Graph Comparison Research Articles

Investigation Related to Intellectual Capital and Scientific Production in Colombia Public Universities: A Review from Scopus

This research aims to provide a general visualization of studies focused on the intellectual capital developed in Colombia. Firstly, detailed information on research articles on this topic was obtained from the Scopus database. Secondly, the information was analyzed using Excel through the direct comparison and analysis of graphs with references and theories related to intellectual capital. Finally, the results exposed that Colombian scientific production is associated with 32 public universities, representing 45,029 elaborate documents by 9,758 authors on three relevant topics: physics and astronomy, agricultural and biological science, and medicine. Also, the data obtained exposed that 56.61% of the institutions are above the average considering the overall public institutions. However, this behavior depends on the institution’s number of researchers and full-time teachers related to intellectual capital. Finally, the hypotheses agree with the results obtained, considering that the quantity and quantity of intellectual capital significantly influence scientific production, the size and location of the public universities of Colombia, and the national and international scientific collaboration.

Measurements of high phase transition temperatures and construction of Ni-Si-Zr phase diagram

There is a hindrance in modeling Gibbs energies of high melting alloys via the CALPHAD approach because of the scarcity of experimental results. All the conventional methods suffer to overcome the practical difficulties at high temperatures. Despite these setbacks, this paper provides a reliable way of using an arc-melting device in conjunction with a pyrometer to measure high-temperature phase transitions and developing a hybrid approach to construct a Ni-Si-Zr phase diagram. First, the preparation of Ni-Si-Zr alloy is via arc-melting technique. Forty-two as-cast sample analyses provide the primary solidifying phase encompassing the Ni-Si-Zr composition domain. Heat-treatment of fifteen samples at 1073 K refines the isothermal section, emphasizing the Zr-rich corner. Ni-Si-Zr alloy’s temperature recording using pyrometer in the arc-melting device while cooling after melting reveals novel Ni-Si-Zr’s invariant reactions. The time versus temperature graph comparison with its as-cast electron micrograph reveals several phase transitions. The notable research output of this research work is the hybrid approach, including the experiments circumventing hurdles at high temperatures and the first-principles method to calculate the intermetallic phase’s formation energy. The hybrid approach in the present work has established a phase diagram and the thermodynamic description over the extensive temperature and composition ranges for a highly complicated ternary system. The presently developed hybrid methodology is also applicable to construct a phase diagram for other complex ternary systems up to ≈ 3700 K.

CFD SIMULATION COMPARISON OF BOILER COMBUSTION TEMPERATURE USING CONVENTIONAL COMBUSTION AIR AND OXY-FUEL COMBUSTION

The burning of fossil fuels is the cause of the largest carbon dioxide (CO2) emissions in the world today. CO2 emissions may cause the greenhouse effect and global countermeasures. Boilers are steam generators that use fossil fuels such as coal and natural gas in their environment. The boiler itself is commonly used in power plants and industrial steam generators. In its operation, conventional boilers fueled by natural gas use air for the combustion system where the content of Nitrogen (N2) gas in ordinary air reaches 79% which dissolves CO2 gas in the exhaust gas so that the CO2 gas is carried into the atmosphere. Several technologies that aim to reduce CO2 emissions include pre-combustion, post-combustion, and combustion of oxygen fuel. Numerical methods such as Computational Fluid Dynamics (CFD) are used because the oxy-fuel technology has limited use in the industrial and utility world, and requires large procurement costs. Based on the simulation results, the combustion temperature in the fire system in 1917 o C originating from the combustion system, the resulting temperature is 1857 o C. Even so, the temperature distribution in the oxyfuel combustion system is more even in the combustion system so that the combustion system will be better. This is evidenced by the comparison of temperature comparison graphs on each furnace wall. The result was an increase in the temperature of the wall furnace by 5 o C to 20 o C.

FGOT: Graph Distances Based on Filters and Optimal Transport

Graph comparison deals with identifying similarities and dissimilarities between graphs. A major obstacle is the unknown alignment of graphs, as well as the lack of accurate and inexpensive comparison metrics. In this work we introduce the filter graph distance. It is an optimal transport based distance which drives graph comparison through the probability distribution of filtered graph signals. This creates a highly flexible distance, capable of prioritising different spectral information in observed graphs, offering a wide range of choices for a comparison metric. We tackle the problem of graph alignment by computing graph permutations that minimise our new filter distances, which implicitly solves the graph comparison problem. We then propose a new approximate cost function that circumvents many computational difficulties inherent to graph comparison and permits the exploitation of fast algorithms such as mirror gradient descent, without grossly sacrificing the performance. We finally propose a novel algorithm derived from a stochastic version of mirror gradient descent, which accommodates the non-convexity of the alignment problem, offering a good trade-off between performance accuracy and speed. The experiments on graph alignment and classification show that the flexibility gained through filter graph distances can have a significant impact on performance, while the difference in speed offered by the approximation cost makes the framework applicable in practical settings.

