The Coherence Field in the Field Perturbation Theory of Superconductivity

We re-examine the Nambu-Gorkov perturbation theory of superconductivity on the basis of the Bogoliubov-Valatin quasi-particles. We show that two different fields (and two additional analogous fields) may be constructed, and that the Nambu field is only one of them. For the other field- the coherence field- the interaction is given by means of two interaction vertices that are based on the Pauli matrices tau1 and tau3. Consequently, the Hartree integral for the off-diagonal pairing self-energy may be finite, and in some cases large. We interpret the results in terms of conventional superconductivity, and also discuss briefly possible implications to HTSC

Do stochastic inhomogeneities affect dark-energy precision measurements?

The effect of a stochastic background of cosmological perturbations on the luminosity-redshift relation is computed to second order through a recently proposed covariant and gauge-invariant light-cone averaging procedure. The resulting expressions are free from both ultraviolet and infrared divergences, implying that such perturbations cannot mimic a sizable fraction of dark energy. Different averages are estimated and depend on the particular function of the luminosity distance being averaged. The energy flux, being minimally affected by perturbations at large z, is proposed as the best choice for precision estimates of dark-energy parameters. Nonetheless, its irreducible (stochastic) variance induces statistical errors on \Omega_{\Lambda}(z) typically lying in the few-percent range.Comment: 5 pages, 3 figures. Comments and references added. Typos corrected. Version accepted for publication in Phys. Rev. Let

Supervised Associative Learning in Spiking Neural Network

In this paper, we propose a simple supervised associative learning approach for spiking neural networks. In an excitatory-inhibitory network paradigm with Izhikevich spiking neurons, synaptic plasticity is implemented on excitatory to excitatory synapses dependent on both spike emission rates and spike timings. As results of learning, the network is able to associate not just familiar stimuli but also novel stimuli observed through synchronised activity within the same subpopulation and between two associated subpopulations

Staying afloat on Neurath's boat - Heuristics for sequential causal learning

Causal models are key to flexible and efficient exploitation of the environment. However, learning causal structure is hard, with massive spaces of possible models, hard-to-compute marginals and the need to integrate diverse evidence over many instances. We report on two experiments in which participants learnt about probabilistic causal systems involving three and four variables from sequences of interventions. Participants were broadly successful, albeit exhibiting sequential dependence and floundering under high background noise. We capture their behavior with a simple model, based on the “Neurath’s ship” metaphor for scientific progress, that neither maintains a probability distribution, nor computes exact likelihoods

Dynamical Phase Transitions In Driven Integrate-And-Fire Neurons

We explore the dynamics of an integrate-and-fire neuron with an oscillatory stimulus. The frustration due to the competition between the neuron's natural firing period and that of the oscillatory rhythm, leads to a rich structure of asymptotic phase locking patterns and ordering dynamics. The phase transitions between these states can be classified as either tangent or discontinuous bifurcations, each with its own characteristic scaling laws. The discontinuous bifurcations exhibit a new kind of phase transition that may be viewed as intermediate between continuous and first order, while tangent bifurcations behave like continuous transitions with a diverging coherence scale.Comment: 4 pages, 5 figure

How single-photon nonlinearity is quenched with multiple quantum emitters: Quantum Zeno effect in collective interactions with Λ\Lambda-level atoms

Single-photon nonlinearity, namely the change in the response of the system as the result of the interaction with a single photon, is generally considered an inherent property of a single quantum emitter. Understanding the dependence of the nonlinearity on the number of emitters is important both fundamentally and practically, as strong light-matter coupling is more readily achieved through collective interactions than with a single emitter. Here, we theoretically consider a system that explores the transition from a single to multiple emitters with a $\Lambda$-level scheme. We show that the single-photon nonlinearity indeed vanishes with the number of emitters. Interestingly, the mechanism behind this behavior is the quantum Zeno effect, manifested in the slowdown of the photon-controlled dynamics.Comment: 6 pages, 4 figures + Supplementary material

