Land price and the viability of delivering affordable homes on 'Rural Exception Sites' in England

Registered providers (RPs) of social housing struggle to build genuinely affordable homes in rural England’s private land market. Land allocated for market development commands a high price in amenity areas. Since 1991, RPs have developed homes on unallocated ‘rural exception sites’ (RES) in villages, seeking affordable land on which to build homes for local need. However, landowner expectation of the price achievable for these sites has risen, largely because government has sought to substitute public grants with market mechanisms to achieve project viability. Drawing on research into local practices on RES, this paper explores evolving viability challenges and solutions, concluding with a call to fix land price for affordable housing on rural exception sites

Endo-FASt3r: Endoscopic Foundation Model Adaptation for Structure from Motion

Accurate depth and camera pose estimation is essential for achieving high-quality 3D visualisations in robotic-assisted surgery. Despite recent advancements in foundation model adaptation to monocular depth estimation of endoscopic scenes via self-supervised learning (SSL), no prior work has explored their use for pose estimation. These methods rely on low rank-based adaptation approaches, which constrain model updates to a low-rank space. We propose Endo-FASt3r, the first monocular SSL depth and pose estimation framework that uses foundation models for both tasks. We extend the Reloc3r relative pose estimation foundation model by designing Reloc3rX, introducing modifications necessary for convergence in SSL. We also present DoMoRA, a novel adaptation technique that enables higher-rank updates and faster convergence. Experiments on the SCARED dataset show that Endo-FASt3r achieves a substantial 10% improvement in pose estimation and a 2% improvement in depth estimation over prior work. Similar performance gains on the Hamlyn and StereoMIS datasets reinforce the generalisability of Endo-FASt3r across different datasets. Our code is available at: https://github.com/Mona-ShZeinoddin/Endo_FASt3r.git

The new commute: Is teleworking stimulating residential and workplace relocations?

As teleworking becomes increasingly embedded in contemporary work arrangements, its broader impacts on mobility and settlement patterns remain unclear. This study investigates the extent to which teleworking, alongside sociodemographic, household, employment, travel, and residential factors, influences job and residential relocation behaviour. Using data from an online survey of 1290 workers in East Flanders, Belgium, conducted between October 2023 and January 2024, this study estimates two multinomial logit models: one assessing relocation behaviour over the past three years, and another examining future relocation intentions. Results indicate that job changes and job change intentions are partially associated with telework-related factors, such as unmet telework preferences and dual-teleworker household status. However, residential relocation decisions are shaped more strongly by life stage, household composition, housing conditions, and neighbourhood and commuting experiences. These findings highlight the need to understand teleworking within its broader socio-spatial context, as teleworking alone does not appear to drive large-scale shifts in residential or employment location

Out-of-Distribution Detection in Gastrointestinal Vision by Estimating Nearest Centroid Distance Deficit

The integration of deep learning tools in gastrointestinal vision holds the potential for significant advancements in diagnosis, treatment, and overall patient care. A major challenge, however, is overconfident predictions, even when encountering unseen or newly emerging disease patterns, which undermines the reliability of such tools. We address this critical issue of reliability in gastrointestinal vision through the lens of out-of-distribution (OOD) detection, which handles previously unseen or emerging diseases as OOD samples. To this end, we hypothesize that the features of an in-distribution example will cluster closer to the centroids of their ground truth class, resulting in a shorter distance between the example and the nearest centroid. In contrast, OOD examples maintain more or less an equal distance from all class centroids. Based on this hypothesis, we propose a novel Nearest-Centroid Distance Deficit (NCDD) score in the feature space for gastrointestinal OOD detection. Evaluations across Resnet, ViT, DeiT and MLPmixer and two publicly available benchmarks, Kvasir2 and Gastrovision, demonstrate the effectiveness of our approach compared to several state-of-the-art methods. The code is available at: bhattarailab/NCDD