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Inverse problems have numerous important applications in science, engineering, medicine, and other disciplines. In this study, we present a numerical solution for a one-dimensional parabolic inverse problem with energy overspecification at a fixed spatial point, using the radial basis function (RBF) method. The collocation matrix arising in RBF-based approaches is typically highly ill-conditioned, and the method’s performance is strongly influenced by the choice of the radial basis function and its shape parameter. Unlike previous studies that focused primarily on Gaussian radial basis functions, this work investigates and compares the performance of three RBF types—Gaussian (GRBF), Multiquadrics (MQRBF), and Inverse Multiquadrics (IMQRBF). By transforming the inverse problem into an equivalent direct problem, we apply the RBF collocation method in both space and time. Numerical experiments on two test problems with known analytical solutions are conducted to evaluate the approximation error, optimal shape parameters, and matrix conditioning. Results indicate that both MQRBF and IMQRBF generally provide better accuracy than GRBF. Furthermore, IMQRBF enhances numerical stability due to its lower condition number, making it a more robust choice for solving ill-posed inverse problems where both stability and accuracy are critical.

It is often claimed that languages with more non-native speakers tend to become morphologically simpler, presumably because non-native speakers learn the language imperfectly. A growing number of studies support this claim, but there is a dearth of experiments that evaluate it and the suggested explanatory mechanisms. We performed a large-scale experiment which directly tested whether imperfect language learning simplifies linguistic structure and whether this effect is amplified by iterated learning. Members of 45 transmission chains, each consisting of 10 one-person generations, learned artificial mini-languages and transmitted them to the next generation. Manipulating the learning time showed that when transmission chains contained generations of imperfect learners, the decrease in morphological complexity was more pronounced than when the chains did not contain imperfect learners. The decrease was partial (complexity did not get fully eliminated) and gradual (caused by the accumulation of small simplifying changes). Simplification primarily affected double agent-marking, which is more redundant, arguably more difficult to learn and less salient than other features. The results were not affected by the number of the imperfect-learner generations in the transmission chains. Thus, we provide strong experimental evidence in support of the hypothesis that iterated imperfect learning leads to language simplification.

In this article, we compare autometrics and machine learning techniques including Minimax Concave Penalty (MCP), Elastic Smoothly Clipped Absolute Deviation (E-SCAD), and Adaptive Elastic Net (AEnet). For simulation experiments, three kinds of scenarios are considered by allowing the multicollinearity, heteroscedasticity, and autocorrelation conditions with varying sample sizes and the varied number of covariates. We found that all methods show improved their performance for a large sample size. In the presence of low and moderate multicollinearity and low and moderate autocorrelation, the considered methods retain all relevant variables. However, for low and moderate multicollinearity, excluding AEnet, all methods keep many irrelevant predictors as well. In contrast, under low and moderate autocorrelation, along with AEnet, the Autometrics retain less irrelevant predictors. Considering the case of extreme multicollinearity, AEnet retains more than 93 percent correct variables with an outstanding gauge (zero percent). However, the potency of remaining techniques, specifically MCP and E-SCAD, tends towards unity with augmenting sample size but capturing massive irrelevant predictors. Similarly, in case of high autocorrelation, E-SCAD has shown good performance in the selection of relevant variables for a small sample, while in gauge, Autometrics and AEnet are performed better and often retained less than 5 percent irrelevant variables. In the presence of heteroscedasticity, all techniques often hold all relevant variables but also suffer from overspecification problems except AEnet and Autometrics which circumvent the irrelevant predictors and establish the true model precisely. For an empirical application, we take into account the workers’ remittance data for Pakistan along its twenty-seven determinants spanning from 1972 to 2020 for Pakistan. The AEnet selected thirteen relevant covariates of workers’ remittance while E-SCAD and MCP suffered from an overspecification problem. Hence, the policymakers and practitioners should focus on the relevant variables selected by AEnet to improve workers' remittance in the case of Pakistan. In this regard, the Pakistan government has devised policies that make it easy to transfer remittances legally and mitigate the cost of transferring remittances from abroad. The AEnet approach can help policymakers arrive at relevant variables in the presence of a huge set of covariates, which in turn produce accurate predictions.

Margin occurs where a design is overspecified with respect to the minimum required. Margin may be desirable to mitigate risk and absorb future changes, but at the same time, may be undesirable if the overspecification deteriorates the design’s performance. In this article, the margin value method (MVM) is introduced to analyse an engineering design, localise the excess margin, and quantify it considering change absorption potential in relation to design performance deterioration. The method provides guidance for improving a design by prioritising excess margin that provides relatively little advantage at high cost, and that could, therefore, be eliminated to improve design performance. It shows how the value of excess margin depends on its localisation in the design parameter network, the importance of design performance parameters, and the importance of absorbing potential future changes. The method is applied to a belt conveyor design. This case indicates that the method is practicable, reveals implications, and suggests opportunities for further work.

