Le giustificazioni interpretative nella pratica dell’interpretazione giuridica

The question at issue in the present essay is whether there is a reason to doubt the seemingly indubitable fact that the function of interpretive justifications of judges and lawyers is to show that certain interpretive judgments are correct. The conclusion is that there is a reason to doubt this fact: the reason is that judges and lawyers do not seem to attribute this function to their interpretive justifications, because their justifications do not have the content which is needed to perform such a function. Four theses are developed to support this conclusion. First, there are three meanings in which interpretive judgments could be said correct: grounded on facts; grounded on norms belonging to the legal system; grounded on moral norms. Second, an examination of interpretive argumentation indicates that interpretive judgments are to be conceived as moral judgments, whose correctness depends on moral norms. Third, to claim that an interpretive judgment is correct, the interpretive justification which is offered must have a premise that states a methodological principle, i.e. a moral norm prescribing a hierarchy of interpretive arguments to be used to attribute a meaning to legal texts. Fourth, the premise that states a certain methodological principle is a missing element in the interpretive justifications of judges and lawyers

Explanations of Machine Learning Models in Repeated Nested Cross-Validation: An Application in Age Prediction Using Brain Complexity Features

SHAP (Shapley additive explanations) is a framework for explainable AI that makes explanations locally and globally. In this work, we propose a general method to obtain representative SHAP values within a repeated nested cross-validation procedure and separately for the training and test sets of the different cross-validation rounds to assess the real generalization abilities of the explanations. We applied this method to predict individual age using brain complexity features extracted from MRI scans of 159 healthy subjects. In particular, we used four implementations of the fractal dimension (FD) of the cerebral cortex—a measurement of brain complexity. Representative SHAP values highlighted that the most recent implementation of the FD had the highest impact over the others and was among the top-ranking features for predicting age. SHAP rankings were not the same in the training and test sets, but the top-ranking features were consistent. In conclusion, we propose a method—and share all the source code—that allows a rigorous assessment of the SHAP explanations of a trained model in a repeated nested cross-validation setting

Alcune osservazioni sull'antipaternalismo moderato di Maniaci

Espongo qui alcune osservazioni sul libro di Giorgio Maniaci, Contra el paternalismo jurídico, che intende difendere una teoria antipaternalistica moderata. Dapprima mi occupo del concetto di paternalismo. Discuto poi della possibilità di giustificare un divieto di danneggiare se stessi sulla base di ragioni non paternalistiche. Infine esamino i caratteri che, secondo Maniaci, un’azione deve possedere per evitare interferenze paternalistiche, mostrando che la sua posizione può essere considerata paternalistica anziché antipaternalistica.Here, I make some remarks on Giorgio Maniaci’s book, Contra el paternalismo jurídico, aimed at defending a soft antipaternalistic theory. Firstly, I deal with the concept of paternalism. Then, I discuss the possibility of justifying a prohibition of self-harming activities on the basis of nonpaternalistic reasons. At last I analyse the properties that, according to Maniaci, an action should have to avoid paternalistic interferences, and I show that his position may be considered paternalistic instead of antipaternalistic

Early prediction of Autism Spectrum Disorders through interaction analysis in home videos and explainable artificial intelligence

There is considerable discussion about the advantages and disadvantages of early ASD diagnosis. However, the development of easily understandable and administrable tools for teachers or caregivers in order to identify potentially alarming behaviours (red flags) is usually considered valuable even by scholars who are concerned with very early diagnosis. This study proposes an AI pre-screening tool with the aim of creating an easily administrable tool for non-competent observers useful to identify potentially alarming signs in pre-verbal interactions. The use of these features is evaluated using an explainable artificial intelligence algorithm to assess which of the proposed new interaction characteristics were more effective in classifying individuals with ASD vs. controls. We used a rating scale with three core sections - sensorimotor, behavioural, and emotional - each further divided into four items. By seeing home videos of children doing everyday activities, two experienced observers rated each of these items from 1 (highly typical interaction) to 8 (extremely atypical interaction). Then, a machine learning model based on XGBoost was developed for identifying ASD children. The classification obtained was interpreted through the use of SHAP explanations, obtaining an area under the receiver operating curve of 0.938 and 0.914 for the two observers, respectively. These results demonstrated the significance of early detection of body-related sensorimotor features

The "Peeking" Effect in Supervised Feature Selection on Diffusion Tensor Imaging Data

We read with great interest the article by Haller et al[1][1] in the February 2013 issue of the American Journal of Neuroradiology . The authors used whole-brain diffusion tensor imaging–derived fractional anisotropy (FA) data, skeletonized through use of the standard tract-based spatia

Fractal Analysis of MRI Data at 7 T: How Much Complex Is the Cerebral Cortex?

