Finite objects and automorphisms

We show that every K-finite decidable object X of an elementary topos E is Dedekind-finite, i.e., that every monic endomorphism of X is an automorphism. As an easy corollary, every epic endomorphism of X is likewise an automorphism. The proof depends in part on an analysis of the finite cardinals of E and in part on the equivalence, in any Boolean tbpos, of Kfiniteness and Tarski-finiteness (Theorem 6). Here X is Tarski-finite iff every inhabited collection of arbitrary elements of ω ^X (subobjects of X ) contains a ¨—minimal element.     

Meta-synthesis approach to complex system modeling

Meta-synthesis method is proposed to tackle with open complex giant system problems which cannot be effectively solved by traditional reductionism methods by a Chinese system scientist Qian, Xuesen (Tsien HsueShen) around the early 1990s. The method emphasizes the synthesis of collected information and knowledge of various kinds of experts, and combining quantitative methods with qualitative knowledge. Since then, continuous endeavors have been taken to put those ideas into practice. In this paper, firstly we review meta-synthesis approach and other research relevant to complex system modeling briefly. Then we discuss two main issues, model integration and opinion synthesis, which are often confronted when applying meta-synthesis approach, together with an exhibit of the development of an embryonic meta-synthetic support prototype. Such a demonstration shows how to model complex problems, such as macro-economic problems in Hall for Workshop on Meta-Synthetic Engineering with versatile resources in information collection, model integration and opinion synthesis. Finally, some future work is indicated.     

Performance modeling of heterogeneous data networks

A heterogeneous data network consists of Local Area Networks (LANs) interconnected with either leased lines, packet-switched networks, Metropolitan Area Networks (MANs), or combinations thereof. Heterogeneous networks are characterized by bursty traffic, nested segmentation and reassembly of packets, window flow control and round-robin channel access. We develop a performance methodology for estimating user perceived delay and buffer overflow in heterogeneous data networks. The methodology is based on well-known two-moment approximation schemes. Modifications of these schemes are proposed in order to model the bandwidth allocation policies of the MANs and to capture the burstiness of the traffic. The methodology is applied to several LAN-MAN and LAN-MAN-WAN network examples.Supported partially through AT&T Grant No. 5-20491 and partially through NSF Grant No. NCR-8914447.     

Finite element approach to the Stokes problem

We approximate the Stokes problem by using a finite element method. This method utilizes the approach of Kleiser–Schumann, in which a boundary condition for the pressure is implicitly defined by a condition on the velocity. We consider a suitable splitting of the unknowns that allows one to reduce the Stokes problem to a cascade of classical Dirichlet problems and to a boundary integral equation.     

An agent-based approach to modeling insider threat

This paper describes the modeling of the potential of an organization to develop an insider threat given certain attributes of its culture. The model represents all employees of the organization and their likelihood of becoming an insider threat. Each employee is instantiated in an agent-zero construct, which accounts for affective, rational, and social behavioral influences. The main driver of behavior is the employee’s level of disgruntlement against the organization. The simulation is run over a period of 10 years and the total number of employees that exceed a certain threshold of becoming an insider threat are computed. This number is compared with survey data on work force ethics as a measure of validity of the simulation results.     

Finite element modeling of welding processes

This work deals with the simulation of fusion welding by the Finite Element Method. The implemented models include a moving heat source, temperature dependence of thermo-physical properties, elasto-plasticity, non-steady state heat transfer, and mechanical analysis. The thermal problem is assumed to be uncoupled from the mechanical one, so the thermal analysis is performed separately and previously to the mechanical analysis at each time step. The mechanical problem is based on the thermal history. A special treatment is performed on mechanical elements during the liquid/solid and solid/liquid phase changes to account for stress states. The three-dimensional stress state of a butt-welded joint is obtained as an example of an application.     

A new approach to fuzzy wavelet system modeling

In this paper, we propose simple but effective two different fuzzy wavelet networks (FWNs) for system identification. The FWNs combine the traditional Takagi–Sugeno–Kang (TSK) fuzzy model and discrete wavelet transforms (DWT). The proposed FWNs consist of a set of if–then rules and, then parts are series expansion in terms of wavelets functions. In the first system, while the only one scale parameter is changing with it corresponding rule number, translation parameter sets are fixed in each rule. As for the second system, DWT is used completely by using wavelet frames. The performance of proposed fuzzy models is illustrated by examples and compared with previously published examples. Simulation results indicate the remarkable capabilities of the proposed methods. It is worth noting that the second FWN achieves high function approximation accuracy and fast convergence.     

