In this paper we investigate the sensitivity of the LWR model on network to its parameters and to the network itself. The quantification of sensitivity is obtained by measuring the Wasserstein distance between two LWR solutions corresponding to different inputs. To this end, we propose a numerical method to approximate the Wasserstein distance between two density distributions defined on a network. We found a large sensitivity to the traffic distribution at junctions, the network size, and the network topology.

In this paper we investigate the possibility of reducing the complexity of a system composed of a large number of interacting agents, whose dynamics feature a symmetry breaking. We consider first order stochastic differential equations describing the behavior of the system at the particle (i.e., Lagrangian) level and we get its continuous (i.e., Eulerian) counterpart via a kinetic description. However, the resulting continuous model alone fails to describe adequately the evolution of the system, due to the loss of granularity which prevents it from reproducing the symmetry breaking of the particle system. By suitably coupling the two models we are able to reduce considerably the necessary number of particles while still keeping the symmetry breaking and some of its large-scale statistical properties. We describe such a multiscale technique in the context of opinion dynamics, where the symmetry breaking is induced by the results of some opinion polls reported by the media.

We consider homogeneous linear Volterra Discrete Equations and we study the asymptotic behaviour of their solutions under hypothesis on the sign of the coefficients and of the first- and second-order differences. The results are then used to analyse the numerical stability of some classes of Volterra integrodifferential equations.

The self and cross diffusion action on the dynamic of the nonlinear continu- ous duopoly model introduced in [22], is investigated. Under Robin boundary conditions the longtime behavior and the linear and nonlinear stability of the steady states, are studied. The self and cross diffusion parameters guaran- teeing the spreading of the firms outputs, are characterized.

Well-balanced schemes, nowadays mostly developed for both hyperbolic and kinetic equations, are extended in order to handle linear parabolic equations, too. By considering the variational solution of the resulting stationary boundary-value problem, a simple criterion of uniqueness is singled out: the C1 regularity at all knots of the computational grid. Being easy to convert into a finite-difference scheme, a well-balanced discretization is deduced by defining the discrete time-derivative as the defect of C1 regularity at each node. This meets with schemes formerly introduced in the literature relying on so-called L-spline interpolation of discrete values. Various monotonicity, consistency and asymptotic-preserving properties are established, especially in the under-resolved vanishing viscosity limit. Practical experiments illustrate the outcome of such numerical methods.

Proteins are the core and the engine of every process in cells thus the study of mechanisms that drive the regulation of protein expression, is essential. Transcription factors play a central role in this extremely complex task and they synergically co-operate in order to provide a fine tuning of protein expressions. In the present study, we designed a mathematically well-founded procedure to investigate the mutual positioning of transcription factors binding sites related to a given couple of transcription factors in order to evaluate the possible association between them. We obtained a list of highly related transcription factors couples, whose binding site occurrences significantly group together for a given set of gene promoters, identifying the biological contexts in which the couples are involved in and the processes they should contribute to regulate. Studio delle sinergie tra fattori di trascrizione nei promotori

The large amount of work on community detection and its applications leaves unaddressed one important question: the statistical validation of the results. A methodology is presented that is able to clearly detect if the community structure found by some algorithms is statistically significant or is a result of chance, merely due to edge positions in the network. Given a community detection method and a network of interest, the proposal examines the stability of the partition recovered against random perturbations of the original graph structure. To address this issue, a perturbation strategy and a null model graph, which matches the original in some of its structural properties, but is otherwise a random graph, is specified. A set of procedures is built based on a special measure of clustering distance, namely Variation of Information, using tools set up for functional data analysis. The procedures determine whether the obtained clustering departs significantly from the null model. This strongly supports the robustness against perturbation of the algorithm used to identify the community structure. Results obtained with the proposed technique on simulated and real datasets are shown and discussed.

