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In topic modeling, identifiability of the topics is an essential issue. Many topic modeling approaches have been developed under the premise that each topic has a characteristic anchor word that only appears in that topic. The anchor-word assumption is fragile in practice, because words and terms have multiple uses; yet it is commonly adopted because it enables identifiability guarantees. Remedies in the literature include using three- or higher-order word co-occurence statistics to come up with tensor factorization models, but such statistics need many more samples to obtain reliable estimates, and identifiability still hinges on additional assumptions, such as consecutive words being persistently drawn from the same topic. In this work, we propose a new topic identification criterion using second order statistics of the words. The criterion is theoretically guaranteed to identify the underlying topics even when the anchor-word assumption is grossly violated. An algorithm based on alternating optimization, and an efficient primal-dual algorithm are proposed to handle the resulting identification problem. The former exhibits high performance and is completely parameter-free; the latter affords up to 200 times speedup relative to the former, but requires step-size tuning and a slight sacrifice in accuracy. A variety of real text copora are employed to showcase the effectiveness of the approach, where the proposed anchor-free method demonstrates substantial improvements compared to a number of anchor-word based approaches under various evaluation metrics. [ABSTRACT FROM AUTHOR]

Highlights • This paper applies a recent concept, namely the concept of stratification. • The dynamicity of the decision environment when a decision is made is considered. • The method mimics the brain's decision making process and has the potential for future applications in artificial intelligence. • The method will be useful to researchers who are applying different MCDM methods. Abstract Multiple Criteria Decision Making (MCDM) methods generally require the decision maker to evaluate alternatives with respect to decision criteria and also to assign importance weightings to the criteria. Then, based on the assigned weightings, the best alternative can be selected. However, after a decision is made it often happens that the decision maker becomes doubtful whether the right weightings have been assigned to the criteria given that a variety of eventualities may occur in the near future. The main aim of this paper is to address this concern and improve the application of MCDM methods by addressing possible fluctuations in the criteria weightings. The recently proposed concept of stratification (CST) is used in conjunction with MCDM methods to stratify the decision environment. The method is then applied to a supplier selection problem. The stratified MCDM (SMCDM) approach is in its early stages only and requires further research to reach its maturity. [ABSTRACT FROM AUTHOR]

In this article Jℱ-class operators are introduced and some basic properties of Jℱ-vectors are given. The Jℱ-class operators include the J-class operators and Jmix-class operators introduced by Costakis and Manoussos in 2008. This class also includes the Jwmix-class and Jerg-class operators defined by Zhang [2012]. Furthermore, for the unilateral weighted backward shifts on a Fréchet sequence space, we establish a criterion under which the shift operators belong to the Jℱ-class. From the criterion it is easy to obtain the existing criteria of hypercyclic backward shifts and of the topological mixing backward shifts. The obtained criterion also reveals the characteristic of Jℱ-class shift operators by the recurrence property. Meanwhile, we obtain infinite topological entropy when the shifts have stronger recurrence property, which generalizes the related results by Brian et al. in 2017. [ABSTRACT FROM AUTHOR]

This letter studies the tracking performance of a stochastic gradient-based adaptive algorithm, namely the maximum correntropy criterion algorithm, where a random walk is used to model the non-stationarity. In our analysis, we use the energy conservation argument to derive expressions for the steady-state excess mean square error (EMSE). We consider two different cases for measurement of noise distribution including the Gaussian noise and general non-Gaussian noise. For the Gaussian case, we derive a fixed-point equation that can be solved numerically to find steady-state EMSE value. For the general non-Gaussian case, we derive an approximate closed-form expression for EMSE. For both cases, unlike the stationary environment, the EMSE curves are not increasing functions of step size parameter. We use this observation to find the optimum step size learning parameter for general non-Gaussian case. The validity of the theoretical results are justified via simulation results. [ABSTRACT FROM AUTHOR]