Privacy has long been an important issue for IT systems that handle personal information, and is further aggravated as technology for collecting and analyzing massive amounts of data is becoming increasingly effective. There are methods to help practitioners analyze the privacy implications of a system during the design time. However, this is still a difficult task, especially when dealing with Internet of Things scenarios. The problem of privacy can become even more unmanageable with the introduction of overspecifications during the system development life cycle. In this paper, we carried out a controlled experiment with students performing an analysis of privacy implications using two different methods. One method aims at reducing the impact of overspecifications through the application of a goal-oriented analysis. The other method does not involve a goal-oriented analysis and is used as a control. Our initial findings show that conducting a goal-oriented analysis early during design time can have a positive impact over the privacy friendliness of the resulting system.

In this paper, we study the inverse problem of determining the density function modeling the vector external source for the linear momentum of particles, in a mathematical model for the bioconvective flow problem. The model consists of three equations: linear momentum of particles, a conservation law for the microorganisms, and the incompressibility condition. We analyze the direct problem obtaining results for the well posedness. We prove the existence of weak solutions under general assumptions and the uniqueness of weak solutions for a particular class of density functions. To solve the inverse problem, we assume that an integral overspecification condition is given. Then, we prove the local uniqueness of the inverse problem. The proof is based on the characterization of the inverse problem solutions using an operator equation of second kind, the introduction of several a priori estimates, and the application of the Tikhonov fixed point theorem.

Engineering design is characterized, in many cases, by the involvement of multiple stakeholders. The variety of stakeholders’ expectations with regards to the output and outcome of a vessel design situation, and the differences in background, culture and information asymmetry among stakeholders, make it difficult to arrive at a common set of requirements and a mutually accepted vessel design solution. In this paper, we show how poor handling of expectations in multi-stakeholder arrangements may lead to overspecified design solutions and thereby, negatively affect business outcomes. We propose and test a set of metrics to measure the level of misalignment among stakeholders’ expectations to identify and measure overspecification in vessel design alternatives. The measure can be used in tradeoff analysis against cost, in the decision process for selection among design alternatives. Hence, at equal cost, a higher degree expectation fulfillment may be preferred and selected. A case study is presented for the design of an offshore ship design based on a joint-venture ownership.

This work compares Autometrics with dual penalization techniques such as minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) under asymmetric error distributions such as exponential, gamma, and Frechet with varying sample sizes as well as predictors. Comprehensive simulations, based on a wide variety of scenarios, reveal that the methods considered show improved performance for increased sample size. In the case of low multicollinearity, these methods show good performance in terms of potency, but in gauge, shrinkage methods collapse, and higher gauge leads to overspecification of the models. High levels of multicollinearity adversely affect the performance of Autometrics. In contrast, shrinkage methods are robust in presence of high multicollinearity in terms of potency, but they tend to select a massive set of irrelevant variables. Moreover, we find that expanding the data mitigates the adverse impact of high multicollinearity on Autometrics rapidly and gradually corrects the gauge of shrinkage methods. For empirical application, we take the gold prices data spanning from 1981 to 2020. While comparing the forecasting performance of all selected methods, we divide the data into two parts: data over 1981–2010 are taken as training data, and those over 2011–2020 are used as testing data. All methods are trained for the training data and then are assessed for performance through the testing data. Based on a root-mean-square error and mean absolute error, Autometrics remain the best in capturing the gold prices trend and producing better forecasts than MCP and SCAD.

We develop algorithms for detecting multiple changepoints in functional data when the number of changepoints is unknown (unsupervised case), when it is specified apriori (supervised case), and when certain bounds are available (semi-supervised case). These algorithms utilize the maximum mean discrepancy (MMD) measure between distributions on Hilbert spaces. We develop an oracle analysis of the changepoint detection problem which reveals an interesting relationship between the true changepoint locations and the local maxima of the oracle MMD curve. The proposed algorithms are shown to detect general distributional changes by exploiting this connection. In the unsupervised case, we test the significance of a potential changepoint and establish its consistency under the single changepoint setting. We investigate the strong consistency of the changepoint estimators in both single and multiple changepoint settings. In both supervised and semi-supervised scenarios, we include a step to merge consecutive groups that are similar to appropriately utilize the prior information about the number of changepoints. In the supervised scenario, the algorithm satisfies an order-preserving property: the estimated changepoints are contained in the true set of changepoints in the underspecified case, while they contain the true set under overspecification. We evaluate the performance of the algorithms on a variety of datasets demonstrating the superiority of the proposed algorithms compared to some of the existing methods.

This paper presents the Stars2 corpus of definite descriptions for referring expression generation (REG). The corpus was produced in collaborative communication involving speaker-hearer pairs, and includes situations of reference that are arguably under-represented in similar work. Stars2 is intended as an incremental contribution to the research in REG and related fields, and it may be used both as training/test data for algorithms of this kind, and also to gain further insights into reference phenomena in general, with a particular focus on the issue of attribute choice in referential overspecification.