Relative, local and global dimension in complex networks

Dimension is a fundamental property of objects and the space in which they are embedded. Yet ideal notions of dimension, as in Euclidean spaces, do not always translate to physical spaces, which can be constrained by boundaries and distorted by inhomogeneities, or to intrinsically discrete systems such as networks. To take into account locality, finiteness and discreteness, dynamical processes can be used to probe the space geometry and define its dimension. Here we show that each point in space can be assigned a relative dimension with respect to the source of a diffusive process, a concept that provides a scale-dependent definition for local and global dimension also applicable to networks. To showcase its application to physical systems, we demonstrate that the local dimension of structural protein graphs correlates with structural flexibility, and the relative dimension with respect to the active site uncovers regions involved in allosteric communication. In simple models of epidemics on networks, the relative dimension is predictive of the spreading capability of nodes, and identifies scales at which the graph structure is predictive of infectivity. We further apply our dimension measures to neuronal networks, economic trade, social networks, ocean flows, and to the comparison of random graphs.

ESTIMATION OF NDVI FOR CLOUDY PIXELS USING MACHINE LEARNING

Abstract. The Normalized Difference Vegetation Index (NDVI) is a useful index for vegetation monitoring. However, due to cloud cover the observations of NDVI are discrete and vary in the intensity. Therefore, there is a need to estimate the NDVI during cloud cover using alternative sources of satellite observations. The main objective of this study is to estimate NDVI during cloudy conditions using moderate resolution multi-spectral and synthetic aperture radar (SAR) observations. Two approaches were identified: 1) pixel replacement and 2) machine learning based regression analysis to estimate cloud free NDVI. Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day NDVI composite, Sentinel-1 SAR and cloud masked Sentinel-2 multi-spectral observations were collected for entire cropping season. The satellite observations were selected only for agricultural areas by applying the agriculture, non-agriculture land use land cover mask. Machine learning algorithms such as Linear Regression (LR), Random Forest Regression (RFR), and Support Vector Regression (SVR) were used for NDVI estimation. Regression analysis was performed using Sentinel-2 NDVI as an independent variable and VV, VH, Cross Ratio (i.e., VV/VH), and MODIS NDVI as dependent variables. NDVI of the cloudy pixel was estimated using the trained regression models over the agriculture areas. A regression model was trained and applied to each Sentinel-2 tile that covers an area of 100 km × 100 km. The RFR and SVR showed the highest R2 of 0.73 and a RMSE of 0.12. A visual comparison of time series graphs showed good alignment between actual (Sentinel-2) and predicted NDVI and usual crop growth trend.

On the Similarity Between von Neumann Graph Entropy and Structural Information: Interpretation, Computation, and Applications

The von Neumann graph entropy is a measure of graph complexity based on the Laplacian spectrum. It has recently found applications in various learning tasks driven by the networked data. However, it is computationally demanding and hard to interpret using simple structural patterns. Due to the close relation between the Laplacian spectrum and the degree sequence, we conjecture that the structural information, defined as the Shannon entropy of the normalized degree sequence, might be a good approximation of the von Neumann graph entropy that is both scalable and interpretable. In this work, we thereby study the difference between the structural information and the von Neumann graph entropy named as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">entropy gap</i> . Based on the knowledge that the degree sequence is majorized by the Laplacian spectrum, we for the first time prove that the entropy gap is between 0 and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\log _{2} e$ </tex-math></inline-formula> in any undirected unweighted graphs. Consequently we certify that the structural information is a good approximation of the von Neumann graph entropy that achieves provable accuracy, scalability, and interpretability simultaneously. This approximation is further applied to two entropy-related tasks: network design and graph similarity measure, where a novel graph similarity measure and the corresponding fast algorithms are proposed. Meanwhile, we show empirically and theoretically that maximizing the von Neumann graph entropy can effectively hide the community structure, and then propose an alternative metric called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spectral polarization</i> to guide the community obfuscation. Our experimental results on graphs of various scales and types show that the very small entropy gap readily applies to a wide range of simple/weighted graphs. As an approximation of the von Neumann graph entropy, the structural information is the only one that achieves both high efficiency and high accuracy among the prominent methods. It is at least two orders of magnitude faster than SLaQ (Tsitsulin <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2020) with comparable accuracy. Our structural information based methods also exhibit superior performance in downstream tasks such as entropy-driven network design, graph comparison, and community obfuscation.

