Learning interaction activities are the key part of tracking and evaluating learning behaviors, that plays an important role in data-driven autonomous learning and optimized learning in interactive ...learning environments. In this study, a big data set of learning behaviors with multiple learning periods is selected. According to the instance characteristics of data attributes and learning behaviors, the analysis of learning interaction activities and their relationships is mapped to the optimized design of Markov random fields. Appropriate models and multi-level algorithms are constructed to demonstrate the maximal cliques that affect the learning behaviors and mine the critical paths, then cooperative inference strategies of learning interaction activities are deduced. In order to verify the effectiveness of the random fields and analysis technology of learning interaction activities, several approximate algorithms are introduced to fully compare multiple statistical indexes; To verify the feasibility of collaborative recommendation strategies, the improvement effect of learning behaviors and learning effect is tested by applying it to the actual teaching process. Research and analysis show that the random fields of learning interaction activities and the collaborative inference strategies have advantages in data analysis, topological relationship construction and teaching practice guidance.
Partitioning multi-view data is a recent challenge in clustering methods, which traditionally consider single-view data. In clustering techniques, finding the similarity or distance between objects, ...handled by metrics in
R
n
, plays a central role in community detection. Under this framework, different algorithms have been developed where the output relies on an exact distance calculated based on the objects’ features. As feature information might be qualitative data defined in an ambiguous environment, this study offers a new class of metrics, so-called
S
-distance, as a dual of a fuzzy
T
-similarity, which successfully produces a collective distance based on all views/observers and provides a more flexible framework to define distance under uncertainty. Besides, most existing approaches handle multi-view clustering by aggregating each view’s clusters or using an iterative optimization method; both are time-consuming. Here, by transforming the multi-view clustering problem into node clustering, we suggest a new approach without iteration for multi-view and multi-observer data. Our proposed method, GM
S
kNN, uses an attribute-structural similarity relation between nodes to get more coherent clusters. To this end, we first build a
k
-nearest neighbor (kNN) directed graph using the proposed
S
-distance, then transform it into an undirected graph based on the neighborhood information of the nodes so that the resultant graph is characterized based on nodes interactions and initial features information of the nodes. Next, a new maximal-clique-based clustering is designed to complete the node partitioning. The proposed clustering algorithm is programmed and tested on synthetic and four real-world datasets using the R software. The clustering results are analyzed based on several indexes. This analysis shows the efficiency of the proposed algorithm compared to the traditional clustering methods.
Governments implemented countermeasures to mitigate the spread of the COVID-19 virus. This had a severe effect on the economy. We examine convergence patterns in the evolution of COVID-19 deaths ...across countries. We aim to investigate whether countries that implemented different measures managed to limit the number of COVID-19 deaths. We extend the most recent macro-growth convergence methodology to examine convergence of COVID-19 deaths. We combine a long memory stationarity framework with the maximal clique algorithm. This provides a rich and flexible club formation strategy that goes beyond the stationary/non stationary approach adopted in the previous literature. Our results suggest that strict measures (even belated) or an aggressive vaccination scheme can confine the spread of the disease while maintaining the strictness of the measures steady can lead to a burst of the virus. Finally, we observe that fiscal measures did not have an effect on the containment of the virus.
The explosive growth of spatial data and the widespread use of spatial databases emphasize the need for spatial data mining. The subsets of features frequently located together in a geographic space ...are called spatial co-location patterns. It is difficult to discover co-location patterns because of the huge amount of data brought by the instances of spatial features. A large fraction of the computation time is devoted to generating row instances and candidate co-location patterns. This paper makes three main contributions for mining co-location patterns. First, the definition of maximal instances is given and a row instance (RI)-tree is constructed to find maximal instances from a spatial data set. Second, a fast method for generating all row instances and candidate co-locations is proposed and the feasibility of this method is proved. Third, a maximal instance algorithm with no join operations for mining co-location patterns is proposed. Finally, experimental evaluations using synthetic data sets and a real data set show that maximal instance algorithm is feasible and has better performance.
In this paper we present a family of maximal cliques of size q+12 or q+32, accordingly as q≡1(4) or q≡3(4), in Paley graphs of order q2, where q is an odd prime power. After that we use the new ...cliques to define a family of eigenfunctions corresponding to both non-principal eigenvalues and having support size q+1, which is the minimum possible value by the weight-distribution bound. Finally, we prove that the constructed eigenfunction comes from an equitable partition.
Clear cell renal cell carcinoma (ccRCC) is a common type of fatal malignancy in the urinary system. As the therapeutic strategies of ccRCC are severely limited at present, the prognosis of patients ...with metastatic carcinoma is usually not promising. Revealing the pathogenesis and identifying hub candidate genes for prognosis prediction and precise treatment are urgently needed in metastatic ccRCC.
