Phase transitions in wave turbulence

We consider turbulence within the Gross-Pitaevsky model and look into the creation of a coherent condensate via an inverse cascade originating at small scales. The growth of the condensate leads to a spontaneous breakdown of statistical symmetries of overcondensate fluctuations: First, isotropy is broken, then a series of phase transitions marks the changing symmetry from twofold to threefold to fourfold. We describe respective anisotropic flux flows in the k space. At the highest level reached, we observe a short-range positional and long-range orientational order (as in a hexatic phase). In other words, the more one pumps the system, the more ordered the system becomes. The phase transitions happen when the system is pumped by an instability term and does not occur when pumped by a random force. We thus demonstrate nonuniversality of an inverse-cascade turbulence with respect to the nature of small-scale forcing.

Threshold-induced phase transitions in perceptrons

Error rates of a Boolean perceptron with threshold and either spherical or Ising constraint on the weight vector are calculated for storing patterns from biased input and output distributions derived within a one-step replica symmetry breaking (RSB) treatment. For unbiased output distribution and non-zero stability of the patterns, we find a critical load, α p, above which two solutions to the saddlepoint equations appear; one with higher free energy and zero threshold and a dominant solution with non-zero threshold. We examine this second-order phase transition and the dependence of α p on the required pattern stability, κ, for both one-step RSB and replica symmetry (RS) in the spherical case and for one-step RSB in the Ising case.

Phase transitions and memory effects in the dynamics of Boolean networks

The generating functional method is employed to investigate the synchronous dynamics of Boolean networks, providing an exact result for the system dynamics via a set of macroscopic order parameters. The topology of the networks studied and its constituent Boolean functions represent the system's quenched disorder and are sampled from a given distribution. The framework accommodates a variety of topologies and Boolean function distributions and can be used to study both the noisy and noiseless regimes; it enables one to calculate correlation functions at different times that are inaccessible via commonly used approximations. It is also used to determine conditions for the annealed approximation to be valid, explore phases of the system under different levels of noise and obtain results for models with strong memory effects, where existing approximations break down. Links between Boolean networks and general Boolean formulas are identified and results common to both system types are highlighted. © 2012 Copyright Taylor and Francis Group, LLC.

Computing with noise: phase transitions in Boolean formulas

Computing circuits composed of noisy logical gates and their ability to represent arbitrary Boolean functions with a given level of error are investigated within a statistical mechanics setting. Existing bounds on their performance are straightforwardly retrieved, generalized, and identified as the corresponding typical-case phase transitions. Results on error rates, function depth, and sensitivity, and their dependence on the gate-type and noise model used are also obtained.

A hierarchy of phase transitions in optimal neuronal coding:from binary to M-ary discrete optimal codes

We have investigated how optimal coding for neural systems changes with the time available for decoding. Optimization was in terms of maximizing information transmission. We have estimated the parameters for Poisson neurons that optimize Shannon transinformation with the assumption of rate coding. We observed a hierarchy of phase transitions from binary coding, for small decoding times, toward discrete (M-ary) coding with two, three and more quantization levels for larger decoding times. We postulate that the presence of subpopulations with specific neural characteristics could be a signiture of an optimal population coding scheme and we use the mammalian auditory system as an example.

Identifying and correcting non-Markov states in peptide conformational dynamics

Conformational transitions in proteins define their biological activity and can be investigated in detail using the Markov state model. The fundamental assumption on the transitions between the states, their Markov property, is critical in this framework. We test this assumption by analyzing the transitions obtained directly from the dynamics of a molecular dynamics simulated peptide valine-proline-alanine-leucine and states defined phenomenologically using clustering in dihedral space. We find that the transitions are Markovian at the time scale of ˜ 50 ps and longer. However, at the time scale of 30–40 ps the dynamics loses its Markov property. Our methodology reveals the mechanism that leads to non-Markov behavior. It also provides a way of regrouping the conformations into new states that now possess the required Markov property of their dynamics.

Computational mechanics of molecular systems:quantifying high-dimensional dynamics by distribution of Poincaré recurrence times

A framework that connects computational mechanics and molecular dynamics has been developed and described. As the key parts of the framework, the problem of symbolising molecular trajectory and the associated interrelation between microscopic phase space variables and macroscopic observables of the molecular system are considered. Following Shalizi and Moore, it is shown that causal states, the constituent parts of the main construct of computational mechanics, the e-machine, define areas of the phase space that are optimal in the sense of transferring information from the micro-variables to the macro-observables. We have demonstrated that, based on the decay of their Poincare´ return times, these areas can be divided into two classes that characterise the separation of the phase space into resonant and chaotic areas. The first class is characterised by predominantly short time returns, typical to quasi-periodic or periodic trajectories. This class includes a countable number of areas corresponding to resonances. The second class includes trajectories with chaotic behaviour characterised by the exponential decay of return times in accordance with the Poincare´ theorem.

