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Background: The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was first detected in China at the end of 2019 and it has since spread in few months all over the World. Italy was one of the first Western countries who faced the health emergency and is one of the countries most severely affected by the pandemic. The diffusion of Coronavirus disease 2019 (COVID-19) in Italy has followed a peculiar spatial pattern, however the attention of the scientific community has so far focussed almost exclusively on the prediction of the evolution of the disease over time. Methods: Official freely available data about the number of infected at the finest possible level of spatial areal aggregation (Italian provinces) are used to model the spatio-temporal distribution of COVID-19 infections at local level. An endemic-epidemic time-series mixed-effects generalized linear model for areal disease counts has been implemented to understand and predict spatio-temporal diffusion of the phenomenon. Results: Three subcomponents characterize the fitted model. The first describes the transmission of the illness within provinces; the second accounts for the transmission between nearby provinces; the third is related to the evolution of the disease over time. At the local level, the provinces first concerned by containment measures are those that are not affected by the effects of spatial neighbours. On the other hand, the component accounting for the spatial interaction with surrounding areas is prevalent for provinces that are strongly involved by contagions. Moreover, the proposed model provides good forecasts for the number of infections at local level while controlling for delayed reporting. Conclusions: A strong evidence is found that strict control measures implemented in some provinces efficiently break contagions and limit the spread to nearby areas. While containment policies may potentially be more effective if planned considering the peculiarities of local territories, the effective and homogeneous enforcement of control measures at national level is needed to prevent the disease control being delayed or missed as a whole. This may also apply at international level where, as it is for the European Union or the United States, the internal border checks among states have largely been abolished. [ABSTRACT FROM AUTHOR]

Background: The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was first detected in China at the end of 2019 and it has since spread in few months all over the World. Italy was one of the first Western countries who faced the health emergency and is one of the countries most severely affected by the pandemic. The diffusion of Coronavirus disease 2019 (COVID-19) in Italy has followed a peculiar spatial pattern, however the attention of the scientific community has so far focussed almost exclusively on the prediction of the evolution of the disease over time. Methods: Official freely available data about the number of infected at the finest possible level of spatial areal aggregation (Italian provinces) are used to model the spatio-temporal distribution of COVID-19 infections at local level. An endemic-epidemic time-series mixed-effects generalized linear model for areal disease counts has been implemented to understand and predict spatio-temporal diffusion of the phenomenon. Results: Three subcomponents characterize the fitted model. The first describes the transmission of the illness within provinces; the second accounts for the transmission between nearby provinces; the third is related to the evolution of the disease over time. At the local level, the provinces first concerned by containment measures are those that are not affected by the effects of spatial neighbours. On the other hand, the component accounting for the spatial interaction with surrounding areas is prevalent for provinces that are strongly involved by contagions. Moreover, the proposed model provides good forecasts for the number of infections at local level while controlling for delayed reporting. Conclusions: A strong evidence is found that strict control measures implemented in some provinces efficiently break contagions and limit the spread to nearby areas. While containment policies may potentially be more effective if planned considering the peculiarities of local territories, the effective and homogeneous enforcement of control measures at national level is needed to prevent the disease control being delayed or missed as a whole. This may also apply at international level where, as it is for the European Union or the United States, the internal border checks among states have largely been abolished. [ABSTRACT FROM AUTHOR]

This research paper collects the outcomes of an experimental study focused on the energy benefits achieved by coupling an energy efficient building with a sunspace in winter season and Mediterranean climate. Acting as passive Solar heating elements, sunspaces are intended to be a valuable tool among sustainable and energy-efficient constructive and/or retrofitting strategies. In order to assess the energy saving potential of the above-mentioned coupling and relate it to the main design parameters, two operative steps were carried out: the numerical step allowed different simulated configurations to be evaluated in order to identify the optimal one from an energy perspective and physically realize it; the monitoring phase served the double purpose of calibrating the simulation model and provide on-site validated data for subsequent assessments and comparisons. Three main sunspace configurations were tested: irradiative single-glazed, irradiative double-glazed and convective double-glazed. The convective double glazed configuration was found to outperform the others since the daily energy consumption reduction exceeded the 27% (compared to the irradiative, double glazed counterpart) for all weather conditions. Besides, the activation of a mechanical ventilation system was slightly counterproductive in terms of discomfort phenomena since a less stable indoor air temperature was guaranteed. Comparing the two irradiative configurations, it was found that the addition of an extra glass layer was responsible of a quite constant reduction of 1 kW h/day, thus increasing the percent heating energy demand saving achieved through the insertion of the sunspace from approximately 3,3%–8,7%, while minimizing indoor air temperature fluctuations too. [ABSTRACT FROM AUTHOR]

