Simple Additive Models Research Articles

Peridynamic Framework to Model Additive Manufacturing Processes

AbstractThe study presents a framework for analyzing Additive Manufacturing processes within the Peridynamics (PD) software PeriLab. This framewor k employs a mesh‐free, point‐based numerical approach to approximate the continuum PD equations. Implemented within this framework are thermal, thermo‐mechanical, and simple additive models. These models have been validated against analytical solutions, Finite Element (FE) models, and Peridigm simulations. To leverage the PD mesh‐free implementation, the study introduces a novel boundary detection algorithm. This algorithm is essential because the outer surface area may change during the manufacturing process. It operates without requiring surface or topology information, relying instead on the comparison of neighborhood volume to sphere volume. Additionally, the study introduces a wrapper that generates the mesh necessary for simulating the printing process, based on the G‐code machine input path. Finally, the study presents a comprehensive analysis of an L‐shaped profile utilizing the developed features, comparing the results with those obtained from an Abaqus solution.

Using a Multivariate Virtual Experiment for Uncertainty Evaluation with Unknown Variance

Virtual experiments are a digital representation of a real measurement and play a crucial role in modern measurement sciences and metrology. Beyond their common usage as a modeling and validation tool, a virtual experiment may also be employed to perform a parameter sensitivity analysis or to carry out a measurement uncertainty evaluation. For the latter to be compliant with statistical principles and metrological guidelines, the procedure to obtain an estimate and a corresponding measurement uncertainty requires careful consideration. We employ a Monte Carlo sampling procedure using a virtual experiment that allows one to perform a measurement uncertainty evaluation according to the Monte Carlo approach of JCGM-101 and JCGM-102, two widely applied guidelines for uncertainty evaluation in metrology. We extend and formalize a previously published approach for simple additive models to account for a large class of non-linear virtual experiments and measurement models for multidimensionality of the data and output quantities, and for the case of unknown variance of repeated measurements. With the algorithm developed here, a simple procedure for the evaluation of measurement uncertainty is provided that may be applied in various applications that admit a certain structure for their virtual experiment. Moreover, the measurement model commonly employed for uncertainty evaluation according to JCGM-101 and JCGM-102 is not required for this algorithm, and only evaluations of the virtual experiment are performed to obtain an estimate and an associated uncertainty of the measurand. We demonstrate the efficacy of the developed approach and the effect of the underlying assumptions for a generic polynomial regression example and an example of a simplified coordinate measuring machine and its virtual representation. The results of this work highlight that considerable effort, diligence, and statistical considerations need to be invested to make use of a virtual experiment for uncertainty evaluation in a way that ensures equivalence with the accepted guidelines.

Delay Prediction for ASIC HLS: Comparing Graph-Based and Nongraph-Based Learning Models

While high-level synthesis (HLS) tools offer faster design of hardware accelerators with different area versus delay tradeoffs, HLS-based delay estimates often deviate significantly from results obtained from ASIC logic synthesis (LS) tools. Current HLS tools rely on simple additive delay models which fail to capture the downstream optimizations performed during LS and technology mapping. Inaccurate delay estimates prevent fast and accurate design-space exploration without performing time-consuming LS tasks. In this work, we exploit different machine learning models which automatically learn to map the different downstream optimizations onto the HLS critical paths. In particular, we compare graph-based and nongraph-based learning models to investigate their efficacy, devise hybrid models to get the best of the both worlds. To carry out our learning-assisted methodology, we create a dataset of different HLS benchmarks and develop an automated framework, which extends a commercial HLS toolchain, to extract essential information from LS critical path and automatically matches this information to HLS path. This is a nontrivial task to perform manually due to difference in level of abstractions. Finally, we train the proposed hybrid models through inductive learning and integrate them in the commercial HLS toolchain to improve delay prediction accuracy. Experimental results demonstrate significant improvements in delay estimation accuracy across a wide variety of benchmark designs. We demonstrate that the graph-based models can infer essential structural features from the input design, while incorporating them into traditional nongraph-based models can significantly improve the model accuracy. Such “hybrid” models can improve delay prediction accuracy by 93% compared to simple additive models and provide <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$175\times $ </tex-math></inline-formula> speedup compared to LS. Furthermore, we discuss key insights from our experiments, identifying the influence of different HLS features on model performance.

