First-principles quantum dynamics in interacting Bose gases II: stochastic gauges

First principles simulations of the quantum dynamics of interacting Bose gases using the stochastic gauge representation are analysed. In a companion paper, we showed how the positive-P representation can be applied to these problems using stochastic differential equations. That method, however, is limited by increased sampling error as time evolves. Here, we show how the sampling error can be greatly reduced and the simulation time significantly extended using stochastic gauges. In particular, local stochastic gauges (a subset) are investigated. Improvements are confirmed in numerical calculations of single-, double- and multi-mode systems in the weak-mode coupling regime. Convergence issues are investigated, including the recognition of two modes by which stochastic equations produced by phase-space methods in general can diverge: movable singularities and a noise-weight relationship. The example calculated here displays wave-like behaviour in spatial correlation functions propagating in a uniform 1D gas after a sudden change in the coupling constant. This could in principle be tested experimentally using Feshbach resonance methods.

First-principles quantum dynamics in interacting Bose gases: I. The positive P representation

The performance of the positive P phase-space representation for exact many- body quantum dynamics is investigated. Gases of interacting bosons are considered, where the full quantum equations to simulate are of a Gross-Pitaevskii form with added Gaussian noise. This method gives tractable simulations of many-body systems because the number of variables scales linearly with the spatial lattice size. An expression for the useful simulation time is obtained, and checked in numerical simulations. The dynamics of first-, second- and third-order spatial correlations are calculated for a uniform interacting 1D Bose gas subjected to a change in scattering length. Propagation of correlations is seen. A comparison is made with other recent methods. The positive P method is particularly well suited to open systems as no conservation laws are hard-wired into the calculation. It also differs from most other recent approaches in that there is no truncation of any kind.

Many-body quantum dynamics of polarization squeezing in optical fibers

We report new experiments that test quantum dynamical predictions of polarization squeezing for ultrashort photonic pulses in a birefringent fiber, including all relevant dissipative effects. This exponentially complex many-body problem is solved by means of a stochastic phase-space method. The squeezing is calculated and compared to experimental data, resulting in excellent quantitative agreement. From the simulations, we identify the physical limits to quantum noise reduction in optical fibers. The research represents a significant experimental test of first-principles time-domain quantum dynamics in a one-dimensional interacting Bose gas coupled to dissipative reservoirs.

Molecular dynamics characterization of n-octyl-beta-D-glucopyranoside micelle structure in aqueous solution

n-Octyl-beta-D-glueopyranoside (OG) is a non-ionic glycolipid, which is used widely in biotechnical and biochemical applications. All-atom molecular dynamics simulations from two different initial coordinates and velocities in explicit solvent have been performed to characterize the structural behaviour of an OG aggregate at equilibrium conditions. Geometric packing properties determined from the simulations and small angle neutron scattering experiment state that OG micelles are more likely to exist in a non-spherical shape, even at the concentration range near to the critical micelle concentration (0.025 M). Despite few large deviations in the principal moment of inertia ratios, the average micelle shape calculated from both simulations is a prolate ellipsoid. The deviations at these time scales are presumably the temporary shape change of a micelle. However, the size of the micelle and the accessible surface areas were constant during the simulations with the micelle surface being rough and partially elongated. Radial distribution functions computed for the hydroxyl oxygen atoms of an OG show sharper peaks at a minimum van der Waals contact distance than the acetal oxygen, ring oxygen, and anomeric carbon atoms. This result indicates that these atoms are pointed outwards at the hydrophilic/hydrophobic interface, form hydrogen bonds with the water molecules, and thus hydrate the micelle surface effectively. (c) 2005 Elsevier Inc. All rights reserved.

Predicting population dynamics of weed biological control agents: science or gazing into crystal balls?

Various factors can influence the population dynamics of phytophages post introduction, of which climate is fundamental. Here we present an approach, using a mechanistic modelling package (CLIMEX), that at least enables one to make predictions of likely dynamics based on climate alone. As biological control programs will have minimal funding for basic work (particularly on population dynamics), we show how predictions can be made using a species geographical distribution, relative abundance across its range, seasonal phenology and laboratory rearing data. Many of these data sets are more likely to be available than long-term population data, and some can be incorporated into the exploratory phase of a biocontrol program. Although models are likely to be more robust the more information is available, useful models can be developed using information on species distribution alone. The fitted model estimates a species average response to climate, and can be used to predict likely geographical distribution if introduced, where the agent is likely to be more abundant (i.e. good locations) and more importantly for interpretation of release success, the likely variation in abundance over time due to intra- and inter-year climate variability. The latter will be useful in predicting both the seasonal and long-term impacts of the potential biocontrol agent on the target weed. We believe this tool may not only aid in the agent selection process, but also in the design of release strategies, and for interpretation of post-introduction dynamics and impacts. More importantly we are making testable predictions. If biological control is to become more of a science making and testing such hypothesis will be a key component.

