Charge dynamics effects in conductance through a large semi-open quantum dot

Fano lineshapes in resonant transmission in a quantum dot imply interference between localized and extended states. The influence of the charge accumulated at the localized levels, which screens the external gate voltage acting on the conduction channel is investigated. The modified Fano q parameter and the resonant conduction is derived starting from a microscopic Hamiltonian. The latest experiments on ‘charge sensing’ and ‘Coulomb modified Fano sensing’ compare well with the results of the present model.     

Herd behaviour experimental testing in laboratory artificial stock market settings. Behavioural foundations of stylised facts of financial returns

Many scholars express concerns that herding behaviour causes excess volatility, destabilises financial markets, and increases the likelihood of systemic risk. We use a special form of the Strongly Typed Genetic Programming (STGP) technique to evolve a stock market divided into two groups—a small subset of artificial agents called ‘Best Agents’ and a main cohort of agents named ‘All Agents’. The ‘Best Agents’ perform best in term of the trailing return of a wealth moving average. We then investigate whether herding behaviour can arise when agents trade Dow Jones, General Electric, and IBM financial instruments in four different artificial stock markets. This paper uses real historical quotes of the three financial instruments to analyse the behavioural foundations of stylised facts such as leptokurtosis, non-IIDness, and volatility clustering. We found evidence of more herding in a group of stocks than in individual stocks, but the magnitude of herding does not contribute to the mispricing of assets in the long run. Our findings suggest that the price formation process caused by the collective behaviour of the entire market exhibit less herding and is more efficient than the segmented market populated by a small subset of agents. Hence, greater genetic diversity leads to greater consistency with fundamental values and market efficiency.     

Public debates driven by incomplete scientific data: The cases of evolution theory, global warming and H1N1 pandemic influenza

Public debates driven by incomplete scientific data where nobody can claim absolute certainty, due to the current state of scientific knowledge, are studied. The cases of evolution theory, global warming and H1N1 pandemic influenza are investigated. The first two are of controversial impact while the third is more neutral and resolved. To adopt a cautious balanced attitude based on clear but inconclusive data appears to be a lose-out strategy. In contrast overstating arguments with incorrect claims which cannot be scientifically refuted appears to be necessary but not sufficient to eventually win a public debate. The underlying key mechanisms of these puzzling and unfortunate conclusions are identified using the Galam sequential probabilistic model of opinion dynamics (Galam, 2002 [4], Galam, 2005 [18], Galam and Jacobs, 2007 [19]). It reveals that the existence of inflexible agents and their respective proportions are the instrumental parameters to determine the faith of incomplete scientific data in public debates. Acting on one’s own inflexible proportion modifies the topology of the flow diagram, which in turn can make irrelevant initial supports. On the contrary focusing on open-minded agents may be useless given some topologies. When the evidence is not as strong as claimed, the inflexibles rather than the data are found to drive the opinion of the population. The results shed a new but disturbing light on designing adequate strategies to win a public debate.     

Binary choices in small and large groups: A unified model

Two different ways to model the diffusion of alternative choices within a population of individuals in the presence of social externalities are known in the literature. While Galam’s model of rumors spreading considers a majority rule for interactions in several groups, Schelling considers individuals interacting in one large group, with payoff functions that describe how collective choices influence individual preferences. We incorporate these two approaches into a unified general discrete-time dynamic model for studying individual interactions in variously sized groups. We first illustrate how the two original models can be obtained as particular cases of the more general model we propose, then we show how several other situations can be analyzed. The model we propose goes beyond a theoretical exercise as it allows modeling situations which are relevant in economic and social systems. We consider also other aspects such as the propensity to switch choices and the behavioral momentum, and show how they may affect the dynamics of the whole population.     

Volume-of-fluid algorithm with different modified dynamic material ordering methods and their comparisons

Volume-of-fluid (VOF) interface reconstruction methods are used to define material interfaces to separate different materials in a mixed cell. These material interfaces are then used to evaluate transport flux at each cell edges in multi-material hydrodynamic calculations. Most of the VOF interface reconstruction methods and volume transport schemes rely on an accurate material order unique to each computational cell. Similarly, to achieve overshoot-free volume fractions, a non-intersecting interface reconstruction procedure has to be performed with the help of a ‘material-order list’ determined prior to interface reconstruction. It is, however, the least explored area of VOF technique especially for ‘onion-skin’ or ‘layered’ model. Also, important technical details how to prevent intersection among different material interfaces are missing in many literature. Here, we present an efficient VOF interface tracking algorithm along with modified ‘material order’ methods and different interface reconstruction methods. The relative accuracy of different methods are evaluated for sample problems. Finally, a convergence study with respect to mesh-size is performed.     