Authoritarianism, economic threat, and the limits of multiculturalism in post‐migration crisis Germany

Objective The purpose of this article is to investigate the associations between submissive authoritarianism, multiculturalism, and perceived economic threat. More specifically, it explores whether the effects of multiculturalism on authoritarianism are moderated by perceived economic anxieties. Methods An original data set was collected using the online platform Qualtrics, which consisted of a stratified sample of 1010 complete responses from a German non-probability sample in January 2019. Results In simple additive models, multiculturalist attitudes show a statistically significant depressing effect on authoritarianism, while economic threat significantly increases it. However, when multiculturalist attitudes are interacted with perceived economic threats, the depressing effect of multiculturalism on authoritarianism wanes. While low levels of economic threat demonstrate a statistically significant depressing effect of multiculturalism on authoritarianism, at higher levels of economic threat, this effect wanes and becomes statistically insignificant. Conclusion This article shows that turning away from inclusionary policies may lead not only to a withering of multiculturalism but also to an increased tendency to succumb to authoritarian temptations, particularly in a climate of rising economic anxieties.

OASIS\u2009+: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality

BackgroundSeverity scores assess the acuity of critical illness by penalizing for the deviation of physiologic measurements from normal and aggregating these penalties (also called “weights” or “subscores”) into a final score (or probability) for quantifying the severity of critical illness (or the likelihood of in-hospital mortality). Although these simple additive models are human readable and interpretable, their predictive performance needs to be further improved.MethodsWe present OASIS +, a variant of the Oxford Acute Severity of Illness Score (OASIS) in which an ensemble of 200 decision trees is used to predict in-hospital mortality based on the 10 same clinical variables in OASIS.ResultsUsing a test set of 9566 admissions extracted from the MIMIC-III database, we show that OASIS + outperforms nine previously developed severity scoring methods (including OASIS) in predicting in-hospital mortality. Furthermore, our results show that the supervised learning algorithms considered in our experiments demonstrated higher predictive performance when trained using the observed clinical variables as opposed to OASIS subscores.ConclusionsOur results suggest that there is room for improving the prognostic accuracy of the OASIS severity scores by replacing the simple linear additive scoring function with more sophisticated non-linear machine learning models such as RF and XGB.

Use and Misuse of MCDA to Support Decision Making Informed by Risk.

Recent guidelines for risk-informed decision making (RIDM) provide a gold-standard for how to incorporate probabilistic risk models in conjunction with other considerations in a decision process. Nevertheless, risk quantification using probabilistic and statistical methods is difficult in situations where threat, vulnerability, and consequences are highly uncertain and risk quantification. In such situations a wider variety of methods could be employed, which we call decision making informed by risk (DMIR) combining risk and decision analytics. Risk informed decision making (RIDM) can be considered as a special case of DMIR. Multi criteria decision analysis (MCDA) often serves as a basis for DMIR in order to flexibly accommodate different levels of analytical detail. DMIR often involves artful use of proxy variables that correlate with, and are more measurable than, underlying factors of interest. This article introduces the notion of DMIR and discusses the use of MCDA in its application in the context of risk-based problems. MCDA-based risk analyses identify metrics associated with threats of concern and system vulnerabilities, characterize the way in which alternative actions can affect these threats and vulnerabilities, and ultimately synthesize this information to compare, prioritize, or select alternative mitigation strategies. Simple linear additive MCDA models often integrate these inputs, but the same simplicity can limit such approaches and create pitfalls and more advanced models including multiplicative relationships can be warranted. This essay qualitatively explores the critical practitioner questions of how and when the use of linear multicriteria models creates significant problems, and how to avoid them.