This paper has two main objectives: one is to describe some extensions of an adaptive Algebraic Multigrid (AMG) method of the form previously proposed by the first and third authors, and a second one is to present a new software framework, named BootCMatch, which implements all the components needed to build and apply the described adaptive AMG both as stand-alone solver and as preconditioner in a Krylov method. The adaptive AMG presented is meant to handle general symmetric and positive definite (SPD) sparse linear systems, without assuming any a priori information of the problem and its origin; the goal of adaptivity is to achieve a method with a prescribed convergence rate. The presented method exploits a general coarsening process based on aggregation of unknowns, obtained by a maximum weight matching in the adjacency graph of the system matrix. More specifically, a maximum product matching is employed to define an effective smoother subspace (complementary to the coarse space), a process referred to as compatible relaxation, at every level of the recursive two-level hierarchical AMG process. Results on a large variety of test cases and comparisons with related work demonstrate the reliability and efficiency of the method and of the software.

Introduction : Neuroimaging studies have shown that in adults, the motor learning induced alterations of the functional connectivity assessed during Resting State Networks (RSN) is age-dependent (Mary et al., 2017). Motor learning relies on the build-up of new sensori-motor representations, which has been studied using the bar-man task in adults (Barlaam, Vaugoyeau, Fortin, Assaiante, & Schmitz, 2016; Paulignan, Dufossé, Hugon, & Massion, 1989) and in children (Schmitz et al, 2002).. The aim of this study was to investigate the modulations of functional connectivity after a motor learning task in the childs resting state network. Method : 20 children aged 7 to 12 (12 boys; age 9y 9m ; age 1y et 8m) took part in the study. The resting state tasks consisted of a 3 session closed-eyes recording of 45 seconds. The first one was used as a baseline and the next two have been set around the motor learning task to evaluate the effect of motor learning on the connectivity in the RSN. The motor learning task was a load-lifting task where the participant was asked to lift a weight using its right hand which triggered the fall of a weight attached to the supporting left arm (Paulignan et al, 1989). We continuously recorded the neuromagnetic signals using a 275 channels CTFMEG system. To quantify the functional connectivity between brain regions, coherency analyses have been conducted, using the imaginary part of the coherency, corresponding to the correlation coefficient between two signals in the alpha and beta frequency bands. Moreover, graph theory analysis has provided an overview of the network organisation after the motor task. All resultshave been analysed using data-related permutation statistic with a 0.005 significance threshold (calculated as a relation between the number of conditions (n=2) and the number of subjects (N=20); = 1/, therefore < 0.005). 2 of 3 Results : The motor learning behavioural performances were assessed using a learning curve model throughout the trials which revealed a significant global learning effect (F(7 ;19) = 50,62 ; p <0,0001). In the alpha band (8-12Hz) : Permutation analysis showed an increase of the functional connectivity in the RSN (<0.005) when contrasting before and after the sensorimotor learning in the primary motor cortices (M1) along with the inferior frontal gyrus (IFG) and the premotor cortices. In the beta band (15-29Hz) : Significant increase of the functional connectivity was also found in this frequency band when contrasting before and after the sensorimotor learning in the somatosensory cortex andin the precuneus gyrus. Interestingly, we found that functional connectivity measured in pairs of brain areas (such as the premotor cortex and the cerebellum) in the pre-learning RSN was predictive of the behavioural learning performance. Conclusion : Our study showed that, after a motor learning task, the functional connectivity measured in the RSN increases between regions involved in the build-up of sensorimotor representations (such as the supplementary motor area (SMA); the primary motor dorsal area (PMd) ; the primary motor cortex (M1) and even the somatosensory cortexs (S1 & S2)), in children. References : Barlaam, F., Vaugoyeau, M., Fortin, C., Assaiante, C., & Schmitz, C. (2016). Shift of the muscular inhibition latency during on-line acquisition of anticipatory postural adjustments. PLoS ONE, 11(5). https://doi.org/10.1371/journal.pone.0154775 Mary, A., Wens, V., Op De Beeck, M., Leproult, R., De Tiège, X., & Peigneux, P. (2017). Resting-state Functional Connectivity is an Age-dependent Predictor of Motor Learning Abilities. Cerebral Cortex, 27(10), 49234932. https://doi.org/10.1093/cercor/bhw286 Paulignan, Y., Dufossé, M., Hugon, M., & Massion, J. (1989). Acquisition of co-ordination between posture and movement in a bimanual task. Experimental Brain Research, 77(2), 337348. https://doi.org/10.1007/BF00274991

