this chapter discusses secure management of virtualized resources in a Cloud

this Chapter discusses secure assessment and authorization for private Clouds

Connections between microscopic follow-the-leader and macroscopic fluid-dynamics traffic flow models are already well understood in the case of vehicles moving on a single road. Analogous connections in the case of road networks are instead lacking. This is probably due to the fact that macroscopic traffic models on networks are in general ill-posed, since the conservation of the mass is not sufficient alone to characterize a unique solution at junctions. This ambiguity makes more difficult to find the right limit of the microscopic model, which, in turn, can be defined in different ways near the junctions. In this paper we show that a natural extension of the first-order follow-the-leader model on networks corresponds, as the number of vehicles tends to infinity, to the LWR-based multi-path model introduced in [4, 5].

In this paper we are concerned with multiscale modeling, control, and simulation of self-organizing agents leaving an unknown area under limited visibility, with special emphasis on crowds. We first introduce a new microscopic model characterized by an exploration phase and an evacuation phase. The main ingredients of the model are an alignment term, accounting for the herding effect typical of uncertain behavior, and a random walk, accounting for the need to explore the environment under limited visibility. We consider both metrical and topological interactions. Moreover, a few special agents, the leaders, not recognized as such by the crowd, are "hidden" in the crowd with a special controlled dynamic. Next, relying on a Boltzmann approach, we derive a mesoscopic model for a continuum density of followers, coupled with a microscopic description for the leaders' dynamics. Finally, optimal control of the crowd is studied. It is assumed that leaders aim at steering the crowd towards the exits so to ease the evacuation and limit clogging effects, and locally optimal behavior of leaders is computed. Numerical simulations show the efficiency of the control techniques in both microscopic and mesoscopic settings. We also perform a real experiment with people to study the feasibility of such a bottom-up control technique.

Presentazione a convegno

The RapidCell (RC) model was originally developed to simulate flagellar bacterial chemotaxis in environments with spatiotemporally varying chemoattractant gradients. RC is best suited for motility simulations in unbounded nonfluid environments; this limits its use in biomedical applications hinging on bacteria-fluid dynamics in microchannels. In this study, we eliminated this constraint by coupling the RC model with the colloidal lattice Boltzmann (LB) model. RC-LB coupling was accomplished by tracking positions of chemoreceptors on particle surfaces that vary with particles' angular and translational velocities, and by including forces and torques due to particles' tumbling and running motions in particle force-and torque-balance equations. The coupled model successfully simulated trajectories of particles in initially stagnant fluids in bounded domains, involving a chemoattractant contained in a confined zone with a narrow inlet or concentric multiringed inline obstacles, mimicking tumor vasculature geometry. Chemotactically successful particles exhibited higher attractant concentrations near the receptor clusters, transient increases in the motor bias, and transient fluctuations in methylated proteins at the cell scale, while exhibiting more frequent higher particle translation velocities and smaller angular velocities than chemotactically unsuccessful particles at the particle scale. In these simulations, the chemotactic particles reached the chemoattractant with the success rates of 20-72 %, whereas nonchemotactic particles would be unsuccessful. The coupled RC-LB model is the first step toward development of a multiscale simulation tool that bridges cell-scale signal and adaptation dynamics with particle-scale fluid-particle dynamics to simulate chemotaxis-driven bacterial motility in microchannel networks, typically observed in tumor vasculatures, in the context of targeted drug delivery.

The main steps taking the Lattice Boltzmann (LB) method beyond the realm of continuum hydrodynamics are discussed along with an appraisal of future prospects for coupling LB with other computational kinetic methods, such as Bird's Direct Simulation Monte Carlo and/or Discrete Velocity Models.

We present a Lattice Boltzmann method for the simulation of a wide range of Knudsen regimes. The method is assessed in terms of normalised discharge for flow across parallel plates and three-dimensional flows in porous media. Available analytical solutions are well reproduced, supporting the the method as an appealing candidate to bridge the gap between the hydrodynamic regime and free molecular motion.