Joint Semantic Synthesis and Morphological Analysis of the Derived Word

Much like sentences are composed of words, words themselves are composed of smaller units. For example, the English word questionably can be analyzed as question+ able+ ly. However, this structural decomposition of the word does not directly give us a semantic representation of the word’s meaning. Since morphology obeys the principle of compositionality, the semantics of the word can be systematically derived from the meaning of its parts. In this work, we propose a novel probabilistic model of word formation that captures both the analysis of a word w into its constituent segments and the synthesis of the meaning of w from the meanings of those segments. Our model jointly learns to segment words into morphemes and compose distributional semantic vectors of those morphemes. We experiment with the model on English CELEX data and German DErivBase (Zeller et al., 2013) data. We show that jointly modeling semantics increases both segmentation accuracy and morpheme F1 by between 3% and 5%. Additionally, we investigate different models of vector composition, showing that recurrent neural networks yield an improvement over simple additive models. Finally, we study the degree to which the representations correspond to a linguist’s notion of morphological productivity.

Interactions between species attributes explain population dynamics in stream fishes under changing climate

AbstractSpecies responses to climate change have been shown to vary in both direction and magnitude. Understanding these idiosyncratic responses is crucial if we are to predict extinction risk and set up efficient conservation strategies. The variations observed across species have been related to several species attributes including intrinsic traits such as physiological tolerances or life‐history strategies but also to niche characteristics (e.g., niche breadth [NB], niche position [NP]). However, although previous studies have successfully linked species attributes to population dynamics or range shifts, few have considered synergistic effects to explain responses to climate variations. Here, we assessed whether five species attributes (fecundity, thermal safety margin, trophic position [TP], NB, and NP) explained interspecific differences in four parameters influencing population dynamics of 35 stream fish species at the French scale. We used Bayesian N‐mixture models to estimate posterior distributions of the growth rate, the strength of density dependence, and the influence of both mean temperature and temperature variability on populations for each species. We then used phylogenetic generalized least squares (PGLS) models to investigate the influence of species attributes and their interactions on interspecific differences in each of the four parameter driving population dynamics. The percentage of variance explained by the PGLS models was relatively high (around 40% on average), indicating that species attributes are good predictors of species population dynamics. Furthermore, we showed that the influence of these single attributes was mediated by other attributes, especially NP and TP. Importantly, we found that models including interaction terms had greater support over simple additive models in explaining interspecific differences in population dynamics. Taken together, these results point to the importance of considering the interplay between species attributes in unraveling the mechanisms involved in population dynamics and understanding the vulnerability of species to global change.

Visualizing Fit and Lack of Fit in Complex Regression Models with Predictor Effect Plots and Partial Residuals

Predictor effect displays, introduced in this article, visualize the response surface of complex regression models by averaging and conditioning, producing a sequence of 2D line graphs, one graph or set of graphs for each predictor in the regression problem. Partial residual plots visualize lack of fit, traditionally in relatively simple additive regression models. We combine partial residuals with effect displays to visualize both fit and lack of fit simultaneously in complex regression models, plotting residuals from a model around 2D slices of the fitted response surface. Employing fundamental results on partial residual plots along with examples for both real and contrived data, we discuss and illustrate both the strengths and limitations of the resulting graphs. The methods described in this paper are implemented in the effects package for R.

Body shape differences in a pair of closely related Malawi cichlids and their hybrids: Effects of genetic variation, phenotypic plasticity, and transgressive segregation.

Phenotypic differences may have genetic and plastic components. Here, we investigated the contributions of both for differences in body shape in two species of Lake Malawi cichlids using wild‐caught specimens and a common garden experiment. We further hybridized the two species to investigate the mode of gene action influencing body shape differences and to examine the potential for transgressive segregation. We found that body shape differences between the two species observed in the field are maintained after more than 10 generations in a standardized environment. Nonetheless, both species experienced similar changes in the laboratory environment. Our hybrid cross experiment confirmed that substantial variation in body shape appears to be genetically determined. The data further suggest that the underlying mode of gene action is complex and cannot be explained by simple additive or additive‐dominance models. Transgressive phenotypes were found in the hybrid generations, as hybrids occupied significantly more morphospace than both parentals combined. Further, the body shapes of transgressive individuals resemble the body shapes observed in other Lake Malawi rock‐dwelling genera. Our findings indicate that body shape can respond to selection immediately, through plasticity, and over longer timescales through adaptation. In addition, our results suggest that hybridization may have played an important role in the diversification of Lake Malawi cichlids through creating new phenotypic variation.

