Predictable Errors Research Articles

Robust expected violation criterion for constrained robust design problems and its application in automotive lightweight design

Metamodeling techniques are commonly used to replace expensive computer simulations in robust design problems. Due to the discrepancy between the simulation model and metamodel, a robust solution in the infeasible region can be found according to the prediction error in constraint responses. In deterministic optimizations, balancing the predicted constraint and metamodeling uncertainty, expected violation (EV) criterion can be used to explore the design space and add samples to adaptively improve the fitting accuracy of the constraint boundary. However in robust design problems, the predicted error of a robust design constraint cannot be represented by the metamodel prediction uncertainty directly. The conventional EV-based sequential sampling method cannot be used in robust design problems. In this paper, by investigating the effect of metamodeling uncertainty on the robust design responses, an extended robust expected violation (REV) function is proposed to improve the prediction accuracy of the robust design constraints. To validate the benefits of the proposed method, a crashworthiness-based lightweight design example, i.e. a highly nonlinear constrained robust design problem, is given. Results show that the proposed method can mitigate the prediction error in robust constraints and ensure the feasibility of the robust solution.

Effectiveness of a glossary of security expressions in error predictability and translation performance of movie translators

Hundreds of thousands of strategic movies and TV series are being produced every year and almost all of them get translated into so many different languages. The present paper is an attempt to examine the role of a particular type of glossary on the translation performance of movie translators. In this study, efforts have been made to develop the first cinematic glossary of security terms for movie translators. This bilingual glossary is compiled based on the errors Iranian movie translators have committed while translating this genre of movies. The main body of this glossary was obtained from the translated screen plays of 30 popular action movies in English. In the second phase of the project, 30 translators were assigned with a translation task comprised of movie extracts. The translators had to do the translation task without having the proposed glossary in the pretest stage and in the posttest stage, with an interval of one month, the same translation task was given to the same translators with the glossary. Data was analyzed in the SPSS Software and the results of a t-test showed that the difference between participants` pretest and posttest scores was statistically significant indicating that the proposed glossary was effective in the translation performance of the participants. Findings further showed that the proposed glossary had the potentials to predict the possible errors of movie translators. Key words: Terminology, cinematic glossary, movie translators.

Integration of dynamic assessment and instructional conversations to promote development and improve assessment in the language classroom

This article explores how a primary school teacher utilized the frameworks of dynamic assessment (DA) and the instructional conversation (IC) within a Spanish as a foreign language classroom. DA was used to construct zones of proximal development with individuals in the classroom context. A menu of pre-scripted assisting prompts, used to respond to predictable lexical and grammatical errors, permitted the teacher to assess students while also promoting development. ICs were used to co-construct a group zone of proximal development (ZPD) in response to less predictable student errors or inquiries. The flexible mediation provided by the teacher in these instances allowed for the active involvement of more students as well as more responsive dialogue. This language teacher drew upon these two frameworks to navigate dual goals of instruction and assessment while providing mediation attuned to the ZPD of the learners. As students studied interrogative formation to complete the pedagogical task of an interview, the teacher alternated between these two frameworks based on her goal for each interaction. Class transcripts are analysed to reveal how these two complementary frameworks can be used in conjunction to meet both the students’ and teacher’s needs.

Predictability during active break phases of Indian summer monsoon in an ensemble prediction system using climate forecast system

This study examines the phase dependant temporal and spatial error evolution and prediction of active break spells of Indian summer monsoon rainfall in an ensemble prediction system (EPS) on a pentad time scale using climate forecast system (CFS). The EPS system shows systematic wet bias (overestimation) over west coast over the Arabian Sea and Myanmar coast and dry bias (underestimation) over Indian land mass even at pentad 1 lead and these biases consistently increase up to 4 pentad lead and saturate thereafter. Irrespective of the phases of the monsoon, the lower bound of predictability is 2 pentads, while upper bound of predictability for initial conditions starting from active phase saturates at 3 pentads and for break and transition phases predictability error saturates at a later stage at about 5 pentad. Initial conditions started from transition phase shows higher potential predictability followed by break phase and then active phase.