We discuss the intriguing ability of minimal kinetic theory to describe a broad variety of complex non-equilibrium flows across scales of motion. It is argued that, besides major computational progress, minimal kinetic theory also provides a new conceptual framework to investigate the complexities of flowing matter far from equilibrium.

Nature routinely presents us with spectacular demonstrations of organization and orchestrated motion in living species. Efficient information transfer among the individuals is known to be instrumental to the emergence of spatial patterns (e.g. V-shaped formations for birds or diamond-like shapes for fishes), responding to a specific functional goal such as predatory avoidance or energy savings. Such functional patterns materialize whenever individuals appoint one of them as a leader with the task of guiding the group towards a prescribed target destination. It is here shown that, under specific conditions, the surrounding hydrodynamics plays a critical role in shaping up a successful group dynamics to reach the desired target.

New method: We computed the lead-field matrix by using a novel routine provided by the OpenMEEG software. We performed an analysis of the numerical stability of the ECoG inverse problem by computing the condition number of the lead-field matrix for different configurations of the electrodes grid. We applied a Linear Constraint Minimum Variance (LCMV) beamformer to both synthetic data and a set of real measurements recorded during a rapid visual categorization task. Background: Electrocorticography (ECoG) measures the distribution of the electrical potentials on the cortex produced by the neural currents. A full interpretation of ECoG data requires solving the ill-posed inverse problem of reconstructing the spatio-temporal distribution of the neural currents. This study addresses the ECoG source modeling developing a beamformer method.

With the exponential increase in data dimension and methodological complexities, conducting brain network analyses using MEG and EEG is becoming an increasingly challenging and time-consuming endeavor. To date, most of the MEG/EEG processing is done by combining software packages and custom tools which often hinders reproducibility of the experimental findings. Here we describe NeuroPype, which is a free open-source Python package we developed for efficient multi-thread processing of MEG and EEG studies. The proposed package is largely based on the NiPype framework and the MNE-Python software and benefits from standard Python packages such as NumPy and SciPy. It also incorporates several existing wrappers, such as a Freesurfer Python-wrapper for multi-subject MRI segmentation. The NeuroPype project includes three different packages: I Neuropype-ephy includes pipelines for electrophysiology analysis; current implementations allow for MEG/EEG data import, data pre-processing and cleaning by an automatic removal of eyes and heart related artefacts, sensor or source-level connectivity analyses II Neuropype-graph: functional connectivity exploiting graph-theoretical metrics including modular partitions III Neuropype-gui: a graphical interface wrapping the definition of parameters. NeuroPype provides a common and fast framework to develop workflows for advanced MEG/EEG analyses (but also fMRI and iEEG). Several pipelines have already been developed with NeuroPype to analyze different MEG and EEG datasets: e.g. EEG sleep data, MEG resting state measurements and MEG recordings in Autism. NeuroPype will be be made available via Github. Current developments will increase its compatibility with existing Python packages of interest such as machine learning tools.

The phenomenologyand reported effects of meditation vary according to the technique practiced.While numerous studies have explored the cerebral mechanisms involved inmeditation, little research provides direct comparisons between the neuronalnetwork dynamics involved in different meditation techniques. Here, we exploreand compare brain signals recorded with magnetoencephalography (MEG) during (a)resting state, (b) focused-attention meditation (FAM) and (c) open-monitoringmeditation (OMM) in a group of expert meditators (12 monks).To this end, weestimated MEG source time courses using a minimum-norm solution and computed (1)spectral power in multiple frequency bands (delta, theta, alpha, beta andgamma), (2) graph theoretical measures, (3) long-range coupling using imaginarycoherence and weighed phase-lag index and (4) multifractal scaling parameters using Wavelet Leader-based Multifractal formalism. We compared all the measures in the three conditions(OMM, FAM and resting state) and tested for statistical significance using permutationtest (paired t-test) corrected by maximum statistics. We also used a machinelearning framework in order to see which features provide the highestclassification across conditions. Our findings reveal several differencesbetween FAM, OMM and the resting-state condition. Compared to OMM, FAM isassociated with an increase in power in regions involved in attention andperformance monitoring. In OMM, increases in activity were observed in regionsinvolved in memory and emotion processing. Moreover, OMM seems to have strongestand more connections, while resting state have connections that are weaker andfewer in number compared to OMM and FAM. We discuss these results in thecontext of previous cognitive neuroimaging studies of meditation and paths forfuture research are proposed.

