Spherical Diffusion for Surface Smoothing and Denoising

Data defined on spherical domains occurs in various applications, such as surface modeling, omnidirectional imaging, and the analysis of keypoints in volumetric data. The theory of spherical signals lacks important concepts like the Gaussian function, which is permanently used in planar image processing. We propose a definition of a spherical Gaussian function as the Green\u27s function of the spherical diffusion process. This allows to introduce a linear scale space on the sphere. We apply this new filter to the smoothing of 3D object surfaces

Hypercomplex Spectral Signal Representations for the Processing and Analysis of Images

In the present work hypercomplex spectral methods of the processing and analysis of images are introduced. The thesis is divided into three main chapters. First the quaternionic Fourier transform (QFT) for 2D signals is presented and its main properties are investigated. The QFT is closely related to the 2D Fourier transform and to the 2D Hartley transform. Similarities and differences of these three transforms are investigated with special emphasis on the symmetry properties. The Clifford Fourier transform is presented as nD generalization of the QFT. Secondly the concept of the phase of a signal is considered. We distinguish the global, the local and the instantaneous phase of a signal. It is shown how these 1D concepts can be extended to 2D using the QFT. In order to extend the concept of global phase we introduce the notion of the quaternionic analytic signal of a real signal. Defining quaternionic Gabor filters leads to the definition of the local quaternionic phase. The relation between signal structure and local signal phase, which is well-known in 1D, is extended to 2D using the quaternionic phase. In the third part two application of the theory are presented. For the image processing tasks of disparity estimation and texture segmentation there exist approaches which are based on the (complex) local phase. These methods are extended to the use of the quaternionic phase. In either case the properties of the complex approaches are preserved while new features are added by using the quaternionic phase

Manipulating Anger Does Not Affect Risky Decision Making

To date, multiple studies have examined the influence of negative mood on per­formance on behavioral decision-making tasks. Self-reported negative mood was inconsis­tently associated with subsequent decision making, and a similar inconsistent pattern was seen when negative mood was manipulated in the study session. The present study sought to examine how deliberately inducing a particular negative mood, anger, would affect risky decision making. College student participants reported their political beliefs, then were randomly assigned to one of several mood manipulation conditions (political anger, anger, sadness, fear, control) prior to completion of standard behavioral risky decision-making tasks including the Iowa Gambling Task, Game of Dice Task, Balloon Analogue Risk Task, and Columbia Card Task. Results indicated an increase in negative mood in the anger condition following the study manipulation, but only minimal effects of negative mood on risky decision making across tasks. Future assessments of mood and decision making should address multiple negative mood affects in addition to manipulation tech­niques in order to determine if a specific mood and/or manipulation is contributing to an individuals’ risky decision making

The Impact of Motion Correction on Lesion Characterization in DCE Breast MR Images

ABSTRACT In the context of dynamic contrast enhanced breast MR imaging we analyzed the effect of motion compensating registration on the characterization of lesions. Two registration techniques were applied: 1) rigid registration and 2) elastic registration based on the Navier-Lamé equation. Interpreting voxels that exhibit a decline in image intensity after contrast injection (compared to the non-contrasted native image) as motion outliers, it can be shown that the rate of motion outliers can be largely reduced by both rigid and elastic registration. The performance of lesion features, including maximal signal enhancement ratio and variance of the signal enhancement ratio, was measured by area under the ROC curve as well as Cohen's κ and showed significant improvement for elastic registration, whereas features derived from rigidly registered images did not in general exhibit a significant improvement over the level of unregistered data

Supporting Learning Engagement with Online Students

University students are increasingly demanding that traditionally taught courses are converted to an online platform. While quality standards are in place for the format and organization of online courses, professors often are left wondering what activities contribute to learning engagement for their online students. The research question driving this study was, what activities contribute to learning engagement for online students? To investigate this question, an online survey was conducted in one state university of all students taking an online course during the spring semester. With responses from 417 students and using three standardized scale variables for learning engagement, as well as two open-ended questions, course components related to strong learning engagement were identified and examined. Initial findings indicated a statistically significant moderate correlation of learning engagement with the use of higher-order learning and reflective and integrative learning techniques. Specifically, students who reported being highly engaged connected ideas from other courses, changed their understanding of a topic or concept, found connections between their learning and societal problems, and had fun. A regression model using these variables, along with control variables of student age, gender, and out-of-school work, resulted in an R2 of 0.484, suggesting that almost half of the variance in learning engagement can be explained via this model. Further analysis of the qualitative data identified certain aspects of online discussions and assignments as engaging, such as discussions and interactive assignments that are not merely “fun” from a student perspective but also integrate previous learning and connect to current social issues. This includes prompting students with thought-provoking questions that relate to “real-world” situations and inviting students to share diverse opinions as well as develop personal perspectives