Purpose -- The purpose of this paper is to present the contents and the didactic approach that characterize, respectively, the "Introductory Statistics with R" and "Statistics and Foresight" courses of the Master in Social Foresight. Design/methodology/approach -- The two courses "Introductory Statistics with R" and "Statistics and Foresight" are designed to provide an introduction to quantitative methods in the social sciences with specific applications to social foresight. In particular, the first course introduces students to data analysis providing the necessary tools to study and represent socio-economic phenomena through graphical summaries and numerical measures. During the course, example applications based on the use of the open-source software R are shown. At the end, the students should be able to perform data management, conduct descriptive analysis of categorical and quantitative variables and analyze bivariate distributions. The subsequent course "Statistics and Foresight" presents the most efficient methods to make decisions in a context of uncertainty while visualizing the potential errors of wrong decisions and computing the probability of their occurrence. Findings -- This paper is a description of an interesting and promising way of teaching applied statistics in social sciences. Practical implications -- With the main aim of learning the correct use of statistics, specific attention is devoted to the use and interpretation of the aforementioned methods rather than to their theoretical aspects. Even in the second course, an important role is played by the treatment of real data by the use of the R software. Originality/value -- This paper attempts to systematize a method of teaching statistics based on the practical use of open-source software. [ABSTRACT FROM AUTHOR]

The spatial concentration of firms has long been a central issue in economics under both the theoretical and the applied point of view mainly due to the important policy implications. A popular approach to its measurement, which does not suffer from the problem of the arbitrariness of the regional boundaries, makes use of micro data and looks at the firms as if they were dimensionless points distributed in the economic space. However, in practical circumstances the points (firms) observed in the economic space are far from being dimensionless and are conversely characterized by different dimension in terms of the number of employees, the product, the capital, and so on. In the literature, the works that originally introduce such an approach disregard the aspect of the different firm dimension and ignore the fact that a high degree of spatial concentration may result from the case of both many small points clustering in definite portions of space and only few large points clustering together (e.g., few large firms). We refer to this phenomenon as clustering of firms and clustering of economic activities. The present article aims at tackling this problem by adapting the popular K-function to account for the point dimension using the framework of marked point process theory. [ABSTRACT FROM AUTHOR]

In automatic speech recognition applications, due to significant differences in voice characteristics, adults and children are usually treated as two population groups, for which different acoustic models are trained. In this paper, age-independent acoustic modeling is investigated in the context of large vocabulary speech recognition. Exploiting a small amount (9h) of children’s speech and a more significant amount (57h) of adult speech, age-independent acoustic models are trained using several methods for speaker adaptive acoustic modeling. Recognition results achieved using these models are compared with those achieved using age-dependent acoustic models for children and adults, respectively. Recognition experiments are performed on four Italian speech corpora, two consisting of children’s speech and two of adult speech, using 64k word and 11k word trigram language models. Methods for speaker adaptive acoustic modeling prove to be effective for training age-independent acoustic models ensuring recognition results at least as good as those achieved with age-dependent acoustic models for adults and children. [Copyright &y& Elsevier]

This paper presents several acoustic analyses carried out on read speech collected from Italian children aged from 7 to 13 years and North American children aged from 5 to 17 years. These analyses aimed at achieving a better understanding of spectral and temporal changes in speech produced by children of various ages in view of the development of automatic speech recognition applications. The results of these analyses confirm and complement the results reported in the literature, showing that characteristics of children’s speech change with age and that spectral and temporal variability decrease as age increases. In fact, younger children show a substantially higher intra-and inter-speaker variability with respect to older children and adults. We investigated the use of several methods for speaker adaptive acoustic modeling to cope with inter-speaker spectral variability and to improve recognition performance for children. These methods proved to be effective in recognition of read speech with a vocabulary of about 11k words. [Copyright &y& Elsevier]

