Multi-temporal images and 3D dense models for archaeological site monitoring in Hierapolis of Phrygia (TK)

and range-based measurement systems have become increasingly interesting in excavation processes for monitoring purposes and large scale mapping, both from a terrestrial and aerial point of view. The paper will focus on the great challenge of monitoring sites over time, integrating and conforming multiple data coming from previous metric survey projects and image data collected in the past for different purposes. The test-site was the complex archaeological landscape of the ancient city of Hierapolis in Phrygia on which the MAIER – Italian Archaeological Mission of Hierapolis has operated since the 1960s and where the Politecnico di Torino conducted several survey campaigns. A set of multi-temporal datasets acquired in a series of campaigns in 1997, 2002, 2007, 2012, 2015 are presented, as well as their 3D multi-sensor models; the older dense models generated with archival images are intended to be compared and integrated with newer models generated by the LiDAR scans in 2012 and the UAV systems employed in the last mission in 2015. In particular, the case study was the massive complex of the ancient Bath-Church in the northern part of the city below the Northern Necropolis, and Building A of the Apollo Sanctuary, in the central Sacred Area near the Ancient Theatre. In these sites, many different sensors have been experimented with over the years and preliminary multi-temporal data integration has been tested in order to up-date and improve older archival records based on collected images and related to newer and updated documentation projects

A general aspect-term-extraction model for multi-criteria recommendations

In recent years, increasingly large quantities of user reviews have been made available by several e-commerce platforms. This content is very useful for recommender systems (RSs), since it reflects the users' opinion of the items regarding several aspects. In fact, they are especially valuable for RSs that are able to exploit multi-faceted user ratings. However, extracting aspect-based ratings from unstructured text is not a trivial task. Deep Learning models for aspect extraction have proven to be effective, but they need to be trained on large quantities of domain-specific data, which are not always available. In this paper, we explore the possibility of transferring knowledge across domains for automatically extracting aspects from user reviews, and its implications in terms of recommendation accuracy. We performed different experiments with several Deep Learning-based Aspect Term Extraction (ATE) techniques and Multi-Criteria recommendation algorithms. Results show that our framework is able to improve recommendation accuracy compared to several baselines based on single-criteria recommendation, despite the fact that no labeled data in the target domain was used when training the ATE model

Technologies and techniques offering new interpretations of the landscape evolution

The need for specific documents in terms of environment and landscape has its origin in late eighties, when in Italy both at European and Regional level several dispositions related to the improvement of the landscape quality came out to guarantee the compatibility of territorial transformations. Within this traditional context, how innovative, mainly BIM-oriented, technologies can be useful in offering new ways of interpretation for the landscape planning and safeguarding? The aim of the present work is to read and translate landscape elements using digital BIM-oriented platforms, in order to develop an optimized procedure to collect/organize/implement data and define guidelines for landscape and visual impact assessment; furthermore, the aim is to understand the limits that are still present within available tools. After having read the features of the territory another key point is the creation of digital parametric libraries to represent environmental mitigation works. As far as visual mitigation works are concerned, visibility analysis of the infrastructure is possible through Virtual and Mixed Reality. Once the modeling of specific components is performed, they can be located within the contextual model, where outputs related to the “whole” can be extracted and updated afterwards. New digital technologies and techniques undoubtedly offer new interpretation scenarios of the landscape evolution; nevertheless, there are still strong limits related to the interactions among these tools. The present work provides a valid methodology to involve landscape and urban planning in the BIM process. The research also proposes a series of BIM oriented tools and software to develop a typical output (landscape report) required during the design process, useful for landscape architects

Ocular Refraction at Birth and Its Development During the First Year of Life in a Large Cohort of Babies in a Single Center in Northern Italy

The purpose of this study was to investigate refraction at birth and during the first year of life in a large cohort of babies born in a single center in Northern Italy. We also aimed to analyze refractive errors in relation to the gestational age at birth. An observational ophthalmological assessment was performed within 24 h of birth on 12,427 newborns. Refraction was examined using streak retinoscopy after the administration of tropicamide (1%). Values in the range of between +0.50 ≤ D ≤ +4.00 were defined as physiological refraction at birth. Newborns with refraction values outside of the physiological range were followed up during the first year of life. Comparative analyses were conducted in a subgroup of babies with known gestational ages. The following distribution of refraction at birth was recorded: 88.03% of the babies had physiological refraction, 5.03% had moderate hyperopia, 2.14% had severe hyperopia, 3.4%, had emmetropia, 0.45%, had myopia, 0.94% had astigmatism, and 0.01% had anisometropia. By the end of the first year of life, we observed reductions in hyperopia and astigmatism, and stabilization of myopia. Preterm babies had a four-fold higher risk of congenital myopia and a three-fold higher risk of congenital emmetropia as compared to term babies. Refraction profiles obtained at birth changed during the first year of life, leading to a normalization of the refraction values. Gestational age at birth affected the incidence of refractive errors and amblyopia

