scholarly journals A Similarity Measurement Method Based on Graph Kernel for Disconnected Graphs

2019 ◽  
Author(s):  
Jun Gao ◽  
Jianliang Gao
Keyword(s):  
Real World ◽  
Connected Graph ◽  
Measurement Method ◽  
Dimensional Space ◽  
Similarity Measurement ◽  
Graph Kernel ◽  
The Real ◽  
Connected Subgraphs ◽  
Low Dimensional ◽  
Disconnected Graphs

Disconnected graphs are very common in the real world. However, most existing methods for graph similarity focus on connected graph. In this paper, we propose an effective approach for measuring the similarity of disconnected graphs. By embedding connected subgraphs with graph kernel, we obtain the feature vectors in low dimensional space. Then, we match the subgraphs and weigh the similarity of matched subgraphs. Finally, an intuitive example shows the feasibility of the method.

scholarly journals Relation Structure-Aware Heterogeneous Information Network Embedding

2019 ◽  
Vol 33 ◽  
pp. 4456-4463 ◽  
Author(s):  
Yuanfu Lu ◽  
Chuan Shi ◽  
Linmei Hu ◽  
Zhiyuan Liu
Keyword(s):  
Real World ◽  
Dimensional Space ◽  
Structural Characteristics ◽  
Information Network ◽  
Network Embedding ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Real World Datasets ◽  
Low Dimensional ◽  
Embedding Methods

Heterogeneous information network (HIN) embedding aims to embed multiple types of nodes into a low-dimensional space. Although most existing HIN embedding methods consider heterogeneous relations in HINs, they usually employ one single model for all relations without distinction, which inevitably restricts the capability of network embedding. In this paper, we take the structural characteristics of heterogeneous relations into consideration and propose a novel Relation structure-aware Heterogeneous Information Network Embedding model (RHINE). By exploring the real-world networks with thorough mathematical analysis, we present two structure-related measures which can consistently distinguish heterogeneous relations into two categories: Affiliation Relations (ARs) and Interaction Relations (IRs). To respect the distinctive characteristics of relations, in our RHINE, we propose different models specifically tailored to handle ARs and IRs, which can better capture the structures and semantics of the networks. At last, we combine and optimize these models in a unified and elegant manner. Extensive experiments on three real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods in various tasks, including node clustering, link prediction, and node classification.

scholarly journals An overview of augmented visualization: observing the real world as desired

2018 ◽  
Vol 7 ◽  
Author(s):  
Shohei Mori ◽  
Hideo Saito
Keyword(s):  
Real World ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Open Problems ◽  
The Real ◽  
Real Objects ◽  
Point Image ◽  
Set Up ◽  
Background Image

Over 20 years have passed since a free-viewpoint video technology has been proposed with which a user's viewpoint can be freely set up in a reconstructed three-dimensional space of a target scene photographed by multi-view cameras. This technology allows us to capture and reproduce the real world as recorded. Once we capture the world in a digital form, we can modify it as augmented reality (i.e., placing virtual objects in the digitized real world). Unlike this concept, the augmented world allows us to see through real objects by synthesizing the backgrounds that cannot be observed in our raw perspective directly. The key idea is to generate the background image using multi-view cameras, observing the backgrounds at different positions and seamlessly overlaying the recovered image in our digitized perspective. In this paper, we review such desired view-generation techniques from the perspective of free-view point image generation and discuss challenges and open problems through a case study of our implementations.

Location memory in the real world: Category adjustment effects in 3-dimensional space

Cognition ◽  
2013 ◽  
Vol 128 (1) ◽  
pp. 45-55 ◽  
Author(s):  
Mark P. Holden ◽  
Nora S. Newcombe ◽  
Thomas F. Shipley
Keyword(s):  
Real World ◽  
Dimensional Space ◽  
The Real ◽  
3 Dimensional ◽  
Location Memory

scholarly journals Adversarially Regularized Graph Autoencoder for Graph Embedding