A theory of cosmic rays

We present a theory of non-solar cosmic rays (CRs) in which the bulk of their observed flux is due to a single type of CR source at all energies. The total luminosity of the Galaxy, the broken power-law spectra with their observed slopes, the position of the ‘knee(s)’ and ‘ankle’, and the CR composition and its variation with energy are all predicted in terms of very simple and completely ‘standard’ physics. The source of CRs is extremely ‘economical’: it has only one parameter to be fitted to the ensemble of all of the mentioned data. All other inputs are ‘priors’, that is, theoretical or observational items of information independent of the properties of the source of CRs, and chosen to lie in their pre-established ranges. The theory is part of a ‘unified view of high-energy astrophysics’ — based on the ‘Cannonball’ model of the relativistic ejecta of accreting black holes and neutron stars. The model has been extremely successful in predicting all the novel properties of Gamma Ray Bursts recently observed with the help of the Swift satellite. If correct, this model is only lacking a satisfactory theoretical understanding of the ‘cannon’ that emits the cannonballs in catastrophic processes of accretion.     

Opinion dynamics in a three-choice system

We generalize Galam’s model of opinion spreading by introducing three competing choices. At each update, the population is randomly divided in groups of three agents, whose members adopt the opinion of the local majority. In the case of a tie, the local group adopts opinion A, B or C with probabilities α, β and (1-α-β) respectively. We derive the associated phase diagrams and dynamics by both analytical means and simulations. Polarization is always reached within very short time scales. We point out situations in which an initially very small minority opinion can invade the whole system.     

Coevolution with weights of names in structured language games

We propose a coevolutionary version to investigate the naming game, a model recently introduced to describe how shared vocabulary can emerge and persist spontaneously in communication systems. We base our model on the fact that more popular names have more opportunities to be selected by agents and then spread in the population. A name’s popularity is concerned with its communication frequency, characterized by its weight coevolving with the name. A tunable parameter governs the influence of name weight. We implement this modified version on both scale-free networks and small-world networks, in which interactions proceed between paired agents by means of the reverse naming game. It is found that there exists an optimal value of the parameter that induces the fastest convergence of the population. This illustration indicates that a moderately strong influence of evolving name weight favors the rapid achievement of final consensus, but very strong influences inhibit the convergence process. The rank-distribution of the final accumulated weights of names qualitatively explains this nontrivial phenomenon. Investigations of some pertinent quantities are also provided, including the time evolution of the number of different names and the success rate, as well as the total memory of agents for different parameter values, which are helpful for better understanding the coevolutionary dynamics. Finally, we explore the scaling behavior in the convergence time and conclude a smaller scaling parameter compared to the previous naming game models.     

Particles movement and surface quality in PLD/PR systems

A three-dimensional model based on Monte-Carlo and Finite Elements techniques has been used for simulating plume behavior, ‘micron-sized particles’ movement and interaction with obstacles in a Pulsed Laser Deposition with Plasma Reflection (PLD/PR) system. Have been investigated the influences of mass, surface size and emission time on trajectory and film surface quality as well. Droplet and ‘big-size particles’ deposition statistics are presented and a comparison between theoretical and experimental results upon thin film surface quality as well. One can observe that particles mass and surface size have a strong influence on the particles trajectory by affecting the collisions parameters during the entire propagation process. The emission time should influence the particles trajectory by affecting the probability of interaction with other particles. By making a 10,000 particles statistic for a normal distribution of these investigated parameters, we obtain reasonable good results in modeling ‘big-size particles’ tendency to be deposited at lower reflection angles. These results sustain assumption of ‘big particles’ deflection by plume fine particles during the propagation process.     

Lattice gas simulation and experiment study of evacuation dynamics

In this paper, evacuation dynamics in an office building is studied by experiment and simulation. A lattice gas (LG) model is developed. A parameter called ‘exit bias’ is introduced into the model to describe the occupants’ familiarity with different exits in a building. The evacuation experiment, which consists of seven scenarios under various conditions, is conducted to verify the model and calibrate the model’s input parameters such as pedestrian speed and exit bias. The effect of exit width on flow rate, and the effect of occupants’ familiarity with the building on their route selections, are studied. It is found that the accuracy of simulation depends a lot on the model’s pedestrian speed. The optimal pedestrian speed is decided by not only occupant characteristics, but also flow features determined by people distribution, building structure, environment pressure, etc. LG models with proper pedestrian speed are capable of simulating the dynamic process of orderly emergency evacuations.     

