Multi-domain improves out-of-distribution and data-limited scenarios for medical image analysis

Current machine learning methods for medical image analysis primarily focus on developing models tailored for their specific tasks, utilizing data within their target domain. These specialized models tend to be data-hungry and often exhibit limitations in generalizing to out-of-distribution samples. Recently, foundation models have been proposed, which combine data from various domains and demonstrate excellent generalization capabilities. Building upon this, this work introduces the incorporation of diverse medical image domains, including different imaging modalities like X-ray, MRI, CT, and ultrasound images, as well as various viewpoints such as axial, coronal, and sagittal views. We refer to this approach as multi-domain model and compare its performance to that of specialized models. Our findings underscore the superior generalization capabilities of multi-domain models, particularly in scenarios characterized by limited data availability and out-of-distribution, frequently encountered in healthcare applications. The integration of diverse data allows multi-domain models to utilize shared information across domains, enhancing the overall outcomes significantly. To illustrate, for organ recognition, multi-domain model can enhance accuracy by up to 10% compared to conventional specialized models

Foveation-based Mechanisms Alleviate Adversarial Examples

We show that adversarial examples, i.e., the visually imperceptible perturbations that result in Convolutional Neural Networks (CNNs) fail, can be alleviated with a mechanism based on foveations---applying the CNN in different image regions. To see this, first, we report results in ImageNet that lead to a revision of the hypothesis that adversarial perturbations are a consequence of CNNs acting as a linear classifier: CNNs act locally linearly to changes in the image regions with objects recognized by the CNN, and in other regions the CNN may act non-linearly. Then, we corroborate that when the neural responses are linear, applying the foveation mechanism to the adversarial example tends to significantly reduce the effect of the perturbation. This is because, hypothetically, the CNNs for ImageNet are robust to changes of scale and translation of the object produced by the foveation, but this property does not generalize to transformations of the perturbation. As a result, the accuracy after a foveation is almost the same as the accuracy of the CNN without the adversarial perturbation, even if the adversarial perturbation is calculated taking into account a foveation

Environmental performance of rainwater harvesting strategies in Mediterranean buildings

Purpose: The rapid urbanization and the constant expansion of urban areas during the last decades have locally led to increasing water shortage. Rainwater harvesting (RWH) systems have the potential to be an important contributor to urban water self-sufficiency. The goal of this study was to select an environmentally optimal RWH strategy in newly constructed residential buildings linked to rainwater demand for laundry under Mediterranean climatic conditions, without accounting for water from the mains. Methods: Different strategies were environmentally assessed for the design and use of RWH infrastructures in residential apartment blocks in Mediterranean climates. The harvested rainwater was used for laundry in all strategies. These strategies accounted for (i) tank location (i.e., tank distributed over the roof and underground tank), (ii) building height considering the number of stories (i.e., 6, 9, 12, and 15), and (iii) distribution strategy (i.e., shared laundry, supply to the nearest apartments, and distribution throughout the building). The RWH systems consisted of the catchment, storage, and distribution stages, and the structural and hydraulic calculations were based on Mediterranean conditions. The quantification of the environmental performance of each strategy (e.g., CO2eq. emissions) was performed in accordance with the life cycle assessment methodology. Results and discussion: According to the environmental assessment, the tank location and distribution strategy chosen were the most important variables in the optimization of RWH systems. Roof tank strategies present fewer impacts than their underground tank equivalents because they enhance energy and material savings, and their reinforcement requirements can be accounted for within the safety factors of the building structure without the tank. Among roof tanks and depending on the height, a distribution strategy that concentrates demand in a laundry room was the preferable option, resulting in reductions from 25 to 54 % in most of the selected impact categories compared to distribution throughout the building. Conclusions: These results may set new urban planning standards for the design and construction of buildings from the perspective of sustainable water management. In this sense, a behavioral change regarding demand should be promoted in compact, dense urban settlements.Peer ReviewedPostprint (author's final draft

Llacunes de nova creació

Oasis d'optimisme enmig de la degradaci&oacute

Induction motor with spiral sheet rotor

mprovements in torque at low currents using a rotor with spiral sheets are analyzed. Several rotors and stators have been built combining different constructive and mechanical characteristics of the related elements : inertias, constructive materials, geometrical shapes of the sheets and geometrical disposition of the sheets . These different types of motors have been simulated using computer aided tools and then tested in the laboratory. Finally, four stators (1000, 1500, 1500-type A, and 3000 rpm) with the same constructive parameters, have been simulated and tested with the following rotors types: solid rotor, solid rotor with diamagnetic rings, drag cup, and simple and double squirrel cage rotor; the results have been compared to those obtained with the seven variants of spiral sheet rotor presented in this paper.Peer ReviewedPostprint (published version