Program Graph Research Articles

A Fast Graph Program for Computing Minimum Spanning Trees

When using graph transformation rules to implement graph algorithms, a challenge is to match the efficiency of programs in conventional languages. To help overcome that challenge, the graph programming language GP 2 features rooted rules which, under mild conditions, can match in constant time on bounded degree graphs. In this paper, we present an efficient GP 2 program for computing minimum spanning trees. We provide empirical performance results as evidence for the program's subquadratic complexity on bounded degree graphs. This is achieved using depth-first search as well as rooted graph transformation. The program is based on Boruvka's algorithm for minimum spanning trees. Our performance results show that the program's time complexity is consistent with that of classical implementations of Boruvka's algorithm, namely O(m log n), where m is the number of edges and n the number of nodes.

Learning graph-based heuristics for pointer analysis without handcrafting application-specific features

We present Graphick, a new technique for automatically learning graph-based heuristics for pointer analysis. Striking a balance between precision and scalability of pointer analysis requires designing good analysis heuristics. For example, because applying context sensitivity to all methods in a real-world program is impractical, pointer analysis typically uses a heuristic to employ context sensitivity only when it is necessary. Past research has shown that exploiting the program's graph structure is a promising way of developing cost-effective analysis heuristics, promoting the recent trend of ``graph-based heuristics'' that work on the graph representations of programs obtained from a pre-analysis. Although promising, manually developing such heuristics remains challenging, requiring a great deal of expertise and laborious effort. In this paper, we aim to reduce this burden by learning graph-based heuristics automatically, in particular without hand-crafted application-specific features. To do so, we present a feature language to describe graph structures and an algorithm for learning analysis heuristics within the language. We implemented Graphick on top of Doop and used it to learn graph-based heuristics for object sensitivity and heap abstraction. The evaluation results show that our approach is general and can generate high-quality heuristics. For both instances, the learned heuristics are as competitive as the existing state-of-the-art heuristics designed manually by analysis experts.

Research on aircraft route planning optimization problem with multi-constraints and dual-targets

As the core technology in the field of aircraft, the route planning has attracted much attention. However, due to the complexity of the structure and performance constraints of the aircraft, the route planning algorithm does not have well universality, so it cannot be used in a complex environment. In the paper, a multi-constraints and dual-targets aircraft route planning model was established for the real-time requirements of space flight, the dynamic changes of flight environment with time, the accuracy requirements of positioning errors in the safety area, and the minimum turning radius constraints. Based on the directed graph and dynamic programming ideas, the model simulation and model validation were carried out with the data of F question in the “16th Graduate Mathematical Modeling Contest”. The results showed that the optimal path length obtained in data set 1 was 124.61 km, the number of corrections was 11 times, the solution time was 2.3768 seconds, the optimal path length obtained in data set 2 was 110.00 km, and the number of corrections was 12 times. The solution time was 0.0168 seconds. Multi-constraints and dual-targets aircraft route planning model can plan the flight path of the aircraft intuitively and timely, which confirmed the effectiveness of the model.

NCC Based Correspondence Problem for First- and Second-Order Graph Matching.

Automatically finding correspondences between object features in images is of main interest for several applications, as object detection and tracking, identification, registration, and many derived tasks. In this paper, we address feature correspondence within the general framework of graph matching optimization and with the principal aim to contribute. We proposed two optimized algorithms: first-order and second-order for graph matching. On the one hand, a first-order normalized cross-correlation (NCC) based graph matching algorithm using entropy and response through Marr wavelets within the scale-interaction method is proposed. First, we proposed a new automatic feature detection processing by using Marr wavelets within the scale-interaction method. Second, feature extraction is executed under the mesh division strategy and entropy algorithm, accompanied by the assessment of the distribution criterion. Image matching is achieved by the nearest neighbor search with normalized cross-correlation similarity measurement to perform coarse matching on feature points set. As to the matching points filtering part, the Random Sample Consensus Algorithm (RANSAC) removes outliers correspondences. One the other hand, a second-order NCC based graph matching algorithm is presented. This algorithm is an integer quadratic programming (IQP) graph matching problem, which is implemented in Matlab. It allows developing and comparing many algorithms based on a common evaluation platform, sharing input data, and a customizable affinity matrix and matching list of candidate solution pairs as input data. Experimental results demonstrate the improvements of these algorithms concerning matching recall and accuracy compared with other algorithms.

Safety and Efficacy of Same-Day Hip Resurfacing.