Several evidences of hydrologic data series being nonstationary in nature have been found to date. This has resulted in the conduct of many studies in the area of nonstationary frequency analysis. Nonstationary probability distribution models involve parameters that vary over time. Therefore, it is not a straightforward process to apply conventional goodness-of-fit tests to the selection of an appropriate nonstationary probability distribution model. Tests that are generally recommended for such a selection include the Akaike’s information criterion (AIC), corrected Akaike’s information criterion (AICc), Bayesian information criterion (BIC), and likelihood ratio test (LRT). In this study, the Monte Carlo simulation was performed to compare the performances of these four tests, with regard to nonstationary as well as stationary generalized extreme value (GEV) distributions. Proper model selection ratios and sample sizes were taken into account to evaluate the performances of all the four tests. The BIC demonstrated the best performance with regard to stationary GEV models. In case of nonstationary GEV models, the AIC proved to be better than the other three methods, when relatively small sample sizes were considered. With larger sample sizes, the AIC, BIC, and LRT presented the best performances for GEV models which have nonstationary location and/or scale parameters, respectively. Simulation results were then evaluated by applying all four tests to annual maximum rainfall data of selected sites, as observed by the Korea Meteorological Administration. [ABSTRACT FROM AUTHOR]

Background: Physical activity (PA) offers numerous benefits to health and well-being, but most adults are not sufficiently physically active to afford such benefits. The 10,000 steps campaign has been a popular and effective approach to promote PA. The Transtheoretical Model posits that individuals have varying levels of readiness for health behavior change, known as Stages of Change (Precontemplation, Contemplation, Preparation, Action, and Maintenance). Few validated assessment instruments are available for determining Stages of Change in relation to the PA goal of 10,000 steps per day. The purpose of this study was to assess the criterion-related validity of the SoC-Step, a brief 10,000 steps per day Stages of Change instrument.Methods: Participants were 504 Australian adults (176 males, 328 females, mean age = 50.8 ± 13.0 years) from the baseline sample of the Walk 2.0 randomized controlled trial. Measures included 7-day accelerometry (Actigraph GT3X), height, weight, and self-reported intention, self-efficacy, and SoC-Step: Stages of Change relative to achieving 10,000 steps per day. Kruskal-Wallis H tests with pairwise comparisons were used to determine whether participants differed by stage, according to steps per day, general health, body mass index, intention, and self-efficacy to achieve 10,000 steps per day. Binary logistic regression was used to test the hypothesis that participants in Maintenance or Action stages would have greater likelihood of meeting the 10,000 steps goal, in comparison to participants in the other three stages.Results: Consistent with study hypotheses, participants in Precontemplation had significantly lower intention scores than those in Contemplation (p = 0.003) or Preparation (p < 0.001). Participants in Action or Maintenance stages were more likely to achieve ≥10,000 steps per day (OR = 3.11; 95 % CI = 1.66,5.83) compared to those in Precontemplation, Contemplation, or Preparation. Intention (p < 0.001) and self-efficacy (p < 0.001) to achieve 10,000 steps daily differed by stage, and participants in the Maintenance stage had higher general health status and lower body mass index than those in Precontemplation, Contemplation and Preparation stages (p < 0.05).Conclusions: This brief SoC-Step instrument appears to have good criterion-related validity for determining Stages of Change related to the public health goal of 10,000 steps per day.Trial Registration: Australian New Zealand Clinical Trials Registry reference: ACTRN12611000157976 World Health Organization Universal Trial Number: U111-1119-1755. [ABSTRACT FROM AUTHOR]