A model of the effects of authority on consensus formation in adaptive networks: Impact on network topology and robustness

Opinions of individuals in real social networks are arguably strongly influenced by external determinants, such as the opinions of those perceived to have the highest levels of authority. In order to model this, we have extended an existing model of consensus formation in an adaptive network by the introduction of a parameter representing each agent’s level of ‘authority’, based on their opinion relative to the overall opinion distribution. We found that introducing this model, along with a randomly varying opinion convergence factor, significantly impacts the final state of converged opinions and the number of interactions required to reach that state. We also determined the relationship between initial and final network topologies for this model, and whether the final topology is robust to node removals. Our results indicate firstly that the process of consensus formation with a model of authority consistently transforms the network from an arbitrary initial topology to one with distinct measurements in mean shortest path, clustering coefficient, and degree distribution. Secondly, we found that subsequent to the consensus formation process, the mean shortest path and clustering coefficient are less affected by both random and targeted node disconnection. Speculation on the relevance of these results to real world applications is provided.     

Persistence and financial markets

The persistence phenomenon is studied in a financial context by using a novel mapping of the time evolution of the values of shares in a portfolio onto Ising spins. The method is applied to historical data from the London Financial Times Stock Exchange 100 index (FTSE 100) over an arbitrarily chosen period. By following the time dependence of the spins, we find evidence for a power law decay of the proportion of shares that remain either above or below their ‘starting’ values. As a result, we estimate a persistence exponent for the underlying financial market to be ≈0.5. Preliminary results from computer simulations on persistence in the economic dynamics of a toy model appear to reproduce the behaviour observed in real markets.     

Thermodynamics of relation-based systems with applications in econophysics,sociophysics, and music

A methodology was developed to analyze relation-based systems evolving in time by using the fundamental concepts of thermodynamics. The behavior of such systems can be tracked from the scattering matrix which is actually a network of directed vectors (or pathways) connecting subsequent values, which characterize an event, such as the index values in stock markets. A system behaves in a rigid (elastic) way to an external effect and resists permanent deformation, or it behaves in a viscous (or soft) way and deforms in an irreversible way. It was shown in the past that a formula derived using the slope of paths gives a measure about the extent of viscoelastic behavior of relation-based systems Gündüz (2009) [5] Gündüz and Gündüz (2010) [6]. In this research the ‘work’ associated with ‘elastic’ component, and ‘heat’ associated with ‘viscous’ component were discussed and elaborated. In a simple two subsequent pathway system in a scattering diagram the first vector represents ‘the cause’ and the second ‘the effect’. By using work and heat energy relations that involve force and also storage and loss modulus terms, respectively, one can calculate the energy involved in relation-based systems. The modulus values can be found from the parallel and vertical components of the second vector with respect to the first vector. Once work-like and heat-like terms were determined the internal energy is also easily found from their summation. The parallel and vertical components can also be used to calculate the magnitude of torque and torque energy in the system. Three cases, (i) the behavior of the NASDAQ-100 index, (ii) a social revolt, and (iii) the structure of a melody were analyzed for their ‘work-like’, ‘heat-like’, and ‘torque-like’ energies in the course of their evolution. NASDAQ-100 exhibits highly dissipative behavior, and its work terms are very small but heat terms are of large magnitude. Its internal energy highly fluctuates in time. In the social revolt studied work and heat terms are of comparable magnitude. The melody depicts highly organized structure, and usually has larger work terms than heat terms, but at some intervals heat terms burst out and attain very large magnitudes. Torque terms reach high values when the system is recovering from a minimum value.     

A Lagrangian description of transport associated with a front-eddy interaction: Application to data from the North-Western Mediterranean Sea

We discuss the Lagrangian transport in a time-dependent oceanic system involving a Lagrangian barrier associated with a salinity front which interacts intermittently with a set of Lagrangian eddies — ‘leaky’ coherent structures that entrain and detrain fluid as they move. A theoretical framework, rooted in the dynamical systems theory, is developed in order to describe and analyse this situation. We show that such an analysis can be successfully applied to a realistic ocean model. Here, we use the output of the numerical ocean model DieCAST from Dietrich et al. (2004) [17] and Fernández et al. (2005) [18] studied earlier in Mancho et al. (2008) [15] where a Lagrangian barrier associated with the North Balearic Front in the North-Western Mediterranean Sea was identified. The numerical model provides an Eulerian view of the flow and we employ the dynamical systems approach to identify relevant hyperbolic trajectories and their stable and unstable manifolds. These manifolds are used to understand the Lagrangian geometry of the evolving front-eddy system. Transport in this system is effected by the turnstile mechanism whose spatio-temporal geometry reveals intermittent pathways along which transport occurs. Particular attention is paid to the ‘Lagrangian’ interactions between the front and the eddies, and to transport implications associated with the transition between the one-eddy and two-eddy situation. The analysis of this ‘Lagrangian’ transition is aided by a local kinematic model that provides insight into the nature of the change in hyperbolic trajectories and their stable and unstable manifolds associated with the ‘birth’ and ‘death’ of leaky Lagrangian eddies.     

