Investigation of smoothness-increasing accuracy-conserving filters for improving streamline integration through discontinuous fields

Journal ArticleStreamline integration of fields produced by computational fluid mechanics simulations is a commonly used tool for the investigation and analysis of fluid flow phenomena. Integration is often accomplished through the application of ordinary differential equation (ODE) integrators - integrators whose error characteristics are predicated on the smoothness of the field through which the streamline is being integrated, which is not available at the interelement level of finite volume and finite element data. Adaptive error control techniques are often used to ameliorate the challenge posed by interelement discontinuities

Superaccurate effective elastic moduli via postprocessing in computational homogenization

With the complexity of modern microstructured materials, computational homogenization methods have been shown to provide accurate estimates of their effective mechanical properties, reducing the involved experimental effort considerably. After solving the balance of linear momentum on the microscale, the effective stress is traditionally computed through volume averaging the microscopic stress field. In the work at hand, we exploit the idea that averaging the elastic energy may lead to much more accurate effective elastic properties than through stress averaging. We show that the accuracy is roughly doubled when using energy equivalence instead of strain equivalence for compatible iterates of iterative schemes. Thus, to achieve a prescribed accuracy, the necessary effort is roughly reduced by a factor of two. In addition to the theory, we provide a handbook for utilizing these ideas for modern solvers prominent in FFT-based micromechanics. We demonstrate the superiority of energy averaging through computational examples, discuss the peculiarities of polarization methods with their non-compatible iterates and expose a superaccuracy phenomenon occurring for the linear conjugate gradient method

Visualisation, VISC and scientific insight

CISRG discussion paper ;

Evaluating Biased Attitude Associations of Language Models in an Intersectional Context

Language models are trained on large-scale corpora that embed implicit biases documented in psychology. Valence associations (pleasantness/unpleasantness) of social groups determine the biased attitudes towards groups and concepts in social cognition. Building on this established literature, we quantify how social groups are valenced in English language models using a sentence template that provides an intersectional context. We study biases related to age, education, gender, height, intelligence, literacy, race, religion, sex, sexual orientation, social class, and weight. We present a concept projection approach to capture the valence subspace through contextualized word embeddings of language models. Adapting the projection-based approach to embedding association tests that quantify bias, we find that language models exhibit the most biased attitudes against gender identity, social class, and sexual orientation signals in language. We find that the largest and better-performing model that we study is also more biased as it effectively captures bias embedded in sociocultural data. We validate the bias evaluation method by overperforming on an intrinsic valence evaluation task. The approach enables us to measure complex intersectional biases as they are known to manifest in the outputs and applications of language models that perpetuate historical biases. Moreover, our approach contributes to design justice as it studies the associations of groups underrepresented in language such as transgender and homosexual individuals.Comment: to be published in AIES 202

Energy dissipation in the time domain governed by bosons in a correlated material

In complex materials various interactions play important roles in determining the material properties. Angle Resolved Photoelectron Spectroscopy (ARPES) has been used to study these processes by resolving the complex single particle self energy $\Sigma(E)$ and quantifying how quantum interactions modify bare electronic states. However, ambiguities in the measurement of the real part of the self energy and an intrinsic inability to disentangle various contributions to the imaginary part of the self energy often leave the implications of such measurements open to debate. Here we employ a combined theoretical and experimental treatment of femtosecond time-resolved ARPES (tr-ARPES) and show how measuring the population dynamics using tr-ARPES can be used to separate electron-boson interactions from electron-electron interactions. We demonstrate the analysis of a well-defined electron-boson interaction in the unoccupied spectrum of the cuprate Bi$_{2}$Sr$_{2}$CaCu$_{2}$O$_{8+x}$ characterized by an excited population decay time constant $\tau_{QP}$ that maps directly to a discrete component of the equilibrium self energy not readily isolated by static ARPES experiments.Comment: 19 pages with 6 figure

Metatheory of actions: beyond consistency

Consistency check has been the only criterion for theory evaluation in logic-based approaches to reasoning about actions. This work goes beyond that and contributes to the metatheory of actions by investigating what other properties a good domain description in reasoning about actions should have. We state some metatheoretical postulates concerning this sore spot. When all postulates are satisfied together we have a modular action theory. Besides being easier to understand and more elaboration tolerant in McCarthy's sense, modular theories have interesting properties. We point out the problems that arise when the postulates about modularity are violated and propose algorithmic checks that can help the designer of an action theory to overcome them

What Does Explainable AI Really Mean? A New Conceptualization of Perspectives

We characterize three notions of explainable AI that cut across research fields: opaque systems that offer no insight into its algo- rithmic mechanisms; interpretable systems where users can mathemat- ically analyze its algorithmic mechanisms; and comprehensible systems that emit symbols enabling user-driven explanations of how a conclusion is reached. The paper is motivated by a corpus analysis of NIPS, ACL, COGSCI, and ICCV/ECCV paper titles showing differences in how work on explainable AI is positioned in various fields. We close by introducing a fourth notion: truly explainable systems, where automated reasoning is central to output crafted explanations without requiring human post processing as final step of the generative process

Isotonic distributional regression

Isotonic distributional regression (IDR) is a powerful non-parametric technique for the estimation of conditional distributions under order restrictions. In a nutshell, IDR learns conditional distributions that are calibrated, and simultaneously optimal relative to comprehensive classes of relevant loss functions, subject to isotonicity constraints in terms of a partial order on the covariate space. Non-parametric isotonic quantile regression and non-parametric isotonic binary regression emerge as special cases. For prediction, we propose an interpolation method that generalizes extant specifications under the pool adjacent violators algorithm. We recommend the use of IDR as a generic benchmark technique in probabilistic forecast problems, as it does not involve any parameter tuning nor implementation choices, except for the selection of a partial order on the covariate space. The method can be combined with subsample aggregation, with the benefits of smoother regression functions and gains in computational efficiency. In a simulation study, we compare methods for distributional regression in terms of the continuous ranked probability score (CRPS) and 2 estimation error, which are closely linked. In a case study on raw and post-processed quantitative precipitation forecasts from a leading numerical weather prediction system, IDR is competitive with state of the art techniques