Smart Cities already surround us, and yet they are still incomprehensibly far from directly impacting everyday life. While current Smart Cities are often inaccessible, the experience of everyday citizens may be enhanced with a combination of the emerging technologies Digital Twins (DTs) and Situated Analytics. DTs represent their Physical Twin (PT) in the real world via models, simulations, (remotely) sensed data, context awareness, and interactions. However, interaction requires appropriate interfaces to address the complexity of the city. Ultimately, leveraging the potential of Smart Cities requires going beyond assembling the DT to be comprehensive and accessible. Situated Analytics allows for the anchoring of city information in its spatial context. We advance the concept of embedding the DT into the PT through Situated Analytics to form Fused Twins (FTs). This fusion allows access to data in the location that it is generated in in an embodied context that can make the data more understandable. Prototypes of FTs are rapidly emerging from different domains, but Smart Cities represent the context with the most potential for FTs in the future. This paper reviews DTs, Situated Analytics, and Smart Cities as the foundations of FTs. Regarding DTs, we define five components (physical, data, analytical, virtual, and Connection Environments) that we relate to several cognates (i.e., similar but different terms) from existing literature. Regarding Situated Analytics, we review the effects of user embodiment on cognition and cognitive load. Finally, we classify existing partial examples of FTs from the literature and address their construction from Augmented Reality, Geographic Information Systems, Building/City Information Models, and DTs and provide an overview of future directions.

a) Entering the augmented lobby in the Fused Twins. (b) Coming down the stairs into the augmented lobby.Fig. 1. The Fused Twins are used to augment the building's lobby with visualizations of sensors and agents through a HoloLens 2.Recent advances in Augmented Reality (AR), the Internet of Things (IoT), cloud computing, and Digital Twins transform the types, rates, and volume of information generated in buildings as well as the mediums through which they can be perceived by users. These advances push the standard approach of media architecture to embed screens in the built environment to its limits because screens lack the immersive capacity that newer media afford. To bridge this gap, we propose a novel AR approach to media architecture that uses a Digital Twin as a platform for structuring and accessing data from various sources, including IoT and simulations. Our technical contribution to media architecture is threefold. First, we extend the possibilities of media architecture beyond embedded screens to three dimensions by presenting a Digital Twin using AR with a head-mounted display. This approach results in a shared and consistent augmented experience across large architectural spaces. Second, we use the Digital Twin to integrate and visualize real physical sensor information. Third, we make artificial occupancy simulations accessible to everyday users by presenting them within their natural context in the Digital Twin. Observing the Digital Twin in situ of the Physical Twin also has applications beyond media architecture.Fusing the two twins using AR can reduce the cognitive load of users from consuming big and complex information sources and enhance their experience. We present two use cases of the proposed Fused Twins in a university building at ETH Zürich. In the first use case, we visualize a dense indoor sensor network (DISN) with 390 IoT sensors that collected data from March 2020 to May 2021. In the second use case, we immerse visitors in agent-based simulations to enable insights into the real and projected uses of space. This

A common concern in experimental research is the auditability and reproducibility of experiments. Experiments are usually designed, provisioned, managed, and analyzed by diverse teams of specialists (e.g., researchers, technicians and engineers) and may require many resources (e.g. cloud infrastructure, specialized equipment). Even though researchers strive to document experiments accurately, this process is often lacking, making it hard to reproduce them. Moreover, when it is necessary to create a similar experiment, very often we end up "reinventing the wheel" as it is easier to start from scratch than trying to reuse existing work, thus losing valuable embedded best practices and previous experiences. In behavioral studies this has contributed to the reproducibility crisis. To tackle this challenge, we propose the "Experiments as Code" paradigm, where the whole experiment is not only documented but additionally the automation code to provision, deploy, manage, and analyze it is provided. To this end we define the Experiments as Code concept, provide a taxonomy for the components of a practical implementation, and provide a proof of concept with a simple desktop VR experiment that showcases the benefits of its "as code" representation, i.e., reproducibility, auditability, debuggability, reusability, & scalability.

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The interaction between phenotypic plasticity, e.g. learning, and evolution is an important topic both in Evolutionary Biology and Machine Learning. The evolution of learning is commonly studied in Evolutionary Biology, while the use of an evolutionary process to improve learning is of interest to the field of Machine Learning. This paper takes a different point of view by studying the effect of learning on the evolutionary process, the so-called Baldwin effect. A well-studied result in the literature about the Baldwin effect is that learning affects the speed of convergence of the evolutionary process towards some genetic configuration, which corresponds to the environment-induced plastic response. This paper demonstrates that learning can change the outcome of evolution, i.e., lead to a genetic configuration that does not correspond to the plastic response. Results are obtained both analytically and experimentally by means of an agent-based model of a foraging task, in an environment where the distribution of resources follows seasonal cycles and the foraging success on different resource types is conditioned by trade-offs that can be evolved and learned. This paper attempts to answer a question that has been overlooked: whether learning has an effect on what genotypic traits are evolved, i.e. the selection of a trait that enables a plastic response changes the selection pressure on a different trait, in what could be described as co-evolution between different traits in the same genome.

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