Nonlinear interactions with an ultrahigh flux of broadband entangled photons

We experimentally demonstrate sum-frequency generation (SFG) with entangled photon-pairs, generating as many as 40,000 SFG photons per second, visible even to the naked eye. The nonclassical nature of the interaction is exhibited by a linear intensity-dependence of the nonlinear process. The key element in our scheme is the generation of an ultrahigh flux of entangled photons while maintaining their nonclassical properties. This is made possible by generating the down-converted photons as broadband as possible, orders of magnitude wider than the pump. This approach is readily applicable for other nonlinear interactions, and may be applicable for various quantum-measurement tasks.Comment: 4 pages, 2 figures, Accepted to Phys. Rev. Let

Neuronal assembly dynamics in supervised and unsupervised learning scenarios

The dynamic formation of groups of neurons—neuronal assemblies—is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis suggests that synchronized oscillations underpin their formation and functioning, with a focus on the temporal structure of neuronal signals. In this context, we investigate neuronal assembly dynamics in two complementary scenarios: the first, a supervised spike pattern classification task, in which noisy variations of a collection of spikes have to be correctly labeled; the second, an unsupervised, minimally cognitive evolutionary robotics tasks, in which an evolved agent has to cope with multiple, possibly conflicting, objectives. In both cases, the more traditional dynamical analysis of the system’s variables is paired with information-theoretic techniques in order to get a broader picture of the ongoing interactions with and within the network. The neural network model is inspired by the Kuramoto model of coupled phase oscillators and allows one to fine-tune the network synchronization dynamics and assembly configuration. The experiments explore the computational power, redundancy, and generalization capability of neuronal circuits, demonstrating that performance depends nonlinearly on the number of assemblies and neurons in the network and showing that the framework can be exploited to generate minimally cognitive behaviors, with dynamic assembly formation accounting for varying degrees of stimuli modulation of the sensorimotor interactions

Can distributed delays perfectly stabilize dynamical networks?

Signal transmission delays tend to destabilize dynamical networks leading to oscillation, but their dispersion contributes oppositely toward stabilization. We analyze an integro-differential equation that describes the collective dynamics of a neural network with distributed signal delays. With the gamma distributed delays less dispersed than exponential distribution, the system exhibits reentrant phenomena, in which the stability is once lost but then recovered as the mean delay is increased. With delays dispersed more highly than exponential, the system never destabilizes.Comment: 4pages 5figure

Should radioiodine now be first line treatment for Graves' disease?

Background Radioiodine represents a cost-effective treatment option for Graves’ disease. In the UK, it is traditionally reserved for patients who relapse after initial thionamide therapy. In a change from current practice, the new guidelines of the National Institute for Health and Care Excellence (NICE) recommends that radioiodine should now be first line therapy for Graves’ disease. However, the safety of radioiodine with respect to long-term mortality risk has been the subject of recent debate. This analysis examines evidence from treatment related mortality studies in hyperthyroidism and discusses their implications for future Graves’ disease treatment strategies. Main body Some studies have suggested an excess mortality in radioiodine treated cohorts compared to the background population. In particular, a recent observational study reported a modest increase in cancer-related mortality in hyperthyroid patients exposed to radioiodine. The interpretation of these studies is however constrained by study designs that lacked thionamide control groups or information on thyroid status and so could not distinguish the effect of treatment from disease. Two studies have shown survival advantages of radioiodine over thionamide therapy, but these benefits were only seen when radioiodine was successful in controlling hyperthyroidism. Notably, increased mortality was associated with uncontrolled hyperthyroidism irrespective of therapy modality. Conclusions Early radioiodine treatment will potentially reduce mortality and should be offered to patients with severe disease. However, thionamides are still suitable for patients with milder disease, contraindications to radioiodine, or individuals who choose to avoid permanent hypothyroidism. Ultimately, a patient individualised approach that prioritises early and sustained control of hyperthyroidism will improve long-term outcomes regardless of the therapy modality used