Iconic Destination: a Snapshot of Sustainable Tourism in Pisa

Tourism is one of the world's fastest growing industries. According to the World Tourism Organization, Italy is the fifth most visited country in the world, with more than 47.7 million tourists a year (2013). At the same time, the increasing number of studies focused on sustainable tourism demonstrates a growing interest about the topic. In addition, practitioners’ attitude is changing and the most important actors of the market are acting in a more sustainable way and developing reports on their eco-friendly performances. Nowadays, the entire supply chain maybe environmentally sustainable. From the reservation to the post-holiday phase, it is possible to select the more eco-friendly suppliers. The main companies operating in the different stages of the process are demonstrating a concrete interest on sustainable development. This new challenge is generated through the information flow between local authorities, private firms and final customers. We propose to make a reflection based on the latter actors’ attitude. Our research aims to investigate the level of sensitivity of tourists about environmental sustainability from two different perspectives: self-evaluation and real purchasing behavior. We conducted a face-to-face survey among tourists in Pisa, in Piazza dei Miracoli, during May 2015. By using a structured questionnaire, we gathered primary data from a sample of 406 respondents. We selected respondents randomly. Pisa is the perfect location to obtain information from several typologies of tourists, with different levels of awareness of sustainable issues. Itis one of the most important tourist destination in Italy and it is an iconic destination recognized worldwide thanks to the attractiveness of the leading tower. The results of our study is a snapshot of the current level of awareness among tourists. The analysis of the questionnaires revealed tourist profiles, their eco-friendly behaviors, their concerns about sustainability planning their vacations and their habits during the stay. In the questionnaires, three main aspects of tourist services were considered: transport, accommodation, food and beverage. Our study offers a photography of the state of the art of tourists’ awareness on sustainable issues. It represents a starting point for future investigations on strategic decisions in terms of general and local policies (destination & corporate level). The provided data can be useful to generate new inputs for academic research and to point out managerial implications at destination and corporate level. Moreover, our study generates food for thought with regard to several emerging topics. Further research can investigate the discrepancy between self-evaluation and real behaviors among tourists, the perception of the grade of sustainability of the tourist services and the willingness to pay for more sustainable tourist services

Come to This Heart So Lonely

1. Come to this heart so lonely, Come cheer me with thy smile, Thou has the power only, Each sorrow to beguile. Yes thou hast power only Each sorrow to beguile. When thou art far I languish, Nor taste the sweets of peace, Oh! come and sooth my anguish, And bid each sorrow cease; And bid each sorrow cease. Come to this heart so lonely, Come cheer me with thy smile, Thou has the power only, Each sorrow to beguile; Thou hast the power only, Each sorrow to beguile 2. Oh! calm ye heav\u27n in slumbers, My sorrow for awhile, And send my best beloved, Each sorrow to beguile, And send my best beloved, Each sorrow to beguile. But if my prayrs are fruitless, At least let her return, To bathe with tears of pity, The dust within my urn; The dust within my urn. Oh! calm ye heav\u27n in slumber, My sorrow for a while, And send my best belov\u27d Each sorrow to beguile; And send my best belov\u27d, Each sorrow to beguile

Acoustic Source Localization by Fusing Distributed Microphone Arrays Measurements

Publication in the conference proceedings of EUSIPCO, Lausanne, Switzerland, 200

Contrastive Language-Image Pre-training for the Italian Language

Recently, multi-modal systems such as CLIP (Contrastive Language-Image Pre-training) were introduced to represent images and texts jointly in the same embedding space. These models are trained on massive amounts of image-caption pairs and show impressive performance on zero-shot image classification. However, their usage is limited to English due to their training data. Training the same model for different languages is non-trivial since the amount of natural data in those might not be sufficient, and automatic translations of original captions might not have sufficient quality, harming performance. In this paper, we present the first CLIP model for the Italian Language (CLIP-Italian), trained on more than 1.4 million image-text pairs. Results show that CLIP-Italian outperforms a multilingual CLIP model on image retrieval and zero-shot classification tasks for the Italian language.1 Sistemi multimodali come CLIP (Contrastive Language-Image Pre-training) sono stati proposti di recente al fine di ottenere rappresentazioni di immagini e testo in uno spazio latente condiviso. Questi modelli sono allenati su enormi quantità di immagini associate alle loro didascalie, e dimostrano abilità eccellenti nell'effettuare classificazioni “zero-shot”. Ciononostante, il loro utilizzo è limitato all'inglese, la lingua utilizzata durante il loro addestramento. Ottenere modelli del genere per altre lingue non è cosa da poco, poiché la quantità di dati a disposizione per queste lingue potrebbe non essere sufficiente e la traduzione automatica delle didascalie inglesi originali potrebbe portare a risultati non soddisfacenti. In questo articolo presentiamo il primo modello CLIP per la lingua italiana (CLIP-Italian), addestrato con più di 1.4 milioni di immagini e rispettive didascalie. I risultati riportati dimostrano l'efficacia di CLIP-Italian per l'estrazione e la classificazione zero-shot in italiano, ottenendo risultati migliori di un modello CLIP multilingue.</p

