On the characteristics of emulsion chamber family events produced in low heights

The uncertainty of the primary cosmic ray composition at 10 to the 14th power -10 to the 16th power eV is well known to make the study of the nuclear interaction mechanism more difficult. Experimentally considering, if one can identify effectively the family events which are produced in low heights, then an event sample induced by primary protons might be able to be separated. It is undoubtedly very meaningful. In this paper an attempt is made to simulate the family events under the condition of mountain emulsion chamber experiments with a reasonable model. The aim is to search for the dependence of some experimentally observable quantities to the interaction height

Bi-level Masked Multi-scale CNN-RNN Networks for Short Text Representation

Representing short text is becoming extremely important for a variety of valuable applications. However, representing short text is critical yet challenging because it involves lots of informal words and typos (i.e. the noise problem) but only a few vocabularies in each text (i.e. the sparsity problem). Most of the existing work on representing short text relies on noise recognition and sparsity expansion. However, the noises in short text are with various forms and changing fast, but, most of the current methods may fail to adaptively recognize the noise. Also, it is hard to explicitly expand a sparse text to a high-quality dense text. In this paper, we tackle the noise and sparsity problems in short text representation by learning multi-grain noise-tolerant patterns and then embedding the most significant patterns in a text as its representation. To achieve this goal, we propose a bi-level multi-scale masked CNN-RNN network to embed the most significant multi-grain noise-tolerant relations among words and characters in a text into a dense vector space. Comprehensive experiments on five large real-world data sets demonstrate our method significantly outperforms the state-of-the-art competitors

Dynamic Provable Data Possession Protocols with Public Verifiability and Data Privacy

Cloud storage services have become accessible and used by everyone. Nevertheless, stored data are dependable on the behavior of the cloud servers, and losses and damages often occur. One solution is to regularly audit the cloud servers in order to check the integrity of the stored data. The Dynamic Provable Data Possession scheme with Public Verifiability and Data Privacy presented in ACISP'15 is a straightforward design of such solution. However, this scheme is threatened by several attacks. In this paper, we carefully recall the definition of this scheme as well as explain how its security is dramatically menaced. Moreover, we proposed two new constructions for Dynamic Provable Data Possession scheme with Public Verifiability and Data Privacy based on the scheme presented in ACISP'15, one using Index Hash Tables and one based on Merkle Hash Trees. We show that the two schemes are secure and privacy-preserving in the random oracle model.Comment: ISPEC 201

A local composition model for the prediction of mutual diffusion coefficients in binary liquid mixtures from tracer diffusion coefficients

In a recent publication (Moggride, 2012a), a simple equation was shown to accurately predict the mutual diffusion coefficients for a wide range of non-ideal binary mixtures from the tracer diffusion coefficients and thermodynamic correction factor, on the physical basis that the dynamic concentration fluctuations in the liquid mixture result in a reduction of the mean thermodynamic correction factor relative to the hypothetical case in which such fluctuations do not occur. The analysis was extended to cases where strong molecular association was hypothesised to occur in the form of dimerization of a polar species in mixtures with a non-polar one. This required modification of the average molecular mobility in the form of doubling the tracer diffusivity of the dimerized species (Moggridge, 2012b). Predictions were found to show good accuracy for the mixtures investigated. One of the difficulties with this approach is that it is an a posteriori correction: there is no a priori way of knowing whether strong cluster formation influences the observed molecular mobility, or what the appropriate size of the cluster is. In this work, a modification is made to the average molecular mobility in the original equation by replacing the bulk mole fraction with local mole fraction calculated using the NRTL (non-random two liquid) model, to take account of strong molecular association that results in highly correlated movement during diffusion. The new equation enables an accurate description of mutual diffusion coefficients in mixtures of one strongly self-associating species and one non-polar species, as well as in non-ideal, non-associating mixtures. This result is significant because in this way there is no need of any prior knowledge on the degree of molecular association in the mixture for the prediction of mutual diffusion coefficients from tracer diffusivities.Carmine D’Agostino would like to acknowledge Wolfson College, University of Cambridge, for supporting his research activities.This is the accepted manuscript. The final version is available at http://www.sciencedirect.com/science/article/pii/S0009250915002821

A new 111 type iron pnictide superconductor LiFeP

A new iron pnictide LiFeP superconductor was found. The compound crystallizes into a Cu2Sb structure containing an FeP layer showing superconductivity with maximum Tc of 6K. This is the first 111 type iron pnictide superconductor containing no arsenic. The new superconductor is featured with itinerant behavior at normal state that could helpful to understand the novel superconducting mechanism of iron pnictide compounds.Comment: 3 figures + 1 tabl