Biological systems tend to display complex behaviour with a power-law (1/f - like) distribution. In the brain, this translates into neural activity that exhibits scale-free, temporal or spatial, properties (He, 2014). Scaleinvariance has been observed across different neuroimaging modalities and conditions (Linkenkaer-Hansen, 2001; He, 2014; Ciuciu et al. 2012). Beyond previously used features, recent electrophysiology studies have shown the presence of long-range temporal correlations (LRTCs) in the amplitude dynamics of alpha and beta oscillations (Nikulin et al. 2012). Disease, such as psychosis, can alter the temporal properties of neuronal activity and, consequently, potentially affect information integration (Fernandez et al. 2013). The goals of this study were to: (a) measure and compare scale-free dynamics in schizophrenia patients (SZ) and controls using magnetoencephalography (MEG), and (b) classify subjects using machine-learning tools. Five minutes of resting-state MEG were acquired for 25 SZ patients and 25 controls during eyes open and eyes closed conditions. Detrended Fluctuation analysis (DFA) was applied to resting alpha and beta band oscillatory amplitudes to investigate LRTCs in each subject group, at both the sensor and the source levels. Permutation tests with maximum statistics correction were used to explore group differences (p < 0.01). Finally, machine-learning, using support vector machine (SVM) and a 10-fold cross-validation technique, was applied to classify controls and patients based on DFA values. Statistically significant decoding was assessed using binomial law statistics and permutation tests (p < 0.001). Results/Discussion: Significant group differences were observed between the two groups at both sensor and source levels. Specifically, sensormeasured DFA were found to be significantly attenuated in SZ patients compared to controls over the temporo-parietal areas in the alpha-band, and over central brain regions in the beta-band. Source-level analysis improved anatomical. Relative group differences in DFA were found up to 20% in both alpha and beta bands. Specifically, compared to controls, attenuated DFA values were observed in the frontal, temporal and occipital poles and the cuneus in the alpha band, and in the occipital pole, cuneus and mid frontal gyrus in the beta band. Finally, the machine-learning algorithm successfully classified the groups using the measure of DFA with up to 76% decoding accuracy. The combination of classical statistical measures and machine learning tools in our study illustrate the interest of using features of scale-free dynamics to enhance our understanding of schizophrenia and potentially find a new path for early clinical diagnosis.

Background. With the exponential increase in data dimension and methodological complexities, brain networks analysis with MEG and EEG has become an increasingly challenging and time-consuming endeavor. To date, performing all the data processing steps that are required for a complete MEG/EEG analysis pipeline often require the use of a multitude of software packages and in-house or custom tools (e.g. MRI segmentation, pre-processing, source reconstruction, graph theoretical analysis, statistics). This is not only cumbersome, but may also increase sources of errors and hinders replication of results. Here we describe NeuroPycon, an open-source, multi-modal brain data analysis kit which provides Python-based pipelines for advanced multi-thread processing of fMRI, MEG, and EEG data, with a focus on connectivity and graph analyses [1]. Methods. NeuroPycon is based on the NiPype framework [2] which facilitates data analyses by wrapping numerous commonly-used neuroimaging software solutions into a common python framework. NeuroPycon allows accessing and interfacing with the existing open-science neuroimaging software and signal processing toolboxes, within a unified framework relying on several freely available Python packages which are developed for efficient and fast parallel processing. The current implementation of NeuroPycon comprises three different packages: 2 of 3 - ephypype is mainly based on MNE-Python package [3] and includes pipelines for electrophysiology analyses. Current implementation features MEG/EEG data import, data pre-processing and cleaning via an automatic removal of eyes and heart-related artefacts, and sensor or source-level connectivity analyses - graphpype is based on radatools [4], a set of freely distributed applications aimed at analyses of Complex Networks. It comprises pipelines for functional connectivity studies which heavily exploit graph-theoretical metrics including among other things modular partitions - neuropycon_cli is a command line interface for the ephypype package. Notably, NeuroPycon pipelines can be used in a stand-alone mode but they can also be combined within building blocks to form a larger workflow, in which case the input of one pipeline comes from the outputs of the others. Each pipeline, based upon the nipype engine, is defined by connecting different nodes, with each node being either a user-defined function or a python-wrapped external routine (as MNE-python modules or radatools functions). Results and Discussion. NeuroPycon provides a common and fast framework to develop workflows for advanced neuroimaging data analyses. Several workflows have already been developed to analyze different datasets coming from MEG and EEG studies, such as EEG sleep data and MEG resting state measurements. Furthermore, pipelines defined in graphpype have already been used to perform graph theoretical analysis on a different fMRI datasets. Results visualisation for NeuroPycon is provided through the visbrain (http://visbrain.org/), an open-source multi-purpose python software devoted to graphical representation of neuroscientific data and built on top of VisPy [5], a high-performance visualization library leveraging GPU acceleration. NeuroPycon will shortly be available for download via github (installation via Docker) and is currently being documented (https://neuropycon.github.io/neuropycon_doc/). Future developments will include fusion of multi-modal data (ex. MEG and fMRI or iEEG and fMRI) and feature an increased compatibility with the existing Python packages of interest such as machine learning tools. References: 1. Bullmore E, Sporns O (2009), Nat Rev Neuroscience 2. Gorgolewski et al. (2011) Front. Neuroinformatics 3. Gramfort et al. (2013), Front. Neuroscience 4. http://deim.urv.cat/~sergio.gomez/radatools.php 5. Campagnola et al. (2015), Proceedings of the 14th Python in Science Conference

