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Number magnitude affects spatial decisions: Evidence of spatial-numerical associations with complex movements
No full textThe Spatial-Numerical Association effects describe the spatial relationship between number magnitude and response side, with small numbers usually associated with left sided responses and large numbers with right sided responses. Typically, these effects are demonstrated using response time differences in simple key press tasks, where participants are required to process the magnitude (magnitude classification task) or parity (parity judgement task) of a number. The present study investigated whether similar spatial biases (left/right) also occur for decisions that involve more complex movements, namely walking. Using a free response task, presented in a virtual reality environment, participants were shown a number from 1 to 9, that was presented directly in front of them. At the beginning of each trial participants were required to process either the number's magnitude (Experiment 1) or parity (Experiment 2). They were then asked to walk freely in any direction towards a semi-circular target area, while continuing to process information in working memory. The results showed a higher frequency of leftward walking decisions for smaller numbers and rightward walking decisions for larger numbers in both experiments, as well as compatible deviations of walking trajectory. These findings are consistent with previous literature on SNAs. This study highlights that in a free response task both spatial decisions and spontaneous movements are influenced by number magnitude, both when magnitude is task-relevant and when it is task-irrelevant
DisasterReliefGPT: Multimodal AI for Autonomous Disaster Impact Assessment and Crisis Communication
No full text
Oltre il silenzio accademico: strategie generative per la prevenzione del dropout e l’emersione dei talenti
No full textL’articolo esamina il fenomeno degli studenti silenti all’interno del più ampio quadro del dropout universitario, adottando la cornice teorica della pedagogia generativa e il paradigma della valorizzazione dei talenti. La ricerca presentata si sviluppa in tre fasi qualitative, coinvolgendo docenti, tutor e rappresentanti degli studenti, al fine di triangolare prospettive, esperienze e pratiche. Le evidenze raccolte delineano il silenzio accademico come esito di fattori molteplici e interconnessi che possono condurre a forme di disaffezione, inattività o sospensione del percorso formativo. L’analisi mette in luce il ruolo strategico di dispositivi educativi e relazionali, quali tutorato, accompagnamento personalizzato e setting partecipativi, nel promuovere l’emersione dal silenzio e l’appartenenza alla comunità accademica. L’orientamento generativo, in questo contesto, è inteso come pratica capace di trasformare fragilità in risorsa, intrecciando vocazioni personali e processi formativi, e restituendo all’istituzione universitaria la funzione di spazio di crescita e di empowerment
Towards a better definition of nociplastic pain conditions: a psychological grounded study on fibromyalgia, chronic headache and vulvodynia
No full textSguardi sull'adolescenza. Un'introduzione
No full textThe dossier presented herein seeks to examine,
from a pedagogical standpoint, the existential
phase of adolescence. Through a diversity of voices,
sensitivities, and experiences, the need emerges to
acknowledge the importance of providing a sense
of meaning— a task primarily incumbent upon the
adult, so that the adolescent may also engage in this
process
Optimizing Merkle Proof Size Through Path Length Analysis: A Probabilistic Framework for Efficient Blockchain State Verification
No full textThis study addresses a critical challenge in modern blockchain systems: the excessive size of Merkle proofs in state verification, which significantly impacts scalability and efficiency. As highlighted by Ethereum’s founder, Vitalik Buterin, current Merkle Patricia Tries (MPTs) are highly inefficient for stateless clients, with worst-case proofs reaching approximately 300 MB. We present a comprehensive probabilistic analysis of path length distributions in MPTs to optimize proof size while maintaining security guarantees. Our novel mathematical model characterizes the distribution of path lengths in tries containing random blockchain addresses and validates it through extensive computational experiments. The findings reveal logarithmic scaling of average path lengths with respect to the number of addresses, with unprecedented precision in predicting structural properties across scales from 100 to 300 million addresses. The research demonstrates remarkable accuracy, with discrepancies between theoretical and experimental results not exceeding 0.01 across all tested scales. By identifying and verifying the right-skewed nature of path length distributions, we provide critical insights for optimizing Merkle proof generation and size reduction. Our practical implementation guidelines demonstrate potential proof size reductions of up to 70% through optimized path structuring and node layout. This work bridges the gap between theoretical computer science and practical blockchain engineering, offering immediate applications for blockchain client optimization and efficient state-proof generation
Osservatorio Corte europea dei diritti dell'uomo (Ha natura riparatoria e non punitiva l’ordine di demolizione di una costruzione abusiva + Giustificate le misure che sono state imposte agli operatori sanitari non vaccinati + Giudice parziale in un caso riguardante i licenziamenti compiuti da una grossa società nazionale + Viola la Convenzione l’espulsione dal Paese fondata solo sulla natura e sulla gravità del reato per cui si è stati condannati)
No full textGiurisprudenza Corte ED
Understanding maladaptive daydreaming from the attachment framework: The intertwining roles of parental care, unresolved attachment, depression/anxiety and obsessive-compulsive symptoms
No full textMaladaptive Daydreaming (MD) is an excessive absorption in vivid fantasies interfering with individuals' daily functioning, which has been associated with adverse psychological outcomes and adult attachment insecurities. However, no study to date has addressed the relationships between MD, parental care, unresolved attachment, and psychological symptoms (depression/anxiety and obsessive-compulsive disorder; OCD) in a sample of young adults. In this study, 1295 young adults (401 males) completed an online survey including the Parental Bonding Instrument, Maladaptive Daydreaming Scale, Adult Unresolved Attachment Questionnaire, and the DSM-5 Level 1 Cross-Cutting Symptom Measure. The results evidenced the differential contribution of maternal and paternal care on individuals' psychological symptoms; whilst higher maternal care was negatively related to OCD symptoms, higher paternal care was negatively linked to depression/anxiety symptoms. A relationship between unresolved attachment, MD and psychopathological symptoms emerged; specifically, MD mediated the relationships between unresolved attachment and depression/anxiety and OCD symptoms. Overall, paternal and maternal care may have distinct roles in predicting individuals’ psychopathological outcomes. In the presence of unresolved attachment, MD may represent a dissociative response that allows individuals to deal with negative experiences through psychopathological symptoms. Understanding the specific pathways that lead to different psychopathological outcomes could have important implications in developing preventive clinical interventions
Matching the Expert’s Knowledge via a Counterfactual-Based Feature Importance Measure
No full textTo be employed in real-world applications, explainable artificial intelligence (XAI) techniques need to provide explanations that are comprehensible to experts and decision-makers with no machine learning (ML) background, thus allowing for the validation of the ML model via their domain knowledge.
To this aim, XAI approaches based on feature importance and counterfactuals can be employed, although both have some limitations: the last provide only local explanations, whereas the first can be very computationally expensive. A less computationally-expensive global feature importance measure can be derived by considering the instances close to the model decision boundary and analyzing how often some minor changes in one feature’s values do affect the classification outcome.
However, the validation of XAI techniques in the literature rarely employs the application domain knowledge due to the burden of formalizing it, e.g., providing some degree of expected importance for each feature. Still, given an ML model, it is difficult to determine whether an XAI technique may inject a bias in the explanation (e.g., overestimating or underestimating the importance of a feature) in the absence of such ground truth.
To address this issue, we test our feature importance approach both with the UCI benchmark datasets and real-world smart manufacturing data characterized by annotations provided by domain experts about the expected importance of each feature. If compared to the state-of-the-art, the employed approach results to be reliable and convenient in terms of computation time, as well as more concordant with the expected importance provided by the domain expert