Face Biometric Cloud Authentication Access Using Extreme Learning Class Specific Linear Discriminant Regression Classification Method

The Extreme Learning Class Specific Linear Discriminant Regression Classification used in this proposed system aims at improving the accuracy and recognition rate of the face biometric identification for secured cloud access. The accuracy is improved by maximizing and minimizing the reconstruction error. The between class reconstruction error (BCRE) and within-class reconstruction error (WCRE) are the two values simultaneously increased and decreased for every sample to provide improved accuracy. By selecting the suitable value of WCRE, the learned projection matrix for the discriminant subspace is identified. The class specific representation is implemented for the label created in feature vector to further improve the efficiency of identifying a face. Based on the classification results given by the proposed EL-CSLDRC method, an efficient access of secured data from the big data cloud system is promoted

Explicit MBR All-Symbol Locality Codes

Node failures are inevitable in distributed storage systems (DSS). To enable efficient repair when faced with such failures, two main techniques are known: Regenerating codes, i.e., codes that minimize the total repair bandwidth; and codes with locality, which minimize the number of nodes participating in the repair process. This paper focuses on regenerating codes with locality, using pre-coding based on Gabidulin codes, and presents constructions that utilize minimum bandwidth regenerating (MBR) local codes. The constructions achieve maximum resilience (i.e., optimal minimum distance) and have maximum capacity (i.e., maximum rate). Finally, the same pre-coding mechanism can be combined with a subclass of fractional-repetition codes to enable maximum resilience and repair-by-transfer simultaneously

Forecasting Stock Time-Series using Data Approximation and Pattern Sequence Similarity

Time series analysis is the process of building a model using statistical techniques to represent characteristics of time series data. Processing and forecasting huge time series data is a challenging task. This paper presents Approximation and Prediction of Stock Time-series data (APST), which is a two step approach to predict the direction of change of stock price indices. First, performs data approximation by using the technique called Multilevel Segment Mean (MSM). In second phase, prediction is performed for the approximated data using Euclidian distance and Nearest-Neighbour technique. The computational cost of data approximation is O(n ni) and computational cost of prediction task is O(m |NN|). Thus, the accuracy and the time required for prediction in the proposed method is comparatively efficient than the existing Label Based Forecasting (LBF) method [1].Comment: 11 page

Geometric effects on T-breaking in p+ip and d+id superconductors

Superconducting order parameters that change phase around the Fermi surface modify Josephson tunneling behavior, as in the phase-sensitive measurements that confirmed $d$ order in the cuprates. This paper studies Josephson coupling when the individual grains break time-reversal symmetry; the specific cases considered are $p \pm ip$ and $d \pm id$, which may appear in Sr$_2$RuO$_4$ and Na$_x$CoO$_2 \cdot$(H$_2$O)$_y$ respectively. $T$-breaking order parameters lead to frustrating phases when not all grains have the same sign of time-reversal symmetry breaking, and the effects of these frustrating phases depend sensitively on geometry for 2D arrays of coupled grains. These systems can show perfect superconducting order with or without macroscopic $T$-breaking. The honeycomb lattice of superconducting grains has a superconducting phase with no spontaneous breaking of $T$ but instead power-law correlations. The superconducting transition in this case is driven by binding of fractional vortices, and the zero-temperature criticality realizes a generalization of Baxter's three-color model.Comment: 8 page

Pairing symmetry and properties of iron-based high temperature superconductors

Pairing symmetry is important to indentify the pairing mechanism. The analysis becomes particularly timely and important for the newly discovered iron-based multi-orbital superconductors. From group theory point of view we classified all pairing matrices (in the orbital space) that carry irreducible representations of the system. The quasiparticle gap falls into three categories: full, nodal and gapless. The nodal-gap states show conventional Volovik effect even for on-site pairing. The gapless states are odd in orbital space, have a negative superfluid density and are therefore unstable. In connection to experiments we proposed possible pairing states and implications for the pairing mechanism.Comment: 4 pages, 1 table, 2 figures, polished versio

Identification of Ideal Locations and Stable High Biomass Sorghum Genotypes in semiarid Tropics

The dearth of proper delineation for energy sorghum cultivation has led to a prerequisite for evaluation and identification of test environments for the newly developed lines. This becomes of vital importance as the biomass yield is highly influenced by genotype and environmental (G × E) interactions. Several agronomic traits were considered to assess the biomass yield and the combined analysis of variance for G (genotype), L (location) and interaction effect of G × L. The variations in the yield caused by the interaction of G × L are very essential to acquire knowledge on the specific adaptation of a genotype. Thus, the multi-location trials conducted across locations and years have helped to identify the stable environments with specific adaptation for biomass sorghum. The presence of close association between the test locations suggested that the same information about the genotypes could be obtained from fewer test environments, and hence the potential to reduce evaluation costs. The two genotypes—IS 13762 and ICSV 25333—have shown stable performance for biomass traits across all the locations, in comparison with CSH 22SS (check). The top ten entries with stable and better performance for fresh biomass yield, dry biomass yield, grain yield and theoretical ethanol yield were ICSV 25333, IS 13762, CSH 22SS, IS 25302, IS 25301, IS 27246, IS 16529, DHBM2, ICSSH 28 and IS 17349

Neurogenic inflammation after traumatic brain injury and its potentiation of classical inflammation

Background: The neuroinflammatory response following traumatic brain injury (TBI) is known to be a key secondary injury factor that can drive ongoing neuronal injury. Despite this, treatments that have targeted aspects of the inflammatory pathway have not shown significant efficacy in clinical trials. Main body: We suggest that this may be because classical inflammation only represents part of the story, with activation of neurogenic inflammation potentially one of the key initiating inflammatory events following TBI. Indeed, evidence suggests that the transient receptor potential cation channels (TRP channels), TRPV1 and TRPA1, are polymodal receptors that are activated by a variety of stimuli associated with TBI, including mechanical shear stress, leading to the release of neuropeptides such as substance P (SP). SP augments many aspects of the classical inflammatory response via activation of microglia and astrocytes, degranulation of mast cells, and promoting leukocyte migration. Furthermore, SP may initiate the earliest changes seen in blood-brain barrier (BBB) permeability, namely the increased transcellular transport of plasma proteins via activation of caveolae. This is in line with reports that alterations in transcellular transport are seen first following TBI, prior to decreases in expression of tight-junction proteins such as claudin-5 and occludin. Indeed, the receptor for SP, the tachykinin NK1 receptor, is found in caveolae and its activation following TBI may allow influx of albumin and other plasma proteins which directly augment the inflammatory response by activating astrocytes and microglia. Conclusions: As such, the neurogenic inflammatory response can exacerbate classical inflammation via a positive feedback loop, with classical inflammatory mediators such as bradykinin and prostaglandins then further stimulating TRP receptors. Accordingly, complete inhibition of neuroinflammation following TBI may require the inhibition of both classical and neurogenic inflammatory pathways.Frances Corrigan, Kimberley A. Mander, Anna V. Leonard and Robert Vin