GAN–argcPredNet v1.0: a generative adversarial model for radar echo extrapolation based on convolutional recurrent units

Abstract. Precipitation nowcasting plays a vital role in preventing meteorological disasters, and Doppler radar data act as an important input for nowcasting models. When using the traditional extrapolation method it is difficult to model highly nonlinear echo movements. The key challenge of the nowcasting mission lies in achieving high-precision radar echo extrapolation. In recent years, machine learning has made great progress in the extrapolation of weather radar echoes. However, most of models neglect the multi-modal characteristics of radar echo data, resulting in blurred and unrealistic prediction images. This paper aims to solve this problem by utilizing the features of a generative adversarial network (GAN), which can enhance multi-modal distribution modeling, and design the radar echo extrapolation model GAN–argcPredNet v1.0. The model is composed of an argcPredNet generator and a convolutional neural network discriminator. In the generator, a gate controlling the memory and output is designed in the rgcLSTM component, thereby reducing the loss of spatiotemporal information. In the discriminator, the model uses a dual-channel input method, which enables it to strictly score according to the true echo distribution, and it thus has a more powerful discrimination ability. Through experiments on a radar dataset from Shenzhen, China, the results show that the radar echo hit rate (probability of detection; POD) and critical success index (CSI) have an average increase of 21.4 % and 19 %, respectively, compared with rgcPredNet under different intensity rainfall thresholds, and the false alarm rate (FAR) has decreased by an average of 17.9 %. We also found a problem during the comparison of the result graph and the evaluation index. The recursive prediction method will produce the phenomenon that the prediction result will gradually deviate from the true value over time. In addition, the accuracy of high-intensity echo extrapolation is relatively low. This is a question worthy of further investigation. In the future, we will continue to conduct research from these two directions.

High Resolution Fingerprint Retrieval Based on Pore Indexing and Graph Comparison

High Resolution Fingerprint Retrieval Based on Pore Indexing and Graph Comparison

Применение методов машинного обучения для классификации резонансного движения астероидов

When studying the resonant asteroid dynamics, it is necessary to classify time series of critical arguments on circulation, libration, or mixed case depending on their behavior. It is logical to use modern methods of machine learning to automatize this process. Earlier, a similar problem was solved for artificial satellites of the Earth. The purpose of this paper is to adapt the software attended for distinguishing resonant and nonresonant motion of satellites to solving asteroid dynamics problems. To achieve this goal, it is necessary to modify the program code and to train the created model on time rows obtained during the study of the asteroid orbital evolution. Operation of the modified software can be divided into three stages. At the first stage, to simplify the model-classifier, we make coding of time series of asteroid resonant arguments by vectors of lower dimension using an artificial neural network - an autoencoder. The second stage includes automatic clustering time series of asteroid resonant arguments by the HDBSCAN method (Hierarchical Density-Based Spatial Clustering of Applications with Noise) and their manual labeling to learn the classifier. At the third stage, based on the obtained training set, the artificial neural network-classifier is learned. The results of the classifier operation are estimated by visual comparison of graphs of the time series and received assessments. We may conclude that the classifier works correctly in most cases; some inaccuracies are observed in case of extreme amplitude and in the mixed case when libration passes to circulation. Contribution of the authors: the authors contributed equally to this article. The authors declare no conflicts of interests.

Wasserstein-Based Graph Alignment

A novel method for comparing non-aligned graphs of various sizes is proposed, based on the Wasserstein distance between graph signal distributions induced by the respective graph Laplacian matrices. Specifically, a new formulation for the one-to-many graph alignment problem is casted, which aims at matching a node in the smaller graph with one or more nodes in the larger graph. By incorporating optimal transport into our graph comparison framework, a structurally-meaningful graph distance, and a signal transportation plan that models the structure of graph data are generated. The resulting alignment problem is solved with stochastic gradient descent, where a novel Dykstra operator is used to ensure that the solution is a one-to-many (soft) assignment matrix. The performance of our novel framework is demonstrated on graph alignment, graph classification and graph signal transportation. Our method is shown to lead to significant improvements with respect to the state-of-the-art algorithms on each ofthese tasks.