Contrastive Language-Image Pre-training for the Italian Language

Recently, multi-modal systems such as CLIP (Contrastive Language-Image Pre-training) were introduced to represent images and texts jointly in the same embedding space. These models are trained on massive amounts of image-caption pairs and show impressive performance on zero-shot image classification. However, their usage is limited to English due to their training data. Training the same model for different languages is non-trivial since the amount of natural data in those might not be sufficient, and automatic translations of original captions might not have sufficient quality, harming performance. In this paper, we present the first CLIP model for the Italian Language (CLIP-Italian), trained on more than 1.4 million image-text pairs. Results show that CLIP-Italian outperforms a multilingual CLIP model on image retrieval and zero-shot classification tasks for the Italian language.1 Sistemi multimodali come CLIP (Contrastive Language-Image Pre-training) sono stati proposti di recente al fine di ottenere rappresentazioni di immagini e testo in uno spazio latente condiviso. Questi modelli sono allenati su enormi quantità di immagini associate alle loro didascalie, e dimostrano abilità eccellenti nell'effettuare classificazioni “zero-shot”. Ciononostante, il loro utilizzo è limitato all'inglese, la lingua utilizzata durante il loro addestramento. Ottenere modelli del genere per altre lingue non è cosa da poco, poiché la quantità di dati a disposizione per queste lingue potrebbe non essere sufficiente e la traduzione automatica delle didascalie inglesi originali potrebbe portare a risultati non soddisfacenti. In questo articolo presentiamo il primo modello CLIP per la lingua italiana (CLIP-Italian), addestrato con più di 1.4 milioni di immagini e rispettive didascalie. I risultati riportati dimostrano l'efficacia di CLIP-Italian per l'estrazione e la classificazione zero-shot in italiano, ottenendo risultati migliori di un modello CLIP multilingue.</p

Contrastive Language-Image Pre-training for the Italian Language

Recently, multi-modal systems such as CLIP (Contrastive Language-Image Pre-training) were introduced to represent images and texts jointly in the same embedding space. These models are trained on massive amounts of image-caption pairs and show impressive performance on zero-shot image classification. However, their usage is limited to English due to their training data. Training the same model for different languages is non-trivial since the amount of natural data in those might not be sufficient, and automatic translations of original captions might not have sufficient quality, harming performance. In this paper, we present the first CLIP model for the Italian Language (CLIP-Italian), trained on more than 1.4 million image-text pairs. Results show that CLIP-Italian outperforms a multilingual CLIP model on image retrieval and zero-shot classification tasks for the Italian language.1 Sistemi multimodali come CLIP (Contrastive Language-Image Pre-training) sono stati proposti di recente al fine di ottenere rappresentazioni di immagini e testo in uno spazio latente condiviso. Questi modelli sono allenati su enormi quantità di immagini associate alle loro didascalie, e dimostrano abilità eccellenti nell'effettuare classificazioni “zero-shot”. Ciononostante, il loro utilizzo è limitato all'inglese, la lingua utilizzata durante il loro addestramento. Ottenere modelli del genere per altre lingue non è cosa da poco, poiché la quantità di dati a disposizione per queste lingue potrebbe non essere sufficiente e la traduzione automatica delle didascalie inglesi originali potrebbe portare a risultati non soddisfacenti. In questo articolo presentiamo il primo modello CLIP per la lingua italiana (CLIP-Italian), addestrato con più di 1.4 milioni di immagini e rispettive didascalie. I risultati riportati dimostrano l'efficacia di CLIP-Italian per l'estrazione e la classificazione zero-shot in italiano, ottenendo risultati migliori di un modello CLIP multilingue.</p

The Italian VLBI Network: First Results and Future Perspectives

A first 24-hour Italian VLBI geodetic experiment, involving the Medicina, Noto, and Matera antennas, shaped as an IVS standard EUROPE, was successfully performed. In 2014, starting from the correlator output, a geodetic database was created and a typical solution of a small network was achieved, here presented. From this promising result we have planned new observations in 2016, involving the three Italian geodetic antennas. This could be the beginning of a possible routine activity, creating a data set that can be combined with GNSS observations to contribute to the National Geodetic Reference Datum. Particular care should be taken in the scheduling of the new experiments in order to optimize the number of usable observations. These observations can be used to study and plan future experiments in which the time and frequency standards can be given by an optical fiber link, thus having a common clock at different VLBI stations

Gli studi di genere in Italia: passato, presente e futuro di una sfida ancora aperta

L'articolo riporta una tavola rotonda sugli studi di genere in Italia da molteplici prospettive

Monitoring of hadrontherapy treatments by means of charged particle detection

The interaction of the incoming beam radiation with the patient body in hadrontherapy treatments produces secondary charged and neutral particles, whose detection can be used for monitoring purposes and to perform an on-line check of beam particle range. In the context of ion-therapy with active scanning, charged particles are potentially attractive since they can be easily tracked with a high efficiency, in presence of a relatively low background contamination. In order to verify the possibility of exploiting this approach for in-beam monitoring in ion-therapy, and to guide the design of specific detectors, both simulations and experimental tests are being performed with ion beams impinging on simple homogeneous tissue-like targets (PMMA). From these studies, a resolution of the order of few millimeters on the single track has been proven to be sufficient to exploit charged particle tracking for monitoring purposes, preserving the precision achievable on longitudinal shape. The results obtained so far show that the measurement of charged particles can be successfully implemented in a technology capable of monitoring both the dose profile and the position of the Bragg peak inside the target and finally lead to the design of a novel profile detector. Crucial aspects to be considered are the detector positioning, to be optimized in order to maximize the available statistics, and the capability of accounting for the multiple scattering interactions undergone by the charged fragments along their exit path from the patient body. The experimental results collected up to now are also valuable for the validation of Monte Carlo simulation software tools and their implementation in Treatment Planning Software packages