All-optical processing using cross-phase modulation

The following thesis presents results obtained from both numerical simulation and laboratory experimentation (both of which were carried out by the author). When data is propagated along an optical transmission line some timing irregularities can occur such as timing jitter and phase wander. Traditionally these timing problems would have been corrected by converting the optical signal into the electrical domain and then compensating for the timing irregularity before converting the signal back into the optical domain. However, this thesis posses a potential solution to the problem by remaining completely in the optical domain, eliminating the need for electronics. This is desirable as not only does optical processing reduce the latency effect that their electronic counterpart have, it also holds the possibility of an increase in overall speed. A scheme was proposed which utilises the principle of wavelength conversion to dynamically convert timing irregularities (timing jitter and phase wander) into a change in wavelength (this occurs on a bit-by-bit level and so timing jitter and phase wander can be compensated for simultaneously). This was achieved by optically sampling a linearly chirped, locally generated clock source (the sampling function was achieved using a nonlinear optical loop mirror). The data, now with each bit or code word having a unique wavelength, is then propagated through a dispersion compensation module. The dispersion compensation effectively re-aligns the data in time and so thus, the timing irregularities are removed. The principle of operation was tested using computer simulation before being re-tested in a laboratory environment. A second stage was added to the device to create 3R regeneration. The second stage is used to simply convert the timing suppressed data back into a single wavelength. By controlling the relative timing displacement between stage one and stage two, the wavelength that is finally produced can be controlled.

Noisy random Boolean formulae:a statistical physics perspective

Properties of computing Boolean circuits composed of noisy logical gates are studied using the statistical physics methodology. A formula-growth model that gives rise to random Boolean functions is mapped onto a spin system, which facilitates the study of their typical behavior in the presence of noise. Bounds on their performance, derived in the information theory literature for specific gates, are straightforwardly retrieved, generalized and identified as the corresponding macroscopic phase transitions. The framework is employed for deriving results on error-rates at various function-depths and function sensitivity, and their dependence on the gate-type and noise model used. These are difficult to obtain via the traditional methods used in this field.

On the robustness of random Boolean formulae

Random Boolean formulae, generated by a growth process of noisy logical gates are analyzed using the generating functional methodology of statistical physics. We study the type of functions generated for different input distributions, their robustness for a given level of gate error and its dependence on the formulae depth and complexity and the gates used. Bounds on their performance, derived in the information theory literature for specific gates, are straightforwardly retrieved, generalized and identified as the corresponding typical-case phase transitions. Results for error-rates, function-depth and sensitivity of the generated functions are obtained for various gate-type and noise models. © 2010 IOP Publishing Ltd.

Controlling protein molecular dynamics:how to accelerate folding while preserving the native state

The dynamics of peptides and proteins generated by classical molecular dynamics (MD) is described by using a Markov model. The model is built by clustering the trajectory into conformational states and estimating transition probabilities between the states. Assuming that it is possible to influence the dynamics of the system by varying simulation parameters, we show how to use the Markov model to determine the parameter values that preserve the folded state of the protein and at the same time, reduce the folding time in the simulation. We investigate this by applying the method to two systems. The first system is an imaginary peptide described by given transition probabilities with a total folding time of 1 micros. We find that only small changes in the transition probabilities are needed to accelerate (or decelerate) the folding. This implies that folding times for slowly folding peptides and proteins calculated using MD cannot be meaningfully compared to experimental results. The second system is a four residue peptide valine-proline-alanine-leucine in water. We control the dynamics of the transitions by varying the temperature and the atom masses. The simulation results show that it is possible to find the combinations of parameter values that accelerate the dynamics and at the same time preserve the native state of the peptide. A method for accelerating larger systems without performing simulations for the whole folding process is outlined.

Statistical physics of error-correcting codes

In this thesis we use statistical physics techniques to study the typical performance of four families of error-correcting codes based on very sparse linear transformations: Sourlas codes, Gallager codes, MacKay-Neal codes and Kanter-Saad codes. We map the decoding problem onto an Ising spin system with many-spins interactions. We then employ the replica method to calculate averages over the quenched disorder represented by the code constructions, the arbitrary messages and the random noise vectors. We find, as the noise level increases, a phase transition between successful decoding and failure phases. This phase transition coincides with upper bounds derived in the information theory literature in most of the cases. We connect the practical decoding algorithm known as probability propagation with the task of finding local minima of the related Bethe free-energy. We show that the practical decoding thresholds correspond to noise levels where suboptimal minima of the free-energy emerge. Simulations of practical decoding scenarios using probability propagation agree with theoretical predictions of the replica symmetric theory. The typical performance predicted by the thermodynamic phase transitions is shown to be attainable in computation times that grow exponentially with the system size. We use the insights obtained to design a method to calculate the performance and optimise parameters of the high performance codes proposed by Kanter and Saad.

