Implicit Scale Research Articles

Jetzt hab ich voll die Panik: Prototype effects of NP-external intensifiers in German

Abstract This paper explores two types of degree modifiers in German, namely total and voll. While most degree modifiers are usually placed inside an NP, total and voll frequently appear in a position outside of an NP, giving them the function of intensifiers rather than pure modifiers. Hence, they become NP-external intensifiers. Previous studies have suggested that these NP-external intensifiers either take an adjective inside the subsequent NP in their scope, or, if there is none, the NP head. Based on corpus data from the deCow16B, we suggest an alternative interpretation where the scope of the intensifier always ranges over the subsequent NP as a whole, irrespective of it containing an adjective or not. We suggest that this can be described based on prototype effects and underdetermination, whether there is an adjective present or not. The NP denotes an exemplar of a (prototypical) category: the category membership is understood as scalar and the NP is intensified on this implicit scale.

The things that we can(not) exclaim!

English famously has both wh- and nominal exclamatives (e.g., Portner & Zanuttini 2005). For instance, both What (strange) stories Nina tells! and The (strange) stories that Nina tells! are expressive speech acts about the very high degree that the stories that Nina tells have on some—explicit or implicit—scale. However, puzzlingly enough, Russian only has wh-, but not nominal exclamatives. I propose, partially following Esipova 2021, that both wh- and nominal exclamatives are instances of expressive intensification (akin to She is damn smart), with the expressive component of the expressive intensifier promoted to being the primary speech act (not unlike in Damn {she is / is she} smart!). However, while English can do direct intensification of NPs (e.g., He’s a damn coward) and can, thus, have nominal exclamatives, Russian can only do expressive intensification by predicating a degree property of a free relative over degrees and can, thus, only have wh-exclamatives.

Modeling the Convective Boundary Layer in the Terra Incognita: Evaluation of Different Strategies with Real-Case Simulations

Abstract Modeling atmospheric turbulence in the convective boundary layer is challenging at kilometer and subkilometer resolutions, as the horizontal grid spacing approaches the size of the most energetic turbulent eddies. In this range of resolutions, termed terra incognita or gray zone, partially resolved convective structures are grid dependent and neither traditional 1D mesoscale parameterizations nor 3D large-eddy simulations closures are theoretically appropriate. Leveraging on a new set of one-way nested, full-physics multiscale numerical experiments, we quantify the magnitude of the errors introduced at gray zone resolutions in a real-case application and we provide new perspectives on recently proposed modeling approaches. The new set of experiments is forced by real-time-varying boundary conditions, spans a wide range of scales, and includes traditional 1D schemes, 3D closures, scale-aware parameterizations, and strategies to suppress resolved convection at gray zone resolutions. The study area is Riyadh (Saudi Arabia), where deep CBLs develop owing to strong convective conditions. Detailed analyses of our experiments, including validation with radiosonde data, calculations of spectral features, and partitioning of turbulent fluxes between resolved and subgrid scales, show that (i) grid-dependent convective structures entail minor impacts on the first-order characteristics of the fully developed boundary layer due to some degree of implicit scale awareness of 1D parameterizations and (ii) 3D closures and scale-aware schemes outperform traditional 1D schemes especially in the surface layer, among other findings. The new suite of experiments provides a benchmark of real simulations that can be extended to assess how new turbulence closures perform at gray zone resolutions. Significance Statement As recent advances in high-performance computing are leading to a new era in numerical simulations, regional atmospheric models can now increase their resolution to the widely unexplored kilometer and subkilometer range. While increasing the resolution of atmospheric models is desirable to (i) have more realistic weather and air quality predictions and (ii) better represent boundary conditions for microscale models, kilometer and subkilometer grid spacings pose some theoretical challenges that need to be addressed by the atmospheric modeling community. In this work we run a set of numerical experiments for a real case study that aim to offer new perspectives on recently developed modeling strategies and identify the most promising directions that should be investigated by follow-up studies.

