Engineering non-classical correlation and teleportation with Robust fidelity using Jaynes–Cummings model

In this paper, we propose a model to describe the geometry of quantum correlations and entanglement through their distinct physical significance in quantum information processing and modern communications. However, geometric discord, using trace, Hilbert–Schmidt distances, and entanglement of formation, is engineered to be a well-defined non-classical correlation measure of an atomic field system. It consists of employing Jaynes–Cummings model to study the interaction between an excited atom at two levels and a single electromagnetic field mode inside an electrodynamic cavity in two cases, namely resonance and non-resonance. In fact, the dynamics of these measures depends decisively on the atom-field initial parameters where, importantly, the field parameters can be specified as control settings to implement an optimal teleportation protocol. The obtained results reveal that the behaviors of teleported geometric quantum discord and entanglement are similar to those displayed for maximum fidelity in terms of fully entanglement fraction. Therefore, since fidelity always exceeds the classical limit, one can design a quantum teleportation scheme with robust fidelity superior to any conventional communication protocol.

How are Moral Foundations Associated with Empathic Traits and Moral Identity?

We examined the relationship between moral foundations, empathic traits, and moral identity using an online survey via Mechanical Turk. In order to determine how moral foundations contribute to empathic traits and moral identity, we performed classical correlation analysis as well as Bayesian correlation analysis, Bayesian ANCOVA, and Bayesian regression analysis. Results showed that individualizing foundations (harm/care, fairness/reciprocity) and binding foundations (ingroup/loyalty, authority/respect, purity/sanctity) had various different relationships with empathic traits. In addition, the individualizing versus binding foundations showed somewhat reverse relationships with internalization and symbolization of moral identity. This suggests that moral foundations can contribute to further understanding of empathic traits and moral identity and how they relate to moral behavior in reality. We discuss the implications of these results for moral educators when starting to teach students about moral issues.

Product of random states and spatial (half-)wormholes

We study how coarse-graining procedure of an underlying UV-complete quantum gravity gives rise to a connected geometry. It has been shown, quantum entanglement plays a key role in the emergence of such a geometric structure, namely a smooth Einstein-Rosen bridge. In this paper, we explore the possibility of the emergence of similar geometric structure from classical correlation, in the AdS/CFT setup. To this end, we consider a setup where we have two decoupled CFT Hilbert spaces, then choose a random typical state in one of the Hilbert spaces and the same state in the other. The total state in the fine-grained picture is of course a tensor product state, but averaging over the states sharing the same random coefficients creates a geometric connection for simple probes. Then, the apparent spatial wormhole causes a factorization puzzle. We argue that there is a spatial analog of half-wormholes, which resolves the puzzle in the similar way as the spacetime half-wormholes.

Management of Vataja Shirashula with Kusthadi Shirolepa and Goghrita Pratimarsha Nasya - A Case Report

Shiras, also known as Uttamanga is the most vital part of our body. In Ayurveda, Shiro rogas are restricted to pain or discomfort seen around cranial vault and not the disorders of brain as such. Headache is the most frequent and troublesome reason to seek medical help in our day to day life. Tension type headache is the most common, primary, most neglected and difficult to treat occurring in about three-quarters of the general population. They can range from the occasional mild headaches to daily disabling headaches in some cases. Current allopathic approach is highly limited in treating the disease with just pain killers, which again have lot of side effects. Holistic Ayurveda approach practically is found to yield very good results in the patients of headache. Vataja Shiroroga can be an apt classical correlation of Tension-type headache. Atiuccha bhashana, Vegadharana, Ratrijagarana, Upavasa and Shoka are the major causes of Vataja shirashula. Classical books of Ayurveda advocate the use of Snehana, Swedana, Navana nasya, Snaihika dhumapana and local Vatahara kriya like Lepa and Parisheka as main mode of treatment. Nasya is a special therapy in all Shalakya disorders and Goghrita is considered as best Vatahara dravya. Kushtadi Lepa is indicated in Vataja Shiroroga classically. In this study, Kushtadi shirolepa and Goghrita pratimarsha nasya is done in a 19 year old patient having typical symptoms, to access the overall efficacy. After 1 month of therapy, significant improvement was seen in the symptoms. Mild recurrence was seen post follow-up period.

