A Syntactic Model of Mutation and Aliasing

Traditionally, semantic models of imperative languages use an auxiliary structure which mimics memory. In this way, ownership and other encapsulation properties need to be reconstructed from the graph structure of such global memory. We present an alternative "syntactic" model where memory is encoded as part of the program rather than as a separate resource. This means that execution can be modelled by just rewriting source code terms, as in semantic models for functional programs. Formally, this is achieved by the block construct, introducing local variable declarations, which play the role of memory when their initializing expressions have been evaluated. In this way, we obtain a language semantics which directly represents at the syntactic level constraints on aliasing, allowing simpler reasoning about related properties. To illustrate this advantage, we consider the issue, widely studied in the literature, of characterizing an isolated portion of memory, which cannot be reached through external references. In the syntactic model, closed block values, called "capsules", provide a simple representation of isolated portions of memory, and capsules can be safely moved to another location in the memory, without introducing sharing, by means of "affine' variables. We prove that the syntactic model can be encoded in the conventional one, hence efficiently implemented.Comment: In Proceedings DCM 2018 and ITRS 2018 , arXiv:1904.0956

The Dark Side of Study: When Study Negatively Affects Relationships and School Climate. The Study-Relationships Conflict Scale

This study proposes a new instrument for evaluating the Study-Relationships Conflict, or the conflict that may exist between study and personal relationships with family, friends, schoolmates, and teachers. We recruited a sample of 598 Italian University students (age: M = 22.58 ± 3.85) of different majors. By means of Exploratory and Confirmatory Factor Analyses, we reduced the 16-item pilot version to nine items and three factors: 1) Quarrels at School—QS; 2) Relationship Impairment—RI; 3) Family and Friends' Complaints—FFC. Moreover, we analyzed the correlation between these scales and some academic indicators: Grade Point Average (GPA) and time spent studying. The results showed that the Study-Relationships Conflict Scale (SRCS) has good psychometric properties. In addition, GPA positively correlates with the FFC scale; while time spent studying correlates positively with both the RI and the FFC scales. Finally, QS has a statistically and low significant positive correlation with the hours a day of study before exams. The SRCS will be useful in future research aiming to analyze how studying behaviors could affect social and school relationships. Moreover, it could also be used as a quick screening for detecting student at-risk of high social impairment due to their overstudying, and for developing preventive interventions

Investigation of adaptive optics imaging biomarkers for detecting pathological changes of the cone mosaic in patients with type 1 diabetes mellitus

Purpose To investigate a set of adaptive optics (AO) imaging biomarkers for the assessment of changes of the cone mosaic spatial arrangement in patients with type 1 diabetes mellitus (DM1). Methods 16 patients with 20/20 visual acuity and a diagnosis of DM1 in the past 8 years to 37 years and 20 age-matched healthy volunteers were recruited in this study. Cone density, cone spacing and Voronoi diagrams were calculated on 160x160 μm images of the cone mosaic acquired with an AO flood illumination retinal camera at 1.5 degrees eccentricity from the fovea along all retinal meridians. From the cone spacing measures and Voronoi diagrams, the linear dispersion index (LDi) and the heterogeneity packing index (HPi) were computed respectively. Logistic regression analysis was conducted to discriminate DM1 patients without diabetic retinopathy from controls using the cone metrics as predictors. Results Of the 16 DM1 patients, eight had no signs of diabetic retinopathy (noDR) and eight had mild nonproliferative diabetic retinopathy (NPDR) on fundoscopy. On average, cone density, LDi and HPi values were significantly different (P<0.05) between noDR or NPDR eyes and controls, with these differences increasing with duration of diabetes. However, each cone metric alone was not sufficiently sensitive to discriminate entirely between membership of noDR cases and controls. The complementary use of all the three cone metrics in the logistic regression model gained 100% accuracy to identify noDR cases with respect to controls. PLOS ONE | DOI:10.1371/journal.pone.0151380 March 10, 2016 1 / 14 OPEN ACCESS Citation: Lombardo M, Parravano M, Serrao S, Ziccardi L, Giannini D, Lombardo G (2016) Investigation of Adaptive Optics Imaging Biomarkers for Detecting Pathological Changes of the Cone Mosaic in Patients with Type 1 Diabetes Mellitus. PLoS ONE 11(3): e0151380. doi:10.1371/journal. pone.0151380 Editor: Knut Stieger, Justus-Liebig-University Giessen, GERMANY Received: December 17, 2015 Accepted: February 27, 2016 Published: March 10, 2016 Copyright: © 2016 Lombardo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Funding: Research for this work was supported by the Italian Ministry of Health (5x1000 funding), by the National Framework Program for Research and Innovation PON (grant n. 01_00110) and by Fondazione Roma. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Vision Engineering Italy srl funder provided support in the form of salaries for author GL, but did not have any Conclusion The present set of AO imaging biomarkers identified reliably abnormalities in the spatial arrangement of the parafoveal cones in DM1 patients, even when no signs of diabetic retinopathy were seen on fundoscopy

Relational Neural Machines

Deep learning has been shown to achieve impressive results in several tasks where a large amount of training data is available. However, deep learning solely focuses on the accuracy of the predictions, neglecting the reasoning process leading to a decision, which is a major issue in life-critical applications. Probabilistic logic reasoning allows to exploit both statistical regularities and specific domain expertise to perform reasoning under uncertainty, but its scalability and brittle integration with the layers processing the sensory data have greatly limited its applications. For these reasons, combining deep architectures and probabilistic logic reasoning is a fundamental goal towards the development of intelligent agents operating in complex environments. This paper presents Relational Neural Machines, a novel framework allowing to jointly train the parameters of the learners and of a First--Order Logic based reasoner. A Relational Neural Machine is able to recover both classical learning from supervised data in case of pure sub-symbolic learning, and Markov Logic Networks in case of pure symbolic reasoning, while allowing to jointly train and perform inference in hybrid learning tasks. Proper algorithmic solutions are devised to make learning and inference tractable in large-scale problems. The experiments show promising results in different relational tasks

T-Norms Driven Loss Functions for Machine Learning

Neural-symbolic approaches have recently gained popularity to inject prior knowledge into a learner without requiring it to induce this knowledge from data. These approaches can potentially learn competitive solutions with a significant reduction of the amount of supervised data. A large class of neural-symbolic approaches is based on First-Order Logic to represent prior knowledge, relaxed to a differentiable form using fuzzy logic. This paper shows that the loss function expressing these neural-symbolic learning tasks can be unambiguously determined given the selection of a t-norm generator. When restricted to supervised learning, the presented theoretical apparatus provides a clean justification to the popular cross-entropy loss, which has been shown to provide faster convergence and to reduce the vanishing gradient problem in very deep structures. However, the proposed learning formulation extends the advantages of the cross-entropy loss to the general knowledge that can be represented by a neural-symbolic method. Therefore, the methodology allows the development of a novel class of loss functions, which are shown in the experimental results to lead to faster convergence rates than the approaches previously proposed in the literature