The multiple non-symmetric correspondence analysis (MNSCA) is a useful technique for analysing the prediction of a categorical variable through two or more predictor variables placed in a contingency table. In MNSCA framework, for summarizing the predictability between criterion and predictor variables, the Multiple-TAU index has been proposed. But it cannot be used to test association, and for overcoming this limitation, a relationship with C-Statistic has been recommended. Multiple-TAU index is an overall measure of association that contains both main effects and interaction terms. The main effects represent the change in the response variables due to the change in the level/categories of the predictor variables, considering the effects of their addition. On the other hand, the interaction effect represents the combined effect of predictor variables on the response variable. In this paper, we propose a decomposition of the Multiple-TAU index in main effects and interaction terms. In order to show this decomposition, we consider an empirical case in which the relationship between the demographic characteristics of the American people, such as race, gender and location (column variables), and their propensity to move (row variable) to a new town to find a job is considered. [ABSTRACT FROM AUTHOR]

This research is dedicated to the Production Scheduling Problem in a hybrid flow shop with setup times separated from processing times. The goal is to identify and analyze the current literature to identify papers that develop methods to solve this problem. In this review, it was possible to identify and analyze 72 papers that have addressed this issue since 1991. Analyses were performed using the number of papers published over the years, the approach used in the development of the methods for the solutions, the type of objective function, the performance criterion adopted, and the additional constraints considered. The analysis results provide some conclusions about the state of the art in the subject and also enable us to identify suggestions for future research in this area. [ABSTRACT FROM AUTHOR]

Abstract: A new procedure to reduce uncertainties in reservoir simulation models using statistical inference and observed data is presented in this paper. Observed data is used to guide the obtainment of posterior probability density functions (pdf). The process starts by sampling from prior pdf. Through an iterative procedure, posterior pdf is obtained in each iteration, which is used to sample new models for the next iteration, until a stop criterion is reached. A detailed relationship between all uncertain attributes defined in the analysis and well responses (water and oil rate, flow pressure etc.) is performed. Finally, final ensembles are sampled in order to assess the dispersion of the responses before and after the reduction of uncertainty. In order to assess the robustness of the proposed method, three reservoir models were studied. The first case is a simple reservoir with eight uncertain attributes and was used for validation purposes. The second case, is a more complex synthetic model with 16 uncertain attributes. Finally, the application to a real field is shown. The results showed that the proposed method has potential to deal with the problem of reservoir uncertainty management. [Copyright &y& Elsevier]

Reasonable fracture criteria are crucial for the modeling of dynamic failure in computational spring lattice models. For experiments on the micro and on the meso scale exist successful criteria, which are based on the stress that a spring experiences. In this paper we test the applicability of these failure criteria to large scale models, where gravity plays an important role in addition to the externally applied deformation. The resulting brittle structures do not resemble the outcome predicted by fracture mechanics and geological observations. For this reason we derive an elliptical fracture criterion, which is based on the strain energy stored in a spring. Simulations using the new criterion result in realistic structures. It is another great advantage of this fracture model, that it can be combined with classic geological material parameters: the tensile strength σ0 and the shear cohesionτ0. While we tested the fracture model only for large scale structures, there is strong reason to believe that the model is equally applicable to lattice simulations on the micro and the meso scale. [ABSTRACT FROM AUTHOR]

Recently, the information potential (IP) of order \alpha, defined as the argument of the log in the \alpha-order Renyi entropy, has been successfully used as an information theoretic criterion for supervised adaptive system training. In this paper, we use the survival function (or equivalently the distribution function) of an absolute value transformed random variable to define a new information potential, named the survival information potential (SIP). Compared with the IP, the SIP has some advantages, such as validity in a wide range of distributions, robustness, and the simplicity in computation. The properties of SIP and a simple formula for computing the empirical SIP are given in the paper. Finally, the SIP criterion is applied in adaptive system training, and simulation examples on FIR adaptive filtering, kernel adaptive filtering, and time delay neural networks (TDNNs) training are presented to demonstrate the performance. [ABSTRACT FROM PUBLISHER]

The meaning of experimentally determined characteristic temperatures is analysed in terms of Hruby glass forming criterion. Despite various analysed modifications it reveals that the sensitivity of original criterion is unsurpassable. The applicability of Sestak-Berggren empirical equation for the description of crystallisation kinetics is comparable with the classical modelling based on Johnson-Mehl-Avrami equation. Their employment is certainly dependent to the purpose of evaluation showing their substitution ability and limits. [ABSTRACT FROM AUTHOR]