Removing predictable analyst forecast errors to improve implied cost of equity estimates

Prior research documents a weak association between the implied cost of equity inferred from analyst forecasts and realized returns. It points to predictable errors in analyst forecasts as a possible cause. We show that removing predictable errors from analyst forecasts leads to a much stronger association between implied cost of equity estimates obtained from adjusted forecasts and realized returns after controlling for cash flow news and discount rate news. An estimate of implied risk premium based on the average of four commonly used methods after making adjustments for predictable errors exhibits strong correlations with future realized returns as well as the lowest measurement error. Overall, our results confirm the validity of implied cost of equity estimates as measures of expected returns. Future research using implied cost of equity should remove predictable errors from implied cost of capital estimates and then average across multiple metrics.

Quantitative evaluation of multipath rejection capabilities of GNSS antennas

Multipath error remains the largest error source in many high precision GPS applications. To counteract this problem, solutions at both software and hardware level have been studied. Software processing by means of measurement redundancy or error predictability can be used in order to mitigate the multipath effects. In general, these techniques work properly only when the length of a reflection path exceeds that of the direct path by more than 10---20 m. Unfortunately, in most cases, reflections are generated in the area near the receiving antenna. For this reason, multipath rejection actuated at the antenna level is one of the most valid means to improve the accuracy of GPS systems. The scope of this work is twofold. First, a review of low-multipath reception requirements will be proposed for comparing different classes of high precision GNSS antennas. Based on this discussion, we introduce a quantitative evaluation of multipath rejection capabilities of a GNSS antenna. The proposed assessment technique is focused on the antenna pattern, but, in contrast to other parameters evaluating the antenna radiation characteristics, it is specifically conceived to capture the effects of multipath signals.

Stock Market Liquidity, Aggregate Analyst Forecast Errors, and the Economy

Naes, Skjeltorp, and Odegaard (2011) suggest that stock market liquidity is a good leading indicator of the economy because of the “flight-to-quality” behavior of informed investors, whose sells (buys) tend to lead stock market liquidity to decrease (increase) and tend to occur before economic downturns (expansions). To further understand the mechanism in the economic forecastability of stock market liquidity, we hypothesize that analyst earnings forecast errors have a systematic component, which is predictable and related to changes in the economy, and that smart investors exploit analyst forecast errors, which leads to the economic forecastability of stock market liquidity. Consistent with our hypothesis, we find that there is a strong correlation between detrended aggregate analyst forecast errors and concurrent GDP growth and that a large part of the forecast errors can be predicted using lagged macro variables. Once we control for the predictable forecast errors, the economic forecastability of stock market liquidity disappears. Thus, our study reveals that aggregate analyst forecast errors are very informative about business cycle and contain all the relevant information for stock market liquidity as a leading economic indicator.

