3499 research outputs found
Sort by
Tweet-tales: moods of socio-economic crisis?
No full textThe widespread adoption of highly interactive social media like Twitter, Facebook and other platforms allow users to communicate moods and opinions to their social network. Those platforms represent an unprecedented source of information about human habits and socio-economic interactions. Several new studies have started to exploit the potential of these big data as fingerprints of economic and social interactions.
The present analysis aims at exploring the informative power of indicators derived from social media activity, with the aim to trace some preliminary guidelines to investigate the eventual correspondence between social media indices and available labour market indicators at a territorial level. The study is based on a large dataset of about 4 million Italian-language tweets collected from October 2014 to December 2015, filtered by a set of specific keywords related to the labour market. With techniques from machine learning and user’s geolocalization, we were able to subset the tweets on specific topics in all Italian provinces. The corpus of tweets is then analyzed with linguistic tools and hierarchical clustering analysis. A comparison with traditional economic indicators suggests a strong need for further cleaning procedures, which are then developed in detail. As data from social networks are easy to obtain, this represents a very first attempt to evaluate their informative power in the Italian context, which is of potentially high importance in economic and social research
Programming of CAS Systems by Relying on Attribute-Based Communication
No full textIn most distributed systems, named connections (i.e., channels) are used as means for programming interaction between communicating partners. These kinds of connections are low level and usually totally independent of the knowledge, the status, the capabilities, ..., in one word, of the attributes of the interacting partners. We have recently introduced a calculus, called AbC, in which interactions among agents are dynamically established by taking into account “connection” as determined by predicates over agent attributes. In this paper, we present Open image in new window, a Java run-time environment that has been developed to support modeling and programming of collective adaptive systems by relying on the communication primitives of the AbC calculus. Systems are described as sets of parallel components, each component is equipped with a set of attributes and communications among components take place in an implicit multicast fashion. By means of a number of examples, we also show how opportunistic behaviors, achieved by run-time attribute updates, can be exploited to express different communication and interaction patterns and to program challenging case studies
Sleep reverts changes in human gray and white matter caused by wake-dependent training
No full textAbstract Learning leads to rapid microstructural changes in gray (GM) and white (WM) matter. Do these changes continue to accumulate if task training continues, and can they be reverted by sleep? We addressed these questions by combining structural and diffusion weighted {MRI} and high-density {EEG} in 16 subjects studied during the physiological sleep/wake cycle, after 12 h and 24 h of intense practice in two different tasks, and after post-training sleep. Compared to baseline wake, 12 h of training led to a decline in cortical mean diffusivity. The decrease became even more significant after 24 h of task practice combined with sleep deprivation. Prolonged practice also resulted in decreased ventricular volume and increased {GM} and {WM} subcortical volumes. All changes reverted after recovery sleep. Moreover, these structural alterations predicted cognitive performance at the individual level, suggesting that sleep's ability to counteract performance deficits is linked to its effects on the brain microstructure. The cellular mechanisms that account for the structural effects of sleep are unknown, but they may be linked to its role in promoting the production of cerebrospinal fluid and the decrease in synapse size and strength, as well as to its recently discovered ability to enhance the extracellular space and the clearance of brain metabolites
Conservation integrals for two circular holes kept at different temperatures in a thermoelastic solid
No full textAbstract An explicit analytic solution for thermal stresses in an infinite thermoelastic medium with two circular cylindrical holes of different sizes kept at different constant temperatures, under steady-state heat flux is presented. The solution is obtained by using the most general representation of a biharmonic function in bipolar coordinates. The stress field is decomposed into the sum of a particular stress field induced by the steady-state temperature distribution and an auxiliary isothermal stress field required to satisfy the boundary conditions on the holes. The variations of the stress concentration factor on the surface of the holes are determined for varying geometry of the holes. The concept of the conservation integrals Jk, M and L is extended to steady state thermoelasticity and the integrals are proved to be path-independent. These integrals are calculated on closed contours encircling one or both holes. The geometries of a hole in a half-space and an eccentric annular cylinder are considered as particular cases
A cortical and sub-cortical parcellation clustering by intrinsic functional connectivity