Factors influencing QTL mapping accuracy under complicated genetic models by computer simulation.

The accuracy of quantitative trait loci (QTLs) identified using different sample sizes and marker densities was evaluated in different genetic models. Model I assumed one additive QTL; Model II assumed three additive QTLs plus one pair of epistatic QTLs; and Model III assumed two additive QTLs with opposite genetic effects plus two pairs of epistatic QTLs. Recombinant inbred lines (RILs) (50-1500 samples) were simulated according to the Models to study the influence of different sample sizes under different genetic models on QTL mapping accuracy. RILs with 10-100 target chromosome markers were simulated according to Models I and II to evaluate the influence of marker density on QTL mapping accuracy. Different marker densities did not significantly influence accurate estimation of genetic effects with simple additive models, but influenced QTL mapping accuracy in the additive and epistatic models. The optimum marker density was approximately 20 markers when the recombination fraction between two adjacent markers was 0.056 in the additive and epistatic models. A sample size of 150 was sufficient for detecting simple additive QTLs. Thus, a sample size of approximately 450 is needed to detect QTLs with additive and epistatic models. Sample size must be approximately 750 to detect QTLs with additive, epistatic, and combined effects between QTLs. The sample size should be increased to >750 if the genetic models of the data set become more complicated than Model III. Our results provide a theoretical basis for marker-assisted selection breeding and molecular design breeding.

Exploring Multimodal Data Fusion Through Joint Decompositions with Flexible Couplings

A Bayesian framework is proposed to define flexible coupling models for joint tensor decompositions of multiple data sets. Under this framework, a natural formulation of the data fusion problem is to cast it in terms of a joint maximum a posteriori (MAP) estimator. Data driven scenarios of joint posterior distributions are provided, including general Gaussian priors and non Gaussian coupling priors. We present and discuss implementation issues of algorithms used to obtain the joint MAP estimator. We also show how this framework can be adapted to tackle the problem of joint decompositions of large datasets. In the case of a conditional Gaussian coupling with a linear transformation, we give theoretical bounds on the data fusion performance using the Bayesian Cramer-Rao bound. Simulations are reported for hybrid coupling models ranging from simple additive Gaussian models, to Gamma-type models with positive variables and to the coupling of data sets which are inherently of different size due to different resolution of the measurement devices.