Abstract: In this paper, speaker adaptive acoustic modeling is investigated by using a novel method for speaker normalization and a well known vocal tract length normalization method. With the novel normalization method, acoustic observations of training and testing speakers are mapped into a normalized acoustic space through speaker-specific transformations with the aim of reducing inter-speaker acoustic variability. For each speaker, an affine transformation is estimated with the goal of reducing the mismatch between the acoustic data of the speaker and a set of target hidden Markov models. This transformation is estimated through constrained maximum likelihood linear regression and then applied to map the acoustic observations of the speaker into the normalized acoustic space. Recognition experiments made use of two corpora, the first one consisting of adults’ speech, the second one consisting of children’s speech. Performing training and recognition with normalized data resulted in a consistent reduction of the word error rate with respect to the baseline systems trained on unnormalized data. In addition, the novel method always performed better than the reference vocal tract length normalization method adopted in this work. When unsupervised static speaker adaptation was applied in combination with each of the two speaker normalization methods, a different behavior was observed on the two corpora: in one case performance became very similar while in the other case the difference remained significant. [Copyright &y& Elsevier]

Background: The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was first detected in China at the end of 2019 and it has since spread in few months all over the World. Italy was one of the first Western countries who faced the health emergency and is one of the countries most severely affected by the pandemic. The diffusion of Coronavirus disease 2019 (COVID-19) in Italy has followed a peculiar spatial pattern, however the attention of the scientific community has so far focussed almost exclusively on the prediction of the evolution of the disease over time.Methods: Official freely available data about the number of infected at the finest possible level of spatial areal aggregation (Italian provinces) are used to model the spatio-temporal distribution of COVID-19 infections at local level. An endemic-epidemic time-series mixed-effects generalized linear model for areal disease counts has been implemented to understand and predict spatio-temporal diffusion of the phenomenon.Results: Three subcomponents characterize the fitted model. The first describes the transmission of the illness within provinces; the second accounts for the transmission between nearby provinces; the third is related to the evolution of the disease over time. At the local level, the provinces first concerned by containment measures are those that are not affected by the effects of spatial neighbours. On the other hand, the component accounting for the spatial interaction with surrounding areas is prevalent for provinces that are strongly involved by contagions. Moreover, the proposed model provides good forecasts for the number of infections at local level while controlling for delayed reporting.Conclusions: A strong evidence is found that strict control measures implemented in some provinces efficiently break contagions and limit the spread to nearby areas. While containment policies may potentially be more effective if planned considering the peculiarities of local territories, the effective and homogeneous enforcement of control measures at national level is needed to prevent the disease control being delayed or missed as a whole. This may also apply at international level where, as it is for the European Union or the United States, the internal border checks among states have largely been abolished. [ABSTRACT FROM AUTHOR]

The application of spatial Cliff–Ord models requires information about spatial coordinates of statistical units to be reliable, which is usually the case in the context of areal data. With micro-geographic point-level data, however, such information is inevitably affected by locational errors, that can be generated intentionally by the data producer for privacy protection or can be due to inaccuracy of the geocoding procedures. This unfortunate circumstance can potentially limit the use of the spatial autoregressive modelling framework for the analysis of micro data, as the presence of locational errors may have a non-negligible impact on the estimates of model parameters. This contribution aims at developing a strategy to reduce the bias and produce more reliable inference for spatial models with location errors. The proposed estimation strategy models both the spatial stochastic process and the coarsening mechanism by means of a marked point process. The model is fitted through the maximisation of a doubly-marginalised likelihood function of the marked point process, which cleans out the effects of coarsening. The validity of the proposed approach is assessed by means of a Monte Carlo simulation study under different real-case scenarios, whereas it is applied to real data on house prices. [ABSTRACT FROM AUTHOR]

When conducting firm-level surveys, a relevant aspect in sampling design is to guarantee that the selected sample of business units is representative of the population. Stratified sampling design is used to obtain a representative sample. However, stratification of the main characteristics of establishments may not always be computationally feasible, due to the high number of small and empty strata occurring when the population is highly stratified. A simulation with common sampling designs allows the comparison of the local pivotal method and spatially correlated Poisson sampling in terms of spatial balance and efficiency in estimation. These spatial designs are efficient, whereas stratification is no longer feasible. [ABSTRACT FROM AUTHOR]

