SkyMap: a generative graph model for GNN benchmarking.

Graph Neural Networks (GNNs) have gained considerable attention in recent years. Despite the surge in innovative GNN architecture designs, research heavily relies on the same 5-10 benchmark datasets for validation. To address this limitation, several generative graph models like ALBTER or GenCAT have emerged, aiming to fix this problem with synthetic graph datasets.

Graph neural networks for electroencephalogram analysis: Alzheimer's disease and epilepsy use cases.

Electroencephalography (EEG) is widely used as a non-invasive technique for the diagnosis of several brain disorders, including Alzheimer's disease and epilepsy. Until recently, diseases have been identified over EEG readings by human experts, which may not only be specific and difficult to find, but are also subject to human error. Despite the recent emergence of machine learning methods for the interpretation of EEGs, most approaches are not capable of capturing the underlying arbitrary non-Euclidean relations between signals in the different regions of the human brain.

Metasurface-Programmable Wireless Network-On-Chip.

This paper introduces the concept of smart radio environments, currently intensely studied for wireless communication in metasurface-programmable meter-scaled environments (e.g., inside rooms), on the chip scale.

Radiation Pattern Prediction for Metasurfaces: A Neural Network-Based Approach.

As the current standardization for the 5G networks nears completion, work towards understanding the potential technologies for the 6G wireless networks is already underway. One of these potential technologies for the 6G networks is reconfigurable intelligent surfaces. They offer unprecedented degrees of freedom towards engineering the wireless channel, i.

Reprogrammable Graphene-based Metasurface Mirror with Adaptive Focal Point for THz Imaging.

Recent emergence of metasurfaces has enabled the development of ultra-thin flat optical components through different wavefront shaping techniques at various wavelengths. However, due to the non-adaptive nature of conventional metasurfaces, the focal point of the resulting optics needs to be fixed at the design stage, thus severely limiting its reconfigurability and applicability. In this paper, we aim to overcome such constraint by presenting a flat reflective component that can be reprogrammed to focus terahertz waves at a desired point in the near-field region.

Reconfigurable THz Plasmonic Antenna Based on Few-Layer Graphene with High Radiation Efficiency.

Graphene plasmonic antennas possess two significant features that render them appealing for short-range wireless communications, notably, inherent tunability and miniaturization due to the unique frequency dispersion of graphene and its support for surface plasmon waves in the terahertz band. In this letter, dipole-like antennas using few-layer graphene are proposed to achieve a better trade-off between miniaturization and radiation efficiency than current monolayer graphene antennas. The characteristics of few-layer graphene antennas are evaluated and then compared with those of antennas based on monolayer graphene and graphene stacks, which could also provide such improvements.