Search

Caching popular content at the network edge can benefit both the operator and the client by alleviating the backhaul traffic and reducing access latency, respectively. Recommendation systems, on the other hand, try to offer interesting content to the user and impact her requests, but independently of the caching policy. Nevertheless, it has been recently proposed that designing caching and recommendation policies separately is suboptimal. Caching could benefit by knowing the recommender's actions in advance, and recommendation algorithms could try to favor cached content (among equally interesting options) to improve network performance and user experience. In this paper we tackle the problem of optimally making caching and recommendation decisions jointly, in the context of the recently introduced “soft cache hits” setup. We show that even the simplest (one user, one cache) problem is NP-hard, but that the most generic problem (multiple users, femtocaching network) is approximable to a constant. To the best of our knowledge, this is the first polynomial algorithm with approximation guarantees for the joint problem. Finally, we compare our algorithm to existing schemes using a range of real-world data-sets.

This immunohistochemistry dataset contains the main structures in deep subcortical white matter (axons, astrocytes, and myelinated axons) in a representative cohort of an ageing population. A set of samples from 90 subjects of the Cognitive Function and Ageing Study (CFAS) were analysed, stratified into three groups of 30 subjects each, in relation to the presence of age-associated deep subcortical lesions. High-resolution microscopy data enables the extraction of valuable information, such as volume fractions, for the construction and validation of diffusion MRI (dMRI) models. The dataset provided here was used in Coelho et al. [1].

This study aims to statistically describe histologically stained white matter brain sections to subsequently inform and validate diffusion MRI techniques. For the first time, we characterise volume fraction distributions of three of the main structures in deep subcortical white matter (axons, astrocytes, and myelinated axons) in a representative cohort of an ageing population for which well-characterized neuropathology data is available. We analysed a set of samples from 90 subjects of the Cognitive Function and Ageing Study (CFAS), stratified into three groups of 30 subjects each, in relation to the presence of age-associated deep subcortical lesions. This provides volume fraction distributions in different scenarios relevant to brain diffusion MRI in dementia. We also assess statistically significant differences found between these groups. In agreement with previous literature, our results indicate that white matter lesions are related with a decrease in the myelinated axons fraction and an increase in astrocytic fraction, while no statistically significant changes occur in axonal mean fraction. In addition, we introduced a framework to quantify volume fraction distributions from 2D immunohistochemistry images, which is validated against in silico simulations. Since a trade-off between precision and resolution emerged, we also performed an assessment of the optimal scale for computing such distributions.