Relational time for systems of oscillators

Using an elementary example based on two simple harmonic oscillators, we show how a relational time may be defined that leads to an approximate Schrodinger dynamics for subsystems, with corrections leading to an intrinsic decoherence in the energy eigenstates of the subsystem.

Transcriptional network dynamics in macrophage activation

Transcriptional regulatory networks govern cell differentiation and the cellular response to external stimuli. However, mammalian model systems have not yet been accessible for network analysis. Here, we present a genome-wide network analysis of the transcriptional regulation underlying the mouse macrophage response to bacterial lipopolysaccharide (LPS). Key to uncovering the network structure is our combination of time-series cap analysis of gene expression with in silico prediction of transcription factor binding sites. By integrating microarray and qPCR time-series expression data with a promoter analysis, we find dynamic subnetworks that describe how signaling pathways change dynamically during the progress of the macrophage LPS response, thus defining regulatory modules characteristic of the inflammatory response. In particular, our integrative analysis enabled us to suggest novel roles for the transcription factors ATF-3 and NRF-2 during the inflammatory response. We believe that our system approach presented here is applicable to understanding cellular differentiation in higher eukaryotes. (c) 2006 Elsevier Inc. All rights reserved.

Adsorption dynamics measured by permeation and batch adsorption methods

We have measured the adsorption equilibrium and kinetics of carbon dioxide on a commercially available activated carbon by two methods; permeation and batch adsorption. The two methods are compared and found to yield consistent results. All experiments are performed at low pressure (