An investigation on the impact of natural language on conversational recommendations

In this paper, we investigate the combination of Virtual Assistants and Conversational Recommender Systems (CoRSs) by designing and implementing a framework named ConveRSE, for building chatbots that can recommend items from different domains and interact with the user through natural language. An user experiment was carried out to understand how natural language influences both the cost of interaction and recommendation accuracy of a CoRS. Experimental results show that natural language can indeed improve user experience, but some critical aspects of the interaction should be mitigated appropriately

Central serous chorioretinopathy: Pathogenesis and management

Central serous chorioretinopathy (CSC) is a common retina disease and has a relative high recurrence rate, etiology, and pathogenesis of which remains largely ambiguous. The effects on the retina are usually self-limited, although some people are left with permanent vision loss due to progressive and irreversible photoreceptor damage or retinal pigment epithelium atrophy. There have been a number of interventions used in CSC, including, but not limited to, laser treatment, photodynamic therapy (PDT), intravitreal injection of anti-vascular endothelial growth factor agents, and subthreshold lasers. It is not clear whether there is a clinically important benefit to treating acute CSC, which often resolves spontaneously as part of its natural history. Of the interventions studied to date, PDT and micropulse laser treatment appear the most promising

Small-Angle Neutron Scattering Reveals the Structural Details of Thermosensitive Polymer-Grafted Cellulose Nanocrystal Suspensions.

Thanks to the use of small-angle neutron scattering (SANS), a detailed structural description of thermosensitive polymer-grafted cellulose nanocrystals (CNCs) was obtained and the behavior of aqueous suspensions of these derivatized biosourced particles upon temperature increase was revealed. Although literature data show that the surface grafting of thermosensitive polymers drastically enhances the colloidal properties of CNCs, direct space microscopic investigation techniques fail in providing sufficient structural information on these objects. In the case of CNCs decorated with temperature-sensitive polyetheramines following a peptide coupling reaction, a qualitative and quantitative analysis of SANS spectra shows that CNCs are homogeneously covered by a shell comprising polymer chains in a Gaussian conformation with a thickness equal to their radius of gyration in solution, thus revealing a mushroom regime. An increase of the temperature above the lower critical solution temperature (LCST) of the polyetheramine results in the formation of finite size bundles whose aggregation number depends on the particle concentration and suspension temperature deviation from the LCST. SANS analysis further reveals local changes at the CNC surface corresponding to a release of water molecules and a related denser polymer shell conformation. Noticeably, data show a full reversibility at all length scales when a sample was cooled down to below the LCST after being heated above it. Overall, the results obtained by SANS allow an in-depth structural investigation of derivatized CNCs, which is of high interest for the design of functional materials comprising these biosourced colloids.Institut Carnot PolyNat (ANR N° 16-CARN-025-01), France

A domain-independent framework for building conversational recommender systems

Conversational Recommender Systems (CoRSs) implement a paradigm where users can interact with the system for defining their preferences and discovering items that best fit their needs. A CoRS can be straightforwardly implemented as a chatbot. Chatbots are becoming more and more popular for several applications like customer care, health care, medical diagnoses. In the most complex form, the implementation of a chatbot is a challenging task since it requires knowledge about natural language processing, human-computer interaction, and so on. In this paper, we propose a general framework for making easy the generation of conversational recommender systems. The framework, based on a content-based recommendation algorithm, is independent from the domain. Indeed, it allows to build a conversational recommender system with different interaction modes (natural language, buttons, hybrid) for any domain. The framework has been evaluated on two state-of-the-art datasets with the aim of identifying the components that mainly influence the final recommendation accuracy

A comparison of services for intent and entity recognition for conversational recommender systems

Conversational Recommender Systems (CoRSs) are becoming increasingly popular. However, designing and developing a CoRS is a challenging task since it requires multi-disciplinary skills. Even though several third-party services are available for supporting the creation of a CoRS, a comparative study of these platforms for the specific recommendation task is not available yet. In this work, we focus our attention on two crucial steps of the Conversational Recommendation (CoR) process, namely Intent and Entity Recognition. We compared four of the most popular services, both commercial and open source. Furthermore, we proposed two custom-made solutions for Entity Recognition, whose aim is to overcome the limitations of the other services. Results are very interesting and give a clear picture of the strengths and weaknesses of each solution