Simplified Graphs Research Articles

How do you assert a graph? Towards an account of depictions in scientific testimony

AbstractI extend the literature on norms of assertion to the ubiquitous use of graphs in scientific papers and presentations, which I term “graphical testimony.” On my account, the testimonial presentation of a graph involves commitment to both (a) the in‐context reliability of the graph's framing devices and (b) the perspective‐relative accuracy of the graph's content. Despite apparent disagreements between my account and traditional accounts of assertion, the two are compatible and I argue that we should expect a similar pattern of commitments in a set of cases that extends beyond the graphical one. I end by demonstrating that the account resolves apparent tensions between the demands of honesty and the common scientific practice of presenting idealized or simplified graphs: these “distortions” can be honest so long as there's the right kind of alignment between the distortion and the background beliefs and values of the audience.

An embedding technique in the study of word-representability of graphs

Word-representable graphs, which are the same as semi-transitively orientable graphs, generalize several fundamental classes of graphs. In this paper we propose a novel approach to study word-representability of graphs using a technique of homomorphisms. As a proof of concept, we apply our method to show word-representability of the simplified graph of overlapping permutations that we introduce in this paper. For another application, we obtain results on word-representability of certain subgraphs of simplified de Bruijn graphs that were introduced recently by Petyuk and studied in the context of word-representability.

Simplifying social networks via triangle-based cohesive subgraphs

One main challenge for simplifying node-link diagrams of large-scale social networks lies in that simplified graphs generally contain dense subgroups or cohesive subgraphs. Graph triangles quantify the solid and stable relationships that maintain cohesive subgraphs. Understanding the mechanism of triangles within cohesive subgraphs contributes to illuminating patterns of connections within social networks. However, prior works can hardly handle and visualize triangles in cohesive subgraphs. In this paper, we propose a triangle-based graph simplification approach that can filter and visualize cohesive subgraphs by leveraging a triangle-connectivity called k-truss and a force-directed algorithm. We design and implement TriGraph, a web-based visual interface that provides detailed information for exploring and analyzing social networks. Quantitative comparisons with existing methods, two case studies on real-world datasets, and feedback from domain experts demonstrate the effectiveness of TriGraph.

Graphical Representation of Lipid Panel: A Simplified Time-Series Data Display.

High cholesterol is a risk factor for developing Atherosclerotic Cardiovascular Disease. Poorly designed health data displays cause an undue cognitive burden on clinicians. Simplified line graphs (i.e., sparklines) could support efficient cognitive processing and interpretation of lipid panel results. Clinical concepts for cognitive tasks assessing low-density lipoprotein laboratory results were analyzed according to their internal representations and data scale types. A sparkline external representation aligns more closely with the internal representations for mental tasks associated with identifying abnormalities and assessing trends compared to traditional tabular displays. By simplifying the health data display with sparklines, faster cognitive processing is theoretically supported.

Research on the Revitalization of the Defensive Fortress of the Great Wall Based on the Adversarial Interpretive-Structure Model

This article aims to formulate a revitalization strategy for the affiliated fortress of the Ming Great Wall. The Adversarial Interpretive-Structure Model (AISM) extracts the opposite hierarchical rules and obtains a pair of simplified hierarchical topology graphs. The directed line segments in the adversarial hierarchical topology graph represent the interrelationships between the elements, which are presented in a topological hierarchy and can easily compare the advantages and disadvantages of the revitalization factors, which provides a basis for subsequent revitalization strategy formulation. The adversarial hierarchical topology graph provides a new method for conserving and reusing architectural heritage. Key Words: adversarial hierarchical topology graph, fortress, architectural heritage, conservation and reuse

Evaluation of Deep Learning Architectures for Aqueous Solubility Prediction.

