A Note on the Time Series Measure of Conservatism

Asymmetric persistence of accounting income is often tested in a regression of changes in earnings on lagged changes in earnings, including an interaction term for negative changes (see Basu [1997] or Ball et al. [2009] for a recent overview). In this note we propose an alternative, but closely related measure of conservatism - regressing the changes in earnings on the lagged levels, similar to the threshold-unit root test specification of Enders and Granger [1998]. We argue that this approach has three distinct advantages compared to the conventional setup: (i) a smooth, non-oscillating impulse response pattern to an unexpected shock in earnings (ii) a return to the old equilibrium of earnings in the long run and (iii) it can be extended to higher order autoregressive processes. We illustrate the differences between the two approaches, when applied to a common data set of firms, as well as a data set from a Monte Carlo simulation.Timely loss recognition, Asymmetric persistence, Conservatism

A Note on the Time Series Measure of Conservatism

Asymmetric persistence of accounting income is often tested in a regression of changes in earnings on lagged changes in earnings, including an interaction term for negative changes (see Basu [1997] or Ball et al. [2009] for a recent overview). In this note we propose an alternative, but closely related measure of conservatism - regressing the changes in earnings on the lagged levels, similar to the threshold-unit root test specification of Enders and Granger [1998]. We argue that this approach has three distinct advantages compared to the conventional setup: (i) a smooth, non-oscillating impulse response pattern to an unexpected shock in earnings (ii) a return to the old equilibrium of earnings in the long run and (iii) it can be extended to higher order autoregressive processes. We illustrate the differences between the two approaches, when applied to a common data set of firms, as well as a data set from a Monte Carlo simulation.timely loss recognition, asymmetric persistence, conservatism

Does the Introduction of IFRS Change the Timeliness of Loss Recognition? Evidence from German Firms

In this paper, we re-evaluate the hypothesis that the introduction of the IFRS has an impact on the timeliness of loss recognition. We test this hypothesis in a data set of public German firms that report according to German-GAAP and IFRS, respectively. The parallel use of the two accounting standards in Germany provides a unique opportunity to contribute to the academic discussion, as well as to the current policy debate on regulatory reform in Germany. Starting from the standard time series concept of conditional conservatism that was initially proposed by Basu (1997), we implement a wide range of test specifications, including (i) a threshold unit-root test specification; (ii) a multivariate approach to outlier detection and (iii) various forms of controlling for fixed effects. We do not find evidence that IFRS and German-GAAP firms differ with respect to their timeliness of loss recognition in any of these specifications - a result that appears surprising in light of the more prudent regulation in the German-GAAP, but is consistent with some earlier findings in the literature.IFRS, German-GAAP, Timely loss recognition, Conservatism

Fast Neural Representations for Direct Volume Rendering

Despite the potential of neural scene representations to effectively compress 3D scalar fields at high reconstruction quality, the computational complexity of the training and data reconstruction step using scene representation networks limits their use in practical applications. In this paper, we analyze whether scene representation networks can be modified to reduce these limitations and whether such architectures can also be used for temporal reconstruction tasks. We propose a novel design of scene representation networks using GPU tensor cores to integrate the reconstruction seamlessly into on-chip raytracing kernels, and compare the quality and performance of this network to alternative network- and non-network-based compression schemes. The results indicate competitive quality of our design at high compression rates, and significantly faster decoding times and lower memory consumption during data reconstruction. We investigate how density gradients can be computed using the network and show an extension where density, gradient and curvature are predicted jointly. As an alternative to spatial super-resolution approaches for time-varying fields, we propose a solution that builds upon latent-space interpolation to enable random access reconstruction at arbitrary granularity. We summarize our findings in the form of an assessment of the strengths and limitations of scene representation networks \changed{for compression domain volume rendering, and outline future research directions

Volumetric Isosurface Rendering with Deep Learning-Based Super-Resolution

Rendering an accurate image of an isosurface in a volumetric field typically requires large numbers of data samples. Reducing the number of required samples lies at the core of research in volume rendering. With the advent of deep learning networks, a number of architectures have been proposed recently to infer missing samples in multi-dimensional fields, for applications such as image super-resolution and scan completion. In this paper, we investigate the use of such architectures for learning the upscaling of a low-resolution sampling of an isosurface to a higher resolution, with high fidelity reconstruction of spatial detail and shading. We introduce a fully convolutional neural network, to learn a latent representation generating a smooth, edge-aware normal field and ambient occlusions from a low-resolution normal and depth field. By adding a frame-to-frame motion loss into the learning stage, the upscaling can consider temporal variations and achieves improved frame-to-frame coherence. We demonstrate the quality of the network for isosurfaces which were never seen during training, and discuss remote and in-situ visualization as well as focus+context visualization as potential application