Pansharpening by Convolutional Neural Networks in the Full Resolution Framework

In recent years, there has been a growing interest in deep learning-based pansharpening. Thus far, research has mainly focused on architectures. Nonetheless, model training is an equally important issue. A first problem is the absence of ground truths, unavoidable in pansharpening. This is often addressed by training networks in a reduced resolution domain and using the original data as ground truth, relying on an implicit scale invariance assumption. However, on full resolution images results are often disappointing, suggesting such invariance not to hold. A further problem is the scarcity of training data, which causes a limited generalization ability and a poor performance on off-training test images. In this paper, we propose a full-resolution training framework for deep learning-based pansharpening. The framework is fully general and can be used for any deep learning-based pansharpening model. Training takes place in the high-resolution domain, relying only on the original data, thus avoiding any loss of information. To ensure spectral and spatial fidelity, a suitable two-component loss is defined. The spectral component enforces consistency between the pansharpened output and the low-resolution multispectral input. The spatial component, computed at high-resolution, maximizes the local correlation between each pansharpened band and the panchromatic input. At testing time, the target-adaptive operating modality is adopted, achieving good generalization with a limited computational overhead. Experiments carried out on WorldView-3, WorldView-2, and GeoEye-1 images show that methods trained with the proposed framework guarantee a pretty good performance in terms of both full-resolution numerical indexes and visual quality.

Structural uncertainty through the lens of model building

An important epistemic issue in climate modelling concerns structural uncertainty: uncertainty about whether the mathematical structure of a model accurately represents its target. How does structural uncertainty affect our knowledge and predictions about the climate? How can we identify sources of structural uncertainty? Can we manage the effect of structural uncertainty on our knowledge claims? These are some of the questions that an epistemology of structural uncertainty faces, and these questions are also important for climate scientists and policymakers. I develop three desiderata for an epistemological account of structural uncertainty. In my view, an account of structural uncertainty should (1) identify sources of structural uncertainty, (2) explain how these sources limit the applicability of a model, and (3) show how the severity of structural uncertainty depends on the questions that can be asked of a model. I argue that analyzing structural uncertainty by paying attention to the details of model building can satisfy these desiderata. I focus on parametrizations, which are representations of important processes occurring at scales that are not resolved by climate models. Parametrizations are often thought to be ad-hoc, but I show that some important parametrizations are theoretically justified by explicit or implicit scale separation assumptions. These assumptions can also be supported empirically. Analyzing these theoretical and empirical justificatory roles of the scale separation assumptions can provide insights into how parametrizations contribute to structural uncertainty. I conclude by sketching how my approach can satisfy the desiderata I set out at the beginning, highlighting its importance for policy-relevant scientific statements about the climate.

Confirmatory Factor Analysis of Implicit Beliefs Measure and the Role of Demographics among Pakistani Sample.

Implicit theories (also referred to self-theories) represent a cognitive conceptualization about a matter, generally raised as a belief. It is marked as the primary aspect of cognitive processing among living beings affecting their overall behavior towards others'. In the present study, it is attempted to consider a Pakistani perspective on this phenomenon of self-theories and also to validate the implicit theories Scale. It is a measure of people's beliefs about things to be fixed or changeable. A quantitative approach of correlational methodology was employed. Participants of the study were 355 Pakistani young adults with an age range of 20-30 years (M = 23.08, SD = 1.99). There were 175 males and 180 females (as they reported their gender) from Islamabad. Confirmatory factor analysis was computed to assess the dimensionality of the scale. An adequate model fit indices were found as Root Mean Square Error of Approximation = .04, Comparative Fit Index = .99, Tucker-Lewis Index = .98, Goodness of Fit Index = .97, and Incremental Fit Index = .99, confirming a bidimensional implicit theories measure. The reliability coefficients of Entity Theory and Incremental Theory subscales were assessed through internal consistency and test-retest methods which are found to be in an acceptable range. Demographic specifications are also addressed to reflect upon the indigenous importance of this concept. This will be an additive feature in the literature to consider the cultural specification enabling individuals to align their mindsets in the desired direction of growth and achievement.

