Abstract
The concept of the 15-Minute City aims to enhance urban accessibility by ensuring that essential services are within a short walking distance. This study evaluates the accessibility of Porto, Portugal, particularly for the elderly, by assessing urban density, permeability, and walkability, with a specific focus on crossings and ramps. A five-step methodology was employed, including spatial analysis using QGIS and Place Syntax Tool, proximity assessments, and an in-situ survey of crossings and ramps in the CHP. The results indicate that while the city of Porto offers a dense and walkable urban environment, significant accessibility challenges remain due to inadequate ramp distribution. The data collection identified 80 crossings, of which only 60 were listed in OpenStreetMap, highlighting data inconsistencies. Additionally, 18 crossings lacked curb ramps, posing mobility barriers for elderly residents. These findings highlight the need of infrastructure improvements to support inclusive urban mobility. The study also proposes an automated method to enhance ramp data collection for broader applications. Addressing these gaps is crucial for achieving the equity and sustainability goals of the 15-Minute City model, ensuring that aging populations can navigate urban spaces safely and efficiently.

Abstract
The rapid advancement of wireless technologies, particularly Wi-Fi, has spurred significant research into indoor human activity detection across various domains (e.g., healthcare, security, and industry). This work explores the non-invasive and cost-effective Wi-Fi paradigm and the application of deep learning for human activity recognition using Wi-Fi signals. Focusing on the challenges in machine interpretability, motivated by the increase in data availability and computational power, this paper uses explainable artificial intelligence to understand the inner workings of transformer-based deep neural networks designed to estimate human pose (i.e., human skeleton key points) from Wi-Fi channel state information. Using different strategies to assess the most relevant sub-carriers (i.e., rollout attention and masking attention) for the model predictions, we evaluate the performance of the model when it uses a given number of sub-carriers as input, selected randomly or by ascending (high-attention) or descending (low-attention) order. We concluded that the models trained with fewer (but relevant) sub-carriers are competitive with the baseline (trained with all sub-carriers) but better in terms of computational efficiency (i.e., processing more data per second).

Abstract
Cancer is a leading cause of mortality worldwide, with breast and lung cancer being the most prevalent globally. Early and accurate diagnosis is crucial for successful treatment, and medical imaging techniques play a pivotal role in achieving this. This paper proposes a novel pipeline that leverages generative artificial intelligence to enhance medical images by combining synthetic image generation and super-resolution techniques. The framework is validated in two medical use cases (breast and lung cancers), demonstrating its potential to improve the quality and quantity of medical imaging data, ultimately contributing to more precise and effective cancer diagnosis and treatment. Overall, although some limitations do exist, this paper achieved satisfactory results for an image size which is conductive to specialist analysis, and further expands upon this field's capabilities.

Abstract
The aim of this work is to explore normalising flows to detect anomalous behaviours which is an essential task mainly for surveillance systems-related applications. To accomplish that, a series of ablation studies were performed by varying the parameters of the Spatio-Temporal Graph Normalising Flows (STG-NF) model [3] and combining it with attention mechanisms. Out of all these experiments, it was only possible to improve the state-of-the-art result for the UBnormal dataset by 3.4 percentual points (pp), for the Avenue by 4.7 pp and for the Avenue-HR by 3.2 pp. However, further research remains urgent to find a model that can give the best performance across different scenarios. The inaccuracies of the pose tracking and estimation algorithm seems to be the main factor limiting the models' performance. The code is available at https://github.com/AnaFilipaNogueira/Abnormal-Human-Behaviour-Detection- using-Normalising-Flows-and- Attention-Mechanisms.

Abstract
Contrastive Analysis (CA) detects anomalies by contrasting patterns unique to a target group (e.g., unhealthy subjects) from those in a background group (e.g., healthy subjects). In the context of brain MRIs, existing CA approaches rely on supervised contrastive learning or variational autoencoders (VAEs) using both healthy and unhealthy data, but such reliance on target samples is challenging in clinical settings. Unsupervised Anomaly Detection (UAD) learns a reference representation of healthy anatomy, eliminating the need for target samples. Deviations from this reference distribution can indicate potential anomalies. In this context, diffusion models have been increasingly adopted in UAD due to their superior performance in image generation compared to VAEs. Nonetheless, precisely reconstructing the anatomy of the brain remains a challenge. In this work, we bridge CA and UAD by reformulating contrastive analysis principles for the unsupervised setting. We propose an unsupervised framework to improve the reconstruction quality by training a self-supervised contrastive encoder on healthy images to extract meaningful anatomical features. These features are used to condition a diffusion model to reconstruct the healthy appearance of a given image, enabling interpretable anomaly localization via pixel-wise comparison. We validate our approach through a proof-of-concept on a facial image dataset and further demonstrate its effectiveness on four brain MRI datasets, outperforming baseline methods in anomaly localization on the NOVA benchmark.

Abstract
Medical image captioning is an essential tool to produce descriptive text reports of medical images. One of the central problems of medical image captioning is their poor domain description generation because large pre-trained language models are primarily trained in non-medical text domains with different semantics of medical text. To overcome this limitation, we explore improvements in contrastive learning for X-ray images complemented with soft prompt engineering for medical image captioning and conditional text decoding for caption generation. The main objective is to develop a softprompt model to improve the accuracy and clinical relevance of the automatically generated captions while guaranteeing their complete linguistic accuracy without corrupting the models' performance. Experiments on the MIMIC-CXR and ROCO datasets showed that the inclusion of tailored soft-prompts improved accuracy and efficiency, while ensuring a more cohesive medical context for captions, aiding medical diagnosis and encouraging more accurate reporting.