Detail-Preserving Pooling in Deep Networks

Most convolutional neural networks use some method for gradually downscaling the size of the hidden layers. This is commonly referred to as pooling, and is applied to reduce the number of parameters, improve invariance to certain distortions, and increase the receptive field size. Since pooling by nature is a lossy process, it is crucial that each such layer maintains the portion of the activations that is most important for the network's discriminability. Yet, simple maximization or averaging over blocks, max or average pooling, or plain downsampling in the form of strided convolutions are the standard. In this paper, we aim to leverage recent results on image downscaling for the purposes of deep learning. Inspired by the human visual system, which focuses on local spatial changes, we propose detail-preserving pooling (DPP), an adaptive pooling method that magnifies spatial changes and preserves important structural detail. Importantly, its parameters can be learned jointly with the rest of the network. We analyze some of its theoretical properties and show its empirical benefits on several datasets and networks, where DPP consistently outperforms previous pooling approaches.Comment: To appear at CVPR 201

Formation and Dissolution of Bacterial Colonies

Many organisms form colonies for a transient period of time to withstand environmental pressure. Bacterial biofilms are a prototypical example of such behavior. Despite significant interest across disciplines, physical mechanisms governing the formation and dissolution of bacterial colonies are still poorly understood. Starting from a kinetic description of motile and interacting cells we derive a hydrodynamic equation for their density on a surface. We use it to describe formation of multiple colonies with sizes consistent with experimental data and to discuss their dissolution.Comment: 3 figures, 1 Supplementary Materia

A Concrete Model for the Quantum Permutation Group on 4 Points

In 2019, Jung-Weber gave an example of a concrete magic unitary $M$, which defines a $C^*$-algebraic model of the quantum permutation group $S_4^+$. We show with the help of a computer that there exist no polynomials up to degree $50$ separating the entries of $M$ from the generators of $C(S_4^+)$. This indicates that the magic unitary $M$ might already define a faithful model of $S_4^+$

Single Cs Atoms as Collisional Probes in a large Rb Magneto-Optical Trap

We study cold inter-species collisions of Caesium and Rubidium in a strongly imbalanced system with single and few Cs atoms. Observation of the single atom fuorescence dynamics yields insight into light-induced loss mechanisms, while both subsystems can remain in steady-state. This significantly simplifies the analysis of the dynamics, as Cs-Cs collisions are effectively absent and the majority component remains unaffected, allowing us to extract a precise value of the Rb-Cs collision parameter. Extending our results to ground state collisions would allow to use single neutral atoms as coherent probes for larger quantum systems.Comment: 6 pages, 4 figure

Automatic detection of accommodation steps as an indicator of knowledge maturing

Jointly working on shared digital artifacts – such as wikis – is a well-tried method of developing knowledge collectively within a group or organization. Our assumption is that such knowledge maturing is an accommodation process that can be measured by taking the writing process itself into account. This paper describes the development of a tool that detects accommodation automatically with the help of machine learning algorithms. We applied a software framework for task detection to the automatic identification of accommodation processes within a wiki. To set up the learning algorithms and test its performance, we conducted an empirical study, in which participants had to contribute to a wiki and, at the same time, identify their own tasks. Two domain experts evaluated the participants’ micro-tasks with regard to accommodation. We then applied an ontology-based task detection approach that identified accommodation with a rate of 79.12%. The potential use of our tool for measuring knowledge maturing online is discussed

Where to Start with AI?—Identifying and Prioritizing Use Cases for Health Insurance

Artificial Intelligence (AI) arguably represents a key technology for the digitalization of health care. Specifically, health insurers can benefit from AI as they typically have access to vast amounts of data. However, practitioners struggle to adopt AI in productive use, and extant research lacks an overview of use cases for AI in health insurance as well as prioritization criteria that can guide their implementation. To address this gap, we conduct explorative interviews in the context of the German statutory health insurance system. We identify AI use cases in the areas of predictive health, individualized service, anomaly detection, and operations enhancement. We find that health insurers are likely to prioritize these use cases according to implementation complexity and business orientation, whereas focusing on simple use cases that target cost savings is recommended by experts. Our study advances the understanding of AI adoption in health insurance and supports practitioners in guiding future AI initiatives