Background. Electrocorticography (ECoG) measures the distribution of electrical potentials by means of electrodes grids implanted close to the cortical surface. A full interpretation of ECoG data requires solving the ill-posed inverse problem of reconstructing the spatio-temporal distribution of neural currents responsible for the recorded signals. Only in the last few years some methods have been proposed to solve this inverse problem [1]. Methods. This study [2] addresses the ECoG source modelling using a beamformer method. We computed the lead-field matrix which maps the neural currents onto the sensors space by a novel routine provided by the OpenMEEG framework [3]. The ECoG source-modeling problem requires to invert this matrix by means of a regularization method which reduces its intrinsic numerical instability: we performed an analysis of the condition number of the lead-field matrix for different configurations of the electrodes grid. Finally, we provided quantitative results for source modeling using a Linear Constraint Minimum Variance (LCMV) beamformer [4]. The validation of the effectiveness of beamforming in ECoG was performed both with synthetic data and with experimental data recorded during a rapid visual categorization task. Results. For all considered grids the condition number indicates that the ECoG inverse problem is mildly ill-conditioned. For realistic SNR we found a good performance of the LCMV algorithm for both localization and waveforms reconstruction. The flow of information reconstructed by analyzing real data seems consistent with both invasive monkey electrophysiology studies and non-invasive (MEG and fMRI) human studies. References: 1. Dumpelmann et al., (2012), Human brain mapping, 33(5), 1172-1188 2. Pascarella et al. (2016), Journal of Neuroscience Methods, 263(5), 134-144 3. Kybic et al., (2005), Medical Imaging, IEEE Transactions on, 24(1), 12-28 4. Van Veen et al., (1997), Biomedical Engineering, IEEE Transactions on, 44(9), 867-880

In the present study, we revisit the MEG inverse problem, regularization and depth weighting from a Bayesian hierarchical point of view: the primary unknown is the discretized current density and each dipole has a preferred direction extracted from the MRI of the subject and encoded in the prior distribution. The variance of each dipole is described by its hyperprior density: this hypermodel is used to build the Iterative Alternating Sequential (IAS) algorithm with the novel feature that the parameters are determined using an empirical Bayes approach. We test the performance of the IAS algorithm against synthetic but realistic data. We simulate the neural activity generated by cortical patches located in several cerebral regions including deep regions as Insula, posterior Cingulate, Cerebellum and Hippocampus. Then, we reconstruct the activity by the IAS method with and without the physiological prior. The tests show that the physiological prior significantly improves the localization of the activity also in the case when the neural sources are located in deep regions. We compare the performance of the IAS method against the results obtained using two of the most popolar inversion methods: wMNE and dSPM. A measure based on Bayesian factors is used to quantify the reliability of the reconstructions. Finally, the three inversion methods are applied to a set of auditory real data. The Bayesian hierarchical model provides a very natural interpretation for sensitivity weighting, and the parameters in the hyperprior provide a tool for controlling the quality of the solution in terms of focality, thus leading to a flexible algorithm that can handle both sparse and distributed sources. References 1. Calvetti D, Pitolli F, Somersalo E and Vantaggi B(2015) ArXiv:1503.06844 2. Calvetti D, Pascarella A, Pitolli F, Somersalo E and Vantaggi B(2015) Inverse Problems 31(12) 3. Lin FH et al.(2006) Neuroimage 31 160-171 4. Tadel et al.(2011) Computational intelligence and neuroscience, 2011:8