Spartan random processes in time series modeling

A Spartan random process (SRP) is used to estimate the correlation structure of time series and to predict (interpolate and extrapolate) the data values. SRPs are motivated from statistical physics, and they can be viewed as Ginzburg-Landau models. The temporal correlations of the SRP are modeled in terms of ‘interactions’ between the field values. Model parameter inference employs the computationally fast modified method of moments, which is based on matching sample energy moments with the respective stochastic constraints. The parameters thus inferred are then compared with those obtained by means of the maximum likelihood method. The performance of the Spartan predictor (SP) is investigated using real time series of the quarterly S&P 500 index. SP prediction errors are compared with those of the Kolmogorov-Wiener predictor. Two predictors, one of which is explicit, are derived and used for extrapolation. The performance of the predictors is similarly evaluated.     

Urban road network evolution mechanism based on the ‘direction preferred connection’ and ‘degree constraint’

The urban road network is a complex system that exhibits the properties of self-organization and emergence. Recent theoretical and empirical studies have mainly focused on the structural properties of the urban road networks. This research concentrates on some important parameters such as degree, average degree, meshedness coefficient, betweeness, etc. These parameters of the real road network exhibit specific statistical properties. Some studies show that perhaps these specific statistical properties are caused by a compromise mechanism of the formation of a minimum spanning tree and the greedy triangulation. Inspired by these results, we propose a principle to construct the network (we call it a MG network in this paper) whose structure is located between the minimum spanning tree and the greedy triangulation at first. The structural properties of the MG network are analyzed. We find the formation mechanism of the MG network cannot explain the urban road network evolution well. Then, based on the formation mechanism of the MG network, we add the ‘direction preferred connection’ and ‘degree constraint’ principles to the urban road network evolution simulation process. The result of the simulation network turns out to be a planar network that is in accordance with reality. Compared with the real road network’s structural properties, we find the simulation results are so consistent with it. It indicates the validation of the model and also demonstrates perhaps the ‘direction preferred connection’ and ‘degree constraint’ principle can explain the urban road network evolution better.     

Lost in transactions: The case of the Boulogne s/mer fish market

Starting from some regularities of the Boulogne s/mer fish market, the model proposed here shows that in many circumstances the collective behavior may be ‘reasonable’ whereas the individuals may not be so. The properties which are empirically clear at the aggregate level are not necessarily derived from similar properties at the individual level. Thus, the macroscopic outcomes of the Boulogne s/mer fish market are not directly derived by any of the individual component involved, but are the self-organized outcomes of the agents’ interaction. The simple interaction of noisy and myopic agents leads the system to stabilize itself.     

Graph cohomology classes in the Batalin–Vilkovisky formalism

This paper gives a conceptual formulation of Kontsevich’s ‘dual construction’ producing graph cohomology classes from a differential graded Frobenius algebra with an odd scalar product. Our construction–whilst equivalent to the original one–is combinatorics-free and is based on the Batalin–Vilkovisky formalism, from which its gauge independence is immediate.     

From micro-correlations to macro-correlations

Random vectors with a symmetric correlation structure share a common value of pair-wise correlation between their different components. The symmetric correlation structure appears in a multitude of settings, e.g. mixture models. In a mixture model the components of the random vector are drawn independently from a general probability distribution that is determined by an underlying parameter, and the parameter itself is randomized. In this paper we study the overall correlation of high-dimensional random vectors with a symmetric correlation structure. Considering such a random vector, and terming its pair-wise correlation “micro-correlation”, we use an asymptotic analysis to derive the random vector’s “macro-correlation” : a score that takes values in the unit interval, and that quantifies the random vector’s overall correlation. The method of obtaining macro-correlations from micro-correlations is then applied to a diverse collection of frameworks that demonstrate the method’s wide applicability.     

Electron spectroscopy and density-functional study of ‘ferric wheel’ molecules

The Li-centered ‘ferric wheel’ molecules with six oxo-bridged iron atoms form molecular crystals. We probed their electronic structure by X-ray photoelectron and soft X-ray emission spectroscopy, having calculated in parallel the electronic structure of a single ‘ferric wheel’ molecule from first-principles by tools of the density-functional theory, using, specifically, the Siesta method. The Fe local moments were found to be 4μ_B, irrespective of their mutual orientation. Neighbouring atoms, primarily oxygen, exhibit a noticeable magnetic polarization, yielding effective spin S=5/2 per iron atom, that can get inverted as a ‘rigid’ one in magnetic transitions. Corresponding energy preferences can be mapped onto the Heisenberg model with effective exchange parameter J of about −80 K.