No full textNetwork analysis of resting-state fMRI (rsfMRI) has been widely utilized to investigate the functional architecture of the whole brain. Here we propose a robust parcellation method that first divides cortical and sub-cortical regions into sub-regions by clustering the rsfMRI data for each subject independently, and then merges those individual parcellations to obtain a global whole brain parcellation. To do so our method relies on majority voting (to merge parcellations of multiple subjects) and enforces spatial constraints within a hierarchical agglomerative clustering framework to define parcels that are spatially homogeneous
A thermo-visco-elastic shear-lag model for the prediction of residual stresses in photovoltaic modules after lamination
No full textAbstract The distribution of residual thermo-elastic stresses in encapsulated solar cells arising from lamination is relevant for the characterization of the long term performance of photovoltaic (PV) modules during service. Accurate modelling of the structural response of the laminate in the transient regime during cooling after lamination is a challenging task from the computational point of view. In this work we propose a semi-analytic model based on the Kirchhoff plate theory and the shear-lag approach for the treatment of the polymeric encapsulant layers and accounting for their time and temperature dependency according to a rheological model derived from fractional calculus considerations. Spatially uniform and non-uniform temperature distributions are compared to accurately assess the amount of the residual compressive stresses raised in the Silicon cells after lamination. The use of more realistic non-uniform temperature distributions leads to lower residual compressive stresses in Silicon as compared to the uniform case
How concepts are encoded in the human brain: A modality independent, category-based cortical organization of semantic knowledge
No full textAbstract How conceptual knowledge is represented in the human brain remains to be determined. To address the differential role of low-level sensory-based and high-level abstract features in semantic processing, we combined behavioral studies of linguistic production and brain activity measures by functional magnetic resonance imaging in sighted and congenitally blind individuals while they performed a property-generation task with concrete nouns from eight categories, presented through visual and/or auditory modalities. Patterns of neural activity within a large semantic cortical network that comprised parahippocampal, lateral occipital, temporo-parieto-occipital and inferior parietal cortices correlated with linguistic production and were independent both from the modality of stimulus presentation (either visual or auditory) and the (lack of) visual experience. In contrast, selected modality-dependent differences were observed only when the analysis was limited to the individual regions within the semantic cortical network. We conclude that conceptual knowledge in the human brain relies on a distributed, modality-independent cortical representation that integrates the partial category and modality specific information retained at a regional level
On the behaviour of deviant communities in online social networks
No full textOn-line social networks are complex ensembles of inter-linked communities that interact on different topics. Some communities are characterized by what are usually referred to as deviant behaviours, conducts that are commonly considered inappropriate with respect to the society’s norms or moral standards. Eating disorders, drug use, and adult content consumption are just a few examples. We refer to such communities as deviant networks. It is commonly believed that such deviant networks are niche, isolated social groups, whose activity is well separated from the mainstream social media life. According to this assumption, research studies have mostly considered them in isolation. In this work we focused on adult content consumption networks, which are present in many on-line social media and in the Web in general. We found that few small and densely connected communities are responsible for most of the content production. Differently from previous work, we studied how such communities interact with the whole social network. We found that the produced content flows to the rest of the network mostly directly or through bridge-communities, reaching at least 450 times more users.We also show that a large fraction of the users can be inadvertently exposed to such content through indirect content resharing. We also discuss a demographic analysis of the producers and consumers networks. Finally, we show that it is easily possible to identify a few core users to radically uproot the diffusion process. We aim at setting the basis to study deviant communities in context
From linear to nonlinear MPC: bridging the gap via the real-time iteration
No full textLinear model predictive control (MPC) can be currently deployed at outstanding speeds, thanks to recent progress in algorithms for solving online the underlying structured quadratic programs. In contrast, nonlinear MPC (NMPC) requires the deployment of more elaborate algorithms, which require longer computation times than linear MPC. Nonetheless, computational speeds for NMPC comparable to those of MPC are now regularly reported, provided that the adequate algorithms are used. In this paper, we aim at clarifying the similarities and differences between linear MPC and NMPC. In particular, we focus our analysis on NMPC based on the real-time iteration (RTI) scheme, as this technique has been successfully tested and, in some applications, requires computational times that are only marginally larger than linear MPC. The goal of the paper is to promote the understanding of RTI-based NMPC within the linear MPC community