XAI.it 2020 - Preface to the first italian workshop on explainable artificial intelligence

Artificial Intelligence systems are increasingly playing an increasingly important role in our daily lives. As their importance in our everyday lives grows, it is fundamental that the internal mechanisms that guide these algorithms are as clear as possible. It is not by chance that the recent General Data Protection Regulation (GDPR) emphasized the users’ right to explanation when people face artificial intelligence-based technologies. Unfortunately, the current research tends to go in the opposite direction, since most of the approaches try to maximize the effectiveness of the models (e.g., recommendation accuracy) at the expense of the explainability and the transparency. The main research questions which arise from this scenario is straightforward: how can we deal with such a dichotomy between the need for effective adaptive systems and the right to transparency and interpretability? Several research lines are triggered by this question: building transparent intelligent systems, analyzing the impact of opaque algorithms on final users, studying the role of explanation strategies, investigating how to provide users with more control in the behavior of intelligent systems. XAI.it, the first Italian workshop on Explainable AI, tries to address these research lines and aims to provide a forum for the Italian community to discuss problems, challenges and innovative approaches in the various sub-fields of XAI

Exploring the effects of natural language justifications in food recommender systems

Users of food recommender systems typically prefer popular recipes, which tend to be unhealthy. To encourage users to select healthier recommendations by making more informed food decisions, we introduce a methodology to generate and present a natural language justification that emphasizes the nutritional content, or health risks and benefits of recommended recipes. We designed a framework that takes a user and two food recommendations as input and produces an automatically generated natural language justification as output, which is based on the user's characteristics and the recipes' features. In doing so, we implemented and evaluated eight different justification strategies through two different justification styles (e.g., comparing each recipe's food features) in an online user study (N = 503). We compared user food choices for two personalized recommendation approaches, popularity-based vs our health-aware algorithm, and evaluated the impact of presenting natural language justifications. We showed that comparative justifications styles are effective in supporting choices for our healthy-aware recommendations, confirming the impact of our methodology on food choices

Exploring the effects of natural language justifications in food recommender systems

Users of food recommender systems typically prefer popular recipes, which tend to be unhealthy. To encourage users to select healthier recommendations by making more informed food decisions, we introduce a methodology to generate and present a natural language justification that emphasizes the nutritional content, or health risks and benefits of recommended recipes. We designed a framework that takes a user and two food recommendations as input and produces an automatically generated natural language justification as output, which is based on the user’s characteristics and the recipes’ features. In doing so, we implemented and evaluated eight different justification strategies through two different justification styles (e.g., comparing each recipe’s food features) in an online user study (N = 503). We compared user food choices for two personalized recommendation approaches, popularity-based vs our health-aware algorithm, and evaluated the impact of presenting natural language justifications. We showed that comparative justifications styles are effective in supporting choices for our healthy-aware recommendations, confirming the impact of our methodology on food choices

Survey on Adsorption of Low Molecular Weight Compounds in Cu-BTC Metal–Organic Framework: Experimental Results and Thermodynamic Modeling

This contribution aims at providing a critical overview of experimental results for the sorption of low molecular weight compounds in the Cu-BTC Metal–Organic Framework (MOF) and of their interpretation using available and new, specifically developed, theoretical approaches. First, a literature review of experimental results for the sorption of gases and vapors is presented, with particular focus on the results obtained from vibrational spectroscopy techniques. Then, an overview of theoretical models available in the literature is presented starting from semiempirical theoretical approaches suitable to interpret the adsorption thermodynamics of gases and vapors in Cu-BTC. A more detailed description is provided of a recently proposed Lattice Fluid approach, the Rigid Adsorbent Lattice Fluid (RALF) model. In addition, to deal with the cases where specific self- and cross-interactions (e.g., H-bonding, Lewis acid/Lewis base interactions) play a role, a modification of the RALF model, i.e., the RALFHB model, is introduced here for the first time. An extension of both RALF and RALFHB is also presented to cope with the cases in which the heterogeneity of the rigid adsorbent displaying a different kind of adsorbent cages is of relevance, as it occurs for the adsorption of some low molecular weight substances in Cu-BTC MOF

"Hemolysis, or not hemolysis, that is the question". Use of hydroxychloroquine in a patient with COVID-19 infection and G6PD deficiency

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QED in strong, finite-flux magnetic fields

