Collective Almost Synchronisation in Complex Networks

This work introduces the phenomenon of Collective Almost Synchronisation (CAS), which describes a universal way of how patterns can appear in complex networks for small coupling strengths. The CAS phenomenon appears due to the existence of an approximately constant local mean field and is characterised by having nodes with trajectories evolving around periodic stable orbits. Common notion based on statistical knowledge would lead one to interpret the appearance of a local constant mean field as a consequence of the fact that the behaviour of each node is not correlated to the behaviours of the others. Contrary to this common notion, we show that various well known weaker forms of synchronisation (almost, time-lag, phase synchronisation, and generalised synchronisation) appear as a result of the onset of an almost constant local mean field. If the memory is formed in a brain by minimising the coupling strength among neurons and maximising the number of possible patterns, then the CAS phenomenon is a plausible explanation for it.

Work Precariousness and Risk of Accidents in Civil Construction: A study based on the Collective Work Analysis

This paper aims to describe the construction workers' activities, as well as their perceptions about risks and workload. The study, based on the Collective Work Analysis, is part of a broader public policies project for the improvement of SIVAT (Surveillance System of Work Accidents) - in the city of Piracicaba (Southeastern Brazil). Civil construction was prioritized given the epidemiological magnitude of the occurrence of work accidents and the limited efficacy of traditional surveillance initiatives in this sector due to informal employment practices, outsourcing, high staff turnover, etc. The workers have a high level of awareness concerning the risk of accidents, but they believe that the main preventive measures hinder or even make it impossible for them to carry out the tasks. Our findings question the efficacy of traditional training for adherence to safety practices, thus highlighting the need for a transformative pedagogy for preventive practices and the health promotion of workers.

Observation of Direct-Photon Collective Flow in Au plus Au Collisions at root s(NN)=200 GeV

The second Fourier component v(2) of the azimuthal anisotropy with respect to the reaction plane is measured for direct photons at midrapidity and transverse momentum (p(T)) of 1-12 GeV/c in Au + Au collisions at root s(NN) = 200 GeV. Previous measurements of this quantity for hadrons with p(T) < 6 GeV/c indicate that the medium behaves like a nearly perfect fluid, while for p(T) > 6 GeV/c a reduced anisotropy is interpreted in terms of a path-length dependence for parton energy loss. In this measurement with the PHENIX detector at the Relativistic Heavy Ion Collider we find that for p(T) > 4 GeV/c the anisotropy for direct photons is consistent with zero, which is as expected if the dominant source of direct photons is initial hard scattering. However, in the p(T) < 4 GeV/c region dominated by thermal photons, we find a substantial direct-photon v(2) comparable to that of hadrons, whereas model calculations for thermal photons in this kinematic region underpredict the observed v(2).

Intelligence systems methodology: a systemic approach to the organizational intelligence function

Organizational intelligence can be seen as a function of the viable structure of an organization. With the integration of the Viable System Model and Soft Systems Methodology (systemic approaches of organizational management) focused on the role of the intelligence function, it is possible to elaborate a model of action with a structured methodology to prospect, select, treat and distribute information to the entire organization that improves the efficacy and efficiency of all processes. This combination of methodologies is called Intelligence Systems Methodology (ISM) whose assumptions and dynamics are delimited in this paper. The ISM is composed of two simultaneous activities: the Active Environmental Mapping and the Stimulated Action Cycle. The elaboration of the formal ISM description opens opportunities for applications of the methodology on real situations, offering a new path for this specific issue of systems thinking: the intelligence systems. Knowledge Management Research & Practice (2012) 10, 141-152. doi:10.1057/kmrp.2011.44

Swarm intelligence in computer vision: an application to object tracking

Tesis en inglés. Eliminadas las páginas en blanco del pdf

The social construction of cooperation: exchange, identification and collective intentions within the organization