2018 ◽  
Author(s):  
Shirui Pan ◽  
Ruiqi Hu ◽  
Guodong Long ◽  
Jing Jiang ◽  
Lina Yao ◽  
...  
Keyword(s):  
Real World ◽  
Topological Structure ◽  
Dimensional Space ◽  
Experimental Studies ◽  
Graph Embedding ◽  
Compact Representation ◽  
Graph Visualization ◽  
Graph Data ◽  
Training Scheme ◽  
Low Dimensional

Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics.  Most existing embedding algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph data,  but they have mostly ignored the data distribution of the latent codes from the graphs, which often results in inferior embedding in  real-world  graph data. In this paper, we propose a novel adversarial graph embedding framework for graph data. The framework encodes the topological structure and node content in a graph to a compact representation, on which a decoder is trained to reconstruct the graph structure. Furthermore, the latent representation is enforced to match a prior distribution via an adversarial training scheme. To learn a robust embedding,  two variants of adversarial approaches,  adversarially regularized graph autoencoder (ARGA) and adversarially regularized variational graph autoencoder (ARVGA), are developed. Experimental studies on real-world graphs validate our design and demonstrate that our algorithms outperform baselines by a wide margin in link prediction,  graph clustering, and graph visualization tasks.

Uncertainty representation and information measurement of grey numbers

2020 ◽  
Vol 10 (4) ◽  
pp. 495-512
Author(s):  
Nanlei Chen ◽  
Naiming Xie
Keyword(s):  
Real World ◽  
Measurement Method ◽  
Number Representation ◽  
Information Measurement ◽  
Content Type ◽  
The Real ◽  
True Value ◽  
Uncertainty Representation ◽  
Grey Number ◽  
Available Information

PurposeThe purpose of this paper is to propose an uncertainty representation and information measurement method for characterizing grey numbers, estimating their internal laws and solving how to generate them based on available information data in the real world.Design/methodology/approachThis paper attempts to present a new mathematical methodology in the field of grey numbers. The generalized grey number is defined at first with the concept of information elements and information samples. Then, the probability function of a grey number is proposed to describe the internal law of the grey number. By finding the feasible information elements from information samples, the probability calculation method for the true value of a grey number is presented. Finally, some numerical examples and comparisons are carried out to assess the efficiency and performance.FindingsThe results show that the uncertainty representation and information measurement method is effective in characterizing and quantifying grey numbers based on available information data.Practical implicationsUncertain information is widespread in practical applications. In this manuscript, the grey number is represented and its information is measured through some existing data in discrete or interval forms, which provides a grey information concept that utilizes information elements to represent uncertainty in the real world.Originality/valueThe proposal presents a novel data-driven method to generate a grey number representation from available data rather than the classical whitening weight function constructed from experience, and the dynamic evolution process of a grey number is measured by the increase of information samples.

scholarly journals SpHMC: Spectral Hamiltonian Monte Carlo

2019 ◽  
Vol 33 ◽  
pp. 5516-5524
Author(s):  
Haoyi Xiong ◽  
Kafeng Wang ◽  
Jiang Bian ◽  
Zhanxing Zhu ◽  
Cheng-Zhong Xu ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Probability Distribution ◽  
Real World ◽  
Dimensional Space ◽  
Probability Distributions ◽  
Superior Performance ◽  
High Dimensional ◽  
Hamiltonian Monte Carlo ◽  
Real World Datasets ◽  
Low Dimensional

Stochastic Gradient Hamiltonian Monte Carlo (SGHMC) methods have been widely used to sample from certain probability distributions, incorporating (kernel) density derivatives and/or given datasets. Instead of exploring new samples from kernel spaces, this piece of work proposed a novel SGHMC sampler, namely Spectral Hamiltonian Monte Carlo (SpHMC), that produces the high dimensional sparse representations of given datasets through sparse sensing and SGHMC. Inspired by compressed sensing, we assume all given samples are low-dimensional measurements of certain high-dimensional sparse vectors, while a continuous probability distribution exists in such high-dimensional space. Specifically, given a dictionary for sparse coding, SpHMC first derives a novel likelihood evaluator of the probability distribution from the loss function of LASSO, then samples from the high-dimensional distribution using stochastic Langevin dynamics with derivatives of the logarithm likelihood and Metropolis–Hastings sampling. In addition, new samples in low-dimensional measuring spaces can be regenerated using the sampled high-dimensional vectors and the dictionary. Extensive experiments have been conducted to evaluate the proposed algorithm using real-world datasets. The performance comparisons on three real-world applications demonstrate the superior performance of SpHMC beyond baseline methods.

