A Robust Color Image Watermarking Scheme Using Entropy and QR Decomposition

Internet has affected our everyday life drastically. Expansive volumes of information are exchanged over the Internet consistently which causes numerous security concerns. Issues like content identification, document and image security, audience measurement, ownership, copyrights and others can be settled by using digital watermarking. In this work, robust and imperceptible non-blind color image watermarking algorithm is proposed, which benefit from the fact that watermark can be hidden in different color channel which results into further robustness of the proposed technique to attacks. Given method uses some algorithms such as entropy, discrete wavelet transform, Chirp z-transform, orthogonal-triangular decomposition and Singular value decomposition in order to embed the watermark in a color image. Many experiments are performed using well-known signal processing attacks such as histogram equalization, adding noise and compression. Experimental results show that proposed scheme is imperceptible and robust against common signal processing attacks

Scalar-tensor cosmology at the general relativity limit: Jordan vs Einstein frame

We consider the correspondence between the Jordan frame and the Einstein frame descriptions of scalar-tensor theory of gravitation. We argue that since the redefinition of the scalar field is not differentiable at the limit of general relativity the correspondence between the two frames is lost at this limit. To clarify the situation we analyse the dynamics of the scalar field in different frames for two distinct scalar-tensor cosmologies with specific coupling functions and demonstrate that the corresponding scalar field phase portraits are not equivalent for regions containing the general relativity limit. Therefore the answer to the question whether general relativity is an attractor for the theory depends on the choice of the frame.Comment: 16 pages, 8 figures, version appeared in PR

Proceedings of the inaugural International Summit for Medical Nutrition Education and Research

© 2016 The Royal Society for Public Health Medical Nutrition Education (MNE) has been identified as an area with potential public health impact. Despite countries having distinctive education systems, barriers and facilitators to effective MNE are consistent across borders, demanding a common platform to initiate global programmes. A shared approach to supporting greater MNE is ideal to support countries to work together. In an effort to initiate this process, the Need for Nutrition Education/Innovation Programme group, in association with their strategic partners, hosted the inaugural International Summit on Medical Nutrition Education and Research on August 8, 2015 in Cambridge, UK. Speakers from the UK, the USA, Canada, Australia, New Zealand, Italy, and India provided insights into their respective countries including their education systems, inherent challenges, and potential solutions across two main themes: (1) Medical Nutrition Education, focused on best practice examples in competencies and assessment; and (2) Medical Nutrition Research, discussing how to translate nutrition research into education opportunities. The Summit identified shared needs across regions, showcased examples of transferrable strategies and identified opportunities for collaboration in nutrition education for healthcare (including medical) professionals. These proceedings highlight the key messages presented at the Summit and showcase opportunities for working together towards a common goal of improvement in MNE to improve public health at large

Mycorrhizae and Rhizobacteria on Precambrian Rocky Gold Mine Tailings: II. Mine-Adapted Symbionts Alleviate Soil Element Imbalance for a Better Nutritional Status of White Spruce Seedlings

In the context of a phytorestoration project, the purpose of this study was to assess the respective contribution to the nutritional status of Picea glauca seedlings of ectomycorrhizae and rhizobacteria native or not to the Sigma-Lamaque gold mine wastes in northern Quebec, Canada. In a glasshouse experiment, inoculated plants were grown for 32 weeks on coarse waste rocks or fine tailings obtained from the mining site. The survival, health, growth, and nutritional status of plants were better on coarse waste rocks than on fine tailings. Fe and Ca were especially found at high levels in plant tissues but at much lower concentrations on waste rocks. Interestingly, inoculation of microsymbionts had only minimal effects on N, P, K, and Mg plant status that were indeed close or within the concentration range encountered in healthy seedlings. However, both fungal and bacterial treatments improved Fe and Ca concentrations in plant tissues. Fe concentration in the foliage of plants inoculated with the fungi Tricholoma scalpturatum Tri. scalp. MBN0213 GenBank #KC840613 and Cadophora finlandia Cad. fin. MBN0213 GenBank #KC840625 was reduced by >50%. Both fungi were isolated from the mining site. The rhizobacteria, Azotobacter chroococcum, also improved plant Fe level in some cases. Regarding Ca nutritional status, the native bacterial strain Pseudomonas putida MBN0213 GenBank #AY391278 was the only symbiont that reduced foliar content by up to 23%. Ca concentration was negatively correlated with the fungal mycorrhization rate of seedling roots. This relation was especially strong (r = -0.66, p-value ≤ 0.0001) in the case of C. finlandia. Also, a similar relationship existed with root Fe concentration (r = -0.44, p-value ≤ 0.0001). In fact, results showed that seedling performance was more correlated with elevated Ca and Fe concentration in planta than with nutrient deficiency. Also, native microsymbionts were capable of regulating seedling nutrition in the poor substrate of the Sigma-Lamaque gold mine tailings