Intelligent decision support and modeling

The influence of cognitive science and psychology on decision theory is bringing about changes to assumptions about decision making, and, as a consequence, the way that decisions should be modeled and supported. The three articles published in this Special Issue on intelligent decision support and modeling reflect an emerging literature that incorporates psychology into decision modeling and decision support. This literature represents only a starting point, and much remains to be done in terms of acknowledging the influence of psychology on engineering decisions. Decision support tools will probably never completely make up for engineers’ lack of the cognitive capacity needed to make the multitude of decisions associated with engineering design in a fully informed, unbiased way. Incorporating rational choice theory and psychology into decision support tools seems to be a fruitful path toward promoting optimal decisions in engineering. The last two to three decades have brought about important changes to the field of decision theory, particularly in response to the generally accepted principle that the act of cognitive representation and framing of decisions should follow the axioms of expected utility theory. The standard theory for decision making is based on subjective expected utility theory (e.g., Savage, 1954; Schmeidler, 1989), which involves the enumeration of possibilities, an analysis of the possible outcomes, and the selection of the utility-maximizing decision (Gilboa & Schmeidler, 2001). In engineering design, decision theory is generally applied as a systematic procedure for selecting design variables when there is uncertainty over the preferences associated with the objectives (Thurston, 1991, 2001). In schools of engineering, operations research, computer science, and business around the world, students continue to be taught a set of methodologies consonant with rational choice theory (Wood, 2004), which is founded upon the analysis of information as the basis of decision. In short, decision support and modeling in engineering design generally follows a framework of selecting design variables that optimize the expected utility of the design, and, in so doing, casts engineering design within a rational process (Hazelrigg, 1998). The perceived increased accuracy and rigor of the decisions and the decision support systems built upon the premise of utility maximization can mask the realities of eliciting preferences from engineers, which may be subject to psychological biases. Kahneman and Tversky (Tversky & Kahneman, 1974; Kahneman & Tversky, 1979) drew attention to the realities of human decision making in describing the heuristics that human beings employ in decision making under uncertainty, which are subject to psychological biases that can lead to systematic and predictable errors. In recent years, Kahneman and colleagues have published a series of articles dealing with strategies to correct for these psychological biases (e.g., Kahneman & Lovallo, 1993; Kahneman et al., 2011) and entire fields of behavioral finance, behavioral economics, and behavioral strategy have grown up around the application of cognitive science and psychology to the theory and practice of decision making under uncertainty in specific contexts. This influence is now being felt in the engineering design domain in the modeling and support of engineering decisions, which is built upon a rich heritage of studies on the cognitive and behavioral strategies of engineers. The three articles published in this Special Issue reflect a shift away from normative subjective utility maximization as the only model for decision making and decision support toward greater consideration to the exigencies of engineering decision making in practice. All three articles contribute to decision support and modeling and treat these as integrated issues rather than as compartmentalized problems. The article “Bayesian Project Diagnosis for the Construction Design Process” by Matthews and Philip is perhaps the most “traditional” of the three articles. The article deals with the problem of forecasting potential problems in construction processes. We use traditional in the sense that Bayesian modeling is an accepted method for estimating the probability of an event or outcome of interest (Marshall & Oliver, 1995). They model the construction process as a Markov chain, with transition probabilities associated with progressing through various Reprint requests to: Andy Dong, Faculty of Engineering and Information Technologies, University of Sydney, Engineering Building (J05), Sydney 2006, Australia. E-mail: andy.dong@sydney.edu.au Artificial Intelligence for Engineering Design, Analysis and Manufacturing (2012), 26, 371–373. # Cambridge University Press 2012 0890-0604/12 $25.00 doi:10.1017/S0890060412000248

The relative contributions of radial and laminar optic flow to the perception of linear self-motion

When illusory self-motion is induced in a stationary observer by optic flow, the perceived distance traveled is generally overestimated relative to the distance of a remembered target (Redlick, Harris, & Jenkin, 2001): subjects feel they have gone further than the simulated distance and indicate that they have arrived at a target's previously seen location too early. In this article we assess how the radial and laminar components of translational optic flow contribute to the perceived distance traveled. Subjects monocularly viewed a target presented in a virtual hallway wallpapered with stripes that periodically changed color to prevent tracking. The target was then extinguished and the visible area of the hallway shrunk to an oval region 40° (h) × 24° (v). Subjects either continued to look centrally or shifted their gaze eccentrically, thus varying the relative amounts of radial and laminar flow visible. They were then presented with visual motion compatible with moving down the hallway toward the target and pressed a button when they perceived that they had reached the target's remembered position. Data were modeled by the output of a leaky spatial integrator (Lappe, Jenkin, & Harris, 2007). The sensory gain varied systematically with viewing eccentricity while the leak constant was independent of viewing eccentricity. Results were modeled as the linear sum of separate mechanisms sensitive to radial and laminar optic flow. Results are compatible with independent channels for processing the radial and laminar flow components of optic flow that add linearly to produce large but predictable errors in perceived distance traveled.

Modeling for body growth of crossbred piglets

This paper attempts to describe growth pattern in terms of body weight of crossbred variety (Landrace× desi) of pig for both male and female separately maintained at piggery farm, Indian Veterinary Research Institute (IVRI), Izatnagar. The suitability of the model was established on the criteria of mean absolute error (MAE), root mean squared error (RMSE) and mean absolute predicted error (MAPE). On these criteria, Gompertz model was found to be the best fitted model as the shape of the curve for body weight and predicted weights were also quite closed.

Relationships between the limit of predictability and initial error in the uncoupled and coupled lorenz models

In this study, the relationship between the limit of predictability and initial error was investigated using two simple chaotic systems: the Lorenz model, which possesses a single characteristic time scale, and the coupled Lorenz model, which possesses two different characteristic time scales. The limit of predictability is defined here as the time at which the error reaches 95% of its saturation level; nonlinear behaviors of the error growth are therefore involved in the definition of the limit of predictability. Our results show that the logarithmic function performs well in describing the relationship between the limit of predictability and initial error in both models, although the coefficients in the logarithmic function were not constant across the examined range of initial errors.