Controlled information integration and bayesian inference

One of the oldest hypotheses in cognitive psychology is that controlled information integration1 is a serial, capacity-constrained process that is delimited by our working memory resources, and this seems to be the most uncontroversial aspect also of present-day dual-systems theories (Evans, 2008). The process is typically conceived of as a sequential adjustment of an estimate of a criterion (e.g., a probability), in view of successive consideration of inputs to the judgment (i.e., cues or evidence). The “cognitive default” seems to be to consider each attended cue in isolation, taking its impact on the criterion into account by adjusting a previous estimate into a new estimate, until a stopping rule applies (e.g., Juslin et al., 2008). Considering each input in isolation, without modifying the adjustments contingently on other inputs to the judgment, invites additive integration. The limits on working memory moreover contribute to an illusion of linearity. If people, when pondering the relationship between variables X and Y, are constrained by working memory to consider only two X–Y pairs, the function induced can take no other form than a line. As illustrated by many scientific models, with computational aids people can capture also non-additive and non-linear relations. But without support, this is rather taxing on working memory and additive integration, typically as a weighted average, seems to be the default process (Juslin et al., 2009), and, even more so, considering that additive integration is famously “robust” (Dawes, 1979), allowing little marginal benefit from also considering the putative configural effects of cues. These cognitive constraints therefore define a point toward which our judgments naturally gravitate. This simplistic and probably not overly controversial model of controlled integration immediately has important consequences for our abilities to make judgments, some of which are well-known, some of which may still need to be further digested. At a general level, the most fundamental constraint on people's ability to comprehend and control their environment is this tendency to view it in terms of an “additive caricature,” as if they “looked at the world through a straw,” appreciating each factor in isolation, but with limited ability to capture the interactions and dynamics of the entire system. In more prosaic terms, a wealth of evidence suggests that multiple-cue judgments are typically well described by simple linear additive models (Brehmer, 1994; Karelaia and Hogarth, 2008), even if the task departs from linearity and additivity. There are important exceptions where people transcend this imprisonment in a linear additive mental universe also without external computational aids, in particular, an ability to use a prior input to “contextualize” the meaning of an immediately following input. For example, for a lottery, like a 0.10 chance of winning $100 and $0 otherwise, people have little difficulty with contextualizing the outcome in view of the preceding probability; that is, to discount the “appeal” of the positive outcome of receiving $100 by the fact that the probability of ever seeing it is low. Likewise, people often have little difficulty with understanding normalized probability ratios and appreciate that, say, “30 chances in 100” and “300 chances in 1000” describe comparable states of uncertainty, something that again requires that one input is contextualized by another2. These exceptions are important, but seem to be connected to specific judgment domains.

A comparative validation of simple additive models and integrated addition models for predicting mixture toxicity

A comparative validation of simple additive models and integrated addition models for predicting mixture toxicity

Combined influence of visual scene and body tilt on arm pointing movements: gravity matters!

Performing accurate actions such as goal-directed arm movements requires taking into account visual and body orientation cues to localize the target in space and produce appropriate reaching motor commands. We experimentally tilted the body and/or the visual scene to investigate how visual and body orientation cues are combined for the control of unseen arm movements. Subjects were asked to point toward a visual target using an upward movement during slow body and/or visual scene tilts. When the scene was tilted, final pointing errors varied as a function of the direction of the scene tilt (forward or backward). Actual forward body tilt resulted in systematic target undershoots, suggesting that the brain may have overcompensated for the biomechanical movement facilitation arising from body tilt. Combined body and visual scene tilts also affected final pointing errors according to the orientation of the visual scene. The data were further analysed using either a body-centered or a gravity-centered reference frame to encode visual scene orientation with simple additive models (i.e., ‘combined’ tilts equal to the sum of ‘single’ tilts). We found that the body-centered model could account only for some of the data regarding kinematic parameters and final errors. In contrast, the gravity-centered modeling in which the body and visual scene orientations were referred to vertical could explain all of these data. Therefore, our findings suggest that the brain uses gravity, thanks to its invariant properties, as a reference for the combination of visual and non-visual cues.

Penal reform ‘Canadian style’: Fiscal responsibility and decarceration in Alberta, Canada

To fulfil a political promise to eliminate the provincial fiscal deficit, the (conservative) Premier of Alberta cut all budgets by roughly 20 per cent in 1993–1994. As an unanticipated by-product, this political solution to a political problem resulted in a 32 per cent decrease in provincial imprisonment between 1993 and 1997. Economic imperatives created the catalyst for changes in imprisonment policies. However, the types of change and the mechanisms for achieving them reflected Canada’s specific history, culture and politico-legal structures. Decarceration was consistent with core Canadian values rooted in the long-standing belief in the need for restraint in the use of imprisonment and a lack of faith in its effectiveness as a crime control strategy. On the surface, this case study is yet another example of decarceration. However, the interactive and multi-factorial explanatory model underlying Alberta’s reduction in its prison population raises questions about not only single factors or simple additive models as explanations for changes in penal policies but also uni-dimensional solutions to jurisdictions in need of fiscal restraint. The historical and cultural embeddedness of Alberta’s decarceration alerts us to its country-specific nature and the need to situate imprisonment in a broader set of concerns.