Measurement of the microwave effective permittivity in tensile-strained polyvinylidene difluoride trifluoroethylene filled with graphene

We report an interesting effect in the form of a rise (up to 13%) in the permittivity of graphene (GE) filled polyvinylidene difluoride trifluoroethylene (P(VDF-TrFE)) subjected to a small uniaxial deformation (up to 7% in the principal direction). Our findings differ from GE-PVDF homopolymer samples that show a decrease of permittivity upon elongation. We argue that the VDF content which controls the spontaneous polarization has a profound effect on the charge storage through the addition of interface density by the GE phase. (C) 2014 AIP Publishing LLC

A progressive refinement approach for the visualisation of implicit surfaces

Visualising implicit surfaces with the ray casting method is a slow procedure. The design cycle of a new implicit surface is, therefore, fraught with long latency times as a user must wait for the surface to be rendered before being able to decide what changes should be introduced in the next iteration. In this paper, we present an attempt at reducing the design cycle of an implicit surface modeler by introducing a progressive refinement rendering approach to the visualisation of implicit surfaces. This progressive refinement renderer provides a quick previewing facility. It first displays a low quality estimate of what the final rendering is going to be and, as the computation progresses, increases the quality of this estimate at a steady rate. The progressive refinement algorithm is based on the adaptive subdivision of the viewing frustrum into smaller cells. An estimate for the variation of the implicit function inside each cell is obtained with an affine arithmetic range estimation technique. Overall, we show that our progressive refinement approach not only provides the user with visual feedback as the rendering advances but is also capable of completing the image faster than a conventional implicit surface rendering algorithm based on ray casting

Supervised Domain Adaptation using Graph Embedding

Getting deep convolutional neural networks to perform well requires a large amount of training data. When the available labelled data is small, it is often beneficial to use transfer learning to leverage a related larger dataset (source) in order to improve the performance on the small dataset (target). Among the transfer learning approaches, domain adaptation methods assume that distributions between the two domains are shifted and attempt to realign them. In this paper, we consider the domain adaptation problem from the perspective of dimensionality reduction and propose a generic framework based on graph embedding. Instead of solving the generalised eigenvalue problem, we formulate the graph-preserving criterion as a loss in the neural network and learn a domain-invariant feature transformation in an end-to-end fashion. We show that the proposed approach leads to a powerful Domain Adaptation framework; a simple LDA-inspired instantiation of the framework leads to state-of-the-art performance on two of the most widely used Domain Adaptation benchmarks, Office31 and MNIST to USPS datasets.Comment: 7 pages, 3 figures, 3 table

Phase Space Analysis of Quintessence Cosmologies with a Double Exponential Potential

We use phase space methods to investigate closed, flat, and open Friedmann-Robertson-Walker cosmologies with a scalar potential given by the sum of two exponential terms. The form of the potential is motivated by the dimensional reduction of M-theory with non-trivial four-form flux on a maximally symmetric internal space. To describe the asymptotic features of run-away solutions we introduce the concept of a `quasi fixed point.' We give the complete classification of solutions according to their late-time behavior (accelerating, decelerating, crunch) and the number of periods of accelerated expansion.Comment: 46 pages, 5 figures; v2: minor changes, references added; v3: title changed, refined classification of solutions, 3 references added, version which appeared in JCA