Motor learning induces changes in resting-state (RS) network properties in fronts-parietal (Albert et al, 2009) and sensorimotor (Taubert et al, 2011) networks. This study explores the putative modulations of spontaneous resting-state oscillations following a sensori-motor learning task. The task consisted in lifting a load with the right hand, which triggered the unloading of a load suspended to the left forearm (Paulignan et al., 1989). Because learning stabilizes quickly, a temporal delay was implemented, hence placing the subject in a dynamic learning state. Sixteen adults performed a resting state sessions in which they fixated a grey crosshair on a white background before and after two motor learning conditions: The subjects were instructed to lift with their right arm a load (800 g) placed on the ipsilateral haptic space. In the LEARNED condition, voluntary lifting of the object with the right arm instantaneously triggered the unloading of the load placed on the left arm. In the DYNAMIC LEARNING condition, a time delay was implemented per block between lifting and the resulting unloading. MEG signals were recorded using a 275-channel MEG CTF system. The performance was constant in the LEARNED condition, while postural stabilization increased during the DYNAMIC LEARNING condition (p<.001). Minimum-norm estimation revealed that alpha power (8-12 Hz) generators were located bilaterally within the pre-central gyri, the post-central gyri, the inferior parietal gyri and the superior parietal gyri. Most importantly, comparison of RS power pre and post learning revealed a significant increase of sensori-motor alpha power contralateral to postural side, only after the DYNAMIC LEARNING condition (p<.05). Our RS MEG connectivity and graph theoretical analyses also showed significant changes following motor learning. The RS oscillatory network modulations we observed following dynamic motor learning could be specifically related to sustained sensori-motor learning processes, distinct from novel skill acquisition.

The phenomenology and reported effects of meditation vary according to the technique practiced. While numerous studies have explored the cerebral mechanisms involved in meditation, little research provides direct comparisons between the neuronal network dynamics involved in different meditation techniques. Here, we explore and compare brain signals recorded with magnetoencephalography (MEG) during (a) focused-attention meditation (FAM), and (b) open-monitoring meditation (OMM) in a group of expert meditators (12 monks). To this end, we estimated MEG source time courses using minimum-norm and computed spectral power in multiple frequency bands (delta, theta, alpha, beta and gamma), graph theoretical measures and multifractal scaling parameters in both conditions. Preliminary findings reveal several differences between FAM and OMM. Interestingly, OMM was associated with higher theta power in the right temporal pole. We discuss these results in the context of previous cognitive neuroimaging studies of meditation and paths for future research are proposed.

With the exponential increase in data dimension and complexity, conducting state-of-the-art brain network analyses using MEG and EEG is becoming an increasingly challenging and time-consuming endeavor. Here we describe NeuroPype, a free open-source Python package we developed for efficient multi-thread processing of MEG and EEG studies. The proposed package is based on NiPype and MNE-Python and benefits from standard Python packages such as NumPy and SciPy. The pipeline also incorporates several existing wrappers, such as a Freesurfer Pyhton-wrapper for multi-subject MRI segmentation. Through the efficient combination of multiple neuroimaging and MEG/EEG packages, NeuroPype provides a common and fast framework for advanced MEG/EEG analyses. The highlights of the pipeline, include data pre-processing and cleaning, sensor or source-level connectivity analyses (Imaginary and standard coherence, phase-lag index, phase-locking, etc.), and graph-theoretical metrics (including modular partitions). The pipeline design, data structure and analysis workflow is described and future additions will be discussed.

Here we describe NeuroPype, which is a free open-source Python package, we developed for efficient multi-thread processing of MEG and EEG studies. The proposed package is based on the Nipype framework , a tool developed in fMRI field, which facilitates data analyses by wrapping many commonly-used neuro-imaging software into a common python framework.