Targeted affordable housing subsidies in Flanders: evaluating equity using equivalence scales

In Flanders – the northern region of Belgium – various targeted housing subsidies exist, aimed at improving affordability for low income households. For these subsidies, the income limits differ according to household type, in order to adjust for different consumption needs. In this article, we will show how equivalence scales can be applied to evaluate the horizontal equity – in terms of household composition – of targeted affordability instruments. We compute the ‘implicit’ equivalence scale of each instrument and compare them with well-known equivalence scales (the modified OECD and square root scale). Using the 2013 Flemish Housing Survey, the size and composition of the target group for the main targeted housing subsidies is calculated, both for the implicit (currently used) and commonly applied equivalence scales. A comparison with the results of the well-known scales shows that the currently applied implicit equivalence scale generally favours lone parents, whereas in the case of housing allowances couples are disadvantaged compared to single people. As regards the housing allowance, the entitled share belonging to the second income quintile rose strongly when the OECD or square root scales were used. Applying these scales would therefore improve some equity aspects of the subsidies.

Analytical Description of Optimally Time-Dependent Modes for Reduced-Order Modeling of Transient Instabilities

The optimally time-dependent (OTD) modes form a time-evolving orthonormal basis that captures directions in phase space associated with transient and persistent instabilities. In the original formulation, the OTD modes are described by a set of coupled evolution equations that need to be solved along the trajectory of the system. For many applications where real-time estimation of the OTD modes is important, such as control or filtering, this is an expensive task. Here, we examine the low-dimensional structure of the OTD modes. In particular, we consider the case of slow-fast systems, and prove that OTD modes rapidly converge to a slow manifold, for which we derive an asymptotic expansion. The result is a parametric description of the OTD modes in terms of the system state in phase space. The analytical approximation of the OTD modes allows for their offline computation, making the whole framework suitable for real-time applications. In addition, we examine the accuracy of the slow-manifold approximation for systems in which there is no explicit time-scale separation. In this case, we show numerically that the asymptotic expansion of the OTD modes is still valid for regions of the phase space where strongly transient behavior is observed, and for which there is an implicit scale separation. We also find an analogy between the OTD modes and the Gram--Schmidt vectors (also known as orthogonal or backward Lyapunov vectors), and thereby establish new properties of the former. Several examples of low-dimensional systems are provided to illustrate the analytical formulation.

The mirative and scalar meaning of even

The analysis I develop here draws on the concept of mirativity (DeLancey 1987) and on Kay’s (1990) insight into the scalar nature of even. I show that even calls for the reconstruction of an implicit scale, on which the element highlighted by this adverbial marker stands out as the most (or more) surprising in the set considered. This non-referential, non-propositional meaning of even is likely to trigger a series of inferences, whose status is duly discussed, and to strengthen or weaken other derived assumptions, and even to guide their derivation itself.

Identifying tax implicit equivalence scales

This paper describes a simple and tractable method for identifying equivalence scales that reflect the value judgements implicit in a tax and transfer system. The approach depends on two identifying assumptions and a functional description for transfer payments that can be estimated using common publicly available data sources. We use this approach to evaluate tax implicit equivalence scales for the tax-transfer systems of 12 European countries that applied in 2012. Cross-country averages for the tax implicit scales generate a surprising set of stylised results: at low incomes, each additional household member increases the tax implicit scale by approximately 0.5, relative to 1.0 for the first adult; at high incomes, the average tax implicit scales describe variation that is remarkably similar to the modified OECD scale. However, substantial cross-country variation underlies these average scales, suggesting important differences in value judgements implicit in the respective tax-transfer systems; differences that can otherwise be difficult to discern when systems are complex and opaque.

