The development of technology required for the successful deposition of aluminum alloys and beryllium

Development of technology for electrodeposition of aluminum alloys and berylliu

Research and development of magnesium/aluminum electroforming process for solar concentrators Final report, 13 May 1966 - 10 May 1967

Magnesium and aluminum electroforming process for solar concentrator

Evaluating local explanation methods on ground truth

Evaluating local explanation methods is a difficult task due to the lack of a shared and universally accepted definition of explanation. In the literature, one of the most common ways to assess the performance of an explanation method is to measure the fidelity of the explanation with respect to the classification of a black box model adopted by an Artificial Intelligent system for making a decision. However, this kind of evaluation only measures the degree of adherence of the local explainer in reproducing the behavior of the black box classifier with respect to the final decision. Therefore, the explanation provided by the local explainer could be different in the content even though it leads to the same decision of the AI system. In this paper, we propose an approach that allows to measure to which extent the explanations returned by local explanation methods are correct with respect to a synthetic ground truth explanation. Indeed, the proposed methodology enables the generation of synthetic transparent classifiers for which the reason for the decision taken, i.e., a synthetic ground truth explanation, is available by design. Experimental results show how the proposed approach allows to easily evaluate local explanations on the ground truth and to characterize the quality of local explanation methods

Metabotropic glutamate 2/3 receptors and epigenetic modifications in psychotic disorders: a review

Schizophrenia and Bipolar Disorder are chronic psychiatric disorders, both considered as "major psychosis"; they are thought to share some pathogenetic factors involving a dysfunctional gene x environment interaction. Alterations in the glutamatergic transmission have been suggested to be involved in the pathogenesis of psychosis. Our group developed an epigenetic model of schizophrenia originated by Prenatal Restraint Stress (PRS) paradigm in mice. PRS mice developed some behavioral alterations observed in schizophrenic patients and classic animal models of schizophrenia, i.e. deficits in social interaction, locomotor activity and prepulse inhibition. They also showed specific changes in promoter DNA methylation activity of genes related to schizophrenia such as reelin, BDNF and GAD67, and altered expression and function of mGlu2/3 receptors in the frontal cortex. Interestingly, behavioral and molecular alterations were reversed by treatment with mGlu2/3 agonists. Based on these findings, we speculate that pharmacological modulation of these receptors could have a great impact on early phase treatment of psychosis together with the possibility to modulate specific epigenetic key protein involved in the development of psychosis. In this review, we will discuss in more details the specific features of the PRS mice as a suitable epigenetic model for major psychosis. We will then focus on key proteins of chromatin remodeling machinery as potential target for new pharmacological treatment through the activation of metabotropic glutamate receptors

Explaining Sentiment Classification with Synthetic Exemplars and Counter-Exemplars

We present xspells, a model-agnostic local approach for explaining the decisions of a black box model for sentiment classification of short texts. The explanations provided consist of a set of exemplar sentences and a set of counter-exemplar sentences. The former are examples classified by the black box with the same label as the text to explain. The latter are examples classified with a different label (a form of counter-factuals). Both are close in meaning to the text to explain, and both are meaningful sentences – albeit they are synthetically generated. xspells generates neighbors of the text to explain in a latent space using Variational Autoencoders for encoding text and decoding latent instances. A decision tree is learned from randomly generated neighbors, and used to drive the selection of the exemplars and counter-exemplars. We report experiments on two datasets showing that xspells outperforms the well-known lime method in terms of quality of explanations, fidelity, and usefulness, and that is comparable to it in terms of stability

XAI.it 2021 - Preface to the Second 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 intelligencebased 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 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

Evaluation of MU-MIMO Digital Beamforming Algorithms in B5G/6G LEO Satellite Systems

Satellite Communication (SatCom) systems will be a key component of 5G and 6G networks to achieve the goal of providing unlimited and ubiquitous communications and deploying smart and sustainable networks. To meet the ever-increasing demand for higher throughput in 5G and beyond, aggressive frequency reuse schemes (i.e., full frequency reuse), combined with digital beamforming techniques to cope with the massive co-channel interference, are recognized as a key solution. Aimed at (i) eliminating the joint optimization problem among the beamforming vectors of all users, (ii) splitting it into distinct ones, and (iii) finding a closed-form solution, we propose a beamforming algorithm based on maximizing the users' Signal-to-Leakage-and-Noise Ratio (SLNR) served by a Low Earth Orbit (LEO) satellite. We investigate and assess the performance of several beamforming algorithms, including both those based on Channel State Information (CSI) at the transmitter, i.e., Minimum Mean Square Error (MMSE) and Zero-Forcing (ZF), and those only requiring the users' locations, i.e., Switchable Multi-Beam (MB). Through a detailed numerical analysis, we provide a thorough comparison of the performance in terms of per-user achievable spectral efficiency of the aforementioned beamforming schemes, and we show that the proposed SLNR beamforming technique is able to outperform both MMSE and ZF schemes in the presented SatCom scenario

Evaluation of multi-user multiple-input multiple-output digital beamforming algorithms in B5G/6G low Earth orbit satellite systems

Satellite communication systems will be a key component of 5G and 6G networks to achieve the goal of providing unlimited and ubiquitous communications and deploying smart and sustainable networks. To meet the ever-increasing demand for higher throughput in 5G and beyond, aggressive frequency reuse schemes (i.e., full frequency reuse), combined with digital beamforming techniques to cope with the massive co-channel interference, are recognized as a key solution. Aimed at (i) eliminating the joint optimization problem among the beamforming vectors of all users, (ii) splitting it into distinct ones, and (iii) finding a closed-form solution, we propose a beamforming algorithm based on maximizing the users' signal-to-leakage-and-noise ratio served by a low Earth orbit satellite. We investigate and assess the performance of several beamforming algorithms, including both those based on channel state information at the transmitter, that is, minimum mean square error and zero forcing, and those only requiring the users' locations, that is, switchable multi-beam. Through a detailed numerical analysis, we provide a thorough comparison of the performance in terms of per-user achievable spectral efficiency of the aforementioned beamforming schemes, and we show that the proposed signal to-leakage-plus-noise ratio beamforming technique is able to outperform both minimum mean square error and multi-beam schemes in the presented satellite communication scenario

Opening the black box: a primer for anti-discrimination

The pervasive adoption of Artificial Intelligence (AI) models in the modern information society, requires counterbalancing the growing decision power demanded to AI models with risk assessment methodologies. In this paper, we consider the risk of discriminatory decisions and review approaches for discovering discrimination and for designing fair AI models. We highlight the tight relations between discrimination discovery and explainable AI, with the latter being a more general approach for understanding the behavior of black boxes