In the present study, we conducted a series of bioinformatics studies with the gene expression profiles of ccRCC samples from Gene Expression Omnibus (GEO) and the cancer genome atlas (TCGA) database for identifying and validating the hub gene of metastatic ccRCC. We constructed a co-expression network, divided genes into co-expression modules, and identified ccRCC-related modules by weighted gene co-expression network analysis (WGCNA) with data from GEO. Then, we investigated the functions of genes in the ccRCC-related modules by enrichment analyses and built a sub-network accordingly. A hub candidate gene of the metastatic ccRCC was identified by maximal clique centrality (MCC) method. We validate the hub gene by differentially expressed gene analysis, overall survival analysis, and correlation analysis with clinical traits with the external dataset (TCGA). Finally, we explored the function of the hub gene by correlation analysis with targets of precise therapies and single-gene gene set enrichment analysis.
We conducted WGCNA with the expression profiles of GSE73731 from GEO and divided all genes into 8 meaningful co-expression modules. One module is proved to be positively correlated with pathological stage and tumor grade of ccRCC. Genes in the ccRCC-related module were mainly enriched in functions of mitotic cell division and several proverbial tumor related signal pathways. We then identified
as a hub gene of the metastasis of ccRCC. Validating analyses in external dataset observed the up-regulation of
in ccRCC and its correlation with worse outcomes. Further analyses found that the expression of KIF18B is related to that of targets of precise therapies.
Our study proposed
as a hub candidate gene of ccRCC for the first time. Our conclusion may provide a brand-new clue for prognosis evaluating and precise treatment for ccRCC in the future.
Network connectivity is a necessary prerequisite to realizing distributed formation control. However, it is challenging to maintain the global network connectivity of a formation with local ...information because the communications between topological agents may be disconnected as formation maneuvers due to the range-limited sensing capabilities of agents. In this article, we propose a novel connectivity-preserving formation strategy to address the challenge. The core idea is to decompose global connectivity constraint into the induced-subgraph-preserving problems of each agent. Different from the state-of-the-art methods, our strategy can acquire the local algebraic connectivity directly at the level of maximal cliques, instead of estimating the global algebraic connectivity by hundreds of iterations. Consequently, it yields more flexibility in formation maneuvers and requires lower computing costs. Moreover, we treat the entire formation as a single rigid body and employ a variant of the optimal reciprocal collision avoidance algorithm to optimize the overall movement of the formation so that agents can maintain the desired shape while avoiding obstacles. Here, the position and orientation of the desired formation are negotiated by the agents using only local peer-to-peer information. Simulation results verify that the proposed strategy can drive a group of agents to implement connectivity-preserving formations in obstacle-rich spaces.
On twin preserving spanning subgraph Wahid, Khola; Das, Angsuman; Rani, Anam ...
Journal of intelligent & fuzzy systems,
01/2021, Volume:
40, Issue:
5
Journal Article
Peer reviewed
There are several approaches to lower the complexity of huge networks. One of the key notions is that of twin nodes, exhibiting the same connection pattern to the rest of the network. We extend this ...idea by defining a twin preserving spanning subgraph (TPS-subgraph) of a simple graph as a tool to compute certain graph related invariants which are preserved by the subgraph. We discuss how these subgraphs preserve some distance based parameters of the simple graph. We introduce a sub-skeleton graph on a vector space and examine its basic properties. The sub-skeleton graph is a TPS-subgraph of the non-zero component graph defined over a vector space. We prove that some parameters like the metric-dimension are preserved by the sub-skeleton graph.
A graph G is called a pseudo-core if every endomorphism of G is either an automorphism or a colouring. A graph G is a core if every endomorphism of G is an automorphism. Let Fq be the finite field ...with q elements where q is a power of an odd prime number. The quadratic forms graph, denoted by Quad(n,q) where n≥2, has all quadratic forms on Fqn as vertices and two vertices f and g are adjacent whenever rk(f−g)=1 or 2. We prove that every Quad(n,q) is a pseudo-core. Further, when n is even, Quad(n,q) is a core. When n is odd, Quad(n,q) is not a core. On the other hand, we completely determine the independence number of Quad(n,q).
The lung cancer incidence and mortality in China have always been high. Moreover, due to the limited level of professional technology, misdiagnosis and missed diagnosis of lung cancer often occur. To ...improve the accuracy of diagnosis, this paper proposes an interpretable diagnostic method for lung cancer based on Chinese electronic medical records (EMRs). First, to overcome the difficulty in word segmentation of clinical texts in Chinese EMRs, a dictionary construction method is proposed based on the idea of maximal clique, and 730 medical professional terms related to lung diseases are identified. Then, the ProbSparse self‐attention mechanism and self‐attention distilling operation in Informer are used to improve the Bidirectional Encoder Representations from Transformer (BERT) to realize the representation of long clinical texts with lower time complexity and memory consumption. Finally, the convolutional neural network with an attention mechanism is employed to process the representation results to realize the interpretable prediction of lung cancer. This method is applied to the lung cancer diagnosis of inpatients in a tertiary hospital in Hunan Province, obtaining excellent results of about 0.9 for area under the receiver operating characteristic curve (AUROC) and area under the precision‐recall curve (AUPRC). In addition, the results of the comparative analysis with existing dictionaries, word embedding methods and diagnostic methods further confirm the superiority of the proposed method. Specifically, the proposed method improves the precision by at least 6%, the recall by at least 2.6%, the F1 score by at least 5.2%, AUROC by at least 7.3% and AUPRC by at least 7.7% compared with all these state‐of‐the‐art methods.
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