Abstract
Medical case retrieval plays a crucial role in clinical decision-making by enabling healthcare professionals to find relevant cases based on patient records, diagnostic images, and textual descriptions. Given the inherently multimodal nature of medical data, effective retrieval requires models that can bridge the gap between different modalities. Traditional retrieval approaches often rely on unimodal representations, limiting their ability to capture cross-modal relationships. Recent advances in dense model-based techniques have shown promise in overcoming these limitations by encoding multimodal information into a shared latent space, facilitating retrieval based on semantic similarity. This paper investigates the potential of dense models to enhance multimodal search systems. We evaluate various dense model-based approaches to assess which model characteristics have the greatest impact on retrieval effectiveness, using the medical case-based retrieval task from ImageCLEFmed 2013 as a benchmark. Our findings indicate that different dense model approaches substantially impact retrieval effectiveness, and that applying the CombMAX fusion methodto combine their output results further improves effectiveness. Extending context length, however, yielded mixed results depending on the input data. Additionally, domain-specific models—those trained on medical data—outperformed general models trained on broad, non-specialized datasets within their respective fields. Furthermore, when text is the dominant information source, text-only models surpassed multimodal models

Abstract
With the growing adoption of Deep Learning for imaging tasks in biometrics and healthcare, it becomes increasingly important to ensure privacy when using and sharing images of people. Several works enable privacy-preserving image sharing by anonymizing the images so that the corresponding individuals are no longer recognizable. Most works average images or their embeddings as an anonymization technique, relying on the assumption that the average operation is irreversible. Recently, cold diffusion models, based on the popular denoising diffusion probabilistic models, have succeeded in reversing deterministic transformations on images. In this work, we leverage cold diffusion to decompose superimposed images, empirically demonstrating that it is possible to obtain two or more identically-distributed images given their average. We propose novel sampling strategies for this task and show their efficacy on three datasets. Our findings highlight the risks of averaging images as an anonymization technique and argue for the use of alternative anonymization strategies.

Abstract
[No abstract available]

Abstract
The technology of Reconfigurable Intelligent Surfaces (RISs) is lately being considered as a boosting component for various indoor wireless applications, enabling wave propagation control and coverage extension. However, the incorporation of extremely large RISs, as recently being considered for ultra-high capacity industrial environments at subTHz frequencies, imposes certain challenges for indoor channel characterization. In particular, such RISs contribute additional multipath components and their large sizes with respect to the signal wavelength lead to near-field propagation. To this end, ray tracing approaches become quite cumbersome and need to be rerun for different RIS unit cell designs. In this paper, we present a novel approach for the incorporation of RISs in indoor multipath environments towards their efficient channel characterization. An 100x100 RIS design with 2-bit resolution unit cells realizing a fixed anomalous reflection at 300 GHz is presented, whose radar cross section patterns are obtained via full-wave simulations. It is showcased that the RIS behavior can be conveniently approximated by a three-ray model, which can be efficiently incorporated within available ray tracing tools, and that the far-field approximation is valid for even very small distances from the RIS.

Abstract
This study provides an extensive examination of tourist perceptions during a visit to China, conducted within the framework of the Erasmus+ TourX project. The project includes participants from Spain, Italy, Germany, Greece, Belgium, and Portugal. Tourism is widely acknowledged as a mechanism for cultural exchange, fostering intercultural learning and broadening global perspectives. This research aims to empirically analyze the experiences and behaviors of tourists within an unfamiliar cultural environment. The findings contribute to the broader academic discourse on the role of tourism in facilitating cross-cultural engagement and its implications for the tourism industry in China. 

Abstract
Integrating citizen science with digital twin technology represents a significant development in oceanographic research and marine management. This paper examines how the Iliad project has successfully developed a comprehensive suite of digital twins of the ocean (DTOs) that leverage citizen science contributions to enhance data coverage, improve modelling accuracy, and foster public engagement with marine ecosystems. Through innovative technological solutions, including semantic interoperability frameworks, mobile applications, knowledge graphs, and gamification approaches, the project demonstrates the reciprocal benefits between citizen scientists, scientific research and digital twin ecosystems. The developments presented in this work illustrate how engaging the public in scientific research not only broadens the data foundation for digital twins but also creates pathways for citizens to gain valuable insights from these sophisticated digital representations of ocean environments.