scholarly journals Selective Inference for High-order Interaction Features Selected in a Stepwise Manner

2021 ◽  
Vol 14 (0) ◽  
pp. 1-11
Author(s):  
Shinya Suzumura ◽  
Kazuya Nakagawa ◽  
Yuta Umezu ◽  
Koji Tsuda ◽  
Ichiro Takeuchi
Keyword(s):  
High Order ◽  
High Order Interaction ◽  
Order Interaction ◽  
Stepwise Manner ◽  
Selective Inference

Two-wavelength wrinkling patterns in helicoidal plywood surfaces: imprinting energy landscapes onto geometric landscapes

Soft Matter ◽  
2018 ◽  
Vol 14 (25) ◽  
pp. 5180-5185 ◽  
Author(s):  
P. Rofouie ◽  
Z. Wang ◽  
A. D. Rey
Keyword(s):  
Interfacial Tension ◽  
Soft Matter ◽  
High Order ◽  
Length Scale ◽  
Energy Landscapes ◽  
High Order Interaction ◽  
Free Surfaces ◽  
Order Interaction ◽  
Surface Patterns ◽  
Scale Surface

We present a model to investigate the formation of two-length scale surface patterns in biological and synthetic anisotropic soft matter materials through the high order interaction of anisotropic interfacial tension and capillarity at their free surfaces.

scholarly journals Hypergraph Clustering Based on Game-Theory for Mining Microbial High-Order Interaction Module

2020 ◽  
Vol 16 ◽  
pp. 117693432097057
Author(s):  
Limin Yu ◽  
Xianjun Shen ◽  
Jincai Yang ◽  
Kaiping Wei ◽  
Duo Zhong ◽  
...  
Keyword(s):  
Game Theory ◽  
Microbial Community ◽  
Clustering Algorithm ◽  
Game Problem ◽  
Living Environment ◽  
High Order ◽  
Clustering Methods ◽  
High Order Interaction ◽  
Order Interaction ◽  
Community Function

Microbial community is ubiquitous in nature, which has a great impact on the living environment and human health. All these effects of microbial communities on the environment and their hosts are often referred to as the functions of these communities, which depend largely on the composition of the communities. The study of microbial higher-order module can help us understand the dynamic development and evolution process of microbial community and explore community function. Considering that traditional clustering methods depend on the number of clusters or the influence of data that does not belong to any cluster, this paper proposes a hypergraph clustering algorithm based on game theory to mine the microbial high-order interaction module (HCGI), and the hypergraph clustering problem naturally turns into a clustering game problem, the partition of network modules is transformed into finding the critical point of evolutionary stability strategy (ESS). The experimental results show HCGI does not depend on the number of classes, and can get more conservative and better quality microbial clustering module, which provides reference for researchers and saves time and cost. The source code of HCGI in this paper can be downloaded from https://github.com/ylm0505/HCGI .

High order interaction analysis of SNPs in PEDF (rs12150053, rs12948385) and EPO (rs1617640) genes with clinical determinants of type 2 diabetic retinopathy patients from south India

Meta Gene ◽  
2017 ◽  
Vol 13 ◽  
pp. 92-98
Author(s):  
Kumari Vinita ◽  
Sarangapani Sripriya ◽  
Ferdina Marie Sharmila Philomenadin ◽  
Kulothungan Vaitheeswaran ◽  
Rajiv Raman ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
South India ◽  
Interaction Analysis ◽  
High Order ◽  
High Order Interaction ◽  
Order Interaction ◽  
Type 2 Diabetic ◽  
Type 2 Diabetic Retinopathy

On the evaluation of high-order interaction energies using pseudo state methods

1974 ◽  
Vol 27 (5) ◽  
pp. 1235-1239 ◽  
Author(s):  
J.F. Bukta ◽  
William J. Meath
Keyword(s):  
High Order ◽  
Interaction Energies ◽  
High Order Interaction ◽  
Order Interaction

scholarly journals LCHI: Low-Order Correlation and High-Order Interaction Integrated Model Oriented to Network Intrusion Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Shengwei Lei ◽  
Chunhe Xia ◽  
Tianbo Wang
Keyword(s):  
Intrusion Detection ◽  
High Order ◽  
Network Intrusion Detection ◽  
Feature Interaction ◽  
Detection Accuracy ◽  
High Order Interaction ◽  
Order Interaction ◽  
Feature Interactions ◽  
Network Intrusion ◽  
Low Order

Network intrusion poses a severe threat to the Internet of Things (IoT). Thus, it is essential to study information security protection technology in IoT. Learning sophisticated feature interactions is critical in improving detection accuracy for network intrusion. Despite significant progress, existing methods seem to have a strong bias towards single low- or high-order feature interaction. Moreover, they always extract all possible low-order interactions indiscriminately, introducing too much noise. To address the above problems, we propose a low-order correlation and high-order interaction (LCHI) integrated feature extraction model. First, we selectively extract the beneficial low-order correlation between the same-type features by the multivariate correlation analysis (MCA) model and attention mechanism. Second, we extract the complicated high-order feature interaction by the deep neural network (DNN) model. Finally, we emphasize both the low- and high-order feature interactions and incorporate them. Our LCHI model seamlessly combines the linearity of MCA in modeling lower-order feature correlation and the nonlinearity of DNN in modeling higher-order feature interaction. Conceptually, our LCHI is more expressive than the previous models. We carry on a series of experiments on the public wireless and wired network intrusion detection datasets. The experimental results show that LCHI improves 1.06%, 2.46%, 3.74%, 0.25%, 1.17%, and 0.64% on the AWID, NSL-KDD, UNSW-NB15, CICIDS 2017, CICIDS 2018, and DAPT 2020 datasets, respectively.

scholarly journals Multihop Neighbor Information Fusion Graph Convolutional Network for Text Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Fangyuan Lei ◽  
Xun Liu ◽  
Zhengming Li ◽  
Qingyun Dai ◽  
Senhong Wang
Keyword(s):  
Computational Complexity ◽  
Information Fusion ◽  
Text Classification ◽  
State Of The Art ◽  
High Order ◽  
High Order Interaction ◽  
Convolutional Network ◽  
Graph Representations ◽  
Order Interaction ◽  
Different Order

Graph convolutional network (GCN) is an efficient network for learning graph representations. However, it costs expensive to learn the high-order interaction relationships of the node neighbor. In this paper, we propose a novel graph convolutional model to learn and fuse multihop neighbor information relationships. We adopt the weight-sharing mechanism to design different order graph convolutions for avoiding the potential concerns of overfitting. Moreover, we design a new multihop neighbor information fusion (MIF) operator which mixes different neighbor features from 1-hop to k-hops. We theoretically analyse the computational complexity and the number of trainable parameters of our models. Experiment on text networks shows that the proposed models achieve state-of-the-art performance than the text GCN.

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