Vibrational dynamics and structure of natural feldspars

The present study gives a contribution to the knowledge on the Na-feldspar and plagioclases, extending the database of the Raman spectra of plagioclases with different chemical compositions and structural orders. This information may be used for the future planetary explorations by “rovers”, for the investigation of ceramics nanocrystal materials and for the mineralogical phase identification in sediments. Na-feldspar and plagioclase solid solution have been investigated by Raman spectroscopy in order to determine the relationships between the vibrational changes and the plagioclase crystal chemistry and structure. We focused on the Raman micro-spectroscopy technique, being a non-destructive method, suited for contactless analysis with high spatial resolution. Chemical and structural analyses have been performed on natural samples to test the usefulness of Raman spectroscopy as a tool in the study of the pressure-induced structural deformations, the disordering processes due to change in the Al-Si distribution in the tetrahedral sites and, finally, in the determination of the anorthitic content (Anx) in plagioclase minerals. All the predicted 39 Ag Raman active modes have been identified and assigned to specific patterns of atomic vibrational motion. A detailed comparison between experimental and computed Raman spectra has been performed and previous assignments have been revised, solving some discrepancies reported in recent literature. The ab initio calculation at the hybrid HF/DFT level with the WC1LYP Hamiltonian has proven to give excellent agreement between calculated and experimentally measured Raman wavenumbers and intensities in triclinic minerals. A short digression on the 36 infrared active modes of Na-feldspar has been done too. The identification of all 39 computed Raman modes in the experimentally measured spectra of the fully ordered Na-feldspar, known as low albite, along with the detailed description of each vibrational mode, has been essential to extend the comparative analysis to the high pressure and high temperature structural forms of albite, which reflect the physical–chemical conditions of the hosting rocks. The understanding of feldspar structure response to pressure and temperature is crucial in order to constrain crustal behaviour. The compressional behaviour of the Na-feldspar has been investigated for the first time by Raman spectroscopy. The absence of phase transitions and the occurrence of two secondary compression mechanisms acting at different pressures have been confirmed. Moreover, Raman data suggest that the internal structural changes are confined to a small pressure interval, localized around 6 GPa, not spread out from 4 to 8 GPa as suggested by previous X-rays studies on elasticity. The dominant compression mechanisms act via tetrahedral tilting, while the T-O bond lengths remain nearly constant at moderate compressional regimes. At the spectroscopic level, this leads to the strong pressure dependencies of T-O-T bending modes, as found for the four modes at 478, 508, 578 and 815 cm-1. The Al-Si distribution in the tetrahedral sites affects also the Raman spectrum of Na-feldspar. In particular, peak broadening is more sensitive than peak position to changes in the degree of order. Raman spectroscopy is found to be a good probe for local ordering, in particular being sensitive to the first annealing steps, when the macroscopic order parameter is still high. Even though Raman data are scattered and there are outliers in the estimated values of the degree of order, the average peak linewidths of the Na-feldspar characteristic doublet band, labelled here as υa and υb, as a function of the order parameter Qod show interesting trends: both peak linewidths linearly increase until saturation. From Qod values lower than 0.6, peak broadening is no more affected by the Al-Si distribution. Moreover, the disordering process is found to be heterogeneous. SC-XRD and Raman data have suggested an inter-crystalline inhomogeneity of the samples, i.e., the presence of regions with different defect density on the micrometric scale. Finally, the influence of Ca-Na substitution in the plagioclase Raman spectra has been investigated. Raman spectra have been collected on a series of well characterized natural, low structural plagioclases. The variations of the Raman modes as a function of the chemical composition and the structural order have been determined. The number of the observed Raman bands at each composition gives information about the unit-cell symmetry: moving away from the C1 structures, the number of the Raman bands enhances, as the number of formula units in the unit cell increases. The modification from an “albite-like” Raman spectrum to a more “anorthite-like” spectrum occurs from sample An78 onwards, which coincides with the appearance of c reflections in the diffraction patterns of the samples. The evolution of the Raman bands υa and υb displays two changes in slope at ~An45 and ~An75: the first one occurs between e2 and e1 plagioclases, the latter separates e1 and I1 plagioclases with only b reflections in their diffraction patterns from I1 and P1 samples having b and c reflections too. The first variation represents exactly the e2→e1 phase transitions, whereas the second one corresponds in good approximation to the C1→I1 transition, which has been determined at ~An70 by previous works. The I1→P1 phase transition in the anorthite-rich side of the solid solution is not highlighted in the collected Raman spectra. Variations in peak broadening provide insights into the behaviour of the order parameter on a local scale, suggesting an increase in the structural disorder within the solid solution, as the structures have to incorporate more Al atoms to balance the change from monovalent to divalent cations. All the information acquired on these natural plagioclases has been used to produce a protocol able to give a preliminary estimation of the chemical composition of an unknown plagioclase from its Raman spectrum. Two calibration curves, one for albite-rich plagioclases and the other one for the anorthite-rich plagioclases, have been proposed by relating the peak linewidth of the most intense Raman band υa and the An content. It has been pointed out that the dependence of the composition from the linewidth can be obtained only for low structural plagioclases with a degree of order not far away from the references. The proposed tool has been tested on three mineralogical samples, two of meteoric origin and one of volcanic origin. Chemical compositions by Raman spectroscopy compare well, within an error of about 10%, with those obtained by elemental techniques. Further analyses on plagioclases with unknown composition will be necessary to validate the suggested method and introduce it as routine tool for the determination of the chemical composition from Raman data in planetary missions.

Asymptotic theory of time varying networks with memory and heterogeneous activation pattern

In this thesis work we develop a new generative model of social networks belonging to the family of Time Varying Networks. The importance of correctly modelling the mechanisms shaping the growth of a network and the dynamics of the edges activation and inactivation are of central importance in network science. Indeed, by means of generative models that mimic the real-world dynamics of contacts in social networks it is possible to forecast the outcome of an epidemic process, optimize the immunization campaign or optimally spread an information among individuals. This task can now be tackled taking advantage of the recent availability of large-scale, high-quality and time-resolved datasets. This wealth of digital data has allowed to deepen our understanding of the structure and properties of many real-world networks. Moreover, the empirical evidence of a temporal dimension in networks prompted the switch of paradigm from a static representation of graphs to a time varying one. In this work we exploit the Activity-Driven paradigm (a modeling tool belonging to the family of Time-Varying-Networks) to develop a general dynamical model that encodes fundamental mechanism shaping the social networks' topology and its temporal structure: social capital allocation and burstiness. The former accounts for the fact that individuals does not randomly invest their time and social interactions but they rather allocate it toward already known nodes of the network. The latter accounts for the heavy-tailed distributions of the inter-event time in social networks. We then empirically measure the properties of these two mechanisms from seven real-world datasets and develop a data-driven model, analytically solving it. We then check the results against numerical simulations and test our predictions with real-world datasets, finding a good agreement between the two. Moreover, we find and characterize a non-trivial interplay between burstiness and social capital allocation in the parameters phase space. Finally, we present a novel approach to the development of a complete generative model of Time-Varying-Networks. This model is inspired by the Kaufman's adjacent possible theory and is based on a generalized version of the Polya's urn. Remarkably, most of the complex and heterogeneous feature of real-world social networks are naturally reproduced by this dynamical model, together with many high-order topological properties (clustering coefficient, community structure etc.).

