly understood. Therefore, this study li- gated bilateral carotid arteries in 15-month-old rats to simulate chronic cerebral ischemia in aged humans. Aged rats were then intragastrically administered 3-n-butylphthalide. 3-N-butylphtha- lide administration improved the neuronal morphology in the cerebral cortex and hippocampus of rats with chronic cerebral ischemia, increased choline acetyltransferase activity, and decreased malondialdehyde and amyloid beta levels, and greatly improved cognitive function. These findings suggest that 3-n-butylphthalide alleviates oxidative stress caused by chronic cerebral ischemia, improves cholinergic function, and inhibits amyloid beta accumulation, thereby im- proving cerebral neuronal injury and cognitive deficits.
基金supported in part by the National Natural Science Foundation of China (No. 61572407)
文摘In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Unleashing the power of knowledge in multi-view data is very important in big data mining and analysis. This calls for advanced techniques that consider the diversity of different views,while fusing these data. Multi-view Clustering(MvC) has attracted increasing attention in recent years by aiming to exploit complementary and consensus information across multiple views. This paper summarizes a large number of multi-view clustering algorithms, provides a taxonomy according to the mechanisms and principles involved, and classifies these algorithms into five categories, namely, co-training style algorithms, multi-kernel learning, multiview graph clustering, multi-view subspace clustering, and multi-task multi-view clustering. Therein, multi-view graph clustering is further categorized as graph-based, network-based, and spectral-based methods. Multi-view subspace clustering is further divided into subspace learning-based, and non-negative matrix factorization-based methods. This paper does not only introduce the mechanisms for each category of methods, but also gives a few examples for how these techniques are used. In addition, it lists some publically available multi-view datasets.Overall, this paper serves as an introductory text and survey for multi-view clustering.
文摘Information on the Internet is fragmented and presented in different data sources, which makes automatic knowledge harvesting and understanding formidable for ma- chines, and even for humans. Knowledge graphs have be- come prevalent in both of industry and academic circles these years, to be one of the most efficient and effective knowledge integration approaches. Techniques for knowledge graph construction can mine information from either structured, semi-structured, or even unstructured data sources, and fi- nally integrate the information into knowledge, represented in a graph. Furthermore, knowledge graph is able to organize information in an easy-to-maintain, easy-to-understand and easy-to-use manner. In this paper, we give a summarization of techniques for constructing knowledge graphs. We review the existing knowledge graph systems developed by both academia and industry. We discuss in detail about the process of building knowledge graphs, and survey state-of-the-art techniques for automatic knowledge graph checking and expansion via log- ical inferring and reasoning. We also review the issues of graph data management by introducing the knowledge data models and graph databases, especially from a NoSQL point of view. Finally, we overview current knowledge graph sys- tems and discuss the future research directions.
基金We are grateful for the support of the National Natural Science Foundation of China （U163215, 61472433, 61732022, 61732004, 61672020, and 61502517） and the National Key Research and Development Program （2016YFB0800802, 2016YFB0800803, 2016YFB0800804, 2017YFB0802204, 2016QY03D0601, 2016QY03D0603, and 2016YFB0800303）.
文摘Cyberattack forms are complex and varied, and the detection and prediction of dynamic types of attack are always challenging tasks. Research on knowledge graphs is becoming increasingly mature in many fields. At present, it is very significant that certain scholars have combined the concept of the knowledge graph with cybersecurity in order to construct a cybersecurity knowledge base. This paper presents a cybersecurity knowledge base and deduction rules based on a quintuple model. Using machine learning, we extract entities and build ontology to obtain a cybersecurity knowledge base. New rules are then deduced by calculating formulas and using the path-ranking algorithm. The Stanford named entity rec- ognizer （NER） is also used to train an extractor to extract useful information. Experimental results show that the Stanford NER provides many features and the useGazettes parameter may be used to train a rec- ognizer in the cybersecurity domain in preparation for future work.