Abstract: The protection of process plants from external events is mandatory in the Seveso Directive. Among these events figures the possibility of inundation of a plant, which may cause a hazard by disabling technical components and obviating operator interventions. A methodological framework for dealing with hazards from potential flooding events is presented. It combines an extension of the fault tree method with generic properties of flooding events in rivers and of dikes, which should be adapted to site-specific characteristics in a concrete case. Thus, a rational basis for deciding whether upgrading is required or not and which of the components should be upgraded is provided. Both the deterministic and the probabilistic approaches are compared. Preference is given to the probabilistic one. The conclusions drawn naturally depend on the scope and detail of the model calculations and the decision criterion adopted. The latter has to be supplied from outside the analysis, e.g. by the analyst himself, the plant operator or the competent authority. It turns out that decision-making is only viable if the boundary conditions for both the procedure of analysis and the decision criterion are clear. [Copyright &y& Elsevier]

α-integration and α-GMM have been recently proposed for integrated stochastic modeling. However, there has not been an approach to date for estimating model parameters for αGMM in a statistical way, based on a set of training data. In this letter, parameter updating formulas are mathematically derived based on maximum likelihood criterion using an adapted expectation-maximization algorithm. With this method, model parameters for αGMM are reestimated in an iterative way. The updating formulas were found to be simple and systematically compatible with the GMM equations. This advantage renders the αGMM a superset of the GMM but with similar computational complexity. This method has been effectively applied to realistic speaker recognition applications. [ABSTRACT FROM AUTHOR]

Manufacturing process variations lead to circuit timing variability and a corresponding timing yield loss. Traditional corner analysis consists of checking all process corners (combinations of process parameter extremes) to make sure that circuit timing constraints are met at all corners, typically by running static timing analysis (STA) at every corner. This approach is becoming too expensive due to the increase in the number of corners with modern processes. As an alternative, we propose a linear-time approach for STA which covers all process corners in a single pass. Our technique assumes a linear dependence of delays and slews on process parameters and provides estimates of the worst case circuit delay and slew. It exhibits high accuracy in practice, and if the circuit has m gates and n relevant process parameters, the complexity of the algorithm is O(mn). [ABSTRACT FROM AUTHOR]

We prove a generalization of the well-known Dzyadyk theorem that gives an interesting geometric criterion for the analyticity of functions. [ABSTRACT FROM AUTHOR]

Two different forms of Akaike's information criterion (AIC) are compared for selecting the smooth terms in penalized spline additive mixed models. The conditional AIC (cAIC) has been used traditionally as a criterion for both estimating penalty parameters and selecting covariates in smoothing, and is based on the conditional likelihood given the smooth mean and on the effective degrees of freedom for a model fit. By comparison, the marginal AIC (mAIC) is based on the marginal likelihood from the mixed-model formulation of penalized splines which has recently become popular for estimating smoothing parameters. To the best of the authors' knowledge, the use of mAIC for selecting covariates for smoothing in additive models is new. In the competing models considered for selection, covariates may have a nonlinear effect on the response, with the possibility of group-specific curves. Simulations are used to compare the performance of cAIC and mAIC in model selection settings that have correlated and hierarchical smooth terms. In moderately large samples, both formulations of AIC perform extremely well at detecting the function that generated the data. The mAIC does better for simple functions, whereas the cAIC is more sensitive to detecting a true model that has complex and hierarchical terms. [ABSTRACT FROM AUTHOR]

