Abstract
This article presents a methodology to estimate the evolution of QoS indices, based on investments and maintenance costs carried out in the DN. The indices were estimated at various disaggregated levels, including the global index, 3 different QoS zones (urban, semi-urban and rural) and 278 municipalities, thereby facilitating the mitigation of QoS asymmetries by allocating investments and maintenance actions to specific regions. To achieve this objective, an optimization problem was formulated to allocate investments and maintenance costs to municipalities with higher improvement benefit-cost ratios, potentially exhibiting lower levels of QoS. This methodology was adopted by the Portuguese DSO to establish the future investments plan from 2023 to 2027. The results demonstrate estimations of good performance, considering the stochastic nature of the phenomena affecting QoS (e.g. atmospheric conditions), which are included in this study, thus developing confidence levels for the global indices.

Abstract
Breast cancer remains a significant global health concern. This study presents an image retrieval system to aid specialists in the analysis of mammogram images. The system employs individual classifiers for eight dimensions: laterality, view, breast density, BI-RADS classification, masses, calcifications, distortions, and asymmetries. Four pre-trained networks, ResNet50, VGG16, InceptionV3, and InceptionResNetV2, were used to train these classifiers. The retrieval model combines these classifiers through a weighted sum. Four weight assignment strategies were explored, ranging from equal weights to weights based on empirical, literature-based, and specialist-informed considerations. Results are illustrated using the INBreast database, which comprises 410 images. Besides the native annotations, ground truth to validate retrieval models had to be acquired. Classification accuracy is as high as 100% for some of the dimensions. Results also demonstrate the effectiveness of the proposed weighted-sum approach, with variations in weight assignments impacting model performance. © 2024 Owner/Author.

Abstract
This work presents an implementation of a reference optical cavity based on parasitic cavities on a low coherence interferometric system. This method allows a maximization of the number of sensors to be implemented without occupying additional reading channels.

Abstract
This research addresses the trainability of Parameterized Quantum Circuit-based Softmax policies in Reinforcement Learning. We assess the trainability of these policies by examining the scaling of the expected value of the partial derivative of the log policy objective function. Here, we assume the hardware-efficient ansatz with blocks forming local 2-designs. In this setting, we show that if each expectation value representing the action's numerical preference is composed of a global observable, it leads to exponentially vanishing gradients. In contrast, for n-qubit systems, if the observables are log(n)-local, the gradients vanish polynomially with the number of qubits provided O(log n) depth. We also show that the expectation of the gradient of the log policy objective depend on the entire action space. Thus, even though global observables lead to concentration, the gradient signal can still be propagated in the presence of at least a single local observable. We validate the theoretical predictions in a series of ansatze and evaluate the performance of local and global observables in a multi-armed bandit setting.

Abstract

Abstract