Mesoscopic mean-field theory for spin-boson chains in quantum optical systems

We present a theoretical description of a system of many spins strongly coupled to a bosonic chain. We rely on the use of a spin-wave theory describing the Gaussian fluctuations around the mean-field solution, and focus on spin-boson chains arising as a generalization of the Dicke Hamiltonian. Our model is motivated by experimental setups such as trapped ions, or atoms/qubits coupled to cavity arrays. This situation corresponds to the cooperative (E⊗β) Jahn-Teller distortion studied in solid-state physics. However, the ability to tune the parameters of the model in quantum optical setups opens up a variety of novel intriguing situations. The main focus of this paper is to review the spin-wave theoretical description of this problem as well as to test the validity of mean-field theory. Our main result is that deviations from mean-field effects are determined by the interplay between magnetic order and mesoscopic cooperativity effects, being the latter strongly size-dependent

A large sample analysis of European rivers on seasonal river flow correlation and its physical drivers

The geophysical and hydrological processes governing river flow formation exhibit persistence at several timescales, which may manifest itself with the presence of positive seasonal correlation of streamflow at several different time lags. We investigate here how persistence propagates along subsequent seasons and affects low and high flows. We define the high-flow season (HFS) and the low-flow season (LFS) as the 3-month and the 1-month periods which usually exhibit the higher and lower river flows, respectively. A dataset of 224 rivers from six European countries spanning more than 50 years of daily flow data is exploited. We compute the lagged seasonal correlation between selected river flow signatures, in HFS and LFS, and the average river flow in the antecedent months. Signatures are peak and average river flow for HFS and LFS, respectively. We investigate the links between seasonal streamflow correlation and various physiographic catchment characteristics and hydro-climatic properties. We find persistence to be more intense for LFS signatures than HFS. To exploit the seasonal correlation in the frequency estimation of high and low flows, we fit a bi-variate meta-Gaussian probability distribution to the selected flow signatures and average flow in the antecedent months in order to condition the distribution of high and low flows in the HFS and LFS, respectively, upon river flow observations in the previous months. The benefit of the suggested methodology is demonstrated by updating the frequency distribution of high and low flows one season in advance in a real-world case. Our findings suggest that there is a traceable physical basis for river memory which, in turn, can be statistically assimilated into high- and low-flow frequency estimation to reduce uncertainty and improve predictions for technical purposes

Thermoelectric simulation of electric machines with permanent magnets

The objective of this work is to describe some numerical tools developed to perform the thermoelectric simulation of electric machines. From the electromagnetic point of view, we will focus on the computation of nonlinear 2D transient magnetic fields where the data concerning the electric current sources involve potential drops excitations. From the thermal point of view, once the electromagnetic losses are known, we will show an application of a Galerkin lumped parameter method (GLPM) to simulate the thermal behavior of an electric motor. The proposed methods are applied to the simulation of a permanent magnet synchronous electric motor

Leaking from the phase space of the Riemann-Liouville fractional standard map

In this work we characterize the escape of orbits from the phase space of the Riemann-Liouville (RL) fractional standard map (fSM). The RL-fSM, given in action-angle variables, is derived from the equation of motion of the kicked rotor when the second order derivative is substituted by a RL derivative of fractional order $\alpha$. Thus, the RL-fSM is parameterized by $K$ and $\alpha\in(1,2]$ which control the strength of nonlinearity and the fractional order of the RL derivative, respectively. Indeed, for $\alpha=2$ and given initial conditions, the RL-fSM reproduces Chirikov's standard map. By computing the survival probability $P_{\text{S}}(n)$ and the frequency of escape $P_{\text{E}}(n)$, for a hole of hight $h$ placed in the action axis, we observe two scenarios: When the phase space is ergodic, both scattering functions are scale invariant with the typical escape time $n_{\text{typ}}=\exp\langle \ln n \rangle \propto (h/K)^2$. In contrast, when the phase space is not ergodic, the scattering functions show a clear non-universal and parameter-dependent behavior

Neutron-proton interaction in rare-earth nuclei: Role of tensor force

We investigate the role of the tensor force in the description of doubly odd deformed nuclei within the framework of the particle-rotor model. We study the rare-earth nuclei 174Lu, 180Ta, 182Ta, and 188Re using a finite-range interaction, with and without tensor terms. Attention is focused on the lowest K=0 and K=1 bands, where the effects of the residual neutron-proton interaction are particularly evident. Comparison of the calculated results with experimental data evidences the importance of the tensor-force effects.Comment: 8 pages, 5 figures, to be published on Physical Review

Magnetosensitive e-skins with directional perception for augmented reality

Electronic skins equipped with artificial receptors are able to extend our perception beyond the modalities that have naturally evolved. These synthetic receptors offer complimentary information on our surroundings and endow us with novel means of manipulating physical or even virtual objects. We realize highly compliant magnetosensitive skins with directional perception that enable magnetic cognition, body position tracking, and touchless object manipulation. Transfer printing of eight high-performance spin valve sensors arranged into two Wheatstone bridges onto 1.7-mm-thick polyimide foils ensures mechanical imperceptibility. This resembles a new class of interactive devices extracting information from the surroundings through magnetic tags. We demonstrate this concept in augmented reality systems with virtual knob-turning functions and the operation of virtual dialing pads, based on the interaction with magnetic fields. This technology will enable a cornucopia of applications from navigation, motion tracking in robotics, regenerative medicine, and sports and gaming to interaction in supplemented reality

Intelligent Financial Fraud Detection Practices: An Investigation

Financial fraud is an issue with far reaching consequences in the finance industry, government, corporate sectors, and for ordinary consumers. Increasing dependence on new technologies such as cloud and mobile computing in recent years has compounded the problem. Traditional methods of detection involve extensive use of auditing, where a trained individual manually observes reports or transactions in an attempt to discover fraudulent behaviour. This method is not only time consuming, expensive and inaccurate, but in the age of big data it is also impractical. Not surprisingly, financial institutions have turned to automated processes using statistical and computational methods. This paper presents a comprehensive investigation on financial fraud detection practices using such data mining methods, with a particular focus on computational intelligence-based techniques. Classification of the practices based on key aspects such as detection algorithm used, fraud type investigated, and success rate have been covered. Issues and challenges associated with the current practices and potential future direction of research have also been identified.Comment: Proceedings of the 10th International Conference on Security and Privacy in Communication Networks (SecureComm 2014