Lower bounds are placed on the fermionic determinants of Euclidean quantum electrodynamics in two and four dimensions in the presence of a smooth, finite-flux, static, unidirectional magnetic field $B(r) =(0,0,B(r))$, where $B(r) \geq 0$ or $B(r) \leq 0$, and $r$ is a point in the xy-plane.Comment: 10 pages, postscript (in uuencoded compressed tar file

Usutu virus in blackbirds (Turdus merula) with clinical signs, a case study from northern Italy

Usutu virus (USUV) is a mosquito-borne virus belonging to the family Flaviviridae, genus Flavivirus. Natural transmission cycle of USUV involves mosquitoes and birds, so humans and other mammals are considered incidental hosts. In this study, USUV infection was diagnosed in all wild blackbirds, collected from July to September 2018 in a wildlife recovery center in the province of Bologna, in the Emilia-Romagna region, northern Italy. All blackbirds showed neurological clinical signs, such as overturning, pedaling, and incoordination. Moreover, the subjects died shortly after arriving at the hospitalization center. Virological investigations were performed by real-time PCR on frozen samples of the spleen, kidney, myocardium, and brain for the detection of Usutu (USUV) and West Nile (WNV) viruses. The small and large intestine were used as a matrix for the detection of Newcastle disease virus (NDV). All 56 subjects with neurological clinical signs were positive for USUV, only one subject (1.8%) tested positive for WNV, and no subject was positive for NDV. The most represented age class was class 1 J (58.9%), followed by class 3 (25.0%), and lastly from class 4 (16.1%). Most of the blackbirds before dying were in good (51.8%) and fair (39.3%) nutritional status, while only five subjects (8.9%) were cachectic. The USUV genomes detected in the blackbirds of this study fall within the sub-clade already called EU2 that has been detected since 2009 in the Emilia-Romagna region. Neurological clinical signs in USUV-affected blackbirds are still widely discussed and there are few works in the literature. Although our results require further studies, we believe them to be useful for understanding the clinical signs of Usutu virus in blackbirds, helping to increase the knowledge of this zoonotic agent in wild species and to understand its effect on the ecosystem. The goal of this study was to report—in the context of the regional passive surveillance program—the detection of USUV RNA in its most important amplifying host, the common blackbird, when showing clinical signs before death

Different spatial distribution of inflammatory cells in the tumor microenvironment of ABC and GBC subgroups of diffuse large B cell lymphoma

Diffuse Large B-Cell Lymphoma (DLBCL) presents a high clinical and biological heterogeneity, and the tumor microenvironment chracteristics are important in its progression. The aim of this study was to evaluate tumor T, B cells, macrophages and mast cells distribution in GBC and ABC DLBCL subgroups through a set of morphometric parameters allowing to provide a quantitative evaluation of the morphological features of the spatial patterns generated by these inflammatory cells. Histological ABC and GCB samples were immunostained for CD4, CD8, CD68, CD 163, and tryptase in order to determine both percentage and position of positive cells in the tissue characterizing their spatial distribution. The results evidenced that cell patterns generated by CD4-, CD8-, CD68-, CD163- and tryptase-positive cell profiles exhibited a significantly higher uniformity index in ABC than in GCB subgroup. The positive-cell distributions appeared clustered in tissues from GCB, while in tissues from ABC such a feature was lower or absent. The combinations of spatial statistics-derived parameters can lead to better predictions of tumor cell infiltration than any classical morphometric method providing a more accurate description of the functional status of the tumor, useful for patient prognosis

Fermionic Determinant of the Massive Schwinger Model

A representation for the fermionic determinant of the massive Schwinger model, or $QED_2$, is obtained that makes a clean separation between the Schwinger model and its massive counterpart. From this it is shown that the index theorem for $QED_2$ follows from gauge invariance, that the Schwinger model's contribution to the determinant is canceled in the weak field limit, and that the determinant vanishes when the field strength is sufficiently strong to form a zero-energy bound state

A molecular interpretation of the dynamics of diffusive mass transport of water within a glassy polyetherimide

The diffusion process of water molecules within a polyetherimide (PEI) glassy matrix has been analyzed by combining the experimental analysis of water sorption kinetics performed by FTIR spectroscopy with theoretical information gathered from Molecular Dynamics simulations and with the expression of water chemical potential provided by a non‐equilibrium lattice fluid model able to describe the thermodynamics of glassy polymers. This approach allowed us to construct a convincing description of the diffusion mechanism of water in PEI providing molecular details of the process related to the effects of the cross‐ and self‐hydrogen bonding established in the system on the dynamics of water mass transport