Determining the aqueous solubility of molecules is a vital step in many pharmaceutical, environmental, and energy storage applications. Despite efforts made over decades, there are still challenges associated with developing a solubility prediction model with satisfactory accuracy for many of these applications. The goals of this study are to assess current deep learning methods for solubility prediction, develop a general model capable of predicting the solubility of a broad range of organic molecules, and to understand the impact of data properties, molecular representation, and modeling architecture on predictive performance. Using the largest currently available solubility data set, we implement deep learning-based models to predict solubility from the molecular structure and explore several different molecular representations including molecular descriptors, simplified molecular-input line-entry system strings, molecular graphs, and three-dimensional atomic coordinates using four different neural network architectures—fully connected neural networks, recurrent neural networks, graph neural networks (GNNs), and SchNet. We find that models using molecular descriptors achieve the best performance, with GNN models also achieving good performance. We perform extensive error analysis to understand the molecular properties that influence model performance, perform feature analysis to understand which information about the molecular structure is most valuable for prediction, and perform a transfer learning and data size study to understand the impact of data availability on model performance.

A data structure for studying 3D modeling design behavior based on event logs

In recent years, with the increase of large-scale and complex architecture projects, the architecture, engineering and construction (AEC) industries are changing rapidly towards intelligent and databased direction. As the by-product, 3D modeling event logs could provide quantitative and traceable data for early design stages that can produce butterfly-effect on the later construction stages. In this study, new data structure of command-object graph retrieved from 3D modeling event logs was proposed. It reflects the cause-and-effect relationship between commands and objects to accommodate 3D modeling process. A case study was conducted on 110 students' event logs generated from solving a well-defined façade model. As result: an average trial-and-error ratio was calculated at 0.3357 based on original and simplified graphs. Four main types of ‘modeling bubble’ were summarized base on unit-tag graphs. Key commands were found by graph centrality. Auto-prediction of same-group objects was developed with mis-group rate less than 0.9%.

A suitability assessment using an instrumented impact test of the use of selected structural steel grades on the basis of their changes in response to exposure to fire

In this article, changes occurring in structural steel after exposure to fire are described and discussed. The steel structure before and after fire determines its susceptibility to brittle cracking. The individual phases of cracking are described and interpreted on the basis of a load-displacement graph, directly obtained from the Charpy impact test. The relationship between the intensity of individual fracture energies and the type and appearance of the sample fractures are demonstrated. The program of planned Charpy impact tests and expected hazards after the exposure to fire of selected steel grades are presented. Standard simplified load-displacement graphs are assigned to the steel transition curve. The course of various cracking mechanisms occurring in the case of brittle fractures and plastic fractures are discussed. The aim of this article is to evaluate the possibility of the assessment of structural steel after a fire based on results obtained during the Charpy impact test.

Absorption in Time-Varying Markov Chains: Graph-Based Conditions

We investigate absorption, i.e., almost sure convergence to an absorbing state, in time-varying (non-homogeneous) discrete-time Markov chains with finite state space. We consider systems that can switch among a finite set of transition matrices, which we call the modes. Our analysis is focused on two properties: 1) almost sure convergence to an absorbing state under any switching, and 2) almost sure convergence to a desired set of absorbing states via a proper switching policy. We derive necessary and sufficient conditions based on the structures of the transition graphs of modes. More specifically, we show that a switching policy that ensures almost sure convergence to a desired set of absorbing states from any initial state exists if and only if those absorbing states are reachable from any state on the union of simplified transition graphs. We then show three sufficient conditions for absorption under arbitrary switching. While the first two conditions depend on the acyclicity (weak acyclicity) of the union (intersection) of simplified transition graphs, the third condition is based on the distances of each state to the absorbing states in all the modes. These graph theoretic conditions can verify the stability and stabilizability of absorbing states based only on the feasibility of transitions in each mode.