ESTIMATION OF THE ACCURACY OF FUEL CONSUMPTION MEASUREMENT BASED ON DATA FROM THE ON-BOARD CAN-BUS

Determining the dependence of actual fuel consumption on operating conditions is the most important task in vehicle transport, since the cost of hydrocarbons is a significant part of the cost of transportation. Currently, there are many ways to experimentally measure fuel efficiency: using external flow meters installed in the fuel line, equipping the tank with additional level sensors, using information from the electronic engine control unit. The latter approach is of greatest interest, since it requires minimal financial investment, is easy to install, and there are no significant changes to the design. The question arises about the measurement accuracy according to this method, since the ECU calculates the fuel consumption according to a mathematical model, calibrated and programmed at the factory. This article offers a solution to this problem. At the first stage, NNSTU specialists have developed and installed on a vehicle equipment for measuring fuel consumption simultaneously by two methods – using an external high-precision Kistler Dfl3x-5bar flow meter, installed in the fuel line, and on the basis of data from the engine ECU, obtained using information CAN-bus. At the second stage, tests were carried out (moving in a steady state and the driving cycle) after this, using results of tests, we have concluded a numerical difference in the methods for determining fuel consumption. At the end of the article, tabular results of comparison of measurements and graphs of the determined parameters are demonstrated.

Bayesian Edge Regression in Undirected Graphical Models to Characterize Interpatient Heterogeneity in Cancer

It is well-established that interpatient heterogeneity in cancer may significantly affect genomic data analyses and in particular, network topologies. Most existing graphical model methods estimate a single population-level graph for genomic or proteomic network. In many investigations, these networks depend on patient-specific indicators that characterize the heterogeneity of individual networks across subjects with respect to subject-level covariates. Examples include assessments of how the network varies with patient-specific prognostic scores or comparisons of tumor and normal graphs while accounting for tumor purity as a continuous predictor. In this paper, we propose a novel edge regression model for undirected graphs, which estimates conditional dependencies as a function of subject-level covariates. We evaluate our model performance through simulation studies focused on comparing tumor and normal graphs while adjusting for tumor purity. In application to a dataset of proteomic measurements on plasma samples from patients with hepatocellular carcinoma (HCC), we ascertain how blood protein networks vary with disease severity, as measured by HepatoScore, a novel biomarker signature measuring disease severity. Our case study shows that the network connectivity increases with HepatoScore and a set of hub genes as well as important gene connections are identified under different HepatoScore, which may provide important biological insights to the development of precision therapies for HCC.

Pseudonatural Products Occur Frequently in Biologically Relevant Compounds.

A new methodology for classifying fragment combinations and characterizing pseudonatural products (PNPs) is described. The source code is based on open-source tools and is organized as a Python package. Tasks can be executed individually or within the context of scalable, robust workflows. First, structures are standardized and duplicate entries are filtered out. Then, molecules are probed for the presence of predefined fragments. For molecules with more than one match, fragment combinations are classified. The algorithm considers the pairwise relative position of fragments within the molecule (fused atoms, linkers, intermediary rings), resulting in 18 different possible fragment combination categories. Finally, all combinations for a given molecule are assembled into a fragment combination graph, with fragments as nodes and combination types as edges. This workflow was applied to characterize PNPs in the ChEMBL database via comparison of fragment combination graphs with natural product (NP) references, represented by the Dictionary of Natural Products. The Murcko fragments extracted from 2000 structures previously described were used to define NP fragments. The results indicate that ca. 23% of the biologically relevant compounds listed in ChEMBL comply to the PNP definition and that, therefore, PNPs occur frequently among known biologically relevant small molecules. The majority (>95%) of PNPs contain two to four fragments, mainly (>95%) distributed in five different combination types. These findings may provide guidance for the design of new PNPs.

Developmental graphs comparison strategy for analysis of pattern formation and phylogeny

In most taxa of plant and animal kingdoms the initial steps of embryogenesis and the final morphology of an organism are strongly determined. However, these two phenomena do not correlate from phylogenetic point of view, namely, different unrelated taxa can have the same type of early embryogenesis, while there can be different types of cleavage inside one taxon. Here we discuss an approach enabling giving an insight into the understanding of this phenomenon.First, we propose a strategy for constructing developmental graphs (trees) that provide mathematical formalization of a process of embryogenesis. Second, we suggested an algorithm of trees comparison, developed specifically for this type of labeled graphs, which allows calculating a distance between two developmental trees, and thus clustering them into groups. Next we performed the analysis of correspondence between the obtained clusters and the inception of morphological characters in given clustered groups of organisms, which allows describing several particular cases of interrelation between developmental trends and formation of morphological structures. Here we present some illustrations of the suggested methodology on the analysis of plant angiosperm species belonging to different taxa of various ranks.

