Paul, The Pastor (His Corinthian Ministry)

Here, in this city of lewdness and immorality, of gross dishonesty and crying sins, the Apostle Paul preached the message of Christ Crucified. He rarely stayed in any city as long as he did in Corinth. Christ was the Savior even of publicans and sinners, hence even this stronghold of iniquity and vice was to be conquered with the Gospel of Christ

Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders

Convolutional autoencoders have emerged as popular methods for unsupervised defect segmentation on image data. Most commonly, this task is performed by thresholding a pixel-wise reconstruction error based on an $\ell^p$ distance. This procedure, however, leads to large residuals whenever the reconstruction encompasses slight localization inaccuracies around edges. It also fails to reveal defective regions that have been visually altered when intensity values stay roughly consistent. We show that these problems prevent these approaches from being applied to complex real-world scenarios and that it cannot be easily avoided by employing more elaborate architectures such as variational or feature matching autoencoders. We propose to use a perceptual loss function based on structural similarity which examines inter-dependencies between local image regions, taking into account luminance, contrast and structural information, instead of simply comparing single pixel values. It achieves significant performance gains on a challenging real-world dataset of nanofibrous materials and a novel dataset of two woven fabrics over the state of the art approaches for unsupervised defect segmentation that use pixel-wise reconstruction error metrics

Optimal or antagonistic? muscle force solutions in the lower limb

Provides evidence of the appropriateness of different muscle force distribution protocols in a musculoskeletal model of the lower limb

Revisiting and revitalizing political ecology in the American West

Political ecology, initially conceived to better understand the power relations implicit in management and distribution of natural resources in the developing world, came “home” to the American West in the 1990s and 2000s. This groundswell of research did much to problematize socio-environmental conflicts in the region, long typified by tensions over land and resources, identity and belonging, autonomy and authority. Since first touching down in the West, however, the “big tent” of political ecology has only grown bigger, incorporating new perspectives, epistemologies, and ontologies. At the same time, the nexus of environment and society is perhaps even more salient today, amid a regional conjuncture of populist revolt, climate change, and rapid political economic transformation. Here we reflect on three longstanding regional concerns – energy development, wolf reintroduction, and participatory governance – leveraging the pluralism of contemporary political ecology to better understand their contemporary incarnations. In so doing, we highlight the need to bring together insights from both “traditional” approaches and newer directions to better understand and engage contemporary challenges, with their heightened stakes and complexity. Such an approach demonstrates what we might learn about global processes in this place, as well as what insights regional praxis (often woefully provincial) might gain from elsewhere – new ways of seeing and doing political ecology. Our goal is to generate discussion among and between political ecologists and regional critical scholars, initiating new collaborative engagements that might serve the next wave of political ecology in the 21st century American West

Motives and Incentives for Data Sharing in Industrial Data Ecosystems: An Explorative Single Case Study

The increasing connectivity of the business world leads to economic value being created less and less by one company alone, but rather through the exchange and combination of data by various actors in so-called data ecosystems. However, many companies are not yet willing to participate in data ecosystems because they do not see the added value of their participation. This is partly because the motives of data providers do not match the incentives offered to share their data. So far, there are only very few studies that deal with this issue in detail. Therefore, we close this research gap by adopting a conceptual model to the issue of motives and incentives for data sharing and applying it to the industrial data ecosystem Catena-X in a single case study. Through the case study analysis, we can identify seven different motives and eight incentives for data sharing

Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings

We introduce a powerful student-teacher framework for the challenging problem of unsupervised anomaly detection and pixel-precise anomaly segmentation in high-resolution images. Student networks are trained to regress the output of a descriptive teacher network that was pretrained on a large dataset of patches from natural images. This circumvents the need for prior data annotation. Anomalies are detected when the outputs of the student networks differ from that of the teacher network. This happens when they fail to generalize outside the manifold of anomaly-free training data. The intrinsic uncertainty in the student networks is used as an additional scoring function that indicates anomalies. We compare our method to a large number of existing deep learning based methods for unsupervised anomaly detection. Our experiments demonstrate improvements over state-of-the-art methods on a number of real-world datasets, including the recently introduced MVTec Anomaly Detection dataset that was specifically designed to benchmark anomaly segmentation algorithms.Comment: Accepted to CVPR 202

On the prediction of separation-induced transition by coupling delayed detached-eddy simulation with γ transition model

The computation of industrial turbomachinery applications is still a big challenge for LES methods due to their requirements in terms of mesh resolution, which lead to high computational costs. Therefore, hybrid RANS/LES methods, such as Detached Eddy-Simulation, are gaining more attention. There is potential in combining the strengths of LES (accuracy where required) and RANS (reduced computational costs where possible) within one modelling approach. Nevertheless, so far, hybrid methods have mostly been developed for fully turbulent flow configurations. The application in transitional flows is still not well considered yet, although the phenomenon laminar-to-turbulent flow transition has a noticeable impact on turbomachinery performance. To capture the transition accurately is a key for improving the predictive quality of hybrid RANS/LES methods. Therefore, we propose a coupling of DDES and the γ-transition model. In this paper, we first introduce the underlying turbulence and transition model. A detailed investigation of how these two models interact with each other resulted in a potential coupled DDES-γ model with a modified k-transport equation. We show the application of DDES-γ and discuss numerical results with two exemplary test cases, namely a flat plat boundary layer with adverse pressure gradient, experimentally investigated by Volino & Hultgren (2000) and the low-pressure turbine cascade T106C, experimentally considered by Michálek et al. (2012). Both test cases represent characteristic flow conditions in turbomachinery such as separation-induced transition under low free-stream turbulence intensity. Main focus is the assessment of the proposed DDES-γ with focus on the improvement of predictive quality, but also potential issues, coming up when coupling DDES and the γ-transition model. The flat plate case serves as a starting point to assess the general behavior of DDES in transitional flows and how the γ-model interacts with the DDES. Secondly, the T106C case revealed also predictive improvements when analyzing turbomachinery-relevant values such as wake losses. For a better assessment, we always put the results into context and compare them with RANS and LES results. This supports the need for more sophisticated approaches such as DDES compared to RANS and illustrates the competitiveness of DDES approaches compared to LES. The considered cases helped to understand the model coupling and yield promising results for the DDES-γ model predicting separation-induced transition, while we showed, that the fully-turbulent DDES failed to capture relevant features for this transition type. After initially assessing DDES- γ for separation-induced transition in this paper, future research needs to address bypass transition to get a better sense for the performance of DDES-γ in transitional flows

Modulation of Asymmetric Division Diversity through Cytokinin and SPEECHLESS Regulatory Interactions in the Arabidopsis Stomatal Lineage

Coordinated growth of organs requires communication among cells within and between tissues. In plants, leaf growth is largely dictated by the epidermis; here, asymmetric and self-renewing divisions of the stomatal lineage create two essential cell types—pavement cells and guard cells—in proportions reflecting inputs from local, systemic, and environmental cues. The transcription factor SPEECHLESS (SPCH) is the prime regulator of divisions, but whether and how it is influenced by external cues to provide flexible development is enigmatic. Here, we show that the phytohormone cytokinin (CK) can act as an endogenous signal to affect the extent and types of stomatal lineage divisions and forms a regulatory circuit with SPCH. Local domains of low CK signaling are created by SPCH-dependent cell-type-specific activity of two repressive type-A ARABIDOPSIS RESPONSE REGULATORs (ARRs), ARR16 and ARR17, and two secreted peptides, CLE9 and CLE10, which, together with SPCH, can customize epidermal cell-type composition