A multidimensional intuitionistic fuzzy InterCriteria analysis in the restaurant

The intuitionistic fuzzy InterCriteria analysis (ICrA) is a new method for correlation analysis, which is based on the concepts of index matrices (IMs) and intuitionistic fuzzy sets (IFSs), aiming at detecting of the dependencies between pairs of rating criteria in both clear and uncertain environments. In the present paper, which is an extension of [39], our aim is to extend ICrA to multidimensional ICrA (n-D ICrA) under intuitionistic fuzzy environment for situations where the evaluations of the objects against multidimensional criteria are completely unknown and to show its efficiency through an application in identifying correlations between pairs of criteria when referred to actual data gathered through estimates of a restaurant’s kitchen staff over a three-year period in Bulgaria. We also present a comparative analysis of the correlations between the evaluated criteria of the kitchen staff, on the basis the application of the correlation methods of ICrA, Pearson (PCA), Spearman (SCA) and Kendall (KCA). The four-correlation analysis yielded very similar correlation coefficients, but only the ICrA can be applied to intuitionistic fuzzy evaluations. It is observed that considerable divergence of the ICrA results from those obtained by the other classical correlation analyzes, is only found when the input data contains mistakes.

The Triangle Wave Versus the Cosine: How Classical Systems Can Optimally Approximate EPR-B Correlations

The famous singlet correlations of a composite quantum system consisting of two two-level components in the singlet state exhibit notable features of two kinds. One kind are striking certainty relations: perfect anti-correlation, and perfect correlation, under certain joint settings. The other kind are a number of symmetries, namely invariance under a common rotation of the settings, invariance under exchange of components, and invariance under exchange of both measurement outcomes. One might like to restrict attention to rotations in the plane since those are the ones most commonly investigated experimentally. One can then also further distinguish between the case of discrete rotations (e.g., only settings which are a whole number of degrees are allowed) and continuous rotations. We study the class of classical correlation functions, i.e., generated by classical physical systems, satisfying all these symmetries, in the continuous, planar, case. We call such correlation functions classical EPR-B correlations. It turns out that if the certainty relations and rotational symmetry holds at the level of the correlations, then rotational symmetry can be imposed “for free” on the underlying classical physical model by adding an extra randomisation level. The other binary symmetries are obtained “for free”. This leads to a simple heuristic description of all possible classical EPR-B correlations in terms of a “spinning bi-coloured disk” model. We deliberately use the word “heuristic” because technical mathematical problems remain wide open concerning the transition from finite or discrete to continuous. The main purpose of this paper is to bring this situation to the attention of the mathematical community. We do show that the widespread idea that “quantum correlations are more extreme than classical physics would allow” is at best highly inaccurate, through giving a concrete example of a classical correlation which satisfies all the symmetries and all the certainty relations and which exceeds the quantum correlations over a whole range of settings. It is found by a search procedure in which we randomly generate classical physical models and, for each generated model, evaluate its properties in a further Monte-Carlo simulation of the model itself.

A CNN-Based Correlation Predictor for PRNU-Based Image Manipulation Localization

For PRNU-based forensic detectors, the fundamental test statistic is the normalized correlation between the camera fingerprint and the noise residual extracted from the image in successive overlapping analysis windows. The correlation predictor plays a crucial role in the performance of all such detectors. The traditional correlation predictor is based on predefined hand-crafted features representing intensity, texture and saturation characteristics of the image block under inspection. The performance of such an approach depends largely on the training and test data. We propose a convolutional neural network (CNN) architecture to predict the correlation from image patches of suitable size fed as input. Our empirical finding suggests that the CNN generalizes much better than the classical correlation predictor. With the CNN, we could operate with a common network architecture for various digital camera devices as well as a single network that could universally predict correlations for content from all cameras we experimented with, even including the ones that were not used in training the network. Integrating the CNN with our forensic detector gave state-of-the-art results.

Fractional Fourier Transform for Digital Image Recognition

In the image recognition field, there are several techniques that allow identifying patterns in digital images, correlation being one of them. In a correlation, you have to obtain an output plane that is as clean as possible. To measure the sharpness of the correlation peak and the cleanliness of the output plane, a performance metric called Peak to Correlation Energy (PCE) is used. In this paper, the fractional correlation is applied to recognize real phytoplankton images. This fractional correlation guarantees a higher PCE compared to the conventional correlation. The results of PCE are two-orders of magnitude higher than those obtained with the conventional correlation and manage to identify 91.23% of the images, while the conventional correlation only manages to identify 87.42% of them. This methodology was tested using images in salt and pepper or Gaussian noise, and the fractional correlation output plane always is cleaner and generates a better-defined correlation peak when compared with the classical correlation.