1. Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization 

   Mutti, Mirco; De Santi, Riccardo; Rossi, Emanuele; et al. (2023)

   Proceedings of the AAAI Conference on Artificial Intelligence

   In the sequential decision making setting, an agent aims to achieve systematic generalization over a large, possibly infinite, set of environments. Such environments are modeled as discrete Markov decision processes with both states and actions represented through a feature vector. The underlying structure of the environments allows the transition dynamics to be factored into two components: one that is environment-specific and another ...

   Conference Paper

2. Side-By-Side Might Win: Occlusion Negatively Affects The Performance Of Augmented Reality Task Instructions 

   Wiethuchter, Robin; Kalloori, Saikishore; Lindlbauer, David (2024)

   2024 IEEE CONFERENCE ON VIRTUAL REALITY AND 3D USER INTERFACES ABSTRACTS AND WORKSHOPS, VRW 2024

   We compare different representations for Augmented Reality instructions for a combined pick & place and drawing task. Participants completed the task with instructions presented as 3D AR overlays, as 3D AR offset (side-by-side), or as 2D panels. Results indicate that participants preferred the 3D offset instructions, especially compared to 3D overlays. Our results stand in contrast to prior work where 3D overlays were preferred. Our work ...

   Conference Paper

3. A Global Assessment of the State of Plant Health 

   Savary S.; Andrivon D.; Esker P.; et al. (2023)

   Plant Disease

   The Global Plant Health Assessment (GPHA) is a collective, volunteer-based effort to assemble expert opinions on plant health and disease impacts on ecosystem services based on published scientific evidence. The GPHA considers a range of forest, agricultural, and urban systems worldwide. These are referred to as (Ecoregion × Plant System), i.e., selected case examples involving keystone plants in given parts of the world. The GPHA focuses ...

   Journal Article

4. Developmental stage shapes the realized energy landscape for a flight specialist 

   Nourani E.; Faure L.; Brønnvik H.; et al. (2024)

   eLife

   The heterogeneity of the physical environment determines the cost of transport for animals, shaping their energy landscape. Animals respond to this energy landscape by adjusting their distribution and movement to maximize gains and reduce costs. Much of our knowledge about energy landscape dynamics focuses on factors external to the animal, particularly the spatio-temporal variations of the environment. However, an animal's internal state ...

   Journal Article

5. Microbial dietary preference and interactions affect the export of lipids to the deep ocean 

   Behrendt L.; Alcolombri U.; Hunter J.E.; et al. (2024)

   Science

   Lipids comprise a significant fraction of sinking organic matter in the ocean and play a crucial role in the carbon cycle. Despite this, our understanding of the processes that control lipid degradation is limited. We combined nanolipidomics and imaging to study the bacterial degradation of diverse algal lipid droplets and found that bacteria isolated from marine particles exhibited distinct dietary preferences, ranging from selective to ...

   Journal Article

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