A new approach to fuzzy wavelet system modeling

In this paper, we propose simple but effective two different fuzzy wavelet networks (FWNs) for system identification. The FWNs combine the traditional Takagi–Sugeno–Kang (TSK) fuzzy model and discrete wavelet transforms (DWT). The proposed FWNs consist of a set of if–then rules and, then parts are series expansion in terms of wavelets functions. In the first system, while the only one scale parameter is changing with it corresponding rule number, translation parameter sets are fixed in each rule. As for the second system, DWT is used completely by using wavelet frames. The performance of proposed fuzzy models is illustrated by examples and compared with previously published examples. Simulation results indicate the remarkable capabilities of the proposed methods. It is worth noting that the second FWN achieves high function approximation accuracy and fast convergence.     

A mixed-integer mathematical modeling approach to exam timetabling

This paper explores mathematical programming models for an exam timetabling problem related to Kuwait University (KU). In particular, we consider two subproblems: (a) the ExamTimetabling Problem (ETP), which is concerned with assigning exams to designated exam-periods and classrooms, and (b) the Proctor Assignment Problem (PAP), which deals with the assignment of proctors to exams. While this exam timetabling problem is ubiquitous in many academic institutions worldwide, idiosyncrasies of the problem related to gender-based policies and having multiple exam centers at KU require novel models. A mixed-integer exam timetabling programming model (ETM) is developed for ETP, which takes into account restrictions related to exam-period conflicts, facility and human resources, and commuting and traffic considerations. Assuming that exam-periods are specified for all exams as determined by ETM, another mixed-integer programming model is formulated for PAP, denoted by PAM, which incorporates the proctors’ preferences for specific days and exam-periods. Computational results are reported and analyzed for solving ETM and PAM directly using the CPLEX Optimization Software (version 9.0), and for implementing a specialized sequential LP-based heuristic for solving PAM. The results obtained significantly improve upon those derived via the existing manual approach implemented at KU, in terms of eliminating conflicts as well as from the overall efficiency and equity points of view.     

A parameterized approach to modeling and forecasting mortality

A new method is proposed of constructing mortality forecasts. This parameterized approach utilizes Generalized Linear Models (GLMs), based on heteroscedastic Poisson (non-additive) error structures, and using an orthonormal polynomial design matrix. Principal Component (PC) analysis is then applied to the cross-sectional fitted parameters. The produced model can be viewed either as a one-factor parameterized model where the time series are the fitted parameters, or as a principal component model, namely a log-bilinear hierarchical statistical association model of Goodman [Goodman, L.A., 1991. Measures, models, and graphical displays in the analysis of cross-classified data. J. Amer. Statist. Assoc. 86(416), 1085-1111] or equivalently as a generalized Lee-Carter model with p interaction terms. Mortality forecasts are obtained by applying dynamic linear regression models to the PCs. Two applications are presented: Sweden (1751-2006) and Greece (1957-2006).     

Finite approach to the problem of optimizing theorem-proving algorithms

Theorem-proving algorithms in a finite set S of predicate logic formulas are examined. Let F be a consistent and sufficiently strong formal system and let proof of correctness mean a proof in F. If set S is sufficiently rich, then neither a decision algorithm, whose correctness is provable in F (when card(S)C·length of (F)), nor a practically simple to operate search algorithm in S, in which the proof of correctness would be sufficiently short, is possible.Results announced on Oct. 11, 1973.Translated from Zapiski Nauchnykh Seminarov Leningradskogo Otdeleniya Matematicheskogo Instituta im. V. A. Steklova AN SSSR, Vol. 49, pp. 123–130, 1975.     

The hierarchical approach to modeling knowledge and common knowledge

One approach to representing knowledge or belief of agents, used by economists and computer scientists, involves an infinite hierarchy of beliefs. Such a hierarchy consists of an agent's beliefs about the state of the world, his beliefs about other agents' beliefs about the world, his beliefs about other agents' beliefs about other agents' beliefs about the world, and so on. (Economists have typically modeled belief in terms of a probability distribution on the uncertainty space. In contrast, computer scientists have modeled belief in terms of a set of worlds, intuitively, the ones the agent considers possible.) We consider the question of when a countably infinite hierarchy completely describes the uncertainty of the agents. We provide various necessary and sufficient conditions for this property. It turns out that the probability-based approach can be viewed as satisfying one of these conditions, which explains why a countable hierarchy suffices in this case. These conditions also show that whether a countable hierarchy suffices may depend on the “richness” of the states in the underlying state space. We also consider the question of whether a countable hierarchy suffices for “interesting” sets of events, and show that the answer depends on the definition of “interesting”.     

