Does the economic freedom hinder the underground economy? Evidence from a cross-country analysis.

We evaluate the impact that the economic freedom exerts on the shadow economy for a sample of 152 countries from 1995 to 2017. In order to solve endogeneity issues, we rely on an instrumental variable approach and find that a change in the economic freedom index, induced by the level of independence of financial markets from government actions, adversely affects the hidden economy. To corroborate the interpretation of our results we also show how each subcomponent of the economic freedom index explains the downward change registered in the shadow economy. Further, the negative effect is mainly found in countries characterized by a low level of democracy and strong corruption, whereas in more democratic and less corrupt countries the economic freedom positively affects the size of the shadow economy. Consistent with these findings, we also highlight that the effect of the composite indicator of economic freedom on the hidden economy is U-shaped and this relationship is exclusively driven by both business regulation and the freedom in the legal system and property rights.

Deep networks for behavioral variant frontotemporal dementia identification from multiple acquisition sources.

Behavioral variant frontotemporal dementia (bvFTD) is a neurodegenerative syndrome whose clinical diagnosis remains a challenging task especially in the early stage of the disease. Currently, the presence of frontal and anterior temporal lobe atrophies on magnetic resonance imaging (MRI) is part of the diagnostic criteria for bvFTD. However, MRI data processing is usually dependent on the acquisition device and mostly require human-assisted crafting of feature extraction. Following the impressive improvements of deep architectures, in this study we report on bvFTD identification using various classes of artificial neural networks, and present the results we achieved on classification accuracy and obliviousness on acquisition devices using extensive hyperparameter search. In particular, we will demonstrate the stability and generalization of different deep networks based on the attention mechanism, where data intra-mixing confers models the ability to identify the disorder even on MRI data in inter-device settings, i.e., on data produced by different acquisition devices and without model fine tuning, as shown from the very encouraging performance evaluations that dramatically reach and overcome the 90% value on the AuROC and balanced accuracy metrics.

An embedded toolset for human activity monitoring in critical environments.

In many working and recreational activities, there are scenarios where both individual and collective safety have to be constantly checked and properly signaled, as occurring in dangerous workplaces or during pandemic events like the recent COVID-19 disease. From wearing personal protective equipment to filling physical spaces with an adequate number of people, it is clear that a possibly automatic solution would help to check compliance with the established rules. Based on an off-the-shelf compact and low-cost hardware, we present a deployed real use-case embedded system capable of perceiving people's behavior and aggregations and supervising the appliance of a set of rules relying on a configurable plug-in framework. Working on indoor and outdoor environments, we show that our implementation of counting people aggregations, measuring their reciprocal physical distances, and checking the proper usage of protective equipment is an effective yet open framework for monitoring human activities in critical conditions.

A multiple network-based bioinformatics pipeline for the study of molecular mechanisms in oncological diseases for personalized medicine.

MOTIVATION: Assessment of genetic mutations is an essential element in the modern era of personalized cancer treatment. Our strategy is focused on 'multiple network analysis' in which we try to improve cancer diagnostics by using biological networks. Genetic alterations in some important hubs or in driver genes such as BRAF and TP53 play a critical role in regulating many important molecular processes. Most of the studies are focused on the analysis of the effects of single mutations, while tumors often carry mutations of multiple driver genes. The aim of this work is to define an innovative bioinformatics pipeline focused on the design and analysis of networks (such as biomedical and molecular networks), in order to: (1) improve the disease diagnosis; (2) identify the patients that could better respond to a given drug treatment; and (3) predict what are the primary and secondary effects of gene mutations involved in human diseases. RESULTS: By using our pipeline based on a multiple network approach, it has been possible to demonstrate and validate what are the joint effects and changes of the molecular profile that occur in patients with metastatic colorectal carcinoma (mCRC) carrying mutations in multiple genes. In this way, we can identify the most suitable drugs for the therapy for the individual patient. This information is useful to improve precision medicine in cancer patients. As an application of our pipeline, the clinically significant case studies of a cohort of mCRC patients with the BRAF V600E-TP53 I195N missense combined mutation were considered. AVAILABILITY: The procedures used in this paper are part of the Cytoscape Core, available at (www.cytoscape.org). Data used here on mCRC patients have been published in [55]. SUPPLEMENTARY INFORMATION: A supplementary file containing a more detailed discussion of this case study and other cases is available at the journal site as Supplementary Data.

Tax evasion and government size: evidence from Italian provinces.

We analyze the impact of government size, measured by total spending per capita, on tax evasion at the provincial level in Italy over the period 2001-2015. In order to solve endogeneity issues we rely on a system GMM and find that public expenditure negatively affects tax evasion, as taxpayers perceive the government is efficiently spending resources coming from the tax levy. Results are confirmed when we (1) consider expenditures related to long-term investments, namely capital spending per capita, and (2) directly test the impact of government efficiency on tax evasion. In addition, we show that the impact of public spending is heterogeneous across geographical areas: an increase in public expenditure leads to a downward shift in tax evasion only in the northern part of Italy, characterized by a relatively larger initial level of public goods provision.

A parallel architecture for interactively rendering scattering and refraction effects.

A new method for interactive rendering of complex lighting effects combines two algorithms. The first performs accurate ray tracing in heterogeneous refractive media to compute high-frequency phenomena. The second applies lattice-Boltzmann lighting to account for low-frequency multiple-scattering effects. The two algorithms execute in parallel on modern graphics hardware. This article includes a video animation of the authors' real-time algorithm rendering a variety of scenes.