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Quantifying randomness in real networks
No full textRepresented as graphs, real networks are intricate combinations of order and disorder. Fixing some of the structural properties of network models to their values observed in real networks, many other properties appear as statistical consequences of these fixed observables, plus randomness in other respects. Here we employ the dk-series, a complete set of basic characteristics of the network structure, to study the statistical dependencies between different network properties. We consider six real networks—the Internet, US airport network, human protein interactions, technosocial web of trust, English word network, and an fMRI map of the human brain—and find that many important local and global structural properties of these networks are closely reproduced by dk-random graphs whose degree distributions, degree correlations and clustering are as in the corresponding real network. We discuss important conceptual, methodological, and practical implications of this evaluation of network randomness, and release software to generate dk-random graphs
Big hits, export concentration and volatility
No full textRecent empirical work has documented the high concentration of trade flows, and the large role played by few “big hits” in each country’s export. We propose a simple stochastic benchmark against which we assess each economy’s actual number of “big hits”. We show that most European countries underperform the benchmark, while China, the US and Germany do better. A low number of “big hits” (relative to our prediction) is associated with higher export volatility. Looking at possible determinants of “big hits”, we find they depend on the actual performance of each country, so that industrial policy needs to be country-specific
Unbiased sampling of network ensembles
No full textSampling random graphs with given properties is a key step in the analysis of networks, as random ensembles represent basic null models required to identify patterns such as communities and motifs. An important requirement is that the sampling process is unbiased and efficient. The main approaches are microcanonical, i.e. they sample graphs that match the enforced constraints exactly. Unfortunately, when applied to strongly heterogeneous networks (like most real-world examples), the majority of these approaches become biased and/or time-consuming. Moreover, the algorithms defined in the simplest cases, such as binary graphs with given degrees, are not easily generalizable to more complicated ensembles. Here we propose a solution to the problem via the introduction of a ‘Maximize and Sample’ (‘Max & Sam’ for short) method to correctly sample ensembles of networks where the constraints are ‘soft’, i.e. realized as ensemble averages. Our method is based on exact maximum-entropy distributions and is therefore unbiased by construction, even for strongly heterogeneous networks. It is also more computationally efficient than most microcanonical alternatives. Finally, it works for both binary and weighted networks with a variety of constraints, including combined degree-strength sequences and full reciprocity structure, for which no alternative method exists. Our canonical approach can in principle be turned into an unbiased microcanonical one, via a restriction to the relevant subset. Importantly, the analysis of the fluctuations of the constraints suggests that the microcanonical and canonical versions of all the ensembles considered here are not equivalent. We show various real-world applications and provide a code implementing all our algorithms
Neural and Behavioral Correlates of Extended Training during Sleep Deprivation in Humans: Evidence for Local, Task-Specific Effects
No full textRecent work has demonstrated that behavioral manipulations targeting specific cortical areas during prolonged wakefulness lead to a region-specific homeostatic increase in theta activity (5–9 Hz), suggesting that theta waves could represent transient neuronal OFF periods (local sleep). In awake rats, the occurrence of an OFF period in a brain area relevant for behavior results in performance errors. Here we investigated the potential relationship between local sleep events and negative behavioral outcomes in humans.Volunteers participated in two prolonged wakefulness experiments (24 h), each including 12 h of practice with either a driving simulation (DS) game or a battery of tasks based on executive functions (EFs). Multiple high-density EEG recordings were obtained during each experiment, both in quiet rest conditions and during execution of two behavioral tests, a response inhibition test and a motor test, aimed at assessing changes in impulse control and visuomotor performance, respectively. In addition, fMRI examinations obtained at 12 h intervals were used to investigate changes in inter-regional connectivity.The EF experiment was associated with a reduced efficiency in impulse control, whereas DS led to a relative impairment in visuomotor control. A specific spatial and temporal correlation was observed between EEG theta waves occurring in task-related areas and deterioration of behavioral performance. The fMRI connectivity analysis indicated that performance impairment might partially depend on a breakdown in connectivity determined by a “network overload.”Present results demonstrate the existence of an association between theta waves during wakefulness and performance errors and may contribute explaining behavioral impairments under conditions of sleep deprivation/restriction
Large-scale analysis of neuroimaging data on commercial clouds with content-aware resource allocation strategie
No full textThe combined use of mice that have genetic mutations (transgenic mouse models) of human pathology and advanced neuroimaging methods (such as magnetic resonance imaging) has the potential to radically change how we approach disease understanding, diagnosis and treatment. Morphological changes occurring in the brain of transgenic animals as a result of the interaction between environment and genotype can be assessed using advanced image analysis methods, an effort described as ‘mouse brain phenotyping’. However, the computational methods involved in the analysis of high-resolution brain images are demanding. While running such analysis on local clusters is possible, not all users have access to such infrastructure and even for those that do, having additional computational capacity can be beneficial (e.g. to meet sudden high throughput demands). In this paper we use a commercial cloud platform for brain neuroimaging and analysis. We achieve a registration-based multi-atlas, multi-template anatomical segmentation, normally a lengthy-in-time effort, within a few hours. Naturally, performing such analyses on the cloud entails a monetary cost, and it is worthwhile identifying strategies that can allocate resources intelligently. In our context a critical aspect is the identification of how long each job will take. We propose a method that estimates the complexity of an image-processing task, a registration, using statistical moments and shape descriptors of the image content. We use this information to learn and predict the completion time of a registration. The proposed approach is easy to deploy, and could serve as an alternative for laboratories that may require instant access to large high-performance-computing infrastructures. To facilitate adoption from the community we publicly release the source code
Electoral predictions with Twitter: a machine-learning approach
No full textSeveral studies have shown how to approximately predict public opinion,
such as in political elections, by analyzing user activities in blogging platforms
and on-line social networks. The task is challenging for several reasons.
