The Patenting Behavior of Academic Founders

This study explores why academic entrepreneurs patent their inventions before and after creating a firm. Drawing on start-up data combined with patent data, we specifically examine the impact of five, relatively under-researched factors (scientific field, pace of technological development, technological uncertainty, entrepreneurial orientation, and patent effectiveness. The study shows that some scientific fields, technological uncertainty, and patent effectiveness are positively related to patent propensity, both before and after founding. The effects of pace of technological development and entrepreneurial orientation were timespecific. Our study suggests that patenting by academic entrepreneurs is driven by special rationales and that prior research on full-time scientists and established firms does not necessarily generalize to them. We discuss the implications of our findings both in terms of contribution to the current literature and technology transfer policies. --academic patenting

Dutkat: A Privacy-Preserving System for Automatic Catch Documentation and Illegal Activity Detection in the Fishing Industry

United Nations' Sustainable Development Goal 14 aims to conserve and sustainably use the oceans and their resources for the benefit of people and the planet. This includes protecting marine ecosystems, preventing pollution, and overfishing, and increasing scientific understanding of the oceans. Achieving this goal will help ensure the health and well-being of marine life and the millions of people who rely on the oceans for their livelihoods. In order to ensure sustainable fishing practices, it is important to have a system in place for automatic catch documentation. This thesis presents our research on the design and development of Dutkat, a privacy-preserving, edge-based system for catch documentation and detection of illegal activities in the fishing industry. Utilising machine learning techniques, Dutkat can analyse large amounts of data and identify patterns that may indicate illegal activities such as overfishing or illegal discard of catch. Additionally, the system can assist in catch documentation by automating the process of identifying and counting fish species, thus reducing potential human error and increasing efficiency. Specifically, our research has consisted of the development of various components of the Dutkat system, evaluation through experimentation, exploration of existing data, and organization of machine learning competitions. We have also implemented it from a compliance-by-design perspective to ensure that the system is in compliance with data protection laws and regulations such as GDPR. Our goal with Dutkat is to promote sustainable fishing practices, which aligns with the Sustainable Development Goal 14, while simultaneously protecting the privacy and rights of fishing crews

Efficient Cancer Classification by Coupling Semi Supervised and Multiple Instance Learning

The annotation of large datasets is often the bottleneck in the successful application of artificial intelligence in computational pathology. For this reason recently Multiple Instance Learning (MIL) and Semi Supervised Learning (SSL) approaches are gaining popularity because they require fewer annotations. In this work we couple SSL and MIL to train a deep learning classifier that combines the advantages of both methods and overcomes their limitations. Our method is able to learn from the global WSI diagnosis and a combination of labeled and unlabeled patches. Furthermore, we propose and evaluate an efficient labeling paradigm that guarantees a strong classification performance when combined with our learning framework. We compare our method to SSL and MIL baselines, the state-of-the-art and completely supervised training. With only a small percentage of patch labels our proposed model achieves a competitive performance on SICAPv2 (Cohen's kappa of 0.801 with 450 patch labels), PANDA (Cohen's kappa of 0.794 with 22,023 patch labels) and Camelyon16 (ROC AUC of 0.913 with 433 patch labels). Our code is publicly available at https://github.com/arneschmidt/ssl_and_mil_cancer_classification.European Union's Horizon 2020 Research and Innovation Program through the Marie Skodowska Curie (Cloud Artificial Intelligence For pathologY (CLARIFY) Project) 860627Spanish Government PID2019-105142RB-C2

Solutions to the Cocktail Party Problem in Insects: Selective Filters, Spatial Release from Masking and Gain Control in Tropical Crickets

Insects often communicate by sound in mixed species choruses; like humans and many vertebrates in crowded social environments they thus have to solve cocktail-party-like problems in order to ensure successful communication with conspecifics. This is even more a problem in species-rich environments like tropical rainforests, where background noise levels of up to 60 dB SPL have been measured.Using neurophysiological methods we investigated the effect of natural background noise (masker) on signal detection thresholds in two tropical cricket species Paroecanthus podagrosus and Diatrypa sp., both in the laboratory and outdoors. We identified three 'bottom-up' mechanisms which contribute to an excellent neuronal representation of conspecific signals despite the masking background. First, the sharply tuned frequency selectivity of the receiver reduces the amount of masking energy around the species-specific calling song frequency. Laboratory experiments yielded an average signal-to-noise ratio (SNR) of -8 dB, when masker and signal were broadcast from the same side. Secondly, displacing the masker by 180° from the signal improved SNRs by further 6 to 9 dB, a phenomenon known as spatial release from masking. Surprisingly, experiments carried out directly in the nocturnal rainforest yielded SNRs of about -23 dB compared with those in the laboratory with the same masker, where SNRs reached only -14.5 and -16 dB in both species. Finally, a neuronal gain control mechanism enhances the contrast between the responses to signals and the masker, by inhibition of neuronal activity in interstimulus intervals.Thus, conventional speaker playbacks in the lab apparently do not properly reconstruct the masking noise situation in a spatially realistic manner, since under real world conditions multiple sound sources are spatially distributed in space. Our results also indicate that without knowledge of the receiver properties and the spatial release mechanisms the detrimental effect of noise may be strongly overestimated

Microporous sulfur-doped carbon from thienyl-based polymer network precursors

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Porous sulfur-doped carbon was synthesised by using a thienyl-based polymer network as a precursor. The sulfur amount varies from 5–23 m% while the materials show microporosity with BET surface areas of up to 711 m2 g−1.DFG, EXC 314, Unifying Concepts in Catalysi

Patenting rationales of academic entrepreneurs in weak and strong organizational regimes

This study explores why academic entrepreneurs seek patents for spin-off technology in weak organizational regimes (the employee owns her inventions) and strong organizational regimes (the employer, i.e. the university or research organization, owns these inventions). Specifically, we examine organizational and founding team characteristics as alternative explanations. Matched data of academic spin-offs from both contexts combined with patent data show that founding team characteristics (expert knowledge and entrepreneurial orientation) matter in weak, but not strong regimes. In contrast, organizational patenting norms are the key driver of patenting in strong, but not weak regimes. We discuss the implications of our results for the current literature and technology transfer policies

Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation

Medical image segmentation is a challenging task, particularly due to inter- and intra-observer variability, even between medical experts. In this paper, we propose a novel model, called Probabilistic Inter-Observer and iNtra-Observer variation NetwOrk (Pionono). It captures the labeling behavior of each rater with a multidimensional probability distribution and integrates this information with the feature maps of the image to produce probabilistic segmentation predictions. The model is optimized by variational inference and can be trained end-to-end. It outperforms state-of-the-art models such as STAPLE, Probabilistic U-Net, and models based on confusion matrices. Additionally, Pionono predicts multiple coherent segmentation maps that mimic the rater's expert opinion, which provides additional valuable information for the diagnostic process. Experiments on real-world cancer segmentation datasets demonstrate the high accuracy and efficiency of Pionono, making it a powerful tool for medical image analysis.Comment: 13 pages, 5 figure