Determinants of Net Interest Margin in the Ethiopian Banking Industry

Determinants of Net Interest Margin (NIM) of commercial banks vary from economy to economy due to variation in country, industry and firm specific factors. This study aims to assess and identify the determinants of NIM in the Ethiopian banking industry. It mainly used unbalanced panel data collected from annual reports commercial banks and the National Bank of Ethiopia for the period 1997 to 2014. Specific macroeconomic data such as Real GDP and Inflation were collected from annual reports of Ministry of Finance and Economic Development. In addition, expert opinions are obtained from officials operating in both private and public banks and used to examine the effects of changes in internal and external factors on the performance of banks. Fixed Effect unbalanced panal data model is used for data analysis. The findings of the study indicated that cost efficiency, implicit interest payment, competition and scale efficiency consistently have positive and significant effects on NIM. On the other hand, liquidity risk and management efficiency has negative and significant effect on NIM. However, macroeconomic variables like inflation and gross domestic product do not seem to have significant effect on NIM. From this finding it is possible to conclude that both bank specific and industry factors are indispensable determinants of commercial banks’ performance in Ethiopia. This, in turn, shows that operational efficiency and business growth are important areas of emphasis to register optimal return. Thus, bank executives, bank advisors and the monetary authority of Ethiopia need to focus on these two pillars performance to ensure optimal NIM in banks.

Optimization of an Implicit Large-Eddy Simulation Method for Underresolved Incompressible Flow Simulations

In engineering applications, resolution is often low. In these underresolved regions, the truncation error of the underlying numerical schemes strongly affects the solution. If the truncation error functions as a physically consistent subgrid-scale model (that is, it models the evolution of otherwise resolved scales), resolution may remain low. Thereby, computational efficiency is improved. The sixth-order adaptive central-upwind weighted essentially nonoscillatory scheme with implicit scale separation, denoted as WENO-CU6-M1, potentially allows for physically consistent implicit subgrid-scale modeling, when shaped accordingly. In this work, finding an optimal formulation of WENO-CU6-M1 is considered within a deterministic design optimization framework. Possible surrogate modeling and sampling strategies are considered. Design optimization is based on evaluating the potential of a WENO-CU6-M1 scheme formulation to reproduce Kolmogorov scaling for a Taylor–Green vortex in its quasi-isotropic state. As in the absence of physical viscosity, kinetic energy dissipates exclusively due to the subgrid-scales, the Reynolds number is infinite, and the evolution of the flow is determined by proper subgrid-scale modeling. To complete the work, the effective numerical dissipation rate of the WENO-CU6-M1 model optimized for artificially compressible fluid flows is quantified, and it is compared to the original one. Not only is the zero viscosity limit considered, but the model behavior is benchmarked offdesign, for low to high Reynolds numbers. A comparison to an alternative explicit and implicit subgrid-scale model demonstrates its superior behavior for the chosen test flow.

Test Models for Filtering with Superparameterization

Superparameterization is a fast numerical algorithm to mitigate implicit scale separation of dynamical systems with large-scale, slowly varying “mean” and smaller-scale, rapidly fluctuating “eddy” term. The main idea of superparameterization is to embed parallel highly resolved simulations of small-scale eddies on each grid cell of coarsely resolved large-scale dynamics. In this paper, we study the effect of model errors in using superparameterization for filtering multiscale turbulent dynamical systems. In particular, we use a simple test model, designed to mimic typical multiscale turbulent dynamics with small-scale intermittencies without local statistical equilibriation conditional to the large-scale mean dynamics, and simultaneously force the large-scale dynamics through eddy flux terms. In this paper, we consider the Fourier domain Kalman filter for filtering regularly spaced sparse observations of the large-scale mean variables. We find high filtering and statistical prediction skill with superparameterization (identical to the skill with perfect model), beyond conventional approaches such as the “bare truncation model” that ignores completely the eddy fluxes and the “equilibrium closure” model that crudely approximates the eddy fluxes with classical averaging theory. We show that this high filtering skill is robust even for very sparse observation networks and turbulent signals with a very steep, $-6$, spectrum. This is a counterexample to naive thinking that the small-scale processes are not so important in multiscale turbulent dynamics with steep energy spectrum. We find that the high filtering skill with superparameterization is robust for small enough scale gap, provided that the filter prior model satisfies the classical linear controllability condition. We will demonstrate a spectacular failure of filtering deterministically forced true signals with the exactly perfect model that does not satisfy controllability condition and a dramatic improvement when the controllability condition is restored with additional stochastic forcings. This result reconfirms and justifies the counterintuitive viewpoint that judicious model errors (or noises) can help filtering turbulent signals.