Same-day discharge (SDD) surgery in total hip arthroplasty (THA) has been shown to have similar outcomes to non-SDD THA in select patient populations. Hip resurfacing arthroplasty (HRA) is an alternative to THA for young, active patients, making them ideal candidates for SDD. This study compared the safety and efficacy of non-SDD HRA and SDD HRA for specific postoperative outcomes. An electronic data warehouse query was performed for procedures labeled "hip resurfacing." Data collected included demographics, surgical factors, and quality metrics. Statistical analyses were evaluated using a graphing and statistics software program. Categorical variables were analyzed with chi-square tests and continuous variables with Student's t tests, with P<.05 deemed significant. Sixty-three of 274 total HRAs were enrolled in this SDD HRA protocol. No significant difference was observed between SDD HRA and non-SDD HRA baseline characteristics. On postoperative day 0, 98.41% of SDD HRA recipients were discharged successfully. The SDD HRA recipients had shorter stays, with 1.59% requiring a hospital stay of 2 days or more compared with 56.87% of non-SDD HRA recipients (P<.0001). The non-SDD HRA recipients were found to have shorter surgical times than SDD HRA recipients (104.74 vs 125.51 minutes, P=.01). Rates of infection, periprosthetic fractures, emergency department visits, and hospital readmissions were equivalent (P=.99). Same-day discharge HRA is a safe and effective procedure with similar outcomes to non-SDD HRA regarding infections, fractures, emergency department visits, and readmissions. The major benefit of SDD is a shorter hospital stay that may lead to decreased cost while preserving and enhancing quality of care and patient satisfaction. [Orthopedics. 2020;43(6):e595-e600.].

Study on track planning problem of multi-constrained and double-targeted

Aiming at the positioning error of the aircraft, the error accumulation to a certain extent may lead to the failure of the mission. A track correction method based on oriented graph search was proposed, and the dynamic programming idea was applied to solve the path optimization problem. Firstly, the correction points in the flight space were preprocessed, and the planning of the correction points in the flight space was transformed into a graph theory problem. It effectively solves the problem that traditional methods did not adapt well to dynamic changes in flight space. And the problem of too high complexity in the calculation space. Using the dynamic programming idea to carry out the flight path planning for the flight space, the path-correction number optimal double-objective model is comprehensively established. Finally, the oriented graph and dynamic programming ideas were used to solve the problem, and the visual analysis of the aircraft track was realized. The simulation results show that the established path-corrected optimal double-objective model can calculate an optimal track with relatively small correction times and short length.

Comparative dielectric parameters and conductivity mechanisms of pyridine-based rod-like liquid crystals

ABSTRACT In this study, the dielectric properties and ac conductivity mechanism of pyridine-based rod-like liquid crystals (LC1 and LC2), which show enantiotropic smectic A mesophase, have been investigated by impedance spectroscopy within the frequency interval of 1 kHz–2 MHz. The variation of real and imaginary component of dielectric constant with angular frequency has been investigated. The temperature-dependent absorption coefficient α, relaxation time τ o, dielectric strength value Δε, temperature-dependent changes on crystal, SmA and Iso phases have been given. These values were obtained by fitting the experimental results in ε ′-ω graph with the Cole-Cole equation real equation and Origin Pro Graph program. The frequency dependence of ac conductivities has also been analyzed. Different conductivity mechanisms, i.e. dc conductivity, correlated barrier hoping and quantum mechanical tunneling behaviors have been determined for different frequency regions.

Systemizing Interprocedural Static Analysis of Large-scale Systems Code with Graspan

There is more than a decade-long history of using static analysis to find bugs in systems such as Linux. Most of the existing static analyses developed for these systems are simple checkers that find bugs based on pattern matching. Despite the presence of many sophisticated interprocedural analyses, few of them have been employed to improve checkers for systems code due to their complex implementations and poor scalability. In this article, we revisit the scalability problem of interprocedural static analysis from a “Big Data” perspective. That is, we turn sophisticated code analysis into Big Data analytics and leverage novel data processing techniques to solve this traditional programming language problem. We propose Graspan , a disk-based parallel graph system that uses an edge-pair centric computation model to compute dynamic transitive closures on very large program graphs. We develop two backends for Graspan, namely, Graspan-C running on CPUs and Graspan-G on GPUs, and present their designs in the article. Graspan-C can analyze large-scale systems code on any commodity PC, while, if GPUs are available, Graspan-G can be readily used to achieve orders of magnitude speedup by harnessing a GPU’s massive parallelism. We have implemented fully context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases written in multiple languages such as Linux and Apache Hadoop demonstrates that their Graspan implementations are language-independent, scale to millions of lines of code, and are much simpler than their original implementations. Moreover, we show that these analyses can be used to uncover many real-world bugs in large-scale systems code.