Breast cancer is one of the most common invasive tumors causing high mortality among women. It is characterized by high heterogeneity regarding its biological and clinical characteristics. Several high-throughput assays have been used to collect genome-wide information for many patients in large collaborative studies. This knowledge has improved our understanding of its biology and led to new methods of diagnosing and treating the disease. In particular, system biology has become a valid approach to obtain better insights into breast cancer biological mechanisms. A crucial component of current research lies in identifying novel biomarkers that can be predictive for breast cancer patient prognosis on the basis of the molecular signature of the tumor sample. However, the high dimension and low sample size of data greatly increase the difficulty of cancer survival analysis demanding for the development of ad-hoc statistical methods. In this work, we propose novel screening-network methods that predict patient survival outcome by screening key survival-related genes and we assess the capability of the proposed approaches using METABRIC dataset. In particular, we first identify a subset of genes by using variable screening techniques on gene expression data. Then, we perform Cox regression analysis by incorporating network information associated with the selected subset of genes. The novelty of this work consists in the improved prediction of survival responses due to the different types of screenings (i.e., a biomedical-driven, data-driven and a combination of the two) before building the network-penalized model. Indeed, the combination of the two screening approaches allows us to use the available biological knowledge on breast cancer and complement it with additional information emerging from the data used for the analysis. Moreover, we also illustrate how to extend the proposed approaches to integrate an additional omic layer, such as copy number aberrations, and we show that such strategies can further improve our prediction capabilities. In conclusion, our approaches allow to discriminate patients in high-and low-risk groups using few potential biomarkers and therefore, can help clinicians to provide more precise prognoses and to facilitate the subsequent clinical management of patients at risk of disease.

Hypothesis testing is a statistical decisional process that allows one to choose between two complementary possibilities on the basis of samples drawn from the population(s) of interest. The two possibilities are called the null and alternative hypothesis, respectively. For each decision, two types of errors might occur, i.e., rejecting the null hypothesis when it is true (Type I error) or accepting the null hypothesis when it is false (Type II error). The decision is taken by compromising the two error types. When multiple hypotheses are compared one also has to define and control the overall decisional error.