Altering Perceptions, Visualizing Sub-ground Metal Objects

Smart phones and sensor technology represent a key part of everyday life and are being used in areas such as safety, training, healthcare and others. Utilizing an array of internal sensors and a metal detector requires an evaluation of the precision of the measurements and performance reviews. Metal detectors are versatile, with uses in healthcare as well as recreational, but a common issue often seen in the proprietary equipment is bad presentation of data. Usually the user interface is just numbers on a display, simplified graphs or sounds. By combining smartphone sensors with a metal detector and a custom mount we model a mapping between the virtual and physical model, a digital twin. In this paper we are utilizing the computing capabilities of a smartphone and employing visualization techniques not possible by partial information. In addition, we present an improved graphical user interface without any proprietary accessories. For this purpose, preliminary case studies are included as a part of a prototype in development.

Developing a Theoretical Framework for Evaluating the Quality of mHealth Apps for Adolescent Users: A Systematic Review.

To assess adolescents' preferred mobile app features and to propose a framework for evaluating health-related mobile apps for adolescents. PubMed, CINAHL, PsycINFO, ERIC, HealthIT.gov, and ClinicalTrials.gov were systematically searched in August 2017. Studies pertaining to app development, feasibility, or usability that reported preferred app features and rating criteria on mHealth (mobile health) apps intended for adolescents were included. Quality assessment was performed using the Mixed Methods Appraisal Tool. Qualitative synthesis was performed to develop themes reflecting best practices for evaluating the quality of mHealth apps for adolescents. Using a grounded theory approach, we constructed a theoretical framework of rating criteria that can be used to inform development of an evaluation tool for mHealth apps targeted to adolescents. Thirteen articles were included. Most commonly preferred features include ability to track test results or self-management progress, connect to social media, and gain points or prizes through app gamification. Common rating criteria include degree of app customizability, ease of use, visual appeal, and interactivity. Five emerging dimensions were used in the theoretical framework: Technical Quality; Engagement; Support System; Autonomy; and Safety, Privacy, and Trust. We found that adolescents prefer mHealth apps that are customizable, offer peer support through social media, sustain engagement via gamification, and support the ability to visualize health trends via simplified graphs. Findings may help in the development of mHealth apps that are preferred by adolescents, as well as the development of a quality evaluation tool for mHealth apps targeted to this population.

Simplified interval-valued Pythagorean fuzzy graphs with application

Interval-valued Pythagorean fuzzy set (IVPFS) as a generalization of Pythagorean fuzzy set (PFS) increases its elasticity drastically. However, the expressions and calculations of IVPFS are slightly complicated. To overcome this drawback, in this research study, we greatly simplify the expressions of IVPFS by introducing a new concept of simplified interval-valued Pythagorean fuzzy set (SIVPFS), constituted by two Pythagorean fuzzy numbers (PFNs) with the relationships of intersection and union simultaneously. We develop systematic aggregation operators to aggregate simplified interval-valued Pythagorean fuzzy information. Meanwhile, we propose a new generalization of fuzzy graph, called simplified interval-valued Pythagorean fuzzy graph (SIVPFG), to describe uncertain information in graph theory. We develop a series of operations on two SIVPFGs and investigate their desirable properties. Finally, we develop a SIVPFG-based multi-agent decision-making approach to solve a common kind of situation where the graphic structure of agents is obscure. A numerical example is provided to illustrate the proposed approach as well as the applicability of SIVPFS and SIVPFG in decision making.

Rotation in rectangular and circular reinforced elastomeric bearings resulting in lift-off

Rotation is an important design consideration in reinforced elastomeric bearings (a composite of an elastomer and steel or fiber reinforcement). In certain circumstances, an applied rotation may overcome the hydrostatic compressive pressure due to the applied axial load. If this occurs, a tensile stress will develop in bonded bearings, or lift-off (i.e. the loss of contact between the bearing and the supports) will occur in unbonded bearings. In this paper, analytical solutions are derived to predict the initiation of lift-off or development of tensile stress in infinite strip, rectangular and circular pads including the compressibility of the elastomer and the extensibility of the reinforcement – parameters which are often ignored. The unbonded infinite strip and rectangular pad geometries are further investigated for the impact of rotations exceeding lift-off on the instantaneous geometry and moment-rotation relationship of the bearing. Due to the complexity of the analytical solutions, simplified geometry-specific approximations and graphs are derived that are appropriate for design applications. It is shown that the current code methodology may contain significant error in predicting the initiation of lift-off in unbonded applications or the introduction of a tensile stress in bonded applications.