A responsabilidade profissional na assistência ao parto: discursos de enfermeiras obstétricas

Trata-se de um estudo qualitativo, de natureza exploratória, que objetivou identificar o conhecimento das enfermeiras obstétricas em relação à responsabilidade profissional na assistência ao parto. Foram entrevistadas 11 enfermeiras que atuavam na assistência ao parto em hospitais e/ou domicílio no estado de Santa Catarina, entre março e agosto de 2009. Após análise pelo Discurso do Sujeito Coletivo, emergiram Ideias Centrais que contemplam os temas sobre as relações das enfermeiras obstétricas com os médicos e a instituição; a responsabilização profissional e as repercussões morais e legais do erro. Verificou-se que as enfermeiras conhecem pouco a respeito das repercussões legais do erro. Ao assumir a assistência ao parto, devem dedicar total atenção aos limites da competência e à prevenção de erros previsíveis, tendo em mente que assumirão também a responsabilização por suas falhas. A atualização sobre responsabilidade legal é tão importante quanto a científica e pode contribuir para a autoconfiança profissional.

On finite-size Lyapunov exponents in multiscale systems

We study the effect of regime switches on finite size Lyapunov exponents (FSLEs) in determining the error growth rates and predictability of multiscale systems. We consider a dynamical system involving slow and fast regimes and switches between them. The surprising result is that due to the presence of regimes, the error growth rate can be a non-monotonic function of initial error amplitude. In particular, troughs in the large scales of FSLE spectra are shown to be a signature of slow regimes, whereas fast regimes are shown to cause large peaks in the spectra where error growth rates far exceed those estimated from the maximal Lyapunov exponent. We present analytical results explaining these signatures and corroborate them with numerical simulations. We show further that these peaks disappear in stochastic parametrizations of the fast chaotic processes, and the associated FSLE spectra reveal that large scale predictability properties of the full deterministic model are well approximated, whereas small scale features are not properly resolved.

Handedness and proprioceptive position estimation: Are left handed people more accurate in self representation and is this representation resistant to manipulation by the Rubber Hand Illusion?

In the absence of visual information, predictable errors are seen in proprioceptive estimation of body position. Are certain individuals better able to locate their limbs when no visual information is present? Strongly right handed individuals ‘over-represent’ the right side of extrapersonal and body-space resulting in less accurate implicit body-representation compared to strongly left handed or ambidextrous individuals (termed psuedoneglect). The current study sought to investigate whether these differences in body-representation exist for static perceptual judgements of limb position. Proprioceptive acuity was compared across hand used and handedness at varying spatial locations with respect to the body midline. Secondly, we investigated manipulability of proprioceptive position using the Rubber Hand Illusion. We predicted left handed individuals would display greater position acuity overall and that the RHI would be less effective in shifting their position sense – compared to right-handed and ambidextrous individuals. Position errors at baseline indicated a natural positional recalibration towards the location of the shoulder of the hand used, rather than the midline (as predicted by previous research). Contrary to our predictions, evidence for accuracy differences and leftward spatial biases in right handed individuals was equivocal. Interestingly, in both right and left handers there was some evidence to suggest the RHI was more effective in shifting the position of the non-dominant hand. In conclusion, our results suggest that the effects of functional hemispheric asymmetry on processing of body-space are modulated by attention to hand-space. Results will be discussed in relation to laterality of brain function and the role of interhemispheric communication in updating body representation.

Categorization Bias in the Stock Market

This paper provides evidence of categorization bias in nancial markets. Some investors perceive individual rms through the lenses of industries. Such categorical thinking generates mispricing and predictability in stock-returns. We measure the dierence in returns between a rm’s ocial SIC industry and its underlying fundamentals by constructing a basket of closely related rms, based on Hoberg and Phillips (2010a,b). We nd that stocks comove strongly with their ocial industry in the short-term and then revert toward their basket of Hoberg and Phillips comparables. Long-short strategies exploiting mispricing due to industry categorization generate statistically signicant and economically sizable risk-adjusted excess returns. We also provide evidence that nancial analysts are biased by industry categorization: When a rm’s ocial industry is less representative of its fundamentals, analysts sometimes tend to put excessive weight on information related to the ocial industry and thus make predictable forecast errors.