Postprocessing of Ensemble Weather Forecasts Using Permutation-invariant Neural Networks

Statistical postprocessing is used to translate ensembles of raw numerical weather forecasts into reliable probabilistic forecast distributions. In this study, we examine the use of permutation-invariant neural networks for this task. In contrast to previous approaches, which often operate on ensemble summary statistics and dismiss details of the ensemble distribution, we propose networks which treat forecast ensembles as a set of unordered member forecasts and learn link functions that are by design invariant to permutations of the member ordering. We evaluate the quality of the obtained forecast distributions in terms of calibration and sharpness, and compare the models against classical and neural network-based benchmark methods. In case studies addressing the postprocessing of surface temperature and wind gust forecasts, we demonstrate state-of-the-art prediction quality. To deepen the understanding of the learned inference process, we further propose a permutation-based importance analysis for ensemble-valued predictors, which highlights specific aspects of the ensemble forecast that are considered important by the trained postprocessing models. Our results suggest that most of the relevant information is contained in few ensemble-internal degrees of freedom, which may impact the design of future ensemble forecasting and postprocessing systems.Comment: Submitted to Artificial Intelligence for the Earth System

Analytical modeling of mine water rebound: Three case studies in closed hard-coal mines in Germany

Purpose. In this paper we present and validate an analytical model of water inflow and rising level in a flooded mine and examine the model robustness and sensitivity to variations of input data considering the examples of three closed hard-coal mines in Germany. Methods. We used the analytical solution to a boundary value problem of radial ground water flow to the shaft, treated as a big well, and water balance relations for the series of successive stationary positions of a depression cone to simulate a mine water rebound in the mine taking into account vertical distribution of hydraulic conductivity, residual volume of underground workings, and natural pores. Findings. The modeling demonstrated very good agreement with the measured data for all the studied mines. The maximum relative deviation for the mine water level during the measurement period did not exceed 2.1%; the deviation for the inflow rate to a mine before its flooding did not exceed 0.8%. Sensitivity analysis revealed the higher significance of the residual working volume and hydraulic conductivity for mine water rebound in the case of thick overburden and the growing significance of the infiltration rate and the flooded area size in the case of lower overburden thickness. Originality. The developed analytical model allows realistic prediction of transient mine water rebound and inflow into a mine with layered heterogeneity of rocks, irregular form of the drained area, and with the inflow/outflow to a neighboring mine and the volume of voids as a distributed parameter without gridding the flow domain performed in numerical models. Practical implications. The study demonstrated the advantages of analytical modeling as a tool for preliminary evaluation and prediction of flooding indicators and parameters of mined out disturbed rocks. In case of uncertain input data, modeling can be considered as an attractive alternative to usually applied numerical methods of modeling ground and mine water flow.Мета. У роботі представлена та протестована аналітична модель припливу води та підйому його рівня в затоплюваній шахті. Досліджено стійкість моделі та її чутливість до змін вихідних даних на прикладах трьох закритих шахт з видобутку кам’яного вугілля в Німеччині. Методика. Використане аналітичне рішення крайової задачі радіального потоку підземних вод у шахтному стволі, який розглядається як великий колодязь, а також співвідношення водного балансу для послідовності стаціонарних положень конуса депресії при моделюванні відновлення рівня води в шахті з урахуванням вертикального розподілу коефіцієнту фільтрації, залишкового об’єму підземних гірничих виробок та природних пор. Результати. Моделювання продемонструвало досить добру узгодженість для всіх досліджуваних шахт. Максимальне відносне відхилення рівня шахтних вод протягом періоду вимірювань не перевищило 2.1%, а для припливу в шахту до її затоплення – 0.8%. Аналіз чутливості виявив більш високу значущість залишкового об’єму виробок та коефіцієнту фільтрації для відновлення рівня шахтних вод у випадку потужних перекриваючих порід та зростаючу значущість швидкості інфільтрації та розміру області затоплення у випадку меншої товщини цих порід. Наукова новизна. Розроблена аналітична модель дозволяє реалістично прогнозувати нестаціонарне відновлення й приплив води в шахту з шаруватою неоднорідністю гірських порід, неправильною формою дренованої ділянки, припливом або відтоком до сусідньої шахти, а також об’ємом пустот як розподіленим параметром без сіткової дискретизації області фільтрації, виконуваної в чисельних моделях. Практична значимість. Дослідження продемонструвало переваги аналітичного моделювання як інструменту попереднього оцінювання та прогнозування показників затоплення та параметрів підробленого порушеного породного масиву. В разі невизначе-ності вхідних даних це можна розглядати як привабливу альтернативу зазвичай застосовуваним чисельним методам моделювання течій підземних та шахтних вод.Цель. В статье представлена и протестирована аналитическая модель притока воды и подъема ее уровня в затапливаемой шахте. Исследована надежность модели и ее чувствительность к изменениям исходных данных на примерах трех закрытых каменно-угольных шахт в Германии. Методика. Использовано аналитическое решение краевой задачи о радиальном потоке подземных вод в шахтном стволе, который рассматривается как большой колодец, а также соотношения водного баланса для серии последовательных стационарных положений депрессионного конуса при моделировании восстановления уровня воды в шахте с учетом вертикального распределения коэффициента фильтрации, остаточного объема подземных горных выработок и естественных пор. Результаты. Моделирование продемонстрировало достаточно хорошую согласованность с измеренными данными для всех исследованных шахт. Максимальное относительное отклонение за период измерений уровня шахтных вод не превысило 2.1%, а для притока в шахту до ее затопления – 0.8%. Анализ чувствительности выявил более высокую значимость остаточного объема выработок и коэффициента фильтрации для восстановления уровня шахтных вод в случае мощных перекрывающих пород, и возрастающую значимость скорости инфильтрации и размера области затопления в случае меньшей толщины этих пород. Научная новизна. Разработанная аналитическая модель позволяет реалистически прогнозировать нестационарное восстановление уровня шахтных вод и приток в шахту при слоистой неоднородности горных пород, неправильной формой дренируемой области, притоком или оттоком в соседнюю шахту, а также объемом пустот как распределенным параметром без сеточной дискретизации области фильтрации, выполняемой в численных моделях. Практическая значимость. Исследование продемонстрировало преимущества аналитического моделирования как инструмента предварительной оценки и прогноза показателей и параметров затопления подработанного нарушенного породного массива. В случае неопределённостей исходных данных это можно рассматривать как привлекательную альтернативу обычно применяемым численным методам моделирования течений подземных и шахтных вод.We would like to thank RAG Aktiengesellschaft and DMT GmbH & Co. KG (both Essen, Germany) for providing documents and data required to develop the analytical model

