Irreversibility-inversions in 2 dimensional turbulence

In this paper we consider a recent theoretical prediction (Bragg \emph{et al.}, Phys. Fluids \textbf{28}, 013305 (2016)) that for inertial particles in 2D turbulence, the nature of the irreversibility of the particle-pair dispersion inverts when the particle inertia exceeds a certain value. In particular, when the particle Stokes number, ${\rm St}$, is below a certain value, the forward-in-time (FIT) dispersion should be faster than the backward-in-time (BIT) dispersion, but for ${\rm St}$ above this value, this should invert so that BIT becomes faster than FIT dispersion. This non-trivial behavior arises because of the competition between two physically distinct irreversibility mechanisms that operate in different regimes of ${\rm St}$. In 3D turbulence, both mechanisms act to produce faster BIT than FIT dispersion, but in 2D turbulence, the two mechanisms have opposite effects because of the flux of energy from the small to the large scales. We supplement the qualitative argument given by Bragg \emph{et al.} (Phys. Fluids \textbf{28}, 013305 (2016)) by deriving quantitative predictions of this effect in the short time limit. We confirm the theoretical predictions using results of inertial particle dispersion in a direct numerical simulation of 2D turbulence. A more general finding of this analysis is that in turbulent flows with an inverse energy flux, inertial particles may yet exhibit a net downscale flux of kinetic energy because of their non-local in-time dynamics

Towards the Application of Association Rules for Defeasible Rules Discovery

In this paper we investigate the feasibility of Knowledge Discovery from Database (KDD) in order to facilitate the discovery of defeasible rules that represent the ratio decidendi underpinning legal decision making. Moreover we will argue in favour of Defeasible Logic as the appropriate formal system in which the extracted principles should be encoded

Weak measurement of quantum dot spin qubits

The theory of weak quantum measurements is developed for quantum dot spin qubits. Building on recent experiments, we propose a control cycle to prepare, manipulate, weakly measure, and perform quantum state tomography. This is accomplished using a combination of the physics of electron spin resonance, spin blockade, and Coulomb blockade, resulting in a charge transport process. We investigate the influence of the surrounding nuclear spin environment, and find a regime where this environment significantly simplifies the dynamics of the weak measurement process, making this theoretical proposal realistic with existing experimental technology. We further consider spin-echo refocusing to combat dephasing, as well as discuss a realization of "quantum undemolition", whereby the effects of quantum state disturbance are undone.Comment: 8 pages, 2 figure

Broadband solid-state MAS NMR of paramagnetic systems.

The combination of new magnet and probe technology with increasingly sophisticated pulse sequences has resulted in an increase in the number of applications of solid-state nuclear magnetic resonance (NMR) spectroscopy to paramagnetic materials and biomolecules. The interaction between the paramagnetic metal ions and the NMR-active nuclei often yields crucial structural or electronic information about the system. In particular the application of magic-angle spinning (MAS) has been shown to be crucial to obtaining resolution that is sufficiently high for studying complex systems. However such systems are generally extremely difficult to study as the shifts and shift anisotropies resulting from the same paramagnetic interaction broaden the spectrum beyond excitation and detection, and the paramagnetic relaxation enhancement (PRE) shortens the lifetimes of the excited signals considerably. One specific area that has therefore been receiving significant attention in recent years, and for which great improvements have been seen, is the development of broadband NMR sequences. The development of new excitation and inversion sequences for paramagnetic systems under MAS has often made the difference between the spectrum being unobtainable, and a complete NMR study being possible. However the development of the new sequences must explicitly take account of the modulation of the anisotropic shift interactions due to the sample rotation, with the resulting spin dynamics often being complicated considerably. The NMR sequences can either be helped or hindered by MAS, with the efficiency of some pulse schemes being destroyed, and others being greatly enhanced. This review describes the pulse sequences that have recently been proposed for broadband excitation, inversion, and refocussing of the signal components of paramagnetic systems. In doing so we define exactly what is meant by "broadband" under spinning conditions, and what the perfect pulse scheme should deliver. We also give a unified description of the spin dynamics under MAS which highlights the strengths and weaknesses of the various schemes, and which can be used as guidance for future research in this area. All the reviewed pulse schemes are evaluated both with simulations and experimental data obtained on the battery material LiFe(0.5)Mn(0.5)PO(4) which is typical of the complexity of the paramagnetic systems that are currently under study.A.J.P. was supported by the LABEX iMUST (ANR-10-LABX-0064) of the Université de Lyon, within the program Investissements d’Avenir (ANR-11-IDEX-0007) operated by the Agence Nationale de la Recherche (ANR). The research leading to these results has received funding from the People Programme (Marie Curie Actions Initial Training Networks (ITN)) of the European Union’s Seventh Framework Programme FP7/2007-2013/ under REA grant agreement No. 317127, the “pNMR” project.This is the accepted manuscript. The final version is available from Elsevier at http://www.sciencedirect.com/science/article/pii/S0079656514000910#

Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware

In recent years the field of neuromorphic low-power systems that consume orders of magnitude less power gained significant momentum. However, their wider use is still hindered by the lack of algorithms that can harness the strengths of such architectures. While neuromorphic adaptations of representation learning algorithms are now emerging, efficient processing of temporal sequences or variable length-inputs remain difficult. Recurrent neural networks (RNN) are widely used in machine learning to solve a variety of sequence learning tasks. In this work we present a train-and-constrain methodology that enables the mapping of machine learned (Elman) RNNs on a substrate of spiking neurons, while being compatible with the capabilities of current and near-future neuromorphic systems. This "train-and-constrain" method consists of first training RNNs using backpropagation through time, then discretizing the weights and finally converting them to spiking RNNs by matching the responses of artificial neurons with those of the spiking neurons. We demonstrate our approach by mapping a natural language processing task (question classification), where we demonstrate the entire mapping process of the recurrent layer of the network on IBM's Neurosynaptic System "TrueNorth", a spike-based digital neuromorphic hardware architecture. TrueNorth imposes specific constraints on connectivity, neural and synaptic parameters. To satisfy these constraints, it was necessary to discretize the synaptic weights and neural activities to 16 levels, and to limit fan-in to 64 inputs. We find that short synaptic delays are sufficient to implement the dynamical (temporal) aspect of the RNN in the question classification task. The hardware-constrained model achieved 74% accuracy in question classification while using less than 0.025% of the cores on one TrueNorth chip, resulting in an estimated power consumption of ~17 uW

Is There Co-Movement of Agricultural Commodities Futures Prices and Crude Oil?

Demand and Price Analysis, Risk and Uncertainty,

Linkage between World and Domestic Prices of Rice under the regime of Agricultural Trade Liberalization in Bangladesh

The paper examines the relationship between the world market and domestic market prices of rice for Bangladesh in the regime of agricultural trade liberalization. The long run price relationship information is an important piece of information for the policy makers in formulating domestic polices and negotiating trade policies at the international level. The monthly data used for this study are taken from different sources, the Food outlook, FAO and Global Information and Early Warning System, FAO and the Bangladesh Bank for the period June 1998 to July 2007. Both Engle-Granger bi-variate and Johansen multivariate cointegration tests were used for the results sensitivity. We sequentially proceed to estimate the standard error correction model. The results showed that there is a long run equilibrium relationship between the world and the domestic prices and the relationship is uni-directional, meaning that, the domestic prices adjust to the world prices but not vice-versa. So the policy to ensure food security (via food price stability and price risk management) should be carefully designed as the movement of the world market price is higher and distorted and many consumers depend on the markets for their food, especially in the case of Bangladesh.Market integration, Domestic price, World price, Error correction model, Marketing,