Data:Lab Munich (Germany)

When retrieving the surface soil moisture over agricultural fields using Synthetic Aperture Radar (SAR), vegetation absorbs and scatters the signal, which then hinders the analysis of the underlying soil [1,2]. One method to circumvent this is by decomposing the radar signal into three components: surface, dihedral, and volume scattering [3,4]. Recent advancements have extended these models into a tensor framework and incorporated spatial information to then invert the geophysical parameters of the models and retrieve soil moisture for a wider range of crop scenarios [5]. The soil moisture is retrieved through numerical optimization of the models' geophysical parameters. In this work, we compare the information loss when retrieving soil moisture using the tensor-based decomposition between full-polarisation and dual-polarisation inversion. We assess the ambiguity of parameter retrieval for different combinations of dual-polarisation channels and conclude on which set-up of dual-polarisations with VV, VH, and/or HH provides the most constrained and thus most optimal soil moisture retrieval with the tensor decomposition technique. This work is implemented using the full-pol airborne F-SAR data from DLR and soil moisture retrieval from the inversion is compared with ground measurements taken during the AgriROSE-L campaign around Munich, Germany, in 2025. [1] I. Hajnsek, E. Pottier and S. R. Cloude, "Inversion of surface parameters from polarimetric SAR," in IEEE Transactions on Geoscience and Remote Sensing, vol. 41, no. 4, pp. 727-744, April 2003, doi: 10.1109/TGRS.2003.810702.[2] Dipankar Mandal, Vineet Kumar, Debanshu Ratha, Subhadip Dey, Avik Bhattacharya, Juan M. Lopez-Sanchez, Heather McNairn, Yalamanchili S. Rao, Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data, Remote Sensing of Environment, https://doi.org/10.1016/j.rse.2020.111954.[3] Freeman, Anthony, and Stephen L. Durden. "A three-component scattering model for polarimetric SAR data." IEEE transactions on geoscience and remote sensing 36.3 (2002): 963-973.[4] Yamaguchi, Yoshio, et al. "Four-component scattering model for polarimetric SAR image decomposition." IEEE Transactions on geoscience and remote sensing 43.8 (2005): 1699-1706[5] Basargin, N., Alonso-González, A., & Hajnsek, I. “Model-based tensor decompositions for soil moisture estimation.” Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR (2024)

Abstract The dawn of Foundation Models has on the one hand revolutionised a wide range of research problems, and, on the other hand, democratised the access and use of AI-based tools by the general public. We even observe an incursion of these models into disciplines related to human psychology, such as the Affective Computing domain, suggesting their affective, emerging capabilities. In this work, we aim to raise awareness of the power of Foundation Models in the field of Affective Computing by synthetically generating and analysing multimodal affective data, focusing on vision, linguistics, and speech (acoustics). We also raise awareness of evaluation problems related to the use of Foundation Models in this research area.

As of March 2020, with the start of the COVID-19 pandemic, Italians are moving less to get to work, but the car remains the preferred means of transport. In general, there has been a sharp drop in demand for public transport. In order to overcome this critical situation, it is necessary not only to ensure constant sanitation and control on board to mitigate contagion, but also to guarantee a certain flexibility in transport services. In fact, greater flexibility in the service makes it easier to reach people and places that are not part of the daily routes or timetables of travelers. Demand responsive transport (DRT) is the flexible solution that fits into the space between buses and taxis. Through the use of minibuses, DRT services provide a “social” aspect to offer niche services to users with limited mobility (disabled or elderly). They also connect areas defined as weak demand areas, which are not only peripheral areas but also those areas that have been characterized by reduced connections due to the recent pandemic. This paper focuses on a city in the Sicilian hinterland and presents a comparison of the current public transport service connecting the train station with the area of the historic centre and the expansion areas with a hypothetical DRT service. The analysis was conducted by applying an analytical model characterized by a cost function of the transport operator and a cost function of the PT user for each of the services. Assessing the transport cost as the sum of the costs received by the service provider and the user makes it possible to analyze the social and the results provide a basis for better planning of public mobility services and thus an increase in sustainable mobility.

When solving propositional logic satisfiability (specifically 3SAT) using quantum annealing, we analyze the effect the difficulty of different instances of the problem has on the quality of the answer returned by the quantum annealer. A high-quality response from the annealer in this case is defined by a high percentage of correct solutions among the returned answers. We show that the phase transition regarding the computational complexity of the problem, which is well-known to occur for 3SAT on classical machines (where it causes a detrimental increase in runtime), persists in some form (but possibly to a lesser extent) for quantum annealing.

Nerve tissue contains a high density of chemical synapses, about 1 per µm3 in the mammalian cerebral cortex. Thus, even for small blocks of nerve tissue, dense connectomic mapping requires the identification of millions to billions of synapses. While the focus of connectomic data analysis has been on neurite reconstruction, synapse detection becomes limiting when datasets grow in size and dense mapping is required. Here, we report SynEM, a method for automated detection of synapses from conventionally en-bloc stained 3D electron microscopy image stacks. The approach is based on a segmentation of the image data and focuses on classifying borders between neuronal processes as synaptic or non-synaptic. SynEM yields 97% precision and recall in binary cortical connectomes with no user interaction. It scales to large volumes of cortical neuropil, plausibly even whole-brain datasets. SynEM removes the burden of manual synapse annotation for large densely mapped connectomes.

Allergic diseases are influenced by both genes and environment. A 70-kb haplotype block in the G protein-coupled receptor for asthma susceptibility gene (GPR154; alias GPRA) on chromosome 7p was recently identified to influence susceptibility to asthma and elevated total serum IgE levels in adults. To assess the impact of GPR154 on childhood allergic disease, including allergic sensitization, asthma, and rhinoconjunctivitis, in study populations with diverse environmental backgrounds. We studied farm children, Steiner school children, and two reference groups from five Western European countries in the cross-sectional PARSIFAL (Prevention of Allergy Risk factors for Sensitization In children related to Farming and Anthroposophic Lifestyle) study and a sample of children from the Swedish birth cohort study BAMSE. DNA samples from 3,113 PARSIFAL and 800 BAMSE children were genotyped for 7 GPR154 polymorphisms and haplotypes were inferred. The proportions of alleles and haplotypes (H1-H7) were compared in affected children with their healthy counterparts. Data indicate a global association of the haplotype block to sensitization (allergen-specific serum IgE > or = 0.35 kU/L, p = 0.022), with significant haplotype-specific associations for H1, H5, and H6. Haplotypes H1 and H5 were also significantly associated with childhood allergic asthma (p = 0.045 and p = 0.023, respectively), and H5 to asthma regardless of sensitization. A broader involvement of GPR154 in allergic diseases was further supported in allergic rhinoconjunctivitis (H3: p = 0.046). The associated haplotypes could be allocated into risk (H5/H6) and nonrisk (H1/H3) groups, a pattern supported by allelic association of single nucleotide polymorphisms (SNPs) rs324384 and rs324396. Our results indicate that polymorphisms and haplotypes in the haplotype block of GPR154 are associated with asthma, rhinoconjunctivitis, and sensitization in European children.