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Leadership in a global world
(2017)
We are interested in computing a mini-batch-capable end-to-end algorithm to identify statistically independent components (ICA) in large scale and high-dimensional datasets. Current algorithms typically rely on pre-whitened data and do not integrate the two procedures of whitening and ICA estimation. Our online approach estimates a whitening and a rotation matrix with stochastic gradient descent on centered or uncentered data. We show that this can be done efficiently by combining Batch Karhunen-Löwe-Transformation [1] with Lie group techniques. Our algorithm is recursion-free and can be organized as feed-forward neural network which makes the use of GPU acceleration straight-forward. Because of the very fast convergence of Batch KLT, the gradient descent in the Lie group of orthogonal matrices stabilizes quickly. The optimization is further enhanced by integrating ADAM [2], an improved stochastic gradient descent (SGD) technique from the field of deep learning. We test the scaling capabilities by computing the independent components of the well-known ImageNet challenge (144 GB). Due to its robustness with respect to batch and step size, our approach can be used as a drop-in replacement for standard ICA algorithms where memory is a limiting factor.
The Lake Constance region is due to its scenic attractiveness one of the most visited destinations in German-speaking countries. Scenic attractiveness as well as so-called landscape stereotypes also play a decisive role in tourism marketing. Tour operators reproduce supra-individual landscape concepts and establish mental geographies that ultimately influence the choice of destinations. A growing trend in tourism is the emergence of creative narratives in tourism marketing and tourism offers induced by creative companies. By means of a discourse-analytical investigation, whose theoretical and conceptual frame of reference is the hegemony and discourse theory of Laclau and Mouffe (1985), recurring landscape stereotypes are identified in tourist promotional material for the destination Bodensee. Based on these results as well as expert interviews with regional tourism stakeholders, a discussion of the creative economic potential for regional tourism marketing will take place. The investigation shows that these potentials are currently not being exhausted. At the same time, creative tourism can help a rural region, such as Lake Constance, to position itself as an alternative to city tourism, while at the same time addressing the lucrative target group 60plus.
A physics lab-setup has been developed for engineering students in their first year at university. The so-called LabTeamCoaching helps to improve general lab skills, such as preparing an experiment, writing a documentation, using graphs and drawing conclusions. By using a flipped classroom approach, students get better involved than in our former physics labs when we applied classical methods. This approach will be described and an overview of our 10 years of experience using this method will be given.
Know when you don't know
(2018)
Deep convolutional neural networks show outstanding performance in image-based phenotype classification given that all existing phenotypes are presented during the training of the network. However, in real-world high-content screening (HCS) experiments, it is often impossible to know all phenotypes in advance. Moreover, novel phenotype discovery itself can be an HCS outcome of interest. This aspect of HCS is not yet covered by classical deep learning approaches. When presenting an image with a novel phenotype to a trained network, it fails to indicate a novelty discovery but assigns the image to a wrong phenotype. To tackle this problem and address the need for novelty detection, we use a recently developed Bayesian approach for deep neural networks called Monte Carlo (MC) dropout to define different uncertainty measures for each phenotype prediction. With real HCS data, we show that these uncertainty measures allow us to identify novel or unclear phenotypes. In addition, we also found that the MC dropout method results in a significant improvement of classification accuracy. The proposed procedure used in our HCS case study can be easily transferred to any existing network architecture and will be beneficial in terms of accuracy and novelty detection.
Vortrag
Purpose
In order to combat climate change and safeguard a liveable future we need fundamental and rapid social change. Climate communication can play an important role to nurture the public engagement needed for this change, and higher education for sustainability can learn from climate communication.
Approach
The scientific evidence base on climate communication for effective public engagement is summarised into ten key principles, including ‘basing communication on people’s values’, ‘conscious use of framing’, and ‘turning concern into action’. Based on the author’s perspective and experience in the university context, implications are explored for sustainability in higher education.
Findings
The article provides suggestions for teaching (e.g. complement information with consistent behaviour by the lecturer, integrate local stories, and provide students with basic skills to communicate climate effectively), for research (e.g. make teaching for effective engagement the subject of applied research), for universities’ third mission to contribute to sustainable development
in the society (e.g. provide climate communication trainings to empower local stakeholders), andgreening the campus (develop a proper engagement infrastructure, e.g. by a university storytelling exchange on climate action).
Originality
The article provides an up-to-date overview of climate communication research, which is in itself original. This evidence base holds interesting learnings for institutions of higher education, and the link between climate communication and universities has so far not been explored comprehensively.
In multi-extended object tracking, parameters (e.g., extent) and trajectory are often determined independently. In this paper, we propose a joint parameter and trajectory (JPT) state and its integration into the Bayesian framework. This allows processing measurements that contain information about parameters and states. Examples of such measurements are bounding boxes given from an image processing algorithm. It is shown that this approach can consider correlations between states and parameters. In this paper, we present the JPT Bernoulli filter. Since parameters and state elements are considered in the weighting of the measurement data assignment hypotheses, the performance is higher than with the conventional Bernoulli filter. The JPT approach can be also used for other Bayes filters.
The digital transformation of business processes and the integration of IT systems leads to opportunities and risks for small and medium-sized enterprises (SMEs). Risks that can result in a lack of IT Governance, Risk and Compliance (GRC). The purpose of this paper is to present the Design and Evaluation phase of creating an artefact, to reduce these risks. With this, the Design Science Research approach based on Hevner is using. The artefact will be developed by selecting relevant existing frameworks and the identification of SME-specific competencies. The method enables IT-GRC managers to transfer or adapt the frameworks to an SME organizational structure. The results from ten interviews and further three feedback loops showed that the method can be applied in practice and that a tailoring of established frameworks can take place. Contrary to the previous basic orientation of the research, this paper focuses on the concretization of approaches.