My aim is to develop a theory of cooperation within the organization and empirically test it. Drawing upon social exchange theory, social identity theory, the idea of collective intentions, and social constructivism, the main assumption of my work implies that both cooperation and the organization itself are continually shaped and restructured by actions, judgments, and symbolic interpretations of the parties involved. Therefore, I propose that the decision to cooperate, expressed say as an intention to cooperate, reflects and depends on a three step social process shaped by the interpretations of the actors involved. The first step entails an instrumental evaluation of cooperation in terms of social exchange. In the second step, this “social calculus” is translated into cognitive, emotional and evaluative reactions directed toward the organization. Finally, once the identification process is completed and membership awareness is established, I propose that individuals will start to think largely in terms of “We” instead of “I”. Self-goals are redefined at the collective level, and the outcomes for self, others, and the organization become practically interchangeable. I decided to apply my theory to an important cooperative problem in management research: knowledge exchange within organizations. Hence, I conducted a quantitative survey among the members of the virtual community, “www.borse.it” (n=108). Within this community, members freely decide to exchange their knowledge about the stock market among themselves. Because of the confirmatory requirements and the structural complexity of the theory proposed (i.e., the proposal that instrumental evaluations will induce social identity and this in turn will causes collective intentions), I use Structural Equation Modeling to test all hypotheses in this dissertation. The empirical survey-based study found support for the theory of cooperation proposed in this dissertation. The findings suggest that an appropriate conceptualization of the decision to exchange knowledge is one where collective intentions depend proximally on social identity (i.e., cognitive identification, affective commitment, and evaluative engagement) with the organization, and this identity depends on instrumental evaluations of cooperators (i.e., perceived value of the knowledge received, assessment of past reciprocity, expected reciprocity, and expected social outcomes of the exchange). Furthermore, I find that social identity fully mediates the effects of instrumental motives on collective intentions.

Structure learning of graphical models for task-oriented robot grasping

In the collective imaginaries a robot is a human like machine as any androids in science fiction. However the type of robots that you will encounter most frequently are machinery that do work that is too dangerous, boring or onerous. Most of the robots in the world are of this type. They can be found in auto, medical, manufacturing and space industries. Therefore a robot is a system that contains sensors, control systems, manipulators, power supplies and software all working together to perform a task. The development and use of such a system is an active area of research and one of the main problems is the development of interaction skills with the surrounding environment, which include the ability to grasp objects. To perform this task the robot needs to sense the environment and acquire the object informations, physical attributes that may influence a grasp. Humans can solve this grasping problem easily due to their past experiences, that is why many researchers are approaching it from a machine learning perspective finding grasp of an object using information of already known objects. But humans can select the best grasp amongst a vast repertoire not only considering the physical attributes of the object to grasp but even to obtain a certain effect. This is why in our case the study in the area of robot manipulation is focused on grasping and integrating symbolic tasks with data gained through sensors. The learning model is based on Bayesian Network to encode the statistical dependencies between the data collected by the sensors and the symbolic task. This data representation has several advantages. It allows to take into account the uncertainty of the real world, allowing to deal with sensor noise, encodes notion of causality and provides an unified network for learning. Since the network is actually implemented and based on the human expert knowledge, it is very interesting to implement an automated method to learn the structure as in the future more tasks and object features can be introduced and a complex network design based only on human expert knowledge can become unreliable. Since structure learning algorithms presents some weaknesses, the goal of this thesis is to analyze real data used in the network modeled by the human expert, implement a feasible structure learning approach and compare the results with the network designed by the expert in order to possibly enhance it.

Collective effects for the LHC injectors: non-ultrarelativistic approaches

The upgrade of the CERN accelerator complex has been planned in order to further increase the LHC performances in exploring new physics frontiers. One of the main limitations to the upgrade is represented by the collective instabilities. These are intensity dependent phenomena triggered by electromagnetic fields excited by the interaction of the beam with its surrounding. These fields are represented via wake fields in time domain or impedances in frequency domain. Impedances are usually studied assuming ultrarelativistic bunches while we mainly explored low and medium energy regimes in the LHC injector chain. In a non-ultrarelativistic framework we carried out a complete study of the impedance structure of the PSB which accelerates proton bunches up to 1.4 GeV. We measured the imaginary part of the impedance which creates betatron tune shift. We introduced a parabolic bunch model which together with dedicated measurements allowed us to point to the resistive wall impedance as the source of one of the main PSB instability. These results are particularly useful for the design of efficient transverse instability dampers. We developed a macroparticle code to study the effect of the space charge on intensity dependent instabilities. Carrying out the analysis of the bunch modes we proved that the damping effects caused by the space charge, which has been modelled with semi-analytical method and using symplectic high order schemes, can increase the bunch intensity threshold. Numerical libraries have been also developed in order to study, via numerical simulations of the bunches, the impedance of the whole CERN accelerator complex. On a different note, the experiment CNGS at CERN, requires high-intensity beams. We calculated the interpolating Hamiltonian of the beam for highly non-linear lattices. These calculations provide the ground for theoretical and numerical studies aiming to improve the CNGS beam extraction from the PS to the SPS.

Progettazione di un sistema di Social Intelligence e Sentiment Analysis per un'azienda del settore consumer goods

Progettazione di un sistema di Social Intelligence e Sentiment Analysis per un'azienda del settore consumer goods