Using scattered hyperspectral imagery data to map the soil properties of a region

Airborne hyperspectral imagery has been recently proved to be a successful technique for predicting soil properties of the bare soil surfaces that are usually scattered in the landscape. This new soil covariate could much improve the digital soil mapping (DSM) of soil properties over larger areas. To illustrate this, we experimented with digital soil mapping in a 24.6‐km2area located in the vineyard plain of Languedoc. As input data, we used 200 points with clay content measurements and 192 bare soil fields representing 3.5% of the total area in which the clay contents of the soil surface were successfully mapped at 5‐m resolution by hyperspectral remote sensing. The clay contents were estimated from CR2206, a spectrometric indicator that quantifies specific absorption features of clay at 2206 nm. We demonstrated by cross‐validation that the co‐kriging procedure based on our co‐regionalization model provided accurate error estimates at the clay measurement sites. Then, we applied a block co‐kriging model to map the mean clay content at increasing resolutions (50 , 100, 250 and 500 m). The results showed the following: (i) using hyperspectral data significantly increased the accuracy of the mean clay content estimations; (ii) a block co‐kriging procedure with reliable estimates of error variance can be used to estimate mean clay contents over larger areas and at coarser resolutions with acceptable and predictable errors and (iii) various maps can be produced that represent different compromises between prediction accuracy and spatial resolution.

Some considerations of classification for high dimension low-sample size data

We review in this article several classification methods, especially for high-dimensional and low-sample size data. We discuss several desirable properties for classifiers in such settings, including predictability, consistency, generality, stability, robustness and sparsity. Specifically, a good classifier should have a small prediction error (predictability); converge to the Bayes-rule classifier asymptotically (consistency); be stable when adding/removing an observation (generality); be stable for different data sets of the same kind (stochastic stability); be stable when there are a small number of contaminated observations (robustness); and have a small number of variables in the classifier (interpretability or sparsity). Several simulation examples and real applications are used to illustrate the usefulness of the existing popular classifiers and compare their performance.

Predictability error growth of turbulent flows

Recently, alternative viewpoints were suggested that is in contrast to the conventional picture of predictability error growth in the spectral domain. We survey key historical and current literatures and suggest that the traditional perspective has not been invalidated.

A case study of the error growth and predictability of a Meiyu frontal heavy precipitation event

The Advanced Regional Eta-coordinate Model (AREM) is used to explore the predictability of a heavy rainfall event along the Meiyu front in China during 3–4 July 2003. Based on the sensitivity of precipitation prediction to initial data sources and initial uncertainties in different variables, the evolution of error growth and the associated mechanism are described and discussed in detail in this paper. The results indicate that the smaller-amplitude initial error presents a faster growth rate and its growth is characterized by a transition from localized growth to widespread expansion error. Such modality of the error growth is closely related to the evolvement of the precipitation episode, and consequently remarkable forecast divergence is found near the rainband, indicating that the rainfall area is a sensitive region for error growth. The initial error in the rainband contributes significantly to the forecast divergence, and its amplification and propagation are largely determined by the initial moisture distribution. The moisture condition also affects the error growth on smaller scales and the subsequent upscale error cascade. In addition, the error growth defined by an energy norm reveals that large error energy collocates well with the strong latent heating, implying that the occurrence of precipitation and error growth share the same energy source—the latent heat. This may impose an intrinsic predictability limit on the prediction of heavy precipitation.

Properties of implied cost of capital using analysts’ forecasts

We evaluate the influence of measurement error in analysts’ forecasts on the accuracy of implied cost of capital estimates from various implementations of the ‘implied cost of capital’ approach, and develop corrections for the measurement error. The implied cost of capital approach relies on analysts’ short- and long-term earnings forecasts as proxies for the market’s expectation of future earnings, and solves for the implied discount rate that equates the present value of the expected future payoffs to the current stock price. We document predictable error in the implied cost of capital estimates resulting from analysts’ forecasts that are sluggish with respect to information in past stock returns. We propose two methods to mitigate the influence of sluggish forecasts on the implied cost of capital estimates. These methods substantially improve the ability of the implied cost of capital estimates to explain cross-sectional variation in future stock returns, which is consistent with the corrections being effective in mitigating the error in the estimates due to analysts’ sluggishness.