An approach to modeling asymmetric multivariate spatial covariance structures

We propose a framework in light of the delay effect to model the asymmetry of multivariate covariance functions that is often exhibited in real data. This general approach can endow any valid symmetric multivariate covariance function with the ability of modeling asymmetry and is very easy to implement. Our simulations and real data examples show that asymmetric multivariate covariance functions based on our approach can achieve remarkable improvements in prediction over symmetric models.     

On an approach to the modeling of oil wells

In this paper, a numerical study of a diffusion problem in the presence of wells on which integral boundary conditions are used is performed. It is shown that a method proposed earlier is fully efficient and offers certain advantages as compared with direct modeling of wells based on the finite element method. The results of calculations for two wells are presented.     

Understanding non-linear modeling of measurement invariance in heterogeneous populations

This study examined how a non-linear modeling of ordered categorical variables within multiple-group confirmatory factor analysis supported measurement invariance. A four-item classroom disciplinary climate scale used in cross-cultural framework was empirically investigated. In the first part of the analysis, a separated categorical confirmatory factor analysis was initially applied to account for the complex structure of the relationships between the observed measures in each country. The categorical multiple-group confirmatory factor analysis (MGCFA) was then used to conduct a cross-country examination of full measurement invariance namely the configural, metric, and scalar levels of invariance in the classroom discipline climate measures. The categorical MGCFA modeling supported configural and metric invariances as well as scalar invariance for the latent factor structure of classroom disciplinary climate. This finding implying meaningful cross-country comparisons on the scale means, on the associations of classroom disciplinary climate scale with other scales and on the item-factor latent structure. Application of the categorical modeling appeared to correctly specify the factor structure of the scale, thereby promising the appropriateness of reporting comparisons such as rankings of many groups, and illustrating league tables of different heterogeneous groups. Limitations of the modeling in this study and future suggestions for measurement invariance testing in studies with large numbers of groups are discussed.     

A data driven equivariant approach to constrained Gaussian mixture modeling

Maximum likelihood estimation of Gaussian mixture models with different class-specific covariance matrices is known to be problematic. This is due to the unboundedness of the likelihood, together with the presence of spurious maximizers. Existing methods to bypass this obstacle are based on the fact that unboundedness is avoided if the eigenvalues of the covariance matrices are bounded away from zero. This can be done imposing some constraints on the covariance matrices, i.e. by incorporating a priori information on the covariance structure of the mixture components. The present work introduces a constrained approach, where the class conditional covariance matrices are shrunk towards a pre-specified target matrix \(\varvec{\varPsi }.\) Data-driven choices of the matrix \(\varvec{\varPsi },\) when a priori information is not available, and the optimal amount of shrinkage are investigated. Then, constraints based on a data-driven \(\varvec{\varPsi }\) are shown to be equivariant with respect to linear affine transformations, provided that the method used to select the target matrix be also equivariant. The effectiveness of the proposal is evaluated on the basis of a simulation study and an empirical example.     

A semigroup approach to an integro-differential equation modeling slow erosion

The paper is concerned with a scalar conservation law with nonlocal flux, providing a model for granular flow with slow erosion and deposition. While the solution u=u(t,x)$u=u(t,x)$ can have jumps, the inverse function x=x(t,u)$x=x(t,u)$ is always Lipschitz continuous; its derivative has bounded variation and satisfies a balance law with measure-valued sources. Using a backward Euler approximation scheme combined with a nonlinear projection operator, we construct a continuous semigroup whose trajectories are the unique entropy weak solutions to this balance law. Going back to the original variables, this yields the global well-posedness of the Cauchy problem for the granular flow model.     

A copula approach to test asymmetric information with applications to predictive modeling

In this article, we present a copula regression model for testing asymmetric information as well as for predictive modeling applications in automobile insurance market. We use the Frank copula to jointly model the type of coverage and the number of accidents, with the dependence parameter providing for evidence of the relationship between the choice of coverage and the frequency of accidents. This dependence therefore provides an indication of the presence (or absence) of asymmetric information. The type of coverage is in some sense ordered so that coverage with higher ordinals indicate the most comprehensive coverage. Henceforth, a positive relationship would indicate that more coverage is chosen by high risk policyholders, and vice versa. This presence of asymmetric information could be due to either adverse selection or moral hazard, a distinction often made in the economics or insurance literature, or both. We calibrated our copula model using a one-year cross-sectional observation of claims arising from a major automobile insurer in Singapore. Our estimation results indicate a significant positive coverage-risk relationship. However, when we correct for the bias resulting from possible underreporting of accidents, we find that the positive association vanishes. We further used our estimated model for other possible actuarial applications. In particular, we are able to demonstrate the effect of coverage choice on the incidence of accidents, and based on which, the pure premium is derived. In general, a positive margin is observed when compared with the gross premium available in our empirical database.