ZraP is a periplasmic molecular chaperone and a repressor of the zinc-responsive two-component regulator ZraSR

The bacterial envelope is the interface with the surrounding environment and is consequently subjected to a barrage of noxious agents including a range of compounds with antimicrobial activity. The ESR (envelope stress response) pathways of enteric bacteria are critical for maintenance of the envelope against these antimicrobial agents. In the present study, we demonstrate that the periplasmic protein ZraP contributes to envelope homoeostasis and assign both chaperone and regulatory function to ZraP from Salmonella Typhimurium. The ZraP chaperone mechanism is catalytic and independent of ATP; the chaperone activity is dependent on the presence of zinc, which is shown to be responsible for the stabilization of an oligomeric ZraP complex. Furthermore, ZraP can act to repress the two-component regulatory system ZraSR, which itself is responsive to zinc concentrations. Through structural homology, ZraP is a member of the bacterial CpxP family of periplasmic proteins, which also consists of CpxP and Spy. We demonstrate environmental co-expression of the CpxP family and identify an important role for these proteins in Salmonella's defence against the cationic antimicrobial peptide polymyxin B

Towards an MLOps Architecture for XAI in Industrial Applications

Machine learning (ML) has become a popular tool in the industrial sector as it helps to improve operations, increase efficiency, and reduce costs. However, deploying and managing ML models in production environments can be complex. This is where Machine Learning Operations (MLOps) comes in. MLOps aims to streamline this deployment and management process. One of the remaining MLOps challenges is the need for explanations. These explanations are essential for understanding how ML models reason, which is key to trust and acceptance. Better identification of errors and improved model accuracy are only two resulting advantages. An often neglected fact is that deployed models are bypassed in practice when accuracy and especially explainability do not meet user expectations. We developed a novel MLOps software architecture to address the challenge of integrating explanations and feedback capabilities into the ML development and deployment processes. In the project EXPLAIN, our architecture is implemented in a series of industrial use cases. The proposed MLOps software architecture has several advantages. It provides an efficient way to manage ML models in production environments. Further, it allows for integrating explanations into the development and deployment processes

D2.4. Building a Personal Learning Environment with Language-Technology-based Widgets: Services v2 - integrated thread

Hoisl, B., Haley, D., Wild, F., Anastasiou, L., Buelow, K., Koblische, R., Burek, G., Loiseau, M., Markus, T., Rebedea, T., Drachsler, H., Kometter, H., Westerhout, E., & Posea, V. (2010). D2.4. Building a Personal Learning Environment with Language-Technology-based Widgets: Services v2 - integrated thread. LTfLL-project.This deliverable reports on the results achieved by the LTfLL work packages in their efforts toward interoperability of the LTfLL tools and services. There are two aspects: one is the pedagogical utility of achieving interoperability; the other aspect involves the technical features. The technical basis of the interoperability is to use Wookie widgets in Elgg and is thoroughly described here. Finally, the deliverable provides details and screen shots of each widget for each LTfLL service embedded in the Elgg environment.The work on this publication has been sponsored by the LTfLL STREP that is funded by the European Commission's 7th Framework Programme. Contract 212578 [http://www.ltfll-project.org

Opportunities for improving recognition of coastal wetlands in global ecosystem assessment frameworks

Vegetated coastal wetlands, including seagrass, saltmarsh and mangroves, are threatened globally, yet the need to avert these losses is poorly recognized in international policy, such as in the Convention on Biological Diversity and the United Nations (UN) Sustainable Development Goals. Identifying the impact of overlooking coastal wetlands in ecosystem assessment frameworks could help prioritize research efforts to fill these gaps. Here, we examine gaps in the recognition of coastal wetlands in globally applicable ecosystem assessments. We address both shortfalls in assessment frameworks when it comes to assessing wetlands, and gaps in data that limit widespread application of assessments. We examine five assessment frameworks that track fisheries, greenhouse gas emissions, ecosystem threats, and ecosystem services. We found that these assessments inform management decisions, but that the functions provided by coastal wetlands are incompletely represented. Most frameworks had sufficient complexity to measure wetland status, but limitations in data meant they were incompletely informed about wetland functions and services. Incomplete representation of coastal wetlands may lead to them being overlooked by research and management. Improving the coverage of coastal wetlands in ecosystem assessments requires improving global scale mapping of wetland trends, developing global-scale indicators of wetland function and synthesis to quantitatively link animal population dynamics to wetland trends. Filling these gaps will help ensure coastal wetland conservation is properly informed to manage them for the outstanding benefits they bring humanity