Horizontal gene transfer for recombining graphs

We introduce a form of neutral horizontal gene transfer (HGT) to evolving graphs by graph programming (EGGP). We introduce the mu times lambda evolutionary algorithm (EA), where mu parents each produce lambda children who compete only with their parents. HGT events then copy the entire active component of one surviving parent into the inactive component of another parent, exchanging genetic information without reproduction. Experimental results from symbolic regression problems show that the introduction of the mu times lambda EA and HGT events improve the performance of EGGP. Comparisons with genetic programming and Cartesian genetic programming strongly favour our proposed approach. We also investigate the effect of using HGT events in neuroevolution tasks. We again find that the introduction of HGT improves the performance of EGGP, demonstrating that HGT is an effective cross-domain mechanism for recombining graphs.

Unicast based reactive &amp; proactive routing simulation in MANET: A comparative study for protocols in mobility scenarios

A challenging issue for (MANETs) is finding an effective routing mechanism, the wireless connection must have the capability to interact while traveling randomly, since MANETs are self-configuring mobile node networks. Traditional fixed network routing protocols are useless with MANET. Based on this, MANETs introduced new routing protocols. In this study, the routing process was investigated, particularly (DSR), (AODV) and (DSDV). Simulations are implemented in the NS2 simulation environment and evaluated their performance under various scenarios. The results of the simulation study were summarized with a number of graphs using NAM (Network Animation Environment) and Trace Graph programs and a general evaluation was made. The results of the simulation studies presented that DSR and AODV showed better efficiency than the table based DSDV protocol. In addition, DSR and AODV protocols may present a variable performance for the different data packets and network sizes due to their algorithmic differences.

Unicast based reactive & proactive routing simulation in MANET: A comparative study for protocols in mobility scenarios

A challenging issue for (MANETs) is finding an effective routing mechanism, the wireless connection must have the capability to interact while traveling randomly, since MANETs are self-configuring mobile node networks. Traditional fixed network routing protocols are useless with MANET. Based on this, MANETs introduced new routing protocols. In this study, the routing process was investigated, particularly (DSR), (AODV) and (DSDV). Simulations are implemented in the NS2 simulation environment and evaluated their performance under various scenarios. The results of the simulation study were summarized with a number of graphs using NAM (Network Animation Environment) and Trace Graph programs and a general evaluation was made. The results of the simulation studies presented that DSR and AODV showed better efficiency than the table based DSDV protocol. In addition, DSR and AODV protocols may present a variable performance for the different data packets and network sizes due to their algorithmic differences.

Algorithm of the Cyclic-Order Graph Program (Implementation and Usage)

Algorithm of the Cyclic-Order Graph Program (Implementation and Usage)

Rule-based Graph Repair

Model repair is an essential topic in model-driven engineering. Since models are suitably formalized as graph-like structures, we consider the problem of rule-based graph repair: Given a rule set and a graph constraint, try to construct a graph program based on the given set of rules, such that the application to any graph yields a graph satisfying the graph constraint. We show the existence of repair programs for specific constraints, and show the existence of rule-based repair programs for specific constraints compatible with the rule set.

Interpreting and predicting social commerce intention based on knowledge graph analysis

There have been significant efforts to understand, describe, and predict the social commerce intention of users in the areas of social commerce and web data management. Based on recent developments in knowledge graph and inductive logic programming in artificial intelligence, in this paper, we propose a knowledge-graph-based social commerce intention analysis method. In particular, a knowledge base is constructed to represent the social commerce environment by integrating information related to social relationships, social commerce factors, and domain background knowledge. In this study, knowledge graphs are used to represent and visualize the entities and relationships related to social commerce, while inductive logic programming techniques are used to discover implicit information that can be used to interpret the information behaviors and intentions of the users. Evaluation tests confirmed the effectiveness of the proposed method. In addition, the feasibility of using knowledge graphs and knowledge-based data mining techniques in the social commerce environment is also confirmed.

A Comparative Study of Alternative Approaches to Estimate Productivity

Theoretically, for single output-single input, annual productivity are expected to be identical across index, non-parametric programming and parametric statistical approaches. The following models within each approach is considered—index (Tornqvist-Theil and Ideal Fisher), the non-parametric programming (Malmquist input, output and graph; Malmquist total factor productivity) and parametric (Input and Output; total factor productivity) regression. Empirically, for single output-single input, this research show differences in annual productivity and productivity growth rate between and within each of the three approaches using Nebraska agriculture data from 1936 to 2004. The annual productivity growth rate from 1936 to 2004 was identical across non-parametric Malmquist output, input, graph and Malmquist total factor productivity, and parametric Malmquist total factor productivity. However annual productivity estimated by parametric Malmquist total factor productivity is identical to Ideal Fisher productivity.