HIERARCHICAL PATH PLANNING FOR WALKING (ALMOST) ANYWHERE

Abstract. Computerized path planning, not constrained to transportation networks, may be useful in a range of settings, from search and rescue to archaeology. This paper develops a method for general path planning intended to work across arbitrary distances and at the level of terrain detail afforded by aerial LiDAR scanning. Relevant information about terrain, trails, roads, and other infrastructure is encoded in a large directed graph. This basal graph is partitioned into strongly connected subgraphs such that the generalized diameter of each subgraphs is constrained by a set value, and with nominally as few subgraphs as possible. This is accomplished using the k-center algorithm adapted with heuristics suitable for large spatial graphs. A simplified graph results, with reduced (but known) position accuracy and complexity. Using a hierarchy of simplified graphs adapted to different length scales, and with careful selection of levels in the hierarchy based on geodesic distance, a shortest path search can be restricted to a small subset of the basal graph. The method is formulated using matrix-graph duality, suitable for linear algebra-oriented software. Extensive use is also made of public data, including LiDAR, as well as free and open software for geospatial data processing.

Robust Structure‐Based Shape Correspondence

AbstractWe present a robust method to find region‐level correspondences between shapes, which are invariant to changes in geometry and applicable across multiple shape representations. We generate simplified shape graphs by jointly decomposing the shapes, and devise an adapted graph‐matching technique, from which we infer correspondences between shape regions. The simplified shape graphs are designed to primarily capture the overall structure of the shapes, without reflecting precise information about the geometry of each region, which enables us to find correspondences between shapes that might have significant geometric differences. Moreover, due to the special care we take to ensure the robustness of each part of our pipeline, our method can find correspondences between shapes with different representations, such as triangular meshes and point clouds. We demonstrate that the region‐wise matching that we obtain can be used to find correspondences between feature points, reveal the intrinsic self‐similarities of each shape and even construct point‐to‐point maps across shapes. Our method is both time and space efficient, leading to a pipeline that is significantly faster than comparable approaches. We demonstrate the performance of our approach through an extensive quantitative and qualitative evaluation on several benchmarks where we achieve comparable or superior performance to existing methods.

Monte Carlo method based QSAR modelling of natural lipase inhibitors using hybrid optimal descriptors

Obesity is one of the most provoking health burdens in the developed countries. One of the strategies to prevent obesity is the inhibition of pancreatic lipase enzyme. The aim of this study was to build QSAR models for natural lipase inhibitors by using the Monte Carlo method. The molecular structures were represented by the simplified molecular input line entry system (SMILES) notation and molecular graphs. Three sets – training, calibration and test set of three splits – were examined and validated. Statistical quality of all the described models was very good. The best QSAR model showed the following statistical parameters: r2 = 0.864 and Q2 = 0.836 for the test set and r2 = 0.824 and Q2 = 0.819 for the validation set. Structural attributes for increasing and decreasing the activity (expressed as pIC50) were also defined. Using defined structural attributes, the design of new potential lipase inhibitors is also presented. Additionally, a molecular docking study was performed for the determination of binding modes of designed molecules.