The human brain is a highly complex structure, which can be only partially described by conventional metrics derived from magnetic resonance imaging (MRI), such as volume, cortical thickness, and gyrification index. In the last years, the fractal dimension (FD) - a useful quantitative index of fractal geometry - has proven to well express the morphological complexity of the cerebral cortex. However, this complexity is likely higher than that we can observe using MRI scanners with 1.5 T or 3 T field strength. Ultrahigh-field MRI (UHF-MRI) improves imaging of smaller anatomical brain structures by exploring down to a submillimetric spatial resolution with higher signal-to-noise and contrast-to-noise ratios. Accordingly, we hypothesized that UHF-MRI might reveal a higher level of the structural complexity of the cerebral cortex. In this study, using an improved box-counting algorithm, we estimated the FD of the cerebral cortex in six public or private T1-weighted MRI datasets of young healthy subjects (for a total of 87 subjects), acquired at different field strengths (1.5 T, 3 T, and 7 T). Our results showed, for the first time, that MRI-derived FD values of the cerebral cortex imaged at 7 T were significantly higher than those observed at lower field strengths. UHF-MRI provides an anatomical definition not achievable at lower field strengths and can improve unveiling the real structural complexity of the human brain

Characterizing cardiac autonomic dynamics of fear learning in humans

Understanding transient dynamics of the autonomic nervous system during fear learning remains a critical step to translate basic research into treatment of fear-related disorders. In humans, it has been demonstrated that fear learning typically elicits transient heart rate deceleration. However, classical analyses of heart rate variability (HRV) fail to disentangle the contribution of parasympathetic and sympathetic systems, and crucially, they are not able to capture phasic changes during fear learning. Here, to gain deeper insight into the physiological underpinnings of fear learning, a novel frequency-domain analysis of heart rate was performed using a short-time Fourier transform, and instantaneous spectral estimates extracted from a point-process modeling algorithm. We tested whether spectral transient components of HRV, used as a noninvasive probe of sympathetic and parasympathetic mechanisms, can dissociate between fear conditioned and neutral stimuli. We found that learned fear elicited a transient heart rate deceleration in anticipation of noxious stimuli. Crucially, results revealed a significant increase in spectral power in the high frequency band when facing the conditioned stimulus, indicating increased parasympathetic (vagal) activity, which distinguished conditioned and neutral stimuli during fear learning. Our findings provide a proximal measure of the involvement of cardiac vagal dynamics into the psychophysiology of fear learning and extinction, thus offering new insights for the characterization of fear in mental health and illness

Artificial intelligence-based models for reconstructing the critical current and index-value surfaces of HTS tapes

For modelling superconductors, interpolation and analytical formulas are commonly used to consider the relationship between the critical current density and other electromagnetic and physical quantities. However, look-up tables are not available in all modelling and coding environments, and interpolation methods must be manually implemented. Moreover, analytical formulas only approximate real physics of superconductors and, in many cases, lack a high level of accuracy. In this paper, we propose a new approach for addressing this problem involving artificial intelligence (AI) techniques for reconstructing the critical surface of high temperature superconducting (HTS) tapes and predicting their index value known as n-value. Different AI models were proposed and implemented, relying on a public experimental database for electromagnetic specifications of HTS tapes, including artificial neural networks (ANN), eXtreme Gradient Boosting (XGBoost), and kernel ridge regressor (KRR). The ANN model was the most accurate in predicting the critical current of HTS materials, performing goodness of fit very close to 1 and extremely low root mean squared error. The XGBoost model proved to be the fastest method, with training computational times under 1 s; whilst KRR could be used as an alternative solution with intermediate performance

Longitudinal study of the effect of a 5-year exercise intervention on structural brain complexity in older adults. A Generation 100 substudy

Physical inactivity has been identified as an important risk factor for dementia. High levels of cardiorespiratory fitness (CRF) have been shown to reduce the risk of dementia. However, the mechanism by which exercise affects brain health is still debated. Fractal dimension (FD) is an index that quantifies the structural complexity of the brain. The purpose of this study was to investigate the effects of a 5-year exercise intervention on the structural complexity of the brain, measured through the FD, in a subset of 105 healthy older adults participating in the randomized controlled trial Generation 100 Study. The subjects were randomized into control, moderate intensity continuous training, and high intensity interval training groups. Both brain MRI and CRF were acquired at baseline and at 1-, 3- and 5-years follow-ups. Cortical thickness and volume data were extracted with FreeSurfer, and FD of the cortical lobes, cerebral and cerebellar gray and white matter were computed. CRF was measured as peak oxygen uptake (VO2peak) using ergospirometry during graded maximal exercise testing. Linear mixed models were used to investigate exercise group differences and possible CRF effects on the brain's structural complexity. Associations between change over time in CRF and FD were performed if there was a significant association between CRF and FD. There were no effects of group membership on the structural complexity. However, we found a positive association between CRF and the cerebral gray matter FD (p < 0.001) and the temporal lobe gray matter FD (p < 0.001). This effect was not present for cortical thickness, suggesting that FD is a more sensitive index of structural changes. The change over time in CRF was associated with the change in temporal lobe gray matter FD from baseline to 5-year follow-up (p < 0.05). No association of the change was found between CRF and cerebral gray matter FD. These results demonstrated that entering old age with high and preserved CRF levels protected against loss of structural complexity in areas sensitive to aging and age-related pathology