Evolving graphs with semantic neutral drift

We introduce the concept of Semantic Neutral Drift (SND) for genetic programming (GP), where we exploit equivalence laws to design semantics preserving mutations guaranteed to preserve individuals’ fitness scores. A number of digital circuit benchmark problems have been implemented with rule-based graph programs and empirically evaluated, demonstrating quantitative improvements in evolutionary performance. Analysis reveals that the benefits of the designed SND reside in more complex processes than simple growth of individuals, and that there are circumstances where it is beneficial to choose otherwise detrimental parameters for a GP system if that facilitates the inclusion of SND.

Dynamic Network Planning of Underground Logistics System on Uncertainty Graph

When designing the underground logistics system, it is necessary to consider the uncertainty of logistics nodes, high cost, and high risk. This paper employed the theories of uncertain graph and dynamic programming to solve the network planning problem of underground logistics system. Firstly, we proposed the concepts of uncertainty measure matrix and vertices structure uncertainty graph by using uncertainty measure and uncertainty graph. Secondly, vertices structure uncertainty graph of the underground logistics system was constructed based on our proposed vertices structure uncertainty graph and the uncertainty of logistics nodes. Thirdly, the dynamic programming model of the underground logistics system was established, and its solution algorithm was also designed by improving simulated annealing. Finally, the correctness and feasibility of the method was validated by using a numerical example of the underground logistics system in Xianlin district, Nanjing City in China.

A calculus of concurrent graph-rewriting processes

State-of-the-art approaches to controlled graph rewriting focus on the specification of an external control layer over graph-rewriting rule applications and on the input-output semantics of the resulting systems, leading to a decreased relevance of many interesting operational aspects of graph transformation, as studied in the classical theory of algebraic graph rewriting.We propose a novel approach to controlled graph rewriting where we aim at bridging the gap between these two complementary approaches, defining an operational semantics for which classical concepts and results related to independence and parallelism of graph derivations can be recast.The calculus we propose is based on a control layer specified using (a fragment of) Milner's Calculus of Communicating Systems (CCS), where the actions specify the application of graph transformation rules to the current state, according to the Double-Pushout approach (DPO).In particular, we address the following aspects for our controlled graph-rewriting processes: (i) expressiveness of the control language, compared to a minimally complete reference language, Graph Programs, proposed by Habel and Plump; (ii) process equivalence based on labeled transition systems and a structural operational semantics; (iii) a unifying treatment of action independence, considering the corresponding notions from both process algebra and graph-rewriting theory; and (iv) parallelism and concurrency based on the notion of asynchronous (labeled) transition systems, obtained from an asynchronous version of the proposed calculus.

The Theory Graph Modeling and Programming Systems from Module Elements to the Application Areas

The mathematical basics of graph modeling and paradigm programming of applied systems (AS) are presented. The vertices of graph are been the functional elements of the systems and the arcs define the connections between them. The graph is represented by an adjacency and reach ability matrix. A number of graph of program structures and their representation by mathematical operations (unions, connections, differences, etc.) are shown. Given the characteristics of graph structures, complexes, units, and systems created from the modules of the graph. The method of modeling the system on the graph of modules, which describe in the programming languages (LP) and calling them with operations (link, assembling, building, etc.). The standard of configuration (2012) Assembly of heterogeneous software elements in AS of different fields of knowledge is made. Brief descriptions of modern and future programming paradigms for formal theoretical creation of systems from service-components for Internet in the near future are given.

VIABILITY OF VERO E6 CELLS EXPOSED TO THE Bothrops leucurus SNAKE CRUDE VENOM

Primary and continuous cell culture systems are a good in vitro alternative for the quantitative assessment of snake venom toxicity. There are still few studies evaluating the cellular response against Bothrops leucurus venom (BlV). This study aimed to evaluate the cytotoxic effect of crude BlV on culture of VERO E6 cells. Neutral Red incorporation (NR) and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium bromide (MTT) tests were performed for viable cells, and Trypan Blue (TB) dye exclusion test was performed for non-viable cells. The differences between the groups were evaluated by ANOVA-One-Way and Dunnet through the program Graph Pad 5.0 with significance of 5%. The 50% cytotoxic concentration (CC50) of BlV was 7.86 μg/mL. There was a trend of dose-dependent reduction on cell viability in all assays evaluated. However, MTT and NR tests were more sensitive for the evaluation of BlV cytotoxicity on the VERO E6 cell line.