The biodiversity crisis is maybe the most pressing issue of our time, which makes measuring the biodiversity an important precursor for improvement. Biodiversity assessment is limited by data management and analysis tools. Prior work has approached this problem by exploring the space of tools that can aide workflows of biodiversity Read more…
Ophthalmo-AI Age-related macular degeneration (AMD) is the primary cause of severe vision loss in the western world. One cause is the retinal accumulation of harmful byproducts of metabolism processes occurring in the eye. Treatment aims at slowing down disease progression and is performed through the injection of anti-VEGF agents into Read more…
Medical AI is about to become more powerful and versatile thanks to the fast creation of reusable AI models that can learn from different types of data. We call this new approach a foundation model for medical AI (FMAI). FMAI models will be able to do many different tasks with Read more…
Detecting animal sounds is an essential aspect of scientific research and conservation efforts. It provides valuable insights into biodiversity, species behavior, and ecosystem health. Monitoring of terrestrial and marine wildlife is a way to decrease biodiversity loss and improve species conservation. To achieve this goal passive acoustic monitoring (PAM) is Read more…
Passive acoustic monitoring (PAM), the recording of sounds using microphones (e.g. in biosphere reserves), is an increasingly popular method for continuous, reproducible, scalable and cost-effective monitoring of wildlife [Sugai et al., 2018]. It is widely employed in various fields, including ecology, marine biology, and conservation, to study animal behavior, biodiversity, Read more…
Figure 1. An overview of the transfer learning framework with BERT-SNER. Information extraction from clinical text has the potential for clinical research and personalized care, but annotating large data for customized requirements is prohibitive. We present a German medical Named Entity Recognition (NER) system (Liang et al., 2023) that transfers Read more…
Explainable AI (XAI) has demonstrated its potential in deciphering discriminatory features in machine learning (ML) decision-making processes. Specifically, XAI’s feature attribution methods shed light on individual decisions made by ML models. However, despite their visual appeal, these attributions can be unfaithful. To ensure the faithfulness of feature attributions, it is Read more…
Funding period: 01/2023 – 06/2026 Many industries are transitioning to Industry 4.0 production models by adopting robots in their processes. In parallel, Extended Reality (XR) technologies have reached sufficient maturity to enter the industrial applications domain, with early success cases often related to training workers, remote assistance, access to contextual Read more…