Relaxation system based sub-grid scale modelling for large eddy simulation of Burgers' equation

An implicit sub-grid scale model for large eddy simulation is presented by utilising the concept of a relaxation system for one dimensional Burgers' equation in a novel way. The Burgers' equation is solved for three different unsteady flow situations by varying the ratio of relaxation parameter (ε) to time step. The coarse mesh results obtained with a relaxation scheme are compared with the filtered DNS solution of the same problem on a fine mesh using a fourth-order CWENO discretisation in space and third-order TVD Runge-Kutta discretisation in time. The numerical solutions obtained through the relaxation system have the same order of accuracy in space and time and they closely match with the filtered DNS solutions.

Multiscale modeling of longleaf pine (Pinus palustris)

There are few remaining longleaf pine ( Pinus palustris Mill.) ecosystems left in the southeastern coastal plain of the United States. Restoration and maintenance of these remaining habitats requires an understanding of ecosystem processes at multiple scales. The focus of this study was to develop and evaluate a modeling framework for analyzing longleaf pine dynamics at the spatially explicit landscape scale and at the spatially implicit population scale. The landscape disturbance and succession (LANDIS) model was used to simulate landscape fire dynamics in a managed forest in north-central Florida. We constructed a density-dependent longleaf pine population matrix model using data from a variety of studies across the southeastern United States to extend an existing model. Sensitivity analyses showed that the most sensitive parameters were those from the original pine model, which was based on extensive observations of individual trees. A hybrid approach integrated the two models: the fire frequencies output from the LANDIS model were input to the matrix model for specific longleaf pine populations. These simulations indicated that small isolated longleaf pine populations are more vulnerable to fire suppression and that landscape connectivity is a critical concern. A frequent prescribed fire regime is nonetheless necessary to maintain even large longleaf pine sandhill communities that have better landscape connectivity.

Rankings, standards, and competition: Task vs. scale comparisons

Research showing how upward social comparison breeds competitive behavior has so far conflated local comparisons in task performance (e.g. a test score) with comparisons on a more general scale (i.e. an underlying skill). Using a ranking methodology ( Garcia, Tor, & Gonzalez, 2006) to separate task and scale comparisons, Studies 1–2 reveal that an upward comparison on the scale (e.g. being surpassed in rank), rather than in the mere task (e.g., being outperformed), is necessary to generate competition among rivals proximate to a standard (e.g. ranked #3 vs. 4, near “the top”); rivals far from a standard (e.g. ranked #203 vs. 204), on the other hand, still tend to cooperate. Study 3 illustrates this finding with player trades in Major League Baseball. Study 4 further shows how an implicit scale comparison, instead of the commonly assumed explicit task comparison, may account for those classical competition findings in the literature. Study 5 then reveals how scale ranking becomes all important in the proximity of a standard, leading rivals to tolerate even an upward scale comparison to increase their proximity to the standard. Implications for the increasingly popular “forced ranking” management systems (e.g., at General Electric) are also discussed.

