A new technique for nonparametric regression of multichannel signals is presented. The technique is based on the use of the Rational-Dilation Wavelet Transform (RADWT), equipped with a tunable Q-factor able to provide sparse representations of functions with different oscillations persistence. In particular, two different frames are obtained by two RADWT with different Q-factors that give sparse representations of functions with low and high resonance. It is assumed that the signals are measured simultaneously on several independent channels and that they share the low resonance component and the spectral characteristics of the high resonance component. Then, a regression analysis is performed by means of the grouped lasso penalty. Furthermore, a result of asymptotic optimality of the estimator is presented using reasonable assumptions and exploiting recent results on group-lasso like procedures. Numerical experiments show the performance of the proposed method in different synthetic scenarios as well as in a real case example for the analysis and joint detection of sleep spindles and K-complex events for multiple electroencephalogram (EEG) signals.

We propose a discrete in continuous mathematical model describing the in vitro growth process of biophsy-derived mammalian cardiac progenitor cells growing as clusters in the form of spheres (Cardiospheres). The approach is hybrid: discrete at cellular scale and continuous at molecular level. In the present model cells are subject to the self-organizing collective dynamics mechanism and, additionally, they can proliferate and differentiate, also depending on stochastic processes. The two latter processes are triggered and regulated by chemical signals present in the environment. Numerical simulations show the structure and the development of the clustered progenitors and are in a good agreement with the results obtained from in vitro experiments.

We consider some discrete approximation polynomials, namely discrete de la Vallée Poussin means, which have been recently deduced from certain delayed arithmetic means of the Fourier-Jacobi partial sums, in order to get a near-best approximation in suitable spaces of continuous functions equipped with the weighted uniform norm. By the present paper we aim to analyze the behavior of such discrete de la Vallée means in weighted L1 spaces, where we provide error bounds for several classes of functions, included functions of bounded variation. In all the cases, under simple conditions on the involved Jacobi weights, we get the best approximation order. During our investigations, a weighted L1 Marcinkiewicz type inequality has been also stated.

Longitudinal defects of the internal coated surface of a metal pipe can be evaluated in a fast, precise and cheap way from thermal measurements on the external surface. In this paper, we study two classes of real situations in which the thickness of the coating is much smaller than the thickness of the metal tube: the transportation of potable water and crude oil. A very precise and stable reconstruction of damages is obtained by means of perturbation methods. To do this, first we translate a composite (coating-plus-tube) boundary value problem in a virtual one on the metallic part only. The information about possible damages is now included in the deviations delta h of the effective heat transfer coefficient from a known background value. Finally, we determine delta h by means of Thin Plate Approximation. (C) 2017 Elsevier Ltd. All rights reserved.

The literature on k-splittable flows, see Baier et al. (2002) Baier et al. (2005), provides evidence on how controlling the number of used paths enables practical applications of flows optimization in many real-world contexts. Such a modeling feature has never been integrated so far in Quickest Flows, a class of optimization problems suitable to cope with situations such as emergency evacuations, transportation planning and telecommunication systems, where one aims to minimize the makespan, i.e. the overall time needed to complete all the operations, see Pascoal et al. (2006) Pascoal et al. (2006). In order to bridge this gap, a novel optimization problem, the Quickest Multicommodity k-Splittable Flow Problem (QMCkSFP) is introduced in this paper. The problem seeks to minimize the makespan of transshipment operations for given demands of multiple commodities, while imposing restrictions on the maximum number of paths for each single commodity. The computational complexity of this problem is analyzed, showing its NP-hardness in the strong sense, and an original Mixed-Integer Programming formulation is detailed. We propose a matheuristic algorithm based on a hybridized Very Large-Scale Neighborhood Search that, utilizing the presented mathematical formulation, explores multiple search spaces to solve efficiently large instances of the QMCkSFP. High quality computational results obtained on benchmark test sets are presented and discussed, showing how the proposed matheuristic largely outperforms a state-of-the-art heuristic scheme frequently adopted in path-restricted flow problems.

The petrol station replenishment problem consists in delivering fuel oils from a set of storage depots to a set of petrol stations during a few days planning horizon. This problem is addressed by an oil company which, for example, has to decide simultaneously the weekly fuel oil replenishment plan for each station, and, for each day of the week, the tank truck (vehicle) routes from depots to stations, in order to deliver the planned fuel oil replenishment amounts to petrol stations. Assuming a fleet of homogeneous tank trucks, the aim is to minimize the total distance travelled by tank trucks during the week, while loading tank trucks possibly near to their capacity in order to maximize the resource utilization. We model the problem as a generalization of the Multi-Depot Periodic Vehicle Routing Problem (MDPVRP) and provide a mathematical formulation. Due to the large size of the real instances which the company has to deal with, we solve the problem heuristically. We propose a hybrid genetic algorithm that successfully address the problem. The algorithm is derived from a known hybrid genetic algorithm for the MDPVRP, and adopts additional techniques and features tailored for the particular fuel oil distribution problem. It is specifically designed to deal with real instances derived from the fuel oil distribution in the European context that are profoundly different from the MDPVRP instances available from the literature. The proposed algorithm is evaluated on a set of real case studies and on a set of randomly generated instances that hold the same characteristics of the former.

Several researches in the scientific, industrial and commercial fields are supporting the reduction of traditional combustion cars' use. The main purpose is to increase the quality of life into the metropolitan cities through the reduction of CO2 emissions and global warming. Accordingly, one of the most successful models is the carpooling system. Currently, people are investigating the sustainability and durability of carpooling business model from both economic and organizational point of view. The present research aims to develop a Multicriteria Decision Support System (MDSS) in order to offer a carpooling system's platform based on different criteria. The MDSS is developed from driver's point of view and settled on two levels of optimization. Firstly, a genetic algorithm is proposed to solve an orienteering problem that optimizes the total revenue of driver based on the car's capability and the time schedule. Secondly, the best optimization solutions are compared with multicriteria analysis respect to other criteria not included in the first optimization. The outcome of MDSS is a schedule for drivers, which gives maximum satisfaction in terms of profitability, punctuality and comfort of the travel.

The paper proposes a decision support system (DSS) for the supply chain of packaged fresh and highly perishable products. The DSS combines a unique tool for sales forecasting with order planning which includes an individual model selection system equipped with ARIMA, ARIMAX and transfer function forecasting model families, the latter two accounting for the impact of prices. Forecasting model parameters are chosen via two alternative tuning algorithms: a two-step statistical analysis, and a sequential parameter optimisation framework for automatic parameter tuning. The DSS selects the model to apply according to user-defined performance criteria. Then, it considers sales forecasting as a proxy of expected demand and uses it as input for a multi-objective optimisation algorithm that defines a set of non-dominated order proposals with respect to outdating, shortage, freshness of products and residual stock. A set of real data and a benchmark - based on the methods already in use - are employed to evaluate the performance of the proposed DSS. The analysis of different configurations shows that the DSS is suitable for the problem under investigation; in particular, the DSS ensures acceptable forecasting errors and proper computational effort, providing order plans with associated satisfactory performances.

A comprehensive approach to Sobolev-type embeddings, involving arbitrary rearrangement-invariant norms on the entire Euclidean space R^n, is offered. In particular, the optimal target space in any such embedding is exhibited. Crucial in our analysis is a new reduction principle for the relevant embeddings, showing their equivalence to a couple of considerably simpler one-dimensional inequalities. Applications to the classes of the Orlicz-Sobolev and the Lorentz-Sobolev spaces are also presented. These contributions fill in a gap in the existing literature, where sharp results in such a general setting are only available for domains of finite measure.

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