An abstract fixed-point theorem for Horn formula equations

Graph matching is a classical NP-hard problem, and it plays an important role in many applications in computer science. In this paper, we propose an approximate graph matching method. For two graphs to be matched, our method first constructs an association graph with nodes representing the candidate correspondences between the two original graphs. It then constructs an affinity matrix based on the local and global distance information between the original graphs' nodes. Each element of the matrix represents the mutual consistency of a pair of nodes of the association graph. After simulating random walks on the association graph, a stable quasi-stationary distribution is obtained. With the Hungarian algorithm, our method finally discretizes the distribution to achieve an approximate matching between the two original graphs. Experiments on two commonly used datasets demonstrate the effectiveness of our method on graph matching.

This chapter describes four interdisciplinary fields originated and defined by Ashu M. G. Solo in 2011 called political engineering, public policy engineering, computational politics, and computational public policy. Political engineering is the application of engineering, computer science, mathematics, or natural science to solving problems in politics. Computational politics is the application of computer science or mathematics to solving problems in politics. Political engineering and computational politics include, but are not limited to, principles and methods for political decision-making, analysis, modeling, optimization, forecasting, simulation, and expression. Public policy engineering is the application of engineering, computer science, mathematics, or natural science to solving problems in public policy. Computational public policy is the application of computer science or mathematics to solving problems in public policy. Public policy engineering and computational public policy include, but are not limited to, principles and methods for public policy formulation, decision-making, analysis, modeling, optimization, forecasting, and simulation. The chapter describes the scope of research and development in these fields, provides examples of research and development in these fields, and provides possible university curricula for academic programs in these fields.

We consider constrained Horn clause solving from the more general point of view of solving formula equations. Constrained Horn clauses correspond to the subclass of Horn formula equations. We state and prove a fixed-point theorem for Horn formula equations which is based on expressing the fixed-point computation of a minimal model of a set of Horn clauses on the object level as a formula in first-order logic with a least fixed point operator. We describe several corollaries of this fixed-point theorem, in particular concerning the logical foundations of program verification, and sketch how to generalise it to incorporate abstract interpretations.

The purpose of the study is to assess the application of Computer science in the fractional distillation of petroleum. The study sought to: Determine the merit of using Computer science in fractional distillation of petroleum. Evaluate the cost effectiveness to the application of Computer science in fractional distillation of petroleum to the welfare of citizens. Determine if Computer science can help in controlling the natural effects of fractional distillation of petroleum on the environment. Computer science has gained wider application in so many areas of human endeavor ranging from the homes to more scientific and industrial applications, hence its application of Computer science in fractional distillation of petroleum cannot be overemphasized. Irrespective of the benefits of the application of Computer science in fractional distillation of petroleum as a whole, it has become pertinent to ask how effective and efficient its application of Computer science in fractional distillation of petroleum has been. This research work tries to investigate how effective the application of Computer science in fractional distillation of petroleum. The project exertion covered statement of the problem, purpose of the study, research questions/hypotheses were formulated to enable the researcher find out facts about the study, highlighted in division one. Subdivision two was based on the literature review of this study. The study covers the research methodology which guided the study, the next stage was based on the presentation of data and its’ analysis as well as the discussion of findings. The final stage of the research was concerned with the conclusion as well as recommendations and suggestions for further studies

Geometric Algebra (GA) has proven to be an advanced language for mathematics, physics, computer science, and engineering. This review presents a comprehensive study of works on Quaternion Algebra and GA applications in computer science and engineering from 1995 to 2020. After a brief introduction of GA, the applications of GA are reviewed across many fields. We discuss the characteristics of the applications of GA to various problems of computer science and engineering. In addition, the challenges and prospects of various applications proposed by many researchers are analyzed. We analyze the developments using GA in image processing, computer vision, neurocomputing, quantum computing, robot modeling, control, and tracking, as well as improvement of computer hardware performance. We believe that up to now GA has proven to be a powerful geometric language for a variety of applications. Furthermore, there is evidence that this is the appropriate geometric language to tackle a variety of existing problems and that consequently, step-by-step GA-based algorithms should continue to be further developed. We also believe that this extensive review will guide and encourage researchers to continue the advancement of geometric computing for intelligent machines.

As our economy and society continue to evolve, computer technology is also evolving and innovating. The emergence of computer technology has greatly facilitated human work and learning, and has brought great influence to all walks of life. For a long time, computer technology has deeply penetrated into people's production and life, and become an indispensable and important part. To a certain extent, computers have contributed to the development of the national economy, and at the same time, it has also brought a lot of convenience to the people. This initiative not only significantly improves the quality of life and productivity of the general public, but also an important indicator of the overall quality of a country's population. Through the study, it is found that with the continuous development of science and technology, the trend of combining computer technology and communication technology is becoming more and more obvious and has made certain achievements. Although the development of computer science and technology is very short, but the scope of its application for people's lives and the reform and development of related work is unparalleled by any other technology. The application of computer science and technology basically involves all aspects of domestic life, from the most basic food, clothing, housing and transportation to human communication and all kinds of production work, at the same time, computer science and technology is also the core of other technological reforms and innovations, and other technological advances relying on computer science and technology, cloud computing technology, cloud storage and its intelligent regulation and control of the new technology have achieved a certain degree of fundamental independent innovation. This research will focus on the computer science and technology, which is the core of other technological reforms and innovations. This study will systematically analyze the practical application and development of computer science and technology, and carefully sort out the main reasons for the development of computer science and technology and the future development of the market outlook.