In this article, we use the cross-entropy method for noisy optimization for fitting generalized linear multilevel models through maximum likelihood. We propose specifications of the instrumental distributions for positive and bounded parameters that improve the computational performance. We also introduce a new stopping criterion, which has the advantage of being problem-independent. In a second step we find, by means of extensive Monte Carlo experiments, the most suitable values of the input parameters of the algorithm. Finally, we compare the method to the benchmark estimation technique based on numerical integration. The cross-entropy approach turns out to be preferable from both the statistical and the computational point of view. In the last part of the article, the method is used to model the probability of firm exits in the healthcare industry in Italy. Supplemental materials are available online. [ABSTRACT FROM PUBLISHER]

The presence of knowledge spillovers and shared human capital is at the heart of the Marhall–Arrow–Romer externalities hypothesis. Most of the earlier empirical contributions on knowledge externalities; however, considered data aggregated at a regional level so that conclusions are based on the arbitrary definition of jurisdictional spatial units: this is the essence of the so-called modifiable areal unit problem. A second limitation of these studies is constituted by the fact that, somewhat surprisingly, while concentrating on the effects of agglomeration on firm creation and growth, the literature has, conversely, largely ignored its effects on firm survival. The present paper aims at contributing to the existing literature by answering to some of the open methodological questions reconciling the literature of Cox proportional hazards model with that on point pattern and thus capturing the true nature of spatial information. We also present some empirical results based on Italian firm demography data collected and managed by the Italian National Institute of Statistics (ISTAT). [ABSTRACT FROM AUTHOR]

Due to the increasing availability of georeferenced microdata in several fields of research, surveys can benefit greatly from the use of the most recent spatial sampling methods. These methods facilitate selection of spatially balanced samples, which lead to particularly efficient estimates, by incorporating the distances between the exact locations of statistical units into the design. Unfortunately, since locations of units are rarely exact in practice due to imperfections in the geocoding processes, the implementation of spatial sampling designs is actually often limited. This paper aims at demonstrating that spatial sampling designs can be effectively carried out even when spatial information is not completely accurate. By means of Monte Carlo simulation studies, this paper proves that even when the geocoding of population is not exact, spatial sampling methods still facilitate more spatially balanced samples and more efficient estimates. [ABSTRACT FROM AUTHOR]

In spite of recent advances in automatic speech recognition, the performance of state-of-the-art speech recognisers fluctuates depending on the speaker. Speaker normalisation aims at the reduction of differences between the acoustic space of a new speaker and the training acoustic space of a given speech recogniser, improving performance. Normalisation is based on an acoustic feature transformation, to be estimated from a small amount of speech signal. This paper introduces a mixture of recurrent neural networks as an effective regression technique to approach the problem. A suitable Viterbi-based time alignment procedure is proposed for generating the adaptation set. The mixture is compared with linear regression and single-model connectionist approaches. Speaker-dependent and speaker-independent continuous speech recognition experiments with a large vocabulary, using Hidden Markov Models, are presented. Results show that the mixture improves recognition performance, yielding a 21% relative reduction of the word error rate, i.e. comparable with that obtained with model-adaptation approaches. [ABSTRACT FROM AUTHOR]

In this paper, spatial shift-share decomposition is analysed when applied to Italian data on regional business change at plant level, over the period 2004-2009. A new type of spatial decomposition, which looks more effectively at neighbourhood influence, is introduced here. Notable results emerge from the empirical investigation. First, it can be seen that the spatial level of aggregation greatly affects results. Second, evidence of neighbourhood advantage in the Southern NUTS 3 regions is found, together with opposite results for the Central- Northern NUTS 3 regions. Finally, evidence of positive industrial mix effects is only found in Central- Northern Italy. [ABSTRACT FROM AUTHOR]

A series of recent papers have introduced some explorative methods based on Ripley's K-function (Ripley in J R Stat Soc B 39(2):172-212, ) analyzing the micro-geographical patterns of firms. Often the spatial heterogeneity of an area is handled by referring to a case-control design, in which spatial clusters occur as over-concentrations of firms belonging to a specific industry as opposed to the distribution of firms in the whole economy. Therefore, positive, or negative, spatial dependence between firms occurs when a specific sector of industry is seen to present a more aggregated pattern (or more dispersed) than is common in the economy as a whole. This approach has led to the development of relative measures of spatial concentration which, as a consequence, are not straightforwardly comparable across different economies. In this article, we explore a parametric approach based on the inhomogeneous K-function (Baddeley et al. in Statistica Nederlandica 54(3):329-350, ) that makes it possible to obtain an absolute measure of the industrial agglomeration that is also able to capture spatial heterogeneity. We provide an empirical application of the approach taken with regard to the spatial distribution of high-tech industries in Milan (Italy) in 2001. [ABSTRACT FROM AUTHOR]