Ever more organizations, both private and public, are placing a greater importance on employee engagement as a means of generating better organizational climate and higher levels of performance. Actually, employee engagement is part of the strategic management of high performance organization, which pay always more attention to human resource initiatives. Moreover, forms of involvement in the decision processes make more motivating and more satisfying the activity for employees, as they create the conditions for greater inspiration and, in turn, contribute to their well-being. Besides, several studies show that when employees believe they have opportunities for voice in decision-making, such awarenesscanpositively affectthe organisational commitment.Based on the foregoing premise, this study proposes a new "employee voice framework"for stimulating employee voice andemployee participation in strategic decision-making. The first step of the framework prescribes to organizea number of "World Cafè"events dedicated toa specific subject of the strategic decision-making. The World Cafèmethodis a structured conversational process for knowledge sharing in which an informal climate allows groups of voluntary participants (in this case employees) to discuss a specific topic, enhancing creativity and cross-pollination of ideas. In the second step, the proposals emerged from the World Cafèevents are included in a questionnaire to be submitted to all employeesthat should be involved in the decision-making process. Each proposal is evaluated on the basis oftwo variables: "importance" and "feasibility". The top-management has to answer the same questions to which employees respond. The third step of the frameworkprescribesthe creation of "importance/feasibility matrices"that allowscomparing employee and top-management viewpoints on the proposals. The matrices offer an opportunity for employees and managers to exchange views. Therefore, the matrices give insightinto which proposals should be implementedas they result the most important for employees but also feasible for the top-management.The paper concludes with a real case study application to the Italian National Research Council (CNR), the largest research organization in Italy. The application of the "employee voice framework"involved all CNR employees and concluded with the formulation of various proposals for the design of a new performance evaluation and incentive system.

The knowledge production provided by universities is essential to sustaining a country's long-term economic growth and international competitiveness. Many nations are thus driving to create sustainable and effective funding environments. The evaluation of university knowledge, productivity and research quality becomes critical, with ever increasing share of public funding allocated on the basis of research assessment exercises. Nevertheless, the existing methods to assess the universities' knowledge production are often affected by limits and biases, extensively discussed in the scientific literature. In this paper we study how to reduce the effect of size-related bias due to university size on the indicators of knowledge performance used in evaluation exercises. We propose an innovative utilization of the scale-free property of the power laws as a scaling relationship, to normalize research productivity indicators, and provide results independent by the university size. Our method has evident policy implications and gives a contribution for the future design of assessment exercises. We apply our findings in a recent Italian research assessment exercise.

E-commerce is a sector in continual growth in all countries and, in particular, the increase in B2C (Business to Consumer) e-commerce market has important effects on last-mile deliveries in city areas. The delivery of a parcel to a consumer's address involves not only high costs for both couriers (extended car routes) and consumers (high prices) and also greater environmental pollution. The growing demand for deliveries in urban areas involves increases in traffic and congestion problems and, consequently, environmental issues. In recent years, many studies have focused on alternative measures to reduce the negative aspects and impact of last-mile deliveries. Good practice to rationalize last-mile delivery should involve the use of various systems, such as reception boxes, delivery boxes, controlled access systems, collection points and lockers. This paper compares two alternative options to home delivery. In particular, it makes comparisons between point-to-point and lockers, states the pro and cons of both, and defines the best positions to locate lockers to reduce consumers' deviations. The proposed method is applied to a real case: the Italian municipality of Dolo (near Venice).

Linear regression models a dependent variable Y in terms of a linear combination of p independent variables X=[X1|...|Xp] and estimates the coefficients of the combination using independent observations (x_i,Y_i ),i=1,...,n. The Gauss-Markov conditions guarantees that the least squares estimate of the regression coefficients constitutes the best linear estimator. Under the assumption of white noise, it is possible to test the significance of each regression coefficient, evaluate the uncertainty/goodness of fit, and use the fitted model for predicting novel outcomes. When p>n, classical linear regression cannot be applied, and penalized approaches such as ridge regression, lasso or elastic net have to be used.

Network flows and specifically water flow in open canals can be modeled by systems of balance laws defined on graphs. The shallow water or Saint-Venant system of balance laws is one of the most used model and present two phases: fluvial or sub-critical and torrential or super-critical. Phase transitions may occur within the same canal but transitions related to networks are less investigated. In this paper we provide a complete characterization of possible phase transitions for a case study of a simple scenario with two canals and one junction. However, our analysis allows the study of more complicate networks. Moreover, we provide some numerical simulations to show the theory at work.