Sample bias and automatic understanding of textual content are two of several
non trivial issues.
In this work we study how Twitter can provide some interesting insights concerning
the primary elections of an Italian political party. State-of-the-art approaches
rely on indicators based on tweet and user volumes, often including sentiment
analysis. We investigate how to exploit and improve those indicators in order to
reduce the bias of the Twitter users sample. We propose novel indicators and a
novel content-based method. Furthermore, we study how a machine learning approach
can learn correction factors for those indicators. Experimental results on
Twitter data support the validity of the proposed methods and their improvement
over the state of the art
Quantitative Analysis of Probabilistic Models of SoftwareProduct Lines with Statistical Model Checking
No full textWe investigate the suitability of statistical model checking techniques for analysing quantitative prop-erties of software product line models with probabilistic aspects. For this purpose, we enrich thefeature-oriented language FLANwith action rates, which specify the likelihood of exhibiting par-ticular behaviour or of installing features at a specific moment or in a specific order. The enrichedlanguage (called PFLAN) allows us to specify models of software product lines with probabilis-tic configurations and behaviour, e.g. by considering a PFLANsemantics based on discrete-timeMarkov chains. The Maude implementation of PFLANis combined with the distributed statisticalmodel checker MultiVeStA to perform quantitative analyses of a simple product line case study. Thepresented analyses include the likelihood of certain behaviour of interest (e.g. product malfunction-ing) and the expected average cost of product
Is unpaid work conducive of well-being? The case of within-household unpaid work in the Modena District
No full textIn this paper we document the pattern of unpaid domestic and care work, disaggregated on the basis of the type of work and the care recipient, and its partial correlation with subjective well-being as measured by reported life satisfaction. We explore gender-specific effects since domestic and unpaid work have an intrinsic relational dimension that, at least in the current Italian society, has an important gender-specific component. The data used come from the 2012 Survey on the Economic and Social Conditions in the Modena District (ICESmo3) which is a unique dataset that contains disaggregated data on unpaid work. In particular, this dataset allows us to look at the correlations between the different types of unpaid work and reported life satisfaction. The overall picture that emerges from the data is one where unpaid work within the household is more likely to be conducive of well-being if it is more likely to be genuinely voluntary and intrinsically motivated. Although we must admit that this conclusion is highly speculative, we think it is a good starting point for further analyses in this regard
CARMA: Collective Adaptive Resource-sharing Markovian Agents
No full textIn this paper we present CARMA, a language recently defined to support specification and analysis of collective adaptive systems. CARMA is a stochastic process algebra equipped with linguistic constructs specifically developed for modelling and programming systems that can operate in open-ended and unpredictable environments. This class of systems is typically composed of a huge number of interacting agents that dynamically adjust and combine their behaviour to achieve specific goals. A CARMA model, termed a collective, consists of a set of components, each of which exhibits a set of attributes. To model dynamic aggregations, which are sometimes referred to as ensembles, CARMA provides communication primitives that are based on predicates over the exhibited attributes. These predicates are used to select the participants in a communication. Two communication mechanisms are provided in the CARMA language: multicast-based and unicast-based. In this paper, we first introduce the basic principles of CARMA and then we show how our language can be used to support specification with a simple but illustrative example of a socio-technical collective adaptive system