Advantages of nonclassical pointer states in postselected weak measurements

We investigate, within the weak measurement theory, the advantages of non-classical pointer states over semi-classical ones for coherent, squeezed vacuum, and Schr\"{o}inger cat states. These states are utilized as pointer state for the system operator $\hat{A}$ with property $\hat{A}^{2}=\hat{I}$, where $\hat{I}$ represents the identity operator. We calculate the ratio between the signal-to-noise ratio (SNR) of non-postselected and postselected weak measurements. The latter is used to find the quantum Fisher information for the above pointer states. The average shifts for those pointer states with arbitrary interaction strength are investigated in detail. One key result is that we find the postselected weak measurement scheme for non-classical pointer states to be superior to semi-classical ones. This can improve the precision of measurement process.Comment: 8 pages, 5 figure

Identification Techniques Applied to a Passive Elasto-magnetic Suspension

The paper presents an experimental passive elasto-magnetic suspension based on rare-earth permanent magnets, characterized by negligible dependence on mass of its natural frequency. The nonlinear behaviour of this system, equipped with a traditional linear elastic spring coupled to a magnetic spring, is analysed in time domain, for non-zero initial conditions, and in frequency domain, by applying sweep excitations to the test rig base. The dynamics of the system is very complex in dependence of the magnetic contribution, showing both hardening behaviour in the elasto-magnetic setup, and softening motion amplitude dependent behaviour in the purely magnetic case. Hence it is necessary to adopt nonlinear identification techniques, such as non-parametric restoring force mapping method and direct parametric estimation technique, in order to identify the system parameters in the different configurations. Finally, it is discussed the ability of identified versus analytical models in reproducing the nonlinear dependency of frequency on motion amplitude and the presence of jump phenomen

Angle-Resolved Photoemission Spectroscopy of Tetragonal CuO: Evidence for Intralayer Coupling Between Cupratelike Sublattices

We investigate by angle-resolved photoemission the electronic structure of in situ grown tetragonal CuO, a synthetic quasi-two-dimensional edge-sharing cuprate. We show that, in spite of the very different nature of the copper oxide layers, with twice as many Cu in the CuO layers of tetragonal CuO as compared to the CuO2 layers of the high-T-c cuprates, the low-energy electronic excitations are surprisingly similar, with a Zhang-Rice singlet dispersing on weakly coupled cupratelike sublattices. This system should thus be considered as a member of the high-T-c cuprate family, with, however, interesting differences due to the intralayer coupling between the cupratelike sublattices.open1199sciescopu

Antifungal potential of essential oil and ethanol extracts of Lonicera japonica Thunb. against dermatophytes

The antifungal potential of essential oil and ethanolic leaf extracts of Lonicera japonica Thunb. was evaluated for controlling the growth of dermatophytes. The oil (1,000 ppm) and extracts (1,500 ppm) of L. japonica revealed 55.1–70.3 % and 40.1–65.5 % antidermatophytic effect against Microsporum canis KCTC 6348, 6349, 6591, Trichophyton rubrum KCTC 6345, 6352, 6375, Trichophyton mentagrophytes KCTC 6077 and 6085, respectively, along with their respective minimum inhibitory concentrations ranging from 62.5-500 and 125-1,000 μg/ml. Also, the oil had strong detrimental effect on spore germination of all the tested dermatophytes as well as concentration and time-dependent kinetic inhibition of M. canis KCTC 6348. The results demonstrated that L. japonica oil and extracts could be potential sources of natural fungicides to protect human and animals from fungal infections

Latent Space Model for Multi-Modal Social Data

With the emergence of social networking services, researchers enjoy the increasing availability of large-scale heterogenous datasets capturing online user interactions and behaviors. Traditional analysis of techno-social systems data has focused mainly on describing either the dynamics of social interactions, or the attributes and behaviors of the users. However, overwhelming empirical evidence suggests that the two dimensions affect one another, and therefore they should be jointly modeled and analyzed in a multi-modal framework. The benefits of such an approach include the ability to build better predictive models, leveraging social network information as well as user behavioral signals. To this purpose, here we propose the Constrained Latent Space Model (CLSM), a generalized framework that combines Mixed Membership Stochastic Blockmodels (MMSB) and Latent Dirichlet Allocation (LDA) incorporating a constraint that forces the latent space to concurrently describe the multiple data modalities. We derive an efficient inference algorithm based on Variational Expectation Maximization that has a computational cost linear in the size of the network, thus making it feasible to analyze massive social datasets. We validate the proposed framework on two problems: prediction of social interactions from user attributes and behaviors, and behavior prediction exploiting network information. We perform experiments with a variety of multi-modal social systems, spanning location-based social networks (Gowalla), social media services (Instagram, Orkut), e-commerce and review sites (Amazon, Ciao), and finally citation networks (Cora). The results indicate significant improvement in prediction accuracy over state of the art methods, and demonstrate the flexibility of the proposed approach for addressing a variety of different learning problems commonly occurring with multi-modal social data.Comment: 12 pages, 7 figures, 2 table

Generating Multimode Entangled Microwaves with a Superconducting Parametric Cavity

In this Letter, we demonstrate the generation of multimode entangled states of propagating microwaves. The entangled states are generated by parametrically pumping a multimode superconducting cavity. By combining different pump frequencies, applied simultaneously to the device, we can produce different entanglement structures in a programable fashion. The Gaussian output states are fully characterized by measuring the full covariance matrices of the modes. The covariance matrices are absolutely calibrated using an in situ microwave calibration source, a shot noise tunnel junction. Applying a variety of entanglement measures, we demonstrate both full inseparability and genuine tripartite entanglement of the states. Our method is easily extensible to more modes.Comment: 5 pages, 1 figures, 1 tabl