Evidence for carbon dioxide and moisture interactions from the leaf cell up to global scales: Perspective on human-caused climate change

It is of utmost interest to further understand the mechanisms behind the potential interactions or synergies between the greenhouse gases (GHG) forcing(s), particularly as represented by CO 2, and water processes and through different climatic scales down to the leaf scale. Toward this goal, the factor separation methodology introduced by Stein and Alpert [Stein U. and Alpert, P. 1993. Factor separation in numerical simulations, J. Atmos. Sci., 50, 2107–2115.] that allows an explicit separation of atmospheric synergies among different factors, is employed. Three independent experiments carried out recently by the present authors, are reported here, all strongly suggest the existence of a significant CO 2–water synergy in all the involved scales. The experiments employed a very wide range of up-to-date atmospheric models that complement the physics currently introduced in most Global Circulation Models (GCMs) for global climate change prediction. Three modeling experiments that go from the small/micro scale (leaf scale and soil moisture) to mesoscale (land-use change and CO 2 effects ) and to global scale (greenhouse gases and cloudiness) all show that synergies between water and CO 2 are essential in predicting carbon assimilation, minimum daily temperature and the global Earth temperature, respectively. The study also highlights the importance of including the physics associated with carbon–water synergy which is mostly unresolved in global climate models suggesting that significant carbon–water interactions are not incorporated or at least well parameterized in current climate models. Hence, there is a need for integrative climate models. As shown in earlier studies, the climate involves physical, chemical and biological processes. To only include a subset of these processes limits the skill of local, regional and global models to simulate the real climate system. In addition, our results provide explicit determination of the direct and the interactive effect of the CO 2 response on the terrestrial biosphere response. There is also an implicit scale interactive effect that can be deduced from the multiscale effects discussed in the three examples. Processes at each scale-leaf, regional and global will all synergistically contribute to increase the feedbacks — which can decrease or increase the overall system's uncertainty depending on specific case/setup and needs to be examined in future coupled, multiscale studies.

Heavy to light meson exclusive semileptonic decays in effective field theory of heavy quarks

We present a general study on exclusive semileptonic decays of heavy (B, D, B_s) to light (pi, rho, K, K^*) mesons in the framework of effective field theory of heavy quark. Transition matrix elements of these decays can be systematically characterized by a set of wave functions which are independent of the heavy quark mass except for the implicit scale dependence. Form factors for all these decays are calculated consistently within the effective theory framework using the light cone sum rule method at the leading order of 1/m_Q expansion. The branching ratios of these decays are evaluated, and the heavy and light flavor symmetry breaking effects are investigated. We also give comparison of our results and the predictions from other approaches, among which are the relations proposed recently in the framework of large energy effective theory.

Wilsonian flows and background fields

We study exact renormalization group flows for background field dependent regularizations. It is shown that proper-time flows are approximations to exact background field flows for a specific class of regulators. We clarify the rôle of the implicit scale dependence introduced by the background field. Its impact on the flow is evaluated numerically for scalar theories at criticality for different approximations and regularizations. Implications for gauge theories are discussed.

CONTACT QUALITY AND ATTITUDES TOWARD THE ELDERLY

The contact hypothesis (Allport, 1954) states that cooperative contact with individual members of an out-group can lead to a general more positive attitude toward the out-group as a whole. The research reported in this article investigated the validity of the contact hypothesis within the context of interage interactions. Specifically, the study examined the relationship between young adults' contact with older adults and their attitudes toward the elderly in general. Previous research has emphasized that various factors related to contact quality strongly influence the formation of positive attitudes. In accordance with this assertion, we hypothesized that a single factor, participants' self-reported quality of contact with older individuals, would be related to their attitudes toward the elderly as measured by an implicit attitude scale. Participants' self-reported frequency of contact with older adults was expected to have no effect on their attitudes. College-aged participants were given a single questionnaire that assessed (a) the frequency and quality of their contact with elderly men and women and (b) their implicit attitudes toward the elderly. The results confirmed that self-reported favorable quality, but not frequency, of contact was significantly related to more positive attitudes toward the elderly.