In many applications, it is important to find the mixture with fewest number of components, known as the mixture complexity, that provides a satisfactory fit to the data. This article focuses on developing an estimator of mixture complexity that is consistent when the form of component densities are unknown but are postulated to be members of some parametric family and is simultaneously robust against model misspecification. We treat the estimation of mixture complexity as a model selection problem and construct an estimator of mixture complexity as a byproduct of minimizing a Hellinger information criterion. This estimator is shown to be consistent for any parametric family of mixtures. When the model is correctly specified, Monte Carlo simulations for a wide variety of normal mixtures show that our estimator is very competitive with several others in the literature in correctly identifying the true mixture complexity. The basic construction, being firmly rooted in the minimum Hellinger distance approach, enables our estimator to naturally inherit the property of robustness, which is examined, through simulations, under symmetric departures from postulated component normality. In terms of correctly identifying the mixture complexity under model misspecification, our estimator performs much better than an estimator based on the Kullback--Leibler distance due to James, Priebe, and Marchette. An example concerning hypertension is revisited to further illustrate the performance of our estimator. [ABSTRACT FROM AUTHOR]

There are several interpretations of the Keynes's economical theory which corrival about what is the truly insight of this theory. Even the mathematical or logical models of this theory do not coincide in some essential aspects. The proposal of this work is that a structuralist reconstruction of the Keynes's theory, because of its emphasis on the T-theoretical terms, can be a help for the clarification of the identity of the said theory. Then, on the basis of an analysis about the criterions for the T-theoretical terms, and about some structuralist reconstructions of the Keynesian theory, I suggest here which Keynes-theoretical terms can give us an important part of the identity of that theory. [ABSTRACT FROM AUTHOR]

The aim of this paper is to propose a number of semiautomatic criteria for the joint selection of the model and the information set in the heteroskedastic context considered in Lejeune [Lejeune, B. (1997). Second order pseudo-maxim urn likelihood estimation and conditional variance misspecification. University of Liege. ERUDITE and CORE]. Particular cases of this methodology are the posterior information criterion (PIC) and the posterior information criterion for a forecasts (PICF) of Phillips [Phillips. P. C. B. (1996). Econometric model determination. Econometrica 64:763-812] and Phillips and Ploberger [Phillips. P. C. B., Ploberger. W. (1994). Posterior odds testing for a unit root with data-based model setection. Econometric Theory 10:774-808] and the Bayesian information criterion (BIC) criterion of Schwarz [Schwarz. G. (1978). Estimating the dimension of a model. Ann, Statist, 6:461-464]. Sufficient conditions for the weak and strong consistencies of the proposed criteria are provided. Furthermore, the behaviors of these criteria are analyzed in the presence of sinieturat changes. [ABSTRACT FROM AUTHOR]

Examines the actual properties of voting procedure. Concept of Condorcet winner criterion; Consideration of the size of the voting population; Details on Pareto criterion and monotonicity criterion.

The article offers the simplex model as a tool for making inferences about change processes given data collected at one point in time. The fruitfulness of the simplex used in this way will be illustrated by restating George Peter Burdock's theory of change in social organization into simplex terms and fitting data drawn from a recent study by Alvin NW. Guilder and the author to the model. The simplex is a mathematical criterion for testing the structure of variables ordered in terms of an a priori scheme. A perfect simplical ordering of variables can be illustrated with words and hypothetical coefficients of correlation. The simplex is a criterion for ordering variables. The meaning given to the ordering must be supplied by the researcher. In some, the ordering of data in simplifical form has been interpreted in terms of degree of complexity, psychological distance and similar avenues. Simplical ordering has not been interpreted as showing the interrelations of system elements over time.

The guard region for a randomly select user is introduced based on the stochastic geometry model. The guard region, through which a frequency reuse criterion is proposed, is calculated based on parameters such as user position, path loss exponents and transmission power. A dynamic frequency reuse scheme based on the guard regions of macro-cell users is proposed to mitigate inter-layer interference. A frequency reuse criterion, which changes along with user position adaptively and is derived through stochastic geometry theory, is believed to be the first effort that can be found in the literature. [ABSTRACT FROM AUTHOR]