Planetary transmission load sharing: Manufacturing errors and system configuration study

This paper addresses the effect of manufacturing errors such as eccentricity and planet pin positioning errors on the quasi-static behavior of a 3 planet planetary transmission, taking into account different configurations regarding the bearing condition of the sun gear shaft. The aim of the paper is to shed light on some untouched aspects of the load sharing behavior of planetary transmissions, such as the effect of radial positioning errors of the planets when different pressure angles are used, and the impact of the different loadings per planet on the actual load per tooth.A modeling approach is employed, and physical explanations and simplified graphs are provided to help understand the behavior of the transmission when the sun is allowed to float and errors are introduced. The model used, developed by the authors and presented and validated in previous works, hybridizes analytical solutions with finite element models in order to compute the contact forces.The results obtained show that the teeth loads are much lower than expected compared to the planet uneven loads, both in the non-defected and defected transmission, and that radial positioning errors have non-negligible effect on the load sharing ratio under certain operating conditions.

Volume and Direction of the Atlantic Slave Trade, 1650-1870: Estimates by Markov Chain Carlo Analysis

This article presents methods and results in the application of the Markov Chain Monte Carlo (MCMC) analysis to a problem in missing data. The data used here are from The Atlantic Slave Trade Database (TASTD), 2010 version, available online. Of the currently known 35,000 slaving voyages, original data on the size of the cargo of captives exist for some 25 percent of voyage embarkations in Africa and for about 50 percent of arrivals in the Americas. Previous efforts to estimate the missing data (and project the to- tal number of captives who made the transatlantic migration) have proceeded through eclectic projections of maximum likelihood estimates of captives per voyage, without error margins. This paper creates new estimates of total mi- grant flow through two methods: one is a formally frequentist set of multiple methods, and the other is through Markov Chain Monte Carlo methodology. Comparison of the three methods, all based on the same raw data, show that the results of the two new methods are fairly close to one another and they yield total flows of migrant captives of more than 20 percent higher than the previous estimates. Quantitative results, presented in simplified graphs and tables within the text and in detailed spreadsheets available online, provide a new estimate of the volume of African embarkations and American arrivals in the transatlantic slave trade for the period from 1650 to 1870, by decade, for eleven African regions of embarkation and seven American and European regions of arrival.

Energy landscapes and persistent minima.

We consider a coarse-graining of high-dimensional potential energy landscapes based upon persistences, which correspond to lowest barrier heights to lower-energy minima. Persistences can be calculated efficiently for local minima in kinetic transition networks that are based on stationary points of the prevailing energy landscape. The networks studied here represent peptides, proteins, nucleic acids, an atomic cluster, and a glassy system. Minima with high persistence values are likely to represent some form of alternative structural morphology, which, if appreciably populated at the prevailing temperature, could compete with the global minimum (defined as infinitely persistent). Threshold values on persistences (and in some cases equilibrium occupation probabilities) have therefore been used in this work to select subsets of minima, which were then analysed to see how well they can represent features of the full network. Simplified disconnectivity graphs showing only the selected minima can convey the funnelling (including any multiple-funnel) characteristics of the corresponding full graphs. The effect of the choice of persistence threshold on the reduced disconnectivity graphs was considered for a system with a hierarchical, glassy landscape. Sets of persistent minima were also found to be useful in comparing networks for the same system sampled under different conditions, using minimum oriented spanning forests.

Improving the Detection of Malware Behaviour Using Simplified Data Dependent API Call Graph

Malware stands for malicious software. It is software that is designed with a harmful intent. A malware detector is a system that attempts to identify malware using Application Programming Interface (API) call graph technique and/or other techniques. Matching the API call graph using graph matching algorithm have NP-complete problem and is slow because of computational complexity .In this study, a malware detection system based on API call graph is proposed. Each malware sample is represented as data dependent API call graph. After transforming the input sample into a simplified data dependent graph, graph matching algorithm is used to calculate similarity between the input sample and malware API call graph samples stored in a database. The graph matching algorithm is based on Longest Common Subsequence (LCS) algorithm which is used on the simplified graphs. Such strategy reduces the computation complexity by selecting paths with the same edge label in the API call graph. Experimental results on 85 samples demonstrate 98% detection rate and 0% false positive rate for the proposed malware detection system.