New dimensions on the economics of the entrepreneurial process

This thesis examines the transition of employees into entrepreneurship, with particular emphasis on the role of workplace characteristics in influencing this movement. The first main chapter examines whether the determinants of becoming an intrapreneur differ from those that support transitions into independent entrepreneurship. The results show that intrapreneurs resemble employees rather than entrepreneurs, contrary to what the entrepreneurship theory would suggest. Yet it shows that those intrapreneurs that expect to acquire an ownership stake in the business, unlike the rest of intrapreneurs, possess traditional entrepreneurial traits. Chapter 3 investigates how workers’ degree of specialisation determines their decision to found a firm. It shows that entrepreneurs emerging from small firms, i.e. generalists, transfer knowledge from more diverse aspects of the business and create firms more related to the main activity of their last employer. Workers in large firms, however, benefit from higher returns to human capital that increase their opportunity costs to switch to entrepreneurship. Since becoming an entrepreneur would make part of their specialised skills unutilised, the minimum quality of the idea at which they would be willing to leave will be higher and, therefore, entrepreneurs emerging from large firms will be of highest quality. Chapter 4 analyses whether the reason to terminate an employment contract is associated with the fact that the majority of entrepreneurs appear to set up their business after having worked for a small firm. Moreover, it studies how this pattern varies as the labour market conditions worsen. The effect of layoffs turns out to be a key driver in the entry to entrepreneurship and it is found to exert a greater effect the smaller the firm workers are dismissed from. This has been reflected in an overall larger flow of employees from small firms moving into entrepreneurship over the recession.

Magneto-vibratory separation of glass and bronze granular mixtures immersed in a paramagnetic liquid

A fluid-immersed granular mixture may spontaneously separate when subjected to vertical vibration, separation occurring when the ratio of particle inertia to fluid drag is sufficiently different between the component species of the mixture. Here, we describe how fluid-driven separation is influenced by magneto-Archimedes buoyancy, the additional buoyancy force experienced by a body immersed in a paramagnetic fluid when a strong inhomogeneous magnetic field is applied. In our experiments glass and bronze mixtures immersed in paramagnetic aqueous solutions of MnCl2 have been subjected to sinusoidal vertical vibration. In the absence of a magnetic field the separation is similar to that observed when the interstitial fluid is water. However, at modest applied magnetic fields, magneto-Archimedes buoyancy may balance the inertia/fluid-drag separation mechanism, or it may dominate the separation process. We identify the vibratory and magnetic conditions for four granular configurations, each having distinctive granular convection. Abrupt transitions between these states occur at well-defined values of the magnetic and vibrational parameters. In order to gain insight into the dynamics of the separation process we use computer simulations based on solutions of the Navier-Stokes' equations. The simulations reproduce the experimental results revealing the important role of convection and gap formation in the stability of the different states.

Antioxidants, reactive oxygen and nitrogen species, gene induction and mitochondrial function

Redox-sensitive cell signalling Thiol groups and the regulation of gene expression Redox-sensitive signal transduction pathways Protein kinases Protein phosphatases Lipids and phospholipases Antioxidant (electrophile) response element Intracellular calcium signalling Transcription factors NF-?B AP-1 p53 Cellular responses to oxidative stress Cellular responses to change in redox state Proliferation Cell death Immune cell function Reactive oxygen and nitrogen species – good or bad? Reactive oxygen species and cell death Reactive oxygen species and inflammation Are specific reactive oxygen species and antioxidants involved in modulating cellular responses? Specific effects of dietary antioxidants in cell regulation Carotenoids Vitamin E Flavonoids Inducers of phase II enzymes Disease states affected Oxidants, antioxidants and mitochondria Introduction Mitochondrial generation of reactive oxygen and nitrogen species Mitochondria and apoptosis Mitochondria and antioxidant defences Key role of mitochondrial GSH in the defence against oxidative damage Mitochondrial oxidative damage Direct oxidative damage to the mitochondrial electron transport chain Nitric oxide and damage to mitochondria Effects of nutrients on mitochondria Caloric restriction and antioxidants Lipids Antioxidants Techniques and approaches Mitochondrial techniques cDNA microarray approaches Proteomics approaches Transgenic mice as tools in antioxidant research Gene knockout and over expression Transgenic reporter mice Conclusions Future research needs