A challenging scenario is addressed in which a distant-talking speech recognizer operates in a noisy office environment with model adaptation. The use of a single far microphone as well as that of a microphone array input are investigated.In addition to the benefits from the application of microphone array processing, system robustness is improved by training hidden Markov models (HMMs) with a contaminated version of a clean corpus. This artificial corpus is produced by exploiting information extracted from “real world" acoustic scenarios. The resulting models are then used as a starting point for unsupervised incremental adaptation.Experimental results show that improvements in recognition accuracy due to multiple microphones, HMM training on contaminated speech and incremental adaptation are additive on a connected digits task. Moreover, the results show that unsupervised incremental adaptation receives the benefits of starting from models trained using contaminated speech. A final contribution of the paper refers to the influence of accuracy of speech activity detection, which seems to be relevant when moving towards real applications. [Copyright &y& Elsevier]

This article contributes to the literature on firm demography and regional development in at least three different ways. First, consumption, rather than employment, which is the most common variable seen in literature, is used to measure the impact of firm demography on regional development. Second, while the literature is mainly focused on the relationship between new business formation and regional development, we investigate both entry and exit flows of firms. Third, we decompose each of these flows into spatial and sectoral components. The empirical investigation looks at the Italian regions with reference to the period 2004-2009. Results seem to be substantially divergent between the South and the rest of Italy. [ABSTRACT FROM AUTHOR]

This paper describes the development of a multilingual and multigenre manually annotated speech dataset, freely available to the research community as ground truth for the evaluation of automatic transcription systems and spoken language translation systems. The dataset includes two video genres-television broadcast news and talk-shows-and covers Flemish, English, German, and Italian, for a total of about 35 h of television speech. Besides segmentation and orthographic transcription, we added a very rich annotation on the audio signal, both at the linguistic level (e.g. filled pauses, pronunciation errors, disfluencies, speech in a foreign language) and at the acoustic level (e.g. background noise and different types of non-speech events). Furthermore, a subset of the transcriptions is translated in four directions, namely Flemish to English, German to English, German to Italian and English to Italian. The development of this dataset was organized in several phases, relying on expert transcribers as well as involving non-expert contributors through crowdsourcing. We first conducted a feasibility study to test and compare two methods for crowdsourcing speech transcription on broadcast news data. These methods are based on different transcription processes (i.e. parallel vs. iterative) and incorporate two different quality control mechanisms. With both methods, we achieved near-expert transcription quality-in terms of word error rate-for English, German and Italian data. Instead, for Flemish data we were not able to get a sufficient response from the crowd to complete the offered transcription tasks. The results obtained demonstrate that the viability of methods for crowdsourcing speech transcription significantly depends on the target language. This paper provides a detailed comparison of the results obtained with the two crowdsourcing methods tested, describes the main characteristics of the final ground truth resource created as well as the methodology adopted, and the guidelines prepared for its development. [ABSTRACT FROM AUTHOR]

This study investigates whether a computer assisted pronunciation training (CAPT) system can help young learners improve word-level pronunciation skills in English as a foreign language at a level comparable to that achieved through traditional teacher-led training. The pronunciation improvement of a group of learners of 11 years of age receiving teacher-fronted instruction was compared to that of a group receiving computer assisted pronunciation training by means of a system including an automatic speech recognition component. Results show that 1) pronunciation quality of isolated words improved significantly for both groups of subjects, and 2) both groups significantly improved in pronunciation quality of words that were considered particularly difficult to pronounce and that were likely to have been unknown to them prior to the training. Training with a computer-assisted pronunciation training system with a simple automatic speech recognition component can thus lead to short-term improvements in pronunciation that are comparable to those achieved by means of more traditional, teacher-led pronunciation training. [ABSTRACT FROM AUTHOR]