The Social Sustainability of the Infrastructures: A Case Study in the Liguria Region

One of the indicators that measures the economic development of a territory is its infrastructural endowment (road, rail, etc.). The presence of roads, railways, and airports are essential elements in creating the optimal conditions for the establishment or development of productive activities and economic growth; and also to generate benefits. However, the presence of infrastructure can have strong impacts on the environment and the living conditions of the population and infrastructure can be subject to actions related to contrast and opposition. Therefore, in parallel with the economic and environmental sustainability assessment, it is essential to decide whether or not to build new infrastructure. In addition, social sustainability is also pursued on the basis of an assessment that takes into account various aspects that relate the work to the population, also in order to identify the most satisfactory design solution. Alongside the adopted methodology, the assessment must be identified suitable criteria which are capable of taking into account the various impacts generated by the infrastructure, not only of an economic and environmental type, but also social and attributed relative importance (or weight) that is congruous with the correct balance of the three aspects of sustainability. This contribution deals with the identification of criteria for assessing the social sustainability of infrastructure projects, by taking as reference the 24 infrastructure projects in the planning and construction phase in the Liguria Region that make use of the Regional Law n. 39/2007 on the "Regional Strategic Intervention Programs-P.R.I.S." (Regional Strategic Intervention Programs); which guarantees citizens affected by the infrastructure. In this research work, the selection is performed through the involvement of local stakeholders as well as the subjects and institutions that operate within the decision-making process of a work (designers, technicians from public administrations). The selected criteria are then weighted through the pairwise comparison method used in the multi-criteria technique of ThomasSaaty-Analytic Hierarchy Process (AHP). The goal is to identify the useful criteria for assessing social sustainability and the weights attributed by the various parties involved in the decision-making process by citizens directly or indirectly affected by the infrastructure

Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification

This paper studies the problem of deriving fast and accurate classification algorithms with uncertainty quantification. Gaussian process classification provides a principled approach, but the corresponding computational burden is hardly sustainable in large-scale problems and devising efficient alternatives is a challenge. In this work, we investigate if and how Gaussian process regression directly applied to classification labels can be used to tackle this question. While in this case training is remarkably faster, predictions need to be calibrated for classification and uncertainty estimation. To this aim, we propose a novel regression approach where the labels are obtained through the interpretation of classification labels as the coefficients of a degenerate Dirichlet distribution. Extensive experimental results show that the proposed approach provides essentially the same accuracy and uncertainty quantification as Gaussian process classification while requiring only a fraction of computational resources

Estimating Koopman operators with sketching to provably learn large scale dynamical systems

The theory of Koopman operators allows to deploy non-parametric machine learning algorithms to predict and analyze complex dynamical systems. Estimators such as principal component regression (PCR) or reduced rank regression (RRR) in kernel spaces can be shown to provably learn Koopman operators from finite empirical observations of the system's time evolution. Scaling these approaches to very long trajectories is a challenge and requires introducing suitable approximations to make computations feasible. In this paper, we boost the efficiency of different kernel-based Koopman operator estimators using random projections (sketching). We derive, implement and test the new “sketched” estimators with extensive experiments on synthetic and large-scale molecular dynamics datasets. Further, we establish non asymptotic error bounds giving a sharp characterization of the trade-offs between statistical learning rates and computational efficiency. Our empirical and theoretical analysis shows that the proposed estimators provide a sound and efficient way to learn large scale dynamical systems. In particular our experiments indicate that the proposed estimators retain the same accuracy of PCR or RRR, while being much faster. Code is available at https://github.com/Giodiro/NystromKoopman

Valutazione multicriterio e stime di massa: un’applicazione ad un patrimonio immobiliare pubblico

The paper presents an application of multi-criteria evaluation developed to select the most significant property characteristics of a real estate portfolio. The selected characteristics are utilized within a multi-parameter model to estimate the most probable market value of a large public property portfolio owned by the Bank of Italy. The multi-criteria evaluation is based on the involvement of some key actors of the decision process. The goal is overcoming the difficulties presented by econometric models due to the scarcity of a large sample real estate data. The application has shown that the selection and weighting of real estate characteristics allows the development of a reliable mass appraisal without the need for large amounts of data necessary for the application of regression models

Large-scale Nonlinear Variable Selection via Kernel Random Features

We propose a new method for input variable selection in nonlinear regression. The method is embedded into a kernel regression machine that can model general nonlinear functions, not being a priori limited to additive models. This is the first kernel-based variable selection method applicable to large datasets. It sidesteps the typical poor scaling properties of kernel methods by mapping the inputs into a relatively low-dimensional space of random features. The algorithm discovers the variables relevant for the regression task together with learning the prediction model through learning the appropriate nonlinear random feature maps. We demonstrate the outstanding performance of our method on a set of large-scale synthetic and real datasets.Comment: Final version for proceedings of ECML/PKDD 201

Greater numbers of antral follicles in the ovary are associated with increased concentrations of glucose in uterine luminal fluid of beef heifers

Increased antral follicles are associated with greater fertility and a uterine environment that is more supportive of early embryonic development in beef heifers. Glucose is a primary energy source for embryos, and glucose concentrations are elevated in uterine luminal fluid (ULF) of pregnant heifers. We hypothesized that ULF glucose concentrations and endometrial transcript abundance for glucose transporters on d16 after insemination would be greater in heifers with increased numbers of antral follicles. Heifers classified with either increased or diminished antral follicle counts were artificially inseminated following the CO-Synch protocol (d0). On d16 after insemination, reproductive tracts of heifers were collected at an abattoir to retrieve conceptuses to determine pregnancy. Uterine luminal fluid was collected, endometrium was biopsied, total RNA was extracted and glucose transporter transcript abundances were determined. Data were analyzed using the MIXED procedure of SAS with antral follicle group, pregnancy status, and the interaction as fixed effects. Glucose concentrations in ULF were greater in heifers with increased antral follicle numbers. Glucose ULF concentrations increased in pregnant heifers. Facilitated glucose transporter member 1 (SLC2A1) transcript abundance was increased in the endometrium of pregnant heifers but was not different due to antral follicle number or the interaction. Differences in uterine concentrations of glucose associated with antral follicle number could be due to another mechanism, since glucose transporters were not different between antral follicle numbers. Therefore, heifers with increased number of antral follicles have increased energy availability in the uterus to support trophoblast proliferation and function

The inner centromere is a biomolecular condensate scaffolded by the chromosomal passenger complex.

The inner centromere is a region on every mitotic chromosome that enables specific biochemical reactions that underlie properties, such as the maintenance of cohesion, the regulation of kinetochores and the assembly of specialized chromatin, that can resist microtubule pulling forces. The chromosomal passenger complex (CPC) is abundantly localized to the inner centromeres and it is unclear whether it is involved in non-kinase activities that contribute to the generation of these unique chromatin properties. We find that the borealin subunit of the CPC drives phase separation of the CPC in vitro at concentrations that are below those found on the inner centromere. We also provide strong evidence that the CPC exists in a phase-separated state at the inner centromere. CPC phase separation is required for its inner-centromere localization and function during mitosis. We suggest that the CPC combines phase separation, kinase and histone code-reading activities to enable the formation of a chromatin body with unique biochemical activities at the inner centromere