The inverse Weibull distribution (IWD) is the complementary Weibull distributionand plays an important role in many application areas. In Bayesian analysis, Soland’smethod can be considered to avoid computational complexities. One limitation of thisapproach is that parameters of interest are restricted to a nite number of values. Thispaper introduce nonparametric Bayesian estimator in the context of record statisticsvalues from the exponentiated inverse Weibull distribution (EIWD). In stead of Soland’sconjugate piror, stick-breaking prior is considered and the corresponding Bayesian esti-mators under the squared error loss function (quadratic loss) and LINEX loss functionare obtained and compared with other estimators. The results may be of interest espe-cially when only record values are stored.Keywords: Exponentiated inverse Weibull distribution, nonparametric Bayesian esti-mation, record statistics, stick-breaking prior. 1. Introduction The inverse Weibull distribution (IWD) is the complementary Weibull distribution andplays an important role in many applications including the dynamic components of dieselengines, the times to breakdown of an insulating uid subject to the action of constanttensioin and ood data (Nelson, 1982; Maswadah, 2003). Also, it has been used quite exten-sively when the data indicate a monotone hazard function beacuse of the exibility of thepdf and its corresponding hazard function. Studies for the inverse Weibull distribution wereconducted by many authors. Calabria and Pulcini (1994) studied Bayes 2-sample predictionfor the inverse Weibull distribution. Mahmoud et al. (2003) considered the order statisticsarising from the inverse Weibull distribution and derived the exact expression for the singlemoments of order statistics. They also obtained the variances and covariances based on themoments of order statistics.

Computational Science is so important such that more and more institutions start to offer courses or even programs in this area recently. What should be incorporated into computational science curriculum? Generating Functions (GFs) are one of the most useful tools, GFs have been playing an important role in many applications, including analysis of algorithms and in combinatorics, etc, GFs transform discrete data into continuous functions, so we can apply all the mathematical theories and methodologies for manipulating functions to study discrete data or data science. Recently, it was showed that GFs can be used beyond traditional applications in computation [1, 4, 5, 12]. So we believe it is very important to incorporate GFs into computational science education, including both traditional and new applications.

This extended abstract provides a brief overview of two new interdisciplinary fields defined by Ashu M. G. Solo called political engineering and computational politics. Political engineering is the application of engineering, computer science, mathematics, or natural science to solving problems in politics. Computational politics is the application of computer science or mathematics to solving problems in politics. Political engineering and computational politics include, but are not limited to, principles and methods for political decision making, analysis, modeling, optimization, forecasting, simulation, and expression. The definition of these two new fields will greatly increase the pace of research and development in these important fields.

This extended abstract provides a brief overview of two new interdisciplinary fields defined by Ashu M. G. Solo called public policy engineering and computational public policy. Public policy engineering is the application of engineering, computer science, mathematics, or natural science to solving problems in public policy. Computational public policy is the application of computer science or mathematics to solving problems in public policy. Public policy engineering and computational public policy include, but are not limited to, principles and methods for public policy formulation, decision making, analysis, modeling, optimization, forecasting, and simulation. The definition of these two new fields will greatly increase the pace of research and development in these important fields.

This chapter describes two new interdisciplinary fields defined by Ashu M. G. Solo called “political engineering” and “computational politics.” Political engineering is the application of engineering, computer science, mathematics, or natural science to solving problems in politics. Computational politics is the application of computer science or mathematics to solving problems in politics. Political engineering and computational politics include, but are not limited to, principles and methods for political decision making, analysis, modeling, optimization, forecasting, simulation, and expression. The definition of these two new fields will greatly increase the pace of research and development in these important fields.

This chapter describes two new interdisciplinary fields defined by Ashu M. G. Solo called “public policy engineering” and “computational public policy.” Public policy engineering is the application of engineering, computer science, mathematics, or natural science to solving problems in public policy. Computational public policy is the application of computer science or mathematics to solving problems in public policy. Public policy engineering and computational public policy include, but are not limited to, principles and methods for public policy formulation, decision making, analysis, modeling, optimization, forecasting, and simulation. The definition of these two new fields will greatly increase the pace of research and development in these important fields.

This chapter describes two new interdisciplinary fields defined by Ashu M. G. Solo called “political engineering” and “computational politics.” Political engineering is the application of engineering, computer science, mathematics, or natural science to solving problems in politics. Computational politics is the application of computer science or mathematics to solving problems in politics. Political engineering and computational politics include, but are not limited to, principles and methods for political decision making, analysis, modeling, optimization, forecasting, simulation, and expression. The definition of these two new fields will greatly increase the pace of research and development in these important fields.

This chapter describes two new interdisciplinary fields defined by Ashu M. G. Solo called “public policy engineering” and “computational public policy.” Public policy engineering is the application of engineering, computer science, mathematics, or natural science to solving problems in public policy. Computational public policy is the application of computer science or mathematics to solving problems in public policy. Public policy engineering and computational public policy include, but are not limited to, principles and methods for public policy formulation, decision making, analysis, modeling, optimization, forecasting, and simulation. The definition of these two new fields will greatly increase the pace of research and development in these important fields.

Modelling can provide intellectual frameworks that are necessary to translate data into knowledge. Mathematical modelling has played an important role in many applications, such as ecology, genetics, engineering, psychology, sociology, physics and computer science, in recent years. This study focused on reviewing mathematical modelling and its applications to biological systems by tracing many metabolic activities of cellular interactions on the one hand and between the spread of epidemics and population growth on the other hand. Various mathematical equations have played fundamental roles in the formation of these systems for model development procedures by describing them mathematically and establishing relationships that characterise the dynamics of a biological phenomenon. Consequently, the creation of new mathematical representations and simulation algorithms is important to the success of biological modelling initiatives. Finally, the optimisation approach performs its primary role in directing and controlling interactions by adjusting the parameters that provide the best possible result for the system