Heterogeneous Feature Representation for Digital Twin-Oriented Complex Networked Systems

• URL: http://arxiv.org/abs/2309.13229v1
• Date: Sat, 23 Sep 2023 01:40:56 GMT
• Title: Heterogeneous Feature Representation for Digital Twin-Oriented Complex Networked Systems
• Authors: Jiaqi Wen, Bogdan Gabrys, Katarzyna Musial
• Abstract summary: Building models of Complex Networked Systems that can accurately represent reality forms an important research area. This study aims to improve the expressive power of node features in Digital Twin-Oriented Complex Networked Systems.
• Score: 13.28255056212425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Building models of Complex Networked Systems (CNS) that can accurately represent reality forms an important research area. To be able to reflect real world systems, the modelling needs to consider not only the intensity of interactions between the entities but also features of all the elements of the system. This study aims to improve the expressive power of node features in Digital Twin-Oriented Complex Networked Systems (DT-CNSs) with heterogeneous feature representation principles. This involves representing features with crisp feature values and fuzzy sets, each describing the objective and the subjective inductions of the nodes' features and feature differences. Our empirical analysis builds DT-CNSs to recreate realistic physical contact networks in different countries from real node feature distributions based on various representation principles and an optimised feature preference. We also investigate their respective disaster resilience to an epidemic outbreak starting from the most popular node. The results suggest that the increasing flexibility of feature representation with fuzzy sets improves the expressive power and enables more accurate modelling. In addition, the heterogeneous features influence the network structure and the speed of the epidemic outbreak, requiring various mitigation policies targeted at different people.

Related papers

• Neural Networks Decoded: Targeted and Robust Analysis of Neural Network Decisions via Causal Explanations and Reasoning [9.947555560412397]
  We introduce TRACER, a novel method grounded in causal inference theory to estimate the causal dynamics underpinning DNN decisions. Our approach systematically intervenes on input features to observe how specific changes propagate through the network, affecting internal activations and final outputs. TRACER further enhances explainability by generating counterfactuals that reveal possible model biases and offer contrastive explanations for misclassifications.
  arXiv  Detail & Related papers  (2024-10-07T20:44:53Z)
• The Dynamic Net Architecture: Learning Robust and Holistic Visual Representations Through Self-Organizing Networks [3.9848584845601014]
  We present a novel intelligent-system architecture called "Dynamic Net Architecture" (DNA) DNA relies on recurrence-stabilized networks and discuss it in application to vision.
  arXiv  Detail & Related papers  (2024-07-08T06:22:10Z)
• Going Beyond Neural Network Feature Similarity: The Network Feature Complexity and Its Interpretation Using Category Theory [64.06519549649495]
  We provide the definition of what we call functionally equivalent features. These features produce equivalent output under certain transformations. We propose an efficient algorithm named Iterative Feature Merging.
  arXiv  Detail & Related papers  (2023-10-10T16:27:12Z)
• Digital Twin-Oriented Complex Networked Systems based on Heterogeneous Node Features and Interaction Rules [13.28255056212425]
  This study proposes an extendable modelling framework for Digital Twin-Oriented Complex Networked Systems. We conduct experiments on simulation-based DT-CNSs that incorporate various features and rules about network growth and different transmissibilities related to an epidemic spread on these networks.
  arXiv  Detail & Related papers  (2023-08-18T01:54:48Z)
• A Generic Shared Attention Mechanism for Various Backbone Neural Networks [53.36677373145012]
  Self-attention modules (SAMs) produce strongly correlated attention maps across different layers. Dense-and-Implicit Attention (DIA) shares SAMs across layers and employs a long short-term memory module. Our simple yet effective DIA can consistently enhance various network backbones.
  arXiv  Detail & Related papers  (2022-10-27T13:24:08Z)
• Polysemanticity and Capacity in Neural Networks [1.4174475093445233]
  Individual neurons in neural networks often represent a mixture of unrelated features. This phenomenon, called polysemanticity, can make interpreting neural networks more difficult.
  arXiv  Detail & Related papers  (2022-10-04T20:28:43Z)
• SpatioTemporal Focus for Skeleton-based Action Recognition [66.8571926307011]
  Graph convolutional networks (GCNs) are widely adopted in skeleton-based action recognition. We argue that the performance of recent proposed skeleton-based action recognition methods is limited by the following factors. Inspired by the recent attention mechanism, we propose a multi-grain contextual focus module, termed MCF, to capture the action associated relation information.
  arXiv  Detail & Related papers  (2022-03-31T02:45:24Z)
• Perception-and-Regulation Network for Salient Object Detection [8.026227647732792]
  We propose a novel global attention unit that adaptively regulates the feature fusion process by explicitly modeling interdependencies between features. The perception part uses the structure of fully-connected layers in classification networks to learn the size and shape of objects. An imitating eye observation module (IEO) is further employed for improving the global perception ability of the network.
  arXiv  Detail & Related papers  (2021-07-27T02:38:40Z)
• Spatio-Temporal Representation Factorization for Video-based Person Re-Identification [55.01276167336187]
  We propose Spatio-Temporal Representation Factorization module (STRF) for re-ID. STRF is a flexible new computational unit that can be used in conjunction with most existing 3D convolutional neural network architectures for re-ID. We empirically show that STRF improves performance of various existing baseline architectures while demonstrating new state-of-the-art results.
  arXiv  Detail & Related papers  (2021-07-25T19:29:37Z)
• Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition [80.17419621762866]
  We propose a novel Feature Decomposition and Reconstruction Learning (FDRL) method for effective facial expression recognition. FDRL consists of two crucial networks: a Feature Decomposition Network (FDN) and a Feature Reconstruction Network (FRN)
  arXiv  Detail & Related papers  (2021-04-12T02:22:45Z)
• Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image Restoration [65.11022516031463]
  We present a deep interleaved network (DIN) that learns how information at different states should be combined for high-quality (HQ) images reconstruction. In this paper, we propose asymmetric co-attention (AsyCA) which is attached at each interleaved node to model the feature dependencies. Our presented DIN can be trained end-to-end and applied to various image restoration tasks.
  arXiv  Detail & Related papers  (2020-10-29T15:32:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.