scholarly journals Estimación de la ubicación relativa de una cámara utilizando un sistema de visión monocular

2019 ◽  
pp. 18-23
Author(s):  
Saúl Martínez-Díaz
Keyword(s):  
Real World ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Reference Points ◽  
Reference Object ◽  
Relative Location ◽  
The Real ◽  
Processing Power ◽  
Fixed Reference ◽  
Three Dimensional Space

Objective: Estimate the location of a camera with respect to objects in the real world, using monocular vision. Methodology: In this paper we introduce a method to calculate the relative location of the camera with respect to a group of points located in the three-dimensional space. The method requires only three fixed reference points of which the real distance between each pair of points must be known. With this information it is possible to estimate the relative location of the camera when it is moved, using successive images that contain the same points. Contribution: In recent years, processing power of computers has grown considerably and, with this, the interest of the scientific community in visual odometry has also increased. For this purpose, in many cases, it is convenient to use a single camera (monocular system). Unfortunately, a monocular system allows to estimate the location of the camera with respect to some object in the real world only up to a scale factor. The main contribution of this work is the estimation of the location of the camera in real world coordinates with respect to a reference object.

scholarly journals Computing the Riemannian curvature of image patch and single-cell RNA sequencing data manifolds using extrinsic differential geometry

2021 ◽  
Author(s):  
Duluxan Sritharan ◽  
Shu Wang ◽  
Sahand Hormoz
Keyword(s):  
Differential Geometry ◽  
Single Cell ◽  
Real World ◽  
Dimensional Space ◽  
Small Sample ◽  
Topological Classification ◽  
High Dimensional ◽  
Dimensional Manifold ◽  
Riemannian Curvature ◽  
Low Dimensional

Most high-dimensional datasets are thought to be inherently low-dimensional, that is, datapoints are constrained to lie on a low-dimensional manifold embedded in a high-dimensional ambient space. Here we study the viability of two approaches from differential geometry to estimate the Riemannian curvature of these low-dimensional manifolds. The intrinsic approach relates curvature to the Laplace-Beltrami operator using the heat-trace expansion, and is agnostic to how a manifold is embedded in a high-dimensional space. The extrinsic approach relates the ambient coordinates of a manifold's embedding to its curvature using the Second Fundamental Form and the Gauss-Codazzi equation. Keeping in mind practical constraints of real-world datasets, like small sample sizes and measurement noise, we found that estimating curvature is only feasible for even simple, low-dimensional toy manifolds, when the extrinsic approach is used. To test the applicability of the extrinsic approach to real-world data, we computed the curvature of a well-studied manifold of image patches, and recapitulated its topological classification as a Klein bottle. Lastly, we applied the approach to study single-cell transcriptomic sequencing (scRNAseq) datasets of blood, gastrulation, and brain cells, revealing for the first time the intrinsic curvature of scRNAseq manifolds.

scholarly journals Visual Hyper Reality of Teenagers in Social Media With Photographs as Communication Media

2017 ◽  
Vol 2 (4) ◽  
pp. 329
Author(s):  
Adita Miranti
Keyword(s):  
Social Media ◽  
Virtual Reality ◽  
New Media ◽  
Real World ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Virtual Communication ◽  
The Real ◽  
Digital World ◽  
The People

In postmodern era, technology has evolved so rapidly that brings the people into the digital world (cyberspace), a new space to present the virtual reality and to provide free space for every individual to take any action that ends the simulation of reality. The development of digital technology has been brought through human fantasy boundaries, creating a three-dimensional space of the following items inside, going to the stage where virtual reality has exceeded manipulation and visual imagery so we step from the real world into a fantasy world. By reviewing the virtual communication through social media in cyberspace and how the virtual communication through new media (internet), and the formation of identity, the identity of both the real and virtual identities. Freedom and comfort of a virtual entered into a structured system, then to minimize misperceptions, prejudices and miss understanding should be built communications balanced relationship between the real world and the virtual world.

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