Exploration of Lagged Associations using Longitudinal Data

Several statistical approaches for the analysis of longitudinal data require that models be correctly specified for the association between a current outcome and the full history of past outcomes and time-dependent exposures. It is empirically challenging to determine the specific aspects of the outcome and/or exposure history that are predictive of a current outcome because the potential number of variables representing the history can be quite large. The purpose of this article is to outline statistical methods that can characterize lagged effects and to provide a structured approach for data analysis with the goal of appropriate model development. One of the main contributions of the article is to emphasize the possibility that in practice transition models may frequently require more than simple additive and linear models for the predictors representing the history of the outcome and covariate processes. We illustrate the concepts using an example from anemia treatment for dialysis patients and show how linear models can be specified with flexible dependence on exposure and/or outcome histories.

Radiation and Smoking Effects on Lung Cancer Incidence by Histological Types Among Atomic Bomb Survivors

While the risk of lung cancer associated separately with smoking and radiation exposure has been widely reported, it is not clear how smoking and radiation together contribute to the risk of specific lung cancer histological types. With individual smoking histories and radiation dose estimates, we characterized the joint effects of radiation and smoking on type-specific lung cancer rates among the Life Span Study cohort of Japanese atomic bomb survivors. Among 105,404 cohort subjects followed between 1958 and 1999, 1,803 first primary lung cancer incident cases were diagnosed and classified by histological type. Poisson regression methods were used to estimate excess relative risks under several interaction models. Adenocarcinoma (636 cases), squamous-cell carcinoma (330) and small-cell carcinoma (194) made up 90% of the cases with known histology. Both smoking and radiation exposure significantly increased the risk of each major lung cancer histological type. Smoking-associated excess relative risks were significantly larger for small-cell and squamous-cell carcinomas than for adenocarcinoma. The gender-averaged excess relative risks per 1 Gy of radiation (for never-smokers at age 70 after radiation exposure at age 30) were estimated as 1.49 (95% confidence interval 0.1-4.6) for small-cell carcinoma, 0.75 (0.3-1.3) for adenocarcinoma, and 0.27 (0-1.5) for squamous-cell carcinoma. Under a model allowing radiation effects to vary with levels of smoking, the nature of the joint effect of smoking and radiation showed a similar pattern for different histological types in which the radiation-associated excess relative risk tended to be larger for moderate smokers than for heavy smokers. However, in contrast to analyses of all lung cancers as a group, such complicated interactions did not describe the data significantly better than either simple additive or multiplicative interaction models for any of the type-specific analyses.

Stickbreaking: A Novel Fitness Landscape Model That Harbors Epistasis and Is Consistent with Commonly Observed Patterns of Adaptive Evolution

In relating genotypes to fitness, models of adaptation need to both be computationally tractable and qualitatively match observed data. One reason that tractability is not a trivial problem comes from a combinatoric problem whereby no matter in what order a set of mutations occurs, it must yield the same fitness. We refer to this as the bookkeeping problem. Because of their commutative property, the simple additive and multiplicative models naturally solve the bookkeeping problem. However, the fitness trajectories and epistatic patterns they predict are inconsistent with the patterns commonly observed in experimental evolution. This motivates us to propose a new and equally simple model that we call stickbreaking. Under the stickbreaking model, the intrinsic fitness effects of mutations scale by the distance of the current background to a hypothesized boundary. We use simulations and theoretical analyses to explore the basic properties of the stickbreaking model such as fitness trajectories, the distribution of fitness achieved, and epistasis. Stickbreaking is compared to the additive and multiplicative models. We conclude that the stickbreaking model is qualitatively consistent with several commonly observed patterns of adaptive evolution.

Aging properties of the lifetime in simple additive degradation models

This paper deals with the simple additive degradation models with single random effect. The authors further study the link between the aging property of the implied lifetime and that of the random variation. It is found that both the aging property of the random variation and the analytical behavior of the mean degradation path influence the aging behavior of the implied lifetime.