Connecting a direct and a Galerkin approach to slow manifolds in infinite dimensions

In this paper, we study slow manifolds for infinite-dimensional evolution equations. We compare two approaches: an abstract evolution equation framework and a finite-dimensional spectral Galerkin approximation. We prove that the slow manifolds constructed within each approach are asymptotically close under suitable conditions. The proof is based upon Lyapunov-Perron methods and a comparison of the local graphs for the slow manifolds in scales of Banach spaces. In summary, our main result allows us to change between different characterizations of slow invariant manifolds, depending upon the technical challenges posed by particular fast-slow systems.

Preparation of Calcium Citrate Malate Using Citric Acid and Malic Acid with Calcium Hydroxide Synthesized from Duck Eggshell

This research work presents an optimum setting at which the production of calcium citrate malate would have the highest yield. The factors that were varied are temperature and the concentrations of citric acid and malic acid. Duck eggshells were used as the source of calcium. The temperature settings were at room temperature (25°C) and at 80°C, the concentrations of citric acid at 1.5 M and 1.7 M and that of malic acid at 2.2 M and at 2.5 M. Using 23 factorial design of experiment (8 settings), the main effects and the interaction effects were plotted to derive an empirical model to predict the yield of calcium citrate malate. Furthermore, a comparison of x-ray diffraction graphs was conducted to evaluate the final product.

A novel visualization approach for data provenance

SummaryData provenance has led to a developing need for the technologies to empower end‐users to assess and take action on the data life cycle. In the Big Data era, companies' amount of data over the world increases each day. As data increases, metadata on the data origin and lifecycle of data also overgrows. Thus, this requires innovations that can provide a better understanding and interpretation of data using data provenance. This study addresses the challenge of extracting data in the form of graphs from scientific workflows and facilitating demanded visualization approaches such as graph comparison, summarization, backward‐forward querying, and stream data visualization. W3C‐PROV‐O provenance specification is implemented via a visualization tool to assess the applicability of proposed algorithms. The proposed algorithms are tested on a large‐scale provenance dataset to explore their performance. In addition, this study discusses the details of a comprehensive usability study of the prototype visualization tool. Results indicate that proposed visualization approaches are usable and processing overhead is insignificant.

Optimization of pressure drop model for shale gas wells in area X based on correlation coefficient method

The bottom hole pressure is an important parameter in the production process of a gas well, and it is of great significance to many aspects such as gas well productivity evaluation, dynamic analysis and production allocation. A large number of shale gas wells in area X were lack of the bottom hole pressure data. In order to solve this problem, based on extensive research in many articles, this paper selected eight kinds of calculation models as candidate objects, such as Hagadorn-Brown and Beggs-Brill. Different models were used to calculate the bottom hole pressure of only two shale gas wells with measured pressure data in this area, and the calculated results were compared with the measured data to select the best model for calculating the bottom hole pressure of shale gas wells in area X. In the optimization analysis, it was found that the conventional average relative error comparison method or the graph comparison method could not reflect the application situation of the calculation models well in this area. Therefore, on the basis of the two comparison methods, the correlation coefficient method was introduced to quantify the overall difference between the calculated pressure and the measured pressure. The results showed that the calculated result of Orkiszewski model fitted the measured pressure best.

An application specific dynamic behaviour model using function-call sequence and memory access-graph for execution integrity verification

Computing systems demand stringent security checks to guard themselves against powerful attack vectors. Deployment of the Control Flow Integrity(CFI) checks limit the control flow of a program to a set of valid destinations, derived from the static analysis of the code. CFI checks guarantee the integrity of the executed code, but, they do not guarantee the integrity of execution, as sophisticated attack vectors create attacks that comply with the normal control flow of the program. This paper proposes an application specific, dynamic, execution integrity verification scheme, to detect static and dynamic integrity breaches. The system builds a behaviour model consisting of function call sequences and memory access graphs, to verify the semantic behaviour exhibited by the application. Dynamic variation of the function behaviour is further recorded as Access Graph Variance Vector (AGVV). Temporal sequencing of function calls, together with the memory access graph comparison, yields a two-level integrity verification system. Apart from detecting the deviations from normal behaviour, our proposed method limits the abnormal behaviour from being propagated. Experimental evaluation of the proposed method using applications from MiBench benchmark suite against static integrity breaches shows cent percent verification accuracy with less than 5% false positive rate. The system also identifies the dynamic integrity violation caused by ROP based attacks from the RIPE benchmark suite, indicating an accuracy of 94.44% against dynamic integrity breaches.