Towards representing thermokarst processes in land surface models

Large-scale Earth system and land surface models often lack an adequate representation of subgrid-scale processes in permafrost landscapes. Small-scale processes such as thermokarst formation might, however, considerably impact the energy and carbon budgets in way which is not resolved within large-scale models. Since a spatially high-resolved simulation of such processes is not feasible, novel techniques for up-scaling subgrid processes are demanded. Within this work a one-dimensional model of the ground thermal regime of land surfaces, CryoGrid 3, is employed to conceptually represent small-scale features of permafrost landscapes, particularly those related to thermokarst. For example, the model has been shown to adequately describe the degradation of permafrost underneath waterbodies in a warming climate. Using tiling approaches such point-wise realizations can be up-scaled in a statistical way in order to represent larger land surface units. The model development is closely linked to field campaigns to the Lena River Delta in Siberia which offers very diverse land surface features such as polygonal tundra and thermos-erosional valleys. These features are related to the region’s diverse soil stratigraphies, in particular the occurrence of ice-rich ground. Combining field measurements with modelling ultimately allows an improvement in the qualitative and quantitative understanding of the typical geomorphological processes in permafrost landscapes and their representation in large-scale models

Sparse Surface Constraints for Combining Physics-based Elasticity Simulation and Correspondence-Free Object Reconstruction

We address the problem to infer physical material parameters and boundary conditions from the observed motion of a homogeneous deformable object via the solution of an inverse problem. Parameters are estimated from potentially unreliable real-world data sources such as sparse observations without correspondences. We introduce a novel Lagrangian-Eulerian optimization formulation, including a cost function that penalizes differences to observations during an optimization run. This formulation matches correspondence-free, sparse observations from a single-view depth sequence with a finite element simulation of deformable bodies. In conjunction with an efficient hexahedral discretization and a stable, implicit formulation of collisions, our method can be used in demanding situation to recover a variety of material parameters, ranging from Young's modulus and Poisson ratio to gravity and stiffness damping, and even external boundaries. In a number of tests using synthetic datasets and real-world measurements, we analyse the robustness of our approach and the convergence behavior of the numerical optimization scheme