Magnetic properties and Spin dynamics in magnetic Molecular Rings

Molecular nanomagnets are spin clusters whose topology and magnetic interactions can be modulated at the level of the chemical synthesis. They are formed by a small number of transition metal ions coupled by the Heisenberg's exchange interactions. Each cluster is magnetically isolated from its neighbors by organic ligands, making each unit not interacting with the others. Therefore, we can investigate the magnetic properties of an isolated molecular nanomagnet by bulk measurements. The present thesis has been mostly devoted to the experimental investigation of the magnetic properties and spin dynamics of different classes of antiferromagnetic (AF) molecular rings. This study has been exploiting various techniques of investigations, such as Nuclear Magnetic Resonance (NMR), muon spin relaxation (muSR) and SQUiD magnetometry. We investigate the magnetic properties and the phonon-induced relaxation dynamics of the first regular Cr9 antiferromagnetic (AF) ring, which represents a prototype frustrated AF ring. The magnetically-open AF rings like Cr8Cd are model systems for the study of the microscopic magnetic behaviour of finite AF Heisenberg chains. In this type of system the different magnetic behaviour depends length and on the parity of the chain (odd or even). In order to study the local spin densities on the Cr sites, the Cr-NMR spectra was collected at low temperature. The experimental result confirm the theoretical predictions for the spin configuration. Finally, the study of Dy6, the first rare-earth based ring that has been ever synthesized, has been performed by AC-SQuID and muSR measurements. We found that the dynamics is characterized by more than one characteristic correlation time, whose values depend strongly on the applied field.

Description and training of neural network dynamics

Attractor properties of a popular discrete-time neural network model are illustrated through numerical simulations. The most complex dynamics is found to occur within particular ranges of parameters controlling the symmetry and magnitude of the weight matrix. A small network model is observed to produce fixed points, limit cycles, mode-locking, the Ruelle-Takens route to chaos, and the period-doubling route to chaos. Training algorithms for tuning this dynamical behaviour are discussed. Training can be an easy or difficult task, depending whether the problem requires the use of temporal information distributed over long time intervals. Such problems require training algorithms which can handle hidden nodes. The most prominent of these algorithms, back propagation through time, solves the temporal credit assignment problem in a way which can work only if the relevant information is distributed locally in time. The Moving Targets algorithm works for the more general case, but is computationally intensive, and prone to local minima.

Noisy fitness evaluation in genetic algorithms and the dynamics of learning

A theoretical model is presented which describes selection in a genetic algorithm (GA) under a stochastic fitness measure and correctly accounts for finite population effects. Although this model describes a number of selection schemes, we only consider Boltzmann selection in detail here as results for this form of selection are particularly transparent when fitness is corrupted by additive Gaussian noise. Finite population effects are shown to be of fundamental importance in this case, as the noise has no effect in the infinite population limit. In the limit of weak selection we show how the effects of any Gaussian noise can be removed by increasing the population size appropriately. The theory is tested on two closely related problems: the one-max problem corrupted by Gaussian noise and generalization in a perceptron with binary weights. The averaged dynamics can be accurately modelled for both problems using a formalism which describes the dynamics of the GA using methods from statistical mechanics. The second problem is a simple example of a learning problem and by considering this problem we show how the accurate characterization of noise in the fitness evaluation may be relevant in machine learning. The training error (negative fitness) is the number of misclassified training examples in a batch and can be considered as a noisy version of the generalization error if an independent batch is used for each evaluation. The noise is due to the finite batch size and in the limit of large problem size and weak selection we show how the effect of this noise can be removed by increasing the population size. This allows the optimal batch size to be determined, which minimizes computation time as well as the total number of training examples required.

Dynamics of learning with restricted training sets

The dynamics of supervised learning in layered neural networks were studied in the regime where the size of the training set is proportional to the number of inputs. The evolution of macroscopic observables, including the two relevant performance measures can be predicted by using the dynamical replica theory. Three approximation schemes aimed at eliminating the need to solve a functional saddle-point equation at each time step have been derived.