Interactions of cosmological gravitational waves and magnetic fields

The energy momentum tensor of a magnetic field always contains a spin-2 component in its anisotropic stress and therefore generates gravitational waves. It has been argued in the literature (Caprini & Durrer \cite{CD}) that this gravitational wave production can be very strong and that back-reaction cannot be neglected. On the other hand, a gravitational wave background does affect the evolution of magnetic fields. It has also been argued (Tsagas et al. \cite{Tsagas:2001ak},\cite{Tsagas:2005ki}) that this can lead to very strong amplification of a primordial magnetic field. In this paper we revisit these claims and study back reaction to second order.Comment: Added references, accepted for publication in PR

Identifying the lights position in photometric stereo under unknown lighting

Reconstructing the 3D shape of an object from a set of images is a classical problem in Computer Vision. Photometric stereo is one of the possible approaches. It stands on the assumption that the object is observed from a fixed point of view under different lighting conditions. The traditional approach requires that the position of the light sources is accurately known. It has been proved that the lights position can be estimated directly from the data, when at least 6 images of the observed object are available. In this paper, we give a Matlab implementation of the algorithm for solving the photometric stereo problem under unknown lighting, and propose a simple shooting technique to solve the bas-relief ambiguity.Comment: new versio

Demographic Fairness in Multimodal Biometrics: A Comparative Analysis on Audio-Visual Speaker Recognition Systems

In urban scenarios, biometric recognition technologies are being increasingly adopted to empower citizens with a secure and usable access to personalized services. Given the challenging environmental scenarios, combining evidence from multiple biometrics at a certain step of the recognition pipeline has been often proved to increase the performance of the biometric-enabled recognition system. Despite the increasing accuracy achieved so far, it still remains under-explored how the adopted biometric fusion policy impacts on the quality of the decisions made by the biometric system, depending on the demographic characteristics of the citizen under consideration. In this paper, we investigate the extent to which state-of-the-art multimodal recognition systems based on facial and vocal biometrics are susceptible to unfairness towards legally-protected groups of individuals, characterized by a common sensitive attribute. Specifically, we present a comparative analysis of the performance across groups for two deep learning architectures tailored for facial and vocal recognition, under seven fusion policies that cover different pipeline steps (feature, model, score and decision). Experiments show that, compared to the unimodal systems alone and the other fusion policies, the multimodal system obtained via a fusion at the model step leads to the highest overall accuracy and the lowest disparity across groups

Recency, Popularity, and Diversity of Explanations in Knowledge-based Recommendation

Modern knowledge-based recommender systems enable the end-to-end generation of textual explanations. These explanations are created from learnt paths between an already experience product and a recommended product in a knowledge graph, for a given user. However, none of the existing studies has investigated the extent to which properties of a single explanation (e.g., the recency of interaction with the already experience product) and of a group of explanations for a recommended list (e.g., the diversity of the explanation types) can influence the perceived explanation quality. In this paper, we summarize our previous work on conceptualizing three novel properties that model the quality of the explanations (linking interaction recency, shared entity popularity, and explanation type diversity) and proposing re-ranking approaches able to optimize for these properties. Experiments on two public data sets showed that our approaches can increase explanation quality according to the proposed properties, while preserving recommendation utility. Source code and data: https://github.com/giacoballoccu/explanation-quality-recsys

Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations

Existing explainable recommender systems have mainly modeled relationships between recommended and already experienced products, and shaped explanation types accordingly (e.g., movie "x"starred by actress "y"recommended to a user because that user watched other movies with "y"as an actress). However, none of these systems has investigated the extent to which properties of a single explanation (e.g., the recency of interaction with that actress) and of a group of explanations for a recommended list (e.g., the diversity of the explanation types) can influence the perceived explaination quality. In this paper, we conceptualized three novel properties that model the quality of the explanations (linking interaction recency, shared entity popularity, and explanation type diversity) and proposed re-ranking approaches able to optimize for these properties. Experiments on two public data sets showed that our approaches can increase explanation quality according to the proposed properties, fairly across demographic groups, while preserving recommendation utility. The source code and data are available at https: //github.com/giacoballoccu/explanation-quality-recsys

XRecSys: A framework for path reasoning quality in explainable recommendation

There is increasing evidence that recommendations accompanied by explanations positively impact on businesses in terms of trust, guidance, and persuasion. This advance has been made possible by traditional models representing user–product interactions augmented with external knowledge modeled as knowledge graphs. However, these models produce textual explanations on top of reasoning paths extracted from the knowledge graph without considering relevant properties of the path entities. In this paper, we present XRecSys, a Python framework for the optimization of the reasoning path selection process according to properties deemed relevant by users (e.g., time relevance of the linking interaction or popularity of the entity linked to the explanation). Our framework leads to a higher reasoning path quality in terms of the considered properties and, consequently, textual explanations more relevant for the users

Estimating the trace of matrix functions with application to complex networks

The approximation of trace(f(Ω)), where f is a function of a symmetric matrix Ω, can be challenging when Ω is exceedingly large. In such a case even the partial Lanczos decomposition of Ω is computationally demanding and the stochastic method investigated by Bai et al. (J. Comput. Appl. Math. 74:71–89, 1996) is preferred. Moreover, in the last years, a partial global Lanczos method has been shown to reduce CPU time with respect to partial Lanczos decomposition. In this paper we review these techniques, treating them under the unifying theory of measure theory and Gaussian integration. This allows generalizing the stochastic approach, proposing a block version that collects a set of random vectors in a rectangular matrix, in a similar fashion to the partial global Lanczos method. We show that the results of this technique converge quickly to the same approximation provided by Bai et al. (J. Comput. Appl. Math. 74:71–89, 1996), while the block approach can leverage the same computational advantages as the partial global Lanczos. Numerical results for the computation of the Von Neumann entropy of complex networks prove the robustness and efficiency of the proposed block stochastic method

Impact of Horse Grazing on Floristic Diversity in Mediterranean Small Standing-Water Ecosystems (SWEs)

Small standing-Water Ecosystems (SWEs), despite their pivotal ecological role due to their participation in hydrogeological processes and their richness in biodiversity, seem to be often overlooked by the scientific community. In this study, the vascular plant diversity in some representative SWEs, that host a peculiar assemblage of plant and animal species, was investigated in relation to the disturbance effects of a wild horse population. A total of 50 plots, equally distributed in small and large SWEs, were surveyed and a level of disturbance was attributed to each plot. We found greater species richness in small and undisturbed SWEs, which suggests the negative impact of horse grazing on the richness of plant species in this type of habitat. Significant differences in plant assemblage were found according to the disturbance level, whereas, contrary to what was observed for species richness, no differences were detected based on their size. The diversity indices, used to evaluate the richness and diversity in these areas, recorded the highest values for small and undisturbed areas. This result highlights that the disturbance of the horse grazing plays a pivotal role in affecting the diversity and richness of species in the SWEs. These findings suggest that SWE systems should be analyzed considering these areas as unique in order to allow the conservation of the plant richness and biodiversity of the SWE systems in conjunction with the protection of horses